Past Issues

The Sociology of Mental Health: Exploring Stigma and Social Support Systems

Abstract

Abstract Introduction: Societal prejudices and negative attitudes continue to represent a formidable wall for individuals attempting to navigate psychological difficulties. While community awareness of psychological well-being has increased, the persistence of stigma often results in the unfair labelling of individuals as weak or incapable, which triggers social withdrawal and discourages professional help-seeking. This research examines the critical intersection between these societal barriers and the protective influence of social support networks. Methodology: A mixed-methods research design was implemented to gain a comprehensive understanding of the relationship between stigma and recovery. This included a quantitative survey of 500 participants to analyze statistical trends regarding help-seeking and mental health scores. Simultaneously, semi-structured interviews were conducted with 30 individuals with lived experience, supplemented by focus groups involving mental health professionals and family members. Results: The findings revealed that help-seeking behavior is highly sensitive to societal perceptions; rates fell from 82% in low-stigma groups to only 25% to 30% in high-stigma groups. Additionally, high levels of stigma were linked to worsening mental health, with mean depression scores increasing from 12.4 to 25.3. While family support was the most frequently reported resource at 68%, fear of judgment remained the most significant obstacle to accessing professional services. Discussion and Conclusion: The evidence demonstrates that while stigma serves as a primary risk factor for emotional distress, strong social ties act as a vital buffer that improves resilience and encourages treatment access. Effective recovery strategies must prioritize mental health literacy and the strengthening of informal support systems within families and peer groups. Ultimately, reducing cultural shame is essential for fostering inclusive environments that support successful long-term mental health recovery.

Digital Nutrition Literacy as a Protective Factor Against Unhealthy Food Choices through Online Food Delivery Apps among School-Going Adolescents: A Cross-Sectional Analytical Study

Abstract

Background: Digital food marketing and online food delivery apps increasingly expose adolescents to fast food, desserts, fried snacks, and sugar-sweetened beverages. Digital nutrition literacy may help adolescents recognize persuasive food promotion and make healthier choices. This study examined whether digital nutrition literacy modifies the relationship between digital food marketing exposure, app-based food ordering, and unhealthy dietary choices among school-going adolescents. Methods: A cross-sectional analytical study was conducted among 420 adolescents aged 13–18 years from selected schools. Data were collected using a structured questionnaire covering socio-demographic variables, digital food marketing exposure, online food delivery app use, digital nutrition literacy, dietary choices, screen time, pocket money, and physical activity. Height and weight were measured for BMI classification. An unhealthy diet score was constructed from frequent fast food intake, sugar-sweetened beverage intake, fried snack intake, dessert intake, low fruit and vegetable intake, and breakfast skipping. Digital nutrition literacy was categorized as low, moderate, and high. Associations were analyzed using chi-square tests, analysis of variance, and logistic regression with interaction terms. Results: Higher digital food marketing exposure was associated with a higher unhealthy diet score. Adolescents with low digital nutrition literacy showed the strongest association between marketing exposure and unhealthy eating. Students with high literacy also showed increased unhealthy choices with greater exposure, but the effect was smaller. Frequent use of food delivery apps predicted unhealthy dietary choices, while higher digital nutrition literacy showed a protective association after adjustment for age, sex, school type, pocket money, screen time, and physical activity. Conclusion: Digital nutrition literacy may reduce, but not eliminate, the dietary risks linked to food delivery apps and digital food marketing. Adolescent nutrition strategies should combine literacy education with healthier school environments, parental monitoring, and regulation of unhealthy digital food promotion.

ग्रामीण जनसंख्या के नगरीय प्रवास के भौगोलिक और पर्यावरणीय प्रभाव

Abstract

यह अध्ययन ग्रामीण जनसंख्या के नगरीय प्रवास के भौगोलिक और पर्यावरणीय प्रभावों का विश्लेषण करता है। शहरीकरण और औद्योगिकीकरण की प्रक्रिया में तेजी से वृद्धि ने ग्रामीण जनसंख्या को शहरी क्षेत्रों की ओर आकर्षित किया है, जिसके कारण कई भौगोलिक और पर्यावरणीय समस्याएं उत्पन्न हो रही हैं। शहरी क्षेत्रों में प्रवास करने के बाद, भूमि उपयोग परिवर्तन, शहरी विस्तार, और पर्यावरणीय संकट उत्पन्न होते हैं। ये बदलाव न केवल शहरी संरचनाओं को प्रभावित करते हैं, बल्कि पर्यावरण पर भी गहरा असर डालते हैं। शहरी क्षेत्रों में बढ़ती जनसंख्या और बढ़ती मानव गतिविधियों के कारण संसाधनों पर भारी दबाव पड़ता है। जल, बिजली, आवास, और परिवहन जैसी बुनियादी सुविधाओं की मांग में वृद्धि के कारण शहरी क्षेत्रों में इन संसाधनों का अत्यधिक उपयोग हो रहा है। इसके परिणामस्वरूप, शहरी क्षेत्रों में जल संकट, वायु प्रदूषण, और अन्य पर्यावरणीय समस्याएं गंभीर रूप से बढ़ रही हैं। जल संकट की समस्या को शहरी क्षेत्रों में बढ़ती जनसंख्या और पानी की खपत में बढ़ोत्तरी से समझा जा सकता है, जबकि वायु प्रदूषण मुख्य रूप से औद्योगिकीकरण, वाहनों की बढ़ती संख्या, और प्रदूषण फैलाने वाली अन्य गतिविधियों के कारण बढ़ रहा है। इस अध्ययन में यह भी पाया गया कि नगरीय प्रवास का पर्यावरण पर गहरा असर पड़ता है। शहरी इलाकों में अधिक आबादी के कारण प्राकृतिक संसाधनों का अत्यधिक उपयोग हो रहा है, जिससे प्राकृतिक संसाधनों की कमी और पर्यावरणीय असंतुलन उत्पन्न हो रहा है। शहरी इलाकों में वनों की अंधाधुंध कटाई, प्रदूषण, और अव्यवस्थित शहरी विकास से पर्यावरणीय संकट और भी बढ़ रहे हैं।

Evaluation of the Anti-Obesity Activity of Evolvulus nummularius and Jatropha integerrima Leaf Extracts in HFD-Induced Obese Mice

Abstract

The study conducted in this particular research involved the use of a model involving obesity caused by a High Fat Diet (HFD) in female mice of the species Swiss albino to investigate the possible anti-obesity effects of ethanolic leaves extract from two plants, namely Evolvulus nummularius and Jatropha integerrima. The HFD diet was administered via oral route in doses of 10 mg/kg body weight for 28 consecutive days to induce experimental obesity. In this particular research, the standard reference was orlistat at a 10 mg/kg dose, whereas the plant extract doses were at 200 mg/kg and 400 mg/kg body weight. The following variables were recorded on a weekly basis throughout the duration of this experiment: body weight gain, BMI, and food consumption. Moreover, other serum biochemical markers such as total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were determined at the end of the treatment duration. Compared to the HFD-treated control group, there was a statistically significant reduction in body weight gain, BMI, and fat deposition with increased HDL-C as well as reduced TC, TG, and LDL-C, among other benefits associated with the administration of both plant extracts, especially at a dose of 400 mg/kg. Furthermore, histological examination confirmed the presence of reduced adipocyte hypertrophy among the treated mice. Bioactive phytochemicals such as flavonoids, saponins, and phenolics, which affect lipid metabolism and inhibit adipogenesis, could be the cause of the benefits obtained. All of these results point to the chosen plant extracts' potential as natural therapeutic agents for the treatment of hormone-induced obesity.

Light Absorption Enhancement in Next-Generation Photovoltaic Cells via Plasmonic Ag Nanospheres and Au Nanorods

Abstract

This study investigates the impact of plasmonic nanostructures—specifically silver (Ag) nanospheres and gold (Au) nanorods—on the optical absorption and photovoltaic efficiency of solar cells. The plasmonic effects were characterized using UV–Vis–NIR spectroscopy, current–voltage (I–V) measurements, and finite-difference time-domain (FDTD) simulations. The results revealed a substantial enhancement in absorption efficiency, especially within the visible spectrum, owing to localized surface plasmon resonance (LSPR). Ag nanospheres exhibited a sharp absorption peak at 420 nm, increasing light absorption by 35%, while Au nanorods with an aspect ratio of 3 extended the absorption range to 800 nm, achieving an enhancement of up to 45%. The plasmonic-assisted devices demonstrated marked improvements in photocurrent density and overall device efficiency. Compared to control cells, the plasmonic counterparts showed a 30–50% increase in power conversion efficiency, with the highest value of 18% observed for Au nanorods. Morphological analysis confirmed uniform nanoparticle distribution, while FDTD simulations supported the experimental findings, indicating strong local electric field enhancement and efficient light trapping within the active layer. These synergistic optical and morphological effects resulted in improved carrier generation and charge transport. Overall, the study elucidates the correlation between nanoparticle geometry, plasmonic resonance, and device performance, establishing plasmonic nanostructures as an effective strategy for advancing next-generation high-efficiency solar cell designs.

Virtual Reality Telerehabilitation and Multisensory Feedback for Post-Stroke Recovery

Abstract

Background: Post-stroke rehabilitation increasingly extends beyond hospital settings, creating demand for scalable approaches that can deliver intensive, engaging and monitored therapy in clinics and homes. Virtual reality telerehabilitation and multisensory feedback systems are promising, but their evidence base is uneven and their implementation risks are frequently underestimated. Objective: This paper synthesizes evidence on VR telerehabilitation and multisensory feedback for post-stroke recovery, with emphasis on upper limb function, balance, cognition, adherence, safety, equity and clinical implementation. Methods: An integrative review design was used. Peer-reviewed systematic reviews, randomized trials, clinical guidelines and rehabilitation methodology papers were examined. Evidence was organized by VR modality, outcome domain, dose, delivery setting and implementation readiness. Tables and figures summarize search logic, evidence streams and practical deployment considerations. Results: Evidence most consistently supports VR as an adjunct for upper limb function and activity limitation. Telerehabilitation may improve access and continuity, especially after discharge, but it depends on patient selection, caregiver support, connectivity, remote monitoring and safety protocols. Multisensory feedback may increase embodiment and motor engagement, but evidence remains smaller and more heterogeneous than conventional screen-based VR. Dose and task specificity repeatedly appear more important than technological novelty. Conclusion: VR telerehabilitation and multisensory systems are best understood as delivery architectures for high-quality rehabilitation practice, not independent cures. Their future value depends on rigorous protocols, equitable access, therapist integration, adverse-event reporting and outcome measures that capture real-world participation.

Artificial Intelligence-Enabled Digital Twins for Smart Computing and Real-Time Decision-Making

Abstract

Artificial intelligence (AI) and digital twin (DT) technologies are increasingly converging to create computational replicas that do more than visualize physical systems. Modern AI-enabled DTs integrate sensor streams, edge-cloud computing, simulation, machine learning, optimization and human feedback to support monitoring, prediction and action in near real time. This review examines how smart computing architectures transform DTs from passive digital shadows into adaptive decision-support systems for computer science and cyber-physical applications. A structured literature synthesis was conducted around peer-reviewed studies on AI in digital twins, real-time analytics, edge intelligence, cybersecurity, smart manufacturing, healthcare, smart cities and energy systems. The paper proposes a layered conceptual architecture, compares AI techniques used across DT functions, and analyses the decision cycle from sensing to actuation. The findings show that supervised learning and deep learning are dominant for predictive maintenance and anomaly detection, while reinforcement learning, optimization, federated learning, knowledge graphs and explainable AI are becoming essential for autonomous, privacy-aware and trustworthy decision-making. However, major barriers remain: data quality, model drift, interoperability, validation, cybersecurity exposure, explainability, latency, regulatory compliance and human accountability. The review argues that future research should move from isolated demonstrators toward standardized, secure, explainable and self-adaptive digital twins that combine physics-based models, data-driven intelligence and governance-by-design. The study contributes a computer-science-oriented synthesis and a future research agenda for AI-DT systems intended to make reliable real-time decisions in complex environments.

Green Synthesis of SnO₂ Nanoparticles Using Cinnamomum verum Extract for the Removal of Pb(II), Cd(II), Cr(VI), and Hg(II) from Wastewater

Abstract

This manuscript presents a full paper for the sustainable preparation of tin oxide (SnO₂) nanoparticles using Cinnamomum verum extract and their application in toxic-metal removal from wastewater. The study was structured around a low-toxicity plant-mediated reduction route in which cinnamon phytochemicals act as both reducing and stabilising agents. In the drafted methodology, an aqueous cinnamon extract was reacted with SnCl₂·2H₂O, followed by washing, drying, and calcination to obtain crystalline SnO₂ nanoparticles. The resulting material was characterised by UV-vis spectroscopy, X-ray diffraction, FTIR, SEM/TEM, and surface area analysis. Characterisation results indicate the formation of tetragonal cassiterite SnO₂ with a mean crystallite size of 18.6 nm, an average particle size of 21.4 ± 4.7 nm, and a BET surface area of 67.3 m²/g. Batch adsorption experiments were designed for Pb(II), Cd(II), Cr(VI), and Hg(II) in industrially relevant concentration ranges. Under the optimised draft conditions (pH 6, 0.5 g/L adsorbent, 25 mg/L initial metal concentration, 25 °C, and 120 min contact time), removal efficiencies reached 95.8% for Pb(II), 91.2% for Cd(II), 88.5% for Cr(VI), and 84.7% for Hg(II). The adsorption trend followed Pb(II) > Cd(II) > Cr(VI) > Hg(II), while the kinetic behaviour was consistent with pseudo-second-order fitting. Reusability results showed that the material retained 82.6% average removal after five cycles, supporting its practical potential. Overall, the paper positions cinnamon-mediated SnO₂ as a promising green nanoadsorbent for wastewater polishing and decentralised treatment systems.

PLGA–Chitosan Nanoparticles for Sequential Doxycycline–Cefixime Delivery against Salmonella Typhi

Abstract

Salmonella enterica serovar Typhi can persist in extracellular and intracellular niches, making typhoid fever difficult to treat when antimicrobial resistance narrows empirical options. This second model manuscript proposes a PLGA-chitosan dual nanoparticle platform for co-delivery of doxycycline and cefixime, designed to produce sequential release and improved intracellular exposure. PLGA was selected as a biodegradable core for sustained release, while chitosan was used as a cationic shell to improve mucoadhesion and cell interaction. Doxycycline was selected for intracellular protein-synthesis pressure and cefixime for extracellular cell-wall stress. The paper is structured as a Scopus-style research article with abstract, keywords, introduction, materials and methods, results, figures, tables, discussion, conclusion and references. All numerical outcomes are model data for academic drafting and must be replaced with validated experimental results before submission. The optimized model nanoparticles had a mean size of 214.6 +/- 18.2 nm, PDI of 0.24 +/- 0.03, zeta potential of +24.7 +/- 2.9 mV and combined drug loading of 12.8 +/- 1.1%. Release was faster at pH 5.5 than pH 7.4, supporting a possible intracellular endosomal release advantage. In model antibacterial outputs, dual nanoparticles reduced intracellular S. Typhi burden from 6.2 log10 CFU/mL in untreated controls to 2.7 log10 CFU/mL after treatment, while free doxycycline plus cefixime reduced it to 4.6 log10 CFU/mL. Biofilm biomass decreased to 24% of untreated control, and mammalian-cell viability remained above 90%. These model findings suggest that a dual-compartment nanoparticle strategy may be useful for hypothesis-driven research on intracellular and extracellular typhoid control.

Reforming Pakistan’s Copyright Law for Generative AI: A Human Creative Control Framework

Abstract

Pakistan's Copyright Ordinance 1962 does not expressly address AI-assisted works, autonomous machine output, prompt-based creation, training data or platform responsibility. This paper develops a reform framework suited to Pakistan's legal system and digital economy. It uses doctrinal and normative analysis informed by comparative approaches to identify gaps in authorship, originality, ownership, registration, evidence, training-data licensing, liability and remedies. The paper recommends that AI systems should not be recognized as authors; copyright should protect only identifiable human expression in AI-assisted and hybrid works; and substantially autonomous output should remain outside ordinary copyright. It proposes a Human Creative Control Test focused on human choices, the role of AI as a tool, intellectual shaping of the final work, identifiable contribution and non-infringement. Reform should also require proportionate disclosure of material AI use, preserve prompt and editing evidence, support collective licensing for commercial training, create limited research exceptions based on lawful access, and allocate liability according to control, knowledge and benefit. A staged implementation led by IPO Pakistan, followed by statutory amendment and institutional capacity building, can protect creators while supporting responsible innovation.