Policy Research Journal (PRJ) , ISSN (E): 3006-7030 ISSN (P) : 3006-7022, published by the Pinnacle Academia Research & Education, serves as a distinguished platform for scholarly discourse. As a multidisciplinary, open-access, and quarterly journal, it stands at the forefront of academic inquiry. Policy Research Journal upholds rigorous standards of excellence. Committed to fostering a culture of academic integrity and transparency, Policy Research Journal prioritizes the expeditious completion of the double-blind peer-review process. With an unwavering dedication to scholarly rigor, our team endeavors to provide timely feedback to authors within one month of submission, ensuring a seamless and efficient publication experience. The journal welcomes papers of the highest quality in the following domains, both established and emerging; Business & Economics Education, Language, & Literature Journalism, Law, & Political Sciences History & Regional Studies Psychology & Sociology Arts & Humanities Sports Sciences Engineering Agriculture Sciences Earth Sciences Data Sciences [ Mathematics, Statistics, Computer Science, and Artificial Intelligence] The respective authors hold copyrights of all content published in the journal.
UNPACKING THE GLOBALIZATION AND TERRORISM INTERFACE IN SAARC: EMPIRICAL EVIDENCE ACROSS ECONOMIC, SOCIAL & POLITICAL DIMENSIONS
The primary objective of this study is to investigate the nexus between globalization and terrorism in South Asian countries from 2003 to 2017 using the PMG autoregressive distributed lag (ARDL) model. This study examines three components of globalization: economic, social, and political globalization. The empirical analysis is conducted in two steps. First, the panel ARDL model is estimated to derive short-run and long-run elasticities. Second, the Granger causality test is conducted to examine the causal relationships among the selected variables. The panel ARDL results indicate a long-run relationship between terrorism and the components of globalization. Political and economic globalization have a positive and statistically significant impact on terrorism, while total globalization also has a positive impact. Additionally, the Granger causality results reveal unidirectional causality running from terrorism to the Political Globalization Index (PGI), unidirectional causality from the Social Globalization Index (SGI) to terrorism, and unidirectional causality from the Total Globalization Index to terrorism.
CLIMATE-INDUCED MIGRATION AND BORDER MILITARIZATION IN SOUTH ASIA: A CRITICAL ANALYSIS OF SECURITIZATION DYNAMICS, STATE RESPONSES, AND HUMAN RIGHTS
The nexus between climate change, forced displacement, and state securitization constitutes one of the most complex and underexplored intersections in contemporary International Relations scholarship. In South Asia, a region that harbours nearly a quarter of the world's population and is classified by the Intergovernmental Panel on Climate Change (IPCC) as a "high-risk" zone for climate-related hazards, millions of individuals are being compelled to migrate due to sea-level rise, cyclonic intensification, riverine flooding, desertification, and glacial melt. Yet, rather than addressing this displacement as a humanitarian phenomenon, regional states have progressively adopted militarized border architectures, framing climate migrants as security threats through a process theorized in this study as eco-securitization. The study employs a mixed-methods approach combining qualitative document analysis of state policy instruments, bilateral treaty frameworks, and UN reports (2010–2025) with quantitative analysis of displacement data drawn from the Internal Displacement Monitoring Centre (IDMC), UNHCR, and the World Bank. Structured elite interviews with 24 policymakers, security officials, and civil society actors from Bangladesh, India, and Pakistan are integrated with critical discourse analysis of securitization narratives across all three nations. The study finds that between 2015 and 2024, climate-related displacement in South Asia increased by 218% while border infrastructure investment grew by 340% along the India-Bangladesh frontier alone. Paradoxically, militarization does not reduce irregular crossings but increases vulnerability, mortality, and documented human rights violations. A novel Securitization-Vulnerability Paradox (SVP) framework is developed and empirically validated across three country cases, demonstrating that enhanced border militarization inversely correlates with humanitarian outcomes for climate-displaced populations. This research advances a transformative policy architecture termed the Climate-Mobility Governance Framework (CMGF), calling for a paradigm shift from coercive border securitization to cooperative, rights-based regional mobility governance. The findings contribute three novel theoretical propositions to International Relations scholarship and carry direct implications for HEC-Priority research on climate diplomacy, South Asian regional security, and human rights law
IMPALED KNIFE IN NECK ZONE I — A CASE REPORT
Penetrating neck injuries are uncommon yet potentially fatal surgical emergencies due to the density of vital neurovascular and aerodigestive structures within a confined anatomical space. We present a rare case of a deeply impacted (approximately 15 cm) knife impalement injury in Zone I of the neck, successfully managed through meticulous surgical planning and multidisciplinary coordination. The case underscores the principle of controlled removal under direct vision and highlights the preservation of all major cervical structures, including the carotid artery, jugular vein, recurrent laryngeal nerve, vertebral arteries, spinal cord, and brachial plexus.
The nexus between climate change, forced displacement, and state securitization constitutes one of the most complex and underexplored intersections in contemporary International Relations scholarship. In South Asia, a region that harbours nearly a quarter of the world's population and is classified by the Intergovernmental Panel on Climate Change (IPCC) as a "high-risk" zone for climate-related hazards, millions of individuals are being compelled to migrate due to sea-level rise, cyclonic intensification, riverine flooding, desertification, and glacial melt. Yet, rather than addressing this displacement as a humanitarian phenomenon, regional states have progressively adopted militarized border architectures, framing climate migrants as security threats through a process theorized in this study as eco-securitization. The study employs a mixed-methods approach combining qualitative document analysis of state policy instruments, bilateral treaty frameworks, and UN reports (2010–2025) with quantitative analysis of displacement data drawn from the Internal Displacement Monitoring Centre (IDMC), UNHCR, and the World Bank. Structured elite interviews with 24 policymakers, security officials, and civil society actors from Bangladesh, India, and Pakistan are integrated with critical discourse analysis of securitization narratives across all three nations. The study finds that between 2015 and 2024, climate-related displacement in South Asia increased by 218% while border infrastructure investment grew by 340% along the India-Bangladesh frontier alone. Paradoxically, militarization does not reduce irregular crossings but increases vulnerability, mortality, and documented human rights violations. A novel Securitization-Vulnerability Paradox (SVP) framework is developed and empirically validated across three country cases, demonstrating that enhanced border militarization inversely correlates with humanitarian outcomes for climate-displaced populations. This research advances a transformative policy architecture termed the Climate-Mobility Governance Framework (CMGF), calling for a paradigm shift from coercive border securitization to cooperative, rights-based regional mobility governance. The findings contribute three novel theoretical propositions to International Relations scholarship and carry direct implications for HEC-Priority research on climate diplomacy, South Asian regional security, and human rights law.
THE SETTLEMENT OF INTERNATIONAL DISPUTES BY MEANS OF ADJUDICATIVE METHODS UNDER PUBLIC INTERNATIONAL LAW
This research examines how countries resolve their disputes through courts and tribunals in Public International Law. The focus is on how these neutral judicial bodies help maintain peace and legal order. It explains that approaching judicial institutions provides a mechanism for countries to settle disagreements using international law. Countries refer their disputes to judicial institutions to work them out amicably. The study also takes a look at the historical development of this system. It starts from arbitration and moves on to the creation of permanent bodies like the International Court of Justice. At the same time it examines the legal framework that supports dispute settlement. This includes treaties, customary international law and the UN system. The research also examines how these methods work in practice. There are problems, such as weak enforcement, political interference, limited jurisdiction and inconsistent state compliance. For example the Corfu Channel case and Nicaragua v. United States show both the power and limitations of adjudication. In the end the study concludes that while going to court promotes conflict resolution more needs to be done. It relies on enforcement, better country cooperation and improved adherence to judicial rulings for real impact. The International Court of Justice plays a central role in this process. Countries must work together to make international adjudication more effective.
METABOLOMIC AND PROTEOMIC CHARACTERIZATION OF INDIGENOUS MEDICINAL PLANTS FOR TARGETED ANTICANCER DRUG DISCOVERY IN PAKISTAN
Cancer remains a major global health burden characterized by genetic heterogeneity, therapeutic resistance, and dysregulation of multiple signaling pathways. Conventional anticancer therapies are often limited by toxicity, high cost, and lack of specificity, particularly in low- and middle-income countries. In this context, indigenous medicinal plants of Pakistan represent a promising yet underexplored source of bioactive compounds with potential anticancer properties. This study aimed to systematically characterize the metabolomic and proteomic profiles of selected medicinal plants and to identify multi-target anticancer mechanisms through integrated systems biology and network pharmacology approaches. A multi-omics analytical framework was employed, utilizing LC–MS/MS and GC–MS for metabolomic profiling and shotgun proteomics for protein identification and functional annotation. Bioinformatics tools were applied for data processing, differential analysis, and pathway enrichment. Identified metabolites and proteins were mapped onto cancer-related signaling networks, including PI3K/Akt, MAPK, p53, and NF-κB pathways. Network pharmacology analysis was conducted to construct compound–target–pathway interaction maps and to elucidate synergistic mechanisms of action. The integrated analysis revealed that selected medicinal plants possess diverse secondary metabolites, including flavonoids, alkaloids, terpenoids, and phenolic compounds, along with functionally active proteins associated with apoptosis, stress response, and detoxification processes. Pathway enrichment analysis demonstrated significant modulation of key oncogenic signaling cascades involved in cell proliferation, survival, and apoptosis regulation. The findings suggest that these plants exert anticancer effects through a multi-target, systems-level mechanism rather than single-compound activity. This study provides a comprehensive omics-based framework for anticancer drug discovery from indigenous medicinal plants of Pakistan and bridges the gap between ethnopharmacological knowledge and modern computational biology. The results offer a scientific basis for further experimental validation and potential development of novel, cost-effective anticancer therapeutics.
DEVELOPMENT, CHARACTERIZATION, AND THERAPEUTIC EVALUATION OF FUNCTIONAL COCOFEE GUMMIES FOR TYPE 2 DIABETES MANAGEMENT IN WISTAR ALBINO RATS
The present study was conducted to develop, characterize, and evaluate the therapeutic potential of functional Cocofee gummies for the management of type 2 diabetes mellitus. The gummies were formulated and analyzed for their proximate composition, revealing that the gummies contained 28.2% moisture, 6.0% crude fat, 3.3% crude protein, 2.8% crude fiber, 2.5% ash, and 42.8% nitrogen-free extract (NFE). Phytochemical evaluation demonstrated appreciable bioactive potential with total phenolic content of 23.6 mg GAE/100 g and total flavonoid content of 4.7 mg CE/100 g. Antioxidant activity, determined by the DPPH radical-scavenging assay, was 21.48%. Color analysis showed L*, a*, and b* values of 29.2, 5.7, and 10.5, respectively, with C*, WI, and ΔE values of 26.23, 14.12, and 22. Sensory evaluation indicated good consumer acceptability, with appearance, texture, taste, aroma, and overall acceptability scores of 8.5, 7.8, 6.2, 4.2, and 5.7, respectively. For therapeutic efficacy, diabetes was induced in Wistar albino rats using alloxan, and animals were supplemented with low-dose (1 gummy per day) and high-dose (2 gummies per day) cocofee gummies for 30 days. The high-dose treatment group exhibited the greatest reduction in body weight (6.2%) and random blood glucose levels (11.0%) compared with the diabetic control group. The outcomes suggest that functional Cocofee gummies possess hypoglycemic effects which attributed to the synergistic action of coffee-derived bioactive compounds and dietary fiber from coconut husk that improve glucose metabolism, metabolic regulation, and antidiabetic properties.
DIPLOMACY AND WAR: AN ANALYTICAL STUDY OF US-IRAN CONFLICT
Examining the US and Iran conflict with historical background till current situation by adopting the mixed methodology analysis case study approach. How the conflicted rooted in Israel because it is actual a US and Israel foreign policy. Role of Diplomates here’s it discussed in the case of US-Isreal and Iran conflict. Negotiations possible in war time or in normal situations shows the mediator countries relations with others states, here like Pakistan’s relations with US and Iran attracted both for trust. Iran is an oldest Islamic country of the world and become more prominent by their 1979 revolution. Israel become the Jewish State by the UNO Partition plan to resolve the issue between Arabs and Jewish people in 1948 but it changed the whole situation. We will discuss the major issues on both sides in the dispute which are the basis of the conflict. Proxies, IRGC to direct invasion. How the negotiators tried but failed to reach a deal or how the Islamabad talks were unable to reach a comprehensive deal. Role of Pakistan’s Prime Minister and CDF Asim Minur in Islamabad Talks. How make the situations worst the assassinations of Iranian’s and destruction in Iran and Iranian attacks on Isreal and gulf countries. Blockade of Straint of Hurmoz. Foreign policies of countries how affecting the whole scenario because of Iran foreign policy against US-Isreal and US-Isreal foreign policy against Islamic Iran. America is known for economy and power in the world and have the hegemonic style. In this paper it will be explain the entire background of the conflict, efforts for conflict resolution and temporary ceasefire, peace settlements between USA and Iran. Iran assets, sanctions on Iran and no nuclear deal are the major challenges and on the other hand US-Isreal policies for not reaching deal or peace settlements and it’s still a continuous situation.
XAI FOR INDUSTRIAL PROCESS MONITORING: APPLYING SHAP TO DETECT ANOMALIES IN CHEMICAL MANUFACTURING DATA
Industrial process monitoring is essential for maintaining operational safety, product quality, equipment reliability, and overall efficiency in chemical manufacturing environments. The growing adoption of Industry 4.0 technologies has enabled the collection of large volumes of real-time process data, creating opportunities for machine learning (ML)-based anomaly detection. While advanced ML models can accurately identify abnormal operating conditions, their black-box nature often limits transparency and trust among engineers and plant operators. To address this challenge, this research proposes an Explainable Artificial Intelligence (XAI)-driven anomaly detection framework that integrates machine learning with the Shapely Additive explanations (SHAP) method. The proposed framework uses SHAP values to explain model predictions by quantifying the contribution of individual process variables to detected anomalies. Experimental evaluation is performed using a benchmark chemical manufacturing dataset, where data preprocessing, feature engineering, and supervised learning techniques are employed to develop a robust anomaly detection model. Results demonstrate high detection performance in terms of accuracy, precision, recall, and F1-score while simultaneously providing meaningful explanations for abnormal process behavior. SHAP analysis identifies critical variables such as reactor temperature, reactor pressure, feed flow rate, and cooling system parameters as major contributors to anomaly predictions. Furthermore, the generated explanations support effective root-cause analysis, enabling engineers to diagnose faults more efficiently and make informed operational decisions. The findings indicate that integrating SHAP with machine learning significantly enhances model transparency, interpretability, and user trust without compromising predictive performance. Therefore, the proposed framework offers a practical and scalable solution for explainable industrial process monitoring and contributes to the adoption of trustworthy AI in chemical manufacturing systems.
RESPONSE OF DIFFERENT LENTIL CULTIVARS AGAINST APHIDS AT DISTRICT PESHAWAR, KHYBER PAKHTUNKHWA
The cowpea aphid (Aphis caraccivora) is a major pest of lentil crop in Pakistan and caused significant losses each year. The study was conducted under field condition at Newly zdevelopmental Farm, The University of Agriculture, Peshawar during 2022-23. Total three cultivars were selected for the trial namely, cultivar Sanli-bey, Atacan and Masoor-93 arranged in Randomized Complete Block design (RCBD) having three replications. The aphid population was consistently observed from two leaf stage till crop maturity/aphid disappeared. The aphids first invaded the tested plants during second week of February 2023. The black pea aphid (Aphis caraccivora) was first attracted by cultivar Atcan followed by Sanli-bey and Masoor-93. The local variety Masoor-93 showed strong response against aphid followed by cultivar Sanli-bey and Atacn. The cultivar Masoor-93 was found best with lowest aphids infestation (7.52) followed by Sanli-bey (10.61) which was found no-significant to Atcan (11.22). The population density was gradually decreased due to multiple factors and disappeared at end of March 2023. It is concluded that Masoor-93 is suitable for cultivation in Peshawar region due to high potential of well growth and has high resistance against aphid infestation.
RETHINKING CEMETERY ARCHITECTURE IN MUSLIM CITIES: A SUSTAINABLE FRAMEWORK FOR VERTICAL BURIAL DEVELOPMENT
The unprecedented rate of urbanization in developing countries has been putting pressure on land resources, which has made provision of services in the urban areas, including burial, a great challenge. Traditionally used for ground burial, Muslim cemeteries are experiencing problems of land scarcity, inefficient management, overcrowding and problems with grave identification. Such difficulties have been more noticeable in the more rapidly expanding cities where the growth of the cities has been in direct competition with the allocation of land within the cemeteries. The idea of vertical cemeteries is therefore born, as a possible architectural and planning solution to meet future requirements of burying the deceased with the conservation of precious urban space. This study focuses on the feasibility of vertical Muslim cemetery as sustainable urban burial solution in densely populated urban area. The research is based on secondary data, religious literature, scholarly publications, international case studies, and contemporary practice of cemetery design, and examines the connections between Islamic burial practices and the consumption of urban land, as well as the management of cemeteries and the innovation in architecture. Special consideration is paid to the compatibility of vertical burial methods and Islamic beliefs about death, burial and remembrance. The study also examines models from abroad and examines some of the examples of religious buildings from Brazil, India, the Philippines, Austria, Russia, and Bangladesh to identify design approaches that take into account religious requirements, environmental considerations and urban efficiency. The results suggest that if designed appropriately for religious needs, vertical cemeteries have the potential to use significantly less land, increase accessibility and management of the cemetery, improve its environmental aspects and promote sustainable urban development. The study concludes that vertical Muslim cemeteries are a promising solution for the future, which could be effective in solving the shortage of burial plots while maintaining the values and traditions related to Islamic burial
THE EMERGENCE OF CONSTITUTIONAL COURTS IN PAKISTAN: RESTRUCTURING JUDICIAL POWER AND CONSTITUTIONAL ADJUDICATION
This paper explores the role of the 26th and 27th Constitutional Amendments in reforming judicial power and constitutional adjudication in Pakistan, and how they have affected judicial independence, constitutional supremacy, and the balance of power among state institutions. This analysis will begin by explaining the pre-amendment status of the judiciary, in which the Supreme Court served as the main protector of the Constitution by reviewing its own decisions, appointing judges based on seniority, and exercising Suo motu power. The paper argues that, despite this model's drawbacks, particularly the persistent conflicts between the judiciary and the political branches, it has managed to retain a relatively cohesive system of constitutional checks and balances. It then reviews the 26th Amendment, noting four major changes: the tenure of the Chief Justice, the reconstitution of the Judicial Commission, the restriction of Suo motu powers, and the establishment of constitutional benches. The paper concludes that these reforms, though introduced to enhance institutional continuity, accountability, and specialization, also introduced political control over the appointment of judges and undermined the judiciary's independent checking role. The 27th Amendment was introduced as the next, more profound structural change, establishing the Federal Constitutional Court (FCC) and removing the Supreme Court's constitutional preeminence. The study notes that this development created parallel apex courts, heightened the risk of conflicting constitutional interpretations, and weakened the judicial hierarchy. The study finds that the two amendments were packaged as efficiency-based reforms but, in reality, reshaped judicial power in ways that can jeopardize fair adjudication and expose the courts to indirect executive influence.
IDENTIFICATION OF THE FACTORS PROMOTING THE ADOPTION OF ENERGY-EFFICIENT VEHICLES IN KARACHI, PAKISTAN
The main subject of this research paper is to examine the critical factors that encourage the use of energy-efficient vehicles in Karachi, which is the largest metropolitan city in Pakistan. The research adopts the Diffusion of Innovation Model by Rogers to examine the effect of perceived characteristics, social factors, and attitude toward behavior in influencing customer acceptance of energy-efficient vehicles. The survey was a structured survey of 104 respondents to analyze the socio-economic, environmental, and technological determinants in influencing purchase decisions. The results are expected to help the policy-makers, marketers, and urban planners to determine the strategic steps that will help to increase people's awareness, supportive policies, and speed up the internalization of energy-saving cars, helping to make the transportation system more sustainable and environmentally resilient in Karachi.
ALGORITHMIC GOVERNANCE AND CONSTITUTIONAL RIGHTS IN PAKISTAN: LEGAL IMPLICATIONS OF AI-BASED PUBLIC DECISION-MAKING
This study critically examines algorithmic governance and its constitutional implications in Pakistan with a focus on AI-based public decision-making systems. The increasing deployment of artificial intelligence in administrative processes has introduced new dimensions of efficiency and automation; however, it has simultaneously raised significant legal concerns regarding transparency, accountability, due process, and protection of fundamental rights. The research employed a qualitative doctrinal and comparative legal analysis to evaluate constitutional provisions, judicial precedents, policy frameworks, and international AI governance standards. The findings reveal that Pakistan’s existing legal framework lacks explicit provisions for regulating algorithmic decision-making, resulting in a governance gap between technological adoption and constitutional safeguards. Key issues identified include algorithmic opacity, risk of discriminatory outcomes, weak accountability mechanisms, and limited explainability of automated decisions. Comparative analysis with international frameworks such as the EU AI Act, OECD principles, and UNESCO guidelines highlights significant regulatory deficiencies in Pakistan’s approach to AI governance. The study concludes that algorithmic governance, while beneficial for administrative efficiency, poses substantial risks to constitutional rights unless accompanied by robust legal and regulatory safeguards. It recommends the development of a comprehensive AI governance framework incorporating transparency requirements, human oversight mechanisms, and enforceable rights to explanation.
CLIMATE CHANGE EFFECTS ON POLLINATOR DIVERSITY AND AGRICULTURAL PRODUCTIVITY IN PAKISTAN
This study investigates the impact of climate change on pollinator diversity and its subsequent effects on agricultural productivity in Pakistan. Increasing climatic variability, including rising temperatures, erratic rainfall patterns, and extreme weather events, has significantly disrupted ecological balance and threatened pollination services essential for crop production. The study employed a quantitative correlational research design using secondary time-series data on climatic indicators, pollinator diversity, and agricultural yield performance across major agro-ecological zones of Pakistan. The findings reveal a strong negative relationship between climate change variables and pollinator diversity, indicating that increasing temperature anomalies and rainfall variability significantly reduce pollinator populations. Moreover, pollinator diversity was found to have a strong positive effect on agricultural productivity, confirming its critical role in sustaining crop yields. Mediation analysis further demonstrated that pollinator diversity significantly mediates the relationship between climate change and agricultural productivity, highlighting its role as a key ecological link in climate-agriculture interactions. The study concludes that climate change poses a substantial threat to agricultural sustainability in Pakistan through its adverse effects on pollinator ecosystems. Strengthening biodiversity conservation and integrating pollinator protection into climate adaptation strategies are essential for ensuring long-term food security and ecological resilience.
THE DIGITAL MEDIATOR: POLICY FRAMEWORKS FOR INTEGRATING ARTIFICIAL INTELLIGENCE INTO ALTERNATIVE DISPUTE RESOLUTION
The international landscape of Alternative Dispute Resolution (ADR) finds itself on the cusp of a great technological paradigm shift. A successful mediation is always anchored entirely on the human element, such as a mediator’s ability to utilize emotional intelligence, empathy, and neutrality. However, the emergence of Generative Artificial Intelligence (AI) and its predictive power has firmly established itself in the mediation room. Algorithms are no longer passive “fourth party” instead, they have evolved into an active “fifth party agent” capable of guiding negotiations and suggesting settlement options independently. This paper examines the profound policy and regulatory gap created by AI advancement by focusing on procedural disclosure gap and algorithmic-ingestion paradox. While analyzing how the Large Language Models, (LLM) clashes with the key mediation principles under the EU AI Act, The UNCITRAL Model Law and domestic frameworks like Punjab ADR Act 2019 are also put to the test. What happens when a settlement under the Singapore Convention is mediated by an algorithm? This paper argues that the answer should worry us. Consequently, this paper argues that the integrity of the process cannot be compromised just for the sake of expeditious computational output. It proposes the solution by step-by-step framework positioned on algorithmic sandboxing, transparent disclosure rules, a mandatory human-in-the-loop standard to uphold the integrity of digital justice.
THE MODERATING ROLE OF FINANCIAL DEVELOPMENT IN THE HUMAN CAPITAL–GROWTH NEXUS: EVIDENCE FROM MIDDLE INCOME COUNTRIES
This study investigates the moderating role of financial development in the relationship between human capital and economic growth in middle-income countries. Using panel data from the World Development Indicators (WDI), the study employs Ordinary Least Squares (OLS), Fixed Effects (FE), Random Effects (RE), Structural Equation Modeling (SEM), and MEDSEM analysis. Human capital is measured through a PCA-based Human Capital Index (HCI) and the World Bank Human Capital Development Index (HCD), while financial development is proxied by domestic credit to the private sector. The findings reveal that both human capital and financial development positively and significantly influence economic growth. The interaction term between human capital and financial development is also positive and significant, indicating that financial development strengthens the growth-enhancing effect of human capital. Furthermore, mediation analysis shows that financial development partially transmits the impact of human capital on economic growth. The study concludes that human capital and financial development are complementary drivers of economic growth and recommends simultaneous investment in education and financial sector development to achieve sustainable economic growth in middle-income countries.
FIDELITY AND UTILITY OF SYNTHETIC TABULAR HEALTH DATA: A CROSS-PARADIGM BENCHMARKING OF A CORRELATION-PRESERVING STATISTICAL PIPELINE AND A CONDITIONAL GENERATIVE ADVERSARIAL NETWORK
Background: The generation of synthetic tabular health data has emerged as a pivotal methodology for statistical simulation, privacy preservation, and methodological validation. While classical statistical approaches offer interpretable, correlation-preserving synthesis through explicit parametric modeling, generative adversarial networks (GANs) provide a flexible, non-parametric alternative that learns the full joint distribution directly from empirical data. Despite the rapidly expanding application of both paradigms, a rigorous, cross-disciplinary empirical comparison tailored to the specific challenges of mixed-type health datasets with small-to-moderate sample sizes remains absent. Aims & Objective: This study evaluates whether a GAN-based approach (CTGAN) offers demonstrable advantages over a modern classical statistical pipeline for synthetic health data generation, and under which practical conditions each method excels. Methodology: Six publicly available tabular health datasets were selected, encompassing continuous, binary, and time-to-event outcomes with mixed continuous and categorical predictors. The statistical pipeline comprised the Modgo method for covariate matrix simulation, followed by parametric model-based outcome generation (linear, logistic, or Cox models). The CTGAN architecture was trained for 1000 epochs. Synthetic data quality was assessed through quantitative distributional similarity metrics (moments, Kullback–Leibler divergence, Kolmogorov–Smirnov statistic, Jensen–Shannon distance, Wasserstein distance), categorical concordance, pairwise correlation recovery, and downstream predictive utility measured via mean squared error, balanced accuracy, and concordance index. Sensitivity to variable scaling and training epoch selection was systematically examined. Results & Findings: Both methods successfully reproduced univariate distributions and joint dependence structures across all datasets. The statistical approach demonstrated superior fidelity in recovering pairwise linear correlations, attributable to its explicit correlation matrix estimation. Consequently, it yielded marginally higher prediction performance for binary classification (balanced accuracy 0.85 vs. 0.78) and survival analysis (C-index 0.71 vs. 0.65). The CTGAN approach achieved lower mean squared error in linear regression (1.02 vs. 2.82) and exhibited pronounced robustness to feature scaling, in contrast to the statistical method which failed catastrophically without prior standardization. CTGAN-generated densities occasionally displayed discretized artifacts, particularly in small samples. Both methods performed comparably in categorical distribution recovery and overall downstream utility. Conclusions: Classical statistical and GAN-based synthetic data generation methods are both viable tools for health simulation studies, each with distinct operational strengths. The statistical pipeline is recommended when the precise reproduction of known correlation structures or parametric outcome relationships is critical, provided that rigorous data preprocessing is undertaken. The CTGAN approach is preferable for datasets with complex multimodality, heavy tails, or when minimal preprocessing and automated implementation are desired. These findings sanction the principled integration of AI-based generators into the statistical simulation repertoire and underscore the importance of context-dependent method selection. Future research should prioritize the incorporation of privacy preservation mechanisms and the development of hybrid architectures that couple the interpretability of statistical models with the distributional flexibility of deep generative networks.
THERAPEUTIC POTENTIAL OF AJWAIN (TRACHIPTERUS AMMI) AGAINST DIET-INDUCED OBESITY AND DYSLIPIDEMIA IN RAT
The aim of present study was to assess the protective effect of carom seed powder against obesity and hyperlipidemia by using animal model. Sixteen rats were given high-fructose and high-fat diet to induce obesity, then powder of carom seeds was given to the treatment groups, G1, G2 and G3 according to their per kg body weight are about to 100 mg, 300 mg and 500 mg respectively. This trial period was continued for 6-weeks on daily basis. During this practice serum leptin, blood glucose and lipid profile were checked 0, 21 and 42 day and anthropometric measurements were taken 0, 21, 28, 42 days. The data thus obtain was analyzed statistically. The effect was seen in G3 (500 mg carom powder) that reduced low density lipoprotein cholesterol (69.75 mg/dl to 57.50 mg/dl), triglycerides (50.75 mg/dl to 39.50 mg/dl), total cholesterol (79.01 mg/dl to 42.25 mg/dl), serum leptin (19.20 ng/ml to 13.57 ng/ml), serum blood glucose (119.0 mg/dl to 78.75 mg/dl), body weight (213.9 g to 179.1 g) and body mass index (151.32 g/cm2 to 140.10 g/cm2). The data has shown significant decreased in LDL, triglycerides, cholesterol, blood glucose, serum leptin and body weight while significant increase in high density lipoprotein HDL (20.01 mg/dl to 25.06 mg/dl) due to uptake of carom powder. It is concluded from present study that carom powder has ability to influence the above-described obesity biomarkers. Concisely, carom powder has the quality to lower the causes of some metabolic ailments such as hyperlipidemia and hyperglycemia complications.
CYBERSECURITY FRAMEWORK FOR SECURE CLOUD COMPUTING USING BLOCKCHAIN AND FEDERATED DEEP LEARNING
Cloud computing is now a backbone of modern digital services because it offers scalable resources and reduced costs. Yet centralized cloud setups are easy targets for distributed denial-of-service (DDoS) attacks, data breaches, spoofing, insider threats, and malicious traffic. Most existing intrusion detection systems (IDS) train models on a single server, which creates privacy risks, bottlenecks, and poor scalability. This paper proposes a Cybersecurity Framework for Secure Cloud Computing Using Blockchain and Federated Deep Learning (CFSC-BFDL). The framework pairs blockchain-based trust management with a federated convolutional neural network (CNN) so that cloud nodes can detect attacks together without ever sharing raw data. Blockchain handles authentication, tamper-proof logging, and safe model aggregation, while each node trains its own CNN locally and sends only encrypted weight updates to a central aggregator. Experiments on the CICIDS2017 and UNSW-NB15 datasets, validated with 5-fold cross-validation, show an average detection accuracy of 98.4% (+/- 0.31), precision of 97.9%, recall of 97.5%, and F1-score of 97.7%. These numbers beat several existing centralized and blockchain-based IDS approaches. The framework also cut communication overhead by roughly 32% and kept detection latency at 14.2 ms per sample, confirming that combining blockchain with federated deep learning offers a practical path toward stronger cloud security.
A STUDY OF PROBLEMS FACED BY SENIOR CITIZENS AND THE REASONS OF JOINING OLD AGE HOMES
Population ageing has emerged as a significant social and public health concern worldwide. Rapid socio-economic transformations, including urbanization, migration, and the gradual weakening of traditional family support systems, have contributed to an increasing number of senior citizens residing in old age homes. The present study aimed to investigate the reasons for joining old age homes and the problems faced by senior citizens living in these institutional settings. A quantitative descriptive research design was employed. Data were collected from 95 senior citizens residing in five selected old age homes in Islamabad and Rawalpindi through a self-developed structured questionnaire. The collected data were analyzed using the Statistical Package for the Social Sciences (SPSS), and descriptive statistical techniques such as frequency distributions, bar charts, histograms, and pie charts were applied for data presentation. The findings revealed that the primary reasons for admission into old age homes included the absence of caregivers, family conflicts with children, lack of familial support, and personal choice. A substantial proportion of respondents reported infrequent or no visits from their children. Although most participants indicated satisfactory access to medical care, adequate nutrition, and generally stable physical health, many still experienced emotional loneliness, adjustment difficulties, chronic health conditions, and financial insecurity, particularly due to the absence of pension support. The study highlights the increasing vulnerabilities of older adults in contemporary society and underscores the need to strengthen family support systems, improve institutional elderly care services, and develop effective social welfare policies to enhance the overall quality of life of senior citizens residing in old age homes.
CORRELATION BETWEEN NURSING STUDENTS’ CLIMATE CHANGE KNOWLEDGE, ANXIETY, AND SUSTAINABILITY ATTITUDES: A CROSS-SECTIONAL STUDY
Background: Climate change is a growing public health concern affecting physical and mental health globally. Among nursing students, awareness, anxiety, and attitudes toward sustainability may influence future professional practice in healthcare. Methods: A descriptive cross-sectional study was conducted among 161 nursing students at Shalamar Nursing College, Lahore. Data were collected using a structured questionnaire assessing climate change awareness, climate change anxiety, and sustainability attitudes. Data were analyzed using SPSS version 23. Descriptive statistics, Pearson correlation, and multiple regression analysis were applied. Results: Most participants demonstrated good climate change awareness (49.1%) and high sustainability attitudes (61.5%), while moderate climate change anxiety was most common (67.1%). Significant positive correlations were found between awareness and sustainability attitudes (r = 0.559, p < 0.001), anxiety and awareness (r = 0.384, p < 0.001), and anxiety and sustainability attitudes (r = 0.310, p < 0.001). Regression analysis showed that climate change awareness significantly predicted sustainability attitudes (p < 0.001), whereas anxiety did not (p = 0.116). Conclusions: Climate change awareness is a key determinant of sustainability attitudes among nursing students, while anxiety plays a limited predictive role. Strengthening climate education in nursing curricula may enhance sustainable healthcare attitudes.
IMAGE-BASED CANOLA SEED DETECTION AND COUNTING USING YOLOV8N AND YOLO11N OBJECT DETECTION MODELS
Accurate seed counting is a key component in seed quality assessment, breeding programs, germination and crop productivity analysis, and counting canola (Brassica napus L.) seeds is essential. The traditional methods of seed counting are time-consuming, labor-intensive, and subject to human errors, particularly in the processing of large quantities of samples. The development of computer vision and deep learning techniques has been seen as a very promising approach to the automated recognition and counting of objects in agriculture in recent years. This paper proposes an image-based automatic canola seed detection and counting method based on YOLOv8n model and YOLO11n models. A customized dataset of 833 images of canola seeds in natural light was created. The images in the dataset comprised different numbers of seeds (from one to ten). Each image was annotated by hand in the Computer Vision Annotation Tool (CVAT) with only one class, “canola_seed”. The dataset was split into 666 train images and 167 validation images. Both YOLOv8n and YOLO11n models were trained for 50 epochs and evaluated using F1-score, mean average precision (mAP@50), and confusion matrix analysis. The results on the experimental set indicate that the YOLOv8n model had a mAP@50 of 0.965 and an optimal F1 score of 0.95, and the YOLO11n model had a mAP@50 of 0.969 and an optimal F1 score of 0.97. YOLOv8n and YOLO11n had mAP@50-95 of 0.270 and 0.266, respectively. Moreover, YOLO11n had fewer false-positive and false-negative results compared with YOLOv8n, demonstrating the reliability of the detection and counting process. The results show that YOLO11n was superior to automated canola seed detection and counting. The proposed framework is a solution that can be used for seed quality testing, agricultural phenotyping, and future smart agriculture applications, which is low-cost, accurate, and efficient.
IMPACT OF WORKING CAPITAL MANAGEMENT ON FIRMS' PROFITABILITY: EVIDENCE FROM PAKISTAN'S OIL AND GAS EXPLORATION SECTOR
Working capital management plays a key role in determining how financially healthy and efficient a company is, especially in high-cost industries like oil and gas exploration. In Pakistan, this sector is extremely important for the country's energy supply and economic growth, yet it faces serious challenges such as high operational costs, long project timelines, unstable global oil prices, and significant financial risks. Despite its importance, very little research has explored how working capital management actually affects the profits of oil and gas companies in Pakistan, and this study aims to fill that gap. The study looks at key elements of working capital management including the cash conversion cycle, the time taken to collect payments from customers, how long inventory is held, and how long it takes to pay suppliers, and compares these against profitability measures like return on assets and return on equity. Data is taken from publicly listed oil and gas exploration companies in Pakistan and analyzed using statistical methods, while also accounting for factors like company size, debt levels, and growth opportunities. The findings are expected to be highly useful for company managers, investors, and government policymakers, helping them understand the best working capital practices that can boost profits without causing cash flow problems. Overall, this study highlights why smart and careful financial management matters deeply in capital intensive industries and provides a strong foundation for future research across Pakistan's energy sector and other emerging markets.
INVESTIGATING THE EFFECT OF TOURISM, FINANCIAL DEVELOPMENT, AND GLOBALIZATION ON ECOLOGICAL FOOTPRINT IN THE CONTEXT OF PAKISTAN
The purpose of this study to contribute to the existing literature by investigating at the impact of Globalization, Tourism, and Financial Development on Ecological footprint in the Pakistan over the period from 1995 to 2023. The theoretical and statistical framework has been developed to investigate the results. For this purpose, Time series data has been used and analyzed through E-views software by using Regression technique. Data was collected from authentic sources such as the World Bank, Global Footprint Network, and Globalization index. The Environmental Kuznets curve theory were used to explain the purpose of the study. The results of this study indicate significant and positive relationship exists between Ecological Footprint, Financial Development, Globalization, and Tourism. Policy makers should promote sustainable tourism and financial practices while applying regulations to reduce ecological impact. They should also encourage globalization strategies that support ecological protection.
BALOCHISTAN INSURGENCY AND STATE RESPONSE IN POST-MUSHARRAF PAKISTAN: DYNAMICS, CHALLENGES, AND IMPLICATIONS FOR NATIONAL SECURITY
This research analyzes the Balochistan insurgency and the state response in the post-Musharraf era. The main objective of this research is to explore the major aspects behind the creation and continuity of the Balochistan insurgency in Pakistan. They focus on the internal issues, governance challenges, corruption, weaken institution capacity, lack of accountability, and ineffective policy implementation. It also examines the grassroots challenges and the state's response to the Balochistan insurgency. The impact of development projects like the China-Pakistan Economic Corridor on the advancement. The study chooses a qualitative method and uses secondary data such as scholarly literature, articles, journals, and official reports. By using theoretical and analytical structures to demonstrate the evolving aspects of the Balochistan insurgency and its interconnection of internal and external causes. According to the theoretical framework informed by the fragile political system, perceived injustice and historical grievances provide a better understanding of the reasons for the insurgency in a rich resource region. General Pervaiz Musharraf's era shows that the ruling period created a hurdle for the Balochistan insurgency and a highly influential military presence in the Baloch regions. Many military operations were held in Balochistan to counter the Balochistan insurgency. The findings of this research explore governance weakness, unfair resource distribution, and poverty, which are not the only major reasons. The CPEC plays an important role in the development of Balochistan. The CPEC gives fewer opportunities to local people and limited participation in their policies. The role of India and Afghanistan is significant for the development of the Baloch Insurgents. The Gwadar port is also crucial for the development of Balochistan. In conclusion, the Balochistan insurgency in the post-Musharraf era has multifaceted and many aspects of governance issues, political instability, and unequal development, creating political marginalization and changing the local perception of Baloch nationalists. The state should adopt effective administrative policies, equal representation in decision-making, and fair distribution for the sustainable development of Balochistan and the security of Pakistan.
PERFORMANCE OF DISTRICT TEACHER EDUCATORS AND IMPLEMENTATION GAPS IN CONTINUOUS PROFESSIONAL DEVELOPMENT OF PRIMARY SCHOOL TEACHERS: EVIDENCE FROM PAKISTAN
Continuous professional development (CPD) is widely recognized as a key mechanism for improving teachers’ professional competence and classroom practices. However, the effectiveness of CPD programs largely depends on the role of instructional support providers who translate policy frameworks into school-level practices. This study evaluates the performance of District Teacher Educators (DTEs) in implementing CPD practices among primary school teachers in Pakistan. Using a quantitative survey design, data were collected from primary school teachers, DTEs, and cluster heads through a structured instrument based on nine CPD performance indicators, including lesson planning, assessment practices, classroom management, student interaction, and activity-based teaching. The findings reveal that DTE performance was above average but remained below the expected level of implementation. Significant gaps were identified between DTE self-reported practices and teachers’ perceptions regarding the execution of CPD activities. The results indicate a disconnect between the intended CPD framework and its practical implementation at the school level. The study highlights the need for strengthening mentoring systems, improving monitoring mechanisms, and enhancing district-level professional support structures to ensure effective teacher development. The findings contribute to understanding the challenges of translating teacher professional development policies into sustainable classroom practices in developing education systems.
PHARMACOGENOMICS AND ARTIFICIAL INTELLIGENCE FOR PRECISION DRUG THERAPY AND SURGICAL OUTCOME OPTIMIZATION IN PAKISTAN
The integration of pharmacogenomics and artificial intelligence (AI) has emerged as a transformative approach in precision medicine, enabling individualized drug therapy and enhanced surgical outcome optimization through genetic profiling and predictive analytics. This study examines the synergistic role of pharmacogenomics and AI in improving therapeutic efficacy, reducing adverse drug reactions, and supporting clinical decision-making within Pakistan’s healthcare system, where precision medicine adoption remains limited. A quantitative, cross-sectional research design was employed, using structured survey data from healthcare professionals to assess the influence of pharmacogenomics, AI adoption, and healthcare system readiness on precision drug therapy and surgical outcomes. Statistical techniques, including regression analysis and structural equation modeling, were applied to evaluate relationships among variables. The findings reveal that both pharmacogenomics and AI significantly and positively influence precision drug therapy and surgical outcome optimization. AI demonstrated a stronger predictive impact on surgical outcomes, while pharmacogenomics played a dominant role in drug response optimization. However, healthcare system readiness emerged as a critical moderating constraint, limiting the full implementation of precision medicine practices in Pakistan. The study concludes that effective integration of pharmacogenomics and AI can substantially enhance clinical outcomes, but requires strengthened genomic infrastructure, AI-enabled clinical systems, and workforce capacity development in Pakistan’s healthcare sector.
TRANSITION METAL NANOSTRUCTURED CATALYSTS FOR ENERGY CONVERSION AND STORAGE APPLICATIONS: A COMPREHENSIVE REVIEW
The global energy landscape is undergoing a significant transition due to increasing concerns over fossil fuel depletion, rising energy demands, and environmental pollution [27,28,36]. In this scenario, the development of efficient and sustainable energy conversion and storage systems has become a major scientific priority [29,36]. Among the various materials investigated, transition metal nanostructured catalysts have attracted considerable attention owing to their unique physicochemical properties and broad application potential [32,34,38]. These materials, particularly based on Fe, Co, Ni, Mn, and Cu and their corresponding compounds such as oxides, sulfides, phosphides, and carbides, offer a cost-effective alternative to noble metal catalysts [32,38]. Their nanoscale dimensions provide a high density of active sites, improved electron transfer behavior, and tunable surface chemistry, all of which directly influence catalytic performance [30,38]. In electrochemical energy systems, these catalysts play a central role in key reactions such as hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and oxygen reduction reaction (ORR) [4–6,10,19]. These reactions are fundamental to technologies like water electrolysis, fuel cells, and metal–air batteries. In parallel, transition metal nanostructures have also shown promising results in energy storage devices, including lithium-ion batteries, sodium-ion batteries, and supercapacitors, where they enhance capacity, rate performance, and cycling stability [23,24,27]. Despite these advantages, several challenges still limit their large-scale application. Issues such as structural instability under operating conditions, particle agglomeration, and insufficient long-term durability remain critical concerns [30,31]. Recent research has therefore focused on improving material design through strategies such as heterostructure formation, doping, defect engineering, and development of single-atom catalysts [30,31,38]. Overall, transition metal nanostructured catalysts represent a rapidly evolving research area that bridges fundamental materials chemistry with practical energy technologies, offering promising pathways toward sustainable energy systems [32,34].
SMART SAFETY INTEGRATION IN TRADITIONAL CHAFF CUTTER MACHINES TO PREVENT UPPER LIMB AMPUTATIONS IN SOUTH ASIA
The use of chaff cutter machines is common in South Asia’s rural communities when harvesting food for animals. While chaff cutter machines are vital in agriculture, they are the leading contributor of severe upper limb injuries, which include amputations in the fingers and hands. According to epidemiological studies done in various parts of Pakistan, India, and Nepal, chaff cutters are responsible for 45% of injuries sustained in agricultural machinery. Chaff cutter injuries account for about 40% of 500 recorded injuries due to agricultural machinery in Pakistan within six months. These injuries result in amputations, the rate of which varies between 35% and 60%. Most of the people injured using chaff cutter machines are less than 30 years old. The main reason behind such injuries arises from faulty engineering designs of the machinery. Typically, the conventional chaff cutter functions with exposed feed rollers along with a rotating blade without having any automatic detecting device. After entrapment, due to the pull of the feed rollers and the momentum of the flywheel, release becomes highly unlikely, often leading to irreversible harm. In order to deal with the issue, this study combines evidence collected from epidemiology with an engineering approach to improve on safety design. Such an improvement comprises of a hand detecting system, an automatic braking system for stopping the blade rotation, an enhanced blade cover along with an emergency switch. This new design intends to interfere in the process
CONSTITUTIONAL IMPLICATIONS OF ALGORITHMIC GOVERNANCE: REGULATING AI-BASED PUBLIC DECISION-MAKING IN PAKISTAN’S LEGAL FRAMEWORK
The rapid expansion of artificial intelligence (AI) in public administration has transformed traditional governance structures through the emergence of algorithmic governance, where automated systems increasingly support or replace human decision-making. While such systems enhance efficiency, consistency, and scalability in public service delivery, they simultaneously raise critical constitutional challenges related to due process, transparency, accountability, equality before law, and judicial review. In Pakistan, the constitutional framework guarantees fundamental rights under the 1973 Constitution; however, it does not explicitly regulate AI-based public decision-making systems, creating a significant governance and legal gap. This study examined the constitutional implications of algorithmic governance in Pakistan’s legal framework and evaluated the adequacy of existing constitutional and administrative safeguards in regulating AI-driven public decision-making. A qualitative doctrinal and comparative research design was employed, using constitutional analysis, judicial interpretation, and comparative regulatory frameworks, particularly from the European Union and OECD guidelines. The findings indicate that algorithmic governance poses substantial constitutional risks, including opacity in decision-making processes, algorithmic bias, weakened procedural fairness, and limited judicial review capacity. The study concludes that Pakistan requires a constitutionally aligned regulatory framework incorporating principles of transparency, explainability, human oversight, and algorithmic accountability to ensure that AI deployment in public administration remains consistent with fundamental rights and the rule of law.
THE INFLUENCE OF ACADEMIC STRESS AND CAFFEINE DEPENDENCE ON SLEEP DISTURBANCES AMONG UNIVERSITY STUDENTS
The present study was conducted to find out the influence of academic stress and caffeine dependence on sleep disturbances among university students. In the current study‚ students aged 18-30 years who were enrolled in HEC recognized universities in Rawalpindi Pakistan were assessed to evaluate the impact of academic stress and caffeine dependence on sleeping problems among students of Rawalpindi․ A cross sectional research design was adopted․ A simple random sampling technique was used to select HEC based universities for the present study and then through convenience sampling participants were selected. Questionnaires employed to assess academic stress and caffeine dependence‚ were the Perception of Academic Stress Scale (PASS; Bedewy & Gabriel‚ 2015) and the Caffeine Use Disorder Questionnaire (CUDQ; Meredith et al․‚ 2013) ․ The Pittsburgh Sleep Quality Index (PSQI‚ Buysse et al․‚ 1989) was used to assess sleep disturbances. The Pearson correlation coefficient was used to evaluate the relationship between the three variables․ Caffeine dependency had a meaningful positive correlation with sleep disturbances(r = ․278‚ p < ․01) ‚ and academic stress had a statistically nonsignificant correlation with sleep disturbances (r = -․071‚ p > ․05)․ The regression analysis revealed that academic stress and caffeine dependency accounted for 7.9% of the variance in sleep disturbances (R² = .079), and caffeine dependence was the only significant predictor, while academic stress did not contribute significantly. This suggests that, while academic stress alone may have a limited effect, its combination with caffeine dependence can meaningfully lead to sleeplessness among university students. In order to lower sleep problems in university students, health services at the university may make programs to help students sleep better. Universities can control drinks with a lot of caffeine on campus. Public health groups can teach people about stress, safe caffeine use, and restful slumber habits for students.
CLIMATE CHANGE AND PUBLIC HEALTH EDUCATION IN PAKISTAN: EXAMINING THE CONTRIBUTION OF TEACHER EDUCATION TO AWARENESS, ADAPTATION, AND RESILIENCE
Pakistan stands at the crossroads of two converging crises: a deepening climate crisis and a chronically under-resourced public health system. The health burden continues to grow on Pakistani communities, particularly in the ecologically fragile province of Sindh, as extreme weather events become more frequent and severe. Vector-borne diseases, heat-related illness, waterborne infections, respiratory disorders and climate-induced psychological distress are increasing, but institutional responses remain reactive rather than preventive. This study investigates the under-utilized potential of teacher education as a proactive mechanism for climate-health awareness and community-level resilience building in Sindh, Pakistan. A qualitative phenomenological design was employed and eight purposively selected educators from four districts of Sindh Province were interviewed using a semi-structured interview approach. Thematic analysis identified a key structural gap: teachers lack the climate-health literacy, institutional mandate, and pedagogical tools needed to translate personal awareness into effective community education. Informed by the Social-Ecological Systems (SES) framework and Paulo Freire's transformative pedagogy, this paper argues that repositioning teacher education as a pillar of climate-health governance is both feasible and urgently necessary. Evidence-based policy recommendations are advanced for curriculum reform, professional development, and interdepartmental collaboration.
RESTORATION OF PADDY ECOSYSTEMS: ASSESSING BIODIVERSITY AND BIOTIC INTERACTIONS ACROSS AGRICULTURAL LANDSCAPES OF PAKISTAN
Paddy ecosystems represent multifunctional agroecosystems that contribute simultaneously to food production, biodiversity conservation, and ecosystem service provision. In Pakistan, intensive rice cultivation across Punjab and Sindh has increasingly led to biodiversity loss, habitat simplification, and disruption of ecological processes due to agrochemical intensification and landscape fragmentation. This study evaluates the effects of ecological restoration practices on biodiversity patterns and biotic interactions in paddy ecosystems across major rice-growing regions of Pakistan, including Gujranwala, Hafizabad, Sheikhupura, Narowal, and Larkana. A comparative field-based assessment was conducted over two rice-growing seasons (2022–2024), involving restored and conventional paddy systems. Multi-trophic biodiversity was assessed, including plant communities, arthropods, pollinators, amphibians, birds, and soil microorganisms, along with ecological network and landscape analyses. Results demonstrated that restored paddy ecosystems significantly enhanced biodiversity across all trophic levels. Plant species richness increased from 31 to 48 species, while arthropod diversity shifted towards beneficial functional groups, with predator abundance increasing by more than 70%. Pollinator populations increased by 56%, amphibian species richness by 80%, and bird abundance by 42% in restored systems. Soil microbial biomass carbon increased by 42.2%, indicating improved belowground ecosystem functioning. Ecological network analysis revealed stronger trophic connectivity and higher ecosystem stability in restored fields. Landscape heterogeneity, vegetated field margins, and wetland connectivity emerged as key drivers of biodiversity enhancement. Overall, the findings demonstrate that ecological restoration significantly improves biodiversity, strengthens biotic interactions, and enhances ecosystem resilience in rice agroecosystems. The study highlights the potential of biodiversity-friendly farming practices to achieve sustainable rice production while supporting ecological conservation in Pakistan.
THE ECONOMIC FALLOUT: IMPACT OF CLIMATE CHANGE AND NATURAL DISASTERS ON PAKISTAN’S GDP
Pakistan is among the countries most vulnerable to climate change, experiencing frequent floods, heatwaves, and droughts that directly damage agriculture, infrastructure, and livelihoods. This paper investigates how climate‑induced natural disasters affect Pakistan’s GDP growth in the short and long term, identifies the most vulnerable economic sectors, evaluates the effectiveness of current institutional frameworks and policies, and compares the economic resilience of rural versus urban areas. The analysis draws on national‑level studies, post‑disaster assessments, and empirical time‑series and sectoral studies on Pakistan’s climate‑economy nexus. Findings show that agriculture‑dependent rural economies are disproportionately affected, while cities face growing strain from concentrated climate risks and migration. The paper concludes that existing institutional frameworks are conceptually strong but weakly implemented, and recommends targeted climate‑resilient investment, rural‑income diversification, and improved urban‑climate‑governance to reduce GDP‑drag from recurrent disasters. [1] [2] [3] [4] [5] [6] [7] [8] [9]
THE RELATIONSHIP BETWEEN BODY IMAGE SATISFACTION AND CLASSROOM PARTICIPATION ANXIETY AMONG UNIVERSITY STUDENTS
This study examined the relationship between body image satisfaction and classroom participation anxiety among university students. Body image played an important role in shaping how students felt about themselves, and concerns about physical appearance could affect their confidence and willingness to participate in classroom activities. Although appearance-related stress and anxiety had been widely studied in different countries, limited research had explored this relationship among university students in Pakistan. Therefore, the present study investigated whether body image satisfaction was associated with classroom participation anxiety. It was hypothesized that there would be a significant relationship between body image satisfaction and classroom participation anxiety among university students. A quantitative, cross-sectional correlational research design was used. Using convenience sampling, 304 university students (147 females and 157 males) from public and private universities in Karachi participated in the study through online surveys. Most participants were enrolled in undergraduate BS programs. Data were collected using the Body-Image Questionnaire (BIQ; Koleck et al., 2002; α = .82) and the Classroom Anxiety Measure (CAM; Richmond et al., 2001; α = .90). The data were analyzed using SPSS Version 27. The findings revealed a significant negative relationship between body image satisfaction and classroom participation anxiety r (304) = −.489, p < .001. Students who were more satisfied with their appearance reported lower levels of anxiety when participating in class, whereas students who were less satisfied with their appearance experienced greater classroom participation anxiety. The study's limitations, recommendations for future research, and counseling implications were also discussed.
ROLE OF LOW-TECH TEACHING STRATEGIES IN ENHANCING STUDENTS’ CLASSROOM ENGAGEMENT: A STUDY OF PUBLIC SECONDARY SCHOOLS
Low-tech teaching methods are counted as a fundamental component of teaching in resource-poor environments as a key element of the digital divide. This research investigates the impact of the various non-digital instructional materials, such as physical charts, chalkboards, and interactive storytelling and role playing, on student engagement in public secondary schools in Hala. While many classrooms in this region are trying to adopt the latest technologies, others are still teaching in the old-fashioned style because of limitations in infrastructure, so it is important to recognize how these "low-tech" methods can be used to enhance the participation and learning of students. The study is descriptive-correlational research, which is used to assess the relationship between traditional active-learning methods and three dimensions of engagement (behavioral, emotional, and cognitive). A representative sample of teachers and students was asked to complete questionnaires to obtain data. This analysis centres on the links between the content of the curriculum and the interests of the students made by the tactile and interpersonal quality of low-technology tools. Results showed that low-tech instructional strategies like group discussion, storytelling, role play, peer learning, and physical supports to the learning environment led to significant increases in students' behavioral, emotional, and cognitive involvement. The use of low-tech teaching strategies had a strong positive correlation with student engagement. Moreover, the students in classrooms that used low-tech interactive approaches showed significantly higher levels of engagement than the students in traditional classrooms that were taught in a lecture manner. The study concludes that there is no need to spend a lot of money on technology to enhance student engagement, but rather the creative and systematic use of available resources. It suggests that equipping the educational authorities with innovative and low-tech teaching methods is a pressing need for facilitating access to quality, equitable education for all learners in the area
CHALLENGES AND READINESS OF PRIMARY SCHOOL TEACHERS IN TEACHING GRADE 5 ENGLISH TEXTBOOKS: EVIDENCE FROM GOVERNMENT SCHOOLS OF PAKISTAN
This study examined the problems encountered by the primary school teachers and their preparation to teach Grade 5 English textbooks in government schools of Pakistan. A quantitative correlational research design was used. A total of 200 primary school teachers were randomly sampled based on stratification, and data were collected from these teachers. A Likert-scale questionnaire was used to assess teaching challenges and the readiness of the teachers. Descriptive statistical methods, correlation, t-test and ANOVA were used to analyze data. The findings showed that English language proficiency, pedagogical capabilities, and resources are moderately to highly demanding for teachers. The teacher’s readiness was determined to be at the moderate level. There was a strong negative correlation between the teaching challenges and teacher readiness (r = -0.62, p < .01). The study underscores the importance of teacher training for English that is aligned with the curriculum and tailored to specific teachers to increase teaching effectiveness in the primary level.
REGULATING ARTIFICIAL INTELLIGENCE AND SMART CONTRACTS IN PAKISTAN'S COMMERCIAL TRANSACTIONS: A MERCANTILE LAW FRAMEWORK FOR DIGITAL TRADE AND CONSUMER PROTECTION
The increasing integration of artificial intelligence (AI) and smart contracts into commercial transactions has significantly reshaped the structure and operation of digital trade systems worldwide. These technologies enhance efficiency, transparency, and automation in commercial dealings; however, they simultaneously introduce complex legal and regulatory challenges related to contractual validity, liability allocation, consumer protection, and regulatory oversight. In Pakistan, the existing mercantile legal framework remains largely grounded in traditional contract principles and electronic transaction laws, which are insufficient to address the autonomous and self-executing nature of AI-driven systems and blockchain-based smart contracts. This legal gap has created uncertainty for regulators, businesses, and consumers operating within the emerging digital economy. This study examined the regulatory challenges associated with AI and smart contracts in Pakistan’s commercial transactions and proposed a comprehensive mercantile law framework for digital trade and consumer protection. A quantitative, explanatory, and cross-sectional research design was employed, using structured survey data collected from legal practitioners, fintech professionals, regulators, academicians, and technology experts. The data were analysed using descriptive statistics and Structural Equation Modelling (SEM) to test the proposed relationships among variables. The findings revealed that AI adoption and smart contract utilization significantly enhance commercial efficiency and legal certainty in digital transactions. Furthermore, the existence of an effective mercantile regulatory framework plays a critical mediating role in strengthening consumer protection and promoting a sustainable digital economy. The study concludes that Pakistan requires a technology-responsive and adaptive legal framework that integrates AI governance, smart contract recognition, and robust consumer protection mechanisms to ensure secure and sustainable digital trade development
FRAMING, EMOTIONS, AND ENGAGEMENT: POLITICAL PARTIES' COMMUNICATION ON FACEBOOK IN PAKISTAN
The current study is an exploration of the role of political communication in engagement-led discourse in Pakistan which is being analyzed at the level of the framing strategies of major political parties on Facebook. Although there is a lot of research on the polarizing effects of social media, little focus has been placed on the ways in which political actors strategically assemble messages in algorithmic structured environments. This study examines the patterns of framing, emotional tone and user engagement in the response to the question based on quantitative content analysis of 218 Facebook messages from Pakistan Tehreek-e-Insaf (PTI), Pakistan Muslim League (N) (PMLN), and Pakistan Peoples Party (PPP). Results show that the general public tends to be more emotionally, morally, and human interest framed and policy oriented framing is somewhat marginal. There are significant differences in use of conflict and responsibility framing by party with adversarial content getting higher engagement – especially in numbers of shares. The study suggests that the incentives associated with using Facebook as a platform are pushing political communication towards more affective, more conflict, and more strategic messages designed to maximize visibility, rather than to inform citizens. The findings add to the knowledge on the reconfiguration of political communication via digital platforms in emerging democracies.
PSYCHOLOGICAL DISTRESS AND SUICIDAL IDEATION AMONG EMERGING ADULTS OF UOG
The current study was conducted with an aim to investigate psychological distress that is conceived through depression, stress and anxiety which are serving as predictors for suicidal ideations among developing adults. Using quantitative cross- sectional research design, data were collected from 274 participants that aligned with the required the age limit of emerging adulthood from University of Gujrat using Depression, Anxiety, Stress Scale (DASSS-21) and Suicidal Behavioral Questionnaire Revised (SBQ-R). Data was analyzed using SPSS-24, results indicated a positive correlation between depression, stress, anxiety and suicidal ideation with depression having the strongest link with suicidal ideation succeeded by anxiety and stress. The linear regression analysis indicated the depression is the strongest and most significant predictor of suicidal ideation. Anxiety also showed a positively significant but was deemed to be a comparatively weak predictor of suicidal ideation. However, stress was found to be weakest predictor of suicidal ideation. This study highlights the importance of understanding psychological distress, particularly depression and its symptoms in understanding, predicting and alleviating suicidal ideation. This study also emphasizes that early identification and targeted mental health interventions can reduce the risk of suicide in emerging adulthood.
IMPACT OF POSITIVE THINKING IN STRESS MANAGEMENT FOR MENTAL HEALTH OF TEACHER BS EDUCATION STUDENTS AT UNIVERSITY OF GUJRAT
Students studying teacher education at BS Education programs at Pakistani state universities must experience a unique set of academic, professional, and personal stressors, which in combination endanger their mental health and future performance as future educators. Combinations of both the pedagogical content knowledge and at the same time acquiring professional teaching skills under conditions of great classroom size, high-stakes test, practicum anxiety, financial constrainedness and family demands all amounts to chronic exposure to stress, which the literature on mental health issues is consistent in implicating as exposure to burnout, anxiety disorders, depression and cognitive impairment. Positive thinking - defined as a cognitive-affective bias, that comprises positive appraisal to stressful events, positive self-statements, positive future perspectives and the ability to reframe adversity as an opportunity - has been theorised and empirically researched as a buffer of the impact of stress on psychological wellbeing. Although there is a positive evidence internationally, the correlation between positive thinking, stress management, and mental health of BS Education students in Pakistan has not been empirically investigated. The article comprises a quantitative, descriptive-correlational study analyzing the effects of positive thinking on the management of stress, mental well-being of BS Education students at the University of Gujrat, Punjab, Pakistan. Three instruments are used to survey a stratified random sample of 150 students (n = 75 male, n = 75 female) based on the sample of the first four years of study, that is, Year 1-4, to use: the Positive Thinking Scale (PTS), the Perceived Stress Scale (PSS-10), and the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS). Pearson correlation, multiple linear regression analysis, and independent samples t-test can be considered as statistical analysis, and they were performed in the IBM SPSS Version 28.0. Results indicate that positive thinking has a significant negative relationship with perceived stress (r = -0.61, p < .001) and a significant positive relationship with mental wellbeing (r = +0.67, p <.001). The proportion of positive thinking in explaining variation in stress management capacity and mental wellbeing scores is 44 and 52 percent. The paper ends with evidence-based suggestions that could be given in form of research findings to university counsellors, faculty and the BS Education curriculum committee to help incorporating the positive thinking skills in the pre-service teacher education model at the University of Gujrat
A ROLE OF EDUCATIONAL COUNSELLING IN PREPARING STUDENTS FOR UNCERTAIN CAREER FUTURES IN THE AI ERA
Uncertain future careers have many plans, but no clear direction, and this context has been increasingly emerging in Generation Z. This paper elucidates how educational counselling supports students in preparing for uncertain career futures in the era where artificial intelligence is in the hands of every individual. With a dramatic increase in technology and changes in the job market, learners often feel anxious and confused about their career paths. The study focuses on understanding learner’s perceptions and experiences of counselling services in their institutions for helping them develop skills such as adaptability, confidence, social skills, and decision making. A qualitative exploratory approach harnesses interviews with university students to gather in-depth insights. The study is grounded in a theory of career construction that emphasizes career adaptability in a changing era. The findings highlight the important role of counselling in reducing career-related anxiety, improving self-awareness, and making learners look forward to flexible and realistic career choices. The source of data specifically includes semi-structured interviews with pre-graduate university students. The findings suggest that learners have their own perspectives on finding jobs that further polish their AI skills in an AI-driven world. Subjects include university students who are currently enrolled in their graduation program for semi-structured interviews to collect data about their future career plans and the uncertain career paths they are afraid of. Ultimately, this study suggests that effective counselling for career choice can help learners navigate more effectively and prepare them for upcoming challenges in an AI-driven world.
APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN STRUCTURAL FIRE RESISTANCE PREDICTION: A SYSTEMATIC LITERATURE REVIEW
Structural fire resistance prediction has traditionally relied on empirical formulas and costly experimental testing, which often fail to capture the complex thermal and mechanical behaviors of structures under fire exposure. Recent advances in artificial intelligence (AI) and machine learning (ML) offer promising alternatives to these conventional approaches. In this systematic literature review, we aim to comprehensively map and synthesize the existing research on AI and ML applications in structural fire resistance prediction. Our methodology involved a structured search and rigorous screening of peer-reviewed studies published over the past two decades, followed by a thematic analysis across eight identified dimensions, including fire resistance prediction of structural members, post-fire damage assessment and residual properties, material property prediction under elevated temperatures, and explainable AI integration. The review reveals that neural networks, decision trees, and support vector machines are frequently employed to predict fire-induced responses such as temperature distributions, deflection histories, and load-bearing capacities. We further observe a growing emphasis on hybrid models that combine physics-based principles with data-driven techniques, thereby improving generalizability and trustworthiness. Additionally, the literature highlights applications in advanced composite and strengthened structures, real-time response forecasting, and broader fire safety systems. However, significant challenges remain, including limited high-quality experimental datasets and a lack of standardized validation benchmarks. We conclude that AI and ML hold substantial potential to transform structural fire engineering, though future work must prioritize mechanistic interpretability and robust data curation before these methods can be reliably adopted in practice.
MACHINE LEARNING APPROACHES FOR PREDICTING STEEL REINFORCEMENT-CONCRETE BOND STRENGTH: A SYSTEMATIC LITERATURE REVIEW
The bond between steel reinforcement and concrete is fundamental to the structural performance of reinforced concrete elements, and accurate prediction of this bond strength remains a critical challenge in civil engineering. Traditional empirical models often fail to capture the complex, nonlinear interactions arising from material degradation, environmental exposure, and diverse reinforcement types. This systematic literature review aims to synthesize and critically evaluate the existing body of research on machine learning approaches applied to bond strength prediction. We conducted a structured search across major academic databases, followed by a rigorous screening and data extraction process to identify relevant studies. Our methodological framework focused on categorizing the literature according to key thematic dimensions, including algorithm types, corrosion effects, specialized concrete mixes, fiber-reinforced polymer (FRP) interfaces, and the integration of explainable artificial intelligence. The review reveals that ensemble methods and hybrid models generally achieve superior predictive accuracy compared to standalone algorithms, particularly when addressing datasets with high variability. Corrosion-induced degradation is increasingly modeled using neural networks that explicitly incorporate electrochemical parameters, while physics-guided approaches successfully embed mechanical priors to maintain physical consistency. Furthermore, we observe a growing trend toward interpreting model predictions through SHAP and partial dependence plots, which enhances the trustworthiness of these tools in structural engineering practice. The findings also highlight significant gaps in the literature, such as the limited validation of models under fire exposure and the underrepresentation of slip behavior predictions. We conclude that machine learning offers a powerful complement to conventional design equations, yet future work must prioritize standardized benchmark datasets, robust cross-validation protocols, and the development of models that generalize across diverse concrete compositions and environmental conditions.
ARTIFICIAL INTELLIGENCE-BASED DURABILITY ASSESSMENT OF REINFORCED CONCRETE STRUCTURES IN MARINE ENVIRONMENTS: A SYSTEMATIC LITERATURE REVIEW
The durability assessment of reinforced concrete structures in marine environments is a critical challenge due to the aggressive action of chloride-induced corrosion and material degradation. This systematic literature review aims to synthesize and critically evaluate the state of the art in artificial intelligence applications for predicting and managing such durability issues. We conducted a comprehensive methodological search across major academic databases, followed by a structured screening process to identify relevant studies that apply machine learning, deep learning, or other AI techniques to durability-related problems. The review is organized around key thematic dimensions: AI-driven prediction of chloride ingress and corrosion initiation, machine learning for concrete mix optimization and durability enhancement, structural health monitoring and service life forecasting, and the prediction of mechanical properties under aggressive exposure. Our results reveal that artificial neural networks and ensemble methods have become dominant for modeling chloride diffusion and corrosion rates, often outperforming traditional empirical models. Conversely, we find that while mix optimization models show high accuracy in predicting compressive strength and permeability, their generalizability to field conditions remains limited. Structural health monitoring studies increasingly integrate sensor data with AI for real-time damage diagnosis, yet long-term validation data are scarce. In conclusion, this review identifies a growing consensus that AI provides powerful tools for durability assessment, but significant gaps persist regarding data scarcity, model interpretability, and the translation of laboratory findings to full-scale marine structures. We therefore highlight the need for standardized benchmark datasets and hybrid models that combine physics-based knowledge with data-driven learning to advance the field.
AI-DRIVEN OPTIMIZATION OF STEEL STRUCTURAL DESIGN FOR HIGH-RISE BUILDINGS: A SYSTEMATIC LITERATURE REVIEW
The design of steel structures for high-rise buildings involves complex trade-offs among safety, cost, material efficiency, and dynamic performance under wind and seismic loads. Recent advances in artificial intelligence offer transformative potential for addressing these challenges through optimization, prediction, and generative design. In this systematic literature review, we aimed to synthesize and critically evaluate the state of research on AI-driven optimization of steel structural design for high-rise buildings. Our methodology followed the PRISMA guidelines to ensure transparency and replicability; we searched across major academic databases using a structured set of keywords, then screened and classified the retrieved studies according to eight predefined dimensions, including AI-driven optimization algorithms, machine learning for response prediction, generative design, vibration control, topology and shape optimization, integration with BIM and digital twins, and structural health monitoring. The review revealed that evolutionary algorithms and reinforcement learning are widely applied to optimize member sizing and topology, while deep neural networks increasingly serve as surrogate models to accelerate seismic and wind response simulations. Generative adversarial networks and variational autoencoders show promise for producing novel structural layouts, often with improved material efficiency. However, we found that most studies remain limited to simplified benchmark problems or low-rise structures, with few validated against full-scale high-rise building cases. Furthermore, integration with real-time construction management and digital twin frameworks is still nascent. We conclude that the field holds substantial promise but requires more rigorous validation, standardized performance metrics, and closer collaboration between AI researchers and practicing structural engineers to bridge the gap between algorithmic innovation and practical deployment.
MACHINE LEARNING APPROACHES FOR SHEAR STRENGTH PREDICTION OF REINFORCED CONCRETE BEAMS: A SYSTEMATIC LITERATURE REVIEW
Shear failure in reinforced concrete beams is a critical and often brittle mode of failure that poses significant challenges for structural design and assessment. Traditional empirical and semi-empirical equations, while widely used, frequently exhibit limited accuracy across diverse design parameters and material types. This systematic literature review was therefore conducted to comprehensively examine how machine learning approaches have been applied to predict the shear strength of reinforced concrete beams. Our objective was to synthesize the state of the art across four distinct dimensions: standard reinforced concrete beams, beams reinforced or strengthened with fiber-reinforced polymers, fiber-reinforced concrete and advanced cementitious composites, and special structural configurations including degradation effects such as corrosion or fatigue. The methodology involved a structured and replicable process of identifying, screening, and critically appraising relevant studies from the past two decades. We then extracted and analyzed data on model architectures, input features, training strategies, and reported performance metrics. The results reveal that ensemble methods, particularly gradient boosting and random forests, have consistently outperformed single models such as artificial neural networks in terms of predictive accuracy and generalization across all four dimensions. Furthermore, the inclusion of parameters that capture size effect and aggregate interlock was shown to substantially reduce prediction errors for standard beams, while for FRP-strengthened systems, models that integrated delamination-related features proved most effective. For fiber-reinforced composites, the choice of fiber type and volume fraction emerged as critical input variables, and for degraded beams, time-dependent corrosion parameters were essential. We conclude that machine learning offers a powerful and adaptable framework that can capture complex nonlinear interactions often missed by code-based formulas, yet the field still suffers from a lack of standardized benchmark datasets and consistent reporting of model uncertainty. This review thereby provides a roadmap for future research and practical deployment of these predictive tools.
CHALLENGES OF USING ARTIFICIAL INTELLIGENCE APPLICATIONS IN SPEECH-LANGUAGE THERAPY PRACTICE IN PAKISTAN
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PAKISTAN'S EVOLVING MILITARY DOCTRINE UNDER FIELD MARSHAL (CDF) HAFIZ SYED ASIM MUNIR: STRATEGIC VISION AND AIR FORCE MODERNIZATION
This research explores the dynamic transformation of Pakistan’s military doctrine under the leadership of Chief of Army Staff General Asim Munir, with particular emphasis on the modernization of the Pakistan Air Force (PAF). In an era defined by evolving security threats and rapid technological advancement, General Munir’s vision reflects a strategic recalibration aimed at achieving both national sovereignty and economic resilience. The study examines doctrinal shifts from conventional military preparedness to hybrid warfare readiness, highlighting the increasing integration of civil-military planning and counterterrorism efforts. Central to this transformation is the modernization of the Pakistan Air Force, including the acquisition of next-generation fighter jets like the J-10C and upgraded JF-17 Thunder, expansion in UAV capabilities with systems such as Bayraktar TB2 and Shahpar-II, and advancements in electronic warfare and air defense systems. The establishment of the National Aerospace Science and Technology Park (NASTP) underlines a commitment to research-driven innovation and indigenous defense capabilities, signaling a move toward strategic self-reliance. Furthermore, Pakistan’s growing international defense collaborations—particularly with Turkey, China, and Saudi Arabia—are explored through the lens of joint exercises and defense diplomacy. This paper argues that under General Munir’s command, Pakistan is not only responding to traditional regional threats but is also preparing for future asymmetric and technological challenges. Through a multi-disciplinary methodology combining policy analysis, defense strategy, and geopolitical review, this research contributes to the broader understanding of South Asia’s shifting military dynamics. It concludes with policy recommendations aimed at sustaining modernization momentum, strengthening regional stability, and promoting strategic autonomy
CRISPR-BASED CLIMATE-RESILIENT CROP IMPROVEMENT FOR SUSTAINABLE AGRICULTURE IN PAKISTAN
Climate change poses significant threats to agricultural productivity, food security, and environmental sustainability, particularly in developing countries such as Pakistan. Increasing temperatures, water scarcity, soil salinity, floods, droughts, and emerging crop diseases have intensified the need for innovative agricultural technologies capable of enhancing crop resilience and ensuring sustainable food production. This study examined the role of CRISPR-based climate-resilient crop improvement in promoting sustainable agriculture in Pakistan, with climate resilience serving as the mediating mechanism. Guided by Sustainable Agricultural Innovation Theory, the study adopted a quantitative, cross-sectional research design to investigate the relationships among the proposed constructs. Data were collected using a structured questionnaire from agricultural scientists, plant breeders, biotechnology researchers, extension officers, and progressive farmers. The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the measurement and structural models. The findings indicated that CRISPR-based crop improvement significantly enhanced climate resilience and sustainable agriculture. Climate resilience also exerted a significant positive influence on sustainable agriculture and partially mediated the relationship between CRISPR-based crop improvement and sustainable agricultural development. The study demonstrates that precise genome-editing technologies can improve crop tolerance to drought, heat, salinity, floods, pests, and diseases while increasing productivity and resource-use efficiency. The findings provide important theoretical, practical, and policy implications by emphasizing the need for greater investment in agricultural biotechnology, supportive regulatory frameworks, research capacity building, and public–private collaboration to accelerate the adoption of CRISPR technologies for climate-smart agriculture and long-term food security in Pakistan
NONLINEAR DYNAMIC ANALYSIS OF REINFORCED CONCRETE STRUCTURES UNDER PROGRESSIVE COLLAPSE: COMPUTATIONAL MODELING, IMPACT LOADING MECHANISMS, AND ROBUSTNESS ENHANCEMENT STRATEGIES
Progressive collapse of reinforced concrete structures, triggered by localized damage from events like blast or impact, remains a critical challenge in structural engineering, as evidenced by historical failures. This systematic review critically evaluates the state-of-the-art in nonlinear dynamic analysis for such scenarios, focusing on computational modeling techniques, impact loading mechanisms, and strategies for structural robustness enhancement. We synthesized findings from a broad base of peer-reviewed research, following a rigorous and transparent methodology to ensure comprehensive coverage of the field. The analysis reveals a clear evolution from early simplified experimental studies to sophisticated computational approaches, including continuum-based finite element models, discrete applied element methods, and efficient macromodeling strategies. A key finding is that conventional assumptions of idealized column removal can significantly underestimate the structural demand; instead, realistic impact simulations demonstrate a critical downward pulling force that accelerates failure propagation and alters the transition from flexural to catenary action. Furthermore, we systematically categorized and appraised various enhancement approaches, including externally bonded fiber-reinforced polymers, internal detailing modifications, and passive bracing systems, finding their effectiveness consistently linked to improved rotational ductility and enhanced tensile load paths. The review concludes that no single modeling paradigm is universally superior; the appropriate choice depends on the trade-off between predictive accuracy and computational cost for specific research or design objectives. A major practical implication is the need for design codes to explicitly account for impact-induced dynamic forces and to promote mechanisms like catenary and tensile membrane action. Future work should focus on multi-hazard optimization, ensuring that solutions for progressive collapse do not inadvertently compromise seismic performance or economic feasibility.
RECENT ADVANCES IN STRUCTURAL HEALTH MONITORING FOR DYNAMIC DAMAGE DETECTION IN CIVIL INFRASTRUCTURE: A SYSTEMATIC REVIEW OF SENSOR TECHNOLOGIES, MODAL ANALYSIS, ARTIFICIAL INTELLIGENCE, AND REAL-TIME CONDITION ASSESSMENT
Structural health monitoring (SHM) has advanced rapidly over the past decade, enabling dynamic damage detection in civil infrastructure through integrated sensor technologies, modal analysis, artificial intelligence (AI), and real-time condition assessment systems. This systematic review and meta-analysis aims to critically synthesize recent developments in these four domains, focusing on their collective ability to improve damage detection accuracy for bridges, buildings, and other civil assets. A comprehensive literature search was conducted across major engineering databases, followed by rigorous screening and quality assessment of eligible studies. The meta-analysis pooled data from multiple independent experiments, employing a random-effects model to estimate the overall effect size. The pooled analysis yielded a summary odds ratio of 1.64 (95% confidence interval: 1.31 to 1.97) for damage detection accuracy, indicating a statistically significant improvement when using advanced SHM techniques compared to conventional methods. The heterogeneity among studies was considerable (, ), suggesting substantial variability due to differences in sensor type, algorithm choice, and structural application. Our results demonstrate that AI-based modal analysis and real-time monitoring systems consistently outperform traditional approaches, particularly when combined with dense sensor networks. These findings confirm the effectiveness of modern SHM frameworks for early and reliable damage identification. We conclude that ongoing integration of AI models with high-fidelity sensors and continuous condition assessment is critical for advancing dynamic damage detection in civil infrastructure, with important implications for maintenance planning and structural safety.
DYNAMIC BEHAVIOR AND VIBRATION MITIGATION OF LONG-SPAN CABLE-STAYED BRIDGES UNDER MULTI-HAZARD LOADING: A SYSTEMATIC REVIEW OF AERODYNAMIC INSTABILITY, VEHICLE-INDUCED VIBRATIONS, SEISMIC EFFECTS, AND ADVANCED DAMPING TECHNOLOGIES
Long-span cable-stayed bridges are increasingly vulnerable to complex dynamic excitations arising from wind, traffic, and seismic events, yet a quantitative synthesis of their combined effects on structural performance remains scarce. This systematic review and meta-analysis aimed to comprehensively evaluate the dynamic behavior and vibration mitigation strategies for such bridges under multi-hazard loading conditions, with a focus on aerodynamic instability, vehicle-induced vibrations, seismic responses, and advanced damping technologies. A systematic literature search identified eligible studies reporting peak deck displacement as a primary outcome metric, from which pooled effect estimates were derived using a random-effects meta-analysis model. The pooled analysis of peak deck displacement yielded a mean effect of (95% confidence interval: to ) with a heterogeneity statistic of , indicating substantial variability across studies; the summary effect was not statistically significant (). Sensitivity analyses confirmed that no single study disproportionately influenced the pooled estimate. Our findings reveal that while individual damping technologies, such as tuned mass dampers and viscous fluid dampers, consistently reduce peak displacements under specific hazards (e.g., seismic or wind loading), their efficacy becomes less pronounced and more variable under combined multi-hazard scenarios. The meta-analysis further demonstrates that vehicle-induced vibrations and aerodynamic instability interact synergistically with seismic effects, amplifying deck displacements beyond additive predictions. We conclude that no single mitigation approach uniformly outperforms others across all hazard combinations; instead, optimized hybrid damping systems tailored to site-specific multi-hazard profiles are necessary. This review provides a quantitative foundation for future design guidelines and highlights critical gaps in experimental validation under truly coupled loading conditions.
MACHINE LEARNING APPLICATIONS IN STRUCTURAL DYNAMIC RESPONSE PREDICTION OF HIGH-RISE BUILDINGS UNDER WIND AND SEISMIC EXCITATIONS: A SYSTEMATIC REVIEW
The prediction of structural dynamic responses in high-rise buildings under wind and seismic excitations is essential for performance-based engineering, yet traditional physics-based methods remain computationally prohibitive for real-time and large-scale applications. Machine learning has emerged as a promising alternative, but the field lacks a coherent synthesis of its diverse methodological developments and practical limitations. We conducted a systematic review following the PRISMA framework to critically map and evaluate the existing evidence on machine learning applications for this problem. Our objectives were to categorize the predominant ML architectures, identify critical gaps in validation and generalizability, and assess reported effectiveness in terms of accuracy, efficiency, and robustness. A comprehensive search across seven major databases was performed using the Population-Exposure-Outcome framework, with inclusion criteria requiring quantitative validation against physical data or high-fidelity simulations. The review reveals a rapidly expanding field where recurrent neural networks, particularly Long Short-Term Memory networks, dominate time-series prediction tasks and generally outperform feed-forward alternatives by capturing temporal dependencies. A notable methodological shift is the integration of physics-informed neural networks, which embed governing equations into the loss function and thereby improve predictive accuracy and physical plausibility, especially under data scarcity. Transfer learning has also gained traction for adapting models across building typologies and hazard scenarios. However, key challenges persist: data scarcity for extreme events, poor generalizability across diverse structural configurations, and the absence of standardized error metrics preclude meaningful cross-study comparison. We conclude that machine learning provides a computationally efficient paradigm for dynamic response prediction, with hybrid data-physics approaches showing the most promise for overcoming current limitations. Nevertheless, the gap between proof-of-concept studies and validated, generalizable tools remain substantial, primarily due to the lack of standardized development and validation frameworks. Future efforts should prioritize open benchmark datasets and performance metrics that reflect both accuracy and practical decision-making utility.
A SYSTEMATIC REVIEW AND META-ANALYSIS OF OPTIMIZATION ALGORITHMS, ENERGY DISSIPATION EFFICIENCY, AND VIBRATION REDUCTION STRATEGIES FOR TUNED MASS DAMPERS AND ACTIVE CONTROL SYSTEMS IN TALL AND FLEXIBLE STRUCTURES
This systematic review and meta-analysis synthesizes the existing body of research on the performance evaluation of tuned mass dampers and active control systems in structural dynamics, with a particular focus on tall and flexible structures. The background of this work is rooted in the increasing demand for effective vibration mitigation strategies in high-rise buildings and slender structures subjected to wind, seismic, and operational loads. The primary objectives of this review are to critically assess optimization algorithms applied to these control systems, to quantify energy dissipation efficiency metrics, and to evaluate the comparative effectiveness of various vibration reduction strategies across different structural configurations. To achieve these goals, we conducted a comprehensive literature search following the PRISMA guidelines, and we applied random-effects meta-analytic models to pool effect sizes from 47 eligible studies. The methodology involved extracting standardized mean differences and efficiency ratios, and we performed subgroup analyses based on control system type and optimization algorithm category. The results indicate that tuned mass dampers achieve a pooled mean vibration reduction of (95% CI: –) under harmonic excitation, while active control systems yield a higher pooled mean reduction of (95% CI: –), with the difference being statistically significant (). Furthermore, meta-regression revealed that evolutionary optimization algorithms were associated with an additional improvement in energy dissipation efficiency compared to gradient-based methods. Consequently, we conclude that active control systems generally outperform passive tuned mass dampers in terms of vibration attenuation, though the selection of optimization algorithms critically mediates performance gains. These findings provide quantitative benchmarks for engineers and researchers involved in the design of structural control systems for flexible structures.
DRUG TRAFFICKING AND THE GROWING USE OF CRYSTAL METH (ICE) AMONG UNIVERSITY STUDENTS IN SOUTH PUNJAB: A SOCIOLOGICAL PERSPECTIVE
The use of crystal methamphetamine commonly referred to as ‘ice' has emerged as a public health and sociological problem among university students in Pakistan. This study aims to examine sociological factors associated with crystal methamphetamine use: Drug trafficking exposure, peer influence, family environment, economic stress, academic stress, and social media exposure. The study is conducted primarily based on sociological theories and is a quantitative cross sectional study (knowledge survey) conducted among 400 university students of South Punjab using multistage stratified random sampling. The data will be analyzed descriptively, correlated, chi-square and multiple regression. The purpose of this study is to determine the primary social factors related to crystal methamphetamine use and to recommend evidence-based strategies for prevention, intervention, policy planning, and institutional strategies to minimize crystal methamphetamine abuse among university students.
THE ASSOCIATION OF FAMILY COHESION AND RELATIONAL AGGRESSION WITH PSYCHOLOGICAL DISTRESS AMONG YOUNG ADULTS
The aim of the current study was to find out the relationship between Family cohesion, Relational aggression and Psychological Distress in Young Adults. The correlational design was used in the study. The total sample of this present study was 205 undergraduate, graduate and post graduate young adults consisting the age range 18 to 25 from university in Lahore District. Sample was collected through convenient sampling. Data collection was done by using Family Cohesion Scale, Relational Aggression Scale and School Children Problem Scale. Analysis was run by using SPSS 21. Correlation analysis showed a negative significant relationship between Family cohesion and Psychological distress while a positive one between relational aggression and psychological distress in young adult.. The results of this study have significant impact for planning assessments and interventions for young adults.
KNOWLEDGE, PERCEPTIONS, AND PSYCHOSOCIAL CONCERNS REGARDING METFORMIN THERAPY AMONG WOMEN WITH GESTATIONAL DIABETES MELLITUS
Background: Gestational Diabetes Mellitus (GDM) is a prevalent pregnancy complication with rising global incidence. Metformin is increasingly used as an oral therapeutic alternative to insulin; however, maternal perceptions and psychosocial concerns regarding its use remain underexplored, particularly in low-resource settings. Aim: This study aimed to assess knowledge, perceptions, and psychosocial concerns regarding Metformin therapy among women with GDM. Methods: This prospective observational study was conducted from January 2025 to May 2025 at Fatima Hospital, Baqai Medical University, Karachi. A total of 54 pregnant women with a confirmed diagnosis of GDM receiving Metformin therapy were enrolled using consecutive sampling. Data were collected using a structured, pretested, interviewer-administered questionnaire covering maternal knowledge, perceptions regarding Metformin safety and acceptance, and psychosocial concerns including anxiety, fear of fetal harm, emotional distress, and need for counselling support. Statistical analysis was performed using statistical package for social sciences (SPSS) version 16, with a p-value <0.05 considered significant. Results: The mean age of participants was 31.4 ± 4.8 years, with 57.4% (n=31) being multigravida and 72.2% (n=39) in the third trimester. Knowledge assessment revealed moderate knowledge in 46.3% (n=25), poor knowledge in 29.6% (n=16), and good knowledge in 24.1% (n=13). Perceived safety of Metformin was reported as confident by 51.9% (n=28), uncertain by 33.3% (n=18), and negative by 14.8% (n=8). Treatment acceptance was positive in 59.3% (n=32), neutral in 25.9% (n=14), and reluctant in 14.8% (n=8). High anxiety regarding pregnancy outcomes was reported by 35.2% (n=19), fear of fetal harm by 38.9% (n=21), and significant emotional distress by 31.5% (n=17). A strong need for counselling support was expressed by 63.0% (n=34). A statistically significant association was found between knowledge level and psychosocial burden (p=0.02), with poor knowledge linked to higher psychological distress. Conclusion: Women with GDM receiving Metformin demonstrate moderate knowledge, uncertain perceptions regarding medication safety, and considerable psychosocial burden. Poor knowledge is significantly associated with greater psychological distress. Structured, patient-centered counselling addressing both clinical and emotional aspects of GDM care is essential to improve treatment acceptance, psychological well-being, and overall pregnancy outcomes.
DYNAMIC SOIL-STRUCTURE INTERACTION EFFECTS ON FOUNDATION SYSTEMS DURING EARTHQUAKE AND BLAST LOADING: A SYSTEMATIC REVIEW OF NUMERICAL MODELING APPROACHES, WAVE PROPAGATION MECHANISMS, AND SEISMIC ENERGY TRANSFER IN COMPLEX SOIL CONDITIONS
Dynamic soil-structure interaction (SSI) profoundly influences the seismic and blast response of foundation systems, yet a quantitative synthesis of numerical modeling strategies and energy transfer mechanisms across complex soil conditions remains lacking. This systematic review and meta-analysis aimed to consolidate evidence from studies that employed finite element, boundary element, or coupled numerical methods to investigate SSI effects under earthquake or blast loading. We systematically searched peer-reviewed literature and applied predefined inclusion criteria focusing on studies that reported foundation displacement, soil impedance, or energy dissipation metrics under varying soil stiffness, layering, and saturation levels. From the eligible reports, we extracted effect sizes and conducted random-effects meta-analyses to estimate pooled mean outcomes and heterogeneity. The results demonstrated that soft cohesive soils amplified foundation displacements by a mean factor of 1.42 (95% confidence interval: 1.21 to 1.63) relative to stiff soils, while the presence of a water table reduced seismic energy transfer efficiency by approximately 18%. Wave propagation mechanisms, particularly body wave reflection at layer interfaces, contributed to a significant increase in energy dissipation in stratified profiles, with a pooled effect size of (). Blast loading induced substantially higher peak foundation rotations than earthquake loading, with the mean difference reaching 0.34 radians (). We further observed that nonlinear soil behavior and foundation embedment depth were the strongest moderators of the observed heterogeneity. These findings collectively indicate that numerical models that ignore soil nonlinearity or partial saturation risk underestimating foundation distress under extreme events. This review provides a quantified framework for selecting appropriate modeling approaches and highlights critical gaps in the representation of coupled energy transfer paths. The results therefore offer actionable guidance for engineers designing resilient foundation systems in variable geotechnical environments.
A SYSTEMATIC REVIEW AND META-ANALYSIS OF GENETIC ALGORITHMS, PARTICLE SWARM OPTIMIZATION, AND HYBRID AI-BASED METAHEURISTICS IN STRUCTURAL DYNAMIC DESIGN AND VIBRATION CONTROL
Structural dynamic design and vibration control are critical challenges in engineering, where metaheuristic optimization algorithms have emerged as powerful tools for performance enhancement. This systematic review and meta-analysis aimed to evaluate the effectiveness of genetic algorithms, particle swarm optimization, and hybrid artificial intelligence-based methods in improving structural performance under dynamic loading and vibration conditions. We systematically examined studies that reported computational efficiency metrics, specifically convergence generations or function evaluations, as primary outcomes. The meta-analysis incorporated data from multiple eligible studies, and we computed standardized mean differences to quantify the relative performance gains. Results indicated that hybrid approaches significantly outperformed standalone algorithms, with an overall effect size of (95% confidence interval: , ). A subgroup analysis revealed that particle swarm optimization achieved the fastest convergence rates (), while hybrid methods demonstrated the greatest robustness in vibration control applications. The heterogeneity across studies was substantial, suggesting that algorithmic efficacy is highly dependent on structural complexity and loading conditions. Our findings further showed that convergence performance improvements were statistically significant in 82% of the included comparisons. These results lead to the conclusion that metaheuristic algorithms, particularly hybrid configurations, provide reliable and efficient solutions for structural dynamic design problems. However, the observed variability underscores the need for standardized benchmarking frameworks and more extensive validation studies. This review synthesizes current evidence and offers practical guidance for selecting appropriate optimization strategies in structural engineering applications.
DIGITAL TWINS FOR RESILIENT STRUCTURES: A SYSTEMATIC REVIEW AND META-ANALYSIS OF REAL-TIME DYNAMIC RESPONSE ANALYSIS, PREDICTIVE MAINTENANCE, SENSOR-DRIVEN DATA ANALYTICS, AND INTELLIGENT DECISION-MAKING IN SMART INFRASTRUCTURE MONITORING
The integration of digital twins into structural dynamics and smart infrastructure monitoring has attracted considerable attention for its potential to enhance structural resilience through real-time dynamic response analysis, predictive maintenance, sensor-based data analytics, and intelligent decision-making. This systematic review and meta-analysis aimed to synthesize the existing evidence on the effectiveness of digital twin frameworks in these domains, with a particular focus on predictive maintenance accuracy as the primary outcome of interest. A comprehensive literature search was conducted across multiple electronic databases, following the PRISMA guidelines, to identify eligible studies that reported quantitative performance metrics for digital twin-based systems in structural health monitoring. The included studies were subjected to rigorous quality assessment using established risk-of-bias tools, and a random-effects meta-analysis was performed to pool effect sizes where appropriate. From the collected data, we extracted summary statistics for predictive maintenance accuracy, including a reported effect size of with a 95% confidence interval ranging from to , indicating a moderate but statistically non-significant negative association in one key study. The overall pooled estimate across studies was with a corresponding p-value of , suggesting a trend toward improved predictive maintenance performance that did not reach conventional significance thresholds. The heterogeneity among studies was considerable, as reflected by an statistic of 4.54, which underscores the variability in methodologies, sensor configurations, and structural systems examined. Our findings indicate that while digital twin integration shows promise for enhancing predictive maintenance and real-time monitoring, the current evidence base remains limited by small sample sizes and inconsistent outcome definitions. We conclude that future research should standardize performance metrics and adopt larger-scale field validations to confirm these preliminary trends and to advance the deployment of intelligent decision-making systems for resilient infrastructure.
BLAST-RESISTANT STRUCTURAL DESIGN AND DYNAMIC PERFORMANCE ASSESSMENT OF REINFORCED CONCRETE BUILDINGS: A SYSTEMATIC REVIEW AND META-ANALYSIS OF SHOCK WAVE PROPAGATION, ENERGY ABSORPTION MECHANISMS, AND ADVANCED NUMERICAL SIMULATION TECHNIQUES
Blast-resistant design of reinforced concrete (RC) structures remains a critical challenge due to the complex interplay between shock wave propagation, energy dissipation, and material fragmentation. In this systematic review and meta-analysis, we aimed to synthesize the existing evidence on dynamic performance assessment of RC buildings subjected to blast loads, with a particular focus on shock wave propagation mechanisms, energy absorption strategies, and advanced numerical simulation techniques. A comprehensive literature search was conducted without language restrictions, and studies reporting quantitative outcomes such as breach occurrence and spall depth were considered for meta-analytic synthesis. After screening, a total of reports_included studies met the inclusion criteria. The meta-analysis of breach occurrence yielded a pooled log odds ratio of (95% confidence interval: ), indicating a non-significant protective effect of fiber reinforcement against breach formation. A secondary analysis of spall depth reduction produced a mean effect size of (standard error: 0.67), suggesting moderate variability across the included studies. Heterogeneity was substantial (), and subgroup analyses revealed that fiber type and charge weight significantly moderated the observed effects. Numerical simulation studies consistently demonstrated that finite element models incorporating strain-rate-dependent material properties and calibrated damage models provided the closest predictions to experimental data. We conclude that current design practices benefit from integrating fiber reinforcement and optimized member geometries, but that further standardization of testing protocols and validation frameworks is necessary to improve predictive reliability. This review provides a quantitative foundation for future research and practical guidelines for blast-resistant RC structural design
A SYSTEMATIC REVIEW AND META-ANALYSIS OF THE DYNAMIC PERFORMANCE OF COMPOSITE STRUCTURES UNDER CYCLIC AND IMPACT LOADING CONDITIONS
Composite structures are increasingly employed in applications where dynamic loads—both cyclic and impact—predominate, yet a comprehensive quantitative synthesis of their performance under such conditions remains absent. This systematic review and meta-analysis therefore aimed to evaluate the peak contact force during low-velocity impact as a primary outcome, and to identify key structural and material factors that govern dynamic response. We conducted a literature search and systematically screened reports for inclusion. From each eligible study, we extracted means, standard deviations, and sample sizes for the outcome of interest. Meta-analytic synthesis was performed using a random-effects model to account for between-study heterogeneity. The pooled analysis revealed a statistically significant effect: the mean peak contact force was lower in the composite structures featuring hybrid facesheets compared to all-carbon facesheet configurations, with a standard mean difference of and a 95% confidence interval of (). The associated heterogeneity was moderate (). These results indicate that hybridization of the facesheet—specifically the incorporation of glass fiber layers—substantially reduces the peak contact force experienced during low-velocity impact, likely due to increased energy absorption through controlled fiber breakage and reduced stiffness mismatch. Consequently, the meta-analysis confirms that hybrid layup strategies can effectively mitigate damage initiation in composite sandwich panels under impact loading. This evidence provides a quantitative foundation for designing more impact-resistant composite structures and highlights the need for further experimental studies with standardized test protocols
EXPLORING COPING MECHANISMS IN LOW SOCIOECONOMIC STATUS INDIVIDUALS EXPERIENCING PHYSICAL AND EMOTIONAL STRESS STEMMING FROM HOUSEHOLD INTERPERSONAL CONFLICT AND VIOLENCE
INTRODUCTION: People of lower SES are disproportionately exposed to cumulative physical and emotional stressors that magnify pre-existing vulnerabilities to interpersonal conflict and domestic violence. There is a paucity of research on the coping strategies used by economically deprived people to cope with household conflict in South Asian contexts. OBJECTIVES: To explore coping strategies of low socioeconomic status subjects in the Pakistani context, with special reference to interaction between family conflict and economic stress. METHODS: A qualitative design was conducted using Interpretive Phenomenological Analysis (IPA). 18 persons (n=18) took part in community assistance programs affiliated with NUML, Islamabad and partner groups in the Islamabad–Rawalpindi region. In-depth semi-structured interviews were carried out and analysed using IPA. RESULTS: Five coping themes emerged: spiritual/faith-based coping, mobilisation of social support, avoidance and emotional desensitisation, problem-focused behavioural strategies, and meaning-making via adversity. A repeated conflict was noticed between the stigma of vulnerability and the desire to seek help. Systemic barriers, like access to mental health services, housing instability, financial dependency on abusive partners, and patriarchal norms, perpetuated cycles of pain. CONCLUSION: The present study makes a substantial contribution to the current literature on stress and coping methods in the South Asian civilizations. There is a critical need for trauma-informed and culturally relevant interventions that address the material and social situations of low-income Pakistani populations
THE IMPACT OF PATRIARCHAL BELIEFS ON THE ACCEPTANCE OF DOMESTIC VIOLENCE
Background: The presence of patriarchal beliefs significantly influences an individual’s acceptance of domestic violence, therefore, it is important to understand the belief system of the people accepting domestic violence in their culture. Aim: The objective of this research study is to examine the influence of patriarchal beliefs on the acceptance of domestic violence among young adults in Pakistan. Method: Quantitative research with a cross-sectional correlational approach and a convenient sample of 120 men and 130 women (age 18-30) was used in the study from different universities and colleges of Islamabad, Pakistan, through an online questionnaire. It included the Patriarchal Beliefs Scale (PBS) and the Domestic Violence Myth Acceptance Scale (DVMAS). Results: An independent sample t-test showed no significant difference amongst the two genders regarding acceptance of domestic violence, however, male participants overall reported higher scores on the patriarchal beliefs scale. A Pearson product correlation showed that as patriarchal beliefs increased, the acceptability of domestic violence as well as the justification of domestic violence in certain situations also increased. Implications of findings lie in addressing both factors, patriarchal ideology and acceptance of domestic violence in educating not only men but women of the dangers against society. Conclusion: This study can lead to increase in awareness and change in the social structures of our country, which can improve psychological well-being of men and women. It can ultimately contribute to the goal of not only opposing patriarchal structures in society, but also promoting equal and fair relationships where all individuals are viewed as equals who can contribute to society
HYBRID PUBLIC–PRIVATE PARTNERSHIP MODELS AND EDUCATIONAL OUTCOMES IN PAKISTAN: A SECONDARY DATA ANALYSIS
Public–private partnerships (PPPs) have become a growing feature of educational governance in Pakistan, stemming from the country's long-standing issues of restricted access, poor educational outcomes, insufficient capacity, and educational infrastructure. However, there is mixed evidence on the effectiveness of these mixed governance models, especially in terms of learning outcomes and educational equity. The study aims to explore the effectiveness of hybrid PPP models in enhancing the educational outcomes in Pakistan with the help of a qualitative research design using secondary data analysis. The study summarizes peer-reviewed literature, government reports, policy papers and publications from international organizations. The analytical framework for studying the role of collaboration, shared accountability, coordination and stakeholder engagement on PPP performance is developed in the context of Network Governance Theory. The results reveal that hybrid PPPs have helped to extend the access to education, boost enrolment, and enhance school infrastructure, although the effects are not consistent on learning achievement and equity. The study proposes that the quality of the governance, not the ownership of the organizations, is the key factor in the success of partnerships. It is concluded that strengthening institutional coordination, accountability, monitoring, leadership and stakeholder participation is crucial to achieve sustainable and equitable PPPs for educational reform and help realize SDG 4 in Pakistan.
CIVIL–MILITARY RELATIONS AND DEMOCRATIC CONSOLIDATION IN PAKISTAN: INSTITUTIONAL DYNAMICS AND POLITICAL STABILITY IN THE POST-2018 ELECTORAL ERA
Civil–military relations remain a defining feature of democratic governance and political stability in many transitional democracies, particularly Pakistan. The post-2018 electoral era has been characterized by evolving institutional interactions, political polarization, constitutional challenges, and governance reforms, making it imperative to examine the relationship between civil–military relations and democratic consolidation. This study investigated the influence of civil–military relations on democratic consolidation through the mediating role of institutional dynamics and assessed their collective impact on political stability in Pakistan. Grounded in Huntington's Objective Civilian Control Theory, the study adopted a quantitative, cross-sectional, explanatory research design. Primary data were collected from policymakers, public administrators, political scientists, constitutional experts, researchers, legal practitioners, retired military officers, and governance professionals using a structured questionnaire. The proposed conceptual framework was empirically examined using Structural Equation Modeling (SEM). The findings indicate that constructive civil–military relations significantly strengthen institutional dynamics and directly enhance democratic consolidation. Institutional dynamics further contribute positively to democratic consolidation and political stability, while democratic consolidation exerts a significant positive influence on political stability. Mediation analysis demonstrates that institutional dynamics and democratic consolidation serve as significant mechanisms through which civil–military relations promote sustainable political stability. The study extends Huntington's Objective Civilian Control Theory by integrating institutional dynamics and democratic consolidation into a comprehensive framework applicable to emerging democracies. The findings provide important theoretical, practical, and policy implications for strengthening constitutional governance, improving institutional effectiveness, enhancing democratic accountability, and promoting long-term political stability in Pakistan. The study concludes that balanced constitutional relationships among state institutions, reinforced by strong democratic institutions and effective governance mechanisms, are essential for sustaining democratic consolidation and ensuring political stability in Pakistan.
CLIMATE SECURITY, WATER DIPLOMACY, AND GEOECONOMIC CONNECTIVITY: PAKISTAN'S STRATEGIC ROLE IN SOUTH ASIA
Climate change has emerged as a critical non-traditional security challenge that increasingly influences regional stability, transboundary resource governance, and economic integration. South Asia remains one of the world's most climate-vulnerable regions, where climate-induced water insecurity, environmental degradation, and geopolitical complexities necessitate integrated policy responses. This study examined the interrelationships among climate security, water diplomacy, and geoeconomic connectivity while assessing Pakistan's strategic role in fostering sustainable regional cooperation in South Asia. Grounded in Complex Interdependence Theory, the study employed a quantitative, cross-sectional research design. Primary data were collected from policymakers, diplomats, environmental experts, academics, and strategic analysts through a structured questionnaire using purposive sampling. The proposed conceptual framework was empirically tested using Structural Equation Modeling (SEM). The findings demonstrated that climate security significantly influenced both water diplomacy and geoeconomic connectivity. Water diplomacy also exhibited a significant positive effect on regional geoeconomic connectivity and served as a mediating mechanism between climate security and economic integration. The results further indicated that strengthening climate-resilient water governance enhances institutional cooperation, regional trust, and sustainable economic connectivity, thereby reinforcing Pakistan's strategic role in promoting regional stability. The study contributes to the literature by integrating climate security, hydro-diplomacy, and geoeconomic connectivity within a unified analytical framework. It also extends Complex Interdependence Theory by illustrating the multidimensional interactions among environmental governance, diplomatic cooperation, and regional economic development. The findings provide practical and policy-relevant insights for governments, regional organizations, and international development partners seeking to strengthen climate resilience, transboundary water governance, and sustainable regional cooperation in South Asia.
THE MEDIATING ROLE OF CLASSROOM INTERACTION IN THE RELATIONSHIP BETWEEN INSTRUCTIONAL FEEDBACKS AND WRITING STRATEGIES AMONG PAKISTANI ESL LEARNERS
This study examined the mediating effect of classroom interaction on the relationship between instructional feedback and writing strategies among undergraduate learners in the Pakistani English as a Second Language (ESL) context. Grounded in social constructivist and self-regulated learning perspectives, the study investigated whether classroom interaction serves as a mechanism through which instructional feedback enhances students’ use of writing. A quantitative research design utilizing correlation and mediation analysis was employed. The respondents were a total of 600 second-year undergraduate students from various departments in a major Pakistani university. Data were collected using validated, adapted questionnaires measuring the level of instructional feedback, classroom interaction, and writing strategies. Results revealed moderate to high levels of instructional feedback, writing strategy use, and classroom interaction among Pakistani respondents. Correlation analysis showed significant positive relationships among all three variables. Mediation analysis further demonstrated that classroom interaction significantly mediated the relationship between instructional feedback and writing strategies, contributing to over half of the total effect. The findings highlight the necessity of integrating responsive instructional feedback with interactive classroom practices to promote effective writing strategies in the Pakistani higher education sector.
MACHINE LEARNING APPLICATIONS IN SUSTAINABLE CONCRETE MIX DESIGN OPTIMIZATION: A COMPREHENSIVE REVIEW OF PERFORMANCE, DURABILITY, AND ENVIRONMENTAL BENEFITS
Concrete production accounts for approximately 8% of global CO₂ emissions, necessitating urgent adoption of sustainable practices through innovative design methodologies. Machine learning (ML) has emerged as a transformative technology enabling rapid optimization of concrete mix designs that balance mechanical performance, durability, and environmental sustainability. This comprehensive review synthesizes recent advances in ML applications for concrete mix design, examining prediction accuracy, multi-objective optimization frameworks, and integration with supplementary cementitious materials (SCMs). A systematic analysis of 47 peer-reviewed studies from 2016–2026 reveals that ensemble learning algorithms (XGBoost, Random Forest, Gradient Boosting) and deep neural networks consistently achieve prediction coefficients of determination (R²) exceeding 0.90, with mean absolute errors below 5%. Multi-objective optimization approaches have successfully reduced CO₂ emissions by 25–51% while maintaining target strengths of 30–70 MPa. Integration of interpretability tools (SHAP, LIME) demonstrates that water-to-binder ratio, cement content, and supplementary material dosage are dominant performance drivers. This review identifies critical research gaps including long-term durability validation of ML-optimized mixes, real-time field implementation protocols, and standardization of datasets across geographic regions. Future directions emphasize hybrid physics-informed neural networks, automated quality assurance systems, and circular economy applications utilizing construction-demolition waste. ML-driven concrete optimization represents a viable pathway toward achieving sustainable infrastructure goals while reducing material costs by 15–30% and embodied carbon by up to 51%.
RECENT ADVANCES IN SELF-HEALING CONCRETE TECHNOLOGY: MECHANISMS, MATERIALS, PERFORMANCE EVALUATION, AND FUTURE RESEARCH DIRECTIONS FOR INFRASTRUCTURE
Self-healing concrete represents a paradigm shift in construction materials science, addressing the fundamental brittleness and cracking tendency of traditional concrete through mechanisms that enable autonomous or autogenous repair of structural damage. [1] Recent research has demonstrated that self-healing concrete can repair cracks up to 1.8 mm width through bacterial precipitation and calcite production that seals cracks, which could extend the serviceability of concrete. [2] This review synthesizes current knowledge on self-healing mechanisms, materials, performance evaluation methods, and future directions. Five primary strategies for implementing self-healing capability are discussed: (1) autogenous self-healing through intrinsic carbonation and crystallization, (2) biomineralization through bacteria-produced carbonates, (3) polymer-cement composites with autonomous healing, and (4) fiber-based crack inhibition. [1] While autogenous techniques have limited efficacy for cracks exceeding 0.3mm, autonomous techniques have successfully repaired cracks exceeding 2mm in width. [3] Bacterial systems are capable of healing cracks up to 0.5-0.8 millimeters with high recovery of structural strength, with emerging evidence suggesting potential environmental impact benefits through longer structural life and reduced maintenance rates. [4] This comprehensive review identifies critical research gaps and proposes standardized testing protocols to facilitate industrial adoption of self-healing technologies in sustainable infrastructure development.
UTILIZATION OF INDUSTRIAL WASTE MATERIALS IN CONCRETE PRODUCTION: A CRITICAL REVIEW OF SUSTAINABILITY AND MECHANICAL PERFORMANCE
The utilization of industrial waste materials as partial or complete replacements for conventional concrete constituents has emerged as a critical strategy for addressing environmental challenges in the construction industry. This comprehensive review synthesizes peer-reviewed literature on the incorporation of waste materials including fly ash, ground granulated blast furnace slag (GGBS), silica fume, recycled concrete aggregates (RCA), recycled plastic, ceramic waste, and agricultural by-products into concrete matrices. The review evaluates mechanical performance (compressive strength, tensile strength, flexural strength), durability characteristics (water absorption, permeability, chloride penetration, sulfate resistance), and sustainability indicators (carbon footprint reduction, landfill diversion, resource conservation) across 68 peer-reviewed sources. Findings indicate that optimal replacement levels of industrial waste materials can achieve 7–41% enhancements in mechanical properties while simultaneously reducing CO₂ emissions by 16–68%. The review identifies critical research gaps, including long-term durability performance, standardization of waste material quality, and optimization of multi-waste blended systems. This paper provides evidence-based guidance for researchers, practitioners, and industry stakeholders on the technical viability and sustainability benefits of incorporating industrial waste materials into concrete for structural applications.
BODY DISSATISFACTION AMONG GYM-GOING FEMALES: INTERPLAY OF BMI, DIET PREFERENCES AND STRUCTURED FITNESS ROUTINE
The research study was carried out to study body dissatisfaction among adolescent and young adult female gym-goers. The factors studied were assessing the reasons for joining gym or fitness club, whether they observe any positive modified patterns in their physical bodies, which dietary designs they follow, and what were the reasons for them to follow fitness routine. The difference in body image perceptions with regards to dissatisfaction was also studied between both the groups. The sample population comprised of 200 female gym-goers adolescents and young adults, from various gyms and fitness clubs in Karachi. Their consent was obtained and BMI were recorded, followed by presentation of the study questionnaire that required them to fill up demographics and the BSQ-34 scale. Body Shape Questionnaire (BSQ-34) was used to evaluate the body dissatisfaction in sample. The findings were assessed by using SPSS-23 software with descriptive statistics, t-test, Pearson’s Product Moment Correlation Coefficient, Point Biserial Correlation and ANOVA for testing the hypotheses. The results yielded that BMI was significantly associated with body dissatisfaction, the body awareness showed links with body dissatisfaction, the difference was also studied with regards to body dissatisfaction across preferred dietary patterns among the female population of the study.
OPTIMIZING SUPPLY CHAIN VALUE CREATION: THE MEDIATING INFLUENCE OF SUPPLIER NETWORKS ON CUSTOMER SATISFACTION AND SALES PERFORMANCE
The objective of the current research examines the mediating effects of the supplier networks between the operational strategies of the supply chain management, with the satisfaction to increasing firm sales. The research study was based on quantitative research methods to better understanding of customer satisfaction, to increase firm productivity and sales performance. The approach of the lean manufacturing is positive associated with the operations of the supply chain management, and current research study provides the significance and greater understanding and better relationships among the approach of lean manufacturing and the operations of the supply chain management in the manufacturing industry in Pakistan. The data collection through the administrative developed questionnaire with the help non-probability with convenience sampling. The data analysis through the Smart pls and interpretations the results. The results suggested that manufacturing flexibility, productivity, cost reduction, positive associated with the supplier networks, and customer satisfaction has the significance and the statistical impact on the firm’s performance. Thus, the manufacturing flexibility, productivity, cost reduction positive impact on the suppliers’ networks, and the mediating effects of the suppliers’ networks between the productivity, and customer satisfactions cost reduction. Further, customer satisfaction mediates the supplier networks relationship and the firm’s performance, and also customer satisfaction has a positive impact on the firm’s performance. As the managerial implications, the current research study results indicate the experts and the production managers, Supply chain managers to develop supplier relationships, to better efficient productivity, good quality products, to achieve operation alignment with customer expectations
IMPACT OF EBULLIENT SUPERVISION AND GREEN HRM PRACTICES ON INNOVATIVE WORK BEHAVIOR: A MEDIATION–MODERATION ANALYSIS IN PAKISTAN’S HOSPITALITY SECTOR
The objective of this study is to find out the impact of Ebullient supervision (ES) and Green Human Resource Management practices (GHRM) on Innovative work behavior (IWB) of hospitality industry employees of Pakistan. Thus, staff innovation is a critical trait of an employee in today's hospitality sector to ensure that service quality, sustainability and competitiveness can be accepted in the organization. The study also introduces the Componential Theory of Creativity, in order to examine the psychological safety (PS) as mediator and the sense of belongingness (SOB) as moderator. The study was done through quantitative approach with explanatory technique and data was collected from 391 employee of the hotel industry in Pakistan. The analysis of the data was carried out using Partial Least Squares Structural Equation Modeling (SEM) method (Smart PLS, version 4 and SPSS). The results show that ebullient supervision and Green HRM Practices has significant positive effect on the psychological safety and innovative work behavior. Psychological safety is a great mediator between ebullient supervision and green HRM practices and innovative work behavior. In addition, sense of belongingness in a positive way is a moderator on the relationship between psychological safety and innovative work behavior. The research contributes to the literature on leadership, Green HRM and innovation in an integrated mediation-moderation model in a non-Western hospitality context. Results also offer empirical implications for hospitality managers who are looking for ways to foster innovation through supporting supervision practices, sustainable HR management, psychological safety and inclusion-focused relationships at work
COMPARATIVE ANALYSIS OF WRIT JURISDICTION IN PAKISTAN, INDIA, AND THE UK: BALANCING CONSTITUTIONAL SUPREMACY AND PROCEDURAL LIMITATIONS ON THE RULE OF LAW
Writ jurisdiction serves as the ultimate constitutional safeguard against the arbitrary exercise of state power and the violation of fundamental rights. This article provides a critical comparative analysis of the writ jurisdictions in Pakistan, India, and the United Kingdom. While the UK serves as the historical progenitor of prerogative writs operating under an unwritten constitution, Pakistan and India have constitutionally entrenched these powers under Articles 199 & 184(3) and Articles 32 & 226, respectively. By analyzing the interplay between writ jurisdiction and procedural laws such as the Civil and Criminal Procedure Codes, and by examining landmark jurisprudence on the exhaustion of alternative remedies and the quashing of First Information Reports (FIRs), this study highlights the evolving nature of judicial review. It further explores the constitutional principles governing judicial intervention in administrative and executive actions, emphasizing the balance between judicial restraint and judicial activism. The article also evaluates the role of superior courts in ensuring governmental accountability, protecting the rule of law, and maintaining constitutional supremacy in democratic governance. A comparative examination of judicial approaches in the three jurisdictions reveals both convergences and divergences in the interpretation and application of extraordinary constitutional remedies. The findings contribute to the growing body of comparative constitutional scholarship by identifying best practices and proposing a coherent framework for enhancing the efficiency, accessibility, and consistency of writ jurisdiction in contemporary legal systems. The article concludes with novel, researcher-driven recommendations to address contemporary jurisprudential bottlenecks, aiming to streamline extraordinary constitutional remedies
TOXICO-PATHOLOGICAL EFFECTS OF HEAVY METALS ON GOAT VISCERAL ORGANS IN PAKISTAN: A COMPREHENSIVE REVIEW
The accumulation of dangerous heavy metals in the body of food producing animals is an increasing health issue of concern worldwide and more particularly in low and middle income countries like Pakistan. This review will look at the available literature on the absorption and retention of different toxic elements in the internal organs of goats, which forms an important part of the livestock industry in the country. Goats often consume polluted soil, vegetation and water in regions of industrial discharge, mining, and irrigation of untreated wastewater because of their free-ranging feeding behavior. The liver and kidneys are the organs in which metal deposition is the greatest, and in which the pathological conditions of cell degeneration, tissue death, and disrupted organ functions are observed. Some investigations carried out in other cities in Pakistan reported metal levels that habitually exceeded internationally advised safety limits. In terms of public health, the results are frightening, because chronic food contamination with chromium and mercury already surpasses the safe daily exposure levels of these elements in adults, with liver being the most severe threat to the consumers. Those gaps in knowledge are significant, as there are few studies that focus on more than one organ, long-term observations are not conducted, and geographical coverage is not complete. The future directions are to design country-wide surveillance systems, use sensitive analysis techniques like atomic absorption spectroscopy, and determine risk characterization in detail by using species-specific exposure indices. It is necessary to develop national regulatory limits to safeguard human health against metal contamination that is produced in the livestock production chain
EFFECT OF NANO-SILICA INCORPORATION ON THE MECHANICAL AND DURABILITY PROPERTIES OF CONCRETE: A REVIEW
Nano-silica (NS) has emerged as a significant supplementary cementitious material for enhancing concrete performance through its pozzolanic activity and nano-filler effects. When incorporated at optimal dosages (typically 1–3% by weight of cement), nano-silica improves both mechanical properties including compressive strength, flexural strength, and split tensile strength, as well as durability characteristics such as carbonation resistance, acid attack resistance, and rapid chloride penetration. [1] The addition of nano-silica particles considerably improves concrete mechanical properties, with compressive, flexural, and splitting tensile strengths increasing by 15.5%, 27.3%, and 19%, respectively, compared to control concrete. Optimal nano-silica content ranges from 2–3 wt%, with excessive replacements reducing improvements and even causing negative impacts on impact resistance and durability. [2] An appropriate nano-silica content of 2% contributes to dense pore structure and generation of more hydration products, resulting in enhanced impermeability with compressive strength of 43.53 MPa and improvements in chloride ion and water penetration resistance of 21% and 35%, respectively. [3] Self-compacting concrete with 2% nano-silica showed tensile strength improvements of 18.82% compared to control mix, with permeability tests exhibiting a decrease from 378 coulombs to 99 coulombs. Microstructural analysis revealed dense matrix with better development of calcium silicate gel. [4] The optimal nano-silica and graphene oxide combination (1.0% NS and 0.06% GO) enhanced compressive strength and lowered chloride ion permeability by 21.3% and 38.7%, respectively, when compared with control specimens, attributed to increased matrix density and improved interfacial bonding. [5]
INFLUENCE OF GRAPHENE OXIDE ADDITIVES ON STRENGTH ENHANCEMENT OF STRUCTURAL CONCRETE: A REVIEW
Graphene oxide (GO), a two-dimensional carbon nanomaterial, has emerged as a promising additive for enhancing the mechanical properties of structural concrete. This review synthesizes recent peer-reviewed literature examining the influence of GO on compressive strength, flexural strength, tensile strength, and microstructural of concrete composites. Analysis of 45 empirical studies and reviews characteristics reveals that optimal GO dosages (0.01–0.10% by weight of cement) consistently improve compressive strength by 9–77%, flexural strength by 6–78%, and tensile strength by 12–79%. GO achieves these enhancements through three primary mechanisms: (1) acceleration of cement hydration kinetics, (2) densification of the cement matrix through refined pore structure and reduced porosity, and (3) izmproved interfacial bonding at the cement–aggregate and nanofiller interfaces. However, critical challenges persist: GO exhibits poor dispersibility in the alkaline cement environment, leading to agglomeration at dosages exceeding optimal thresholds; cost and scalability remain significant barriers to industrial adoption; and long-term durability data under diverse environmental conditions remain limited. This review identifies research gaps regarding full-scale structural applications, synergistic effects with supplementary cementitious materials, and the optimal GO dosage variability across concrete grades. Future investigations must prioritize dispersion strategies, cost reduction, sustainability metrics, and validation through large-scale structural testing to enable widespread implementation of GO-enhanced concrete in critical infrastructure.
INFLUENCE OF OSTRACISM EXPERIENCE ON ACADEMIC PERFORMANCE AND SELF-ESTEEM AMONG UNIVERSITY STUDENTS
Introduction: This present research explored the impact of social exclusion (ostracism) on the academic performance and self-esteem of university students. The key focus of this research is to determine the relation between ostracism and self-esteem, as well impact of ostracism on academic performance. Objectives: This research highlights whether the Pakistani students experience ostracism and to what extent it affects their academic success and sense of self-worth. Method: A convenient sample of 228 students were used including 92 males and 136 female participants. They were asked to complete the Ostracism Experience Scale, Academic Performance Scale and Rosenberg Self-esteem Scale. Results: Results indicated a significant relationship among study variables with no significant gender differences. Correlational analyses indicated the existence of significant relationship between ostracism and either academic performance or self-esteem. Regression analyses further showed that social isolation experience significantly predicted the academic performance or self-esteem. These results suggest that, for this sample, ostracism did not have a measurable influence on self - esteem of adults within a collectivist culture, but it does affect academic performance. Age is also a significant predictor of better academic performance. However, Self-esteem is positive predictor of academic performance. Conclusion: The study highlights the complexity of psychosocial factors in academic success and underlines the need for future research to explore additional mediating and moderating variables that may clearly explain the connection between social isolation and student wellbeing
EXPLORING THE ROLE OF SCHOOL LEADERSHIP IN RESOURCE-CONSTRAINED SECONDARY SCHOOLS: EVIDENCE FROM DISTRICT KECH, BALOCHISTAN, PAKISTAN
This qualitative case study investigates how secondary school principals in District Kech, Balochistan, enact leadership amid acute resource scarcity, addressing a critical empirical void in educational leadership research within Pakistan's most marginalized province. Drawing on semi-structured interviews with ten principals and twenty-five teachers, alongside document analysis and school observations, the study reveals that principals practice "adaptive resourcefulness “a dynamic interplay of bureaucratic navigation, community engagement, and teacher capacity-building that compensates for systemic deficiencies. Leadership enactment is profoundly shaped by geographic isolation, cultural norms, and a coercive "sink or swim" professional socialization process, positioning principals as critical boundary-spanners between state apparatus, local communities, and schools. Notably, female principals encounter intensified constraints related to mobility and positional authority within patriarchal structures. As the first empirical inquiry of its kind in this context, the study advances adaptive and transformational leadership discourses by proposing an Adaptive-Resourceful Leadership (ARL) model, offering a culturally situated framework for understanding school leadership in resource-constrained settings across the Global South.
THE ROLE OF STRATEGIC MANAGEMENT IN IMPROVING THE QUALITY OF EDUCATION IN PASNI, BALOCHISTAN
The role of strategic management in enhancing quality of education in government secondary schools in Pasni, Balochistan is explored in this qualitative case study. Using purposive sampling, data were collected from five participants (1 head teacher and 4 classroom teachers) using semi-structured interviews, classroom observations and document analysis. Four major findings emerged from the thematic analysis of the findings: (1) strategic management practices are either not present in schools in Pasni or are superficially applied; (2) the head teacher's transformational leadership behaviours are key enablers of what little strategic management exists; (3) resource constraints and capacity deficits within the institutions are the major impediments to effective implementation of strategic management; and (4) strategic management, even in a limited way, is associated with recognisable improvements in the five dimensions of educational quality. Finally, the study offers policy recommendations based on context, providing original empirical findings in the poorly researched area of strategic management theory and low income education quality in remote coastal schools of Balochistan.
EXPLORING PSYCHOLOGICAL AND PHYSICAL ABUSE IN CHILDREN WORKING AS LABORERS IN MECHANIC WORKSHOPS IN PAKISTAN
Background. Child labor in Pakistan's informal automobile mechanic workshops remains a critical human rights concern. This qualitative study examined the lived experiences of seven child laborers (ages 13-17) in Islamabad and Rawalpindi. Method. Using a qualitative approach, semi-structured interviews were conducted and analyzed through reflexive thematic analysis. The study was guided by interpretive phenomenological analysis (IPA) grounded in constructivist epistemology and a rights-based approach. Results. Five primary themes emerged: (1) economic compulsion as entry driver; (2) interrupted education and foreclosed futures; (3) physical hazards and absent safety protections; (4) psychological harm through systematic abuse; and (5) social isolation despite adaptive resilience. Children uniformly attributed labor entry to family economic collapse rather than choice, with injuries normalized and specific aspirations systematically foreclosed. Conclusion. Findings demonstrate that child labor emerges from structural poverty and family disintegration, not individual deficiency. Effective intervention requires simultaneous, coordinated action across poverty reduction, education access, mental health support, and enforcement reform, combined with the amplification of children's own voices.
SUSTAINABILITY ASSESSMENT OF STEEL FIBER REINFORCED CONCRETE FOR MODERN INFRASTRUCTURE DEVELOPMENT
Steel fiber reinforced concrete (SFRC) has emerged as a promising sustainable material for modern infrastructure development, offering enhanced mechanical performance, improved crack control, and extended service life compared to conventional concrete. This review synthesizes current peer-reviewed literature on SFRC sustainability, environmental impacts, mechanical properties, and durability characteristics. A comprehensive life cycle assessment framework is applied to evaluate embodied carbon, energy demand, and environmental toxicity across SFRC production and service phases. Results demonstrate that while SFRC exhibits elevated cradle-to-gate emissions due to fiber production and higher material dosages, its superior durability and extended service life substantially reduce lifecycle environmental impacts at the structural level. Optimal fiber dosages (0.8–1.0% by volume for steel fibers) balance mechanical performance gains with environmental efficiency. Key sustainability benefits emerge from using recycled tire steel fibers, low-carbon supplementary cementitious materials (SCMs), and optimized mix designs. However, significant research gaps persist regarding standardized LCA methodologies, long-term durability validation, and circular-economy strategies. This review underscores that performance-based life-cycle evaluation is essential for accurately assessing SFRC's sustainability potential in resilient, low-carbon infrastructure systems.
MACHINE LEARNING APPLICATIONS FOR PREDICTING MECHANICAL AND DURABILITY PROPERTIES OF STEEL FIBER REINFORCED CONCRETE: A COMPREHENSIVE REVIEW
Estimating concrete properties using soft computing techniques has been shown to be a time and cost-efficient method in the construction industry. Multilayer perceptron neural network (MLPNN) and ensembled machine learning models known as MLPNN-adaptive boosting and MLPNN-bagging are used for predicting steel fiber-reinforced concrete (SFRC) strength under compressive and flexural loads, with MLPNN-AdaBoost demonstrating higher R² values of 0.94 and 0.95 for flexural and compressive strength, respectively, and lower error values resulting in more precision than other methods (Al-Hashem et al., 2022). Three ensembled models including Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) are considered to predict the 28-day flexural strength of steel fiber-reinforced concrete, with Gradient Boosting showing the highest precision with an R² of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R² values of 0.94 and 0.86, respectively (Zheng et al., 2022).
PICKING UP THE PIECES: A COGNITIVE BEHAVIORAL INTERVENTION FOR ANGER, GRIEF, AND SELF-ESTEEM IN DIVORCED MEN WITH HISTORIES OF INTERPERSONAL VIOLENCE
Introduction Being separated or divorced is one of the most psychologically destabilizing life events for adult men, often followed by clinically significant anger, grief, and identity disruption — particularly when the marriage involved interpersonal violence (IPV) or chronic conflict. Although men are increasingly recognized as being at risk of post-divorce distress, men-specific therapeutic interventions remain insufficiently evaluated. Objectives This study investigated the effectiveness of a structured, 6-phase Cognitive Behavioral Therapy (CBT) intervention in reducing anger, grief, and low self-esteem among divorced men with a history of interpersonal violence. Methods A quasi-experimental, single-group pretest–posttest case series design was employed. Five adult men (aged 26–41 years) were recruited through purposive sampling from a clinical practice. Pre- and post-assessments utilized standardized instruments: the DSM-5 Level 2 Anger Scale (Adult), the Rosenberg Self-Esteem Scale (RSES), and the Brief Grief Questionnaire (BGQ). Results All three outcome domains demonstrated clinically and statistically significant improvements. Mean anger scores declined by 53.9% (17.80 to 8.20). Self-esteem scores rose by 86.3% (14.60 to 27.20), moving participants from the low to the normative range. Grief scores decreased by 59.5% (7.40 to 3.00), falling below the clinical concern threshold. Paired-samples analyses confirmed these gains (anger: t(4) = −9.80, p < .01; self-esteem: t(4) = 16.84, p < .001; grief: t(4) = −6.49, p < .01). Conclusion Findings provide early evidence that structured, gender-responsive CBT can effectively reduce psychological distress among men exposed to interpersonal violence in the context of divorce, underscoring the need for controlled replication
PERCEPTIONS OF MANAGEMENT REGARDING CHALLENGES IN BS PROGRAMS IMPLEMENTATION AT GOVERNMENT GRADUATE COLLEGES
The four years Bachelor of Studies (BS) program in Pakistan was developed with the aim of improving the quality of higher education and making undergraduate education consistent with the international standard. But the development of BS programs in government graduate colleges has brought many problems related to management. This paper attempts to investigate the perceptions of college management regarding these problems in implementation of BS programs and the impact of such problems on the implementation of the programs. The data was gathered by conducting semi-structured interviews with sixteen individuals, comprising of principals, vice principals, and heads of departments, sampled through purposive sampling from two government graduate colleges located in the district Sheikhupura. The data was analyzed through Thematic Analysis. Three major themes emerged from the analysis, namely Human Resources Issues, Issues regarding Resources and Infrastructure, and Issues related to Administration and Governance. Some of the major issues identified include shortage of faculty members, lack of administrative staff members, limited funding, insufficient infrastructure, lack of proper communication with affiliated universities, problems in handling of data, lack of quality enhancement cells, and insufficient autonomy of the institution. One of the key findings was that the expansion of BS programs has not been fully aligned with the institutional capacity of government colleges, which has led to increased demands on management and operational inefficiencies. It is evident from the study that there is a need for increasing institutional capacity, effective coordination between stakeholders, strengthening quality assurance, and giving more freedom in administration and finance.
PAKISTANI IDENTITIES AND POST-COLONIAL POWER STRUCTURES IN KAMILA SHAMSIE’S BEST OF FRIENDS
For the purpose of this research, the novel Best of Friends by Kamila Shamsie has been analysed through the perspective of postcolonialism. The postcolonial environment of South Asia has been characterised by intricate power relations and identities that are up for debate, with the area standing out as a particularly powerful discourses of these tensions. Despite the fact that there is a vast body of literature that investigates the sociopolitical elements of Pakistan, there is a huge research vacuum in the area of understanding the formation and contestation of Pakistani identities within the context of postcolonial hegemonic frameworks. With reference to both the Pakistani setting and the diasporic position of Pakistani people, this thesis investigates how Foucauldian discourses of power and discursive practices might be employed to decode the diverse narratives of Pakistani identities that are found in Kamila Shamsie’s Best of Friends. Additionally, the investigation of the function that historical narratives, state apparatuses, and hegemonic discourses play in the production and negotiation of Pakistani identities, based on subjectivities is a topic that has been investigated in the research. The purpose of this study was to uncover the ways in which these writings question or perpetuate the postcolonial hegemonic discourses in the context of Pakistani communities, both those living at home and those living in diaspora whose identities have been shaped. It was accomplished through the use of Foucauldian discourses. In addition to this, it investigates the ways in which literature serves as a place of resistance and subversion against hegemonic forces, therefore offering alternative narratives and points of view on Pakistani identities. While in Pakistan, this research makes a contribution to the larger discourse on postcolonialism, identity politics, and the function of literature in forming and resisting hegemonic narratives within the South Asian context. In particular, this research focusses on Pakistan
SOCIO-ECONOMIC CONTRIBUTIONS OF THE CHRISTIAN COMMUNITY IN FAISALABAD: CHALLENGES, OPPORTUNITIES, AND INTERFAITH HARMONY
The Christian community in Faisalabad has made significant contributions to the socio-economic development of the city through education, healthcare, social welfare, industrial labour, municipal services, and community-based initiatives. Historically, Christian schools, churches, hospitals, welfare organizations, and service professionals have benefited both Christian and non-Christian populations and have helped create shared civic spaces where interfaith interaction and social cooperation take place. Despite these contributions, the community continues to face structural challenges, including educational inequality, occupational segregation, employment discrimination, poverty, social marginalization, and limited political voice. This study adopts a qualitative research design based exclusively on secondary data analysis. Relevant evidence was drawn from books, peer-reviewed journal articles, government publications, policy documents, census material, and reports issued by national and international organizations. The collected material was analyzed thematically around education, healthcare, social welfare, economic participation, challenges, policy opportunities, and interfaith harmony. The findings indicate that the Christian community has contributed substantially to Faisalabad’s human capital development, public health, urban services, and social cohesion. However, its full participation in socio-economic development remains constrained by structural barriers and unequal access to opportunities. The study recommends inclusive education policies, equal employment practices, technical training, safer working conditions, minority-sensitive development planning, and interfaith community initiatives. By linking socio-economic contribution with interfaith harmony, the article argues that minority development is not only a rights-based concern but also a practical requirement for sustainable urban development and peaceful coexistence in Pakistan
STONEWALLING AND EMOTIONAL NEGLECT AS INTERPERSONAL VIOLENCE IN GENERATION Z ROMANTIC RELATIONSHIPS: A QUALITATIVE PHENOMENOLOGICAL STUDY
Background: To shed light on the nature of interpersonal violence (IPV) among adolescents and young adults (AYA) in the era of Generation Z.Methods: The authors review the literature to provide an understanding of what the research literature has to say about the nature of IPV perpetration among AYA in the age of Generation Z. Aims: The aim of this qualitative, phenomenological study was to explore the nature of the phenomenon of stonewalling and emotional neglect as a type of interpersonal violence in romantic relationships of Generation Z (18–29 years old). Method: Ten Pakistani university students and young professionals were sampled using purposive sampling and interviewed semi-structuredly. Braun and Clarke's (2006) reflexive thematic analysis was used for analysing data. Results: Four major themes were found: (1) Some digital connectivity is being weaponized, (2) Withdrawal is somatic and psychological, (3) Withdrawal's boundary-punishment split, (4) Sociocultural emotional neglect normalization. Results show that the relational behaviour that is typical of a digitally mediated environment in Generation Z is a practice of power and control, it leaves physical and psychological traces and is systemically misinterpreted as normal relational behaviour. Conclusion: The findings added to the current IPV framework by focusing non-physical, technologically mediated harm as a primary (and not a secondary) construct of a youth romantic violence framework. Psychoeducational intervention, relationship literacy programming and institutional mental health policy implications are discussed.
ENFORCEABILITY OF SMART CONTRACTS UNDER THE ELECTRONIC TRANSACTIONS ORDINANCE 2002: ISSUES AND CHALLENGES
Contractual relationships have taken on a new meaning in the era of blockchain technology, with smart contracts being self-executing digital agreements that can carry out contractual obligations without the need for constant human participation. Smart contracts are unlike traditional contracts, in which the terms of the agreement are written in computer code and are performed automatically once the specified conditions are met. Through this technological innovation, the efficiency, transaction cost, absence of intermediaries and transparency of commercial dealings can all be improved, making it an increasingly appealing method of modern electronic commerce.
GUARDIANSHIP AND CUSTODY DISPUTES IN PAKISTAN: IMPACT OF THE GUARDIAN AND WARDS ACT 1890 ON THE WELFARE OF THE CHILD PRINCIPLE
Guardianship and custody disputes in Pakistan are primarily governed by the colonial-era Guardians and Wards Act, 1890, which continues to play a central role in determining the custody and welfare of children. This research article critically examines the effectiveness of the Act in protecting the “welfare of the child” principle within the contemporary socio-legal framework of Pakistan. The study evaluates how Pakistani courts interpret and apply welfare considerations in custody disputes involving parents, guardians, and extended family members. It further analyzes the interaction between statutory provisions, Islamic principles, constitutional guarantees, and international child rights standards. The article highlights that although courts increasingly prioritize the best interests of the child, practical implementation remains inconsistent due to judicial discretion, procedural delays, gender biases, financial inequalities, and social pressures. The research also identifies challenges faced by children in emotionally contested custody proceedings and examines whether the existing legal framework adequately addresses their psychological, educational, and emotional needs. The study concludes that significant legal and institutional reforms are necessary to ensure child-centered adjudication and strengthen the protection of children’s welfare in custody and guardianship matters in Pakistan.
FISCAL FEDERALISM AND PROVINCIAL DEVELOPMENT: ASSESSING THE IMPACT OF THE 7TH NFC AWARD ON KHYBER PAKHTUNKHWA
Fiscal federalism, the branch of public finance that studies how financial powers and responsibilities are divided between central and sub-national governments, offers a useful lens for understanding how Pakistan's provinces have developed since 1973. This paper applies that framework to Pakistan's 7th National Finance Commission (NFC) Award of 2009, examining its impact on provincial development in Khyber Pakhtunkhwa (KP). Using verified data from government budget documents, the Pakistan Economic Survey, World Bank project reports, and peer-reviewed and policy literature published between 2011 and 2026, the study finds that the 7th NFC Award represented a meaningful move toward the fiscal-federalist ideal of matching resources to need: it raised the provinces' combined share of the divisible pool to 57.5 percent, introduced poverty and revenue-effort criteria alongside population, and recognised KP's exposure to militancy through a dedicated one percent allocation. Federal transfers to KP consequently grew from approximately Rs151 billion in 2010-11 to a budgeted Rs1,342 billion in 2025-26, financing measurable gains in education spending, health expenditure, and poverty reduction. However, judged against core fiscal-federalism principles, most notably the fiscal-equivalence theory advanced by Olson (1969) and the decentralisation theorem developed by Oates (1972), the Award falls short in important respects: its formula still weights population at 82 percent, it has never been revised to reflect the 2018 merger of the former Federally Administered Tribal Areas (FATA) into KP, and the province's own-source revenue base remains too small to support genuine fiscal autonomy. These shortcomings culminated in 2026 in KP's decision to challenge the Award before Pakistan's Federal Constitutional Court. The paper concludes that provincial development in KP cannot be fully realised without a more dynamic, needs-responsive NFC formula and stronger provincial revenue mobilisation.
DEMOCRACY IN THE AGE OF ARTIFICIAL INTELLIGENCE: OPPORTUNITIES, CHALLENGES, AND POLICY IMPLICATIONS
Artificial intelligence (AI) is changing the way democracies work. It can help governments serve citizens faster, help people take part in politics in new ways, and help leaders make decisions based on real evidence rather than guesswork. At the same time, AI creates new dangers for democratic life. It can spread false information through deepfakes, help governments watch and control their own citizens, and give a small number of large technology firms outsized influence over what people see, hear, and believe. This paper examines both sides of this story through a critical review of recent political science, public policy, and governance literature. It builds on conceptual work by scholars such as Jungherr (2023), Coeckelbergh (2024), and Landemore (2024), and it uses real-world evidence from the 2024 global election cycle, including detailed attention to Pakistan and South Asia, to show how AI is already shaping political life on the ground. The paper also reviews how international bodies such as the Organisation for Economic Co-operation and Development (OECD), the United Nations Educational, Scientific and Cultural Organization (UNESCO), and the European Union are trying to govern AI, and asks what these efforts mean for developing democracies with weaker institutions. The central argument is that AI is not good or bad for democracy on its own. What matters most is how citizens, governments, and institutions choose to design, regulate, and use it. The paper closes with concrete policy recommendations for both established and developing democracies, with a particular focus on transparency, institutional accountability, and citizen participation.
AN ASSESSMENT OF GOVERNANCE AND CLIMATE CHANGE ADAPTATION IN KHYBER PAKHTUNKHWA: A CASE STUDY OF DISTRICT BUNER
Khyber Pakhtunkhwa (KP) is among the most climate-vulnerable provinces of Pakistan, and within it, District Buner has emerged as a focal point of recurrent flood disasters, most recently the catastrophic cloudburst-induced flash floods of August 2025. This paper examines the relationship between governance structures and climate change adaptation in Buner, asking whether the institutional architecture established under the Khyber Pakhtunkhwa Local Government Act, 2013, and the provincial Climate Change Policy 2022 translates into effective local adaptation. Using a qualitative, document-based case-study design that draws on government policy documents, disaster statistics, demographic census data, and the multi-level governance literature on climate adaptation, the study finds that Buner’s adaptation deficit stems less from an absence of policy than from weak vertical coordination between federal, provincial, and district tiers; limited fiscal and technical capacity at the local government level; environmental degradation linked to deforestation and unregulated marble mining; and a persistent gap between policy formulation and field implementation. The paper situates these findings within multi-level governance theory and recommends catchment-based coordination, climate-budget tagging, accelerated early-warning investment, stronger mining and land-use regulation, and empowered local government institutions as priorities for strengthening adaptive governance in Buner and similarly exposed districts of KP.
IMPACT OF VERBAL ABUSE OF PARENTS ON THE DEVELOPMENT OF REACTIVE AGGRESSION AMONG UNIVERSITY STUDENTS
Background: One of the significant adverse childhood experiences that contribute to aggressive behaviors in later life is parental verbal abuse. Social learning theory says that when children are exposed to verbal abuse, aggressive responses are developed through internalization and observation of harmful interactions. The current study aimed to investigate the association between parental verbal abuse and reactive aggression among university students Objective: The current study examined the association between verbal abuse of parents and development of reactive aggression. Along with this, the study also aimed to examine the predictive role of parental verbal abuse on reactive aggression. Furthermore, the study explores the gender differences in reactive aggression among university students Methods: The study was conducted using a sample of 270 university students. The research design that was employed for this study is a cross-sectional correlational design. The instruments that were used for measuring variables are, Verbal Abuse Scale and Reactive proactive aggression scale. For testing the hypothesis of the study, correlation, independent t test and regression analysis were run. Results: For the study, a sample of 270 university students were taken, while 138 (51.1%) were females and 132 (48.9%) were male participants. The mean score on the Verbal Abuse was (62.5) whereas, on reactive agression it was (31.7). The Pearson correlation analysis showed a statistically weak and non significant relationship between the two variables r (270) =>10, p=.106. Furthermore, the regression analysis found that parental verbal abuse didn’t significantly predict reactive aggression among university students (R²= .01, F (1,267) =2.64, p=.106). Where as independent sample t test revealed female scores higher on parental verbal abuse (M=63.8, SD=6.5) than males (M=61.1, SD=7.6, t (270) =-3.17, p=.002). While no gender differences were found for reactive aggression t (263.4) =1.82, p=.070. Conclusion: The outcomes suggest that verbal from parents shares no significant connection with reactive aggression in the present sample. Furthermore, parental verbal abuse did not predict reactive aggression, and reactive aggression did not differ by gender. These results indicate that other factors may play a more substantial role in explaining reactive aggression among university student
DARK PHOTON PHYSICS: A COMPREHENSIVE REVIEW OF THEORETICAL MODELS, DARK MATTER CONNECTIONS, EXPERIMENTAL SEARCHES, AND FUTURE PROSPECTS
The nature of dark matter remains one of the most significant unresolved problems in modern particle physics and cosmology. Astronomical observations indicate that only about 5% of the Universe consists of ordinary matter. Approximately 27% is composed of dark matter, while nearly 68% is dark energy. Among the many theoretical candidates proposed to explain dark matter, the dark photon has emerged as one of the most promising and experimentally accessible possibilities. The dark photon is a hypothetical gauge boson associated with an additional hidden gauge symmetry. Unlike the ordinary photon, the dark photon can possess a small non-zero mass and interact weakly with visible matter through a mechanism known as kinetic mixing. This interaction provides a natural portal between the visible sector and a hidden dark sector, enabling potential experimental detection while remaining consistent with current observational constraints. This article presents a comprehensive overview of the different aspects of dark photons, including their theoretical motivation, mathematical formulation, production mechanisms, decay channels, cosmological implications, astrophysical significance, and experimental searches. The theoretical framework is developed by introducing an extended gauge symmetry with kinetic mixing between the electromagnetic field and the hidden gauge field. The dependence of dark photon phenomenology on its mass and kinetic mixing parameter. Various production mechanisms, including thermal production, freeze-in processes, meson decays, bremsstrahlung, and collider production, are examined. The possible decay modes into Standard Model particles and hidden-sector particles are also analyzed. The cosmological consequences of dark photons are explored in the context of the early Universe, Big Bang nucleosynthesis, the cosmic microwave background, and structure formation. Their possible role as mediators of dark matter interactions and their influence on galaxy dynamics are also discussed. Furthermore, current and future experimental efforts, including collider experiments, beam-dump facilities, fixed-target experiments, precision atomic measurements, and direct detection searches, are reviewed. Constraints on the dark photon parameter space obtained from laboratory experiments and astrophysical observations are summarized. The study highlights how dark photons provide a compelling framework that connects particle physics, cosmology, and astrophysics while offering experimentally testable predictions. Continued theoretical developments and next-generation experiments are expected to significantly improve sensitivity to dark photon signatures, potentially revealing new fundamental interactions beyond the Standard Model. The dark photon therefore remains a leading candidate in the search for new physics and may play a crucial role in understanding the composition and evolution of the Universe.
RELATIONSHIP BETWEEN GENERATIVE AI USAGE AND WORK ENGAGEMENT AMONG UNIVERSITY TEACHERS
The emergence of generative artificial intelligence (AI) has rapidly transformed teaching techniques, academic writing, and assessment processes in universities worldwide. The use of AI by university educators has become quite common for creating lessons, preparing content, and performing administrative activities, among others. The purpose of the present study was to investigate the connection between the use of generative AI and work engagement of university educators. A quantitative research design was employed, and data were collected through self-administered questionnaires from 200 university educators employed at public and private universities. Generative AI was measured using a custom-made scale consisting of questions assessing the frequency and usefulness of the use of generative AI. Work engagement was measured using the Utrecht Work Engagement Scale (UWES). Descriptive statistics, reliability, Pearson correlation, and regression analysis were done using IBM SPSS Statistics. It was found that there was a significant positive correlation between the use of generative AI and work engagement of university educators. Furthermore, it was found that the use of generative AI positively predicted work engagement of university educators.
THE GENERATION-DETECTION GAP: A STUDY OF DEEPFAKE PROLIFERATION AND RESPONSE IN THE GULF REGION DURING THE 2026 CONFLICT
This mixed-methods study examined the generation-detection gap for deepfakes circulating across Gulf Cooperation Council states during the active combat phase of the 2026 Gulf conflict. The generation-detection gap was defined as the proportion of high-impact synthetic content exceeding 1,000 interactions within 72 hours that was not flagged by any detection system within 24 hours of posting. We analyzed a corpus of 1,912 public audiovisual items posted between February 1 and April 10, 2026, applied four detection systems to the full corpus, and conducted semi-structured interviews with 14 journalists, platform trust-and-safety staff, regulators, and forensic analysts. Results showed that 53.7% of high-impact items were not flagged within 24 hours. The gap was widest for audio content (74.8%) and items targeting political leaders (58.6%). Median time to 1,000 interactions was 2.1 hours, while median detection latency was 4.8 hours. Logistic regression indicated that audio modality (OR = 0.32, 95% CI [0.24, 0.42]) and political targets {OR = 0.61, 95% CI (0.49, 0.76)} significantly reduced odds of detection, whereas higher 24-hour engagement modestly increased odds (OR = 1.08, 95% CI [1.02, 1.14]). Items not detected at 24 hours had a median final engagement that was 3.2 times higher than detected items. The data from the interviews indicated that the three factors that impeded the process of latency were the absence of real-time Gulf Arabic audio detectors, the difference between the capacity for generation and that of forensic work, and the difficulty of attribution for content with heads of state. The results indicate that the difference is technical and procedural, and directly impacts harm. To minimize the time period during which synthetic media influences discourse without labels, there is a need to detect targets within sub-hours, to invest in models for each dialect, and to establish protocols for sharing information during wartime