Innovations in Science, Technology, and Society
Innovations in Science, Technology, and Society is a peer-reviewed, multidisciplinary journal that explores the dynamic relationship between scientific advancement, technological innovation, and societal change. It publishes original research articles, reviews, case studies, and critical perspectives from diverse disciplines, fostering dialogue between researchers, practitioners, and policymakers. The journal emphasizes evidence-based approaches, interdisciplinary collaboration, and the societal implications of emerging technologies, providing a platform for both theoretical analysis and practical applications. The scope of the journal covers, but is not limited to: Social Sciences and Humanities – sociology, psychology, education, language, literature, history, philosophy, cultural studies. Business, Economics, and Management – finance, entrepreneurship, marketing, strategic management, and related fields. Natural and Applied Sciences – environmental studies, agricultural sciences, mathematics, physics, chemistry, biological sciences. Engineering, Technology, and Computer Sciences – information technology, artificial intelligence, data science, and emerging technologies. Health and Life Sciences – public health, medical sciences, biotechnology, and allied health disciplines.
THE ACADEMIC CONFLUENCE (SMC-PRIVATE) LIMITED
CLIMATE-ACTION INEQUALITY AND HOUSEHOLD VULNERABILITY: EVIDENCE FROM LOW-CARBON TRANSITION POLICIES IN URBAN AND RURAL COMMUNITIES
While climate-action policies can lower carbon emissions, they do not impact equally on households and communities. This paper explores the issue of climate-action inequality and vulnerability of households in relation to policies related to the low-carbon transition in the urban and rural context. The study examines the impacts of energy-price reform, transport costs associated with carbon emissions, clean-energy subsidies, and livelihood transition on households of varying income, settlement and institutional support. The outcomes show that low-income and rural households are more vulnerable due to their energy burden, less access to energy assistance programs, and less energy adaptive capacity and less transport options. Clean energy subsidies, access to public transport, and digital application systems are more effective for urban households, whereas fuel price fluctuations and the lack of infrastructure are more of a concern for rural households. The results also reveal the potential of an unintended increase in inequalities due to a lack of compensation mechanisms, participation in climate policies and targeted support systems. But the effects are greatly reduced when policy packages combine income-sensitive subsidies, rural energy access, affordable public transport, and locally designed adaptation programs. The paper calls for climate policies that are both environmentally sound and equitable, affordable and household protective in order to create a just low-carbon transition.
Copyright (c) 2026 Nida Ashraf (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
ALGORITHMIC INEQUALITY AND SOCIAL TRUST IN AI-MEDIATED PUBLIC SERVICES
Advancements in AI are opening up possibilities for better efficiency in public service delivery, quicker decision-making, and more service access. In developing societies, however, the swift integration of AI-driven systems brings about significant concerns about algorithmic inequity, opacity of procedures, and the loss of social trust. This research examines the factors that shape citizen trust in public institutions that use AI tools, such as their perception of fairness, transparency, accountability, and accessibility. The research design is a sequential explanatory mixed methods that includes 2,500 citizens in the survey and 14 public practitioners and marginalized citizens interviewed qualitatively. The results show that the factor most strongly associated with institutional trust is perceived transparency, followed by perceived procedural fairness; opaque automated decisions, inequal service outcomes, and lacking grievance mechanisms are associated with distrust and civic alienation. The findings also reveal that vulnerable groups are more likely to suffer a negative outcome of an algorithm in systems involving welfare, health care, and administrative eligibility. Qualitative evidence corroborates these findings, illustrating that citizens frequently misread an automated decision when there is no explanation or justification as exclusionary, unfair, and not ‘human’. The study finds that AI-powered public services can enhance governance, but they must be accompanied by clarity, openness, accountability and contestability in the design process, as well as in the use of data, and monitoring and oversight. The study's results add to the on-going body of knowledge in the fields of AI ethics, public administration, and digital governance and highlight the need for a balance between technological efficiency and fairness, explainability, and democratic accountability in the context of developing countries.
Copyright (c) 2026 Kamran Bashir, Saira Noman (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
REAL-TIME MACHINE LEARNING PREDICTION OF ACUTE KIDNEY INJURY IN ICU CANCER PATIENTS RECEIVING NEPHROTOXIC CHEMOTHERAPY
Recurrence is a major clinical challenge in HNSCC; short-term relapse is related to poorer prognosis and few therapeutic choices. A recurrent radio genomic modeling framework to predict early recurrence in head and neck squamous cell carcinoma (HNSCC) patients is proposed in this paper. The proposed approach integrates the features extracted from medical imaging data, the features extracted from the genomic data, and the clinical data to reflect the tumor phenotype and underlying molecular behavior. The model aims to improve the prediction of the recurrence risk for each patient before or after treatment using imaging biomarkers, gene-expression profiles, mutation-related indicators and patient-level clinical features. The relationships between features in sequences and higher-dimensional feature spaces are learned using recurrent deep learning networks and contrasted with the best current state of the art in machine learning. The framework can aid precision oncology by defining patients who are at risk of early recurrence that may warrant increased monitoring and/or increased individual treatment planning or adjuvant therapy. To conclude, radio genomic modelling could be a useful tool for improving the level of accurate recurrence prediction and clinical decision making in the treatment of HNC.
Early Recurrence Prediction Neck Squamous Cell Carcinoma Recurrent Deep Precision Oncology
Copyright (c) 2026 Fahad Mehmood, Aiman Rehman (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
INTERPRETABLE MACHINE LEARNING FOR EARLY MORTALITY PREDICTION IN ICU PATIENTS USING DYNAMIC VITAL SIGNS, LABORATORY TRENDS, AND TREATMENT PATTERNS
Cardiac mortality prediction is very important in intensive care unit (ICU) patients to take timely clinical interventions, planning and resource allocation. In the paper entitled: Mortali: Interpretable Machine Learning for Early Mortality Prediction in ICU Patients Using Dynamic Vital Signs, Laboratory Trends, and Treatment Patterns, an interpretable machine learning (iML) approach to early mortality prediction for patients in the Intensive Care Unit (ICU) is presented, using continuously collected vital signs, laboratory trends, and treatment-pattern. Unlike traditional static risk scoring systems, the proposed approach is dynamic and can reflect changes over time in a patient's condition, based on dynamic vital signs, lab trends, and dynamic patterns of clinical intervention. Two of the models were designed and tested to see their reliability in predicting, to ultimately choose the best model. The interpretability of the model was discussed using the techniques of Explainable Artificial Intelligence (XAI) to determine the most influential clinical factors contributing to the risk of death. The findings suggest that real-time patient monitoring and machine learning with interpretable models could be valuable tools in improving risk stratification and clinical decision-making in critical care settings. The proposed framework can help clinicians in the ICU to detect high-risk patients earlier and optimise patient outcomes by timely intervention, as it offers clear and transparent predictions.
ICU Mortality Prediction Interpretable Machine Learning Dynamic Vital Signs Laboratory Trends Information Technology And Networks (ITN).
Copyright (c) 2026 Umer Farid, Maria Khalil (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
THE IMPACT OF 3D PRINTING ON THE CONSTRUCTION INDUSTRY: ANALYZING THE POTENTIAL FOR SUSTAINABLE BUILDING PRACTICES
The emergence of 3D printing technology, formally known as Additive Manufacturing (AM), represents a paradigm shift in construction methodologies with significant implications for sustainability, efficiency, and design innovation. This study provides a comprehensive quantitative analysis of 3D printing applications in construction, examining environmental impacts, economic viability, and technological capabilities through a mixed-methods investigation of 180 projects across 35 countries from 2015-2023. Results demonstrate that 3D printed construction reduces material waste by 30-60% compared to conventional methods, decreases construction time by 50-80%, and lowers labor requirements by 45-70% through automation integration. Life Cycle Assessment (LCA) reveals that 3D printed concrete structures achieve 15-35% lower embodied carbon emissions when incorporating geopolymer or recycled materials, though energy consumption during printing operations remains 20-40% higher than traditional methods. Structural performance analysis indicates that printed concrete achieves compressive strengths of 35-65 MPa with controlled layer adhesion, though anisotropic properties result in 20-30% reduced strength perpendicular to printing direction compared to conventional casting. Economic assessment shows initial equipment investment of $150,000-$500,000, with project-level cost savings of 15-40% primarily from reduced formwork (eliminating 95% of temporary structures), labor reductions, and material optimization. However, regulatory frameworks lag behind technological innovation, with only 12 countries establishing formal codes for 3D printed construction, creating barriers to widespread adoption. Case studies of residential, commercial, and infrastructure applications reveal that design freedom enables 40-60% material reduction through topology optimization while maintaining structural integrity. The technology demonstrates particular promise for disaster relief housing, with printing times of 24-48 hours for 50 m² shelters using locally-sourced materials. This research concludes that 3D printing offers transformative potential for sustainable construction, but realizing its full benefits requires addressing material standardization, regulatory harmonization, workforce reskilling, and integration with digital design workflows through coordinated industry-wide initiatives.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
EXPLORING THE ROLE OF ARTIFICIAL INTELLIGENCE IN OPTIMIZING SUPPLY CHAIN MANAGEMENT: A CASE STUDY OF MANUFACTURING INDUSTRIES
The integration of Artificial Intelligence (AI) in supply chain management represents a transformative shift in how manufacturing industries optimize operations, reduce costs, and enhance resilience. This study provides a comprehensive analysis of AI applications across the manufacturing supply chain, examining implementation patterns, performance outcomes, and adoption barriers through a mixed-methods investigation of 500 manufacturing firms across North America, Europe, and Asia from 2018-2023. Results demonstrate that AI-driven supply chain optimization reduces operational costs by 22-35%, decreases inventory carrying costs by 28-42%, and improves forecast accuracy by 45-67% compared to traditional methods. Machine learning algorithms for demand forecasting achieved mean absolute percentage errors (MAPE) of 8.7% versus 21.3% for statistical methods, while reinforcement learning for logistics optimization reduced transportation costs by 19-31% through dynamic routing. Computer vision systems for quality inspection increased defect detection rates from 85% to 98.7% while reducing inspection time by 72%. Natural language processing applications in procurement reduced supplier selection time by 65% and improved contract compliance monitoring by 54%. However, implementation challenges persist: data quality issues affect 58% of AI initiatives, integration complexity with legacy systems hinders 42% of deployments, and skill shortages impact 67% of manufacturing firms. Return on investment analysis reveals an average payback period of 18-36 months for AI implementations, with predictive maintenance applications yielding the highest ROI (3.8:1) followed by demand forecasting (2.9:1) and inventory optimization (2.4:1). The study identifies critical success factors including executive sponsorship (increasing implementation success by 3.2 times), data infrastructure maturity (correlating with AI effectiveness at r=0.71), and change management processes (reducing resistance by 68%). This research concludes that while AI offers substantial optimization potential, realizing benefits requires addressing data governance, talent development, and organizational readiness challenges through holistic transformation strategies that align technological capabilities with operational objectives in manufacturing supply chains.
ANALYZING THE ENVIRONMENTAL IMPACT OF RENEWABLE ENERGY SOURCES: A COMPARATIVE STUDY OF SOLAR, WIND, AND HYDROPOWER
The global transition from fossil fuels to renewable energy is essential for mitigating climate change, but it necessitates a comprehensive understanding of the environmental trade-offs associated with different renewable technologies. This study provides a quantitative comparative analysis of the lifecycle environmental impacts of three dominant renewable energy sources: solar photovoltaic (PV), wind, and hydropower. Employing a problem-based research methodology, the analysis integrates lifecycle assessment (LCA) data, land-use metrics, material intensity, and ecosystem impact indicators from over 150 global projects commissioned between 2010 and 2023. Results indicate that while all three sources offer substantial carbon emission reductions (92-99%) compared to conventional coal, their environmental footprints differ markedly across impact categories. Hydropower exhibits the lowest median greenhouse gas (GHG) emissions at 12 g CO₂-eq/kWh but has the most significant ecosystem disruption and land-use change impacts. Utility-scale solar PV has a higher median carbon intensity (42 g CO₂-eq/kWh), primarily due to manufacturing, but demonstrates lower ecosystem impact per unit of energy generated. Wind power, particularly onshore, shows a favorable balance with a median GHG emission of 15 g CO₂-eq/kWh and moderate land use, though it poses notable challenges for avian and bat populations. The study identifies critical problem areas, including the mineral resource scarcity for solar PV, the biodiversity loss associated with large hydropower reservoirs, and the end-of-life management of composite materials for wind turbines. The findings underscore that there is no single "lowest-impact" source; optimal deployment depends on localized environmental constraints and the specific impact category prioritized. This research concludes that a diversified, spatially optimized renewable energy portfolio, informed by robust local and global environmental impact assessments, is crucial for a sustainable energy transition that genuinely minimizes ecological harm.
THE ROLE OF GENOMIC MEDICINE IN PERSONALIZED HEALTHCARE: A STUDY ON CRISPR-CAS9 AND ITS APPLICATIONS IN GENE THERAPY
Genomic medicine represents a transformative approach to healthcare that utilizes an individual's genomic information to guide clinical decision-making. This research investigates the revolutionary CRISPR-Cas9 gene-editing system and its applications in gene therapy for personalized healthcare. The study employs quantitative problem-based methodology to analyze clinical outcomes, efficacy rates, and safety profiles of CRISPR-based therapies across various genetic disorders. Our comprehensive analysis demonstrates that CRISPR-Cas9 technology has achieved significant breakthroughs in treating monogenic disorders, with an average efficacy rate of 78.3% across completed clinical trials. The research identifies key therapeutic areas including hemoglobinopathies (sickle cell disease and β-thalassemia), inherited retinal diseases, and metabolic disorders where CRISPR interventions show particular promise. However, challenges persist, including off-target effects (observed in approximately 12.7% of cases), immune responses to Cas9 proteins, and ethical considerations surrounding germline editing. The study further reveals that personalized CRISPR approaches, which consider individual genetic variations and patient-specific mutations, yield 34.2% better outcomes compared to standardized protocols. Emerging delivery systems, particularly lipid nanoparticles and adeno-associated virus vectors, have improved targeting efficiency to 89.4% in recent trials. This research concludes that while CRISPR-Cas9 represents a paradigm shift in personalized medicine, successful integration into clinical practice requires addressing technical limitations, establishing robust regulatory frameworks, and ensuring equitable access. The findings underscore the necessity for continued innovation in delivery mechanisms, specificity enhancement, and long-term safety monitoring to realize the full potential of genomic medicine.
ADVANCEMENTS IN QUANTUM COMPUTING: EXPLORING THE POTENTIAL OF QUANTUM ALGORITHMS FOR SOLVING COMPLEX PROBLEMS IN PHYSICS
Quantum computing has emerged as a promising paradigm for addressing computational challenges in physics that remain intractable for classical methods. This study presents a quantitative benchmarking analysis of prominent quantum algorithms applied to representative physics problem classes, with the objective of evaluating their computational performance relative to state-of-the-art classical algorithms. Using standardized performance metrics—including execution time, algorithmic complexity, resource utilization, accuracy, scalability, and quantum advantage ratios—the study systematically compares quantum and classical approaches across multiple experimental configurations. The results demonstrate that quantum algorithms exhibit measurable performance gains for specific problem types, particularly in terms of runtime efficiency and scaling behavior, while also highlighting practical constraints related to qubit requirements and circuit depth. Visual and tabulated analyses further reveal that quantum advantage is highly problem-dependent and influenced by algorithmic structure and system size. Overall, the findings provide empirical evidence that quantum algorithms can offer substantial computational benefits for selected physics applications, while underscoring the importance of quantitative benchmarking in distinguishing theoretical promise from practical performance. This work contributes to ongoing efforts to objectively assess quantum advantage and informs future research directions in quantum algorithm development and physics-based applications.
THE ROLE OF MUSIC IN SOCIAL MOVEMENTS: A CASE STUDY OF PROTEST SONGS IN THE CIVIL RIGHTS MOVEMENT
Music has long served as a powerful instrument of collective expression within social movements, shaping emotions, identities, and political action. This study examines the role of protest songs in the American Civil Rights Movement, focusing on how music functioned as a mechanism for mobilization, emotional synchronization, and collective identity formation. Employing a mixed-methods approach, the research integrates qualitative lyrical analysis with quantitative content analysis of protest songs and participant engagement indicators. The findings demonstrate that protest music played a central role in sustaining morale, reinforcing shared purpose, and facilitating emotional alignment among movement participants. Dominant lyrical themes of freedom, unity, faith, and resistance were consistently associated with heightened engagement and collective efficacy. Quantitative results further reveal strong associations between musical repetition, emotional tone, and mobilization intensity across different performance contexts. By foregrounding the emotional and social dimensions of music, this study highlights protest songs as critical cultural resources that enabled sustained participation and resilience within the Civil Rights Movement. The research contributes to social movement studies and ethnomusicology by providing empirical evidence of music’s capacity to transform individual emotion into collective political action.
ANALYZING THE IMPACT OF URBANIZATION ON THE PRESERVATION OF HISTORIC ARCHITECTURE: A GLOBAL PERSPECTIVE
Rapid urbanization has emerged as one of the most significant challenges to the preservation of historic architecture worldwide, often placing modernization pressures in direct conflict with cultural continuity and architectural authenticity. This study examines the global impact of urbanization on the preservation of historic architecture by integrating qualitative field observations, stakeholder interviews, and quantitative assessments across diverse geographical contexts. Using a mixed-methods approach, the research evaluates urbanization intensity, preservation challenges, architectural alterations, policy effectiveness, and socio-economic outcomes associated with heritage areas. The findings reveal that high levels of urban growth and globalization-driven development are strongly associated with material substitution, façade modification, and loss of visual integrity in historic urban landscapes. While adaptive reuse and heritage tourism generate measurable economic benefits, these gains are frequently accompanied by increased vulnerability of architectural authenticity when governance and community participation mechanisms are weak. The results further demonstrate that integrated preservation frameworks, particularly the Historic Urban Landscape approach, show greater potential for balancing development pressures with heritage conservation. Overall, the study highlights the necessity of dynamic, context-sensitive urban management strategies that reconcile economic growth with the long-term safeguarding of historic architectural identity. The research contributes to global heritage and urban studies by providing empirical evidence to inform sustainable policy, planning, and conservation practices in rapidly urbanizing cities.
AI-Powered Digital Twins for Optimizing Smart City Infrastructure and Energy Management
This study explores the application of AI-powered digital twins in optimizing smart city infrastructure and energy management. By integrating real-time data from IoT-enabled systems with advanced machine learning models, the research demonstrates significant improvements in key urban systems, including energy demand forecasting, traffic optimization, and infrastructure resilience. The AI-driven digital twin models achieved up to a 30% reduction in traffic congestion, 15% peak demand reduction in energy grids, and enhanced system resilience during demand surge events by 40%. These results are supported by simulations and optimization models that reduced energy consumption, improved renewable energy integration, and minimized traffic congestion through predictive modeling. Additionally, the digital twin models optimized water distribution systems and enabled better management of critical urban assets, contributing to more efficient resource utilization. Expert feedback on these models highlighted their practical applicability, showcasing positive reception from urban planners and infrastructure managers. The findings underscore the transformative potential of AI and digital twins in creating more sustainable, resilient, and efficient urban environments. However, the study also identifies challenges such as data privacy concerns and scalability issues, which need to be addressed in future developments. Overall, this research contributes to the evolving field of smart cities, demonstrating how AI-powered digital twins can play a crucial role in urban management and sustainability.
Copyright (c) 2025 Syed Ali Khayam, Imran Razzak (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Sustainable Water Management Practices in Arid Regions
Water scarcity in arid and semi-arid regions represents one of the most urgent sustainability challenges of the 21st century, driven by climate variability, population growth, agricultural expansion, and unsustainable extraction of groundwater. This study employed a mixed-methods experimental approach, integrating quantitative analyses of efficiency indices and statistical modeling with qualitative insights from case studies and policy frameworks, to evaluate sustainable water management practices. Results from nine tables and twelve figures highlight that advanced interventions—such as drip irrigation, wastewater reuse, mulched drip systems, and precision agriculture—consistently outperformed conventional methods, enhancing water-use efficiency by up to 40–60% and improving soil health and crop resilience. Spatial analyses using GIS-based hydrological mapping identified critical recharge zones and stress hotspots, while policy evaluations revealed that community engagement, transparent governance, and justice-oriented frameworks are indispensable for long-term resilience. Importantly, traditional systems such as qanats, liman irrigation, chauka embankments, and taanka cisterns were found to complement modern technologies, demonstrating that cultural heritage knowledge remains relevant when integrated with innovation. Discussion of global and regional case studies further reinforced the multidimensional nature of sustainability, where environmental gains must align with economic feasibility and social equity. The findings conclude that comprehensive strategies—bridging scientific innovation, indigenous practices, and inclusive governance—are essential for transforming water scarcity from a vulnerability into an opportunity for resilience and sustainable development. This research contributes to advancing water security discourse by emphasizing the need for holistic, context-sensitive solutions that prioritize ecological integrity, food security, and intergenerational equity in arid environments.
Copyright (c) 2025 M. Iqbal Choudhary, Muhammad Raza Shah (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
AI in Electoral Prediction: Ethical and Political Analysis
This work is the analysis of artificial intelligence (AI) use to predict elections, the focus of which is on the technical capabilities and the ethical and political implications of AI forecasts. We use a mixed-methods approach, consisting of the qualitative study of the political and societal implications of these technologies and the state-of-the-art machine learning frameworks, such as Logistic Regression, Random Forest, and XGBoost. The quantitative part of the study consists in the application of these models to the historical electoral data which include socioeconomic variables, voting pattern, and demographic characteristics. The prediction models demonstrate a high ability to predict the election outcomes as measured on the basis of accuracy, precision, recall, F1-score, and area under the ROC curve (AUC).
Copyright (c) 2025 Ali Cheema, Usman Qamar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Artificial Intelligence Applications in Personalized Cognitive Behavioral Therapy
This research is a mixed-method experimental investigation into the ways in which Artificial Intelligence (AI) can enhance Personalized Cognitive Behavioural Therapy (AI-CBT). A reinforcement learning framework to dynamically adjust CBT sessions to individual patient characteristics was proposed, supported by multimodal psychometric and natural language processing of therapy transcripts. A randomized controlled trial was carried out (N=120) in three groups: psychoeducational controls, therapist-led CBT, and AI-CBT. The quantitative results indicated that during the baseline, mid-intervention, and post-intervention, participants in the AI-CBT group significantly minimized the symptoms of anxiety and depression measured with the BDI-II and GAD-7 (p < 0.05). Effect size estimates confirmed the high level of AI-motivated personalization impact, and the consistency of therapeutic outcomes was confirmed with repeated measures ANOVA. The presence of positive patient experiences with the involvement, accessibility, and personalization were highlighted through the provision of the complementary qualitative analysis, however, the concerns with the lack of human empathy were also brought up. The thematic insights and the quantitative clinical measures served to enhance the validity of the results and proved AI-CBT to be a successful and scalable intervention. Altogether, the present study provides empirical evidence that AI can transform the method by which therapy is provided by making the interventions context-aware, data-driven, and flexible. This will assist in addressing the growing demand of mental health solutions globally that are readily available. The conclusion of the study addresses implications of human-AI hybrid therapeutic models in the future, as well as ethical issues surrounding digital health, in terms of its acceptance by clinicians.
Copyright (c) 2025 Ayesha Mian, Usman Qamar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Relationship Between Leadership Styles and Employee Motivation
This study investigates the relationship between leadership styles and employee motivation through a mixed-methods approach combining quantitative surveys and qualitative interviews across diverse organizational sectors. The analysis focused on transformational, transactional, and laissez-faire leadership styles, examining their respective impacts on intrinsic and extrinsic motivation. Quantitative results revealed that transformational leadership demonstrated the strongest positive correlation with intrinsic motivation, significantly enhancing empowerment, creativity, and engagement. Transactional leadership exhibited moderate associations, primarily influencing extrinsic motivation through structured rewards and performance monitoring, while laissez-faire leadership consistently showed weak or negative effects on motivation and job satisfaction. Regression analysis confirmed transformational leadership as the most significant predictor of motivational outcomes, supported by ANOVA results showing significant differences across leadership styles. Departmental-level analysis indicated that healthcare and education employees reported the highest motivational responses, while generational differences highlighted younger employees’ stronger alignment with transformational practices. Qualitative findings reinforced these results, identifying recognition, communication, and psychological safety as central themes driving motivation under effective leadership. The integration of quantitative and qualitative insights provided a comprehensive perspective, underscoring that motivation is context-dependent and mediated by demographic, cultural, and organizational factors. Overall, the study concludes that transformational leadership is the most effective style in fostering sustainable motivation, though situational adaptability is essential for optimizing outcomes across diverse workforce segments. These findings have significant implications for leadership development, organizational design, and human resource strategies in contemporary workplaces.
Copyright (c) 2025 Ather H. Akhlaq, Uzma Javed (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Advanced Robotics for Disaster Response and Humanitarian Engineering in Hazard-Prone Regions
The integration of advanced robotics into disaster response has the potential to revolutionize humanitarian engineering in hazard-prone regions. This study introduces and evaluates the Advanced Robotics for Disaster Response and Humanitarian Engineering Framework (ARDR-HEF), designed to enhance safety, efficiency, and ethical deployment in complex disaster scenarios. Through quantitative simulation experiments and qualitative stakeholder analysis, the framework was rigorously tested across multiple hazard environments, including earthquakes, floods, and wildfires. Results from nine experimental tables revealed significant performance improvements: baseline robot performance across terrains demonstrated enhanced navigation speed, detection accuracy increased by over 25% with AI-IoT integration, and fairness metrics in victim detection improved notably in low-visibility conditions. Liability modeling showed balanced distribution of responsibility among agencies, while safety margins were consistently maintained even under stress-tested conditions. Twelve diverse figures further visualized the robustness of the framework, including reductions in disaster response time, improved stakeholder trust indices, and clear correlations between hazard intensity and robotic efficiency. Importantly, multi-agent and hybrid robotic systems showed superior coverage efficiency and ethical compliance compared to traditional approaches. The study concludes that ARDR-HEF provides a resilient, ethically informed, and scalable methodology for robotics deployment in disaster response. By aligning engineering advances with humanitarian imperatives, this framework paves the way for safer, faster, and more transparent disaster management operations in regions most vulnerable to catastrophic events.
Copyright (c) 2024 Faisal Riaz, Khurram Bhatti (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Engineering Ethical and Safe Deployment Frameworks for Autonomous Vehicles in Urban Mobility
The deployment of autonomous vehicles (AVs) in urban mobility systems presents both transformative opportunities and complex challenges, requiring frameworks that integrate engineering safety with ethical accountability. This study proposes and validates an Engineering Ethical and Safe Deployment Framework (EESDF) that combines quantitative simulations, ethical reasoning, and stakeholder engagement. Using high-fidelity urban driving simulations, we observed that ethically guided algorithms reduced the probability of high-severity collisions by 37% compared to baseline rule-based models. Pedestrian detection accuracy improved by 21% through fairness-oriented perception models, while liability distribution models highlighted the importance of transparent governance mechanisms. Qualitative insights from policymakers, engineers, and planners reinforced the significance of explainability, accountability, and regulatory harmonization for public trust. Results from twelve distinct visualization analyses and nine extensive datasets confirmed that the integration of ethical reasoning modules not only enhanced safety margins but also aligned with international standards such as ISO 21448 and UNECE Regulation 157. The EESDF thus provides a robust, adaptable, and socially responsible framework for AV deployment in diverse urban contexts. By embedding fairness, transparency, and explainability into engineering systems, the study demonstrates that the safe integration of AVs depends not only on technical robustness but equally on ethical and governance dimensions, thereby paving the way for sustainable and equitable urban transportation systems.
Copyright (c) 2024 Imran Razzak, Naveed Arshad (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Evolution of Gender Roles in 20th-Century Wars
The paper explores the changing role of gender in light of the 20 th century wars with particular attention on the role played by women in the military industry sector resistance and spy activities as well as politics. The study reveals major shifts in popular opinion about gender roles by reviewing statistics about women enlistment, employment rate, political empowerment, and wartime input into intelligence units. Women have long-term consequences because women who were transferred to defence and industries during the warfare, later contributed on active duties thus helping them gain equal position in the society. The study actually shows that even though many women went back to traditional roles following the war, the changes that happened during the war provided the necessary grounds on which the gender equality movements of the future would build on. As the results indicate, the upheavals caused by the war granted women the opportunity to challenge the gender traditions and occupied themselves with the new roles vital to the efforts of war as well as influenced how genders would interact in the future. This work has built a better understanding of how gender and warfare interact as well as how social change occurs, in that the work shows how the role in which women played during these turbulent times were radical in addition to being highly formative in the course of future feminism.
Copyright (c) 2024 Nukhbah Langah, Farzana Bari (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Industrial Revolution and Its Influence on Urban Social Inequalities
The paper undertakes a mixed-design study of the effects of industrial revolution on urban social inequality by combining a qualitative and a quantitative study. The study focuses on the growth of employment in the manufacturing sector, the wage gap between unskilled an skilled laborers, and on how urbanization impacted housing, healthcare, education, and crime by researching historical archives, censuses, and historical economic statistics of the 19 th century. The results indicate high disparities between socioeconomic groups in that the skilled labourers were better paid, the working population had poor living conditions, limited access to basic amenities and the lifespan deteriorated. Regression analysis and factor analysis, just to name a few of the statistical methods, confirm the extent of income inequality and its relation to poor living conditions i.e. overcrowding and the lack of social amenities. The thematic analysis of historical writings puts historical discoveries into perspective and gives a better understanding of the social consequences of the subject of industrialization. This study broadens the understanding of structural inequalities that occurred during the Industrial Revolution and its continued effects on the current cities.
Copyright (c) 2024 Tahir Kamran, Ali Khan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Determinants of Start-Up Survival in Emerging Markets
This study investigates the determinants of start-up survival in emerging markets, adopting a mixed-methods approach that integrates econometric modeling with qualitative insights from entrepreneurs and ecosystem actors. Drawing on data from multiple emerging economies, the research examines firm-level characteristics, institutional quality, and ecosystem support as key drivers of entrepreneurial resilience. The findings indicate that larger firms and those led by experienced founders are more likely to survive, while innovation and digital adoption further enhance competitiveness. Access to finance emerges as one of the most critical predictors of survival, although its effectiveness is moderated by institutional environments. The study also highlights the vital role of entrepreneurial ecosystems, such as incubators and accelerators, in compensating for weak formal institutions. Sectoral analysis reveals that technology-based ventures display greater longevity than traditional industries. Overall, the results underscore that start-up survival is shaped by systemic interactions between resources, institutions, and networks, rather than by single determinants. The study offers theoretical contributions to entrepreneurship literature by contextualizing survival within emerging market dynamics, and practical implications for policymakers and investors aiming to strengthen entrepreneurial ecosystems and improve long-term sustainability of start-ups.
Copyright (c) 2024 Shahzad Khurram, Faisal Bari (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Global Trade Wars and Their Impact on Emerging Economies
This paper investigates the impact of global trade wars on emerging economies using a mixed-methods approach that combines econometric analysis with qualitative evidence. Drawing on panel data from 2018 to 2024, the study finds that tariff escalations and trade policy uncertainty significantly reduce GDP growth, increase inflation, deter foreign direct investment, and destabilize exchange rates. Sectoral evidence highlights that export-oriented industries and household consumption patterns are particularly vulnerable, with downstream effects on industrial performance and social welfare. Additionally, trade wars exacerbate sovereign debt stress in economies with fragile fiscal structures. Visualizations confirm these relationships, illustrating how trade policy uncertainty and tariff intensity amplify macroeconomic instability. At the same time, regional integration emerges as a partial buffer, mitigating some of the adverse consequences through intra-regional trade flows and institutional cooperation. The study concludes that trade wars are systemic shocks that disproportionately affect developing economies, underscoring the urgency of structural reforms, industrial diversification, and resilience-oriented trade strategies. These insights not only contribute to academic debates but also provide actionable policy implications for governments and international financial institutions navigating the current era of global protectionism.
Copyright (c) 2024 Syed Nawab Haider Naqvi, Naved Hamid (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Role of One Health Approaches in Addressing Zoonotic Diseases in a Globalized World
Zoonotic diseases remain among the most pressing global health challenges of the twenty-first century, accounting for the majority of emerging infectious diseases and representing a critical threat in the context of globalization. The accelerated pace of international travel, global trade, urbanization, and ecological disruption has amplified the risk of zoonotic spillover and cross-border transmission, as illustrated by outbreaks of COVID-19, Ebola, avian influenza, and Nipah virus. This study employed a mixed-method experimental design to evaluate the role of One Health approaches in preventing, detecting, and managing zoonotic diseases between 2020 and 2022. Quantitative analyses of outbreak incidence, mortality rates, and economic impacts were combined with qualitative thematic synthesis of policy frameworks and case studies, enabling a comprehensive assessment of One Health’s effectiveness. Results, presented in nine structured tables and twelve figures, reveal that countries adopting integrated One Health frameworks achieved earlier detection of outbreaks, reduced case fatality ratios, and enhanced cost-effectiveness compared to those relying on siloed health systems. The analysis also highlighted antimicrobial resistance as a critical cross-cutting issue requiring One Health stewardship across human, animal, and environmental domains. While evidence strongly supports the value of One Health in mitigating zoonotic threats, persistent challenges include limited adoption in low- and middle-income countries, governance gaps, and underinvestment in environmental health. Nevertheless, the findings affirm that operationalizing One Health is not only a scientific imperative but also an economic and ethical necessity. By aligning human, animal, and ecosystem health under a unified framework, One Health provides a transformative pathway for global pandemic preparedness, equitable health outcomes, and sustainable coexistence in a rapidly globalizing world.
Copyright (c) 2024 Aftab Ahmed, Saad Abdullah, Rabia Nasir (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Mental Health Crises in Post-Conflict Regions: Interdisciplinary Approaches to Trauma Healing
Post-conflict regions face a dual burden of rebuilding social infrastructures while addressing widespread mental health crises driven by trauma, displacement, and chronic stressors. This study applied a mixed-methods experimental design to evaluate interdisciplinary approaches to trauma healing, combining quantitative assessments of psychological outcomes with qualitative insights from affected communities. Quantitative findings revealed significant reductions in PTSD, depression, and anxiety symptoms among participants engaged in layered psychosocial interventions, with statistical models showing that community-based care integrated with livelihood support produced stronger recovery trajectories than stand-alone treatments. Functional outcomes, such as resilience and daily living capacity, also improved markedly across diverse demographic groups. Cost-effectiveness analysis indicated that integrated interventions yielded favorable incremental cost-effectiveness ratios, underscoring both clinical impact and economic sustainability. Qualitative data highlighted the importance of cultural adaptation, stigma reduction, and gender-sensitive programming, revealing that healing is often experienced not only as symptom relief but as restoration of dignity, social connection, and community trust. The integration of these findings confirmed that interdisciplinary interventions are most effective when clinical care is embedded within social protection, livelihood development, and participatory community structures. However, persistent challenges remain, including limited workforce capacity, inconsistent implementation fidelity, and inequitable access for marginalized groups. Overall, the results demonstrate that trauma healing in post-conflict regions is best advanced through interdisciplinary, context-sensitive strategies that link psychological recovery with broader peacebuilding and development goals.
Copyright (c) 2024 Ayesha Mian, Farah Malik (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Nanotechnology Applications in Water Purification for Addressing Global Freshwater Scarcity
Freshwater scarcity is a mounting global crisis, affecting billions and threatening socio-economic and ecological stability. This study investigates the application of nanotechnology in water purification as a sustainable solution to mitigate this challenge. A mixed-method experimental design was employed, integrating quantitative performance analysis and qualitative evaluation of nanomaterials including graphene oxide, carbon nanotubes, silver nanoparticles, titanium dioxide, and zeolite nanocomposites. The results demonstrate that graphene oxide exhibited the highest adsorption efficiency for heavy metals, achieving over 95% removal under optimized pH conditions. Silver nanoparticles displayed superior antimicrobial performance, eliminating more than 99.9% of E. coli and S. aureus colonies. Titanium dioxide achieved 90–95% degradation of organic dyes through photocatalysis under solar-simulated conditions. Zeolite-based composites provided promising results in desalination, showing a significant reduction in total dissolved solids. Comparative cost-efficiency analysis indicated that carbon nanotubes, despite high production costs, offer excellent regeneration and reuse potential. Twelve complex visualizations—including line plots, bar graphs, pie charts, scatter matrices, heatmaps, and hybrid plots—confirmed the statistical robustness of the findings. Additionally, the pseudo-second-order kinetic model and Langmuir isotherm confirmed strong correlation (R² > 0.98) in adsorption behavior, indicating rapid equilibrium and high surface specificity. While the laboratory-scale data affirm nanomaterials' superior performance, the study also highlights challenges in scalability, material safety, and environmental impact. Ultimately, this research provides compelling evidence that nanotechnology, if guided by safe and cost-effective implementation strategies, can revolutionize global water treatment frameworks and significantly contribute to achieving sustainable development goals.
Copyright (c) 2024 Muhammad Aslam Chaudhry, Rukhsana Kausar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Price Sensitivity and Its Impact on Brand Switching Behavior
This study investigates the impact of price sensitivity on brand switching behavior by employing a mixed-methods experimental design that integrates quantitative surveys and qualitative interviews. A structured survey of 500 consumers across multiple retail categories, complemented by 30 in-depth interviews, provides robust evidence on how price-related factors shape switching intentions. The quantitative findings reveal that price sensitivity exerts a significant positive influence on brand switching, as confirmed by regression and structural equation modeling, while brand loyalty demonstrates a moderating effect that dampens sensitivity-driven defection. Perceived value emerges as a critical mediator, with consumers perceiving low value more likely to abandon established brands even at marginal price differences. The results further indicate that demographic characteristics strongly influence sensitivity, with younger and lower-income consumers displaying higher switching rates than older or more affluent segments.Visual analyses from twelve figures and nine data tables underscore the robustness of these relationships, showing consistent patterns across consumer segments and product categories. Time-series and hybrid plots highlight the role of discounts and promotional strategies in amplifying switching, while scatter and correlation analyses confirm the interactive influence of loyalty and perceived value. The thematic analysis of interviews enriches these findings, revealing psychological underpinnings such as loss aversion, fairness perceptions, and reference pricing that shape switching behavior.Theoretically, this study contributes to the literature by integrating behavioral economics with marketing models to explain the dynamics of price sensitivity. Managerially, the findings suggest that while competitive pricing and promotions can attract high-sensitivity consumers, long-term loyalty requires value-added strategies, emotional branding, and trust-building. In an era of sustainability awareness, firms that effectively balance pricing with authentic ethical attributes can reduce sensitivity and foster durable brand relationships.
Copyright (c) 2024 Samira Azmat, Naeem Akhtar, Kamran Siddiqui (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Role of Sovereign Debt in International Financial Stability
This study investigates the pivotal role of sovereign debt in shaping international financial stability, employing a mixed-method approach that integrates quantitative econometric analysis with qualitative case studies. Using panel data from both advanced and emerging economies between , the research explores the effects of debt-to-GDP ratios, fiscal deficits, inflation, and credit spreads on global financial stability indices. The econometric findings reveal that excessive sovereign debt significantly increases credit spreads and undermines investor confidence, while Granger causality tests confirm a bidirectional relationship between debt accumulation and financial stability. Complementary qualitative analyses of Greece, Argentina, and Sri Lanka further illustrate how political constraints, weak fiscal institutions, and negotiation dynamics exacerbate crises, leading to systemic contagion across international markets. The results, summarized in nine tables and twelve figures, consistently demonstrate that sovereign debt sustainability depends not only on fiscal metrics but also on institutional quality, governance transparency, and macroeconomic credibility. Importantly, the study highlights the vulnerabilities of emerging markets, which face heightened risks due to limited fiscal buffers and exposure to global capital flows. At the same time, the analysis underscores the necessity of international coordination and the role of multilateral institutions in mitigating spillovers and facilitating orderly debt restructuring. Overall, the research contributes to the literature by offering an integrated perspective on sovereign debt management, providing evidence that prudent borrowing, credible fiscal discipline, and cooperative global frameworks are essential for preserving international financial stability.
Copyright (c) 2024 Eatzaz Ahmad, Muhammad Bilal (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Engineering Climate-Resilient Infrastructure for Coastal Mega-Cities under Rising Sea Levels
This study investigates the engineering of climate-resilient infrastructure for coastal mega-cities facing the escalating threat of rising sea levels. Employing a mixed-methods framework that combined quantitative flood modeling, resilience index formulation, and qualitative governance analysis, the research evaluated the comparative performance of gray, green, and hybrid infrastructure strategies across multiple case study cities. The results demonstrated that hybrid green–gray approaches, which integrate engineered structures with ecosystem-based defenses, achieved consistently higher resilience indices compared to traditional gray infrastructure. For example, New York City’s integrated flood barrier designs showed improved adaptive capacity, while Jakarta revealed compounded risks due to land subsidence, necessitating multi-layered interventions. Quantitative simulations further indicated that hybrid systems reduced long-term vulnerability coefficients by up to 35% while lowering projected economic losses relative to singular engineering projects. Technological enhancements, including digital twins and IoT-based flood monitoring, further strengthened adaptability by enabling real-time scenario testing. However, findings highlighted that governance capacity, institutional effectiveness, and financial inclusivity were decisive in determining the sustainability of resilience interventions, with Shanghai’s sponge city initiatives exemplifying successful policy integration. The study concludes that resilient infrastructure must be understood as a dynamic socio-technical system requiring not only engineering innovation but also adaptive governance and community participation. These findings reinforce the need for proactive, multi-disciplinary investments to secure sustainable urban futures in the face of accelerating climate risks.
Copyright (c) 2023 Aqsa Bano, Rimsha Tariq (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Role of Circular Economy Principles in Reducing Electronic Waste in Global Supply Chains
This study examines the role of circular economy (CE) principles in reducing electronic waste (e-waste) within global supply chains, addressing one of the most pressing sustainability challenges of the digital age. Through a mixed-methods approach combining quantitative modeling, case study analysis, and qualitative assessment of corporate and policy frameworks, the research highlights how CE practices contribute to waste minimization, resource conservation, and supply chain resilience. The findings reveal that eco-design and modularity extended electronic product lifecycles by up to 25%, while reverse logistics and take-back systems increased collection rates by nearly 40%. Remanufacturing and refurbishment programs reduced net waste volumes by more than 30%, and advanced recycling technologies achieved recovery efficiencies of over 95% for critical materials such as copper, gold, and rare earth elements. These outcomes underscore the economic and environmental benefits of embedding CE strategies into supply chains, including reduced dependency on virgin resource extraction, stabilization of raw material costs, and the creation of secondary markets. However, the study also identifies persistent barriers such as high initial investment costs, uneven regulatory enforcement, and consumer resistance to non-ownership business models. The analysis concludes that although challenges remain, CE adoption represents both an environmental necessity and a strategic pathway to global supply chain competitiveness. The results affirm that a systemic transition toward circular supply chains is achievable through innovation, collaboration, and harmonized international policy frameworks, thereby contributing significantly to global sustainability targets.
Copyright (c) 2023 Sajid Anwar, M. Arif Khan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Role of High-Temperature Superconductors in Next-Generation Energy Transmission
This paper researches the possibility of high-temperature superconductors (HTS) becoming the basis of the next generation transmission system by combining expertise insights, modelling, and experimental testing. YBCO-based coated conductors were tested under varying load conditions and cooled by cryogenic systems using four-point probe techniques that has enabled the determination of the critical current density and the quench behaviour. The grid-like electromagnetic and thermal responses were simulated with complementary finite element models and results were compared, finding a good match (RMSE < 5%). HTS conductors show vast improvements over conventional metallic conductors with a current-carrying capacity and energy loss performance that is increased by over 20 percent with large currents and recording gains of 20 percent or more in case of high currents. Also, the qualitative perceptions of power grid engineers demonstrated the mechanical durability, cooling costs, and scalability problems that trigger the need of multidisciplinary optimization. The results of quantitative and qualitative analysis show that HTS is transforming energy networks into sustainable, compact and efficient networks that can support the absorption of renewable energies in future. This discussion indicates that HTS technology is a viable solution to remodel the world power transmission infrastructures, although there are still engineering and economic issues.
Copyright (c) 2023 Asim K. Joarder, Zulfiqar Ali (Author) https://creativecommons.org/licenses/by-nc-nd/4.0