Spectrum of Engineering Sciences

MACHINE LEARNING PREDICTION OF SHRINKAGE CRACKING BEHAVIOUR IN ULTRA-HIGH-PERFORMANCE CONCRETE UNDER RESTRAINED CURING CONDITIONS IN BRIDGE DECK SLABS: A COMPREHENSIVE REVIEW

Mon, 04 May 2026 00:00:00 +0500

Ultra-high-performance concrete (UHPC) is increasingly utilized in bridge deck slabs due to its superior mechanical properties and durability. However, its high autogenous shrinkage and the resulting risk of early-age cracking, especially under restrained curing conditions, present significant challenges for long-term structural integrity. Recent advances in machine learning (ML) have enabled more accurate prediction and understanding of shrinkage and cracking behaviors in UHPC, facilitating optimized mix designs and mitigation strategies. This review synthesizes over 100 recent studies on ML-based prediction of shrinkage cracking in UHPC bridge decks, focusing on quantitative model performance, influential material parameters, experimental validation, and practical engineering implications. Ensemble models such as XGBoost, Random Forest, and hybrid approaches consistently achieve high predictive accuracy (R² values up to 0.99), with feature importance analyses highlighting the roles of water-to-binder ratio, fiber content, curing regime, and supplementary cementitious materials. The integration of explainable AI methods (e.g., SHAP) has improved model transparency and practical adoption. Despite these advances, challenges remain regarding data scarcity for field-scale applications and the need for robust models that generalize across diverse environmental conditions. This review concludes with recommendations for future research directions to further enhance the reliability and applicability of ML-driven predictions for UHPC bridge infrastructure.

DEVELOPMENT OF FPGA-BASED FRACTIONAL-ORDER PID CONTROLLER MODULE FOR AUTONOMOUS ROBOTS USING FOR THE APPLICATION INDUSTRY 4.0

Mon, 04 May 2026 00:00:00 +0500

Fractional-order PID controller plays an important role in various industrial applications. The FOPID is the expansion of the conventional PID controller based on fractional calculus. PID controller regulates temperature, flow, pressure, speed, and other process variables in industrial control systems. Therefore, this research intends to design and implement the real-time FPGA-based FOPID controller, which is complex regarding memory issues and energy consumption. Thus, this research uses the hybridized technique of fixed- and floating-point approach using Matlab and Xilinx Vivado. The proposed design is realized on a Zynq-7000 FPGA board, and the performance is improved in terms of dynamic range, speed, unlimited use of resources, efficiency, and less energy consumption.

AI-DRIVEN CYBER THREAT INTELLIGENCE FOR CRITICAL INFRASTRUCTURE PROTECTION IN PAKISTAN: A DEEP LEARNING APPROACH

Muhammad Shahbaz, Syed Ahmed Ali, Sameen Amjad, Rida Zafar, Muhammad Irfan Aslam — Mon, 04 May 2026 00:00:00 +0500

The increasing digitization of critical infrastructure systems in Pakistan has significantly expanded the attack surface for sophisticated cyber threats, including advanced persistent threats, ransomware, and zero-day exploits. Traditional rule-based cybersecurity mechanisms are increasingly insufficient to address these evolving and complex threats. This study proposes an AI-driven Cyber Threat Intelligence (CTI) framework based on deep learning techniques to enhance the protection of critical infrastructure. The proposed model integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Autoencoders to enable real-time anomaly detection, threat classification, and predictive analysis. A quantitative experimental design was employed using benchmark cybersecurity datasets and simulated critical infrastructure environments. The results demonstrate that the hybrid deep learning model outperforms traditional machine learning and signature-based approaches, achieving higher detection accuracy and lower false positive rates. The findings confirm that AI-based CTI significantly improves cybersecurity resilience, enabling proactive threat mitigation in high-risk environments. The study contributes to advancing intelligent cybersecurity frameworks and provides practical implications for strengthening national cyber defense systems in Pakistan.

DEEN TRACKER: TECHNOLOGY-ENHANCED ISLAMIC PRACTICE MONITORING AND AI-BASED RELIGIOUS ACTIVITY TRACKER

Meerub Akhtar, Qoseen Zahra, Khadija Ishaq, Laiba Jabeen, Ateeb Ul Rahman — Mon, 04 May 2026 00:00:00 +0500

The rate that digital developments have been undertaken has transformed how individuals access information related to religion, education and lifestyles. A portal that does not only contribute to spiritual development but also uses the power of technological advances to enhance the process of studying, interaction, and the frequency of prayer becomes increasingly significant even in the setting of the Islamic worship. Deen Tracker is an innovative, artificially intelligent mobile application designed to meet these needs, providing a user of any age with a plethora of features that will definitely attract their attention. This application contains Islamic education programmes such as Ghazwat Explorer, with summaries and videos; Interactive Quranic Learning; Niyyah Tracker is a Niyyah advice using Artificial Intelligence, and Islamic quizzes; and stories told by children. .Deen Tracker can also be applied in Islamic upbringing and education of children through age specific instructions in dua, Salah and Wudu teachings, reading book, and through animated stories. On the same note, Hijab Cam relies on the face recognition shape and lessons to suggest the right type of hijab and there is Qibla Finder that provides the appropriate direction to pray without making use of any type of digital compass. Islamic digital calendar, prayers time module and intelligent alerts are also features of Deen Tracker to ensure a hassle-free experience of worship and life management. Deen Tracker is a combined software that contains AI personalization and learning, prayer, emotional counseling, and contemporary Muslims-friendly practices. It renders the worshiping process easy and appealing among Muslims, yet on the other hand it is an example, which demonstrates how modern technologies can be efficiently employed in the promotion of education and spirituality in the modern world.

OPTION PRICING IN THE LIGHT OF ISLAMIC VISION FOR PAKISTAN STOCK EXCHANGE

Imran Khan, Shahid Khan, Anum Zaib, Ibad Ur Rehman — Mon, 04 May 2026 00:00:00 +0500

It is widely known, The Black-Scholes model is frequently used to establish the behavior of the options trading in the financial market. Throughout this paper we address the issue of Islamic Al-Arboun (the down payment) that is similar to the conventional options trading. We proposed the modified version of the 2-D time-fractional Black-Scholes partial differential c with two assets established on the combination of Finite-Volume Method for unsteady flow and numerical scheme. This work deals with the analytical solution of the European Call option based on financial derivative is so called modified Finite Volume (unsteady) options which numerically solved. Through mathematical analysis it is established that the explicit Finite-Volume scheme is unconditionally stable. After analyzing the conceptual and legal differences between the conventional and Islamic (Al-Arboun) options trading we conclude that Al-Arboun could be the shari’a compliant alternative to the European conventional Call option.

SMART INFRASTRUCTURE DEVELOPMENT: INTEGRATING DIGITAL TECHNOLOGIES INTO CIVIL ENGINEERING PRACTICES

Muhammad Bilal Israr, Noor Thair Abdal Wahid, Pashtoon Ahmad Rayan — Mon, 04 May 2026 00:00:00 +0500

Smart infrastructure development represented a significant advancement in civil engineering through the integration of digital technologies such as artificial intelligence, Internet of Things, and building information modeling. This study examined the impact of digital technology integration on infrastructure performance, operational efficiency, and sustainability outcomes. A quantitative research design was employed, and data were collected from a sample of 220 civil engineering professionals using a structured questionnaire. Statistical analysis included descriptive statistics, correlation, and regression analysis. The results indicated high mean values for digital technology integration (M = 4.08, SD = 0.67), smart infrastructure development (M = 3.95, SD = 0.71), operational efficiency (M = 3.88, SD = 0.69), and sustainability outcomes (M = 3.82, SD = 0.73). Regression findings revealed that digital technology integration significantly influenced smart infrastructure development (β = 0.69, p < 0.001), operational efficiency (β = 0.64, p < 0.001), and sustainability outcomes (β = 0.60, p < 0.001). The study demonstrated that digital technologies enhanced project performance, improved resource utilization, and supported sustainable infrastructure development. Challenges such as implementation cost, technical skill gaps, and data management issues remained key concerns. The study provided practical implications for policymakers and industry professionals to promote digital transformation and achieve efficient and sustainable infrastructure systems.

GENERATIVE AI REVOLUTION IN CYBERSECURITY: A COMPREHENSIVE REVIEW OF THREAT INTELLIGENCE AND OPERATIONS

Muhammad Irfan Aslam — Mon, 04 May 2026 00:00:00 +0500

The rapid advancement of digital technologies has exposed significant limitations in traditional cybersecurity frameworks, creating an urgent demand for intelligent and adaptive security solutions. This study examines the transformative role of Generative Artificial Intelligence (GAI) in modern cybersecurity frameworks. With the increasing frequency and sophistication of cyber threats, traditional security mechanisms are becoming insufficient, creating a demand for intelligent, adaptive solutions. This review highlights how GAI technologies, including Large Language Models (LLMs) and Generative Adversarial Networks (GANs), enhance threat intelligence by enabling real-time data analysis, anomaly detection, and automated incident response. The study emphasizes the ability of generative models to identify novel threats, simulate cyberattacks, and support proactive defense strategies. Furthermore, GAI contributes to operational efficiency by reducing human workload and improving decision-making processes in security operations centers. However, the paper also critically discusses the emerging risks associated with generative AI, particularly its misuse in developing advanced malware, phishing attacks, and deepfake-based cybercrimes. Challenges such as high computational cost, model inaccuracies, and ethical concerns are also explored. The findings suggest that while GAI significantly strengthens cybersecurity capabilities, its dual-use nature requires balanced implementation, robust governance, and continuous monitoring. Overall, the study provides a comprehensive understanding of how generative AI is reshaping threat intelligence and cybersecurity operations in the digital era.

Keywords : Generative Artificial Intelligence, Cybersecurity Threat Intelligence, Anomaly Detection, Adversarial Attacks, Large Language Models, Incident Response

THE ROAD TO DECARBONIZING PAKISTAN'S CONSTRUCTION SECTOR

Tue, 05 May 2026 00:00:00 +0500

Pakistan’s building sector consumes over 40% of national energy, yet low-carbon building (LCB) adoption remains critically low. This study identifies key barriers and develops a contextual framework to accelerate LCB implementation. A mixed-methods design was used: a survey of 153 construction professionals and semi‑structured interviews with ten industry experts across major urban centers. Based on empirical evidence, the study proposes a Three‑Pillar Low‑Carbon Building Design (LCBD) Framework: (1) Policy and Regulatory Foundation (mandatory codes, financial incentives, institutional strengthening); (2) Technical Capacity Development (professional training, curriculum reform, knowledge sharing); and (3) Market Transformation (awareness campaigns, demonstration projects, supply chain development). Complementary outputs include passive design templates for 5 and 10 Marla houses, an Energy‑to‑Mortgage model to improve affordability, and a 10‑year implementation roadmap. The study concludes that overcoming Pakistan’s regulatory vacuum and capacity deficits requires coordinated action. With mandatory codes, green financing, and systemic educational reform, the construction sector can transition from a major carbon emitter to a cornerstone of sustainable development.

SPATIO–TEMPORAL ANALYSIS OF LAND USE/LAND COVER DYNAMICS AND ITS IMPACT ON LAND SURFACE TEMPERATURE USING GEOSPATIAL TECHNIQUES: A CASE STUDY OF MARDAN, PAKISTAN

Tue, 05 May 2026 00:00:00 +0500

Urbanization is presently a worldwide phenomenon. Pakistan, like many other South Asian nations, is experiencing rapid urbanization, with an annual growth rate of 3%. The consequences of urbanization on the climate and environment are critical for the country's natural resource management. One of the most significant aspects of land use change is the relationship between urbanization and the decrease of agriculture as a result of increased economic growth. Furthermore, analyzing dynamic changes in land use is necessary for developing a model for future land use changes. The research examines the city of Mardan's projected land use and land use development for the year 2050. Landsat pictures for the years 1990, 2000, 2010, and 2022 were used in this study. The photos were used to determine the temperature of the earth's surface, recover land use changes in land cover, and derive indices such as NDVI, NDBaI, NDBI, UI, and NDWI. Changes can be seen in built–up regions and agricultural areas, but water bodies and uncultivated places are also affected. Agriculture accounted for 51% of GDP in 1990 and will drop to 40% by 2022. From 1990 to 2022, the Built–up increased from 0.97 percent to 8.01 percent. The total accuracy of the images was between 89 and 90 percent. The LULC model has a significant impact on the projected temperature fluctuation. Additionally, a probability transition image was produced using the Markov model, demonstrating the transition forecast in the LULC model up to 2050, which shows a 35 percent decline in agricultural and a 136 sq.km rise in buildings. LST can be used to reflect the effect of a transition in the LULC model. The average maximum temperature in 1990 was 40 degrees Celsius, rising to 46 degrees Celsius in 2022,

according to seven separate yearly photos acquired by the LANDSAT thermal band. LST was analyzed using linear regression with NDVI, NDBI, NDWI, UI

and NDBaI. The study found that NDVI had a negative relationship with LST.

LST rises when plant cover decreases. While LST has a high positive connection

with NDBI, NDBaI, UI and NDWI. As a result, immediate steps must be taken to limit the rapid disappearance of urbanization in order to minimize environmental, natural resource, and biodiversity devastation by managing the evolution of soil surface temperature

A ROBUST PREPROCESSING AND FEATURE SELECTION FRAMEWORK IS PROPOSED TO ENHANCE HEART DISEASE PREDICTION ACCURACY

Tue, 05 May 2026 00:00:00 +0500

Heart disease is now the leading health issue in the world and requires proper measures to diagnose and prevent heart disease at an early stage. The paper introduces statistical and machine learning methods for forecasting heart disease by analyzing vital health indicators and lifestyle factors. To create a predictive framework, the University of California, Irvine (UCI) Heart Disease Dataset, comprising patient-specific characteristics, is used. The performance of three classification models, including the Logistic Regression, K-Nearest Neighbors (KNN), and the Random Forest, is compared in terms of their predictive performance. The research methodology can be divided into two stages: identification of the most important clinical characteristics that suggest cardiovascular risk; evaluation of the accuracy of the model on the data. The results indicate that machine learning and data mining tools can be used to diagnose and prevent cardiovascular diseases promptly.

EMPIRICAL INSIGHTS INTO VR IMPLEMENTATION CHALLENGES AND PRACTICES FOR CHEMISTRY EDUCATION

Mahboob Ur Rahman, Muhammad Salam, Haseena Noureen, Shah Khalid, Muhammad Fawad — Tue, 05 May 2026 00:00:00 +0500

Context

Virtual Reality (VR) has evolved into a vital tool in education and may make teaching science, particularly Chemistry education, much better. In a computer simulated environment, students can safely perform risky experiments, watch chemical reactions in detail, and learn about challenging chemical reactions. This interactive task assists students to learn more about fundamental concepts and lets the students to perform experiments that may not be feasible in a traditional lab. But even with these advantages, there are still a lot of challenges with using VR in labs and classrooms. These consist of the expensive prices of VR equipment’s, the challenge of blending VR into current educational environment’s, the fact that many teachers lack the technical know-how to use it, the software's constraints, and the unwillingness of schools to accept new technology.

Objectives

The aim of this study is to find out implementation challenges and strategies related with the use of Virtual Reality in Chemistry Education via Systematic Literature Review (SLR) and questionnaire survey. The process will explain how research studies find challenges to Virtual Reality adoption and what methods have worked well to overcome these challenges.

Anticipated Results
The anticipated outcomes include:
(1) A structured list of the challenges in implementation of virtual reality into practice.
(2) A Combination of mitigation strategies.
(3) A conceptual evaluation of the challenges and their solutions.
(4) Data that will help organizations, educators, and students in creating plans for the future use of Virtual Reality.

PREDICTIVE HEART HEALTH ANALYSIS: MACHINE LEARNING WITH THE CARDIOVASCULAR DISEASE DATASET

Tue, 05 May 2026 00:00:00 +0500

Cardiovascular diseases (CVDs) are the leading cause of global mortality, requiring accurate prediction systems for early detection and prevention. This study investigates predictive modeling of CVD risk using two benchmark datasets: Dataset 1 (Kaggle Cardiovascular Disease Risk Prediction, 70,000 records with demographic, clinical, and lifestyle features) and Dataset 2 (Early Medical Risk Dataset, 65,535 samples with clinical symptoms and risk factors). Two deep learning approaches were implemented and compared: a Deep Neural Network (DNN) baseline and a Transformer-based model tailored for tabular healthcare data. The DNN achieved consistent results with accuracies of 85.3% (Dataset 2) and ~90% (Dataset 1), demonstrating balanced precision and recall but limited ability to capture complex feature dependencies. In contrast, the Transformer achieved superior performance, recording precision and recall above 99% with an ROC-AUC of 0.999 on Dataset 2, and consistently higher metrics on Dataset 1. These results confirm that attention-based architectures are more effective in modeling non-linear, interdependent risk factors, offering near-perfect classification outcomes. The findings demonstrate that integrating advanced deep learning models with structured clinical datasets can significantly improve cardiovascular risk prediction, supporting clinical decision-making by reducing misclassification rates and enabling timely, personalized healthcare interventions

MODELING AND ANALYSIS OF A CO-AUTHORSHIP SYSTEM USING COMPLEX NETWORK APPROACH: A CASE STUDY

Tue, 05 May 2026 00:00:00 +0500

Complex systems across various domains including biological, social, environmental, technological, communication, and transportation can be effectively modeled as complex networks. These networks are typically large and intricate due to the vast number of nodes and interconnections among them.This study apply a network science approach to model and analyze co-authorship networks derived from the Journal of Lightwave Technology (JLT). The analysis is conducted using key network metrics such as degree centrality, clustering coefficient, and betweenness centrality.The results indicate that the network exhibit high clustering, short average path lengths, and an inhomogeneous distribution of weighted degree. Furthermore, the findings reveal the presence of influential authors acting as hubs who frequently appear across multiple issues.

FEDERATED HYBRID DEEP LEARNING FOR NETWORK ANOMALY DETECTION WITH ADAPTIVE RESOURCE OPTIMIZATION

Tue, 05 May 2026 00:00:00 +0500

The rapid growth of distributed systems and networked environments has increased the complexity of real-time traffic analysis and management. Traditional centralized approaches face limitations related to latency, scalability, and data privacy. This study proposes a federated hybrid deep learning framework for network anomaly detection combined with adaptive resource optimization. The model integrates Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Autoencoders to capture spatial, temporal, and reconstruction-based anomaly patterns. A federated learning strategy using Federated Averaging (FedAvg) enables decentralized training across multiple edge devices with non-IID data distribution, preserving data privacy. Additionally, a Deep Q-Network (DQN) is employed to dynamically optimize network resource allocation based on detected anomalies and traffic conditions. The framework is evaluated using the UNSW-NB15 dataset and compared with traditional machine learning models and centralized deep learning approaches. Results demonstrate improved detection performance and efficient resource utilization, making the proposed system suitable for real-world distributed network environments.

FRESH AND HARDENED CONCRETE COMBINED WITH LIME STONE FINES AS A SUBSTITUTION FOR CEMENT

Zuhairuddin, Iqra Wahid Lakhiar, Dileep Kumar, Shafique Ahmed — Tue, 05 May 2026 00:00:00 +0500

Cement manufacture emits substantial quantities of carbon dioxide, significantly affecting the climate, while also necessitating considerable energy use. Moreover, the disposal and recycling of conventional concrete constituents may result in environmental deterioration. Utilizing trash in concrete decreases both making cement and utilization of energy. This research aims to assess the characteristics of fresh and hardened concrete by partly substituting cement with limestone fines (LSF). This research included substituting cement with LSF at proportions of 0%, 5%, 10%, 15%, and 20% by weight of cement. The entirety of 30 samples of concrete was prepared using a mix ratio of 1:1.5:3. Cube-sized specimen was evaluated for compressive strength, and density of concrete at 28 days, correspondingly. The optimal result indicated that the compressive strength enhanced by 10.75% when 10% LSF was used as a cement replacement in concrete after 28 days. The slump value and density of concrete decreased with an increase in LSF concentration.

Keywords- Lime Stone Fines, Concrete, slump test, Density, Compressive Strength.

EXPERIMENTAL INVESTIGATION OF SUSTAINABLE CONCRETE SLENDER BEAM UTILIZING FINE COAL BOTTOM ASH AND GROUNDED COIR FIBRE

Sadaquat Hussain, Muhmmad Farooq, Muneer Ahmed, Nizakat Ali — Wed, 06 May 2026 00:00:00 +0500

The current study represents an attempt made to develop eco-friendly structural concrete using different percentages of fine coal bottom ash (FCBA) as a partial cement replacement and grounded coir fiber (GCF) as filler. X-ray diffraction (XRD and key testing were performed to investigate the chemical composition, workability, and splitting tensile strength of the slender concrete beam. The XRD outcomes revealed that the optimal addition of FCBA and GCF in mix can significantly mitigate the pozzolanic activities and create binding effect, thus leading to improving splitting tensile strength by 13.61%. The addition of GCF in concrete mix resulted in enhancement effect to improve the workability of the developed concrete mix by 20.90%. Also, with addition of 10% FCBA and 2% GCF, the flexural strength under four-point bending test showed substantial improvement by 24.50% respectively.

Keywords- Lysimeter, Unaccounted losses, soil EC, Soil pH and Bulk density