International Journal of Innovations in Science & Technology
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Mutual Coupling Reduction in 5G Multiple Input Multiple Output Microstrip Patch Antenna
Full text linkThis study delineates the design and performance assessment of a small 28 GHz single-band Multiple-Input Multiple-Output (MIMO) antenna designed for fifth-generation (5G) wireless communication systems. The proposed antenna employs T-shaped gaps among radiating elements to mitigate mutual coupling, a critical issue in compact MIMO systems. Simulation results demonstrate a significant increase in isolation, with the Return Loss (RL) improved from −17 dB to −46 dB. Furthermore, the overall radiation efficiency increases from 71.5% to 76.8%, indicating an improvement in system performance. The design incorporates polarisation variety to alleviate multipath fading, a common challenge at millimeter-wave frequencies. The proposed antenna, characterized by its exceptional isolation, improved gain, and compact design, is well suited for integration into modern mobile devices and 5G-enabled platforms, including Internet of Things (IoT) networks, autonomous systems, and densely populated urban communication environments
Geospatial Nexus of Land Use Land Cover dynamics and Rapid Population Growth with Emphasis on Trend Prediction of Built-Up Areas in District Hangu, Pakistan
Full text linkThis study aims to analyze the land use land cover (LULC) and predict the patterns in the built-up areas of District Hangu and examine how population growth affects land use and land cover. Rapid population increase remains a continuous threat to the district’s land resources. Projections show that the population will grow from 518,811 in 2017 to 833,964 by 2051. Along with this growth, there is an ongoing expansion of underground utilities and infrastructure, driven by demographic pressures and urban development. The Logistic Regression (LR) model was used to forecast an expansion in the district’s built-up area. Through this model, potential zones for future development are identified, and anticipated changes in planned Land Use/Land Cover (LU/LC) are evaluated. All variables were transformed into raster format and standardized to a 0–1 range using a raster calculator, ensuring uniformity in statistical comparisons. Factor standardization played a central role in the multivariate analysis, where the Variance Inflation Factor (VIF) method in SPSS was applied to resolve multicollinearity issues. Predictors with VIF values exceeding 10 were substituted with alternatives falling below this limit. Land use and land cover data were obtained from Landsat images for the years 1991, 2001, 2011, and 2021, each at a 30-meter resolution. Results indicate that the proportion of built-up areas increased from 8% in 1991 to 11% in 2021, while vegetation cover decreased from 43% to 45%. During the same period, barren land reduced from 47% to 40%, and water bodies expanded from 3% to 4%. Future projections of built-up areas identify the most suitable zones for urban growth. The LR model integrates multiple variables—such as railways, primary roads, tracks, commercial zones, educational and health facilities, and economic hubs—using tools including SPSS, IDRISI, and ArcMap. IDRISI Selva is applied for future land use modeling, estimating that built-up areas will cover 161.22 km² by 2050. The prediction results indicate that population growth will continue to be a significant driver of built-up expansion in Hangu District
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
Climate Change and the Changing Rainfall Patterns in Karachi
Full text linkAbrupt weather phenomena, including heat waves, frequent intense storms, outbreaks of forest fires, glacier melting, and flash floods are experienced throughout the world. Pakistan lies in the South Asian region that falls in the monsoon climatic regime, experiencing summer rainfall. The city of Karachi, which is also highly urbanized situated in southern Sindh and receives secondary monsoon rainfall from the month of July to September. During the 1960s, the city received appreciable rainfall during the monsoon, but the amount of rainfall started declining during the 1980s. At the end of the 20th century, the rainfall pattern was quite abrupt, associated especially with the passage of cyclones, which developed in the Arabian Sea and, after touching Oman, reached Karachi, or, moving from Gujrat in India, reached Karachi. So, the rainfall which received annually is now received within a day. The study represents the statistical analysis as well as the GIS and remote sensing perspective of the changing patterns over the last fifty years
Assessing the Efficacy of Pixel-based and Object-based Classification Techniques and Classifiers for Land Cover Mapping Using Landsat-8 and Sentinel-2 Data in Complex Mountainous Terrain
Full text linkDisaster mitigation and climate-resilient planning heavily depend on accurate Land Use and Land Cover (LULC) datasets. Well-classified LULC data optimizes hazard modeling, surface runoff estimation, and sustainable land use planning, enabling informed decision-making and proactive risk reduction. However, supervised LULC classification faces challenges such as selecting optimal Machine Learning (ML) algorithms, differences in spatial and spectral resolution, and seasonal variability. This study adopts a multi-tiered approach to generate effective LULC maps for Gilgit District, Pakistan, by comparing pixel-based classification and object-based image analysis (OBIA) methods. Pixel-based classification was performed on Google Earth Engine (GEE) using Landsat-8 and Sentinel-2 imagery, applying three classifiers: Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN). OBIA involved multi-resolution segmentation, followed by training and classification on image objects using the same algorithms. Validation using independent samples revealed that object-based maps were visually smoother and more realistic. Quantitatively, pixel-based RF yielded the highest accuracy: 82.9% for Landsat-8 and 78.02% for Sentinel-2. In contrast, OBIA k-NN achieved superior accuracy: 81.3% on Landsat-8 and 83.6% on Sentinel-2. Remaining classifiers also provided nearby results in both classification methods. Lower accuracy in Sentinel-2 may be due to within-class spectral variability at 10m spatial resolution, while Landsat-8’s lower resolution (30m) reduced object-based segmentation performance, resulting in object heterogeneity and misclassification. Although pixel-based classification provided promising results, OBIA ultimately demonstrated superior overall accuracy. This study highlights the importance of resolution-context compatibility and algorithm choice in enhancing LULC classification, which is essential for reliable climate-responsive planning, disaster preparedness, and sustainable development
Self-Powered Robots – A Survey
Full text linkSelf-powered robots represent a significant advancement in autonomous robotics, leveraging renewable energy sources such as solar panels, thermoelectric generators, piezoelectric actuators, microbial fuel cells, and RF energy harvesting to operate independently of traditional power supplies. This study presents a comparative analysis of seven self-powered robotic systems, including the Crabbot, Thermoelectric Quadruped, MilliMobile, and Row-bot, evaluating their energy mechanisms, power consumption, control systems, and application domains. Notable findings include the Crabbot\u27s 85 nm resolution and 150 V piezoelectric actuation for precision tasks, the Thermoelectric Quadruped\u27s 703 J/m gait energy cost for geothermal monitoring, and MilliMobile\u27s submillimeter-scale battery-free operation via RF harvesting. These robots are assessed based on critical parameters such as load-to-weight ratio, energy autonomy, and control architecture. The study highlights the growing role of miniaturized, energy-efficient designs in enabling real-world deployment across sectors like pipeline inspection, remote environmental sensing, and disaster response. By identifying performance benchmarks and gaps, this paper offers insight into next-generation, self-sufficient robotics aimed at sustainability, reliability, and broader societal impact
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 Innovative Machine Learning (ML) Approach in Fabric Defect Detection and Quality Assurance
Full text linkThe garment and textile industries are essential sectors that significantly contribute to a nation\u27s economic development. Fabric defect detection is a complex problem in the textile and technology industries since the efficacy and efficiency of automatic defect detection determine the quality and cost of any textile product. In the past, the textile industry used manual labor to find flaws in the fabric production process. The primary disadvantages of the manual fabric flaw identification technique are human weariness, lack of focus, and time consumption. This article introduces an innovative automated system for detecting garment defects powered by machine learning to revolutionize the traditional system and replace the manual inspection system. This innovative advanced system is trained and assessed using the 500-image dataset from the Artistic Milliners Company in Pakistan. The machine learning algorithm and image processing techniques form the foundation of AI technology, offering the best flaw detection accuracy. This work presents an automated fabric defect detection system driven by a supervised machine learning algorithm, i.e., SVM, that can accurately and precisely detect "hole" and "stain" faults. The system achieves a 72% precision and 74% recall for holes and an 85% precision and 83% recall for stains by utilizing a machine learning algorithm, i.e., SVM. The proposed method throws up vital issues like scalability and fabric sort flexibility. Compared to traditional manual processes, this new method lowers inspection costs by 65%, increasing productivity and setting a standard for automated and sustainable textile quality monitoring
Socially Shared Metacognition of Students in Computer-Supported Programming Tasks and Their Stance on the Difficulty of the Task
Full text linkThe internet has brought much emphasis to online collaborative learning, where learning is connected to co-constructing understanding and knowledge about subjects and tasks through collaboration and conversation. This research centers on several groups of students undertaking a programming project in a Zoom-based environment” or “via Zoom meetings. The paper proposes that socially shared metacognition is most effective in group-based problem-solving. It is a process in which one member of the group helps regulate the whole group’s process of solving a problem and elicits other members’ reactions to this proposal. The feeling of difficulty in performing the task helps ascertain and display the role of group interaction in individual learning. The paper also proposes that the increase in socially shared metacognition decreases the level of difficulty of a problem and thus alleviates individuals’ feelings of task difficulty
Bridging Global Frameworks and Local Realities: Towards Localizing the City Essentials Approach in Pakistan’s Urban Planning Systems
Full text linkCities across the Global South are increasingly exposed to compound and cascading risks—ranging from climate-induced disasters to governance, infrastructure, and institutional failures. Global frameworks such as the UNDRR Making Cities Resilient 2030 (MCR2030) Campaign, the City Resilience Index (CRI), UN-Habitat’s City Resilience Profiling Tool (CRPT), and the ISO 37123 Indicators for Resilient Cities have collectively redefined resilience as a governance-driven, system-wide process. However, their translation into the planning and institutional realities of developing countries remains partial and fragmented. This paper bridges these global frameworks with local contexts through a comparative synthesis that identifies areas of convergence—such as governance, preparedness, and coordination—and divergence in adaptability, innovation, and modularity. Focusing on Pakistan as a representative case, the study examines how the City Essentials Approach under MCR2030 can be embedded within national and local urban planning systems to operationalize resilience. Findings from the comparative review reveal that frameworks like MCR2030 and LGSAT align with Pakistan’s disaster management architecture (NDMA–PDMA), while data-intensive tools such as CRI and ISO 37123 remain constrained by limited institutional capacity. The paper proposes the City Essentials Localization Pathway (CELP) as a conceptual bridge to integrate global principles into local governance, enabling performance-based resilience assessment, policy coherence, and data-driven decision-making within Pakistan’s urban systems