International Journal of Innovations in Science & Technology
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Modernizing Land Records in Tulumba Through GIS: Massavi Reconstruction Under the Pulse Project
Full text linkThis research presents a comprehensive methodology for developing digital Massavis (land record maps) using Geographic Information System (GIS) technologies to address persistent challenges in land record management. The study focuses on the Punjab Urban Land System Enhancement Project (PULSE), specifically examining the digitization of Mouza Tulamba in District Khanewal. Through systematic georeferencing, boundary digitization, and grid adjustment techniques, this study demonstrates how digital technologies can overcome traditional limitations in land record management, including unavailable or damaged physical records and staff reluctance to create new maps. The methodology successfully processed 207 Murabajaat covering 4,189 acres with 8,052 Khasra records, establishing a replicable framework for digital land record transformation
Evaluating the Meteorological Pattern of District Swat Using Different SSP Scenarios
Full text linkThis study investigates the observed and projected impacts of climate change in District Swat, Pakistan, using meteorological records and CMIP6-based projections under SSP2-4.5 and SSP5-8.5 scenarios. Metrological variables, such as temperature and precipitation, were examined for long-term trends, anomalies, and extremes. Machine learning techniques (XGBoost and SHAP) were used to identify the most relevant online datasets and climate models. ERA5 emerged as the most reliable online source, and INM-CM5-0, CNRM-CM6-1, and CMCC-ESM2 were selected as the best-performing GCMs. The Mann-Kendall test showed a significant rise in minimum and maximum temperatures based on future conditions. For instance, the maximum temperature under SSP5-8.5 had a significant increasing trend with a Kendall Tau value of 0.1517, a Sen Slope of 0.00018, and a p-value less than 0.001. In the meantime, the trend of precipitation under SSP2-4.5 was decreasing significantly, which indicated the likelihood of an even more arid future. Under SSP5-8.5, temperature anomalies might be as high as 6.5°C, and precipitation anomalies could be as low as -1.5 mm or as high as +2 mm. Furthermore, Intensity-Duration-Frequency (IDF) analysis indicated that extreme rainfall events are projected to intensify, with rainfall intensities for the 100-year return period increasing from an observed value of 340 mm/hr to 360 mm/hr under SSP5-8.5. These outcomes show a potential rising trend of warmer and possibly drier conditions in the Swat District, and higher vulnerability to severe weather conditions. The results show that we need infrastructure that can handle climate change, flexible water management plans, and aggressive planning to lessen the effects of future extreme weather events
Role of Flood Forecasting and Early Warning System in Flood Management: A Study of the 2010 Flood in the Swat Valley, Pakistan
Full text linkAmong non-structural approaches to flood management, the Flood Forecasting and Early Warning System (FF\&EWS) plays a key role in reducing flood risks. This study focuses on the role of FF&EWS in the Swat Valley. The Swat Valley suffered from numerous floods. Among them, the 2010 flood was a disastrous one. FF&EWS is the main responsibility of the Pakistan Meteorology Department (PMD). To achieve the objectives of the study, data were collected from different sources and processed through different statistical tools. Analysis revealed that rapid change in LULC, encroachment, and deforestation were the major flood-intensifying factors. The increasing population pressure on land resources has pushed people to the flood risk areas, and as a result, people have started development in the flood-prone zone. From the analysis, it was recommended that no construction should be permitted in the vicinity of the River Swat and its tributaries. The development of settlements in vulnerable high-risk zones needs to be restricted. The existing FF&EWS and meteorological network in the Swat valley needs to be extended in order to increase the effectiveness of FF&EWS and minimize the impacts of recurrent floods. The study concludes that the effectiveness of FF and EWS in the Swat Valley can be improved by addressing the issues faced by FEWS
An Integrated Geospatial Approach of GHG Emission Impact on Air Quality Due To Above-Ground Biomass In Rawalpindi Division, Punjab, Pakistan
Full text linkThis study investigates the influence of above-ground biomass (AGB) on greenhouse gas (GHG) emissions and air quality in the Rawalpindi Division, Punjab, Pakistan, from 2018 to 2024. An integrated geospatial approach was applied using Sentinel-2 for vegetation indices, Sentinel-3 for land surface temperature (LST), Sentinel-5P for atmospheric pollutants, and MODIS for active fire detection. Results indicate that while high AGB zones expanded, moderate and low biomass areas declined, suggesting biomass redistribution due to vegetation change. Fire radiative power (FRP) was strongly correlated with AGB (R² = 0.9888), indicating that biomass burning significantly contributed to pollutant concentrations. Linear regression showed strong positive correlations between AGB and NDVI (R² = 0.89), LST (R² = 0.96), and GHGs, including CO₂, CO, NO₂, SO₂, aerosols, and ozone. Notably, LST and pollutant levels peaked during dry seasons. The findings emphasize the dual role of biomass as a carbon sink and emission source, highlighting the utility of remote sensing for environmental monitoring and climate planning
Estimating Soil Erosion Risk in District Diamer, Pakistan Using RUSLE Model: A Spatial Analysis Approach
Full text linkSoil erosion is a critical issue in the hilly regions of Diamer, Pakistan, due to the region\u27s varying topography and significant precipitation patterns. This study uses an effective combination of Geographic Information System (GIS) technologies and the Revised Universal Soil Loss Equation (RUSLE) model to calculate soil erosion rates within the region\u27s complex topography. Different GIS layers, such as rainfall erosivity (R), slope length and steepness (LS) factor, soil erodibility (K), conservation practices (P), and cover management factor (C), were merged by utilizing satellite data and the Normalized Difference Vegetation Index (NDVI). The resulting map showed a maximum soil loss of 2279.3 t/ha/year over the region. Notably, the greatest soil loss was observed in the western regions of Diamer, where rainfall and rainfall erosivity are also recorded as high in these areas. Five separate categories of soil erosion were identified, with a mean soil loss rate of 27 t/ha/year. According to the GIS analysis, 95% of the overall area experienced less severe erosion than the severe erosion classes, accounting for 5%. Additionally, the study included the computation of composite NDVI estimates for 2023 using Google Earth Engine (GEE). This method improved both the scalability and usability of the study by enabling effective processing and storage of data in the cloud. GEE enables the computation of NDVI quickly and precisely. This pioneering study is an important step toward understanding and resolving soil erosion issues in Diamer, Pakistan. The study offers valuable insights for decision-making and management planning initiatives by utilizing cutting-edge GIS tools and RUSLE modeling
Enhancing Pakistani Jaggery Exports: An AHP Driven Analysis
Full text linkThe demand for natural raw products is increasing worldwide, especially in areas that prioritize health and wellness. Jaggery, a non-refined natural sweetener, has emerged as an economic and ecological alternative to processed white sugar. Despite Pakistan’s notable production capacity, the inconsistent quality, inefficient processing technologies, and government policies are hindering its export potential. This study employs the analytical hierarchy process (AHP) to identify and prioritize key factors that influence jaggery export potential. The data for AHP were extracted from a structured questionnaire, which was completed by 100 respondents, including producers, exporters, and farmers. The insights from the analysis revealed that skilled labor, mechanized crushers, and quality of raw material are the most critical factors. However, government policies, water consumption, water wastage, carbon emissions, carbon credits, fair trade, and sustainable fuel are undervalued, which pose a long-term threat to this industry. By prioritizing challenges, decision makers can amicably enhance the sector’s viability. This paper contributes to agro-industrial development by offering recommendations for sustainable jaggery production and export
A Federated Framework for Air Quality Prediction in Smart Cities
Full text linkOver the last couple of decades, due to the constant increase in urbanization and industrialization, the concern in terms of air pollution has become a serious issue. In most cities, the pollution in the air is mostly comprised of Nitrogen Dioxide (NO2), Ozone (O3), Carbon Monoxide, and Particulate Matter, all of which can cause serious health issues. There is an emergent need for a system to detect air pollution. This research presents a framework that uses Federated Learning to lessen the communication overhead during the prediction process and ensure data privacy. The research also uses different Machine Learning algorithms, such as Random Forest, Support Vector Machine (SVM), and Logistic Regression, to train and evaluate the research
A Hybrid Machine-Learning Framework for Intrusion Detection: Comparative Evaluation and Statistical Validation
Full text linkThe increasing sophistication and frequency of cyberattacks have intensified the need for Intrusion Detection Systems (IDS) that are both accurate and adaptive. Traditional IDS, whether signature-based or anomaly-based, provides foundational protection but faces well-documented limitations: signature-based systems struggle against zero-day exploits, while anomaly-based systems often produce high false positive rates. To address these challenges, researchers and practitioners are increasingly turning to Machine Learning (ML) as a means of enhancing IDS capabilities. This paper explores the integration of ML techniques—supervised, unsupervised, and deep learning—into IDS frameworks and evaluates their effectiveness using widely recognized datasets, including NSL-KDD and CICIDS2017. Supervised learning methods such as Random Forest and Support Vector Machines (SVM) demonstrate strong classification abilities, while unsupervised clustering approaches offer promise in identifying novel attacks. Deep learning models, particularly Recurrent Neural Networks (RNNs), show state-of-the-art performance in capturing sequential traffic patterns and detecting subtle anomalies. In addition to model comparisons, this study emphasizes the practical relevance of ML-enhanced IDS by examining its integration with established tools like Snort and Zeek. Our results highlight that ML-driven IDS consistently outperforms traditional approaches, with RNNs and Random Forest achieving the highest balance of accuracy and efficiency. The findings underscore the potential of ML-based IDS to serve as the next frontier in cybersecurity, offering improved detection accuracy, reduced false alarms, and adaptability to evolving threats. At the same time, challenges remain in terms of dataset representativeness, computational demands, and the interpretability of deep learning models. By situating the analysis within both academic research and real-world deployment contexts, this paper contributes to a clearer understanding of the opportunities and trade-offs in advancing IDS through machine learning
Impact of Empty Nest Syndrome on Parental Mental Health: Moderating Role of Coping Styles
Full text linkWhen the kids depart, parents may experience empty nest syndrome (ENS), which is a depressing and negative emotional disturbance, and it in turn affects their mental health. According to studies, there is a need to study single parents and elderly parents who are living in shelter homes. According to attachment theory, the mental health (MH) of parents is greatly impacted by their children as an outcome of the bond between parents and their children. Coping styles assume a pivotal part in how the elderly adjust to the difficulties of (ENS) and keep up with their mental health. Thus, in the recent study moderating role of coping styles was studied. The research design that was used was a cross-sectional survey. A sample of 200 parents was collected, including single parents as well, through purposive sampling techniques. Individuals aged 60 years were included in the study. The Empty Nest Syndrome Questionnaire-Indian Form (ENS-IF), Mental Health Inventory (MHI-5), and Simplified Coping Styles Questionnaire, alongside the demographic data sheet and consent form, were administered. Collected information was analysed through SPSS and Process Macro using correlation, Regression, t-test, and moderation analysis. Future researchers can develop interventions to improve coping styles so that the mental health of empty-nest parents can be enhanced
Parallel Electric Fields Associated with Double Layers in Kappa Distributed Space Plasmas
Full text linkParallel electric field structures associated with double layers (DLs) provide the best explanation for the physical mechanism underlying charged particle energization acceleration at sites of magnetic reconnection. In-situ measurements of reconnection sites by various satellites such as MMS, THEMIS, and FAST confirmed the connection of charged particle energization with the large parallel electric fields in the auroral regions, Earth\u27s plasma sheet, and the separatrix region of the magnetosphere. We employed the fully nonlinear Sagdeev potential technique and multi-fluid theory for electron-ion plasma to find double-layer solutions and the accompanying electric field at the reported sites. Considering electrons to be kappa distributed, we have taken into account the ion inertial effect. Specifically, at non-Maxwellian effective temperature scales, parallel electric fields related to the Alfvénic double layer have been studied and compared with the observations. We have shown that the nonthermal parameter kappa and Alfvénic Mach number ????A considerably alter the properties of DLs and the associated electric field of kinetic Alfvén waves