International Journal of Innovations in Science & Technology
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Novel Results on Refinement of Hermite-Hadamard Type Inequality with Applications Of ∆-Convex Function
Get PDFIn this paper, we recognized novel results on the integral inequalities type of Hermite-Hadamard to explore the applications of ∆-convex functions. Our conclusion extends several established theorems in the literature
Impact Assessment of Climatic Variability on the Streamflows and Predictions for the 21st Century Integrating Global Climate Models
Get PDFWater management needs to investigate the possible consequences of climate variability on hydrological variables. This paper presents the precipitation and temperature trend patterns and their impact on streamflow (1985-2014) for the Astore basin and streamflow predictions by the year 2100. The trend detection of the two parameters was assessed through the Man-Kendall and Sen’s Slope tests. The climate station data were compared with the results of the trends analysis and reported values of two Global Climate Models (GCMs), BCC-CSM1-1 and GFDL-CM3 (each having Representative Concentration Pathway, RCP 2.6 and RCP 8.5 scenarios). No important trend was noted for the precipitation except an increasing trend in September, while there were rising trends in temperature from December to August, whereas declining trends from September to November, which shows that the summer duration is getting longer while the winter is getting shorter with an early start in September. The results indicated that precipitation trends are reciprocating the temperature. The rising trends in temperature can result in extreme events, floods, and droughts due to extensive glacier melt in the near and far future, respectively. The result of GCMs for the two chosen RCPs had a similar pattern of climatic changes all over the century, with slightly higher values for the RCP 8.5 scenario, experiencing a tendency toward less precipitation and, during some seasons, a modest increase in temperature. The stream-flow predictions using GCMs showed rising trends till the mid-21st century and declining trends by the last decade of the century and even onwards. This rise in summer flows will raise the water level in the Tarbela reservoir located on the downstream of Upper Indus River Basin (UIRB) thus providing excess water for Hydropower generation, increasing till the mid-century and there are also chances of inflows to reservoir, beyond its capacity that can cause flooding to its downstream while after 2091, a continues decrease in water level is expected, which in return, can severely affect the power generation capacity form the reservoir, also causing reducing water supply for agriculture needs
Hierarchical Modeling of Barriers to Sustainable Development in the Mining Industry of Pakistan: An ISM and MICMAC-Based Approach
Get PDFMining contributes to economic development but relies on finite and non-renewable resources, posing sustainability challenges. Achieving long-term economic stability and environmental preservation requires a balanced approach that integrates effective resource management with sustainable development strategies. However, sustainable development in mining is complex, as it faces multiple barriers related to governance, economic, structural, and environmental challenges. This study applies Interpretive Structural Modeling (ISM) to explore these barriers and analyze their interdependencies. Data was collected from the literature and analyzed through expert opinions via a structured questionnaire, and an ISM-based model was developed to determine the hierarchical structure of these barriers. The MICMAC (Cross Impact Matrix Multiplication Applied to Classification) analysis further classifies barriers based on their driving and dependence power, providing insights into their relative importance within the system. Findings reveal that all thirty-two barriers influence the sustainability process, with some controlling as a key driving force while others function as dependent factors. Lack of top management commitment and lack of enforcement of rules and regulations emerge as the most influential barriers due to high driving power and low dependence. The absence of autonomous barriers indicates that all identified factors significantly affect the sustainable development of mining. The hierarchical ISM-based model emphasizes the necessity for targeted interventions at different barrier levels. This research contributes to sustainability efforts by offering a structured approach to understanding barrier interrelationships, aiding policymakers and industry stakeholders in formulating effective strategies for responsible and sustainable mining practices
Adaptive Student Assessment Method for Teaching Programming Course
Get PDFComputer programming is a core component of computer science education and is widely recognized as a vital skill for aspiring professionals. Repetitive coding assessments help students improve their programming abilities, but the manual creation and evaluation of these assessments can be time-consuming and challenging for instructors. To address this, we developed an Adaptive Student Assessment System (ASAS) that automatically generates subjective programming questions aligned with Course Learning Objectives (CLOs) and assists in evaluating student responses. The system was evaluated using a controlled study involving two groups: a test group and a control group. Results demonstrated that the test group consistently outperformed the control group across cognitive assessments, with overall performance improvements of 13.5%. Affective feedback collected through a post-term survey showed a 48.20% higher agreement rate in the test group regarding motivation, clarity, and satisfaction with the assessment process. Teacher evaluations further confirmed the system\u27s effectiveness, with improvements of 23.33% in assessment creation, 26.67% in assessment conduction, and 43.33% in result compilation compared to traditional methods. Teachers reported reduced workload, increased efficiency, and a positive attitude toward long-term adoption of the system. These findings highlight that ASAS not only enhances student engagement and academic performance but also improves instructional efficiency, making it a scalable and effective solution for programming education
Assessing Urban Expansion and Land Cover Change in City District Lahore using Multi-Stage Satellite Data: Lahore land use land cover
No full textLahore, a metropolis and 2nd second-largest city of Pakistan, has been experiencing rapid urban expansion over the past five decades. The socio-economic development and growth of the urban population have caused the rapid increase of urban expansion. The increase in the built-up area of Lahore has seen remarkable growth during the past decades. This study is aimed at detecting the Spatio-temporal changes in land use and land cover and evaluating the urban expansion of Lahore since 2003. The conversion of land to other uses is primarily because of growth in urban population, whereas the increase in economic activities is the central reason for the land-use changes. In this study, temporal Landsat imageries were used. The supervised image classification of the maximum likelihood algorithm was applied on Landsat ETM+ (2003) and OLI/TIRs (2023) images, whereas a post-classification comparison technique was employed to detect changes over time. The spatial and temporal analysis revealed that during the past twenty decades, the built-up area of Lahore city has expanded by 486 km². It was found from the analysis that in Lahore city, the urban expansion was primarily at the cost of loss of fertile agricultural land, vegetation, and other cultivable land use. The analysis further revealed that The Total agricultural area in 2003 was 725 KM². The agricultural land to Built-up area is about 325 km². Due to the population increasing, the newly added population needs more space to fulfill their basic needs. The Total Barren Land in 2003 was 7452 KM².The Barren land to Built-up color, which is about 255 km².Rapid Land use changes have been marked for a period of 20 years in Lahore. The increase in the area used for built-up land is 470 Km² in 2003 to 956 Km² in 2023 (overall increase is 28 %), respectively
Command, Control, and Assuasive Measures: Policy-Based Information Dissemination in Environmental Governance of Pakistan
Get PDFSmog is a significant environmental and public health issue in Pakistan, particularly in Punjab, where the intensity of seasonal haze events has increased since 2016. The study analyzes government policies and initiatives from 2017 to 2025, focusing on three mitigation measures: command-and-control, economic, and assuasive, and identifies the weakest link in the current framework. A qualitative content analysis was conducted using national and provincial legislation, policy strategies, implementation reports, and media coverage, guided by the OECD (Organization for Economic Co-operation and Development) environmental policy classification. The findings reveal that Punjab\u27s smog control measures are primarily based on command-and-control measures, including industrial inspections, emission caps, and bans on high-pollution practices. Economic measures, including targeted subsidies for cleaner agricultural machinery, electric vehicle installment schemes, and initial proposals for an Emission Trading System, are emerging, but their scope is limited. Assuasive measures, which involve awareness campaigns and participatory tools, are underdeveloped, seasonal, and poorly integrated with enforcement and incentives. The absence of long-term environmental literacy programs and behavioral change initiatives hinders compliance with regulatory and market-based tools, thereby reducing the effectiveness of the policy. The study concludes that Punjab\u27s long-term smog reduction necessitates a balanced approach to policy, prioritizing continuous, well-funded assuasive measures alongside legal enforcement and economic instruments, to foster a lasting environmental responsibility culture and improve air quality outcomes
Smog, Heatwaves, and the Feminized Face of Climate Distress: Psychological, Geographical, and Engineering Perspectives of Women’s Vulnerability in Central Punjab
Get PDFIn Central Punjab, Pakistan, the rising impacts of climate change, especially frequent heat waves and smog, pose extreme risks to human health. Their impact on women is not gender-neutral; in highly populated regions with socioeconomic limitations, women are disproportionately affected. This study adopts an interdisciplinary approach, incorporating psychology, geography, and environmental engineering to assess women’s vulnerability to climate change. Based on a qualitative research design, in-depth interviews were conducted with women of different ages, professions, and socioeconomic statuses in the urban and semi-urban districts of Central Punjab. Thematic analysis revealed three main areas of vulnerability: psychological distress, risks associated with geography, and inadequate infrastructure. Women frequently experienced increased anxiety, helplessness, and trauma linked to long-term exposure to extreme heat and environmental pollution. Geographical mapping of participants’ residences showed that women living in low-income areas faced greater exposure due to congested housing, lack of green cover, and high levels of vehicle emissions. Their susceptibility was further exacerbated by engineering-related shortcomings, such as poor ventilation, ineffective early warning systems, and unsafe transport in urban environments. These overlapping stressors collectively limited women’s mobility, increased their role strain, and weakened their ability to adapt. The results highlight the urgent need for gender-sensitive, climate-resilient infrastructure and public health interventions. The integration of psychological support, inclusive urban planning, and community sensitization is essential to strengthen women’s resilience to climate-related risks. This interdisciplinary perspective underscores that addressing climate distress requires closing social, spatial, and technological gaps to reduce the disproportionate burden women face from increasing environmental degradation
Advances in AI-Based Land Use and Land Cover Classification: A Review of Deep Learning and Remote Sensing Integration
Get PDFThe integration of Artificial Intelligence (AI) with remote sensing has transformed Land Use and Land Cover (LULC) classification, enabling more accurate, efficient, and scalable environmental monitoring. This review synthesizes recent advancements in AI-driven LULC classification, with a focus on deep learning, transfer learning, hybrid approaches, and explainable AI (XAI). Recent studies demonstrate that AI techniques significantly enhance classification accuracy and adaptability across diverse geospatial datasets, supporting applications such as urban expansion monitoring, ecological assessment, reforestation analysis, and real-time land management. Despite these advancements, challenges remain regarding spectral resolution, model interpretability, computational efficiency, and data scarcity. This review highlights these limitations and discusses emerging solutions, including multimodal data fusion, lightweight AI models, and scalable MLOps frameworks. The findings provide insights for researchers, practitioners, and policymakers to guide future work in sustainable land management and environmental monitoring
An Enhanced Similarity Measure–Driven K-Nearest Neighbor Framework for Categorical Data Classification
Get PDFMachine learning provides effective answers to real-world classification issues by combining supervised approaches (e.g., regression, SVMs, decision trees, neural networks) and unsupervised techniques (e.g., clustering, PCA). Comparing categorical data to numerical data reveals that the former is still understudied. This study compares three variations of the K-Nearest Neighbors (KNN) algorithm, Dice Coefficient KNN (DKNN), Overlap Coefficient KNN (OKNN), and Simple Match Coefficient KNN (SMKNN) on three categorical datasets: Malware Detection, Hospital Readmission (Kaggle) and Mushroom (UCI Repository). Each variation improves classification performance by incorporating a unique similarity metric. Recall, accuracy, precision, and F1-score were used to evaluate the models. According to experimental results, SMKNN consistently performed better than the other variations, obtaining an average F1-score of 93.3%, accuracy of 88.29%, precision of 89.33%, and recall of 98%. With an F1-score of 91% and an average accuracy of 83.89%, OKNN came in second, while DKNN did worse with an accuracy of 73.74%. These results demonstrate the stability and promise of SMKNN as a dependable model for categorical data classification, highlighting its exceptional and flexible performance across a variety of datasets. The study gives useful information for identifying the best KNN variations for data-driven applications
Data Mining for Smarter Administration of TVET Institutes
Get PDFRetaining the trainees is a major problem for the TVET institutes today. The trend of TVET education in Khyber Pakhtunkhwa province has improved in recent decades. Despite its high cultural barriers, resistance in women\u27s education, and dropout rates, on the basis of annual admission, Khyber Pakhtunkhwa holds the 2nd position among all other provinces of Pakistan. In this research, we have tried to decrease the dropout ratio by enhancing the Daily attendance of the trainees and improving their results. Monthly Fee slip, Date Sheet, and Results will be shared with the parents/guardians through SMS/WhatsApp. New TVET institutes will be able to check the trainee\u27s educational record from the previous TVET institute. The Data Mining for Smarter Administration of TVET Institutes will be a Mobile and Web-Based Application and will keep a close relationship between Parents, Teachers, and Administration of the TVET institute