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.
PREDICTIVE MODELING USING MULTIPLE REGRESSION: A CASE STUDY ON SOCIOECONOMIC INDICATORS
Predictive modeling plays a central role in socioeconomic research by enabling analysts to quantify how demographic and macroeconomic factors contribute to income variation. Multiple regression is among the most widely used techniques for this purpose, offering a structured framework to evaluate the combined influence of education, age, employment status, household characteristics, GDP per capita, and inflation on household income. Using a well-structured synthetic dataset, the analysis integrates descriptive statistics, correlation patterns, and regression diagnostics to demonstrate the methodological processes required for rigorous predictive modeling. The findings reveal weak empirical relationships between the selected variables and income an expected outcome given the artificial nature of the dataset yet they provide valuable insights into model construction, assumption testing, and the interpretation of statistical outputs. By emphasizing analytical transparency and methodological clarity, the study illustrates how regression-based predictive frameworks can be applied to socioeconomic indicators, reinforcing their importance for academic inquiry and policy-oriented analysis.
A COMPARATIVE ANALYSIS OF QUALITY EDUCATION ALIGNMENT WITH SUSTAINABLE DEVELOPMENT GOAL 4 (SDG-4) IN GOVERNMENT AND PUNJAB EDUCATION FOUNDATION SCHOOLS AT THE PRIMARY LEVEL
Quality education is education that provides all learners with the knowledge, skills, and values necessary to succeed in life and contribute to society, ensuring equal access and opportunities for everyone. This comparative study assessed the alignment of primary education quality with Sustainable Development Goal 4 (SDG-4) across centralized Government schools and decentralized Punjab Education Foundation (PEF) schools in District Bahawalpur. Employing a rigorous inferential statistical analysis of performance metrics, including Literacy and Numeracy Drive (LND) scores, the research confirmed the PEF model’s effectiveness in fulfilling access mandates. Critically, PEF schools demonstrated statistically comparable learning outcomes to resource-intensive Government schools, as confirmed by inferential statistical tests. However, the analysis identified a critical systemic gap in quality inputs, specifically the lower training levels of PEF teachers. The findings imply that achieving comprehensive SDG-4 compliance requires harmonizing quality inputs across the dual system, necessitating policy intervention to mandate cross-sectoral teacher capacity development.
CHROMATIC EXPRESSION IN CHOLISTANI NOMADIC CULTURAL: AN ANALYSIS THROUGH WESTERN ARTIST COLOR REFERENCES
This research explores the chromatic expression embedded in Cholistani nomadic culture and examines how these traditional color practices can be analyzed, understood, and reinterpreted through references to Western artists. The study investigates the symbolic, environmental, and cultural meanings of colors used by Cholistani nomads in their textiles, attire, crafts, and daily life. By comparing these indigenous color choices with the chromatic strategies observed in selected Western artworks, the research highlights both parallels and contrasts in color theory, emotional impact, and visual communication. Through qualitative analysis, field observations, and visual comparison, the study aims to identify how Western chromatic principles can inform a refined color selection process while maintaining the authenticity of Cholistan’s cultural identity. The findings contribute to cross-cultural design knowledge, offering new insights for artists, designers, and researchers seeking to integrate traditional Pakistani color heritage with global artistic methodologies.
THE ADKAR MODEL AND DIGITALIZATION IN PAKISTAN'S MANUFACTURING SECTOR: A CASE STUDY OF LUCKY CEMENT
This study investigates the impact of Green Human Resource Management (GHRM) practices on organizational sustainability in Pakistani manufacturing firms, focusing on the mediating roles of employee engagement and motivation. Drawing on the Resource-Based View (RBV) and Social Exchange Theory (SET), a quantitative PLS-SEM analysis was conducted using survey data from 250 managerial respondents in Sindh Province's textile sector. Results reveal a stark divide in GHRM efficacy: green recruitment (β=0.472, T=10.241) and green training (β=0.272, T=3.794) significantly enhance employee motivation and engagement, indirectly driving sustainability (R²=0.568). Conversely, green performance appraisal (mean=2.90) and green reward systems (mean=4.20) show non-significant paths, indicating implementation failures and decoupled rewards. Mediation analysis confirms strong indirect effects via front-end practices, explaining 56.8% variance in sustainability outcomes. The study addresses literature gaps by empirically validating psychological mechanisms over direct effects, highlighting "fluffy rewards" and appraisal deficiencies in developing contexts. Theoretical implications refine RBV and SET for GHRM bundles; practical recommendations urge prioritizing recruitment/training investments and overhauling back-end systems. Limitations include cross-sectional design and conceptual mismatches with proposed green innovation mediation. Future research should adopt longitudinal, multi-source approaches to test innovation pathways.
DEVELOPMENT OF THAL GUARA: A HIGH-YIELDING, EARLY MATURING AND DROUGHT TOLERANT GUAR (CYAMOPSIS TETRAGONOLOBA) VARIETY FOR SUSTAINABLE AGRICULTURE
Guar (Cyamopsis tetragonoloba (L.) Taub.) is an important leguminous crop with significant agricultural and industrial value due to its ability to improve soil fertility and serve as a major source of guar gum. The present study focused on the breeding, selection, and multi-environment evaluation of a newly developed guar variety, Thal Guara. Multi-location yield trials conducted from 2014 to 2020 indicated that Thal Guara exhibited superior performance compared with the standard check varieties BR-99 and BR-2017, with yield increases of up to 25.4% under optimal agronomic conditions. The variety was evaluated at different agro-ecological zones and performed best when sown in early June with a fertilization regime of 30-60-60 kg NPK ha⁻¹. Thal Guara also demonstrated enhanced drought resilience, lower pest infestation, and moderate resistance against bacterial blight and Alternaria blight. Biochemical analysis of Thal guara exhibited high guar gum contents, protein, and carbohydrates which are suitability for diverse industrial uses. The stable yield performance and desirable agronomic traits indicate its potential for large-scale adoption of Thal Guara to enhance guar production and promote sustainable agriculture.
RELATIONSHIP BETWEEN INCLUSIVE LEADERSHIP, PSYCHOLOGICAL SAFETY, AND PSYCHOLOGICAL EMPOWERMENT AT THE WORKPLACE: A SYSTEMATIC LITERATURE REVIEW
The presented methodological literature review is a study of the interdependence of the aspects inclusive leadership, psychological safety, and psychological empowerment in contemporary organizations. According to the social exchange theory and the psychological theories of motivation, the review confirms the results of the empirical studies, peer-reviewed sources, and hypothesizes the mediated model where the mediator is the psychological safety between the inclusive leadership and psychological empowerment. The three research hypotheses administered through archival analysis include (H1) Psychological safety is positively predicted by inclusive leadership, (H2) Psychological empowerment is positively predicted by psychological safety, and the hypotheses (H3) includes the relationship between inclusive leadership and psychological empowerment as mediated by psychological safety. In databases: Scopus, Web of science and Google Scholar, 45 databases and articles were searched and reviewed in the period 1995-2024 based on PRISMA. The results indicate that each of the hypotheses is mostly accepted and that the effect sizes are moderate to large (e.g., r =.39 -.59), between safety and leadership; r =.45- 70, between safety and empowerment; indirect 2 =.11- 18, between safety and intermediate variables). Practical implications would include the encouragement of inclusive leadership that may contribute to enhancing the engagement of staff members and their creativity. Limitations and the future research directions are discussed.
PREDICTING COMPRESSIVE STRENGTH OF ULTRA-HIGH-PERFORMANCE CONCRETE USING SUPPORT VECTOR MACHINE REGRESSION: MODEL DEVELOPMENT, FEATURE ANALYSIS, AND VALIDATION
Ultra-High-Performance Concrete (UHPC) is a complex composite material whose mechanical properties depend on intricate interactions between multiple constituents and curing conditions. Traditional mix design relies on extensive experimentation, which is time-consuming and costly. This study develops and validates a Support Vector Machine (SVM) regression model to predict the compressive strength of UHPC using a dataset of 810 mixtures with 13 input features. The model was configured as a ν-SVM with a Radial Basis Function (RBF) kernel and automatic hyperparameter tuning. Performance evaluation on a held-out test set yielded an R² of 0.908, RMSE of 12.205 MPa, and MAPE of 8.969%, indicating high predictive accuracy and generalization. Feature importance analysis, supplemented by k-fold validation, identified silica fume, fiber content, and curing age as the most influential factors—findings consistent with contemporary material science and recent machine learning literature. The study demonstrates that SVM regression, coupled with robust feature selection and validation, provides a reliable, interpretable, and efficient tool for UHPC mix optimization, reducing the need for physical trials and accelerating the development of high-performance concrete formulations.
THE IMPACT OF GOVERNMENT-INDUSTRY-ACADEMIA TRIPLE HELIX MODEL ON PERFORMANCE OF SINDH TECHNICAL EDUCATION & VOCATIONAL TRAINING AUTHORITY (STEVTA): A LONGITUDINAL STUDY
This longitudinal study investigates the impact of the Government-Industry-Academia (GIA) Triple Helix Model on the performance of the Sindh Technical Education and Vocational Training Authority (STEVTA) in Pakistan. Established in 2009 to address the demand for skilled labor, STEVTA faces challenges in aligning its curricula with labor market needs due to fragmented GIA collaboration. Drawing on the Triple Helix framework, this research examines how structured partnerships among government, industry, and academia influence institutional performance, graduate employability, and skills alignment over time. Utilizing a mixed-methods approach, including Partial Least Squares Structural Equation Modeling (PLS-SEM) and qualitative stakeholder interviews, the study analyzes data from 300 respondents, comprising students, faculty, industry partners, and policymakers. Findings reveal that government involvement is the strongest predictor of student performance, mediating the effects of industry and academic collaboration. The study highlights stakeholder perception variations and underscores the need for systematic GIA partnerships to address skills gaps and enhance institutional resilience. These findings contribute to Triple Helix theory by demonstrating its applicability in a developing economy’s vocational education context and offer practical recommendations for policymakers to strengthen STEVTA’s effectiveness through improved coordination, curriculum reform, and stakeholder engagement.
THE INVESTIGATION OF PERFORMANCE APPRAISAL AND ITS IMPACT ON EMPLOYEE RETENTION OF A PRIVATE SCHOOL IN TANDO MUHAMMAD KHAN
This mixed-methods study investigates the impact of performance appraisal systems on teacher retention in private schools of Tando Muhammad Khan, Pakistan. Grounded in Herzberg’s Two-Factor Theory, Equity Theory, and Expectancy Theory, the research addresses a critical gap in understanding how appraisal practices influence job satisfaction and, consequently, teacher retention in under-resourced educational contexts. Using a quantitative survey of 150+ teachers and qualitative interviews with 20 educators, the study applies Partial Least Squares Structural Equation Modeling (PLS-SEM) and thematic analysis. Results reveal that Career Alignment (β = 0.417), Feedback Quality (β = 0.290), and Professional Development Opportunities (β = 0.134) significantly predict Job Satisfaction (R² = 0.586), which in turn strongly predicts Teacher Retention (β = 0.571, R² = 0.526). Qualitative findings expose a stark implementation gap: while a few schools apply structured, fair, and development-oriented appraisals, most rely on informal, biased, and recognition-lacking systems. This inconsistency demotivates teachers and fuels turnover. The study concludes that teacher retention is not hindered by a flawed model but by poor execution. It recommends standardizing appraisal processes, training appraisers for fairness, linking evaluations to professional development, and institutionalizing recognition mechanisms. The findings offer actionable insights for school administrators and policymakers to enhance teacher satisfaction and reduce turnover in similar low-resource settings across Pakistan.
SEISMIC INVERSION FOR RESERVOIR CHARACTERIZATION IN UNCONVENTIONAL SHALE PLAYS
This paper examines the use of seismic inversion as an evaluation method of the reservoir in unconventional shale plays. In the Eagle Ford, Bakken and Marcellus formations, and others that contain the Shale resources, the heterogeneity, anisotropy, and multi-mineralogy of the formation pose a special challenge. Seismic inversion is very important in the conversion of seismic reflection data to quantitative rock properties in the form of acoustic impedance (AI), shear impedance (SI), density, and elastic moduli that are important to detect important features of reservoirs like Total Organic Carbon (TOC), brittleness, and geo-mechanical properties. The research paper describes the process of seismic inversion that involves data conditioning, examination of well logs, AVO/AVA analysis, inversion, interpretation and combination with other geologic as well as operating data. It also talks about the main problems encountered during the characterization of shale reservoirs, such as poor vertical resolution owing to thin beds, complicated rock physics caused by mixture of mineralogy, high anisotropy and lack of well control. Regardless of these difficulties, seismic inversion can be used to give meaningful information on where to find sweet spots, well placement, and estimate of the volume of stimulated rock as well as avoid hazards. Finally, the paper highlights the significance of the latest inversion methods and combining them with rock physics models to become effective in the development and management of shale reservoirs.
PERFORMANCE ANALYSIS OF ENSEMBLE LEARNING ALGORITHMS IN PREDICTING SOIL COMPACTION CHARACTERISTICS
The accurate determination of Maximum Dry Density (MDD) is vital for geotechnical design, yet conventional laboratory methods are resource-intensive and time-consuming. This study presents a comparative machine learning framework to predict MDD using seven index soil properties. A dataset of 486 samples was pre-processed, and feature importance was assessed using both univariate regression and the RReliefF algorithm, revealing a critical divergence: Optimum Moisture Content (OMC) was the strongest linear predictor, whereas Sand Content ranked highest in non-linear, multivariate analysis. Two ensemble algorithms Random Forest (RF) and Gradient Boosting (GB) were developed using an 80-20 train-test split, with hyperparameters controlled for a fair comparison (100 trees, max depth of 3). Model performance was evaluated using a suite of metrics (MSE, RMSE, MAE, MAPE, R², CVRMSE), supplemented by 20-fold cross-validation, error distribution analysis, and SHAP interpretability. Results indicated that both models achieved high accuracy, with RF demonstrating a slight edge on the test set (R² = 0.927, RMSE = 0.489) compared to GB (R² = 0.924, RMSE = 0.498). However, GB exhibited superior stability across cross-validation folds, while RF showed greater performance variability. SHAP analysis identified OMC and Gravel Content as the most influential and physically interpretable features. The study concludes that Random Forest is marginally superior for point prediction accuracy, but the choice between RF and GB should consider the trade-off between test-set performance and model stability. The research underscores the value of multi-method feature selection and model interpretability in developing reliable geotechnical predictive tools.
INVESTIGATION OF POST CAESAREAN SECTION WOUND INFECTIONS AT KAUSAR HOSPITAL KHAIRPUR MIR'S: RISK FACTORS, INCIDENCE RATES, BACTERIAL PROFILE & ANTIMICROBIAL RESISTANCE PATTERN
Caesarean section, also commonly referred to as C-section, is a medical operation that entails cutting of the abdomen and uterus of a mother. Failure to keep proper care after discharge may result in the development of infected surgical wounds. This study was aimed at determining the prevalence, risk factors, incidence, type of bacteria involved and trend of resistance to antibiotics in C-section wound infections in patients of Kausar Hospital, Khairpur Mir, Sindh, Pakistan. Sample pus was collected on sterilized swabs of the various hospital units under the use of sterilized Arnie. Furthermore, the information about possible risk variables was obtained with the help of the standardized questionnaire. The gathered samples were cultivated selectively and differentially using the media. Colony morphology and biochemical testing were used to identify the bacteria, and when necessary, a Micro Scan system was used for confirmation. Clinical and laboratory standards Institute (CLSI) guidelines were observed in carrying out antibiotic and antiseptic susceptibility testing. The most important risk factor was ignored personal hygiene (87%), then there was improper medication adherence (70%), and low socioeconomic status (68%). The rates of the infection were recorded in 97 cases and the maximum number was in the period of June-August during a year. Six different bacterial species were isolated: Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, methicillin-resistant Staphylococcus aureus (MRSA), and Staphylococcus epidermidis. Findings on the antibiotic sensitivity revealed that the isolates were broadly resistant. It was however found that gram-negative bacteria were susceptible to gentamicin and Augmentin, gram-positive bacteria were susceptible to cefoxitin and Augmentin. Spirit was the second-best antiseptic among the ones tried corresponding to Dettol and Pyodine. Based on findings, post C-section wound infection is not uncommon and most of the agents that cause it are becoming resistant to conventional medicines. These results demonstrate the need of clinicians to use careful antibiotic stewardship and do further studies to identify new therapeutic drugs to curb this problem.
OVERLOADED MODEL TEXTBOOKS OF SINGLE NATIONAL CURRICULUM 2020 AND THE CHALLENGE OF CLASSROOM MANAGEMENT WITHIN THE SCHEME OF STUDIES.
The introduction of the Single National Curriculum (SNC) 2020 in Pakistan marked a national effort to ensure educational uniformity, equity, and inclusivity across diverse schooling systems. However, the model textbooks developed under this curriculum have proven to be excessively content-heavy, pedagogically rigid, and incompatible with the time allocation defined in the official scheme of studies. This paper critically analyzes the burdensome nature of SNC model textbooks, highlighting the challenges teachers face in classroom management, instructional delivery, and learner engagement. Subject-wise examples from English, Urdu, General Knowledge, Science, and Islamiat textbooks illustrate how the overambitious design of the SNC has strained classroom dynamics. The study concludes that the curriculum’s vision of equality and quality cannot be realized without rationalizing textbook content, revising the scheme of studies, and empowering teachers with greater pedagogical flexibility.
NAVIGATING CONSUMER PERCEPTIONS OF SUSTAINABILITY: THE INFLUENCE OF GREEN SUPPLY CHAIN MANAGEMENT ON PURCHASING DECISIONS
Purpose: The purpose of this study is to examine the factors influencing consumers’ green purchase intentions in Pakistan. It investigates the roles of brand awareness, advertisement attitude, cognitive and environmental attitudes, and green supply chain management, while assessing the mediating effects of consumer attitudes and the moderating role of cause-related marketing. The study aims to provide both theoretical insights and practical guidance for firms seeking to enhance environmentally responsible consumer behavior. Design: The study was conducted using Partial Least Squares-Structural Equation Modelling (PLS-SEM) and a well- researched survey questionnaire. Findings: The findings show that cognitive attitude is the most predictive of green purchase intention and environmental concern and advertisement attitude. Brand awareness has a major impact on Advertisement Attitude, Cognitive Attitude, Environmental Concern, and Purchase Intention, whereas green supply chain management impacts positively on consumer attitudes and PI. The effects Cognitive Attitude and Environmental Concern have on Purchase Intention are moderated by cause-related marketing and perceived image which reinforces the positive impact of Cognitive Attitude and exhibits a crossover effect with Environmental Concern. The mediation analysis proved the partial mediation of the relationships between Brand Awareness, GSCM, and Purchase Intention by Advertisement Attitude, Cognitive Attitude, and Environmental Concern with Cognitive Attitude being the most effective mediator. In general, the model had good explanatory power using an R square of 0.883 on Purchase Intention. Theoretical Contribution: This study extends the Theory of Planned Behavior and the Stimulus-Organism-Response framework by demonstrating how brand awareness, attitudes, and green supply chain practices jointly influence green purchase intention. It also highlights the moderating role of cause-related marketing and the mediating effects of cognitive and environmental attitudes in sustainable consumer behavior. Practical Implications: The findings guide firms to leverage brand awareness, sustainable supply chain practices, and cause-related marketing to enhance consumer engagement and green purchase intentions. Marketers should focus on shaping cognitive and environmental attitudes to effectively drive environmentally responsible consumer behavior.
INTEGRATING GREEN LEADERSHIP AND STAKEHOLDER ENGAGEMENT TO STRENGTHEN GREEN SUPPLY CHAIN MANAGEMENT
Purpose: This research paper focuses on exploring the contribution of sustainability-oriented capability towards promoting the adoption and implementation of green supply chain practices in Pakistani organizations. It also stresses the significance of Green Human Resource Management, Green Transformational Leadership, and stakeholder engagement in advancing the environmentally friendly practices in the dynamic and resource-limited markets. Additionally, the research will aim at offering useful tips to management and policymakers to facilitate reasonable and sustainable organizational practices in the developing economies. Design/Methodology: Partial Least Squares-Structural Equation Modelling (PLS-SEM) and a well-studied survey questionnaire were used in conducting the study. Findings: The research concluded that good human resource practices have positive impacts on leadership in organizations, stakeholder management, and sustainability of operations in organizations. It was demonstrated that leadership and good relationships with suppliers and customers can increase the influence that HR practices have on sustainable supply chain performance. In general, the results indicate that the combination of internal strengths, leadership, and the involvement of stakeholders is essential in the realization of holistic and long-term sustainability results within organizations. Theoretical Contribution: The study is important to literature as it provides an integrated model that correlates Green Human Resource Management, Green Transformational Leadership, and stakeholder management with green supply chain performance. It gives empirical data on the interaction between internal capabilities and leadership behaviors with the external stakeholder engagement in producing sustainability outcomes. Moreover, it applies the Resource-Based View and Stakeholder Theory to the environment of the developing economies. Practical Implications: The results indicate that Green Human Resource Management practices should be embraced by organizations to encourage green behaviors and create green leaders. Moreover, proactive management of relations with suppliers and customers can increase the efficiency of sustainability efforts. In practice, managers can realize overall green supply chain performance by incorporating HR practices, leadership growth, and stakeholder involvement in their business and strategic operations.
ASSESSING THE EFFECTIVENESS OF CLIMATE SMART AGRICULTURE: A CASE STUDY OF DISTRICT PESHAWAR, PAKISTAN
According to the latest report of the Pakistan Bureau of Statistics (PBS) agriculture is the largest part of Pakistan’s economy, plays a role of like backbone. Twenty four percent are added to the Pakistan’s Gross Domestic Product (GDP) by agricultures sector. According to the Climate Risk Index (CRI-2021) Pakistan is one of the top ten most vulnerable countries in the world to climate change, experiencing impacts of climate change such as; heatwaves, floods, and droughts. The crop production is being improved by Climate-Smart Agriculture (CSA). CSA practices plays an act of sustainable agriculture strategy to cope with the challenges of climate change; availability of water and food security. This study assessed that up to what extent Climate-Smart Agriculture (CSA) has improved agricultural resilience, profitability and productivity in District Peshawar, Pakistan in relation to walk-in tunnel tomato production. Mixed methods approach has been used. Smallholder farmers (including women farmers) are surveyed, participated in focus groups (FGDs), and conducted key informant interviews (KIIs) to get qualitative data and quantitatively from cost-benefit analyses. According to the results the CSA walk-in tunnels produced 797 kg from 480 square feet which is equal to 72,310kg per acre while conventional methods produced 215 Kilograms of tomatoes from 480 square feet (19,511 kg per acre). In comparison to conventional farming practices, the yield of CSA is increased by 270%. Improved yields and off-season market price of CSA walk-in tunnel is 82 PKR per Kg, relative to conventional farming, 45 PKR per Kg led to a net income increase of 271% per 480 square feet area. Training on enhancing farmers knowledge, integrated pest management, and careful input management reduced environmental consequences and resulted in 40 to 160% reduction in fertilizer and pesticides costs and use. Off-season yield reduced risk related to seasonal patterns, while CSA methods increased women's participation in agricultural activities, enhancing food security and farmers’ income. The importance of adoptability was also assumed considering that the main precursors for the awareness were NGOs, social media, and reports from friends, government, and research. Polythene sheet, plastic mulch and packaging materials, raised environmental issues, revealing the necessity for biodegradable alternatives.
ERIK OLIN WRIGHT: UNDERSTANDING SOCIAL CLASS AND ENVISIONING CHANGE
Erik Olin Wright was a leading American sociologist who deeply influenced how we understand social class, inequality, and social change. His work stands out because he went beyond traditional Marxist ideas to develop new ways of analyzing the complex social structures of modern capitalist societies. This paper explores Wright’s main contributions, including his theory of contradictory class locations, his role in analytical Marxism, and his vision of “real utopias” — practical alternatives to capitalism. The paper also discusses how Wright’s ideas remain relevant, especially for countries like Pakistan where social inequality is a pressing concern. Finally, some critiques of his work are considered, emphasizing the importance of adapting his theories to diverse social contexts.
COMPANIONSHIP AND CONTRAST: AN ANALYSIS OF GEORGE AND LENNIE IN STEINBECK’S OF MICE AND MEN
John Steinbeck’s celebrated novella, Of Mice and Men (1937), immerses readers in the harsh world of Depression-era migrant labour, where the poignant bond between George Milton and Lennie Small stands in stark contrast to the pervasive loneliness of the time. Critical discussions have extensively covered the work’s themes—isolation, masculinity, the elusive American Dream. Yet, what has received less focused attention is a systematic, side-by-side analysis of the two central characters themselves. This study aims to fill that gap by applying a structured analytical tool: the FRAN framework, which examines the dimensions of Feelings, Relationship, Actions, and Nature. A close reading through this lens reveals that Steinbeck crafts George and Lennie not merely as opposites but as complementary halves of a whole. Their interconnection embodies essential human contradictions: reason and instinct, autonomy and need, power and fragility. Ultimately, Steinbeck leverages their interdependent bond to deliver a dual critique—one of a brutal socioeconomic system, and another that affirms companionship and shared meaning as fundamental human necessities. The FRAN framework, therefore, proves its utility as a practical model for methodical character study in literary analysis.
NUTRITIONAL INEQUALITIES IN CHILDREN: A REVIEW OF HOW SOCIOECONOMIC POLICIES IN PAKISTAN SHAPE ANATOMY AND GROWTH
Malnutrition is a global issue which means either too much intake of nutrients or fewer intake which further causes physical and mental problems in both early and later life. Under-nutrition means less consumption of nutritious diet that lacks in calories, carbohydrates, proteins and micronutrients. This can cause permanent effects in later life if deficiency occurs in pregnancy period or in first 1000 days of children’s life. Sometimes it may be because of high food prices or food insecurity. PEM (kwashiorkor and marasmus) and micronutrient deficiencies are the severe forms of under-nutrition can cause detrimental effects. On the other side, the problem of obesity as over-nutrition is also emerging in developing countries. Early nutrition pattern plays an important role in long term health. In 2013, 51 million children were wasted majorly in Asia and Africa. 99 million children affected with problem of under-weight and 42 million children being overweight globally in 2013. Gender, education, income also plays crucial role in accessing the nutritional care and healthy option. In Pakistan there was 44.15% prevalence of malnutrition especially in rural areas because of low resources and lack of infrastructure with limited access to health. Sindh and Balochistan have higher rate of malnutrition because of low resources. Maternal education and income also impact the child’s health as she knows how to run house by taking right decisions. Infant and young child feeding practices with exclusive breastfeeding reduces the rate and risk of malnutrition. To address these issues in Pakistan, government launched different programs including BNP and Ehsaas program providing cash payments with supplements and awareness sessions as well. Later these programs have positive impacts and results in country when compared with areas without these programs. Another program started called basic and economical food basket which assess the food preferences, average income, household sizes and then provide food baskets with adequate foods. Study highlights the quick need of maternal education, equal access to nutritious and safe food for low HDS population. Check and balance on working programs, helping small industries to get better quality of food and spreading awareness about importance of early nutrition and health.
PSYCHOLOGICAL FOUNDATIONS OF SCHOLASTIC PERFORMANCE A STUDY ON RELATIONSHIP BETWEEN SELF ESTEEM AND CGPA AMONG VIRTUAL UNIVERSITY STUDENTS
This study investigates the relationship between self-esteem and academic performance among university students at the Virtual University of Pakistan, focusing on potential gender differences and implications for psychological support strategies. Using a quantitative, cross- sectional design, data were collected from 200 students (100 males, 100 females) aged 18–27 via convenience sampling. The Rosenberg Self-Esteem Scale (RSES) measured self-worth, while academic performance was assessed using GPA. Results indicated a moderate positive correlation between self-esteem and academic performance (r = 0.38, p < 0.01), with no significant gender differences observed. The findings align with psychological theories like Bandura's self-efficacy model and Maslow's hierarchy of needs, highlighting the role of self- esteem in academic motivation and resilience. The study underscores the importance of fostering self-esteem through targeted interventions, such as counseling and mentorship programs, to enhance academic outcomes. Limitations include the use of convenience sampling and a cross-sectional design, suggesting the need for future longitudinal and diverse- sample research.
BARRIERS, BURDENS, AND BELONGING: A SOCIOLOGICAL INQUIRY INTO THE EXCLUSION OF WOMEN WITH DISABILITIES IN KOHAT DIVISION, KHYBER PAKHTUNKHWA-PAKISTAN
The present study investigates how the level of participation (independent variable) influences the extent of social exclusion experienced by women with disabilities (dependent variable) in the Kohat Division of Khyber Pakhtunkhwa, Pakistan. A Quantitative Inquiry based on a Cross-Sectional Survey design was employed, and a proportionate stratified sample of 370 respondents was collected from the three districts of the Kohat division. In order to maintain methodological integrity, the researcher collaborated with district-level Social Welfare Offices for participant verification and classification. Trained facilitators were also engaged during interviews with respondents experiencing auditory, verbal, or stature-related impairments to ensure accurate interpretation of questions and responses. The research concentrated on women with physical, auditory, verbal, and stature-related disabilities, while individuals with cognitive or intellectual impairments were excluded due to concerns about response validity. The reliability of the instrument was assessed through Cronbach’s Alpha, producing a coefficient of 0.87, indicating high internal consistency across the measurement items. To examine the association between participation and exclusion, Chi-Square (χ²) Statistics were applied, while Kendall’s Tau-b (Tb)Test was used to determine whether relationships remained spurious or non-spurious when multivariate controls were introduced. The statistical results demonstrated a significant relationship between the level of participation and social exclusion among women with disabilities (χ² = 50.702, p = 0.000; Tb = 0.370). Additional analyses conducted across age and educational categories revealed that the association persisted, confirming that the relationship remained statistically significant rather than disappearing under controlled conditions. Although education moderated the strength of the relationship, it did not eliminate it, establishing a non-spurious association, whereby limited participation continues to predict exclusion, regardless of schooling. Findings further revealed that restricted participation intensifies exclusionary experiences by limiting access to decision-making, physical mobility, and community-based interaction. Interpreted within a rights-based disability perspective, these results affirm that exclusion is not a natural consequence of impairment but is generated by social, attitudinal, and environmental barriers. The study recommends enhanced participation opportunities, accessible environments, and inclusive community initiatives as key approaches to reducing exclusion and promoting social integration for women with disabilities in the region.
ASSESSING THE NEED FOR NATIONAL SOCIAL POLICY: AN APPRAISAL
This research analyses the need for conceptualising a National Social Policy (NSP) in Pakistan through the lens of community service delivery by non-governmental organisations (NGOs). Despite constitutional commitments to social welfare, Pakistan’s fragmented institutional framework and weak policy coordination have resulted in inequitable and inefficient service delivery. NGOs have emerged as critical actors in bridging these gaps, providing essential services in healthcare, education, poverty alleviation, and disaster relief. However, their contributions often occur in isolation from state mechanisms, leading to duplication, limited sustainability, and uneven coverage. Drawing upon Social Capital Theory, the study analyses how trust, networks, and reciprocity enable NGOs to mobilize communities, enhance participation, and extend welfare reach in contexts of limited state capacity. Using a qualitative methodology, semi-structured interviews were conducted with government officials, policymakers, and NGO leaders to explore existing challenges, coordination deficits, and opportunities for policy integration. Thematic analysis revealed four core issues: fragmented welfare systems, community trust as a driver of service delivery, weak data and accountability mechanisms, and the urgent need for a unified policy framework. Findings underscore that a National Social Policy could institutionalize NGO–state collaboration, harmonize welfare schemes, and promote inclusive, sustainable development. The study concludes that integrating NGO-led initiatives within a coherent NSP would not only strengthen social governance and accountability but also transform Pakistan’s welfare landscape from fragmented relief measures to coordinated, rights-based social protection. By embedding social capital into policy design, the research offers a framework for equitable and resilient social development in Pakistan
HOW BACK-CHANNEL DIPLOMACY SHAPES THE TRIANGULAR RELATIONS OF USA-CHINA AND PAKISTAN
This paper examines the unseen mechanisms of statecraft employed by Pakistan to navigate the intensifying geopolitical rivalry between the United States and the People's Republic of China from 2020 to 2025. Moving beyond conventional analysis of public diplomacy and interstate friction, this research posits that strategic stability was primarily achieved through sub-rosa (non-public) communication channels and tacit agreements, which consistently overruled declared foreign policy positions. Following the 2021 U.S. withdrawal from Afghanistan, Pakistan operationalized a 'dual-utility' stabilization effort, simultaneously maintaining Washington’s vital security access while providing Beijing sovereign guarantees for its $62 billion China-Pakistan Economic Corridor (CPEC) investment. The subsequent years (2022–2024) saw increasing systemic stress including financial constraints, CPEC implementation hurdles, and renewed domestic instability which were managed not via multilateral frameworks but through ad hoc, bespoke financial and security assurances negotiated via hidden nodes within the IMF, Chinese lending institutions, and military-to-military contacts. The transactional nature of this governance was acutely demonstrated in 2025: a minor, undeclared U.S.-China tariff accommodation yielded economic relief, immediately preceding the regional crisis triggered by India’s "Operation Sindoor" in May. The unprecedented cessation of hostilities within 96 hours is analyzed as the result of simultaneous, targeted pressure applied through high-level backchannels in both Washington and Beijing. The findings conclude that Pakistan’s short-term political solvency was secured by prioritizing crisis postponement and leveraging great-power dependence. Rather than achieving genuine strategic autonomy, the state remained locked in a high-stakes, perpetual equilibrium, forced to dynamically adjust its posture based on the leverage afforded by the preferred, immediate backchannel of the day.
A NEURAL NETWORK APPROACH FOR COMPRESSIVE STRENGTH PREDICTION OF ULTRA-HIGH-PERFORMANCE CONCRETE: DEVELOPMENT, ANALYSIS, AND ROBUSTNESS TESTING
The development of Ultra-High-Performance Concrete (UHPC) is constrained by the empirical and costly nature of mixture optimization, necessitating robust predictive models that capture the complex, non-linear interactions between its constituent materials. While Artificial Neural Networks (ANNs) have been widely adopted for this purpose, their application is frequently criticized for lack of transparency, inconsistent interpretability, and uncertain generalization beyond training datasets. This study addresses these critical gaps by developing, analysing, and rigorously validating a fully connected ANN model to predict UHPC compressive strength using a substantial and curated dataset of 810 mixture designs. The model, featuring a single hidden layer with 100 neurons and L2 regularization, demonstrated high predictive accuracy on an independent test set, achieving an R² of 0.922, RMSE of 11.267 MPa, and a mean absolute percentage error (MAPE) of 8.305%. Twenty-fold cross-validation confirmed robustness, revealing consistent performance across most folds while identifying specific regions in the mixture design space where predictive accuracy diminished, highlighting data limitations. A critical, multi-method feature importance analysis—employing Univariate Regression, RRelieff, and SHapley Additive exPlanations (SHAP)—provided a nuanced hierarchy of influential variables. While filter methods ranked silica fume and fiber content highest, SHAP analysis revealed the ANN’s internal logic prioritizes curing age, cement content, and fiber dosage as the most dominant predictors, aligning model reasoning with fundamental materials science principles. This work moves beyond a mere performance report to deliver a critical evaluation of ANN utility in UHPC science, explicitly confronting challenges of model interpretability, context-dependent feature significance, and generalization. It concludes that while ANNs are powerful predictors, their effective translation into reliable design tools necessitates larger, more diverse datasets, standardized benchmarking against alternative algorithms, and the integration of explainable AI techniques to yield actionable, physically meaningful insights for engineers.
IOT-INTEGRATED AUTOMATED ROBOT FOR SOLAR PANEL CLEANING
The growing adoption of solar energy as a major contributor to the global energy transition highlights the need to maintain maximum photovoltaic efficiency. One of the primary issues affecting solar panel performance is the buildup of dust, dirt, and environmental pollutants. This study introduces the design, fabrication, and evaluation of an IoT-enabled automated robot developed to mitigate these losses. The system uses four DC geared motors for locomotion, driven by an ESP32 microcontroller connected to the Arduino IoT Cloud for wireless monitoring and control. The robot supports three modes of operation: remote manual control via the internet, a fully autonomous cleaning mode guided by embedded algorithms, and a patrol mode equipped with a water-spray cleaning feature. Experimental evaluation confirms that the robot delivers reliable cleaning performance under diverse surface conditions without causing any physical harm to the solar modules. IoT connectivity further facilitates scheduled or on-demand cleaning, making the system especially suitable for large solar farms and hard-to-reach installations. Overall, the proposed solution reduces labor effort and operational costs while sustaining optimal panel efficiency across residential, commercial, and industrial setups.
OPTIMIZING PREDICTIVE ACCURACY: A COMPARATIVE ANALYSIS OF SUPERVISED MACHINE LEARNING ALGORITHMS FOR REAL-WORLD CLASSIFICATION TASKS
Supervised machine learning methods are widely used for binary classification tasks; however, their effectiveness depends on data characteristics and model assumptions. This study presents a comparative evaluation of commonly used supervised learning algorithms, including Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and Naïve Bayes. The analysis is conducted on a balanced dataset consisting of 400 samples with seven predictive features and a binary target variable. A unified experimental framework is applied to ensure fair comparison, and model performance is assessed using accuracy, precision, recall, F1-score, and Receiver Operating Characteristic Area Under the Curve (ROC–AUC). Visual evaluation techniques, including ROC and precision–recall curves, support interpretability. Experimental results indicate that ensemble and kernel-based models outperform linear and probabilistic approaches. In particular, the Random Forest classifier achieves the highest performance, demonstrating strong accuracy, balanced classification, and superior discriminative capability. The findings provide practical guidance for supervised model selection in structured binary classification problems.
THE APPLICATION OF THE LAZARUS THEORY OF STRESS AND ITS PSYCHOLOGICAL EFFECTS ON LOWER AND MIDDLE MANAGEMENT WORKING IN THE BANKING SECTORS OF SINDH
This study investigates the psychological effects of workplace stress on lower and middle-level managers in the private banking sector of Sindh, Pakistan, utilizing Lazarus' Transactional Theory of Stress. The research examines how workload intensity and work-life conflict contribute to psychological stress and its subsequent impact on physical health problems and burnout. It further explores the moderating roles of perceived job control and coping strategies in mitigating these relationships. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM) with data from 350 managers, the study confirms all hypothesized relationships. Workload intensity (β=0.401, p<0.001) and work-life conflict (β=0.156, p<0.001) significantly predict psychological stress, which strongly influences physical health problems (β=0.558, p<0.001) and burnout (β=0.604, p<0.001). Perceived job control effectively buffers the impact of workload (β=−0.150, p<0.001) and work-life conflict (β=−0.100, p<0.01) on stress, while coping strategies mitigate the progression from stress to physical health issues (β=−0.220, p<0.001) and burnout (β=−0.300, p<0.001). The model explains substantial variance in stress (R²=0.569), with moderate explanatory power for burnout (R²=0.374) and physical health problems (R²=0.318). These findings validate the Job Demand-Control and Conservation of Resources theories, emphasizing the need for structural interventions to enhance job control and individual coping training to foster resilience. Practical recommendations include workload audits, autonomy enhancement, and coping skill development to improve managerial well-being
IMPACT OF E-GOVERNANCE ON CITIZEN ENGAGEMENT: MEDIATING ROLE OF CITIZEN SATISFACTION IN PUBLIC SECTOR INSTITUTIONS
This study explores how e-governance service quality influences citizen engagement, focusing on the mediating Citizen Satisfaction. Drawing upon the Technology Acceptance Model, Expectation Confirmation Theory, and Public Value Theory, salaried employees of public sector universities in the Hazara region of Pakistan. A quantitative research design using simple random sampling was employed, and data were collected through structured questionnaires. Regression and mediation analyses revealed that e-governance service quality significantly enhances citizen engagement, Citizen Satisfaction with acting as partial mediator. The study emphasizes that improving digital service quality can foster stronger public trust, transparency, and participation. Policy implications suggest that prioritizing accessibility, responsiveness, and system reliability can strengthen Pakistan’s digital governance framework.
DESIGN AND OPTIMIZATION OF ENERGY-EFFICIENT SMART GRIDS USING IOT AND MACHINE LEARNING TECHNIQUES
The increasing penetration of renewable energy sources and the growing complexity of power systems have necessitated the development of intelligent and energy-efficient smart grid solutions. This paper presents an integrated framework for the design and optimization of energy-efficient smart grids using Internet of Things (IoT) and machine learning techniques. Real-time data acquired from IoT-enabled sensors are utilized for load and renewable generation forecasting, enabling predictive and adaptive energy management. Machine learning models are employed to capture nonlinear relationships between environmental conditions, consumption patterns, and energy generation. An intelligent control strategy incorporating battery energy storage, demand response, and dynamic pricing is implemented to minimize grid dependency and transmission losses. System performance is evaluated using efficiency metrics, forecasting accuracy, and control action analysis. The results demonstrate that the proposed framework significantly improves energy efficiency, enhances renewable energy utilization, and supports reliable and sustainable smart grid operation.
REINVENTING FAT (THE EVOLUTION AND EMERGING POTENTIAL OF STRUCTURED LIPIDS): A REVIEW
Structured lipids are a new, rapidly developing area in food science, biotechnology, and nutrition. The current evidence indicates that they can improve digestibility, oxidative stability, sensory quality, and targeted health benefits, and can help in the innovation of infant formula, medical foods, and functional bakery fats. Recent progress in the engineering of lipases, green lipids, and highly accurate lipidomic is steering the evolution of the new generation of custom lipids. Nonetheless, the barrier of cost, consumer confidence, and unbalanced regulations are still issues that should be overcome. Organized lipids will increase to be an ingredient of a robust functional food and nutraceutical innovation with coordinated scientific, industrial, and policy efforts, providing technological utility and quantifiable health advantages.
IMPACT OF TEACHER STUDENT RELATIONSHIP ON STUDENTS' WELLBEING AND LEARNING OUTCOMES AT SECONDARY LEVEL AT DISTRICT RAWALPINDI
The present study investigates the impact of student-teacher relationships on student well-being and learning outcomes at the secondary school level in Tehsil Rawalpindi and Gujjar Khan, District Rawalpindi. Recognizing the pivotal role of the teacher-student bond in shaping academic and emotional development, this research aims to examine various dimensions of the relationship such as emotional support; respect, motivation, and academic guidance correlate with students' psychological well-being and academic performance. The study adopted a quantitative research design supplemented by qualitative insights. A total sample of 700 respondents (350 students and 350 teachers) was selected through multi-stage sampling. Proportionate stratified random sampling was used to select schools, and convenience sampling was applied within those institutions. Data were collected through two separate Likert-scale semi-structured questionnaires one for students and one for teachers along with an open-ended question to capture nuanced perceptions and analyzed through SPSS-25. The quantitative data were analyzed using descriptive statistics, reliability analysis, exploratory factor analysis (EFA), t-tests, correlation, and regression analysis. Additionally, thematic analysis was applied to qualitative responses, identifying recurring themes such as Emotional Connection, Mutual Respect, Academic Support, and Teacher Motivation. The study is expected to reveal that a strong, respectful, and emotionally supportive teacher-student relationship significantly contributes to student well-being and enhances academic performance. The findings are anticipated to offer practical implications for educational policy-makers, school administrators, and teachers aiming to foster positive educational environments. This research contributes to the broader discourse on educational psychology and student development by providing empirical evidence from a rural Pakistani context, where such relational dynamics are underexplored. Overall, the study underscores the importance of nurturing teacher-student interactions as a means to promote both emotional health and academic excellence among secondary school learners.
SMART IT FRAMEWORKS FOR AUTOMATION AND QUALITY CONTROL IN CHEMICAL MANUFACTURING
The Industry 4.0, artificial intelligence (AI), and high regulatory standards of quality and data integrity prompt a structural change in the chemical manufacturing sector. The conventional automation architectures that have been based on remote distributed control systems (DCS), programmable logic controllers (PLCs), and laboratory information systems are no longer needed to provide real-time quality assurance, operational agility, and regulatory-compliant data management. This review is based on advances in the field of smart manufacturing, process analytical technology (PAT), soft sensing, predictive quality analytics, digital twins, and multi- agent/service-oriented control architecture with particular attention to its implementation in a Smart IT framework to perform automation and quality control of chemical manufacturing. The paper reviews the current standards and paradigms including Quality by Design (QbD), GAMP 5, ALCOA(+), and real-time release testing (RTRT) after which it evaluates the current IT and control architectures (pyramidal ISA-95 style, SOA, holonic and multi-agent systems) and AI-based quality technologies (predictive quality, computer vision, soft sensors, digital twins). Contributing to this, it suggests a layered Smart IT model that integrates physical properties, connectivity and edge infrastructure, integration services, AI driven intelligence, and governance and compliance into a coherent and closed loop quality and automation structure. The framework puts importance on modularity, service orientation, human-oriented multi-agent coordination, and regulatory-by-design validation of AI components. Lastly, the paper provides implementation roadmaps, illustrative scenarios, and open research directions which incorporate the unified views of the experts in IT, AI, and chemical engineering.