Academic International Journal of Engineering Science
The Academic International Journal of Engineering Science (AIJES) (Electronic ISSN: 2984-7745), published by Academic International Publishers, is a peer-reviewed, open-access scholarly journal in English, committed to disseminating high-quality research across a wide range of engineering sciences and applied engineering disciplines. The journal serves as an international platform for researchers, engineers, academicians, and industry professionals to publish scientific and technical work that adavances engineering knowledge, supports technological innovation, and addresses contemporary engineering challenges. AIJES operates under a fully open-access publishing model, ensuring that all content is freely and permanently accessible online without subscription or registration barriers. The journal assigns a Digital Object Identifier (DOI) to each published article to enhance visibility, discoverability, and long-term accessibility. AIJES follows a continuous publication model and is issued biannually (January–June and July–December). Established in 2022, AIJES obtained its ISSN license in 2023, marking its formal recognition as a scholarly publication. Since its inception, the journal has remained committed to high editorial standards and to supporting the global engineering research community by promoting innovative, interdisciplinary, and applied research across engineering and technology domains. Publishing with AIJES allows authors to reach a broad international and multidisciplinary audience and ensures that all manuscripts undergo a rigorous peer-review process to maintain originality, scientific rigour, technical accuracy, and ethical integrity. The journal accepts original research articles, review articles, and technical studies, providing a platform for work that advances knowledge across engineering sciences and applied technologies. The mission of AIJES is to promote excellence in engineering science by encouraging original, innovative, and interdisciplinary research that contributes to scientific advancement, technological development, and sustainable solutions. The journal seeks to foster international scholarly dialogue and collaboration among researchers, engineers, and academic institutions worldwide. Aims and Scope Aims Advance scientific and technical knowledge in engineering sciences and applied technologies Promote original, rigorous, and innovative engineering research Encourage interdisciplinary and comparative studies across engineering domains Support research addressing contemporary engineering, technological, energy, environmental, and industrial challenges Foster international academic collaboration and scholarly communication Scope The Academic International Journal of Engineering Science (AIJES) publishes original research articles, review articles, and technical studies that demonstrate novelty, methodological rigour, and ethical research standards in the field of Engineering and Technology, with a focus on the following subfields: The journal covers Architectural Engineering, Biomedical Engineering, Chemical Engineering, Civil Engineering, Communication Engineering, Computer Engineering, Computer Science, Data Science, Electrical Engineering, Electronic Engineering, Environmental Engineering, Energy Engineering, Industrial Engineering, Materials Science and Engineering, Mechanical Engineering, Systems Engineering, and other closely related fields in engineering science and technology. Copyright and Licensing Copyright © 2023 Academic International Journal of Engineering Science. Authors retain copyright to their work while granting AIJES the right to publish, reproduce, and distribute articles in all formats and media. All articles are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits sharing, copying, redistribution, adaptation, and reuse for any purpose, provided proper credit is given to the original authors and source. Archiving and Digital Preservation AIJES is committed to the long-term preservation, accessibility, and integrity of its published content. The journal participates in the LOCKSS and CLOCKSS archiving systems, which securely preserve content across a distributed global network of libraries and ensure continued access. A LOCKSS Publisher Manifest is available on the journal website. Authors are permitted to deposit the accepted (post-published) version of their articles in institutional or other repositories of their choice, in accordance with the journal's archiving policy and the terms of the Creative Commons license.
Net-Zero Energy Buildings: Integrating Smart Materials and Passive Design Strategies in Urban Architecture
Building design and operation are critical in mitigation of climate change since the construction industry contributes about 40 percent of all energy used on the planet and 36 percent of carbon dioxide to the environment. This paper will discuss how smart materials and passive design could be integrated to achieve net-zero energy performance in a building within an urban environment. The study examines the performance of enhanced building envelope technologies such as phase-change materials, electrochromic glazing, and self-healing concrete, as well as the passive design such as natural ventilation, optimization of daylighting, and the use of thermal mass. The evaluation of the energy performance of different climate conditions and designs under different design configurations was done by using building information modeling simulation tools. The discussion shows that the strategic incorporation of phase-change materials in building envelopes may decrease cooling loads by 15-30 percent and optimized passive design techniques may help to reduce energy demands by 40-55 percent, in comparison with the traditional building techniques. Use of self-healing materials makes the structure have a longer service life and less energy is used on the structure due to the maintenance process that has been reduced by approximately 20-35. Regional-responsive architectural methods, which simply considered the local climatic conditions, were discovered to contribute to the overall contribution of the building performance and comfort of occupants as well as reducing the dependency on mechanical systems. These results point to the fact that a thorough approach to integrating smart materials with passive design guidelines with the help of sophisticated simulation and modeling tools offers a good opportunity to reach the net-zero energy goal in urbanistic buildings. Not only does this strategy respond to energy efficiency and carbon goals but also facilitates resiliency, flexibility and sustainability of the built environment.
Net-zero energy buildings; Smart materials; Passive design; Building energy efficiency; Phase-change materials.
Copyright (c) 2024 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0
Artificial Intelligence–Driven Predictive Maintenance for Next-Generation Aircraft Systems
The aviation business encounters a major challenge with regard to the cost of aircraft maintenance, operational time, and assurance of safety in the era of rising complexity and demands of the fleet. The conventional reactive and preventive maintenance strategies have been unable to handle the more demanding needs of the modern aircraft systems that consist of advanced material, elaborate electronic systems as well as intricate assemblies of mechanisms. The paper will discuss the application and the performance of predictive maintenance technologies powered by artificial intelligence and specifically in the field of next generation aircraft systems, the use of machine learning to predict faults and the use of the digital twin technology. An analysis of the performance of the different predictive maintenance frameworks was done on several airplane platforms such as commercial aviation fleets and unmanned aerial vehicles. The study shows that machine learning models, especially ensemble algorithms of a random forest, gradient boosting machine, and deep neural network, have a higher accuracy of fault detection of more than 94% and lower false positive rates by about 67% than standard threshold-based monitoring systems. It was found that the implementation of digital twins allowed the real-time estimation of the state of a system with correlation coefficients over 0.92 between the expected and actual patterns of component degradation. Economic analysis indicates that predictive maintenance with the use of artificial intelligence lowers the number of unscheduled maintenance incidents by 48 per cent, reduces aircraft on-ground time by 35 per cent., and saves the company about 1.2 million dollars a year per plane due to optimized maintenance scheduling and improved component life. Commercial operator case studies show that predictive analytics can help to perform dedicated interventions that avoid disastrous failures but do not lead to preventable maintenance interventions, which helps achieve better safety margins and operational efficiency. It was detected that the combination of Internet of Things sensor networks with cloud-based analytics platforms allows scalable implementation in a wide variety of fleet configurations. Nonetheless, the issues associated with implementation such as data quality assurance, the requirements of model interpretability, and consideration of regulatory requirements demand cautiousness in the development, as well as implementation of the system. The results suggest that predictive maintenance based on artificial intelligence is a radical way of managing aviation systems, as it offers significant gains in safety, reliability, and economic operation as well as contributes to the development of autonomous maintenance decision-making algorithms in the future aviation systems.
Predictive maintenance; Artificial intelligence; Machine learning; Digital twin; Aircraft maintenance.
Copyright (c) 2025 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0
Sustainable Construction PracticesThe Use of Green Concrete for Environmental Impact Reduction
The construction industry plays a vital role in economic development; however, it is also a major contributor to environmental degradation due to intensive consumption of natural resources and significant greenhouse gas emissions that accelerate climate change. In response to these challenges, sustainable construction practices have gained increasing attention. Green concrete, defined as concrete formulated to minimize environmental impact through optimized material composition, has emerged as a promising innovation in sustainable building. This paper examines the potential of green concrete to transform the construction industry by reducing carbon emissions, promoting the efficient use of natural resources, and improving overall energy performance. It reviews the key materials used in green concrete production, including recycled aggregates, fly ash, and silica fume, and analyzes their roles in enhancing environmental sustainability while maintaining structural performance. Despite its advantages, the widespread adoption of green concrete faces several challenges, such as higher initial costs, limited availability of sustainable materials in certain regions, and concerns regarding long-term durability and performance. This study discusses these limitations and proposes possible mitigation strategies, including policy incentives, technological advancements, and strengthened collaboration among industry stakeholders. Drawing on case studies, experimental findings, and statistical data, the paper highlights the environmental benefits, material characteristics, and practical challenges associated with green concrete. The findings indicate that green concrete has substantial potential to serve as a core material in the development of sustainable and environmentally responsible built environments.
Green concrete; Sustainable construction; Construction industry; Environmental impact; Carbon emission reduction.
Adaptive Control Strategies for Energy-Efficient Smart Grids
The global move toward sustainable energy systems has led to the creation of smart power distribution networks that are able to balance the various energy sources, changing demand curves and real time operating limits. Adaptive control strategies are another form of new approach to smart grid control, which allows dynamically optimizing the flow of energy, load balancing, and integration of renewable energy. This review looks into the core principles, technological structures, and implementation issues of adaptive control systems in the present-day smart grids. The paper presents major control methodologies such as model predictive control, artificial intelligence-based control and distributed control architecture. It is a critical analysis of how these strategies can improve grid stability, reduce energy losses, and absorb intermittency of the renewable energy generation. Also, the paper reviews regulatory issues, cybersecurity standards, and interoperability standards that regulate smart grid activities across the world. The results reveal that adaptive control systems with the appropriate inclusion of robust communication infrastructure and modern sensing devices can greatly enhance grid efficiency by minimizing energy losses up to 20 percent, reducing operational expenses, and speeding up the process of integrating decentralized energy sources like renewable energy sources but maintaining the reliability and resilience of the system.
Adaptive control, Smart grids, Model predictive control, Renewable energy integration, Artificial intelligence,
Copyright (c) 2023 Academic International Journal of Engineering Science https://creativecommons.org/licenses/by/4.0
Green Synthesis of Nanomaterials for Advanced Water Desalination Systems
Water scarcity around the world has led to increased demands of effective and sustainable desalination processes that can transform sea water and brackish water to drinking water. Nanotechnology solutions are transformational in that the membrane performs better, the membrane is able to endure fouling better, and the membrane can remove contaminants more optimally. Nevertheless, the traditional techniques of producing nanomaterials such as the use of toxic substances, energy intensive processes and production of toxic wastes are against the concept of sustainability required to curb the water problems. Environmentally benign alternatives have been found through green synthesis methods which use environmental friendly biological agents such as plant extracts, microorganisms and biopolymers with high-performance nanomaterials made with minimal ecological footprint. In this paper the analyses will be of green synthesis techniques applicable in the fabrication of nanoparticles, nanocomposites, and nanostructured membranes that are specifically developed to serve the advanced water desalination systems. It has a critical analysis of synthesis mechanisms, techniques of characterization, as well as performance measurements such as salt rejection rates, permeate flux, antifouling properties, and stability during operation. The paper compares the use of applications of reverse osmosis, forward osmosis, membrane distillation, and nanofiltration technologies, the scale-up issue, and economic viability. This study also shows through case studies of Middle Eastern, Asian, and African desalination plants that green-synthesized nanomaterials exhibit salt rejection rates that are more than 99% with energy reduction of up to 30. The paper summarizes that green nanotechnology is a viable way forward in meeting water security by having environmentally responsible desalination plants in line with the concept of the circular economy and the United Nations Sustainable Development Goals.
Green synthesis, Nanomaterials, Water desalination, Reverse osmosis, Membrane technology, Plant-mediated synthesis,
Machine Learning–Based Predictive Maintenance in Industrial Robotics
The fast development of the industry 4.0 technologies is radically changing the operations and maintenance approaches of manufacturing activities in the global arena. Machine learning predictive maintenance is a paradigm shift of the old reactive and preventive methods which allow real time fault detection and predictions of the remaining useful life in industrial robots. In this paper, the technical framework and operation models of predictive maintenance in robotic systems by integrating machine learning algorithms with sensor networks and Industrial Internet of Things infrastructure are analyzed along with their implications. It is a critical analysis of different methodologies such as supervised learning, unsupervised learning, and deep learning approaches, and it discusses implementation issues involving the data quality, model interpretability, and system integration. The paper also assesses the regulatory factors and cybersecurity systems needed to deploy in industries. Using automotive, electronic, and aerospace manufacturing case studies, this study shows that predictive maintenance with the help of ML can reduce unexpected downtimes by up to 40 percent and increase the life of components. It is concluded in the paper that machine learning-driven predictive maintenance systems are revolutionary systems to provide efficient, reliable, and cost-effective industrial operations in the smart manufacturing era.
Predictive maintenance, Machine learning, Industrial robotics, Industry 4.0, Deep learning, Remaining useful life, Condition monitoring
Secure and Scalable Blockchain Frameworks for Internet of Things (IoT) Networks
The increasing exponentially growing Internet of Things (IoT) networks have posed hitherto unseen challenges, in security, data integrity, and scaling in distributed systems. It provides an ideal technology that solves numerous intrinsic weaknesses of traditional IoT designs such as single point of failure, unauthorized access, and data distortion, which can be resolved by the use of blockchain technology. In this paper, the author will discuss how blockchain frameworks can be implemented in conjunction with IoT networks, the technical structure, consensus algorithms, and security protocols intended to improve the level of reliability and trustworthiness of a system. It is a critical analysis of several blockchain applications such as the public as well as the private model and consortium model and also discusses scalability issues of each model of computational overhead, energy usage, and transaction throughput. The paper discusses novel solutions that include lightweight consensus algorithms, off-chain processing, sharding, and directed acyclic graph designs which are specifically designed to support the resource-constrained IoT devices. This study is shown using case studies in smart cities, healthcare, supply chain management and industrial automation industries to show how blockchain-IoT integration can help minimize security breaches by up to 60 percent without compromising system performance. The paper concludes that scalable and secure blockchain platforms are disruptive platforms to construct trusted, transparent and robust IoT networks in the era of pervasive computing.
Blockchain, Internet of Things, IoT security, Distributed ledger, Consensus mechanisms, Smart contracts, Scalability, Edge computing
A Review of Self-Healing Concrete Technologies for Sustainable Infrastructure
Self-healing concrete is a revolutionary innovation in the science of construction materials, which has an autonomous capacity to repair, which is one of the primary drawbacks of concrete cracking and progressive weakening. This review looks at the key self-healing strategies that have been developed over the last 20 years such as autogenous healing in which the surrounding hydration and carbonation promote the healing process, bacterial-based biological systems in which microbial induced calcite precipitation acts to promote healing, and encapsulated chemical healing agents which are released once the crack is formed. The evaluation of each technology is in terms of efficiency of healing, constraints in terms of practical crack width, environmental necessities, cost implications and practicability. The article examines the latest progress in the encapsulation devices, bacterial spore protectant mechanism, and the hybrid systems of combining various mechanisms of healing in order to have the overall effect of crack healing. It has been shown in performance data that bacterial systems are capable of healing cracks with a width of up to 0.5-0.8 millimeters and a high recovery of the strength and that autogenous techniques can seal micro-cracks with a width of less than 0.15 millimeters. Issues of implementation such as scalability of production, standardization of quality control, long-term validation of performance and integration of the construction practices are critically looked at. The life-cycle assessment has shown that the potential environmental impact can be reduced by 20-30 percent by using longer structural life and lowering maintenance rates. The results indicate that self-healing concrete technology, specifically hybrid solutions have a lot of potential to revolutionize sustainable infrastructure construction with a significant portion of construction budgets estimated to be spent on construction repair and maintenance being diminished by a significant margin, and even the carbon emissions produced by concrete production and replacement being reduced significantly.
Self-healing concrete, Autogenous healing, Bacterial concrete, Microbial induced calcite precipitation, Encapsulated healing agents
AI-Driven Security Framework for Internet of Mobility Things: A Mixed-Methods Analysis of Machine Learning Applications in Connected Vehicle Networks
Internet of Mobility Things (IoMT) is the future of transport in a nutshell. It is a car mash-up that combines chat, self-driving technology, and intelligent road technology, utilizing high-tech communications. This article combines two methods to understand how AI can be used to make IoMT safe: a systematic literature review and hands-on experiments. We filtered 45 papers in 202025 and also tested machine-learning models on typical car information. The review featured such AI giants as deep neural networks, federated learning, and adversarial training. At the end of the data, campus area networks CAN bus attack data was proven to yield our convolution neural networks CNNs a 99.2% detection rate and a 0.58% false positive result. Fed-learning models made 97.8% correct with data confidentiality. Altogether, artificial intelligence AI introduces numerous benefits in terms of security; yet we continue to encounter limitations due to the limitations of computer power and the necessity to operate in real-time. We consider that the hybrid AI-plus-traditional model is the most suitable solution to IoMT security. This paper will help establish standard AI security systems for next generation connected mobility.
Internet of Mobility Things, AI security, federated learning, intrusion detection, vehicular networks, cybersecurity.
A Systematic Review of Single-Tuned Passive Filters for Harmonic Mitigation in Distribution Networks
This paper is a systematic literature review on the relevance of passive filtering on eliminating harmonic distortion in distribution networks by optimal use and sizing of single-tuned passive filters (STPFs). The review explains how competing goals of power quality augmentation, loss reduction and cost minimization are balanced in the multi-objective optimization models through a critical analysis of the models. The key findings indicate that properly selected and designed STPFs are important to minimize the Total Harmonic Distortion (THD) and Current Harmonic Distortion (CHD) and at the same time enhance the voltage profiles and minimize power losses. These conclusions can be supported by simulations on IEEE standard test feeders, specifically the IEEE 33-bus system, and by empirical research of real distribution networks. The results have verified that the optimization-oriented filter allocation planning is much better than the traditional methods with significant power quality improvement under operating conditions.
harmonic mitigation, tuned passive filters, distribution system, multi-objective optimization, power quality.
A Comprehensive Review of Top-Loading Effects on Slope Stability: Load Magnitude, Distance, and Geological Heterogeneity
Top-loading slope stability has now been a key issue to infrastructure development on or near slopes. This review will be a synthesis of the empirical, numerical, and analytical research studies that investigate the effect of top-building loads on slope response, especially the magnitude of the load, the distance between the slope shoulder and the load, and the heterogeneity of the load material. Top-surface loads are always noted as influential causes of slope instability in contemporary literature (2020-2023), which can change the possible slip surfaces, shear-strain patterns, and the factors of safety. Combining three-dimensional numerical modelling, parametric tests, and seismic factors prove that the magnitude of loads and the distance to the slope shoulder is a significant parameter of the potential to slip, the movement of the environment, and the safety level. Heterogeneity of rock-contents and geological variability have a great impact on the ultimate loading capacity and failure modes. Dual failure modes, namely, local foundation failure and global slope failure, require integrated assessment frameworks, under the combined effects of both the static and seismic loading. The synthesis highlights that geology-based, holistic and site-specific evaluations are critical in laying plans of buildings on or close to the slope.
slope stability, top building loading, numerical modelling, soil-rock heterogeneity, seismic loading, factor of safety.
Copyright (c) 2023 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0
Advances and Limitations of Level Set Methods for Modeling the Freshwater–Saltwater Interface in Coastal Aquifers: Insights from the Henry Problem
Coastal intrusion of salt water into freshwater aquifers is a major threat to the freshwater security in the world. This paper is a critical review of the level set technique as applied in monitoring and modeling the freshwater-saltwater interface during the coastal aquifer system, based on the case of Henry problem. Combined with high-order numerical discretization, and with a suitable reinitialisation approach, level set formulations have demonstrated the ability to accurately solve interface dynamics and achieve better geometric flexibility than traditional modeling methods. Nevertheless, there remain formidable mathematical problems such as numerical diffusion that affects the sharpness of the interface, inefficient maintenance of mass balance over long simulations and computational inefficiencies, especially in the three-dimensional and long-term modeling cases. The study suggests the hybrid methodological frameworks to help eliminate these limitations by using a combination of level set tracking with the volume-of-fluid corrections to improve mass conservation and make it more applicable to real-life field studies.
saltwater intrusion, level set method, Henry problem, coastal aquifers, interface tracking, density-dependent groundwater flow.
A SPICE-Compatible Physics-Inspired Model for Molecular-Type Atomic Switches in Memory and Neuromorphic Circuits
Molecular-type atomic switches are an emerging technology in solid-electrolyte electrical devices, serving as nonvolatile memory devices and neuromorphic computers. However, there are no compact physics-inspired behavioral models to enable their integration into circuit-level simulations. This paper introduces a simple SPICE-compatible behavioral model that combines ion-density dynamics, filament growth, and exponential resistance growth to recreate the major experimental behaviors of these devices, such as bistable switching, rate-dependent plasticity, and quantized conductance steps. The model is represented as a set of coupled ordinary differential equations using a two-element state vector, and it is then converted into SPICE primitives as behavioral sources and state holding capacitors in practice. Calibration plans for measurable experimental quantities, such as switching time as a function of bias, threshold voltages, and quantized step magnitudes, are suggested. The resulting framework allows for quick exploration of circuits at the circuit level and evaluates the entire system at the system level, while considering material-specific parameterization demands and possible quantum effects refinements. It is a synthesis of proven experimental and theoretical literature, making it directly applicable to modern memory and neuromorphic applications.
Atomic switch; SPICE modeling; Filament growth; Plasticity; Calibration; Neuromorphic computing
Radial Basis Function Neural Networks for Rainfall Prediction and Urban Tree Management in Tropical Malaysia
The current research paper summarizes a wide body of information on Radial Basis Function Neural Networks (RBFNNs) to predict rainfall and its application in urban trees management in tropical Malaysia. Several peer-reviewed articles were analyzed to determine the predictive accuracy, architecture, feature engineering, and integration issues of RBFNNs. It was found that the architectural properties of RBFNNs were appropriate for nonlinearity and nonstationarity in the precipitation processes. Relative performance assessments invariably ranked RBFNNs as one of the most efficient tools compared to other machine learning models, showing better Root Mean Square Error (RMSE) and generalization capabilities across various hydro-climatic regimes. It was identified that incorporating rainfall predictions using RBFNNs with Geographic Information Systems (GIS)-based applications like GeoTrees and the Kuala Lumpur Multi-Hazard Platform (KL-MHP) is a valid solution to facilitate predictive maintenance, reduce costs, and alleviate hazards in tropical urban settings. The results support the use of RBFNNs as a practical and, in many cases, better forecasting element in urban tree management systems, offering quantifiable advantages in maintenance planning, risk reduction, and resource distribution in municipal arboriculture programs.
RBFNN; rainfall prediction; tropical Malaysia; urban tree management; GIS integration; predictive maintenance; urban forestry.
Sustainable Utilization of Sugarcane Bagasse Ash in Concrete: Experimental Insights into Mechanical Performance
This study evaluates the potential of Sugarcane Bagasse Ash (SBA) as a partial substitute for Ordinary Portland Cement (OPC) to enhance the sustainability and mechanical performance of concrete. Given that cement production is a significant contributor to global CO₂ emissions, the investigation focuses on the valorization of agricultural residue as a supplementary cementitious material. Concrete mixtures were prepared with SBA replacement levels of 0%, 5%, 10%, 15%, and 20%, and the resulting specimens were subjected to compressive and tensile strength testing at curing ages of 7, 14, and 28 days. The experimental results indicate that SBA replacement in the range of 5–10% leads to notable improvements in both compressive and tensile strengths, particularly at extended curing periods. This enhancement is attributed to the pozzolanic activity of SBA, wherein its amorphous silica content reacts with calcium hydroxide released during cement hydration to produce additional calcium silicate hydrate (C–S–H) gel, thereby refining the microstructure and enhancing matrix densification. Conversely, higher replacement levels (15–20%) resulted in strength reduction, likely due to the dilution of cementitious phases and insufficient pozzolanic contribution. Overall, the findings demonstrate that SBA, when used at optimal substitution levels, serves as a viable and environmentally responsible alternative to reduce cement consumption, lower construction costs, and promote effective waste utilization, contributing to more sustainable and resilient construction practices.
Smart Logistics Integration: 5G Networks and IoT Technologies in Modern Supply Chain Management
The combination of 5G and internet of things IoT technology is fundamentally a game-changer in smart logistics. This paper will consider the impact of 5G-enabled IoT networks on the entire supply chain, examining innovative network designs such as network slicing, multi-access edge computing (MEC), and low-power wide-area networks (LPWAN). In essence, the paper demonstrating that such technologies can increase warehouse efficiency by 20% and that the use of solutions rooted in narrowband IoT (NB-IoT) may help increase yard dispatch efficiency by 87%. Our systematic digital twin simulations and real-world tests have shown that bespoke network slices and edge computing are highly effective in meeting numerous logistics requirements. They improve throughput, strip off latency and enhance reliability. The paper provides specific, concrete information that IT professionals, telecom engineers, and supply-chain managers can apply in practice when implementing next-generation logistics solutions.
Smart logistics, 5G, Internet of Things, supply chain management, network slicing, edge computing.
Artificial Intelligence driven Customer Behavior Analytics for Healthcare: A Systematic Review of Patient Experience, Engagement, and Decision-Making Patterns
The study discussed systematic literature review regarding the usage of Artificial Intelligence (AI) technologies as tools for analyzing, understanding and predicting consumer behavior in healthcare ecosystems, including patterns of patient engagement, service utilization and decision-making. Design/methodology/approach - A systematic literature review was performed across the major academic databases including PubMed, Scopus, Web of Science and IEEE Xplore, capturing articles published from 1980 to 2024. The literature review provided a synthesis of 87 peer-reviewed articles, which focus on the application of AI for customer behavior analytics in the healthcare context, categorized into five thematic groups: predictive analytics, personalization engines, sentiment analysis, mapping patient journeys, and decision support systems. Findings - AI technologies, in particular machine learning and natural language processing, are making strides in understanding complex patient behaviors, with predictive accuracy rates exceeding 85% in appointment adherence and compliance with treatment plans. Deep learning models are emerging as particularly useful in identifying patients who are at-risk and personalizing healthcare interventions, which have also resulted in increased patient satisfaction scores and lower healthcare costs. Research limitations/implications – Although technology has advanced significantly, obstacles remain related to data privacy, bias in algorithms, the transparency of AI models, and integration with current healthcare information systems. This review identifies a research gap in longitudinal studies and promotes further research into ethical frameworks for AI implementation in healthcare contexts. Practical implications – Healthcare organizations can use patient data and AI-informed behavioral insights to improve patient experiences, better allocate resources, decrease no-show rates, and develop health promotion initiatives targeting specific populations. Administrators and policymakers can utilize the implications presented in this study as actionable frameworks for improvement. Originality/value – This is the first review to comprehensively synthesize AI technology with healthcare customer behavior, to provide taxonomy of AI technologies, and to present implications for future research as well as substantial contributions to the practice of healthcare delivery.
Artificial Intelligence, Healthcare Analytics, Patient Experience, Digital Health, Patient Engagement
Exploring Rice Husk Ash in Uncontrolled Condition as Supplementary Cementitious Materials for Enhanced Performance in Construction Materials
This study investigates the potential of Rice Husk Ash (RHA), an abundant agricultural byproduct in Bangladesh, as a sustainable supplementary cementitious material (SCM) in concrete. Ordinary Portland Cement (OPC) was partially replaced with locally produced, unprocessed RHA at proportions of 5%, 10%, 15%, and 20% by weight, with a control mix (0% RHA) for comparison. Mechanical performance was evaluated through compressive and tensile strength tests at 7, 14, and 28 days, complemented by durability assessments in saltwater conditions. Microstructural and chemical analyses were conducted using X-ray Diffraction (XRD), Energy Dispersive X-ray Spectroscopy (EDX), and Scanning Electron Microscopy (SEM). Results revealed that 10% RHA replacement achieved the optimal balance between strength and sustainability, with compressive strength reaching 22.06 MPa and tensile strength showing improved pozzolanic activity over time. Higher replacement levels (15–20%) significantly reduced strength due to dilution effects. Microstructural studies confirmed the high amorphous silica content and favorable morphology of RHA, supporting its pozzolanic reactivity. Incorporating RHA reduces cement demand, lowers emissions, and offers an environmentally friendly solution for managing agricultural waste. The findings establish that a 10% substitution of OPC with unprocessed RHA provides an effective, eco-friendly, and cost-efficient pathway toward green construction in Bangladesh and other rice-producing regions.
Sustainable Development, Rice Husk Ash, Concrete Strength, Waste Management.
Retrofitting of RCC Beams in the Shear Region Using Carbon Fiber-Reinforced Polymer
In recent years, Fiber Reinforced Polymers (FRPs) have gained significant popularity over traditional materials for enhancing the shear strength of Reinforced Concrete (RC) beams, particularly when they are deficient in shear or prone to shear failure. FRPs are advanced composite materials widely used to retrofit existing RC beams and improve the shear resistance of new ones. Among these, Carbon Fiber Reinforced Polymers (CFRP) stand out due to their lightweight nature, exceptional tensile strength, superior corrosion resistance, and ease of application in construction. Shear failure is commonly observed under seismic and impact loading conditions, making CFRP reinforcement a complex but essential solution. Although extensive research has been conducted in this field, existing studies remain insufficient. This study focuses on strengthening RC beams in shear using CFRP. A total of six simply supported RC beams (150 mm × 200 mm × 1800 mm) were subjected to four-point bending tests. The primary objective is to enhance the shear load-bearing capacity of the beams. The research involved testing RC beams reinforced with CFRP, including two control specimens and four beams wrapped with a single layer of CFRP. The wrapping configurations included two beams with 356 mm CFRP wrapping and two with 508 mm wrapping. The average maximum load capacity of the unwrapped beams was recorded at 84.74 kN, while those wrapped with 356 mm CFRP reached 120.46 kN. Meanwhile, the beams wrapped with 508 mm CFRP demonstrated an increased load capacity of 155.815 kN. Cracking was observed earlier in the unwrapped beams compared to those strengthened with CFRP, indicating improved structural performance. The failure patterns of the beams varied, particularly between those with and without CFRP wrapping. This study highlights CFRP's potential and provides a foundation for future research on optimizing its configurations for infrastructure rehabilitation.
Copyright (c) 2025 Academic International Journal of Engineering Science https://creativecommons.org/licenses/by/4.0/
Review of High-Strength Concrete Structures with Hybrid Fibers
This work provides a thorough evaluation of high strength concrete (HSC) constructions augmented with hybrid fibers, emphasizing their mechanical properties and structural performance. High-strength concrete, characterized by a compressive strength of 55 MPa or greater, is extensively employed in contemporary construction, especially in skyscrapers, owing to its capacity to reduce column dimensions. Nonetheless, conventional HSC demonstrates deficiencies, such as inadequate tensile strength and ductility, which may jeopardize structural integrity. The integration of fibers into concrete mixtures has been increasingly prevalent during the past decade to tackle these issues. Recent research has investigated the utilization of diverse fiber types in hybrid combinations to enhance various performance attributes of HSC. This review consolidates research findings from 2021 onwards, emphasizing the role of hybrid fibers in improving the mechanical properties of high-strength concrete. The paper examines the ramifications of these advancements for future research and practical applications, highlighting the necessity for additional exploration of fiber interactions, performance assessments under varied conditions, and the establishment of standardized protocols for the utilization of hybrid fibers in construction. This review seeks to further the development of concrete technology and its utilization in high-performance constructions.
High strength concrete, fibers, hybrid fibers, ultra high performance
Copyright (c) 2025 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0/
Machine Learning Techniques and Insights for Cardiovascular or Heart Disease Prediction
Heart disease has been one of the major health concerns over the decades regardless of age, weight, or gender. Here in the article, we try to highlight the importance of early detection and the prevention of life-threatening heart-related diseases, including heart attacks and strokes. For this review paper, we include raw data from clinics and also include the famous Framingham Heart Study dataset, which is a popular cardiovascular dataset, including patient records that emphasize important human risk factors including age, blood pressure, cholesterol, smoking status, and family history. Precision, recall, accuracy, F1-score, and ROC-AUC metrics are such techniques and algorithms that are used to evaluate the implementation and performance of advanced machine learning approaches, including Gradient Boosting Machines (GBM), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Convolutional Neural Network (CNN) and many more. Here, we
Heart Disease Prediction, Early Detection, Cardiovascular Health, Gradient Boosting Machines, K- Nearest Neighbors, Artificial Neural Networks.
Slope Stability Considering the Top Building Load
In this paper, the effect of building loads on the stability of slopes was analyzed through three dimensions of the numerical model. Various load pressures that include 120KPa, 200 KPa, and 300KPa were applied to a specified slope model about geometric and mechanical characteristics. The analysis was performed using FLAC3D software for displacement, strain, as well as the safety of the tunnel. The following conclusions can be drawn from the conclusions as they show a strong correlation between different loads and the distances from the shoulder of the slope to determine the stability of the slope and also is valuable information as far as engineering is concerned.
Copyright (c) 2024 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0/
Intrusion Detection Systems: A Revisit of Performance Evaluation Parameters
The effectiveness efficiency of various intrusion detection systems (IDSs) have always been evaluated on various datasets using different techniques. This evaluation normally covers a range of metrics, ranging from those that evaluated their accuracy to those that focus on their usability and performance. The evaluation of several attributes of IDS depends on the use of several metrics which have been proposed and used in various studies. Regrettably, there are no existing benchmark metric for the network intrusion detection systems as studies are still ongoing in this regard. Quantitative evaluation of the performance of IDS is mainly based on the use of several performance but in this article, most of the existing performance metrics were reviewed and used for the evaluation of the performance of various IDSs using different benchmark datasets. Furthermore, this study will aid in better understanding of the various performance metrics used for the evaluation of the performance of novel IDSs.; it will also further the insights to those that have found interest in the uses and the development of IDSs and related areas.
Computer systems security, Intrusion detection system, Network Security, Performance evaluation metrics, Cyber-Physical Systems.
Copyright (c) 2024 Academic international journal of engineering science https://creativecommons.org/licenses/by/4.0
Effect of Training on the Construction Project Performance Using System Dynamic
The sector of construction is important for the country's economic, community, and ecological agendas and therefore takes an important role in Iraq's future; managers of the project are in charge of activities planning and coordinating, and at the same time, obtaining the best charge, period, resources of the human and material so that objectives of the predetermined project are finally gotten. Hence, it is productivity can be increased if the skills of the managers of the projects are improved the paper aims to determine the effect of training on the project's performance in terms of both time and quality to obtain the causal effect on the projects. The paper's methodology consists of a questionnaire distributed to 140 experts in different sectors to obtain the problem that affects the time and quality of the projects and then analyze these problems using the system dynamic technique. Depending on the effort of the research, the subsequent assumptions can be utilized: The training system in Iraqi construction is very weak and very negligible and doesn't contain any supervision Most of the engineers are not multidiscipline, and even if they work on the other discipline, the luck the principle of it. The nature of the construction industry in Iraq tends to use traditional methods in construction and refuses to use modern techniques. There are a series of shortages in communication between different disciplines and that creates a gap that leads to becoming an obstacle in the front of project completion.
Training, System Dynamic, Construction Projects.
Biometric Payment Systems in India – A Case Study of ThumbPay by Proxgy
The rapid evolution of the digital payment ecosystem in India is changing the way citizens access and use financial services. Developed by Proxgy, ThumbPay represents an innovative leap in integrating Aadhaar-based biometric authentication with the Unified Payments Interface, UPI, thereby allowing users to authorize transactions with just their fingerprints. Contrasting with the Amazon One or Tap-and-Pay by Tencent, which have an appeal toward convenience and luxury, ThumbPay has been created with a vision for accessibility and inclusion—solving the problems of unbanked populations and those who cannot use smartphones. This paper examines the technical framework, the model of operation, and the implications of ThumbPay regarding the promotion of financial inclusion in India. It also critically analyzes various issues related to data privacy and security and its readiness concerning regulatory guidelines under the frameworks provided by the RBI, NPCI, and DPDPA (2023). The study concludes that biometric payment systems, like ThumbPay, might act as a transformative agent in achieving an inclusive, secure, and transparent digital financial ecosystem in India.
Biometric payment systems, ThumbPay, Proxgy, Aadhaar, UPI, Financial inclusion, Data privacy, Digital India, FinTech, DPDPA 2023
State of Art: Carbon Fiber Reinforced Concrete: A Review
Because plain concrete can break easily, checking how well it can bend, and handle being pulled apart is important. Even though plain concrete is tough and strong, it's not so good at resisting being stretched. To fix this, the construction industry uses different kinds of fibers to make concrete more flexible, lasting, and able to resist being pulled. Steel fiber is often used because it's strong but can get rusty. Conversely, Carbon Fiber (CF) is a viable choice for Fiber-Reinforced Concrete (FRC). because it doesn't rust, is lightweight, and has strong tensile strength. However, some researchers have noticed that CF hasn't been used as much as it could be. This research gives a summary of CF, its use in fixing and restoring projects, and the properties of CF Reinforced Concrete (CFRC). It looks at the strength, toughness, and bending qualities of CF, and studies if different types of CF can be used to fix and rehab buildings.
Carbon, concrete, Carbon Fiber, Fiber-Reinforced Concrete, Vapor-Grown Carbon Fibers.
Copyright (c) 2024 Journals Academic international journal of engineering science https://creativecommons.org/licenses/by/4.0/
State of Art: Seismic Failure Impact on Concrete Bridges
Seismic failures in concrete bridges can lead to severe structural damage, resulting in partial or complete collapse, significant economic losses, and potential disruptions to transportation networks. Such failures may arise from inadequate design, aging materials, or insufficient seismic detailing, making bridges vulnerable during earthquakes. The impact includes immediate structural damage and long-term consequences for public safety and infrastructure resilience, highlighting the need for effective seismic-resistant designs and retrofitting strategies to minimize risks.
Seismic , Concrete , Bridges , Earthquakes , Flexural.
Roles of AI in Engineering: A Detailed OverviewAI and Data-Driven Engineering: Unlocking Hidden Potential
Advanced technology, specifically Artificial Intelligence (AI) is quickly incorporated into the engineering practices in every industry. This capability of FC together with the ability to analyze data, recognize certain patterns, and make intelligent choices has brought dramatic changes in the design, optimization, virtual simulation, production, and construction. To recap this paper gives an elaborate account of the roles of AI in today's engineering showcasing the major roles in terms of efficiency, creativity, and quality. In design and optimization, AI-enhanced generative design and structural optimization consider solutions that cannot be manually developed because they have to fulfill all the design requirements and constraints while utilizing the least amount of material and minimizing costs. In simulation and modeling, AI boosts processes such as Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), and Multiphysics simulations which include automation of several complex tasks and improved performance results. However, it contributes immensely to the manufacturing and production processes. A Predictive maintenance system anticipates the ability of equipment for maintenance before it degenerates and plans for a time that will allow minimal or no disruption of machinery. Automated inspection mechanisms based on artificial intelligence ensure real-time identification of faulty products, hence enhancing quality. AI interferes in construction to improve project planning, proper use of resources, and smart infrastructure surveillance through drones and sensors, reducing risks and improving operations. These developments such as data analytics, machine learning, and digital twins expand AI use for data analysis, prediction models, and optimizing real-time systems. A shift towards intelligent automation and a specific difference between the holders of engineering disciplines is now possible through the use of AI. Integration of artificial intelligence can help engineers minimize rapture times, minimize mistakes, and design considerably more sustainable designs for complex problems. These are key roles that this paper aims to dissect to provide the reader with the best understanding of how AI reformulates the engineering domain and fosters advancements across sectors.
Artificial Intelligence; Engineering Applications; Generative Design; Simulation and Modeling; Optimization.
CRISPR-Based Synthetic Gene Circuits for Industrial and Therapeutic Applications
Biomolecular engineering has been transformed by the use of Clustered Regularly Interspaced Short Palindromic Repeats technology, which allows the manipulation of genetic systems with precision and also with programmability. This paper discusses how synthetic gene circuits that are based on CRISPR have been developed and used in industrial biomanufacturing and treatment therapies. The study will provide an assessment of programmable gene regulation devices, the development of biosensors, metabolic pathway optimization plans, and the ethical and safety implications of the same. Sophisticated CRISPR platforms such as CRISPR interference, CRISPR activation and base editing platforms were examined using their ability to build complex genetic circuits that dynamically react to environmental signals and cellular conditions. The study indicates that CRISPR-based circuits can be used to statistically regulate gene expression at a temporal resolution of 1530 minutes as well as dynamic ranges of over 100fold, which are many orders of magnitude better than traditional regulatory systems. The biosensor applications of CRISPR-coupled detection saw sensitivity increase of 10-1000-fold over conventional detection mechanisms and was able to detect target molecules at nanomolar to picomolar concentrations. Optimization of metabolic pathways by CRISPR-mediated regulation yielded productivity improvements of 40-300% on several bioprocesses in the industry such as biofuel production, pharmaceutical synthesis, and specialty chemical manufacturing. There were encouraging outcomes in cancer immunotherapy, genetic disease repair, and infectious disease treatment which were shown to be successfully used therapeutically, and some constructs have made it to the stages of clinical trials. The ethical review showed that there were significant issues with germline modification, equitable access to genetic therapies, unintended ecological impact of engineered organisms, and dual-use possibility of biosecurity threats. Risks revealed during safety assessment were off-target effects, immunogenicity, horizontal gene transfer and evolutionary stability of engineered systems. The results show that CRISPR-based synthetic gene circuits are revolutionary instruments of biomolecular engineering with significant applications to industrial biotechnology and precision medicine, and they also require strict ethical systems, detailed safety measures and accountable systems of governance to be enacted to guarantee positive applications.
CRISPR technology; Synthetic gene circuits; Biomolecular engineering; Programmable gene regulation.
Copyright (c) 2024 Academic International Journal of Engineering Science https://creativecommons.org/licenses/by/4.0
Wearable Biosensors and AI Analytics for Continuous Health Monitoring and Early Disease Detection
The advent of wearable biosensor technologies is a paradigm shift in healthcare provision, as this technology allows continuous physiological monitoring beyond the scope of routine clinical practice, and allows the development of disease conditions through real time analysis of the data. This paper looks at how flexible electronics, advanced nanomaterials, and artificial intelligence analytics can work together to create holistic health monitoring systems that can identify the slightest physiological variations that signify the development of pathological conditions. It has been a multi-stage study that included development of flexible sensor arrays using graphene-based nanomaterials and conducting polymers, application of machine learning algorithms to real-time (physiological) signal processing, and biosensor systems integrated with telemedicine platforms to manage patients remotely. The study proves that fabricated flexible biosensor arrays of screen-printed polyimide substrate can be used with mechanical flexibility greater than 10,000 bending cycles without losing electrochemical functionality within 5 percent of their initial performance. A set of nanomaterial-based electrodes with reduced graphene oxide showed the sensitivity enhancement of 340 percent relative to the usual metallic electrodes in detecting biomarkers in physiological fluids. The accuracy of disease state classification of machine learning models trained on continuous data through biosensors was 91.7% of cardiovascular anomalies and 87.3% of metabolic dysfunction, with an average of 72 hours ago before clinical symptoms appear. It was integrated with telemedicine systems which allowed real-time data transfer, automatic triggering of alerts in case of abnormal physiological behavior, and remote physician consultation leading to after-effects of 43% less emergency department visits in monitored patients. The economic analysis shows that a sustained monitoring by the use of wearable biosensors would save healthcare expenditures by about 2840 per patient every year based on early detection, low hospitalization, and better medication therapies. Implementation is however characterized by very serious challenges such as regulatory compliance requirements with reference to certification of medical devices, data security issues pertaining to the constant delivery of sensitive health information, patient acceptance issues, and technical constraint to multi-analyte ability of sensing. The results show that wearable biosensor systems in combination with artificial intelligence analytics could be a valid direction towards personalized, proactive healthcare provision, but their successful implementation should pay close attention to regulatory frameworks, cybersecurity efforts, and clinical validation guidelines to ensure patient safety and data quality.
Wearable biosensors; Flexible electronics; Nanomaterials; Graphene-based sensors; Continuous health monitoring.