Spectrum of Engineering Sciences

GREEN SYNTHESIS CHARACTERIZATION AND ANTIMICROBIAL ACTIVITIES OF SILVER NANOPARTICLES USING LEAVES EXTRACT OF PROSOPIS CINERARIA

Muhammad Shoaib — 2026-03-13

Present work represents ecofriendly green approach for the synthesis of silver nanoparticles using leaf extract of Prosopis cineraria and salt solution of silver nitrate. The synthesized nanoparticles were characterized using ultraviolet visible (UV-Vis), Fourier transform infrared (FT-IR), X-Rays diffraction (XRD) crystalline nature of silver nanoparticles and calculated size was 25 nm, high-resolution transmission electron microscopy (HRTEM), and scanning electron microscopy (SEM). techniques. High-resolution transmission electron microscopic analysis substantiated that the AgNPs were rod-shaped with an average size of 38 nm. SEM analysis revealed that the AgNPs were crystalline rod-shaped with porous morphology. Our SEM findings indicated that the size range of the produced silver nanoparticles exceeded the typical nanoparticle size range due to agglomeration, which should normally be between 25 to 28 nanometers. The proteins that surrounded and were bound to the surface of the produced silver nanoparticles caused their size range to be greater than the preferred size. X-ray diffraction study revealed that the AgNPs were in the face-centered cubic crystal system. The hydroxyl groups of Prosopis cineraria leaf extract acted as reducing agents, which was corroborated by the broad peak infrared spectrum 3400 cm-1. Peak at 3400-1 and a ester carbonyl peak near 1750 cm-1. The silver nanoparticles were confirmed by the presence of a very small peak 2300-1 of peaks along with two other peaks at 1615 cm-1 and 1320 cm-1. The antibacterial activities of silver nanoparticles inhibited the growth of bacterium that showed inhibition zone of 15 nm against Bacillus subtilis, and standard ciprofloxacin showed inhibition zone of 18 nm against the same bacterium. Salmonella typhi showed inhibition zone of 19 nm and standard drug ciprofloxacin showed inhibition zone of 21 nm against the same bacterium.

Keywords : Prosopis cineraria, nickel oxide, nanoparticles, green synthesis, Salmonella typhi, Bacillus subtilis

HYBRID MACHINE LEARNING MODELS: COMBINING DEEP LEARNING WITH CLASSICAL ALGORITHMS

Ans Ali Hussain — 2026-03-10

This paper presents a hybrid machine learning framework that integrates deep learning–based feature extraction with classical machine learning classification to enhance image classification performance. The evaluation of the proposed approach is done on the CIFAR-10 benchmark dataset which has 60,000 color images with 10 classes. Two pre-trained convolutional neural network architectures are used to extract deep feature representations and they are ResNet-50 and VGG16. The deep features are then extracted and grouped via a feature fusion strategy and then the combination is then classified with a Support Vector Machine (SVM) and radial basis function (RBF) kernel. The obtained experimental results reveal that the proposed hybrid model attains an accuracy of 94.8% that is superior to that of the standalone CNN model (91.4%) and the classical SVM that is trained on raw pixel features (62.3%). A rigorous cross-validation and statistical significance test of the framework ensures that the framework is robust and has the ability to be generalized. The results suggest that hybrid architectures are effective in integrating deep representation learning and stable decision boundaries that lead to a higher accuracy of classification, a decrease in overfitting, and an increase of robustness. This is a hybrid learning approach that provides a scalable and practical solution to real-world problems involving image classification in which model stability and accuracy are of vital importance.

Keywords: Hybrid Machine Learning, CIFAR-10, Convolutional Neural Networks, Support Vector Machine, Feature Fusion, Image Classification.

PAKISTAN RENEWABLE ENERGY GENERATION EVOLUTION FROM 1947 TO PRESENT

Shafique Ahmed Soomro — 2026-03-04

Since gaining independence in 1947, Pakistan has faced persistent challenges in meeting its electricity demand due to limited initial infrastructure and rapid population and economic growth. The country’s renewable energy development began with hydropower, which remained the sole significant renewable source for several decades. Early installations provided minimal capacity; however, large reservoir-based projects later established hydropower as a central pillar of the national energy system. Despite this progress, increasing reliance on thermal generation led to rising fuel import dependency and long-term energy security concerns. A major shift occurred after 2015, when prolonged electricity shortages triggered accelerated investment in wind, solar, and distributed generation technologies. Policy reforms, revised renewable energy targets, and the nationwide expansion of net-metering played a critical role in this transition. By 2025, Pakistan’s installed power capacity exceeded 46 GW, with hydropower contributing nearly one-third and modern renewables steadily increasing their share. Nevertheless, the exploitation of Pakistan’s vast renewable potential remains constrained by financial, institutional, and transmission-related barriers. This paper critically evaluates the historical evolution, present status, and future trajectory of renewable energy in Pakistan, providing insight into the gap between policy ambitions and on-ground performance, and identifying key challenges that must be addressed to achieve sustainable and reliable power sector transformation.

FORECASTING OF FUTURE TUBERCULOSIS CASES USING AUTOREGRESSIVE MODELS

Zohaib Ali — 2026-03-04

This paper will provide a time-series analysis of Tuberculosis (TB) cases in Pakistan between 2002 and 2018. The main aim was to capture the trend of TB cases and predict the number of cases in the future using the autoregressive models. To capture the short-term, medium-term, and longer-term temporal dependencies, three models were developed; AR(1), AR(2) and AR(3). The lag variables were developed to make the past observations a predictor in the regression models. To estimate the model parameters, Ordinary Least Squares (OLS) was employed, and all the coefficients were estimated with the help of confidence intervals. To evaluate the performance of the model, good of fit measures such as, , Adjusted and RMSE were computed. The AR(1) model was highly linear and related to the cases of the year before whereas AR(2) and AR(3) were more complex. The numerical findings showed that all the models were fitting the historical data very well with the values of the above 0.95. Recursively calculated predictions of TB cases in 2019-2023 were obtained through the AR(1) model which gives 95 percent confidence intervals of the predictions. The findings show that there is a steady increase of TB cases during the forecast period. Comparison of AR models indicates that higher order models can be used to explain small fluctuations but not necessarily increase forecasting accuracy in the long run. The forecasts and methodology are useful to the health policy makers in Pakistan. This paper demonstrates the role of statistical modeling in disease dynamics and intervention planning strategy. New information can be used to revise the proposed models to make predictions more precise and assist in making evidence-based decisions. In general, this piece of work is relevant to predictive epidemiology and informs TB control and prevention strategies.

AUTOMATED IDENTIFICATION OF BUILD DISCUSSIONS ON MICROSERVICES SYSTEMS: AN EMPIRICAL STUDY

Ayesha Anjum — 2026-03-05

In recent years, microservices architecture has gained widespread popularity over traditional systems, largely due to its flexible development cycle and enhanced scalability. In software development, software quality is a major concern. Issues within the software can significantly impact its overall quality. Microservices developers face several challenges in monitoring and managing issues such as failures, faults, and errors. These challenges often arise from a lack of evidence and understanding, hindering the effective implementation of quality practices in Microservices Architecture (MSA). The process of converting source code into an executable file is known as a build, while any problems that occur during this process are referred to as build issues. In the current literature, the methods available for identifying build issues in microservices are primarily qualitative or rely on manual research approaches. Methods such as thematic analysis (TA) and grounded theory (GT) can be challenging to manage due to their complexity and time-consuming nature. To address this gap, we identify build-related discussions within the existing microservices based systems. We define a build discussion as a developer conversation that addresses challenges and decisions related to the build process, typically presented in the form of paragraphs. This research focuses on identifying build issues, often stemming from poor management and dependencies, using machine learning (ML) techniques. We applied ML and deep learning (DL) models to a manually curated dataset consisting of project discussions and annotations. The results identified 11,663 non-build discussions and 1,997 build-related discussions. The ML models, evaluated using k-fold cross-validation, achieved the following performance metrics: Precision 83.60%, Recall 72.34%, F-score 77.79%, AUC 80.44%, and G-Means 68.55%. Among the three baseline models, DeepM1 performed the best. The validation survey further confirmed that build discussions identified through DeepM1 are beneficial in practice.

RECRYSTALLIZATION STRATEGIES FOR BIOPHARMACEUTICS CLASSIFICATION SYSTEM CLASS II DRUGS: IMPACT ON DISSOLUTION RATE AND ORAL BIOAVAILABILITY

Waqas Ahmad — 2026-03-05

For class II drugs of Biopharmaceutics Classification System (BCS), poor water solubility remained one the greatest challenge in modern pharmaceutical development. Notwithstanding, these drug can pass effortlessly through biological membranes due to their high permeability, their low dissolution in aqueous media prohibit them from absorption. Henceforth, dissolution rate emerges as rate limiting step for their absorption through oral route. Multiple formulation and particle engineering technologies, including solid dispersion, size reduction, lipid based delivery, complexation and recrystallization have been implemented for this purpose. Among them, recrystallization has emerged as a cos-effective, user friendly, and scalable method for refining dissolution by modifying crystal properties devoid of changing chemical structure. Although, it was considered purely as a refining method used during synthesis, recrystallization is now known as prevailing crystal engineering tool proficient for modifying the physical characteristics of active pharmaceutical ingredients without altering their chemical identity. The controlling factors such as temperature, solvent selection and recrystallization condition can be carefully controlled to modify crystal shape, size, surface properties, wettability, and polymorphic form. Ultimately, these changes directly impact dissolution rate and bioavailability of a drug. Here, we explored the scientific basis of recrystallization and discussed about the contribution of the process to enhance dissolution of BCS Class II drugs. Furthermore, the mechanism behind dissolution enhancement has also been elaborated.

A TRADITIONAL LITERATURE REVIEW ON MODELING AND OPTIMIZATION OF SHUNT CURRENT REDUCTION IN LARGE-SCALE ALKALINE ELECTROLYZER (AEL) STACKS

Aqib Yaqub Awan — 2026-03-06

Shunt currents represent a major parasitic loss mechanism in large-scale alkaline electrolyzer (AEL) stacks, adversely affecting electrical efficiency, current uniformity, component durability, and operational safety. As alkaline water electrolysis continues to play a critical role in large-scale hydrogen production, particularly in renewable energy–integrated systems, the mitigation of shunt currents has emerged as a key challenge in stack design and optimization. This traditional literature review systematically synthesizes existing research on the mechanisms, modeling approaches, and optimization strategies associated with shunt current reduction in large-scale AEL stacks, with a strong emphasis on numerical and multiphysics simulation methods. A comprehensive literature search was conducted in accordance with the PRISMA framework across major scientific databases, resulting in the selection of 56 peer-reviewed studies published between 2010 and 2025. The reviewed literature encompasses analytical models, numerical simulations, and fully coupled multiphysics approaches used to analyze current distribution, electrolyte conduction pathways, and stack-level electrical behavior. Particular attention is given to studies employing COMSOL Multiphysics, which has emerged as a widely adopted platform for simulating electrochemical, electrical, thermal, and fluid dynamic interactions within alkaline electrolyzer stacks. The findings indicate that shunt current magnitude is strongly influenced by stack geometry, manifold configuration, electrolyte conductivity, material properties, and operating conditions. Simulation-driven optimization strategies, including geometric redesign, electrical insulation, and flow-field modification, demonstrate significant potential for reducing parasitic current losses while maintaining hydrogen production efficiency. However, the literature reveals persistent gaps in large-scale experimental validation, standardized modeling practices, and integrated optimization frameworks. This review identifies key research trends, methodological limitations, and future research directions aimed at improving the efficiency, scalability, and long-term reliability of large-scale alkaline electrolyzer systems.

ADVANCED THERMAL MANAGEMENT STRATEGIES FOR MICROELECTRONICS: ENHANCING HEAT DISSIPATION THROUGH INNOVATIVE COOLING TECHNIQUES

Waqas Arif — 2026-03-07

Background: As microelectronic devices enter the era where they are being packaged down to the nanoscale with greater processing capabilities this question of how to remove high heat levels effectively becomes a key performance and reliability impediment. To satisfy the thermal needs of the next generation electronics, conventional air and passive cooling techniques are no longer adequate. Newer technologies of thermal management such as increasing efficiency by removal of heat have to be explored, although their effectiveness and challenges to installations and in use are still being discussed.

Objective: The purpose of the research is to examine and compare progressive thermal management techniques used in microelectronics concerning their performance and efficiency in heat removal and their feasibility in the restricted and high-powered conditions.

Method: Simulated (experimental) methodology has been used on four realistic cooling solutions, thermoelectric cooling (TEC), microchannel cooling (MCC), phase change materials (PCM), and two-phase cooling (TPC). The different areas of performance measure consisted of junction temperature, thermal resistance, heat transfer coefficient and pressure drop at controlled IP thermal loads.

Results: TPC showed the best efficiency with junction temperature of up to 32.1 o C and heat transfer coefficient of up to 9,800 W/m 2.K and MCC having a balanced performance and moderate energy costs. Though PCM and TEC showed low thermal performance especially at high transient loads, TEC had the highest thermal resistance (0.35 K/W). The pressure drops showed that TPC was more efficient in flowing than MCC.

Conclusion: the two-phase cooling shows the best heat handling ability, whereas MCC is a convenient option between efficiency and scale. Based on these findings it is possible to recommend selective implementation procedures on thermal optimization of micro electronic designs.

SYNTHESIS AND BIOLOGICAL EVALUATION OF TRANSITION METAL COMPLEXES OF PYRAZOLE SCHIFF BASE LIGAND

Sahrish Younus — 2026-03-07

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UNSTRUCTURED DATA ANALYSIS TECHNIQUE FOR MEASURING COHESION IN SOURCE CODE USING MACHINE LEARNING

Furqan Ashraf — 2026-03-09

One of the major challenges for software community is to find source code quality matrices while using Object Oriented Paradigm. With advancements in Machine Learning and Natural Language Processing (NLP) it is now possible to evaluate code using unstructured data analysis. Many tries have been made to capture this important software quality attribute using traditional structured analysis methods. This research study aims to investigate unstructured data analysis could be performed for calculation of cohesion in source code classes. In this study, we designed an experiment to evaluate cohesion score results of two datasets of source code corpus. Furthermore, unstructured way of measuring high cohesion in source code classes is presented using semantic analysis of class names with method names and class description with methods descriptions. The results gathered through performing experiments yielded that unstructured data analysis technique can be applied for finding cohesion of classes. In this study we calculate LCOM for each class present in obtained datasets. By comparing the experiment result with LCOM we get following results. The study compares the results of cohesion score obtained from this technique with traditional LCOM score of source code classes. It is common practice in software industry to follow naming conventions of classes and writing proper description of classes and methods, unstructured data analysis can be effectively applied for calculating the cohesion score of classes.

ADOPTION OF SMART ENERGY MANAGEMENT SYSTEMS AND INDUSTRIAL ENERGY EFFICIENCY: THE MODERATING ROLE OF ORGANIZATIONAL TECHNOLOGICAL READINESS IN PAKISTAN

Amjad Ali — 2026-03-09

Industrial energy consumption in Pakistan accounts for nearly 37% of total national energy use, making energy efficiency a critical priority for sustainable industrial development. The adoption of Smart Energy Management Systems (SEMS) has emerged as an important technological solution for monitoring, controlling, and optimizing industrial energy consumption. However, the effectiveness of these systems largely depends on the technological capabilities and readiness of organizations. This study examines the impact of SEMS adoption on industrial energy efficiency and investigates the moderating role of Organizational Technological Readiness (OTR) in the industrial sector of Pakistan. Using a quantitative research approach, data were collected through a structured questionnaire from 286 managers and technical employees working in energy-intensive industries, including textiles, cement, and manufacturing firms. The study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed relationships. The results reveal that SEMS adoption significantly improves industrial energy efficiency (β = 0.48, p < 0.001). Furthermore, organizational technological readiness positively moderates the relationship between SEMS adoption and energy efficiency (β = 0.29, p < 0.01), indicating that firms with better technological infrastructure and skilled human resources achieve stronger efficiency outcomes. The findings highlight the importance of technological preparedness, digital infrastructure, and managerial support in maximizing the benefits of smart energy systems. The study offers practical implications for policymakers and industry leaders to promote digital energy solutions for sustainable industrial development in Pakistan.

DEVELOPMENT OF PEROVSKITE-BASED PHOTOVOLTAIC MATERIALS FOR HIGH-EFFICIENCY AND LOW-COST SOLAR ENERGY APPLICATIONS IN PAKISTAN

Muhammad Tayyab — 2026-03-09

The increasing energy demand in Pakistan and the high cost of conventional energy sources have accelerated the need for efficient and low-cost renewable energy technologies. This study focused on the development of perovskite-based photovoltaic materials for high-efficiency and economically viable solar energy applications in Pakistan. Various perovskite compositions, including mixed-cation formulations, were synthesized and integrated into n-i-p and p-i-n device architectures. The photovoltaic performance, environmental stability, and economic feasibility of the fabricated solar cells were systematically evaluated. Results showed that mixed-cation perovskites exhibited superior crystallinity, larger grain size, higher photoluminescence intensity, and tunable bandgaps, resulting in a power conversion efficiency (PCE) of 21%. Stability tests under high temperature and humidity confirmed durability suitable for Pakistan’s climatic conditions. Economic analysis revealed significantly lower production costs and levelized cost of electricity (LCOE) compared to conventional silicon-based solar modules, indicating strong potential for large-scale deployment. These findings suggest that perovskite solar cells are a promising solution for high-efficiency, low-cost, and sustainable solar energy generation in Pakistan.

IMPACT OF ARTIFICIAL INTELLIGENCE-BASED PREDICTIVE ANALYTICS ON IMPROVING ACADEMIC PERFORMANCE IN PAKISTANI UNIVERSITIES: THE MODERATING ROLE OF DIGITAL LITERACY

Amjad Ali — 2026-03-09

The integration of Artificial Intelligence (AI) in higher education has revolutionized academic performance monitoring through predictive analytics. This study investigates the impact of AI-based predictive analytics on improving academic performance in Pakistani universities, with digital literacy as a moderating factor. A quantitative research design was employed, collecting primary data from 300 students across multiple disciplines using a structured questionnaire. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate that AI-based predictive analytics significantly enhances academic performance by providing personalized learning insights and early interventions. Furthermore, digital literacy strengthens this relationship, suggesting that students with higher digital competencies can better utilize AI tools for improved learning outcomes. The findings contribute to the literature on AI adoption in education and emphasize the importance of integrating digital literacy programs to maximize the benefits of AI-driven academic interventions. Practical implications for educators and policymakers include designing AI-supported learning environments and promoting digital skill development to foster student success.

BRIDGING DATA SCARCITY IN SINDHI NER USING MACHINE-LABELED CORPORA AND MULTILINGUAL TRANSFORMERS

Nazish Basir — 2026-03-10

In low-resource languages, Named Entity Recognition (NER) possess many challenges and problems due to the shortage of high-quality annotated corpora and the unequal distribution of entity categories. In this study we inspect the effectiveness incorporating Machine-labeled data in Sindhi NER using two multilingual transformer models Multilingual BERT and XLM RoBERTa under two training settings, (i) direct fine-tuning on gold standard human-annotated data and (ii) machine-labeled pre-training followed by fine-tuning. The performances of models are assessed using entity-level precision, recall, and F1-score, together with learning-curve analysis and confusion matrices. The results indicates that machine-labeled pre-training improves recognition, particularly for mid-frequency and low-frequency entity categories. The XLM-RoBERTa outperform Multilingual BERT in both aggregated and entity-specific evaluations, and the pre-training increases the micro-F1 score for mBERT from 0.50 to 0.63 and for XLM-RoBERTa 0.72 to 0.79, compared to without the pre-training step. These findings indicate that large-scale weak supervision can mitigate data scarcity and improve contextual representation learning for Sindhi and other low-resource languages. The study provides a strong empirical baseline for Sindhi NER and highlights the practical value of machine-labeled pre-training for low-resource language processing.

HIGH-PERFORMANCE AND EFFICIENT BRAIN TUMOR SEGMENTATION FOR ENHANCED CLINICAL ANALYSIS

Meiraj Aslam — 2026-03-10

Automated brain tumor segmentation from MRI images is critical for accurate diagnosis and treatment planning. This study presents a novel ResUNet50-based approach, integrating ResNet50 as an encoder within the U-Net framework to achieve robust and precise segmentation. The proposed model was evaluated on two datasets: a Kaggle-based T1-CE MRI dataset and BraTS 2018, ensuring comprehensive assessment across different imaging conditions. ResUNet50 outperformed state-of-the-art models, achieving Dice coefficients exceeding 0.95 and Jaccard indices above 0.91 on the Kaggle dataset. Additionally, experiments on BraTS 2018 Whole Tumor segmentation across multiple MRI modalities (FLAIR, T1, T1-CE, and T2) demonstrated high accuracy on both High-Grade Gliomas (HGG) and Low-Grade Gliomas (LGG), confirming model generalization. Statistical significance tests (Paired t-tests and Wilcoxon Signed-Rank Tests, p < 0.05) validated the improvements over existing approaches. Furthermore, ResUNet50 reduced parameters by over 60% and accelerated inference time by 4.8× compared to U-Net, enhancing its potential for clinical deployment. Future work will focus on ensemble learning and bio-inspired optimization for improved robustness across multi-center MRI datasets. Explainable AI techniques such as Grad-CAM and saliency maps will be incorporated to enhance interpretability, improving clinical applicability.

https://doi.org/10.5281/zenodo.18998570

NEUROFUSION-X: A HYBRID TRANSFORMER–GNN DEEP LEARNING MODEL FOR PROACTIVE CYBER-ATTACK PREDICTION IN IOT NETWORKS

M Tahseen Alam — 2026-03-10

The rapid expansion of Internet of Things (IoT) networks has introduced significant security challenges due to the heterogeneous, large-scale, and highly dynamic nature of IoT traffic. Contemporary intrusion detection systems (IDS) predominantly rely on traditional machine learning or single-architecture deep learning models, which are largely reactive and insufficient for capturing the complex spatio-temporal characteristics of modern cyber-attacks. In particular, existing approaches often fail to jointly model long-range temporal dependencies and network-level communication topology, limiting their effectiveness in proactive threat mitigation.

This paper proposes NeuroFusion-X, a hybrid deep learning framework that integrates Transformer-based temporal modeling with Graph Neural Network (GNN)-based relational learning for proactive multi-class cyber-attack prediction in IoT networks. The proposed architecture leverages self-attention mechanisms to learn evolving attack patterns across time while simultaneously capturing inter-device communication dependencies through graph-based message passing. A fusion module unifies temporal and topological representations to forecast future attack categories before full attack execution, shifting intrusion detection from reactive classification to predictive cyber defense.

Extensive experiments are conducted on large-scale IoT security datasets, including BoT-IoT and CICIoT2023, encompassing diverse attack types and protocol behaviors. Experimental results demonstrate that NeuroFusion-X consistently outperforms traditional machine learning models, Transformer-only, and GNN-only baselines in terms of macro-F1 score, ROC-AUC, and early prediction accuracy, particularly under severe class imbalance. The findings confirm that spatio-temporal fusion significantly enhances attack separability, prediction stability, and proactive detection capability. Overall, NeuroFusion-X provides a scalable, extensible, and future-ready framework for intelligent cyber-defense in next-generation IoT infrastructures.

CHARACTERIZATION OF PLASTIC DEGRADATION ASSOCIATED BACTERIAL ENZYME CUTINASE THROUGH IN-SILICO TOOLS

Nadia Hussain — 2026-03-10

Plastic pollution is a crucial problem globally. It exerts negative impact on aquatic as well as human life. Bioremediation based on bacterial enzymes is an emerging and sustainable mode of plastic degradation. In current study, two plastic degrading enzymes G8GER6 and E9LVH8 from bacteria bacteria Thermobifida fusca and Thermobifida cellulosilytica were targeted for analysis. UNIPROT database was accessed to retrieve the sequences of enzymes. Followed this, enzymes were analyzed via PROPARAM and SOPMA tool and ALPHAFOLD web server. Analysis revealed the number of amino acids (319 and 262), theoretical pI (9.65 and 6.30), extinction coefficients (38390 and 36900), instability index (41.75 and 36.39), aliphatic index (79.06 and 80.50) and GRAVY values (-0.247 and -0.221) for these two enzymes. Alpha helix, extended strand and random coil content was found in the range of 64-88, 36-44 and 154-195, respectively. Both proteins exhibited tertiary structure with complex folding. Current study findings will be helpful in simulating mutations via site directed mutations that might increase binding affinity of enzyme with plastic and its degradation efficiency.

GRAPH ATTENTION-BASED MULTI-SCALE WAVELET INTELLIGENCE FRAMEWORK FOR HYBRID POWER QUALITY DISTURBANCE CLASSIFICATION

Aslam P. Memon — 2026-03-11

The increasing penetration of renewable energy resources, power electronic converters, electric vehicle infrastructure, and nonlinear industrial loads has significantly increased the occurrence of complex and hybrid power quality disturbances (PQDs) in modern smart grids. Conventional wavelet–ANN frameworks rely on static feature vectors and treat wavelet sub-bands independently, limiting their capability to capture cross-frequency interactions inherent in hybrid disturbances.

This paper proposes a Graph Attention-Based Multi-Scale Wavelet Intelligence (GAMWI) framework for accurate and interpretable classification of IEEE Std. 1159-compliant single and hybrid power quality disturbances. In the proposed approach, multi-resolution wavelet energy features are transformed into structured graph representations that model inter-scale dependencies among frequency bands. A lightweight Graph Attention Network (GAT) dynamically assigns adaptive importance weights to relational frequency interactions, thereby improving disturbance separability and enhancing interpretability of the classification process.

Simulation results demonstrate that the proposed framework achieves a classification accuracy of 99.21%, outperforming conventional DWT-ANN and CNN-based classifiers while maintaining lower computational complexity. The proposed method also exhibits strong robustness under noisy operating conditions, making it suitable for real-time smart grid power quality monitoring applications.

AN ADVANCED WAVELET–AI FAULT CLASSIFICATION FRAMEWORK FOR ENHANCED PROTECTION OF SERIES-COMPENSATED TRANSMISSION SYSTEMS

Aslam P. Memon — 2026-03-11

Series-compensated transmission systems play a vital role in enhancing power transfer capability and improving steady-state stability in long-distance AC networks. However, their dynamic behavior under fault conditions introduces complex transient phenomena, including high-frequency oscillations and sub-synchronous resonance (SSR), which challenge conventional protection strategies. Traditional phasor-based relaying techniques often exhibit reduced reliability due to waveform distortion introduced by compensation devices and nonlinear protective elements.

This paper presents an advanced hybrid Wavelet–Artificial Intelligence (AI) framework for intelligent fault classification in a series-compensated transmission system modeled in MATLAB Simscape Electrical. Multi-resolution analysis using the Daubechies-5 (db5) wavelet at level-5 decomposition is employed to extract transient signatures from three-phase current signals. Statistical features derived from wavelet coefficients are utilized to train and comparatively evaluate Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Probabilistic Neural Network (PNN) classifiers.

The proposed system is validated under single-line-to-ground (SLG), line-to-line (LL), and three-phase-to-ground (LLLG) faults with varying fault resistance and compensation levels. The PNN classifier achieved the highest classification accuracy of 98.5%, outperforming the MLP and RBF networks. Results demonstrate enhanced classification accuracy, robustness, and rapid detection capability, supporting improved protection performance in modern series-compensated transmission networks.

CONSOLIDATION BEHAVIOR OF SOIL STABILIZED WITH CEMENT: EXPERIMENTAL ANALYSIS OF ROHRI CANAL’S SOIL (PAKISTAN)

Dr. Gul Muhammad — 2026-03-11

This research aims to study the consolidation characteristics of clayey soil samples taken from the Rohri Canal in Pakistan, using Ordinary Portland Cement (OPC) as a stabilizer. This study, however, deviates from the general trend of literature that, over the years, has shown a significant decrease in compressibility of soils over time, by revealing a peculiar short-term effect of cement addition, which initially results in an increase in vertical strain and compressibility parameters of soils. The results of the incremental loading Oedometer test on soil samples containing 5% and 10% cement content, after short curing periods, revealed that the 10% cement content-24h sample recorded the highest vertical strain of 27.4%, coefficient of volumetric compressibility (mᵥ = 0.128 m²/MN), and compression index (C꜀ = 0.239), almost tripling the value of the untreated soil samples, which recorded a value of 0.087 for the compression index. This unusual increase in compressibility is due to the formation of a porous soil-cement skeleton, which collapses under high vertical stress levels, up to 640 kPa.

Comparative analysis of sources (2015-2026) indicates that, although the process of long-term curing for 28 to 90 days results in densification of the microstructure through the formation of Calcium Silicate Hydrate, short-term stabilization can increase settlement risks. These results are critical for the design and construction of hydraulic fillings in Pakistan, as immediate mechanical response is the main concern.

LEVERAGING ARTIFICIAL INTELLIGENCE FOR ADVANCE DATA NETWORKING AND CYBERSECURITY

Muhammad Danish Rasheed — 2026-03-11

The increasing complexity of digital infrastructure in the United States has significantly intensified the need for intelligent and adaptive data networking and cybersecurity systems. Rapid advancements in cloud computing, Internet of Things (IoT), 5G connectivity, and large-scale enterprise networks have expanded the digital attack surface. As a result, cyber threats such as ransomware, zero-day exploits, phishing campaigns, and advanced persistent threats (APTs) have become more sophisticated and harder to detect. Traditional rule-based and signature-driven security frameworks are often reactive, relying on predefined patterns of known threats, which limits their ability to respond effectively to evolving and previously unseen attacks. Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has emerged as a transformative solution to these challenges. Unlike conventional systems, AI-driven cybersecurity platforms continuously learn from network behavior, analyze massive volumes of real-time traffic data, and detect anomalies that may indicate malicious activity. Machine learning models enhance intrusion detection systems by identifying patterns beyond human capability, while deep learning algorithms improve malware classification and behavioral analysis. In addition to cybersecurity applications, AI optimizes data networking through predictive traffic management, congestion forecasting, and automated load balancing, thereby improving bandwidth utilization and reducing latency across complex network infrastructures. This study evaluates AI’s quantitative impact on cybersecurity effectiveness and networking efficiency using descriptive and inferential statistical methods. The results demonstrate statistically significant improvements, including threat detection accuracy reaching 97%, incident response time reduced by 50%, network congestion lowered by 30%, and operational cost savings of 25%. Effect size analysis confirms that these improvements are not only statistically meaningful but also practically substantial. These findings highlight AI’s critical role in strengthening digital resilience and modernizing cybersecurity frameworks, positioning it as a foundational technology for securing advanced network ecosystems in the United States.

ARTIFICIAL INTELLIGENCE FOR STRENGTHENING CYBERSECURITY IN EDUCATIONAL TECHNOLOGY SYSTEMS

Amirmohammad Delshadi — 2026-03-11

The rapid adoption of educational technologies (EdTech) in U.S. public schools has significantly transformed teaching, learning, and administrative operations. However, the increasing reliance on digital platforms such as learning management systems, cloud services, and remote learning tools has also exposed schools to a growing number of cybersecurity threats. These threats include ransomware attacks, phishing attempts, data breaches, and unauthorized access to sensitive student information. Artificial Intelligence (AI) has emerged as a promising solution for strengthening cybersecurity in educational environments by enabling automated threat detection, predictive analytics, and rapid incident response. This research examines how AI technologies improve cybersecurity in U.S. public school educational systems. Using a mixed-method approach involving survey data, statistical modeling, and simulated cybersecurity incidents, the study evaluates the effectiveness of AI-based cybersecurity solutions compared to traditional security systems. Results indicate that AI-driven cybersecurity systems significantly improve threat detection accuracy and reduce response time to cyber incidents. Machine learning-based systems have been shown to increase threat detection accuracy to approximately 95.7% compared to 78.4% for traditional rule-based systems, while also significantly reducing incident response time. The findings demonstrate that AI can play a crucial role in strengthening cybersecurity resilience in educational institutions while protecting sensitive student data and digital learning platforms. Furthermore, the study highlights the potential of AI-driven security frameworks to support proactive threat management through continuous monitoring and intelligent anomaly detection. The integration of machine learning techniques enables educational institutions to identify vulnerabilities earlier and mitigate cyber risks before significant damage occurs. In addition, AI-based systems reduce the operational burden on IT administrators by automating routine security monitoring tasks. These findings suggest that adopting AI-driven cybersecurity solutions can significantly enhance the overall security infrastructure of modern educational environments.

CYBERSECURITY RISK ASSESSMENT MODEL FOR INTERNET OF MEDICAL THINGS (IOMT) DEVICES IN HEALTHCARE SYSTEMS

Muhammad Akram — 2026-03-11

The rapid adoption of the Internet of Medical Things (IoMT) in modern healthcare systems has significantly improved patient monitoring, diagnostics, and hospital management. However, the increasing number of interconnected medical devices has also expanded the cybersecurity attack surface in hospital networks. IoMT devices such as infusion pumps, patient monitors, wearable sensors, and imaging systems often operate with limited security mechanisms, outdated software, and weak authentication protocols, making them attractive targets for cybercriminals. Cyberattacks on healthcare infrastructure can compromise patient data, disrupt medical services, and threaten patient safety. Therefore, effective cybersecurity risk assessment mechanisms are essential for protecting hospital networks and connected medical devices. This study proposes a cybersecurity risk assessment model designed specifically for IoMT devices deployed in American hospitals. The proposed framework evaluates cybersecurity threats by integrating vulnerability severity, threat probability, and operational impact to calculate an overall risk score for connected medical devices. A quantitative research approach was used to analyze a dataset consisting of multiple categories of IoMT devices commonly used in hospital environments. Statistical analysis was performed to identify vulnerability patterns and evaluate cybersecurity risk levels across different device types. The results indicate that a significant proportion of IoMT devices exhibit moderate to high cybersecurity risk levels due to software vulnerabilities, weak authentication mechanisms, and legacy system dependencies. Devices such as infusion pumps and hospital information systems were identified as the most vulnerable components within hospital networks. The proposed risk assessment model provides a systematic approach for identifying high-risk devices and prioritizing cybersecurity mitigation strategies. The findings highlight the importance of implementing proactive cybersecurity frameworks in healthcare environments to enhance network security and protect sensitive patient information. The proposed model can assist healthcare institutions in strengthening IoMT security, improving risk management, and supporting the development of resilient hospital cybersecurity infrastructures.

ANALYZING ZERO-KNOWLEDGE PROOF PROTOCOLS FOR PRIVACY-PRESERVING DATABASE QUERY VERIFICATION

Nisar Ahmed Memon — 2026-03-11

The increasing need of data security in the distributed and cloud computing systems has led to the need to develop strong cryptography and cryptography checks that can help in testing the integrity of computations without revealing the underlying data. The research paper has analyzed Zero-Knowledge Proof (ZKP) protocols, namely, zk-SNARKs, zk-STARKs, and Bulletproofs with reference to privacy-friendly database query verification. The researchers used a systematic qualitative-analytical review involving forty-seven peer-reviewed articles found in ePrint archives of IEEE, ACM, and IACR to analyze the theoretical frameworks, performance indicators, and actual implementation issues of these protocols. The evaluation was done by judging each protocol based on its realization with respect to criteria such as computational efficiency, proof size, verification time, and formal security properties such as completeness, soundness and zero-knowledge properties. It was found that zk-SNARKs had compact proofs and could be verified quickly, but at the cost of having a trusted setup, leading to vulnerabilities. zk-STARKs were more transparent but had increased proof sizes. Bulletproofs exhibited significant range proof without trusted preparation. The research determined that there was no one protocol that best met all the database verification needs, and context-driven protocols selection was still a critical step towards the best privacy-preserving implementations.

THE ALGORITHMIC SHIELD: A HYBRID INTELLIGENCE FRAMEWORK FOR PROACTIVE DEFENSE AGAINST AUTONOMOUS AI-DRIVEN CYBER THREATS

Abdul Basit* — 2026-03-06

The rapid evolution of autonomous artificial intelligence has reshaped the cyber threat landscape. Traditional security mechanisms remain largely reactive. They fail to anticipate self-adaptive, AI-driven attacks. This paper introduces a hybrid intelligence framework designed to provide proactive cyber defense against autonomous AI-based threats. The proposed framework integrates machine intelligence with strategic human oversight to create an algorithmic shield capable of early threat perception, adaptive response, and continuous learning. Unlike conventional models, the framework combines unsupervised behavioral analysis, reinforcement-driven policy adaptation, and human-in-the-loop validation. This layered intelligence structure enables the system to detect unknown attack patterns before execution. Context-aware decision making ensures that defensive actions remain precise and explainable. Short feedback cycles allow rapid adjustment under evolving threat conditions. The architecture is evaluated using simulated AI-driven cyberattack scenarios, including autonomous malware propagation and adaptive intrusion strategies. Experimental results demonstrate improved detection latency, higher resilience to zero-day attacks, and reduced false positives when compared with fully automated defense systems. The inclusion of human cognitive input significantly enhances trust and operational stability without sacrificing system agility. This study contributes a novel defense paradigm that shifts cybersecurity from passive protection to anticipatory control. The proposed hybrid intelligence approach establishes a scalable and ethically aligned foundation for securing future AI-dominated digital ecosystems. The framework is particularly suited for critical infrastructures where autonomous threats demand both speed and accountability.

Keywords: Hybrid Intelligence; Proactive Cyber Defense; Autonomous AI Threats; Human-in-the-Loop Security; Adaptive Cybersecurity Framework

AI-DRIVEN FEDERATED MULTI-AGENT ACTOR–CRITIC LEARNING FOR SECURE AND ENERGY-EFFICIENT RESOURCE OPTIMIZATION IN 6G SEMI-GRANT-FREE NOMA-BASED IOT NETWORKS

Maaz Ali Mumtaz — 2026-03-11

Sixth-generation (6G) wireless systems are expected to support ultra-massive machine-type communication through dense deployments of Internet of Things (IoT) devices. Semi-Grant-Free Non-Orthogonal Multiple Access (SGF-NOMA) has emerged as a promising access mechanism for enabling simultaneous transmissions of grant-based and grant-free users while improving spectral efficiency. Dense IoT environments create significant challenges including power-domain collisions, energy inefficiency due to repeated retransmissions, and susceptibility to malicious interference such as jamming attacks. Conventional centralized optimization techniques introduce high signaling overhead and raise privacy concerns, limiting their scalability in large-scale IoT deployments. This work proposes an AI-driven federated hybrid multi-agent actor–critic learning framework for secure and energy-aware resource optimization in 6G SGF-NOMA IoT networks. Each grant-free device operates as an intelligent learning agent that autonomously selects transmission power levels and resource blocks based on local observations. A federated learning architecture enables decentralized model training while periodically aggregating parameters at an edge server using federated averaging. The proposed framework integrates a hybrid exploration–cooperation strategy and a multi-objective reward function that jointly considers throughput gain, collision penalties, energy consumption cost, and security robustness under jamming interference. Simulation-based evaluations demonstrate that the proposed framework significantly improves conditional throughput, reduces power collision probability, and enhances energy efficiency compared with centralized deep reinforcement learning, random access, and conventional SGF-NOMA approaches. Results also indicate faster convergence and improved fairness among IoT devices under ultra-dense deployment scenarios. The proposed solution provides a scalable and privacy-preserving learning architecture suitable for AI-native 6G wireless systems and large-scale IoT networks.

EMPIRICAL ANALYSIS OF PROFESSIONAL PRACTICE GAPS BETWEEN LICENSED SOFTWARE ENGINEERS AND NON-LICENSED DEVELOPERS

Ahmad Farooq — 2026-03-12

Software engineering (as opposed to civil, chemical, and mechanical engineering) is not regulated, unlike its ubiquitous use in the safety-critical infrastructure (healthcare, finance, transportation), where failure results in security breach, loss of money, and loss of human lives. The research is an empirical measure of professional practice differences between licensed software engineers and non-licensed developers, which test the hypothesis that formal licensure would increase the process governance but not reduce technical innovation. Social survey, project audits and DevOps metrics are mixed-method to address 800 developers (400 licensed through NCEES/ABET, 400 not). Performance is benchmarked on the Professional Practice Index (PPI = 0.4 x Quality + 0.3 x Ethics + 0.3 x Risk) of which the Random Forest classification (100 trees) has a test accuracy of 90% (n=160). Multidimensional separation is confirmed by 3D visualization and confusion matrix analysis (95% licensed sensitivity). Licensed engineers have a higher PPI score (0.823 0.087 vs. 0.678 0.118, p=0.001) with documentation compliance (29.2% feature importance) and test coverage (23.1%) prevailing. The advantages of process discipline are proven by geometric feature space segregation, whereas technical parity is proven with equivalent Quality scores (5.3% importance). Licensed teams are also equal to the speed of innovation but are more effective in regulatory traceability demanded in mission-critical systems. Licensing establishes a quantifiable governance upgrading essential in safety critical areas, and facilitates hybrid regulation: licensure required in safety-critical areas (PPI 0.80 systems in healthcare, avionics, finance), voluntary certification in others. The policy should modify NCEES examinations regarding distributed systems, ABET should do more of software accreditation and industry should use PPI benchmarking. This confirms causal facts that industrialize IEEE/ACM professionalization controversies to offer software engineering as civil engineering-level profession that balances responsibility and innovativeness.

SIMULATION-BASED DEBOTTLENECKING OF A CERAMIC TILE PRODUCTION LINE WITH INTEGRATED ENERGY CONSUMPTION AND CARBON EMISSION ANALYSIS

Abdur Rehman Babar — 2026-03-12

Ceramic tile making is considered to be one of the most energy-intensive manufacturing processes due to the drying and firing operations carried out at relatively higher temperatures. Inefficiencies in the production process not only cause reduced production rates, but they also contribute to increased energy consumption and environmental damage. In this paper, the authors proposed a discrete-event simulation-based approach to debottleneck the production process in the ceramic tile industry while concurrently evaluating the energy consumption and environmental damage. Real-time data were used to simulate the production process in an industrial ceramic tile factory using the discrete-event simulation approach. The proposed simulation approach was successfully validated by finding that the deviation between the simulation and actual production rates was merely 0.53%. Two scenarios were proposed to improve the production process in the ceramic tile industry and reduce the bottlenecks in the production system. The analysis found that the kiln is the main bottleneck in the production system due to the longer processing time and the relatively higher defect rates compared to the other production stages. The optimization scenario proposed reduction in the thermal processing times of the double-layer dryer, the single-layer dryer, and the kiln, which improves the production process without causing new queues in the system. The sustainability analysis found that the proposed debottlenecking approach reduced the energy consumption from 80.88 to 74 kWh and the carbon footprint from 66.32 to 60.68 kg CO₂, which is equivalent to the reduction in the energy consumption and the carbon footprint by 6.88 and 5.64 kg CO₂ (8.5%), respectively. The proposed discrete-event simulation approach is successful in debottlenecking the production process and improving the sustainability performance without the need to invest capital in the ceramic tile industry.

NON- INVASIVE GLUCOSE MONITORING DEVICE USING PHOTOPLETHYSMOGRAPHY SIGNAL AND MACHINE LEARNING MODEL

Jawad Ali — 2026-03-12

Diabetes mellitus is a metabolic condition that is permanent and that is why the blood sugar level is under constant monitoring. Conventional surveillance is based on invasive finger-prick tests that may be painful and may decrease patient compliance. In order to deal with this problem, this paper suggests an estimation system of glucose that will be non-invasive and work with photoplethysmography (PPG) signals and machine learning. The system employs a MAX30105 optical sensor to record PPG signals through the fingertip and ESP32 microcontroller to take data, process it, and analyze it in real-time. The synchronized PPG signals along with reference glucose values of a standard glucometer were acquired as a dataset. The PPG waveform was processed to obtain relevant features and was sent through a machine learning model to predict glucose. It has been demonstrated that a low-cost, portable, and non-invasive glucose monitoring system can be developed using PPG signal analysis and machine learning together and be used in wearable healthcare systems.

NUMERICAL MODELING AND OPTIMIZATION OF A HIGH EFFICIENT LEAD-FREE CsSnGeI₃ PEROVSKITE SOLAR CELL USING SOLAR CELL CAPACITANCE SIMULATOR

Qosia Laraib — 2026-03-10

In this work, a lead-free CsSnGeI3 based PSC was numerically investigated using SCAPS-1D simulator. A planar n-i-p structure using ITO/SnO₂/CsSnGeI₃/HTL/Au was used and the effects of various hole transport layer (CuI, P₃HT and CuO) were systematically studied. SnO2 was selected as the electron transport layer because of its good band structure and high electron mobility. The thicknesses of absorber, hole transport layer and electron transport layer were optimized to have better charge transport and reduce recombination losses. The results show that performance of the device highly depends on the thickness of the absorber. Among the investigated hole transport layers, CuI and P₃HT had better photovoltaic performance due to efficient hole extraction and reduced recombination but CuO showed a comparatively lower efficiency. The simulated device showed the best result in optimized condition 34.01% maximum power conversion efficiency, the open-circuit voltage of 1.13V, the short-circuit current density of 35.29 mA/cm² and the fill factor of 84.99%. These results show the great potential of CsSnGeI3 as an environment-friendly absorber for high performance lead-free perovskite solar cells

FROM THE CAPTIVITY TO WILD: A CRITICAL REVIEW OF BREEDING PROGRAMMES FOR ENDANGERED MAMMAL SPECIES

Shahid Mahmood — 2026-03-13

Heavy deforestation leads to the threat of extinction of various mammals’ species in near future due to the habitat loss, which imposes a great risk at survival of these species in a particular area. Another cause of mammal’s extinction is the increasing demand of their body parts that leads to uncontrolled hunting and decrease in their number. Protection of sustainable population of endangered mammals is a great challenge. Therefore, in-situ and ex-situ conservation techniques of captive breeding are being practiced for many years to protect the endangered mammals. These techniques have been proved successful in many cases of mammals. The main problem that we face in ex-situ conservation is the reintroduction of mammals in their natural habitat because natural habit is different from captive environment. However, this problem has been solved to a greater extent by the in-situ conservation in which population of organisms are protected in their natural habitat. In this review we will highlight the history and types of captive breeding. We will also review the in-situ and ex-situ conservation techniques and compare the effectiveness of each of these techniques. It also contains the challenges faced in reintroduction of organisms in their habitat and policies to face these challenges

CLIMATE CHANGE AND THE RESURGENCE OF MALARIA: A REVIEW OF FUTURE ENVIRONMENTAL HEALTH RISKS

Shahid Mahmood — 2026-03-13

Climate change has become a global health concern in the recent time, with a great impact on the epidemiology of the diseases which are transmitted by vectors. Malaria, as a disease caused by climate sensitive Anopheles mosquitoes and parasites, Plasmodium, is in danger of becoming a major threat in areas that were once controlled or malaria-free. The dynamics of climate change and malaria transmission have a complicated intersection, which is examined in this review article in order to comprehensively examine the future environmental health risk. Using a secondary methodological approach, this assessment is based on synthesizing the existing global and regional literature comprising the empirical field data, eco-hydrological modeling, and systematic reviews to examine the exact influence of the climatic and non-climatic factors. The evidence shows that increasing temperature increases the speed of mosquito gonotrophic cycles and also reduced the extrinsic incubation period of the parasite hence maximizing the transmission potential during 25-29°C. In addition to this, changing precipitation patterns are dual-purpose, with moderate precipitation creating more breeding space and extreme incidences leading to flushing of larvae temporarily. As a result, the geographical distribution of malaria is actively displacing towards the poles and elevation, along with significantly long seasonal epidemiologic periods. More importantly, these climatic drivers are synergistically enhanced by land-use transformations, profound socioeconomic susceptibility and biological adaptations like amplified drug and insecticide resistance. The review conclusively concludes that the resurgence of malaria is an issue of critical nexus of environmental risks and biological vulnerability. In order to curb this intensifying menace, international health institutions need to start shifting swiftly towards active emergency management to proactive and climate-responsive approaches. It is paramount to incorporate artificial intelligence to create predictive early warning measures, climate-aware surveillance, advanced vaccines and resilient infrastructure to maintain world eradication efforts in a world that is fast warming.

CLIMATE CHANGE SOURCES, IMPACTS, MITIGATION, AND ADAPTATIONS IN ASIA

Shahid Mahmood — 2026-03-13

Climate change is the greatest challenge to the stability of the world, with its disastrous effects being disproportionately felt among the developing economies of Asia. This review assesses the complex causes of climate change, its socio-economic impacts in Asia, and also identifies a severe imbalance in the vulnerability and adaptiveness of the region. Although Central Asia struggles with unprecedented cryospheric instability and South Asia is dealing with unpredictable monsoon cycles, the local response in the latter is frequently hindered by systemic implementation gaps in policies that are paper-based instead of action-oriented. Moreover, there is a lack of social science research in existing literature, which favors physical modeling and overlooks critical human aspects such as climate justice, gender-sensitive adaptation, and feminization of agriculture. This paper will argue that a radical shift toward a more practical and institutionalized approach to nature-based solutions and climate-smart agriculture is necessary in the Global South. As the review concludes, the unequal coping abilities, which are determined by economic status and technological access, serve to increase the gap between countries. In conclusion, to tackle the climate crisis issue in developing Asia, it is necessary to fill the gap between scientific modeling and local socio-political facts to achieve equitable resiliency, amidst the most vulnerable groups in the world.

CLIMATE CHANGE, ALTITUDE SHIFT AND VIRUS EMERGENCE: A REVIEW ON PTEROPUS BATS IN HIMALAYAN REGION

Shahid Mahmood — 2026-03-13

The potential risk of new infectious diseases to global public health is significant, and climate change has been recognized as a key driver of zoonotic spillover events. The Nipah virus, which has the Pteropus bat (flying fox) as its natural host, is one such disease. The Himalayan region, a biodiversity hotspot and climate change-sensitive area, is a critically understudied region in terms of the ecology of Pteropus bats and the emergence of the Nipah virus. This review aims to compile the knowledge on the distribution and ecology of Pteropus bats in Himalayan region, analyze the expected impact of climate change on the altitudinal distribution of Pteropus bats, and discuss the expected implications of Nipah virus outbreaks. There is very little information available on the ecology of Pteropus bats in the Himalayan region, and only one species-specific distribution modeling study is available from Nepal. Contrary to the expected altitudinal migration pattern in mountainous regions, this study reveals that climate change is not expected to improve the altitudinal distribution of Pteropus medius. Instead, their suitable habitat is expected to be reduced and limited to lower-altitude regions due to their susceptibility to low temperatures. This predicted reduction of range size also overlaps with areas of high human population density and agricultural activity, which could increase the human-bat interface. Among the factors that could play a role in the potential increase in the risk of spillover are the increased viral shedding in the colony because of high population density, stress induced by the disruption of the phenology of fruit tree flowering because of climate change, and the increased opportunities for environmental contamination of date palm sap and food. The interaction of climate change, the unique altitudinal ecology of Pteropus bats, and high human population density in the Himalayan lowlands makes this area a potential perfect storm for the emergence of Nipah virus. A plan to meet this potential risk is to implement immediate, interdisciplinary One Health approaches that include wildlife surveillance, climate modeling, and ecological research to decrease the risk of spillover in this high-risk area.

CLIMATE CHANGE INDUCED EXTREME WEATHER EVENTS AND FOOD SECURITY

Shahid Mahmood — 2026-03-13

Climate change has come to be recognized as one of the key issues affecting food security globally in recent times, especially in the context of the exacerbation of extreme weather events like heat waves, droughts, floods, and cyclones. These extreme events affect agricultural productivity, the quantity of food produced, the health of animals, and the sustainability of fisheries and aquaculture, thereby affecting the food security status of nations in terms of availability, accessibility, utilization, and stability. Studies have shown that even the slightest rise in the average temperature of the earth leads to the reduction of the quantity of staple foods like wheat, maize, rice, and soybean, with yields falling by 3-7% for every 1°C rise in the average earth temperature. Heat waves and rainfall variability affect the health of animals, thereby reducing the quantity of milk and meat produced, as well as the mortality rate of animals. Flooding and rainfall variability affect the quantity of food produced, while marine heat waves reduce the quantity of fish produced. Moreover, regional studies suggest that South Asia, Sub-Saharan Africa, Southeast Asia, and Latin America are the vulnerable regions, and the loss in the crop yield is estimated to vary between 5 and 60%, depending on the crop and the local climatic conditions. Though the adaptation techniques are effective, there are limitations in the institutional, technological, and socio-economic limitations in the mitigation and adaptation techniques. However, this review highlights the importance of addressing the mitigation and adaptation techniques in a manner that would help to strengthen the agricultural sector, addressing the concerns of food security in a changing climate. This is a critical issue that needs to be understood by the policymakers, researchers, and stakeholders who wish to address the concerns of food security in a changing climate.

ECOLOGICAL ROLE OF DETRITIVOROUS INSECTS: IMPACTS AND CHALLENGES IN CLIMATE CHANGE SCENARIOS

Shahid Mahmood — 2026-03-13

Detritivorous insects are an important part of the ecosystem. They aid in the decomposition of organic matter, the recycling of nutrients, and the sustainability of soil fertility. These insects are beetles, termites, springtails, cockroaches, and dipteran larvae, which decompose plant and animal litter into smaller particles, promoting microbial degradation and the nutrient mineralization process. Detritivores also enhance soil aeration and soil water retention through their feeding and burrowing behaviors. Besides that, they have a significant role in detritus-related food webs by transporting energy from dead organic matter to higher trophic levels, including birds, amphibians, reptiles, and predatory arthropods. Nevertheless, the current climate change is a major threat to the existence of detritivore communities and the ecological process. Elevated temperature, changes in precipitation, drought, can influence their metabolic rate, feeding habits, survival, and distribution. These environmental shifts can interfere with the decomposition processes, change the nutrient cycling, and affect the soil health and trophic interactions. Shifts in species distributions and phenology due to climate could also cause a lack of fit between detritivore activity and organic substrate availability and could have a negative impact on ecosystem performance. Although detritivorous insects are ecologically important, they are still under-researched, with much of the research conducted in limited regions and methodological limitations, in addition to problems with predictive ecological modeling. The future research ought to be directed into the areas of long-term ecological monitoring, better taxonomic identification using a molecular tool, and the development of integrative models that would consider various environmental stressors.

IMPACT OF PESTICIDES USE ON SOIL MICROBIAL COMMUNITIES

Shahid Mahmood — 2026-03-13

The increasing global use of agrochemicals in ensuring food security has posed severe ecotoxicological risks to agricultural soil ecosystems. Soil microbes are foundation of soil fertility, playing critical roles in vital biogeochemical processes like carbon sequestration, nitrogen fixation, phosphorus solubilization, and sulfur cycling. However, continuous use and accumulation of diverse pesticide active ingredients have posed severe risks on microbial diversity assembly and stability. Classical pesticide risk assessments have traditionally been based on single active ingredient-based assessments under extremely controlled laboratory conditions. However, modern-day agricultural ecosystems are characterized by high pesticide diversity where co-occurrence of multiple active ingredients like herbicides, insecticides and fungicides poses complex multi-stressor conditions. These chemical cocktails have potential to act synergistically thus increasing the overall ecological stress while reshaping fundamental microbial network topologies. Advances in high-throughput sequencing technologies and metagenomic studies clearly demonstrated that high pesticide diversity leads to reduced stochastic microbial assembly, favoring growth of deterministic specialists and opportunistic microbes with ability to degrade xenobiotics. Consequently, evolutionary forces like genome streamlining led to enhanced expressions of specific functional genes which are associated with rapid nutrient cycling, thus paradoxically accelerating depletion of critical soil resources. This comprehensive review critically evaluates distinct impacts of herbicides, insecticides, and fungicides on soil microbiomes, emphasizing disruption of enzymatic activities such as those of dehydrogenase, urease, and nitrogenase. Furthermore, it explores utility of molecular biomarkers and proposes agronomic interventions specifically nitrogen fertilization and conservation tillage to mitigate Eco toxicological risks. By integrating these molecular insights with agronomic management, this review provides framework for preserving long-term soil health and redefining regulatory pesticide risk assessments

IMPACT OF NANOPARTICLES ON HUMAN HEALTH IN PAKISTAN

Shahid Mahmood — 2026-03-13

Nanotechnology is a rapid emerging field and has found application in medicine in a large-scale agriculture, industry as well as consumer products yet the rate of manufacturing and consumption of nanoparticles. The topic (NPs) has raised growing apprehensions regarding the potential adverse effects of such particles on human health. Nanotechnology is a fast-developing discipline, which has been used extensively in medicine, agriculture, industry, and consumer products but the pace of production and use of nanoparticles (NPs) has brought about increasing concerns about the possible negative impacts of these particles on human health. Nanoparticles, the size of which is generally between 1 and 100 nm, have distinct physicochemical properties including high surface area, high reactivity, and penetration of biological barriers which can have unexpected toxicological implications. The paper is a critical review of the sources, routes of exposure, toxicological pathways and health effects of nanoparticles with special reference to Pakistan. The primary route of human exposure to NPs is via the respiratory, oral, dermal, and occupational exposures (particularly in urban, industrial, and agricultural settings). There is evidence to show that nanoparticles cause toxicity through oxidative stress, inflammatory effects, mitochondrial pathology, and genotoxic effects, which lead to respiratory, cardiovascular, neurological, renal, and reproductive pathologies. Rapid urbanization, traffic emissions, industries, and the growing utilization of nano-enabled agrochemicals and pharmaceuticals are some of the major contributors to engineered and incidental nanoparticle exposure in Pakistan, and distinct monitoring and regulatory systems on nanoparticles are still scarce. This review provides global-based toxicological evidence with regional exposures to bring out the most pertinent public health and occupational risks of nanoparticles in Pakistan. Lastly, it also establishes the gaps that are of interest in the research and it also highlights the urgency of nano safety regulations, exposure surveillance, and evidence-based policymaking to ensure that nanotechnology continues to develop safely and sustainably in the country.

GLACIER MELTS, MICROBIAL DYNAMICS AND ITS ENVIRONMENTAL IMPACTS ON THE CRYOSPHERE

Shahid Mahmood — 2026-03-13

The cryosphere ecosystem like glaciers, ice bergs, ice sheets, permafrost etc are undergoing swift change due to climate change. Glaciers that once known to be lifeless and dormant now considered diverse ecosystem that have variety of microbial communities and performing different activities like their role in biogeochemical cycles. The aim of this review is to provide insight in to the different factors like drivers of glacier melts including albedo reduction, increasing global temperature and black carbon deposition and analyze that how these changes influences the microbiota their functions, distribution and diversity. The metabolic activities of microorganisms affecting glacial melting. This review also emphasizes the current gaps to understand the microbial environmental interaction, spatial variability in glacial system.

THE ECOLOGICAL IMPACT OF URBANIZATION: PATTERNS, PROCESSES, AND PATHS TO SUSTAINABILITY

Shahid Mahmood — 2026-03-13

Urbanization is currently one of the most influential processes changing natural environments around the world. As human populations increase and economic activities expand, cities continue to grow outward. This expansion often occurs at the expense of natural landscapes such as forests, wetlands, grasslands, and farmland, which are frequently converted into residential areas, roads, and other forms of infrastructure. This review explores the various ways in which rapid urban growth influences natural ecosystems, focusing on key aspects such as biodiversity patterns, water systems, soil dynamics, ecological relationships, and the provision of ecosystem services. Findings from studies conducted in different regions of the world consistently demonstrate that urban development significantly alters natural habitats. One of the most immediate consequences is the loss and fragmentation of habitats, which disrupts ecological connectivity and threatens many native species. Urban environments tend to favor species that can easily adapt to human-dominated landscapes, while more sensitive and specialized organisms gradually decline or disappear. As a result, ecological communities in cities often become more uniform, a phenomenon commonly referred to as ecological homogenization. Urban expansion also has profound effects on hydrological processes. The replacement of permeable natural surfaces with impervious materials such as concrete and asphalt increases surface runoff and decreases the natural infiltration of water into the soil. This disrupts groundwater recharge and often contributes to flooding and the degradation of freshwater ecosystems. In addition, the reduction of vegetation cover and changes in soil structure diminish several vital ecosystem services, including carbon storage, climate regulation, pollination, and natural water filtration. Despite these challenges, recent research highlights several promising strategies that can help mitigate the ecological impacts of urbanization. Approaches such as the development of green infrastructure, the creation of ecological corridors, ecosystem-based urban governance, and nature-based restoration initiatives are gaining increasing attention. Furthermore, modern technologies including remote sensing, geographic information systems (GIS), and ecological modeling, provide powerful tools for monitoring environmental changes and supporting informed decision-making. Overall, the evidence suggests that although urbanization presents serious risks to natural ecosystems, its negative effects can be significantly reduced through sustainable urban planning and the integration of ecological considerations into development policies. This review aims to provide valuable insights for researchers, urban planners, and policymakers who seek to design cities that promote both human development and long-term ecological resilience.

STRATEGIES AND BARRIERS FOR GREEN AI ADOPTION: A SURVEY OF ENERGY-EFFICIENT DEEP LEARNING PRACTICES IN PAKISTAN'S TECH ECOSYSTEM

Humair Khan Bughio — 2026-03-13

The emergence of artificial intelligence (AI) has become an issue of concern because of its effect on the environment, especially with the consumption of a lot of energy in deep learning models. This paper explores the strategies and obstacles that affect the use of Green AI practices by AI professionals in the technology ecosystem in Pakistan. The survey was performed using a quantitative method in the survey of 100 software house and start up and research institutions professionals. The analysis of data was done through PLS-SEM that would facilitate the research of the relations between Sustainability Awareness, Technological Readiness, Financial Constraints, Organizational Sustainability Strategies, and Green AI Adoption. The results show that Sustainability Awareness, Technological Readiness, and Organizational Sustainability Strategies have a positive impact on adoption, whereas Financial Constraints have negative implications on implementation. The paper will offer useful suggestions to organizations, policymakers, and stakeholders to improve energy-efficient AI activities and also add to the theoretical literature on sustainable AI implementation in developing economies.

A UNIFIED BENCHMARK OF STATISTICAL, MACHINE LEARNING, AND DEEP LEARNING APPROACHES FOR S&P 500 INDEX FORECASTING

Khansa Shakeel — 2026-03-14

Financial time series forecasting remains one of the most challenging problems in quantitative finance due to the highly volatile, noisy, and non-stationary nature of financial markets. Accurate prediction of stock market indices plays a crucial role in investment decision-making, portfolio optimization, and risk management. In recent years, machine learning and deep learning techniques have gained increasing attention for financial forecasting tasks. However, the comparative effectiveness of traditional statistical models, classical machine learning algorithms, and deep learning architectures under a unified experimental framework remains insufficiently explored.

This study presents a comprehensive empirical evaluation of statistical, machine learning, and deep learning approaches for forecasting the S&P 500 stock market index. Using a dataset consisting of 25 years of daily historical data (2000–2024) including Open, High, Low, Close, and Volume (OHLCV) features, we benchmark eight forecasting models across three methodological categories. The evaluated models include ARIMA as a statistical baseline; logistic regression and support vector machines as classical machine learning methods; random forest and XGBoost as ensemble learning approaches; and deep learning architectures including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid CNN–LSTM model.

To ensure a fair comparison, all models are implemented within a unified experimental pipeline incorporating consistent preprocessing, feature normalization, rolling window segmentation, and chronological train–validation–test splitting to prevent data leakage. Model performance is evaluated using two complementary metrics: Root Mean Squared Error (RMSE) for regression forecasting and directional accuracy for classification-based prediction of market movements.

Experimental results reveal that simpler models can outperform more complex architectures in financial time series forecasting under constrained feature spaces. In particular, logistic regression achieved the highest directional accuracy of 81.96%, significantly outperforming several machine learning and deep learning models. Deep learning architectures such as LSTM and CNN–LSTM demonstrated susceptibility to overfitting and limited generalization capability when trained solely on price-based inputs. Furthermore, feature importance analysis indicates that price-related variables, particularly opening and closing prices, contribute more significantly to predictive performance than trading volume.

The findings challenge the common assumption that deep learning models consistently outperform traditional approaches in financial forecasting tasks. Instead, they highlight the importance of model simplicity, robust validation protocols, and appropriate feature selection when dealing with noisy financial data. This study provides a reproducible benchmarking framework for evaluating forecasting models and offers practical insights for researchers and practitioners developing predictive systems for financial markets. Future research may benefit from incorporating external information sources such as macroeconomic indicators, sentiment analysis, and attention-based architectures to enhance predictive capability.

THE IMPACT OF SYNTHETIC MICROFIBERS FROM LAUNDRY WASTEWATER ON SOIL INVERTEBRATE GUT MICROBIOMES AND NUTRIENT CYCLING

Shahid Mahmood — 2026-03-14

Synthetic microfibers, which are artificially produced during the process of laundry, are a major concern for soils, as a considerable portion of this synthetic material is deposited in agricultural soils via irrigation of these soils using wastewater and sewage sludge or bio-sludge. Though there has been extensive research regarding marine plastic pollution in the past, recent research has been conducted to study the physiological and ecotoxicological effects of synthetic microfibers on soil invertebrates, which are ecosystem engineers and play an essential role in maintaining the structure and fertility of the soil. This study is a systematic review of ten years of research to evaluate the relationship between microfiber exposure, microbiological dysbiosis, and nutrient cycling disorders. High consumption of microplastics results in epithelial damage, obstruction of the intestine, and decreased appetite in some invertebrates, such as earthworms belonging to the family Lumbricidae and springtails belonging to the class Collembola. Microfibers are substrates for recruitment to the solid phase in the gut of these invertebrates, leading to decreased beneficial Proteobacteria and increased Firmicutes, an indicator of inflammatory stress. Decomposition of organic matter is affected if the microbiota of the gut is altered. According to recent statistical data, which goes back to 2025, it has been observed that soils containing microfibers have shown an approximate decrease of 15% in nitrogen mineralization rates and an increase in emissions of nitrous oxide. The microfibers can transfer adsorbed heavy metals and other harmful additives to the stomachs of invertebrates through a "Trojan horse" effect, which increases oxidative stress and harms DNA. According to this study, it is absolutely necessary to conduct long-term field studies to fill the gap between scientific data and actual environmental impacts, despite the presence of dose-response relationships. The mitigation measures should also include textile engineering methods to control microfiber emissions and wastewater filtration systems to maintain significant ecosystems.

HEAVY METAL CONTAMINATION IN GROUND WATER: SOURCES, HEALTH RISKS AND REMEDIATION TECHNOLOGIES

Shahid Mahmood — 2026-03-14

Groundwater is one of the most vital sources of freshwater for drinking purposes, agricultural activities, and industrial use all over the world. Unfortunately, groundwater water quality is facing severe threats of contamination with heavy metals like arsenic, lead, cadmium, chromium, and nickel. These metals are stable in nature and cannot be degraded by biological means; in addition, they have the potential to bio accumulate in living organisms. Therefore, heavy metals pose severe threats to the environment and human health. Heavy metals can enter groundwater systems through geological and anthropogenic activities. Geological activities include natural weathering of rocks and dissolution of minerals in groundwater. Anthropogenic activities include mining activities, industrial effluent discharge into water bodies, agricultural activities, and poor waste management practices. Prolonged exposure to groundwater containing heavy metals may cause various diseases in humans, including neurological disorders, kidney damage, developmental abnormalities, and cancer. Due to low velocities and low self-purification capacity of groundwater systems, it is difficult to remove heavy metals once they have entered groundwater systems. In recent years, various technologies like adsorption, membrane filtration, ion exchange, chemical precipitation, and bioremediation have been developed to remove heavy metals from groundwater systems. This review aims to highlight major sources of heavy metal contamination in groundwater, risks to human health, and available remediation strategies, with emphasis on sustainable management practices and monitoring techniques to maintain groundwater quality.

BENCHMARKING MACHINE LEARNING MODELS FOR POWER FACTOR PREDICTION IN BINARY THERMOELECTRIC COMPOUNDS

Fawad Ali — 2026-03-14

Thermoelectric materials have the ability to directly convert waste heat into electricity, providing a sustainable energy solution. Electrical performance of these materials is determined by the power factor, which is an important parameter of the thermoelectric figure of merit. Nonetheless, high-performance binary thermoelectric compounds are sparsely found experimentally and are costly to synthesize. Machine learning has now become an influential instrument to speed up the discovery of materials, but a systematic benchmarking of algorithm currently existing in binary thermoelectric data is unavailable. In this work, we come up with a thorough machine learning benchmarking system to predict the power factor of binary thermoelectric compounds by relying on both compositional and elemental descriptors. An analysis of a large binary data set consisting of 22750 samples and 26 input variables obtained as the result of first-principles calculations was performed. Machine learning models used in predicting the power factor of cubic binary thermoelectric compounds are compared, such as linear regressors, support vector machines, tree-based ensembles, and neural networks. Following the hyperparameter tuning using randomized search, CatBoost has the highest predictive accuracy with a test R² of 0.9897, followed by Gradient Boosting (0.9869), LightGBM (0.9861), and Random Forest (0.9804). The poor performance of linear models and the support vector regressors implies the non-linearity of the structure-property relationships. The computational efficiency is evaluated by analyzing the training time of each model. The findings indicate that ensemble and gradient boosting models have a better predictive performance than linear and kernel based models. The additional study of model interpretability is conducted with the help of SHAP analysis to reveal the most significant descriptors that control the power factor prediction. Lastly, the importance of the polynomial feature expansion is examined to determine the effects of interaction between the features. The study offers a methodical reference of machine learning models to predict the power factor of binary thermoelectric materials and outlines the promising inquiry of data-driven methods in initial screening of materials.

ENHANCING WILDLIFE CONSERVATION: A DEEP LEARNING FRAMEWORK FOR ACCURATE AND REAL-TIME AMUR TIGER IDENTIFICATION

Faiqa — 2026-03-14

Modern wildlife conservation efforts are hampered by a lack of non-invasive monitoring methods for endangered species, which has generated a need for automated species identification. In this paper, we present a novel deep learning framework that integrates EfficientNetB3 with YOLOv8 for real-time detection of Amur tigers, which would improve automated detection over traditional manual tracking methods. The framework applies transfer learning to improve EfficientNetB3 to recognize tigers by their unique fur patterns and other distinctive morphological features. We generated a dataset of 1,886 images of tigers for training, and then applied multiple preprocessing techniques to increase the efficiency of the training phase (e.g., to improve the robustness of the model to variations in input data we applied resizing, normalization, and augmentation). Our model achieved a test accuracy (97.88%) and macro-average precision and recall (exceeding 95%) that demonstrates a general ability to accurately classify images in a wide range of natural environments. In addition, YOLOv8 real-time video captioning and detection functionality has been incorporated and deployed through a Streamlit web application. Our framework has the highest accuracy compared to traditional methods used for the non-invasive wildlife monitoring, and provides a new scalable approach in this field. We also support ecological research by providing a new reliable automated tool for conservationists that eliminates the need for field personnel to tag or mark animals. The system high potential for mass use in wildlife management and studying biodiversity can be seen from its high performance and ease of use.

MACHINE LEARNING-BASED TIME SERIES MODELING FOR CLIMATE CHANGE VARIABILITY ANALYSIS

Dr. Arzoo Kanwal — 2026-03-14

Climate change has intensified the need for accurate analytical approaches capable of identifying long-term temperature trends and improving climate prediction. This study proposes a machine learning–based time-series modeling framework for analyzing global temperature anomaly data and forecasting climate variability. The dataset consists of monthly temperature anomalies spanning more than a century, providing a comprehensive record for examining historical climate patterns and long-term warming dynamics. Several feature engineering techniques were applied to enhance the predictive capability of the dataset, including lag variables, rolling statistical indicators, exponential moving averages, and difference transformations. These engineered features capture temporal dependencies, seasonal variations, and long-term climate trends within the time series. Multiple predictive models were implemented to evaluate forecasting performance, including ARIMA, Random Forest, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer architectures. Model performance was assessed using standard evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results demonstrate that deep learning models, particularly Transformer and GRU architectures, outperform traditional statistical methods in capturing complex temporal relationships in climate data. The findings confirm the effectiveness of advanced machine learning techniques for modeling climate variability and improving predictive accuracy. This research contributes to climate data analysis by providing a comprehensive framework that integrates feature engineering and comparative machine learning modeling for long-term temperature forecasting.

AI-DRIVEN SMART TRAFFIC MANAGEMENT FOR URBAN CITIES IN PAKISTAN: INTEGRATING REAL-TIME DATA, PREDICTIVE ANALYTICS, AND IOT SENSORS

Dr. Eram Abbasi — 2026-03-14

Rapid urbanization and increasing vehicle ownership have significantly intensified traffic congestion in major Pakistani cities such as Karachi, Lahore, and Islamabad. Conventional traffic control systems based on fixed-time signals are inadequate for managing dynamic traffic flows. This study proposes an AI-driven smart traffic management framework that integrates real-time data acquisition, predictive analytics, and Internet of Things (IoT) sensors to optimize urban traffic operations. The system collects high-frequency data from CCTV cameras, GPS-enabled vehicles, roadside IoT sensors, and mobile traffic platforms, enabling continuous monitoring of traffic density, vehicle speed, and intersection performance. Machine learning algorithms, including Long Short-Term Memory (LSTM) networks and reinforcement learning models, are applied to predict congestion patterns and dynamically adjust traffic signal timings. Simulation-based evaluation using urban traffic datasets indicates that AI-enabled adaptive signal control can reduce average vehicle delay by approximately 25–30%, decrease intersection waiting time by 20%, and improve overall traffic throughput by nearly 30% compared with conventional fixed-time traffic systems. Furthermore, real-time traffic analytics combined with sensor-based vehicle detection can significantly enhance emergency vehicle prioritization and reduce fuel consumption and CO₂ emissions in congested corridors. The proposed architecture integrates edge computing and cloud-based analytics to process large-scale traffic data with minimal latency, enabling proactive congestion management and intelligent decision-making. The findings highlight that AI-IoT–based intelligent transportation systems provide a scalable and sustainable solution for improving urban mobility, road safety, and environmental performance in rapidly growing cities of Pakistan.

CLIMATE CHANGE SOURCES, IMPACTS, MITIGATION, AND ADAPTATIONS IN ASIA

Shahid Mahmood — 2026-03-14

CLIMATE CHANGE, ALTITUDE SHIFT AND VIRUS EMERGENCE: A REVIEW ON PTEROPUS BATS IN HIMALAYAN REGION

Shahid Mahmood — 2026-03-14

A SMART ZERO TRUST SECURITY FRAMEWORK TAILORED FOR MODERN SMES: A REVIEW PAPER

Muhammad Luqman Rasheed — 2026-03-15

Small and Medium Enterprises (SMEs) face in- creasingly sophisticated cyberattacks due to inadequate security infrastructure, limited budgets, and lack of cybersecurity exper- tise. Traditional perimeter-based security models are inadequate for the era of cloud computing, remote working, and BYOD. A new security paradigm that has promise through zero trust architecture (ZTA) which operates under the principle of never trust, always verify has proven to be effective. It is a review of the existing Zero Trust models, their applicability to an SME and the challenges related to adopting it. In addition, it addresses how smart and automated Zero Trust tools, including AI-driven policy and behavioral controls have emerged in recent years. As per the literature reviewed, the paper represents the need to develop the Zero Trust framework that is affordable, scalable, and easy to deploy and specifically oriented to the SMEs. The paper reveals the major gaps in the research and proposes further research opportunities in developing practical Zero Trust solutions that will be accessible to resource-strained organizations.

HYBRID BLOCKCHAIN ARCHITECTURE FOR SECURE AND SCALABLE IOT DATA MANAGEMENT

Muhammad Saleh Shah — 2026-03-16

Massive emergence of Internet of Things (IoT) environments has aggravated the problems in terms of safe and scaled and efficient data administration. To address the weaknesses posed by the traditional centralized blockchain and single layer systems, the paper will show a hybrid blockchain architecture that integrates the public and private blockchain layer. The prototype of sensitive data management was implemented on the permissioned blockchain and the prototype of integrity validation on the public blockchain. It is experimentally evaluated because the suggested architecture improves the data processing speed by 40 percent, reduces the storage costs by 30 percent, and the authenticity of data by 99.9 percent. The analysis of security demonstrates that the 95 percent successful cyber-attack is mitigated in connection with the centralized IoT systems. The fact that scalability testing with higher density of devices demonstrated that this system can only increase its latency by 12 percent as the number of connected devices increase 50,000-fold, however, the sheer existence of the system demonstrates its soundness. To obtain the holistic analysis, Hybrid Blockchain Effectiveness Index (HBEI) was developed that comprised of four dimensions of security (0.40), scalability (0.30), data management (0.20), and interoperability (0.10) with the overall effectiveness score being 0.91. An economic analysis reveals that the investment is 4 times paid, which shows the economic sustainability of the utilization of hybrid blockchain. The findings confirm that hybrid blockchain infrastructure offers a moderate solution regarding decentralization, performance effectiveness, and enhanced security. The investigation offers a numerical data that can be referenced to the introduction of the hybrid blockchain integration as an effective and cost-effective approach in the future to manage the IoT data infrastructure in smart cities, healthcare, and IoT applications in the industries.

COMPONENT-BASED SOFTWARE ENGINEERING FRAMEWORK TO INCREASE THE ACCURACY OF SOFTWARE COST ESTIMATION

Shabir Ahmad — 2026-03-16

This study explores the identification and assessment of reusable factors and variables that have a major impact on software cost estimation (SCE) by using the framework of Component-Based Software Engineering (CBSE). When used in component-driven settings, traditional estimation methods—in particular, object-oriented methods based on Lines of Code (LOC), function points and class metrics—display significant limitations. Although algorithmic models like COCOMO have been widely used, cost estimation that is specifically suited to CBSE has received little scholarly study. This framework treats time as the primary independent variable and suggests an organized process to identify and statistically validate essential parameters. The study determines the most important factors influencing software cost variation using survey-based data collecting and statistical analysis using SPSS, including factor and communalities analysis.

MOLECULAR DESIGN, SYNTHESIS, CHARACTERIZATION AND ANTIBACTERIAL EVALUATION OF (2E)-1-(CYCLOHEXYL(PHENYL)METHYLENE)-2-(2-METHOXYBENZYLIDENE) HYDRAZINE

Najam Ud Din Jamali — 2026-03-16

The Schiff base derivatives have drawn a lot of interest in the field of medicinal chemistry due to their diverse biological activities and versatility in structure. In the current work, (2E)-1-(cyclohexyl(phenyl)methylene)-2-(2-methoxybenzylidene)hydrazine which is a hydrazone derivative of a Schiff base has been synthesized through condensation reaction of (Z)-(cyclohexyl(phenyl)methylene)hydrazine with 2-methoxybenzaldehyde under reflux conditions in ethanol. The product of the synthesis was obtained in 72 % yield and was obtained in the form of brown crystalline solid with a melting point of 178 ^oC. The ¹H NMR and FT-IR spectroscopic methods were used to confirm the molecular structure of the compound. The property of the synthesized compound as an antibacterial agent was tested against Staphylococcus aureus (Gram-positive) and Escherichia coli (Gram-negative) by the agar well diffusion technique. Ciprofloxacin was used as a positive control, whereas dimethyl sulfoxide (DMSO) served as the negative control. The compound formed had a moderate antibacterial activity with 14.0 ± 0.5 mm and 12.0 ± 0.6 mm being the inhibition zones with Staphylococcus aureus and Escherichia coli respectively. Comparatively, the standard antibiotic, ciprofloxacin, had larger areas of inhibition 26.0 ± 0.4 mm and 24.0 ± 0.5 mm, respectively, whereas the negative control did not have an antibacterial effect. The reason why the Gram-positive bacterium is slightly more susceptible to the activity could be the variation in the structural composition of bacterial cell walls. The findings indicate that the synthesized hydrazone derivative of Schiff base has good antibacterial activity and could be used as a useful scaffold in designing new biologically active compounds.

MULTI-OBJECTIVE OPTIMIZATION MODEL OF BATTERY SWAPPING STATIONS TO MINIMIZE COST AND BATTERY DEGRADATION

Sher Muhammad Ghoto — 2026-03-17

Battery Swapping Stations (BSS) offer rapid energy exchange for electric vehicles while functioning as flexible grid assets. This study develops a multi-objective optimization framework utilizing Model Predictive Control (MPC) with convex programming (CVXPY) to balance electricity procurement costs against battery health. We employ a Linearised Throughput Penalty, calibrated from the Wöhler curve at 80% Depth of Discharge (DOD), to serve as a convex proxy for electrochemical degradation. The system is controlled via a 24-hour receding horizon simulated over a 168-hour (one week) operational period to capture diurnal load variances. Cost sensitivity analysis reveals that a conservative degradation penalty (????= $0.05/kWh) creates a robust trade- off, achieving a net weekly revenue of $160.08 while limiting Equivalent Full Cycles (EFC) to Preserving levels. Furthermore, V2G regulatory analysis quantifies the impact of export restrictions: prohibiting grid back-feeding generates an opportunity cost of ~$295/week due to curtailed renewable generation. The CVXPY solver demonstrates varying performance based on horizon length, achieving 45 ms computation time for the 24-hour control loop, validating suitability for real-time deployment.

A STUDY ON THE APPLICATION OF DEEP LEARNING AND MACHINE LEARNING IN ADAPTIVE INTELLIGENT TUTORING

Khowla Khaliq — 2026-03-17

The Intelligent Tutoring Systems (ITS) are now regarded as one of the pillars of modern educational technology, offering a personalized experience in education, adapting to the needs of individual students. The paper addresses the use of ML algorithms in adaptive ITS and how they can be used to create better learning experiences, student interactions, and system efficiency. The paper will examine various machine learning methods, including supervised learning, reinforcement learning, and neural networks, which have been used in ITS. Also, it covers the issues, assessment techniques, and research directions of adaptive intelligent tutoring systems. The Intelligent Tutoring Systems (ITS) are an essential factor in providing individualized education with the help of adaptive learning technologies. Modern ITS has been greatly improved in adaptability and intelligence due to the combination of Machine Learning (ML) and Deep Learning. This paper examines the application of supervised, unsupervised, reinforcement, and deep learning methods in adaptive ITS. Decision Trees, SVM, and Random Forests are supervised models that are applicable in predicting student performance with an accuracy of up to 90. Unsupervised algorithms such as K-Means clustering can be used to define the behavioral patterns of learners to aid in personalized teaching. Reinforcement Learning is a process of maximising tutoring strategies by the use of reward-based policy improvements. Deep Learning methods, especially Deep Knowledge Tracing using LSTM, are effective in structuring temporal learning styles with a greater AUC of more than 0.85. The study puts emphasis on student modeling, involving knowledge tracing and learner profiling, in adaptive systems. Adaptive techniques are reviewed, such as dynamic feedback, content sequencing, which is personal, and real-time assessment. An integrated ITS architecture, based on the use of ML, includes student, tutoring, and analytics modules. The measures of evaluation are the learning gain, the rate of engagement, and the accuracy of prediction (RMSE, MAE). 20-25 percent learning increase by ML-based adaptive conditions when applied. Issues with implementation, such as data privacy and real-time scalability, are also discussed in the study. Afterwards Explainable AI model and multiple models for further personalization are used. All in all, ML-driven ITS proves to have great opportunities of scalable, data-driven, and exceptionally personalized educational system.

DENSITY FUNCTIONAL THEORY ANALYSIS OF LI-DOPED RbMgF3 FOR X-RAY DOSIMETRY AND SENSOR APPLICATIONS

Ramiz Khan — 2026-03-17

Perovskite materials have attracted considerable attention in recent years due to their remarkable structural flexibility and wide range of applications in optoelectronic and photonic devices. Among these materials, fluoride-based perovskites exhibit excellent chemical stability, wide band gaps, and strong optical responses, making them promising candidates for advanced technological applications. In the present study, a comprehensive theoretical investigation of Li-doped RbMgF₃ perovskite is carried out using first-principles calculations based on density functional theory (DFT). The aim of this work is to explore the influence of lithium doping on the structural stability, electronic characteristics, mechanical behavior, optical properties, and X-ray diffraction patterns of the RbMgF₃ crystal. The structural properties of Li-doped RbMgF₃ are first analyzed through geometry optimization using the generalized gradient approximation (GGA) within the plane-wave pseudopotential framework. The optimized lattice parameters confirm that the compound maintains a stable cubic perovskite structure after lithium incorporation. The calculated total energy minimization demonstrates the thermodynamic stability of the doped system. To further evaluate the mechanical stability of the material, the elastic constants are computed. The obtained elastic parameters satisfy the Born mechanical stability criteria for cubic crystals, confirming that Li-doped RbMgF₃ is mechanically stable. Additional mechanical parameters such as bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio are derived from the elastic constants, providing deeper insight into the mechanical strength and ductility of the compound.The electronic properties of Li-doped RbMgF₃ are investigated through band structure calculations and density of states (DOS) analysis. The calculated electronic band structure reveals that the compound exhibits semiconducting behavior with a wide band gap. The total and partial density of states indicate that the valence band is mainly dominated by fluorine states, while the conduction band is primarily contributed by magnesium and rubidium orbitals. The incorporation of lithium introduces slight modifications in the electronic states, which may influence the electrical and optical performance of the material. These electronic characteristics suggest that Li-doped RbMgF₃ can be considered a suitable candidate for optoelectronic and ultraviolet optical devices. Furthermore, the optical properties of the compound are examined by calculating the complex dielectric function and related optical parameters, including refractive index, absorption coefficient, reflectivity, and optical conductivity. The results indicate strong optical absorption in the ultraviolet region and low absorption in the visible range, which is desirable for UV optoelectronic applications. The refractive index and reflectivity spectra also show stable optical behavior across a wide energy range, highlighting the potential of this material for photonic and optical device applications. Finally, simulated X-ray diffraction (XRD) patterns are generated to verify the crystalline structure and phase stability of Li-doped RbMgF₃. The XRD peaks correspond well with the characteristic reflections of the cubic perovskite structure, confirming the preservation of the crystal symmetry after lithium doping. Overall, the present theoretical investigation demonstrates that Li-doped RbMgF₃ possesses favorable structural stability, mechanical robustness, and promising electronic and optical properties. These findings suggest that this material could be a potential candidate for future applications in optoelectronic, photonic, and ultraviolet device technologies.

DEVELOPMENT AND EVALUATION OF PHOTOCATALYTIC AND ANTIMICROBIAL NANOPARTICLES FOR ENHANCED REMOVAL OF PATHOGENS AND ORGANIC POLLUTANTS IN ADVANCED WATER TREATMENT SYSTEMS

Syed Sajjad Hussain — 2026-03-17

Access to safe and clean water remains a critical global challenge due to the increasing presence of organic pollutants and pathogenic microorganisms in water sources. Conventional treatment methods often fail to completely remove these contaminants, necessitating the development of innovative, efficient, and sustainable solutions. This study focuses on the development and evaluation of multifunctional nanoparticles combining photocatalytic and antimicrobial properties for enhanced water treatment. Titanium dioxide (TiO₂) and zinc oxide (ZnO) nanoparticles were synthesized via sol–gel methods, while metallic nanoparticles such as silver (Ag) were prepared through chemical reduction and integrated with metal oxides to form stable nanocomposites. Characterization using SEM, TEM, XRD, FTIR, UV–Vis spectroscopy, and BET analysis confirmed favorable morphology, crystallinity, surface functionalization, and extended light absorption in the visible range. Photocatalytic studies demonstrated that composite nanoparticles achieved up to 90% degradation of model organic pollutants such as methylene blue and rhodamine B under UV and visible light, significantly outperforming pure metal oxides. Antimicrobial evaluation against E. coli, S. aureus, and P. aeruginosa showed substantial inhibition and bacterial inactivation through reactive oxygen species generation and membrane disruption. Bench-scale water treatment tests revealed significant reductions in COD, BOD, turbidity, and microbial load, with immobilized nanoparticles retaining high performance over multiple cycles. The synergistic combination of photocatalytic and antimicrobial functions in these nanocomposites presents a promising, sustainable, and energy-efficient strategy for simultaneous chemical and biological water purification.

ENHANCED MECHANICAL AND PHYSICAL PROPERTIES OF CONVENTIONAL GLASS IONOMER CEMENT BY ADDING ZIRCONIUM NANOPARTICLES

Muhammad Touheed Ul Hassan — 2026-03-17

This study investigated the enhancement of the mechanical properties of conventional glass ionomer cement (GIC) through the incorporation of zirconium dioxide (ZrO₂) nanoparticles to overcome its inherent limitations in strength and durability. ZrO₂ nanoparticles were added at concentrations of 4%, 6%, and 8% to evaluate their effects on tensile strength, hardness, and structural resilience. Advanced characterization techniques were employed to examine the modified materials. X-ray diffraction (XRD) analysis assessed crystalline structure and phase composition, energy-dispersive X-ray spectroscopy (EDX) confirmed elemental composition and nanoparticle dispersion, Fourier-transform infrared (FTIR) spectroscopy identified chemical interactions between ZrO₂ and the GIC matrix, and scanning electron microscopy (SEM) evaluated microstructural changes. The results demonstrated a significant improvement in mechanical properties following nanoparticle incorporation, with the 6% ZrO₂ concentration exhibiting the highest Young’s modulus during tensile testing. Enhanced stress tolerance and structural integrity suggested improved durability and potential longevity of dental restorations. Overall, the findings indicated that ZrO₂ nanoparticle reinforcement substantially improved GIC performance, highlighting the potential of nanotechnology in advancing restorative dental materials.

DEEP LEARNING AND ENSEMBLE MODELS FOR AUTOMATED ECG ARRHYTHMIA CLASSIFICATION

Khaliq Ahmed — 2026-03-17

Electrocardiography (ECG) serves as the primary diagnostic method which medical professionals utilize to identify heart problems and track cardiac function. The process of reading ECG signals requires extended time from doctors because it involves extensive data analysis which increases the probability of mistakes. The progress of artificial intelligence and deep learning technology has led to the creation of automated systems which can identify cardiac arrhythmias with exceptional precision. This research develops an automated ECG beat classification system which uses deep learning to process ECG signals through hybrid systems that employ convolutional neural networks (CNNs) and bidirectional long short-term memory networks (BiLSTM) and attention mechanisms and residual learning methods. The ECG dataset which the system uses includes four different heartbeat types: Normal (N) and Supraventricular Ectopic Beat (SVEB) and Ventricular Ectopic Beat (VEB) and Fusion Beat. Each heartbeat segment contains 160 normalized signal samples which represent the complete ECG waveform. The dataset creates training and validation and testing sets by using stratified sampling method which ensures equal class distribution across all groups. The research team tested three different deep learning models which they designed to compare with three different systems: ECG-ResNet and CNN-BiLSTM and CNN-BiLSTM with Attention. The research findings prove that hybrid systems achieve superior performance because they successfully capture both the spatial and temporal patterns present in ECG data. The CNN-BiLSTM-Attention model achieved the highest accuracy of approximately 97%, followed by CNN-BiLSTM with about 96%, while ECG-ResNet achieved around 92% accuracy. The assessment of performance used three main metrics which included accuracy and precision and recall.

COMPARATIVE EVALUATION OF DEEP LEARNING AND TRADITIONAL MACHINE LEARNING CLASSIFIERS ON DUAE-REDUCED HIGH-DIMENSIONAL GENE EXPRESSION DATA FOR HEAD AND NECK SQUAMOUS CELL CARCINOMA

Aneela Nargis — 2026-03-18

High-dimensional transcriptomic datasets present a major challenge for robust cancer classification because the number of measured genes substantially exceeds the number of available samples, making dimensionality reduction a critical preprocessing step before downstream predictive modeling. In this study, a Deep Under-complete Autoencoder (DUAE) was employed to compress high-dimensional Head and Neck Squamous Cell Carcinoma (HNSCC) gene expression data while preserving discriminative structure for classification. Gene expression data were obtained from The Cancer Genome Atlas (TCGA), and following preprocessing, the DUAE-reduced feature representation was evaluated using four traditional machine learning classifiers, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting Machine (GBM), and four deep learning architectures, WideResNet, DenseNet, VGG, and EfficientNet. Model performance was assessed using accuracy, area under the receiver operating characteristic curve (ROC-AUC), precision, recall, and F1-score. Among all evaluated models, WideResNet achieved the strongest overall performance, with an accuracy of 0.970, ROC-AUC of 0.990, precision of 0.960, recall of 0.950, and F1-score of 0.955, followed by VGG, which also demonstrated strong and balanced classification performance. Among the traditional machine learning baselines, GBM and Random Forest remained competitive, whereas SVM and KNN showed comparatively lower performance. DenseNet and EfficientNet demonstrated moderate predictive capability but did not match the stronger performance profiles of WideResNet and VGG. Overall, the findings indicate that DUAE-based feature compression preserved biologically relevant signal for downstream classification, while the final predictive performance remained strongly dependent on classifier architecture. These results suggest that deep residual learning, particularly WideResNet, may offer substantial advantages for classification of DUAE-reduced gene expression data in HNSCC, and support the use of DUAE-driven representation learning as a practical and effective preprocessing strategy for high-dimensional genomic classification tasks.

MACHINE LEARNING IN THERMO-ELECTROCHEMICAL CELL MATERIAL IDENTIFICATION: FEATURE SELECTION AND ANALYSIS

Riaz Muhammad — 2026-03-18

This research shows the use of Machine Learning models to predict the seebeck coefficient, feature selection, and analysis of ionic thermoelectric material using different feature selection strategies. The approach comprises data collection of ionic thermoelectric material including (matrix + ion donor) combinations with features and target (seebeck coefficient) from different publish papers. Applying different feature selection strategies with cross validation mean absolute error and mean square error to determine optimum feature subset which improve accuracy and generalization of model. Subsequently multiple models were trained on each respective feature subset. To evaluate their performance Decision Tree model was the best model exhibit high R² and low mean absolute error and root mean square error trained on univariate selected feature subset. It is revealed that seeebck coefficient is dominated over few strong predictors, adding more features reduce the accuracy of model and introduce noise or overfitting. This finding also expose that reduction of features significantly accelerates the discovery of matrix, ion donor combinations for thermoelectrochemical cell. Further to achieve additional superior robustness and generalization the top selected model was subjected to hyper parameter optimization process. SHapley Additive exPlanations (SHAP) and correlation analysis was performed to interpret model behavior, determined most influential features, and relationship between features and target seebeck coefficient. FractionCSP3 of the matrix and NumRotatableBonds of the ion donor were identified the most important features using SHAP analysis. It is also found that FractionCSP3 of the matrix show positive correlation, while NumRotatableBonds of the ion donor exhibit negative correlations with seebeck coefficient. The Decision Tree models trained on univariate selected features predicted many promising combinations, especially polyurethane-based, cellulose-based, and PVA-based along with gelatin ionogel, and PAM hydrogel systems with predicted Seebeck coefficients up to 42.8 mV/K.

THE ROLE OF ARTIFICIAL INTELLIGENCE IN STRATEGIC MANAGEMENT DECISIONS

Kirshan Kumar Luhana — 2026-03-18

Artificial Intelligence (AI) is increasingly transforming organizational decision-making processes by enabling firms to analyze complex data and generate predictive insights that support strategic planning. Despite the rapid growth of AI adoption in business environments, limited empirical research has examined its direct influence on strategic management decision quality. This study investigates the role of AI adoption in enhancing strategic decision-making within organizations while considering the influence of digital capability and environmental uncertainty. A quantitative research design was employed using a dataset of 300 firms representing diverse organizational contexts. Descriptive statistics, correlation analysis, and multiple regression techniques were applied to examine the relationships among AI adoption intensity, digital capability, environmental uncertainty, and strategic decision quality. The findings indicate that AI adoption has a significant positive effect on the quality of strategic management decisions, suggesting that organizations utilizing advanced AI-driven analytical tools demonstrate improved decision accuracy and strategic responsiveness. Furthermore, digital capability strengthens the effectiveness of AI implementation, while environmental uncertainty moderates the relationship between AI adoption and decision outcomes. The study contributes to the strategic management literature by providing empirical evidence on the strategic value of AI technologies and highlighting the importance of organizational capabilities in enabling effective AI-driven decision-making.

DEEP LEARNING APPROACHES FOR SECURITY THREAT DETECTION AND MITIGATION IN INTERNET OF THINGS ENVIRONMENTS

Taib Ali — 2026-03-19

Background: The high-speed development of the Internet of Things (IoT) has had an important impact on the contemporary digital ecosystem, as it allows to interconnected smart devices in healthcare, industry, transportation, and smart cities. Nonetheless, IoT, environments are extremely susceptible to cyber-attacks because of limited resources, heterogeneous architectures, and weak authentication systems. Objective: The paper presents a hybrid deep learning model that can effectively detect and mitigation of security threats in IoT environment. Methodology: An experimental research design was chosen as quantitative. It created and tested a hybrid CNN-LSTM model using the IoT-23 dataset. Performance was compared to the conventional machine learning algorithms (SVM, Random Forest) and single models of deep learning (CNN, LSTM). The metrics used in evaluation were Accuracy, Precision, Recall, F1-score, and ROC-AUC. Results: The proposed CNN-LSTM model reached an accuracy of 98.4%, which was higher than comparative models. It showed better recall and F1-score in the detection of botnet, DDoS and malware-based IoT attacks. Conclusion: Hybrid deep learning architectures can improve the performance of IoT threat detectors to a considerable extent and provide real-time mitigation strategies.

ENHANCING DIAGNOSTIC PRECISION: A DEEP LEARNING APPROACH TO AUTOMATED DENTAL CARIES DETECTION IN BITEWING RADIOGRAPHS

Alishba Farrukh — 2026-03-19

Dental Cavities are one of the most common conditions pertaining to oral health, which affect all age groups across the global population. If the condition is not diagnosed in time, the consequences can cause severe complications, including but not restricted to the loss of teeth, infections, and higher costs of treatment. The currently existing technique for diagnosing dental conditions in clinics depends greatly upon the clinical inspection of the dentist and the evaluation of radiographic images, which can be rather time-consuming and impersonal. Also, in less advanced areas, there might not be easy access to competent dentists. This work introduces SmileScan: a web-based AI system developed. It is intended for the automatic detection of dental cavities from dental images using deep learning. The proposed work is interested in the binary classification problem: classifying the image into the cavity class or the non-cavity class. A MobileNetV2 model fine-tuned on a customized dental dataset is utilized. Methods of image preprocessing and augmentation are employed.The deployed model uses the Django backend and the React frontend and helps users upload dental images, obtaining an immediate result set with confidence levels. Experiments show the proposed system reliably works at an accuracy level of 85.71% and helps implement real-time cavity identification. The smile scan system represents an inexpensive and scalable resource that could aid in the early diagnosis and effective delivery of dental services.

A HUMAN-CENTERED AND EXPLAINABLE AI FRAMEWORK FOR LIFECYCLE-ORIENTED SOFTWARE PROJECT MANAGEMENT

Arslan Iftikhar — 2026-03-19

Artificial intelligence (AI) has become an emerging trend in software project management (SPM) with the ability to provide data-driven guidance in the planning, estimation, risk assessment, and decision-making processes. Although the current research on the application of AI-based tools and methods in project settings is getting more and more comprehensive, the current literature is widely dispersed and concerns only individual project management functions instead of integrating lifecycle-wide. The paper is structured as a literature review that conducts a critical discussion of the role of AI in the various stages of a software project management. The review summarizes the outcomes of recent empirical and conceptual research on the topic to establish the current areas of application, such as effort estimation, schedule optimization, risk prediction, resource allocation, and AI-assisted decision support in agile environments. The review shows that AI-based solutions exhibit a positive change in their accuracy in forecasting, efficiency in management, and preventive risk reduction. Nevertheless, there are still major issues such as the lack of explainability, data dependency, organizational resistance, and a lack of focus on governance and adaptability to context. The review has pointed out that there is a need to have a comprehensive and human-based integration framework that balances technical intelligence alongside managerial control and organizational viability. This study, through the consolidated evidence and the existence of gaps in the research, offers a systematic basis on which a future study of lifecycle-integrated and explainable AI-driven software project management could be carried out.

INTEGRATED RADAR-COMMUNICATION SYSTEMS: A UNIFIED MIMO PLATFORM APPROACH

Ebrahim Awadh Hassan — 2026-03-19

This research presents a comprehensive study of a unified MIMO-based radar-communication (RadCom) platform that utilizes Orthogonal Frequency Division Multiplexing (OFDM), aimed at addressing the growing need for multifunctional systems that can simultaneously detect, track, and communicate information using shared hardware and spectral resources. Frequency Shift Keying (FSK) is employed to embed communication data into radar emissions, preserving the radar’s mainlobe integrity while utilizing sidelobe regions for communication. This method is designed to counteract jamming and ensure reliable data delivery even in contested environments. Additionally, a time-domain strategy is introduced using radar rest mode for continuous communication transmission. Performance evaluation is conducted through comprehensive simulations using metrics such as Bit Error Rate (BER), Signal-to-Noise Ratio (SNR), Cramer-Rao Lower Bound (CRLB), and communication throughput per Pulse Repetition Interval (PRI). The results demonstrate that the proposed dual-mode transmission strategy significantly improves data throughput and system resilience, with minimal impact on radar accuracy and resolution. Furthermore, the system enables multi-user connectivity through independently modulated sidelobes and adaptive beampattern control.

INTEGRATING COMPUTATIONAL FLUID MECHANICS AND ARTIFICIAL NEURAL NETWORKS FOR PREDICTING FLUID–STRUCTURE INTERACTIONS IN ADVANCED MECHANICAL MATERIALS

Ghulam Murtaza — 2026-03-20

Fluid–structure interaction (FSI) plays a critical role in advanced mechanical materials used in aerospace, biomedical devices, energy systems, and high-performance engineering applications. Conventional computational fluid dynamics (CFD) and structural mechanics approaches provide accurate predictions of coupled multiphysics behavior; however, they are computationally expensive for nonlinear and transient problems. Recent advances in machine learning and neural surrogate modeling offer promising alternatives for improving computational efficiency while maintaining predictive accuracy.

This study proposes a hybrid computational framework that integrates computational fluid mechanics with artificial neural networks (ANNs) for efficient prediction of FSI responses. The framework utilizes CFD-based simulations for data generation and ANN-based surrogate models to learn complex nonlinear relationships between input parameters and FSI outputs, including displacement, stress distribution, and pressure interactions.

The results show that the ANN model achieves high prediction accuracy with a coefficient of determination (R²) of approximately 0.97 and low error metrics, while maintaining strong agreement with simulation results. Furthermore, the proposed approach reduces computational time from several hours required by conventional CFD–FSI simulations to near-instant predictions, enabling efficient large-scale analysis.

The integration of computational fluid mechanics and artificial intelligence provides a reliable, scalable, and computationally efficient solution for modeling complex FSI systems in advanced mechanical materials.

UTILIZATION OF CONSTRUCTION AND DEMOLITION WASTE (CDW) IN SUSTAINABLE CONCRETE PRODUCTION IN CONSTRUCTION INDUSTRY OF PAKISTAN

Rohan Ahmed — 2026-03-20

Pakistan's escalating solid waste crisis, particularly in Karachi with its annual generation of 49.6 million tons, necessitates the adoption of circular economy principles in construction. This study investigates the feasibility of substituting natural coarse aggregate with Construction and Demolition Waste (CDW) comprising crushed concrete, bricks, and tiles in concrete. Using a 1:2:4 mix design with a constant 0.5 water-cement ratio, six replacement levels (0–100%) were evaluated for compressive strength at 7 and 28 days. Results revealed a nonlinear strength response, with 40% CDW replacement yielding optimal performance (33.12 MPa at 28 days), surpassing the control mix (28.5 MPa). This enhancement is attributed to internal curing from the porous recycled materials. Beyond 60% replacement, however, strength declined due to increased porosity and weakened interfacial transition zones, with 100% replacement achieving only 22.11 MPa. Notably, the 80% replacement mix exhibited the highest strength gain (72.4%) between 7 and 28 days, suggesting potential for non-structural applications. The study concludes that 40% CDW replacement offers an optimal balance between structural integrity and sustainability, providing a viable pathway for waste reduction and resource optimization in Karachi's construction sector.

A CONSISTENCY-AWARE PERSPECTIVE ON EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR FEATURE SELECTION IN SOFTWARE ENGINEERING: A CRITICAL REVIEW AND FRAMEWORK

Adam Khan — 2026-03-18

Explainable Artificial Intelligence (XAI) is become essential to enhance transparency, interpretability and trust in Machine Learning (ML) models in Software Engineering (SE). Although model-agnostic approaches such as Local Interpretable Model-Agnostic Explanations (LIME) or SHapley Additive exPlanations (SHAP) and Permutation Feature Importance (PFI) are increasingly popular for prediction interpretation, their effectiveness in assessing Feature Selection (FS) is an issue of serious concern. Specifically, the ranking of feature importance produced by these methods is often unstable across changes in datasets, model configurations, and validation techniques, and has less practical application in SE decision-making. This study presents a critical and thematic review of XAI methods for FS in SE , with particular emphasis on the explanation consistency. Unlike prior studies, it methodologically examines the shortcomings of current methods in terms of consistency. On the basis of the identified research gaps, we propose the CFXAI-SE framework (Consistent Feature eXplainable AI for Software Engineering). The framework combines dataset perturbation, multi-model analysis and statistical consistency analysis to produce a consistent and reliable feature importance ranking. The findings reveal that consistency is largely unexplored aspect in XAI studies for SE. The proposed framework suggested a systematic context in building reliable, interpretable, and reproducible ML systems. This study contributes to advancing dependable XAI implementation in SE applications, such as defect prediction and effort estimation.