International Journal of Innovations in Science & Technology
International Journal of Innovations in Science and Technology (ISSN 2618-1630) is an international peer-reviewed and "Open Access" journal designed to explore advances in various disciplines of Science and Technology wherein coputer-based Algos and syntaxes have be utilized. The researchers can submit their field observations and the results of laboratory experiments to IJIST within "Aims and Scopes" of the journal. The journal publishes issues quarterly. Rapid Publication: manuscripts are transparently reviewed by international and local reviewers and the first decision is provided to authors within 10-15 Days. High Visibility: indexed in HEC, MDPI, REPEC and other database...... Recognition of Reviewers: Reviewers providing timely decisions, and thorough peer-review reports are acknowledged with a discount in Article Processing Charges.
Assessment of Soil Fertility in Jhelum, Punjab, Pakistan, using Geospatial Technologies
Soil fertility is a key factor influencing agricultural productivity and sustainability. This study evaluates the spatial distribution of essential soil chemical properties-pH, electrical conductivity (EC), available phosphorus (P), available potassium (K), organic matter (OM), and saturation percentage in Tehsil Jhelum, Pakistan. A total of 160 topsoil samples (0–15 cm depth) were collected using random sampling, with GPS coordinates recorded for each site. Laboratory analysis was conducted to assess the nutrient status of the soils, and Ordinary Kriging interpolation was used within a GIS framework to generate spatial distribution maps. The findings revealed notable variability across the region. Soil pH ranged from 4.3 to 7.8 (mean: 7.44), indicating mostly neutral to slightly alkaline conditions. EC values ranged from 0.49 to 1.40 dS/m, suggesting low to moderate salinity. Available phosphorus varied between 1.2 and 7.8 mg/kg, while available potassium ranged from 60 to 180 mg/kg, showing moderate fertility levels. Organic matter content was uniformly low (0.20–0.66%), with a mean of 0.42%, highlighting poor organic inputs. Saturation levels varied from 22% to 72%, displaying a layered spatial pattern. The spatial heterogeneity observed in soil nutrients underscores the need for site-specific nutrient management and precision agriculture practices. The generated maps serve as valuable tools for farmers, agronomists, and policymakers to make informed decisions aimed at improving crop productivity and maintaining soil health in the region.
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An Innovative Machine Learning (ML) Approach in Fabric Defect Detection and Quality Assurance
The garment and textile industries are essential sectors that significantly contribute to a nation's 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.
Artificial Intelligence-Augmented Intrusion Detection Systems for Advanced Threat Taxonomy in Cloud Computing Environments
Over the past few decades, cyber-attacks have emerged as a grave form of criminal activity and a subject of intense scholarly and policy debate. The rapid proliferation of cloud computing services— particularly Software as a Service (SaaS)—has further motivated research to classify security threats and their corresponding countermeasures. Scholars have increasingly focused on the risks, vulnerabilities, and malicious intrusions inherent in such environments, with particular emphasis on MITM (MITM) attacks and their mitigation and detection mechanisms. Host-based virtual software has demonstrated considerable efficacy in detecting malware within localized environments. Building on this foundation, the present study classifies Man-in-the-Middle (MITM) attacks in SaaS platforms through the deployment of Cloud-based Intrusion Detection Systems (CIDS). Our investigation concentrates specifically on attacks that target cloud hosts deployed within SaaS infrastructures. The proposed methodology incorporates the roles of the source cloud, destination cloud, and directional flow of the attack vector. In this context, the cloud ecosystem is understood as a dynamic environment where any participating entity, equipped with sufficient technical expertise, may both launch and be subjected to sophisticated intrusions. Accordingly, adaptive CIDS monitoring architectures are essential to safeguard communication between cloud actors. Moreover, CIDS frameworks furnish modular components capable of aggregating alerts, conducting analysis, and notifying administrators of potential breaches. To further illustrate the threat landscape, we present a statistical analysis of vulnerabilities most frequently exploited in MITM scenarios. This classification not only highlights the evolving tactics of adversaries but also equips readers with a structured understanding of MITM attacks, thereby fostering greater familiarity with contemporary cloud security challenges.
Optimization of Production Planning with Python’s SciPy: A Computational Study
Production planning optimization is the act of effectively distributing limited resources, including labor, materials, and equipment, to achieve production targets while optimizing profit and reducing waste. This study analyzes how optimization methods can be applied to production planning models in the cooking oil sector, with a particular emphasis on how linear programming (LP) can be used to handle usable quality limitations to maximize gross profit. The goal of this study is to find the best values for decision variables across a variety of inventory-based production frameworks. It is important in a manufacturing zone where input bound must be weighed against consumer needs, such as the industry of cooking oil. In order to provide a computational method for determining the perfect production levels, the study establishes a linear programming (LP) model and solves it using Python’s SciPy package. This optimization method uses objective functions involving dense matrices and numerical equations to solve the production planning problem. In calculating output levels and profit margins, the numerical results show a significant convergence, rating the effectiveness and credibility of the suggested approach in providing the optimal solution for practical industrial planning.
DECS: A Deep Learning Approach for EEG Channel Selection in Emotion Classification
The non-stationary nature of Electroencephalogram (EEG) signals often leads to high computational complexity in emotion recognition systems. To address this, we propose a novel framework that integrates optimal channel selection with efficient feature extraction. Our method begins by converting preprocessed EEG signals into two-dimensional spectrograms using a Continuous Wavelet Transform (CWT). These spectrograms are then processed by a GoogLeNet model for deep feature extraction. A key contribution is the Differential Entropy-based Channel Selection (DECS) technique, which identifies and retains the most informative channels. To manage dimensionality, the extracted features are encoded using the Bag-of-Deep-Features (BoDF) method, which employs k-means clustering to create a visual vocabulary and represents features as histograms. Finally, these histogram features are classified using a Support Vector Machine (SVM). Evaluated on the SJTU SEED and DEAP datasets, the proposed model achieves state-of-the-art classification accuracies of 95.1% and 81.1%, respectively, demonstrating its effectiveness and efficiency.
Design of a Novel Compact and High-Efficiency T-Slot Microstrip Antenna for 28 GHz
In this paper, a novel microstrip patch antenna incorporating a T-shaped slot in the radiating patch is proposed to achieve high radiation efficiency and excellent impedance matching for 28 GHz millimeter-wave 5G applications. Utilizing the Rogers RT5880 substrate with a dielectric constant of 2.2, loss tangent of 0.0009, and 0.8mm thickness, the proposed antenna achieves a radiation efficiency of 81.18%, total efficiency of 81.17%, and a peak gain of 7.23 dB over a 2 GHz impedance bandwidth (27–29 GHz). A T-shaped slot is incorporated in the radiating patch to enhance impedance matching and bandwidth. Comparative analysis across ten substrates demonstrates the superiority of Rogers RT5880 in balancing performance, cost, and compactness for mm Wave 5G applications. This innovative microstrip patch antenna design marks a significant advancement in the field, delivering enhanced performance tailored for 5G wireless communication systems.
Python and GLO-DEM Pixel-Based Hypsometry in Upper Indus Basin
Northern Pakistan’s Hunza and Shyok headwaters, where the Karakoram, Himalaya, and Hindukush ranges converge, host some of the largest mid-latitude valley glaciers outside the polar regions and play a decisive role in runoff and hazards of the Upper Indus Basin. Hypsometry provides a rapid, terrain-based approach to assess basin condition in such high mountain settings. In this study, a 30 m digital elevation model was used to delineate 31 sub-catchments, and for each unit, the hypsometric curve and hypsometric integral (HI) were derived. Methods were kept consistent across scales, with HI also recalculated on 1-4 km grid tiles, and spatial organisation tested through Global Moran’s I and the Getis-Ord Gi* statistic. Results reveal coherent belts of high HI aligned with the Main Karakoram Thrust, the Main Mantle Thrust, and the Karakoram Fault, indicating actively rising terrain and focused incision. Lower HI corridors occur in wider valley floors and recent fills, reflecting more mature landscapes and enhanced storage. HI distributions remain stable across tile sizes with mean values below one-half, while significant clustering confirms that these belts are intrinsic terrain signals. Harder crystalline and intrusive lithologies show higher HI on average, though wide variance suggests the combined influence of structure, rock strength, and relief. These geomorphic patterns carry direct hydrological meaning: high-HI belts imply fast translation of snow and ice melt with sharper seasonal peaks, whereas low-HI corridors favour storage and delay. Hypsometry, therefore, offers a cost-effective and reproducible tool for identifying active belts and providing priors for hydrological modelling and hazard planning in the Upper Indus Basin.
Remote Sensing and Machine Learning-Driven Flood Inundation Mapping of September 2025 Ravi Watershed Using Sentinel-1 SAR
Floods is among the most devastating natural hazards in South Asia. The September 2025 flood in the Ravi Basin was triggered by heavy monsoon rainfall and the release of water from cross-border dams. This study utilized Sentinel-1 SAR data, including both ascending and descending passes in VH polarization, to map flood inundation across the basin using a Random Forest classifier. Pre-flood and post-flood composites were prepared for April-May and 27 August to 5 September, respectively. The predictors feature includes VH_pre, VH_post, VH_diff, and VH_ratio. Terrain correction using the NASA DEM and landcover filtering with ESA WorldCover at 10m improved classification accuracy. Results showed that 1,885 km² of land was inundated, representing 5% of the total basin area. Approximately 260 settlements were impacted, including Dera Baba Nanak, Kartarpur, and the low-lying regions of Lahore. Croplands were the most affected class, with 1,610 km² flooded, followed by grasslands (90 km²) and sparse vegetation (62 km²). Built-up areas accounted for 0.7 km² of inundation, though the socio-economic impact was disproportionately high. Precipitation analysis from NOAA CPC confirmed rainfall clustering in the Sialkot and Narowal corridor. The peaks exceeding 800 mm/day cause this region as the epicenter of the flood. News reports corroborated satellite findings, noting that over 2.5 million displaced and more than 100 lives were lost. The study highlights how tributary floods involving the Ravi, Sutlej, and Chenab are emerging as severe hazards for Punjab. Findings underline the need for improved monitoring, resilient agricultural strategies, and disaster preparedness to mitigate future economic and food security risks.
A Hybrid Machine-Learning Framework for Intrusion Detection: Comparative Evaluation and Statistical Validation
The 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.
Socially Shared Metacognition of Students in Computer-Supported Programming Tasks and Their Stance on the Difficulty of the Task
The 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.
Deep Learning Based Sentiment Analysis on Instagram Insights of Consumer Behavior for Improving Business Decision Making
The increasing use of social media platforms such as Instagram has made them a significant source of consumer insights for businesses, highlighting the importance of automated sentiment analysis. This study aims to address the challenge of accurately classifying consumer sentiments in Instagram posts, where informal language, slang, and sarcasm often reduce the effectiveness of traditional models. To overcome this gap, two deep learning approaches were employed: a Bidirectional Long Short-Term Memory (BiLSTM) network as a classical recurrent baseline and transformer-based architectures (BERT and DistilBERT) as state-of-the-art models. A dataset of 184,010 Instagram posts was preprocessed, tokenized, and mapped into positive and negative sentiments, and the models were trained and evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrices. The results demonstrated that BERT achieved the highest performance with an accuracy of 0.91 and an F1-score of 0.91, outperforming BiLSTM (accuracy 0.87, F1-score 0.86), while DistilBERT provided a competitive balance between accuracy (0.89) and efficiency. These findings confirm that transformer-based models, particularly BERT, are better suited for capturing nuanced sentiments in social media text. The study concludes that models can provide actionable insights into consumer behavior, enabling businesses to enhance brand monitoring and customer engagement.
Artificial Intelligence Meets Endocrinology: A Machine Learning-Based Approach to Thyroid Disease Diagnosis Using Feature Selection Methods
Thyroid Disease (TD) arises when the thyroid gland either grows abnormally or does not generate enough thyroid hormones, and might cause serious health issues and consequences. Early and efficient identification of thyroid disease is important for improved clinical intervention and disease management. By combining sophisticated and advanced machine learning models with a range of advanced feature selection strategies, this research study aims to enhance the classification of thyroid disease based on a machine learning based diagnostic system. The preprocessed dataset used in this study and the trials were taken from the machine learning repository at the University of California, Irvine (UCI). We employ two popular feature selection techniques- Chi-Square, and Recursive Feature Elimination, and a dimensionality reduction technique Linear Discriminant Analysis (LDA), and to choose the best features from the dataset for experiments. After selecting the most suitable features, they were then used to train and test the machine learning models: Multi-Layer Perceptron (MLP), Gradient Boost (GB), and Recurrent Neural Network (RNN). Evaluation matrices, accuracy, precision, recall, and F1-score were used to assess models' performance. The experimental results show that the machine learning model Gradient Boost (GB) outperformed the other models and yielded an accuracy of 99%, indicating its ability to classify the Thyroid Disease (TD) accurately. The proposed research work helps to create an intelligent decision-support system for medical diagnostics by offering an understandable and reliable framework for Thyroid Detection.
Industry 5.0: An Energy-Efficient Smart Task Offloading Mechanism for Multi-Access Edge Computing
The industry 5.0 heralds a transformation of industrial systems by integrating artificial intelligence (AI), the Industrial Internet of Things (IIoT), and Multi-Access Edge Computing (MEC) to foster resilience, efficiency, and sustainability. However, managing the massive volume of computation-intensive tasks generated by heterogeneous IIoT devices presents major challenges, particularly in optimizing both latency and energy consumption under dynamic industrial conditions. This research proposes a hybrid task offloading framework Computational Genetic Particle Swarm Optimization Algorithm (CGPCA) to intelligently balance energy efficiency and latency in MEC-enabled IIoT networks. CGPCA integrates the global search capability of Genetic Algorithms (GA) with the fast convergence of Particle Swarm Optimization (PSO), forming a two-layer optimization approach for effective task-device associations and power-bandwidth allocation. The framework is evaluated using iFogSim and Edgelands simulation environments, reflecting realistic industrial scenarios with variable workloads, device capabilities, and server conditions. Results indicate that CGPCA reduces average latency by up to 24%, lowers energy consumption by 18–25%, and maintains a task offloading success rate of 94% surpassing conventional GA, PSO, and heuristic baselines. The framework also achieves improved load balancing and faster convergence time, confirming its suitability for time-sensitive and energy-constrained IIoT environments. This study contributes to the realization of Industry 5.0 by offering an adaptive, intelligent solution that enhances computational efficiency while supporting sustainable and human-centered industrial automation. Future directions include extending CGPCA to highly mobile IIoT contexts and integrating predictive analytics for further performance gains.
Analysis of Periodic Permeability on Free Convective Three-Dimensional Flow with Cattaneo-Christov heat transfer and Slip Effect
The present research paper contributes the slip effects on a three-dimensional viscous fluid flow for free convective boundary conditions with periodic permeability. Free convection fundamentally involves some heat transfer methods. In this work, the Cattaneo-Christov heat transfer method has been employed to develop the knowledge of heat transfer actions in complex flow porous system with periodic permeability. Moreover, the impact of the slip effect is investigated to more effectively deal with the boundary conditions. The mathematical model has designed for incompressible, viscous and laminar flow with free stream specifications. By using the regular perturbation approach, governing highly nonlinear partial differential equations are transferred into the ordinary differential equations in linear form together with certain linear partial differential equations. The separation variables approach is then used for transforming the linear PDEs to ODEs. Analytical solutions are obtained for the pressure, velocity field, components of skin friction, and temperature field. The influence of physical attributes existing in the mathematical representation of the physical occurrence is investigated and illustrated. Both the slip parameter and the Cattaneo-Christov heat flux have an impact of thickness on the thermal boundary layer of observed fluid flow.
Design Evolution and Feature Enhancement Strategies for Advanced Digital Stethoscopes
A stethoscope is a fundamental auscultation and diagnostic device that plays a major role in medication and assists in the identification of sounds inside the body to predict cardiovascular and respiratory diseases. The advent of electronic and artificial intelligence (AI)-enhanced digital stethoscopes is prompted by the limitations of traditional auscultation performance, such as the need for a clinician's experience, failure to detect the required sounds in noisy conditions, and the inability to store patient data. This study focuses on the evolution in the design, relative performance characteristics, and areas of future improvement of stethoscopes, including digital devices that incorporate AI. Several studies show the employment of advanced filters to acquire important auscultating frequency bands, a high-gain amplifier to boost low-frequency internal body sounds, a noise cancellation circuit to block out background noise, Bluetooth for data sharing in real-time signal processing, and syncing with other medical devices. Key features that could be introduced in future versions are adaptive frequency filters, AI-based clustering to classify the sound, remote diagnostic functionality, and an improved data storage system. Protection circuits that take the form of lithium-ion batteries, wireless modules, and processing based on a microcontroller are some of the resource components highlighted in terms of portability and efficiency. This research seeks to develop stethoscopes by incorporating innovations while managing limitations, ultimately enhancing tools for healthcare professionals.
Impact of Empty Nest Syndrome on Parental Mental Health: Moderating Role of Coping Styles
When 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.
Bridging the Divide of Formal and Informal Transit in Urban Areas - Considering Multidimensional Aspects of Sustainability
Public transport in cities across the developing world is fundamentally shaped by the dualism of formal and informal services. Informal transport modes, including minibusses, shared taxis, and auto-rickshaws, are not merely supplementary but are essential components of the urban mobility ecosystem, providing critical connectivity for marginalized communities. Contemporary scholarship advocates for a multifaceted evaluation of these systems to capture their full socio-economic, environmental, and operational impact. This paper conducts a systematic literature review to synthesize existing assessment frameworks for public transport. The findings reveal a significant gap: current methodologies often fail to integrate the core dimensions of sustainability—social, economic, and environmental—with emerging imperatives like climate resilience and comprehensive regulatory and technological considerations. By mapping the state of the art, this review underscores the necessity for a more holistic evaluation paradigm, focusing on frameworks that move beyond a simple formal-informal divide to foster comprehensive understanding and strategic integration.
IoT-Enabled Smart Agriculture: Architectures, Applications, and Future Directions
Introduction/Importance of Study: The integration of Internet of Things (IoT) technologies into agriculture has become essential to tackle challenges of food security, climate change, and resource optimization. Novelty statement: This study introduces the novelty of a unified, low-cost, and modular IoT framework that addresses gaps in scalability, interoperability, and affordability, particularly in developing agricultural regions. Material and Method: A systematic literature review was conducted across IEEE Xplore, SpringerLink, ScienceDirect, MDPI, and Google Scholar, focusing on twelve peer-reviewed studies published between 2019 and 2024. Comparative thematic analysis was applied to examine IoT architectures, communication protocols, and practical implementations. Result and Discussion: Findings highlight that IoT systems commonly adopt a three-layer architecture (perception, network, and application), with LoRa, Zigbee, and fog computing models offering reliable rural connectivity. Reported outcomes include 30–40% water savings through smart irrigation, 15–20% yield increases with IoT-based monitoring, and up to 16% energy efficiency improvements in optimized wireless sensor networks. Despite these advances, challenges remain in cost, interoperability, farmer training, and security mechanisms. Current frameworks also lack adaptability across diverse farming contexts, limiting scalability and long-term sustainability. Concluding Remarks: IoT-enabled agriculture offers significant potential to enhance sustainability and productivity, but future research must prioritize modular platforms, lightweight AI integration, energy harvesting, and context-specific deployment strategies.
Bridging Global Frameworks and Local Realities: Towards Localizing the City Essentials Approach in Pakistan’s Urban Planning Systems
Cities 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.
Performance Analysis of Task Distribution Mechanism in Multi-User’ Collaborative Assembly Task' In 3d Virtual Environment
CVEs are real-time, computer-simulated environments where two or more actors can mutually complete a task using synthetic objects. User performance is one of the major problems that arise due to coordination problems, a good mechanism to divide tasks, or less understanding or interaction among users collaborating. The impact of multi-user collaboration on using the task distribution mechanism remains unexplored. In this study, the impact of TDM on multi-users’ collaborative virtual environment is investigated. The TDM model assigns the task to collaborating users in CVEs on a static or dynamic manner. In static distribution, there exists weak coupling, and the amount of communication during the actual execution of a task is low, while in dynamic distribution, users are tightly coupled and hence need to communicate more. To study the effect of static and dynamic task distribution strategies on user’s performance in CVEs on multi users, a CVE prototype was developed using C++ and OpenGL, simulating an assembly task with distinct roles for multiple users, where twenty (20) group (each consists of two users) perform a task in collaboration under both strategies (static and dynamic) on two users and three users using arrow-casting and audio aids. The result shows that static with arrows-casting for two users takes an average time of 331.15 sec, and for three users, 321.45sec, and for audio (342.73sec and 326.34sec, respectively. Similarly, the dynamic with arrow casting for two users takes 347.76 sec, and for three users, 333.24 sec, and for audio, 350.12 sec and 344.4 sec, respectively. The findings provide valuable insights into how multi-user collaboration, task distribution methods, and cognitive aids can influence task efficiency and teamwork. However, when the number of users increased to three users, there is a chance that the performance will be degraded because, from the experimental data, a lower improvement was observed for three users than for two users. This research contributes to improving task management and collaboration in CVEs, with potential applications in training, education, and remote teamwork.
A Robust Deep Learning Model for Early Glaucoma Detection Using Retinal Imaging
The Glaucoma Detection System is developed in such a way that it can enable early diagnosis of glaucoma by incorporating the latest technology with the patient-centric healthcare paradigm. It uses a user-friendly interface written in the Tkinter language and a Convolutional Neural Network (CNN) model, and is mostly useful in processing medical images. The purpose of the methodology is to democratize ocular care, focus on the insidious nature of glaucoma, and emphasize the need to have a highly accurate CNN model to detect the disease at the earliest stage. The key features are preset structures and real-time image processing, which will speed up detection and allow healthcare professionals to prioritize severe cases. The system encourages the development of multimodal integration and feedback of data in order to promote efficacy, proactive eye health, as well as the principles of fair access to care.
Harnessing Unconventional Malware Detection Techniques to Equip Proactive and Resilient Cyber Defense Strategies Against the Constantly Changing Landscape of Sophisticated Cyber Threats
The rapidly changing intricacy of malware, specifically in the case of highly secured air-gapped networks, necessitates proactive and resilient detection mechanisms that are highly capable of detecting sophisticated, modern, and obfuscated malware. Instant work focuses on a model for malware detection that is vibrant and resilient and uses deep learning models to look at Windows API call patterns that come from executable files. The original dataset from Kaggle had a big class imbalance (malicious: 42,797; benign: 1,079), but the SMOTE approach helped balance the training data. In this regard, a comparison of seven deep learning models, including Simple ANN, MLP, DropConnect Improved ANN, Residual ANN, DenseNet ANN, RBF Network, and hybrid CNN-LSTM, has been conducted over both 50 and 150 training epochs on various metrics such as recall, accuracy, F1-score, precision, and ROC-AUC. As a result, the CNN-LSTM model, enhanced by an attention mechanism, exhibited superior efficacy in differentiating between benign and malicious samples. In this context, the accuracy improvement is minimal at +0.08%, but the most substantial increase in Class 0 recall is +4.1%, and the F1-score shows an enhancement of +2.7%. The most significant contribution of this study is the attention-augmented architecture that apparently diminishes interpretability and enhances focus on significant behavioral attributes.
Computation of Rotational Flow of the Sun Using Satellite Data and Doppler Shift Calculations
Solar rotational flow governs the Sun’s magnetic activity, space weather variability, and long-term dynamo processes. Traditional tracer-based techniques offer limited precision in mapping these flows, creating the need for direct spectroscopic velocity measurements. This study presents a computational framework for deriving full-disk Doppler velocity maps of the Sun using high-resolution Hα spectra from the Chinese H-alpha Solar Explorer (CHASE) mission. The H-alpha Imaging Spectrograph (HIS) data cube (2304 × 2313 × 46 pixels) was processed through a workflow of preprocessing, continuum normalization, Voigt profile fitting, and pixel-wise Doppler conversion to retrieve line-of-sight velocities. The resulting field of ~5.3 million pixels shows clear differential rotation, with blue shifts up to −7.89 km s⁻¹ on the approaching limb and red shifts up to +2.19 km s⁻¹ on the receding limb, corresponding to equatorial and polar rotation periods of ~25 and ~31 days, respectively. Localized asymmetries in active regions further reveal small-scale velocity perturbations. These results validate CHASE–HIS spectroscopy as a reliable tool for global solar flow diagnostics and highlight the utility of Voigt-based Doppler modeling in resolving fine-scale plasma dynamics. The developed approach bridges spectroscopic and Helio seismic methods, offering a reproducible foundation for future studies on solar dynamo modeling and space weather prediction.
Digital Credibility and Social Gratification: Understanding How Generation Z in Pakistan Engages with Misinformation and Algorithmic Influence in the Contemporary Social Media Landscape
In the contemporary digital landscape, Generation Z increasingly relies on social media as a primary source of information, communication, and self-expression. While these platforms foster connectivity, learning, and creativity, they also amplify the circulation of misinformation due to limited regulation and inadequate fact-checking practices. This study investigates the motivations and behavioral patterns of Generation Z in Pakistan concerning online information engagement, focusing on the balance between social gratification and information credibility. Employing a qualitative exploratory design, data were collected through five focus group discussions (FGDs) comprising 25 participants across diverse academic disciplines, including Media Studies, Art & Design, Computer Science, Business Administration, and Allied Health Sciences. Thematic analysis revealed that social validation and entertainment are dominant motivators for content sharing, whereas critical evaluation and fact-checking remain secondary concerns. Instagram and WhatsApp emerged as the most frequently used platforms, followed by X (formerly Twitter), TikTok, and Facebook. Although participants acknowledged the prevalence of misinformation, only 52% consistently verified content prior to sharing. The study highlights how algorithmic reinforcement and emotional engagement contribute to selective exposure and echo chambers, intensifying the challenge of discerning credible information. Findings underscore the need for comprehensive digital literacy initiatives that integrate fact-checking, ethical sharing, and critical thinking into educational frameworks. The research contributes to the broader discourse on media ethics, algorithmic influence, and the sociocognitive dimensions of digital engagement among youth in developing contexts.
Interactive Impacts of Heavy Metals and Soil Amendments on Enzymatic Activities and Microbial Biomass
Both organic and inorganic soil additives are frequently used to increase the bioavailability of lead (Pb) and cadmium (Cd) in polluted soils, but these amendments may also affect microbial activity in soils by modifying heavy metal solubility. This research assessed the influence of different soil additives on enzymatic activity and the solubility of Pb and Cd in spiked soils. Soils were spiked with Pb (0, 1000, 1500 mg kg⁻¹) and Cd (0, 100, 150 mg kg⁻¹) artificially. Incubation experiments were carried out with various amendments, such as citric acid (CA; 0, 10 mmol kg⁻¹), ammonium nitrate (AN; 0, 10 mmol kg⁻¹), EDTA (0, 5 mmol kg⁻¹), compost (CO; 0, 10%), and titanium dioxide nanoparticles (TNPs; 0, 100 mg kg⁻¹). The microbial biomass carbon (Cmic) and dehydrogenase activity (DHA) declined by 66% and 47% in Pb₁₅₀₀, and by 54% and 35% in Cd₁₅₀ treatments, respectively. In control soil, compost addition gave the highest value of Cmic and DHA, followed by TNPs, CA, AN, and EDTA. But the mixed application of Pb, Cd, and soil additives caused an overall reduction in microbial activity. Among all the treatments, EDTA alone and in combination with Pb and Cd showed maximum toxicity to soil microorganisms.
Data Mining for Smarter Administration of TVET Institutes
Retaining 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's 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's 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.
Advanced Deep Learning-Based Potato Defect Identification Leveraging YOLOv8 for Smart Agriculture
This paper presents the design of an effective deep learning model to identify and rank potato defects, enabling intelligent farming and post-harvest tasks. The primary goal is to automate the quality measurement of potatoes in several categories: healthy, damaged, defective, fungal-diseased, and sprouted, with the help of an optimized YOLOv8 model. The data set on potato images was annotated and gathered in the real-world agricultural conditions in a wide variety of images. Data augmentation and transfer learning were used to train the model and enhance generalization and detection rates in different conditions. The experiment showed that the detection performance was high and it reached 95.3% training accuracy, 93.8% validation accuracy, and 92.5% test accuracy with an F1-score of 92.9. The results verify that the suggested approach plays a crucial role in detecting defects in potatoes in real time, which can be used to support comprehensive, computerized, and accurate agricultural surveillance.