Research Corridor Journal of Engineering Science
About the Journal The Research Corridor Journal of Engineering Science (RCJES) is multidisciplinary academic journal that focuses on disseminating research across various engineering disciplines. It serves as a platform for the publication of original research and review articles in fields Engineering. The primary aim of RCJES is to facilitate the sharing of advancements in engineering, providing an open-access forum where scholars, researchers, and professionals can contribute and access knowledge freely. Being a peer-reviewed journal, RCJES upholds rigorous standards for the publication of scholarly work, ensuring that the research it publishes is both original and of high quality.
This is an open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access. Articles are licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
A Conceptual Model for AI-Based Technology Management in Smart Engineering Enterprises
The integration of Artificial Intelligence (AI) into engineering enterprises has dramatically outpaced the evolution of traditional technology management (TM) frameworks. Conventional TM models—often linear, static, and heuristic-based—fail to leverage the dynamic, data-driven capabilities of Industry 4.0 and 5.0 environments, leading to a dangerous "strategy latency" where decision-making lags behind technological innovation. This article proposes a novel conceptual model for AI-Based Technology Management (AI-TM) in smart engineering enterprises. By synthesizing recent literature (2021–2026) and applying systems theory and dynamic capabilities frameworks, the study develops an integrative architecture where AI agents act as the central nervous system for technology planning, acquisition, deployment, and governance. Unlike legacy models that rely on periodic audits, the proposed model shifts TM to a continuous, real-time adaptive cycle driven by algorithmic sensing and automated seizing. Theoretical contributions include the redefinition of "technology assessment" as a continuous stochastic prediction function rather than a discrete deterministic audit. Managerial implications offer a detailed roadmap for transitioning from digitized to cognitive enterprises, emphasizing the need for a "Strategic Digital Twin." A validation strategy utilizing the Delphi method is proposed to test the model's robustness against expert consensus.
Artificial Intelligence; Technology Management; Smart Engineering Enterprises; Conceptual Model; Industry 4.0; IT-Enabled Dynamic Capabilities; Algorithmic Management; Strategic Digital Twins.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Multimodal AI for Cloud Security: Intelligent Correlation of Network Traffic, Audit Logs, and System Metrics
The rapid evolution of hyper-scale cloud infrastructures has created a security paradox: while observability data is abundant, actionable intelligence is hindered by fragmentation. Heterogeneous telemetry—including network flows, audit logs, and system metrics—is typically analyzed in isolation by traditional Intrusion Detection Systems (IDS) and SIEM platforms. This "fragmented observability" fails to detect sophisticated, multi-stage attacks that manifest as subtle, sub-threshold indicators across multiple domains simultaneously.This article presents a novel Multimodal Artificial Intelligence architecture designed to bridge these gaps by intelligently correlating heterogeneous data through a unified representation learning framework. Unlike simplistic ensemble methods, the proposed system integrates specialized encoders—CNN-Transformers for traffic, BERT-based models for logs, and Bidirectional LSTMs for metrics—with a novel "Cross-Source Intelligence Fabric." This core innovation enforces behavioral consistency, performs adaptive temporal alignment for asynchronous streams, and models deep contextual dependencies.Experimental evaluation on complex attack datasets demonstrates that this multimodal approach achieves a detection effectiveness score of 0.94 F1-score, representing a 41.48% improvement over best-in-class unimodal baselines. Furthermore, the model significantly reduces operational friction by lowering false positive rates by 35% and decreasing detection latency by 28%. The architecture effectively reveals stealthy attack patterns, such as "low-and-slow" data exfiltration and cryptojacking, which remain invisible to isolated monitoring systems. These findings establish that intelligent cross-modal correlation represents a necessary paradigm shift in cloud security analytics, moving the industry beyond siloed, reactive detection toward holistic, context-aware defense.
Multimodal AI; Cloud Security; Data Correlation; Threat Detection; Audit Logs; System Telemetry; Cross-Modal Fusion; Security Observability; Deep Learning; Anomaly Detection; Graph Neural Networks; Behavioral Analysis
Operationalizing Information Security Governance: From Framework Adoption to Control Effectiveness
Despite the global ubiquity of information security governance (ISG) frameworks such as ISO/IEC 27001 and NIST CSF, empirical evidence suggests a persistent "decoupling" where framework adoption fails to correlate linearly with reduced breach susceptibility. This article critiques the prevailing compliance-centric paradigm, arguing that certification often represents "symbolic adoption"—a legitimacy-seeking exercise—rather than substantive defensive capability. By proposing a "Governance-to-Control Operationalization Model," this research bridges the critical execution gap between abstract governance decision rights and tangible operational efficacy. The study reframes governance not as static documentation, but as a dynamic cybernetic system requiring continuous energy input to combat IT entropy. Findings indicate that organizations emphasizing governance execution—characterized by continuous monitoring and active feedback loops—achieve significantly higher control effectiveness than those reliant on static, checklist-based compliance. This research offers a theoretical pathway from high-level governance structures to measurable security resilience.
Information Security Governance; IT Governance; Control Effectiveness; Risk Management; Compliance; Assurance; Decoupling; Institutional Theory; Cybernetics; Continuous Monitoring; Symbolic Adoption; Resilience Engineering; Normalization of Deviance.
The Role of Artificial Intelligence in Enhancing the Effectiveness of Internal Audit Functions
Purpose: The rapid digitization of business processes and the exponential growth of organizational data have rendered traditional, cyclical internal audit methodologies insufficient for addressing the velocity, volume, and variety of modern corporate risks. This study critically examines the transformative role of Artificial Intelligence (AI) in reshaping Internal Audit Functions (IAF) from reactive, retrospective assurance providers into proactive, strategic advisors. The research aims to develop a robust conceptual framework that explicitly models the integration of specific AI technologies—Machine Learning (ML), Natural Language Processing (NLP), and Process Mining—into the diverse stages of the audit lifecycle. By doing so, it seeks to resolve the "governance gap" created by the latency inherent in manual auditing practices and provide a roadmap for the digital transformation of the assurance profession.
Artificial Intelligence; Internal Audit; Audit Effectiveness; Corporate Governance; Risk Management; Continuous Assurance; Machine Learning; Process Mining; Algorithmic Governance.
Developing an AI-Based Internal Audit Effectiveness Model in Modern Organizations
The digital transformation of modern organizations has rendered traditional, cyclical internal audit methodologies insufficient for addressing real-time risks and high-volume data environments. As organizations increasingly rely on automated decision-making systems and complex algorithmic trading, the latency inherent in periodic sampling creates a significant "governance gap." This temporal lag exposes stakeholders to undetected anomalies, fraud, and operational inefficiencies that crystallize before traditional assurance cycles can identify them. This study aims to develop and critically analyze a comprehensive AI-Based Internal Audit Effectiveness Model (AI-IAEM). Utilizing a conceptual research design synthesized from a systematic review of literature published between 2021 and 2026, the research integrates Agency Theory and the Technology-Organization-Environment (TOE) framework to map artificial intelligence capabilities—specifically machine learning, natural language processing, and process mining—against the internal audit lifecycle. The findings present a validated model demonstrating how AI integration shifts internal auditing from a retrospective assurance function to a continuous, predictive governance mechanism. The study contributes to auditing theory by redefining "audit effectiveness" to include algorithmic transparency, "coverage density," and real-time risk coverage, while providing practitioners with a structural blueprint for AI adoption that enhances audit quality, efficiency, and governance oversight. The resulting framework addresses the critical need for a new paradigm where the speed of assurance matches the velocity of risk generation.
Artificial Intelligence; Internal Audit; Audit Effectiveness; Continuous Auditing; Corporate Governance; Algorithmic Assurance; Predictive Analytics; Machine Learning Integration; Natural Language Processing; Process Mining.
Information Security Governance in Distributed and Decentralized IT Systems: A Coordination-Theoretic Perspective
The rapid proliferation of distributed and decentralized IT architectures—ranging from cloud-native microservices and edge computing to blockchain-based ecosystems—has fundamentally eroded the efficacy of traditional, centralized information security governance (ISG). As organizational perimeters dissolve into federated, multi-actor, and ephemeral environments, the hierarchical model of a single governing authority enforcing uniform policy becomes not only obsolete but actively detrimental to system resilience. This research examines the critical governance tension between the necessity for central control to manage aggregate risk and the operational reality of local autonomy required for distributed system performance. By adopting a coordination-theoretic lens, this article conceptualizes security governance not as a static command structure but as a dynamic, distributed coordination problem. The study identifies and analyzes specific mechanisms for aligning security responsibilities, decision rights, and assurance processes across autonomous nodes without relying on a single root of trust or a monolithic control plane. Key insights reveal that effective governance in decentralized environments depends on the implementation of polycentric decision-making frameworks, the utilization of automated policy-as-code enforcement, and the adoption of consensus-based assurance mechanisms. The findings suggest that a shift from "governance by mandate" to "governance by protocol" is essential for securing the next generation of digital infrastructure.
Information Security Governance; Distributed Systems; Decentralized IT; Cloud and Edge Computing; Digital Ecosystems; Polycentric Governance; Coordination Theory; Policy-as-Code.
AI-Driven Technology Management Frameworks for Automating Complex Engineering Systems
This study develops a theoretically grounded framework explaining how artificial intelligence (AI)-driven technology management capabilities enable decision automation, governance enhancement, and performance optimization in complex engineering systems. The research resolves prevailing ambiguities regarding the distinction between engineering task automation and managerial decision automation.Grounded in recent reinterpretations of Systems Theory, Control Theory, the Resource-Based View (RBV), and Socio-Technical Systems (STS) Theory, the study employs conceptual synthesis followed by a rigorous quantitative research design. AI-driven technology management capability (AI-TMC) is conceptualized as a formative higher-order construct comprising technical infrastructure, governance routines, and adaptive learning. The research design utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) with a target sample of 350–400 engineering managers and system architects across two temporal waves to mitigate common method bias.
Artificial Intelligence; Technology Management; Complex Engineering Systems; Decision Automation; Managerial Decision Automation Extent; Socio-Technical Systems; Governance Capability
From Black-Box Alerts to Actionable Intelligence: Explainable Artificial Intelligence in Cloud Anomaly Detection
The ubiquitous adoption of cloud computing, characterized by ephemeral microservices, hybrid architectures, and elastic scaling, has fundamentally altered the cybersecurity landscape. To defend these vast and distributed infrastructures, organizations have been forced to deploy increasingly complex Deep Learning (DL) models capable of analyzing high-velocity telemetry. While these advanced neural architectures—spanning Long Short-Term Memory (LSTM) networks to Transformer-based models—excel at identifying subtle, non-linear deviations in high-dimensional data, the resulting detection systems often operate as opaque "black boxes." They generate high volumes of statistical alerts based on complex feature interactions but fail to provide the semantic context necessary for human understanding. This disconnect between the mathematical output of a detection model and the cognitive comprehension required by security operators leads to severe operational dysfunctions, including "alert fatigue," inconsistent triage decisions, and suboptimal incident response times.
Explainable AI; Cloud Anomaly Detection; Actionable Intelligence; Cybersecurity Decision Support; Trustworthy AI; Security Operations Center (SOC) Automation; Human-AI Teaming; Deep Learning Interpretability.
Industrial Management Approaches to Hazardous Waste Reduction and Reuse
The increasing production of hazardous waste is a consequence of globalization in the form of industrialization and urbanization. It is a major concern in environmental management, with crucial effects on public health and ecosystems. This article critically reviews the industrial management of hazardous waste to highlight the need to shift from the current linear disposal models to more sustainable solutions based on the principles of a circular economy. It presents a holistic set of management hierarchies, prioritizing waste prevention and reduction at the source, followed by reuse, recycling, and waste-to-energy conversion. The review also provides details of the advanced treatment technologies and the various proper disposal methods. Moreover, the article discusses the role of data-driven approaches for estimation and management of waste. The review also presents a summary of some major international policies, legislation, and conventions on global waste management. The sustainable management of hazardous waste should take a holistic approach toward economic prosperity, environmental sustainability, and well-being of the society to achieve the overall sustainable development goals.
Hazardous Waste Management, Sustainable Development, Waste Reduction, Recycling, Waste-to-Energy, Industrial Waste, Pollution Control
Copyright (c) 2025 Research Corridor Journal of Engineering Science
Integrating Mobile Phone Data with Traditional Census Figures to Create Dynamic Population Estimates for Disaster Response and Resource Allocation
Accurate and up-to-date information on where people are and in what numbers is essential for disaster management and the fair distribution of public goods and services. However, official population counts are typically only available from censuses, which are often outdated and lack temporal and spatial resolution. Here we develop and validate a scalable approach that combines anonymized, aggregated mobile phone data (call detail records and passive network events) and census baselines to produce up-to-date, error-quantified population estimates at sub-district spatial resolutions and hourly to daily temporal frequencies. We normalize mobile activity to resident population with a Bayesian hierarchical model that accounts for carrier market share, multi-SIM ownership, inactive devices, and diurnal/weekly seasonality effects. We allocate the normalized activity across space with dasymetric mapping using ancillary layers (land use, building footprints, night-time lights, road networks) to restrict to likely inhabited areas and disentangle through-mobility from residential population. A state-space data assimilation framework fuses multiple activity signals (tower handovers, app pings, emergency alert traffic) to provide near–real-time population updates in the presence of hazard-induced mobility changes. We assess performance against ground truth from household surveys, administrative registries, smart-meter rollups, and post-event field counts, reporting accuracy as mean absolute percentage error, coverage probabilities for credible intervals, and resilience to simulated network outages. We illustrate applications for storm evacuation and flood response, showing how the estimates can inform the pre-positioning of supplies, dynamic routing of ambulances, siting of mobile clinics and shelters, and prioritization of damage assessment teams. The framework incorporates privacy-by-design measures (k-anonymity thresholds, differential privacy noise, strict governance and retention policies) and generates confidence surfaces to enable risk-aware decision-making. Our method effectively transforms static census baselines into living population surfaces, helping responders and planners see where people are—and how they move—before, during, and after a disaster. Beyond the emergency context, the approach can support the routine distribution of health, education, and transportation resources, offering a practical, ethically sound pathway to data-driven public service delivery in rapidly urbanizing and peri-urban environments. This integrated methodology advances state-of-the-art approaches by delivering higher accuracy in complex urban contexts and enabling more impactful healthcare planning during post-disaster recovery periods.
Mobile Phone Data Integration; Dynamic Population Estimation; Bayesian Hierarchical Modeling; Dasymetric Mapping; Disaster Response and Resource Allocation
Copyright (c) 2024 Research Corridor Journal of Engineering Science
Transforming electronics lifecycle through expert refurbishment, quality assurance, and transparent environmental impact.
The modern electronics industry faces a dual challenge: sustaining rapid technological progress while mitigating its significant environmental footprint. This study explores how expert refurbishment, rigorous quality assurance, and transparent environmental reporting can transform the lifecycle of electronic devices into a sustainable model of circular economy. Expert refurbishment extends product usability by restoring performance to near-original standards, reducing the need for new manufacturing and conserving valuable resources such as rare earth metals. Quality assurance ensures that refurbished electronics meet industry standards, fostering consumer trust and expanding the market for sustainable alternatives. Transparent environmental impact assessment—through life-cycle analysis (LCA) and carbon footprint reporting—further enhances accountability, encouraging both producers and consumers to make responsible decisions. The integration of these elements can create a systemic shift from the traditional “take-make-dispose” model to a “repair-reuse-recycle” framework, promoting sustainable consumption and production. Moreover, the adoption of standardized refurbishment protocols and certification systems can boost the global trade of refurbished devices, providing economic benefits alongside environmental gains. This approach aligns with the United Nations Sustainable Development Goals (SDGs), particularly SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action), positioning refurbishment as a critical pathway toward sustainable industrial transformation. The research concludes that embracing expert refurbishment, coupled with robust quality assurance and transparent environmental metrics, can redefine the electronics lifecycle, reduce e-waste, and foster a culture of environmental accountability within the digital economy.
electronics lifecycle, expert refurbishment, quality assurance, environmental impact, circular economy, e-waste reduction, sustainable development, lifecycle analysis, carbon footprint, responsible consumption.
POST-QUANTUM CRYPTOGRAPHY FOR HEALTHCARE: FUTURE-PROOFING POPULATION HEALTH DATABASES AGAINST QUANTUM COMPUTING THREATS
In the face of quantum computing advancements, classical encryption methods used in protecting health data are expected to become insecure within the next 10-15 years. This paper is the first to provide a comprehensive, detailed framework for healthcare organizations, specifically targeting population health databases, to migrate their cryptographic systems to quantum-resistant algorithms without disrupting performance or violating compliance standards. We provide implementation and performance analysis of the four NIST standard candidate post-quantum algorithms CRYSTALS-Kyber, CRYSTALS-Di lithium, FALCON, and SPHINCS+, all tuned for high-throughput, low-latency healthcare workloads. Our empirical data demonstrates that Kyber-1024 is best-suited for health record encryption tasks with minimal performance overhead (2.3x slower) compared to AES-256, and Dilithium-5 offers the most efficient trade-off for long-term signature security for audit logging (4.1x slower than RSA-2048). The research introduces an innovative "crypto agility" system design, facilitating seamless transitioning between traditional and post-quantum cryptographic methods. This design mitigates transitional risks and enables concurrent support for both legacy and quantum-resistant cryptographic processes. Protocols for negotiating between different cryptographic algorithms automatically, based on a combination of data sensitivity, retention policies, and prevailing threat models, are also established. Empirical evidence from deployment within a production-grade population health system, which currently processes 50 million patient records, indicates the transition to post-quantum cryptography can occur with only 0.03% total downtime, 18% additional storage overhead, and 31% additional compute overhead, well within the tolerance of most healthcare IT budgets. The paper includes a risk assessment that establishes population health databases, which contain sensitive genetic data, disease profiles, and long-term biometric information with relevance extending over a century, as the most critical assets to be protected against quantum cryptographic attacks. Additionally, the cost-benefit analysis included shows that the U.S. healthcare industry could avoid up to $47 billion in breach-related expenses by adopting post-quantum cryptography proactively. Supporting the migration, performance optimization, and regulatory adherence, the framework consists of practical migration tooling, a guide for fine-tuning performance, and evidence to show that post-quantum cryptographic implementations meet the necessary conditions for HIPAA encryption safe harbor and are robust against future quantum-computing-specific regulatory requirements.
Post-quantum cryptography, Quantum computing threats, Healthcare data protection, NIST PQC standards, Crypto-agility, Long-term data security, Population health databases.
THE INFLUENCE OF FAMILY INVOLVEMENT ON THE QUALITY OF CARE FOR AGED ADULTS: A COMPARATIVE STUDY
Background: The growing population of older adults worldwide necessitates the provision of high-quality elderly care. Family involvement is a crucial aspect of caregiving, impacting the physical and emotional well-being of the elderly. While previous studies have focused on family engagement in specific settings, there is a need for a comparative analysis of its role in home care, nursing homes, and assisted living facilities. Methods: This research employed a mixed-methods approach, utilizing both quantitative surveys of family members and professional caregivers and qualitative, in-depth interviews. The quantitative data focused on the frequency of family involvement, participation in decision-making processes, and perceived outcomes of quality of care. These outcomes included measures such as physical health status and mental well-being of the elderly. Qualitative interviews delved into the lived experiences, cultural factors, and perceived benefits and barriers to family participation. The analysis aimed to compare findings across the three care settings, seeking to understand commonalities, differences, and context-specific dynamics. Results: The results of the study reveal that family involvement significantly improves the quality of care, with marked positive effects on emotional support, patient advocacy, and adherence to care plans. The home care environment was found to offer the most significant benefits, attributed to the flexibility and direct collaboration between family members and caregivers. In institutional settings, however, family involvement shifted more towards shared decision-making rather than active care provision. Notable barriers include conflicting expectations between family members and professional caregivers, caregiver burden, and cultural differences in caregiving roles. Conclusion: Family involvement emerges as a complex construct that positively impacts care quality when effectively integrated into service delivery. Tailored strategies are necessary to enhance the collaboration between family members and professional carers in various care contexts. Policies and training programs should focus on bridging communication gaps, managing expectations, and promoting culturally sensitive engagement to optimize care outcomes for older adults.
Family involvement, aged care quality, caregiver participation, elderly healthcare outcomes, mixed-methods research, patient-centered care, long-term care settings
Securing Big Data Pipelines in Healthcare: A Framework for Real-Time Threat Detection in Population Health Systems
Purpose: The paper aims to develop a comprehensive security framework for big data pipelines in healthcare, focusing on real-time threat detection and mitigation. It addresses the increasing security and privacy risks associated with the rapid growth of health data streams from IoT devices, electronic health records, and wearable technologies. Methodology: The study extends previous work on real-time survival risk prediction by designing a layered security architecture that integrates Apache Kafka for stream processing and Splunk SIEM for event monitoring. A machine learning-based anomaly detection algorithm is implemented to identify potential security breaches within 500 milliseconds, achieving 97.3% detection accuracy and a false positive rate of 0.02%. The framework is evaluated in a simulated population health system processing 2.5 million health events per second and is specifically designed to tackle five key security challenges: unauthorized data access, data injection attacks, privacy breaches, insider threats, and compliance violations. Findings: The results show that the proposed framework: Achieves near-instantaneous threat detection (500 ms) Delivers 97.3% detection accuracy with 0.02% false positive rate Reduces mean time to threat detection (MTTD) by 84% compared to batch-processing systems Maintains HIPAA compliance throughout the data pipeline Detects multi-stage and sophisticated attack patterns by correlating threats across multiple data streams. Contribution: This research provides a practical and scalable solution for securing healthcare big data infrastructures while enabling advanced population health analytics. The combination of Apache Kafka, Splunk SIEM, and ML-based anomaly detection offers significant improvements in detection speed, accuracy, and compliance. The work contributes to the field by presenting a real-time, multi-layered security framework that can be adopted by healthcare organizations to enhance data security, privacy, and operational resilience.
Healthcare cybersecurity, Real-time threat detection, Big data security, Stream processing, Population health, Apache Kafka, Splunk, SIEM, Anomaly detection
Artificial Intelligence-Driven Optimization Techniques in Modern Engineering Systems
The integration of artificial intelligence (AI) in engineering has revolutionized the way complex systems are designed, analyzed, and optimized. AI-driven optimization techniques, including machine learning algorithms, genetic algorithms, and deep reinforcement learning, provide engineers with powerful tools to solve multi-variable, non-linear, and large-scale problems that were previously challenging using conventional methods. This study investigates the role of AI in enhancing engineering efficiency across diverse fields such as structural engineering, energy systems, manufacturing, and transportation. Emphasis is placed on the capability of AI to reduce computational costs, improve design accuracy, and enable predictive maintenance. The research also explores emerging hybrid models that combine AI with traditional optimization approaches, offering enhanced adaptability and reliability. Findings suggest that AI not only accelerates innovation but also fosters sustainability and resilience in engineering practices. The paper concludes with challenges related to data quality, interpretability, and ethical considerations, providing insights for future directions in AI-enabled engineering optimization.
Artificial intelligence; Optimization; Machine learning; Engineering systems; Genetic algorithms; Predictive maintenance; Deep reinforcement learning; Sustainable engineering
Advancements in Smart Materials for Sustainable Engineering Applications
Smart materials have emerged as a transformative innovation in engineering sciences, offering unique properties such as self-healing, shape-memory, and adaptability to environmental changes. These materials are increasingly being integrated into sustainable technologies to address global challenges in energy, infrastructure, healthcare, and environmental protection. This study explores the latest advancements in smart materials, focusing on their applications in renewable energy systems, eco-friendly construction, and intelligent manufacturing. It also examines the role of nanotechnology and artificial intelligence in enhancing the efficiency and functionality of these materials. By analyzing recent case studies and technological breakthroughs, the research highlights how smart materials are driving sustainability, reducing energy consumption, and promoting eco-innovation. The paper concludes with potential future directions and the challenges of commercialization, emphasizing the need for interdisciplinary collaboration to unlock their full potential.
Smart materials; Sustainable engineering; Nanotechnology; Renewable energy; Intelligent manufacturing; Eco-innovation; Artificial intelligence
Investigating the Impact of Climate Change on Water Requirements of Sweet Pepper Crop grown in Qassim Region.
Climatic and environmental changes are important issues for present day research. In this paper, the effect of climatic and environmental changes on water requirements of Sweet Pepper crop grown in Qassim-Saudi Arabia have been investigated by incorporating the impacts of climatic and environmental changes on precipitation and temperature in the Qassim region. The base period for data is taken as 1999 -2024. The changes in precipitation and temperature have been predicted by Global Circulation Model (GCM) under RCP 2.6 (Representative Concentration Pathway 2.6) for a period of 25 years. Evapotranspiration has been estimated using Penman-Monteith equation. Its best parameters have been estimated by the General Reduced Gradient (GRG) technique. The water requirements of Sweet Pepper crop grown in Qassim have been estimated using CROPWAT model. Finally, the impact of temperature and precipitation variations on crop water requirements have been estimated. It is observed that the water requirements of Sweet Pepper will slightly increase in the future in the Qassim region. The results of this research will be useful for sustainable water planning, development and management.
Global Circulation Model, General Reduced Gradient, Evapotranspiration, Crop Water Requirements
Building Information Modeling framework for improved Visualization, Management, and Simulation
The discipline of civil engineering has witnessed a revolutionary approach in the form of advanced software of BIM due to the expeditious progress observed in construction technologies. This study offers a thorough analysis that examines conventional building techniques with BIM-based techniques, highlighting the efficiency of each approach for project management and execution. An actual building structure is used in the research to demonstrate the practical implementation of BIM tools and it results in efficiency, accuracy, and flexibility. The results show that BIM substantially lowers construction costs and duration while raising overall project quality because of better decision-making offered by BIM during the designing and planning stage.
BIM Tools, Conventional Tools, Comparison of methods, Estimation, Management , Visualization, Simulation
Investigating the Structural Performance of Precast Concrete Pipes with Steel Fiber-Reinforced Geopolymer Concrete
Recent developments in materials science have identified geopolymer as a promising substitute for Ordinary Portland Cement (OPC), with industrial byproducts serving as its primary ingredients. Studies on geopolymer concrete have revealed notable improvements in early strength development and superior resistance to chemical reactions, in contrast to OPC. These properties of geopolymer prefer its use in precast concrete pipes production. This study recognizes the need to interpret the structural response of geopolymer concrete under load-deflection behavior of geopolymer concrete pipes. In collaboration with “Allah Ho Yasir” precast pipe factory located in Lahore, Punjab, Pakistan. R.C.C pipes of 450mm diameter using OPC and GPC were manufactured at industrial scale. For the geopolymer concrete (GPC) locally produced steel fibers are added with the dosage of 30 kg/m³ along with conventional steel cage. These pipes were manufactured by conventional methodology. For structural analysis performed destructive and non-destructive test, were performed. Destructive testing includes compressive strength test, core cutter compressive test, and three-edge bearing test which provided critical insights regarding maximum load-bearing capacity and failure characteristics. The core cutter compressive test was particularly valuable in assessing the in-situ compressive strength of the concrete within the pipes. Additionally, bond strength tests were performed to assess the adhesion between the steel bars and the concrete matrix, which is essential for structural strength of the pipes. Non-destructive testing such as ultrasonic pulse velocity and rebound/Schmidt hammer test, were performed to assess the quality and uniformity of the concrete without causing damage. These combined testing approaches ensured a comprehensive understanding of the mechanical properties, bond strength, and structural strength of the geopolymer concrete pipes, providing its potential application in R.C.C projects.
Unconfined Precast Pipe, Steel Fiber-Reinforced, Geopolymer Concrete.
POTENTIAL OF ALKALI SILICA REACTION IN CONCRETE USING KASHMIR AGGREGATE
The mountainous belt of Kashmir, bearing complex geological formations, provides aggregates the have potential to be used in construction industry for both small as well as large scale projects. However, a very expansive reaction, known as Alkali-Silica Reaction (ASR) must be carefully evaluated to ensure their suitability and to minimize durability problems in concrete structures. The present study aims to analyze the potential of Alkali silica reactivity in aggregate samples collected from three different quarries of Kashmir region. These aggregates were examined through chemical analysis, petrographic analysis and standard test procedures, including ASTM C 227 and ASTM C1260. Based on the standard expansion limits for mortar bars, all the aggregates sources were identified as potentially reactive. Mortar cubes were also analyzed at different ages to determine the mechanical properties under different ASR conditions. The results reveal that reduction observed in the concrete properties varied among different aggregate sources, depending on their chemical and mechanical properties of aggregates. It was observed that the reduction in strength was found to be inversely proportional to the expansion observed in the mortar bars.
Alkali Silica Reaction, Chemical analysis, Petrographic Analysis, Cubes, Prisms, Mortar Bars
Shear Strength Improvement of Fine-Grained Soils Using Lime Stabilization
An essential component of any civil engineering building is soil. Buildings, bridges, highways, tunnels, dams, towers, and other civil engineering structures are constructed on or below the earth's surface. Proper foundation soil is necessary for their stability. Fine-grained soils often exhibit weak shear strength, posing challenges in construction projects. This study focuses on enhancing the shear strength of such soils through chemical stabilization using lime, a cost-effective and widely available material. In this experimental investigation, lime was mixed with fine-grained soil at varying dosages of 2%, 4%, 6%, 8%, and 10% by weight. The prepared samples were subjected to curing periods of 7, 14, and 28 days. The unconfined compressive strength (UCS) test was conducted to evaluate the shear strength development over time. The results revealed a significant increase in shear strength with both lime content and curing duration. The maximum shear strength of 1007.7 kPa was observed at 8% lime content after 28 days of curing, which is approximately five times greater than that of the untreated (in-situ) soil. The study concludes that lime stabilization is an effective technique for improving the shear strength of fine-grained soils, with optimal performance achieved at 8% lime content after sufficient curing. These findings contribute to the development of sustainable ground improvement strategies in civil engineering.
Enhancing Shear Strength, Lime stabilization, Soil Strength Improvement, Unconfined Compressive Strength (UCS).
Developing a Composite Index to Measure the Integration of BIM, IPD, and Lean Construction: Analysis and Framework
Abstract: The integration of Building Information Modeling (BIM), Integrated Project Delivery (IPD), and Lean Construction (LC) presents a transformative opportunity to improve collaboration, efficiency, and sustainability in construction projects. However, the lack of a unified metric to evaluate the maturity of this integration limits strategic benchmarking and implementation. This paper introduces a novel composite index, titled the BIM-IPD-Lean Integration Index (BILI), to measure integration maturity across these three methodologies. A structured questionnaire was administered to 27 construction firms, and response data were used to develop and apply the index. The analysis revealed that most firms exhibit high to exceptional integration readiness. To bridge perception with practice, the study also proposes a conceptual framework for practical implementation of the index, enabling real-world auditing based on documented project evidence. The BILI model offers a scalable and replicable approach for evaluating integrated construction practices and contributes a structured tool for industry benchmarking and improvement.
Building information modeling, Integrated project delivery, Lean construction
A Review on the Mechanical and Microstructural Performance of Sustainable Geopolymer Concrete
With high levels of CO₂ emissions and resource depletion more attention has been paid to environmental aspects of ordinary Portland cement (OPC) production, especially in view of the need for better sustainable construction materials. The good news to the usage of waste product in GPC as an eco-friendly construction material through the use of fly ash, ground granulated blast furnace slag and Meta kaolin. These differences could be attributing to the lack of aluminosilicate rich substances which are required for GPC to occur using regular cement as well as better environmental advantages.
Geopolymer concrete, microstructure, compressive strength, aluminosilicate, N-A-S-H, durability, green construction, SEM, sustainability, activators
HYDRAULIC AND STRUCTURAL ASSESSMENT OF THE RAVI SYPHON STRUCTURE FOR UP GRADATION
The canal system in District Lahore and Kasur Irrigation Divisions have been suffering from shortage of water for a long time, due to reduction in capacity of Ravi Syphon (4200 Cs) as compared to design capacity (4853 Cs) at BRBD Link Canal which is outlived being a 70 years old structure. Irrigation Department carried out an underwater inspection of the Ravi Syphon with the help of divers in 2007.About 33% of the overall length could only be examined due to limitations and constraints. The inspection of Ravi Syphon structure is carried out recently though out the length of barrels (100%) by Irrigation Department and consultant whereas I supervise the inspection of barrels as Sub Divisional Officer, Ravi Syphon. Underwater and dry inspection of barrels were done through divers and recorded the images and videos for assessment of heath of structure. The results of underwater inspection revealed that the concrete face has been eroded at the syphon inlet leaving coarse aggregate exposed and in some places reinforcement steel bars are exposed. Many of the barrel joints were found to be leaked. Traverse joints in all five barrels were found to be in poor condition. Cracks in the concrete of the five barrels were observed. Two large holes were found in one of the barrels. Water stops are exposed at many places. In this research repairing of existing barrels is recommended by using sprayed concrete (shotcreting or guniting) for the top and sides of the barrel with reinforced concrete at the base. The proposed thickness of sprayed concrete is 2 inches (50 mm) and that of reinforced concrete is 4 inches (100 mm). Repairing of the damaged upstream bed protection is essential and proposed that scour holes should be rock filled.
Advancing Population Health Segmentation Using Explainable AI in Big Data Environments
Population health segmentation creates cohorts of individuals with similar health needs to help develop targeted healthcare interventions. The U.S. healthcare system faces potential benefits and obstacles when using diverse datasets comprising electronic health records and social determinants for patient segmentation. Although complex machine learning models improve segmentation precision, they function as "black boxes" that obstruct clinical acceptance. XAI methods, especially SHAP (Shapley Additive exPlanations), solve the problem of model opacity by clarifying which features contribute to model decisions. We present a framework that combines Explainable AI methods with big data analytics to create transparent population segmentation. The proposed framework uses Apache Spark MLlib to segment patient populations with diabetes, cardiovascular disease, and chronic respiratory illnesses. Our research shows that SHAP-based explanations effectively reveal main factors (e.g., lab values, comorbidities, social factors) that drive population segments. SHAP-based explanations allow clinicians to understand critical drivers such as lab values, medical comorbidities, and social factors for each patient segment, improving clinical decision-making. Our case studies and realistic examples demonstrate how explainable segmentation leads to optimal resource allocation while allowing for personalized care plans and ethical supervision. bias detection) In large-scale health systems. This discussion presents the technical and clinical benefits of implementing XAI-driven segmentation within U.S. healthcare systems to enhance population health outcomes through transparency and trust-building.
Explainable Artificial Intelligence (XAI), Population Health Segmentation, Big Data Analytics, SHAP (SHapley Additive Explanations), Healthcare Risk Stratification.
The Impact of Artificial Intelligence on Business Model Transformation in E-Commerce
The rapid advancement of Artificial Intelligence (AI) technologies has catalyzed a fundamental shift in the business models of e-commerce enterprises. This research investigates the transformative impact of AI on key components of e-commerce, focusing on how data-driven intelligence is reshaping value creation, customer engagement, and operational efficiency. Through a detailed analysis of AI applications—such as machine learning algorithms, predictive analytics, and natural language processing—the study explores how businesses are leveraging these tools to enhance personalization, streamline processes, and drive smarter decision-making. AI-powered recommendation systems and customer segmentation techniques have revolutionized the way online retailers interact with consumers, leading to significantly improved conversion rates and tailored user experiences. In parallel, automation tools such as chatbots, robotic process automation (RPA), and intelligent order processing systems have reduced operational costs while increasing speed and accuracy in customer service and logistics. The integration of AI in demand forecasting and inventory management has further enabled companies to anticipate market trends, optimize stock levels, and reduce waste. Additionally, this paper examines real-world case studies of AI implementation in e-commerce giants, evaluating their outcomes and implications for competitiveness and long-term strategy. Despite its many benefits, the research also highlights potential risks and limitations, including algorithmic bias, data privacy concerns, and the high costs associated with AI infrastructure. Overall, this study provides a comprehensive evaluation of how AI is not merely enhancing existing processes but actively transforming the foundational architecture of e-commerce business models.
Artificial Intelligence, E-Commerce, Business Model Transformation, Personalization, Automation, Predictive Analytics, Recommendation Systems, Competitive Advantage
Use of Artificial Intelligence for Tax Planning Optimization and Regulatory Compliance
Integrating Artificial Intelligence (AI) into financial systems has revolutionized numerous aspects of tax planning and regulatory compliance. This research explores the transformative role of AI in automating tax processes for both individuals and corporate entities. The study investigates how AI-based tools, particularly those leveraging machine learning and natural language processing, optimize tax burdens, detect anomalies in tax returns, and ensure compliance with increasingly complex domestic and international tax regulations. A multi-method approach was employed, combining a critical literature review with simulated model testing and analysis of current industry practices. The paper examines the efficiency of AI in identifying tax risks, automating transfer pricing assessments, and enhancing decision-making in tax consulting. Various AI models—including decision trees, neural networks, and anomaly detection algorithms—were evaluated for their performance in predictive accuracy and compliance automation. Visual tools such as charts, tables, and workflow diagrams are used to support the comparative analysis and demonstrate the effectiveness of AI applications. Key findings indicate that AI can reduce tax compliance time by up to 40%, improve anomaly detection accuracy by over 85%, and significantly minimize manual errors in tax reporting. However, the study also identifies critical challenges, including data privacy risks, algorithmic bias, and the interpretability of AI decisions in legal contexts. The implications of this research are twofold: First, AI presents a scalable and adaptive solution for tax optimization and regulatory alignment in an increasingly digitized global economy. Second, organizations must adopt ethical AI frameworks and robust data governance policies to mitigate the associated risks. This study serves as a foundational reference for policymakers, financial technologists, and tax professionals aiming to harness AI for smarter, compliant, and efficient tax systems.
Artificial Intelligence, Tax Planning, Regulatory Compliance, Machine Learning, Anomaly Detection, Transfer Pricing, Tax Automation, Data Privacy
Ethical Considerations in AI-Powered Decision-Making Systems
AI-powered decision-making systems are increasingly being adopted across various sectors, from healthcare to finance, and education to law enforcement, offering enhanced efficiency and accuracy. However, the integration of AI in decision-making processes raises significant ethical concerns that must be addressed to ensure fairness, accountability, and transparency. These systems often rely on complex algorithms that may inadvertently reinforce biases, leading to discriminatory outcomes. Moreover, the "black-box" nature of many AI systems complicates the process of understanding how decisions are made, making it difficult for stakeholders to hold the system accountable. This paper explores the ethical implications of AI-driven decision-making, focusing on the importance of ensuring fairness in algorithmic outcomes, protecting individual privacy, and promoting transparency in AI systems. The paper also emphasizes the need for regulatory frameworks and ethical guidelines to mitigate risks such as bias, discrimination, and lack of accountability. By examining real-world case studies and ongoing efforts to address these challenges, this paper aims to provide recommendations for the responsible development and deployment of AI decision-making systems.`
AI-Powered Solutions in Climate Change Mitigation and Environmental Sustainability
Climate change is a pressing global challenge requiring innovative, scalable solutions. Artificial Intelligence (AI) is emerging as a powerful tool in mitigating climate change and advancing environmental sustainability. AI applications span diverse areas, including climate modeling, renewable energy optimization, carbon capture, and resource management. Machine learning algorithms enhance climate prediction models, enabling policymakers to anticipate and adapt to future environmental conditions effectively. In renewable energy, AI improves the efficiency of solar and wind energy systems through predictive maintenance, resource forecasting, and grid optimization. Similarly, AI-driven solutions facilitate precision agriculture by optimizing water usage, reducing fertilizer dependency, and minimizing crop waste, thereby promoting sustainable farming practices. AI also plays a crucial role in monitoring deforestation, ocean health, and biodiversity loss via satellite imagery and data analytics, offering timely insights for conservation efforts. However, the adoption of AI in climate initiatives poses challenges, including energy-intensive training processes, ethical considerations, and equitable access to technology. Addressing these issues requires a collaborative effort between governments, industries, and academic institutions to ensure that AI technologies are developed and deployed responsibly. Through leveraging AI, humanity has a transformative opportunity to combat climate change and create a more sustainable future.