KHWARIZMIA (E_ISSN: 3078-2694) is a specialized academic journal dedicated to publishing cutting-edge research in various engineering disciplines. As a publication of Peninsula Publishing Press in Dubai, KHWARIZMIA serves as a platform for researchers, scholars, and industry professionals to share their latest advancements in fields such as mechanical engineering, civil engineering, electrical engineering, and environmental engineering. The journal fosters innovation and aims to advance knowledge by promoting high-quality, peer-reviewed research that addresses both theoretical and practical challenges within the engineering domain.
Impact of Post-mining Transformations on Slope Stability and Sustainability Processes in the Base Titanium Mining Area, Kenya
The gradual transformations of the open-pit titanium mining legacy in the south coast of Kwale, County in Kenya, has led to the formed gently rolling terrains into steep, unstable slopes that have endangered both the environmental and community safety. This study delves to (1) measure changes of landforms caused by post mining modifications at base titanium geosites, (2) assess how these changes impact slope stability, and (3) identify opportunities designing more sustainable post mining landforms and practices. Using drone (UAV) photogrammetry and satellite images, the research generated high-resolution digital surface and terrain models which were applied to compare pre- and post-mining topography within a transected area of 2 km x 2 km. Slope angles derived from the normalized digital surface model were categorized into stable (<30°) and high-risk (>30°) groups. Changes in vegetation cover were analyzed with the normalized difference vegetation index (NDVI) and field measurements were used to verify remote sensing data on slopes and land cover. Results show that unclaimed mine pits have an average slope of 35°, which is above the stability threshold. Areas that have been actively reclaimed through topsoil replacement and strategic revegetation have slopes below 25° and dense plant cover. High-risk zones match closely with bare-soil tailings dumps and eroded pit walls where poor drainage has increased the risk of mass wasting; however, reclaimed embankments with engineered drainage and early-stage vegetation are resistant to rainfall-induced erosion. These results prove that integrated planning for slopes, drainage systems, and ecological restoration is important in securing post-mining landscapes. In conclusion, sustainable landforms after mining can be actualized when the reclamation focus is on (a) optimization of steep slope angles to below 30°, (b) installation of cut-offs and controlling water flows using toe drains, and (c) quick establishment of native plants for soil stabilization and reduction of surface runoffs. Lastly, adopting these mitigation practices would enhance geo-hazards adaptation and resilience. In addition, this will also enhance promotion of ecological succession and enabling future sustainable resource nexus land use approaches such as water storage, water recharge, agroforestry and ecotourism, thus resulting in transformations of former mining area into stable multifunctional and sustainable landscapes.
Copyright (c) 2026 Fredrick Kayusi, Petros Chavula https://creativecommons.org/licenses/by/4.0
Solving the Heat Conduction Equation Using Butterfly Algorithm Guided by Machine Learning
In this paper, we propose a hybrid computational framework based on a machine-learning model for the one-dimensional transient heat conduction equation, where the hybrid model is optimized using the Butterfly Optimization Algorithm (BOA). Rather than using classical numerical discretization techniques, the temperature distribution is modeled by a feed-forward neural network capable of learning the spaio-temporal relation of space and time coordinates. Inspired by its potential as a derivative-free metaheuristic optimizer, BOA guides the training process to approximate the best parameters of the network, within physical constraints. We use a hybrid loss formulation that satisfies boundary conditions and initial conditions, interior supervised temperature samples, and the governing heat-equation residual to maintain numerical accuracy and consistency with physical principles. This BOA–ML solver is then assessed by representative simulations of fundamental heat conduction problems, with results provided in the form of convergence curves, temperature-field heatmaps, and absolute errors distributions. The results from the various simulation cases validate that on the one hand the proposed combination of data-driven and physics-based learning approach leverages the stable temperature prediction capability of the hybrid BOA-guided learning approach, which consistently yields smaller approximation errors than purely data-driven and purely physics-based training variants. The new proposed method may serve as a flexible alternative for heat-transfer modeling, and extensions to higher-dimensional conduction problems and non-simplified thermal boundary conditions are possible directions for future work.
Copyright (c) 2026 Habeeb Al-thabhawee, Ghassan AL-Thabhawee, Hussein Alkattan https://creativecommons.org/licenses/by/4.0
Statistical Assessment of Long-Term Bridge Deterioration Using Field-Measured Structural Response Data
This study aimed to assess the impact of environmental conditions on bridge deterioration by leveraging a comprehensive dataset comprising 1340 records and 15 variables. The dataset included numeric variables such as acceleration, temperature, humidity, wind speed, and categorical variables like bridge and sensor IDs, structural condition, and damage class. Using advanced predictive modeling techniques, we integrated key environmental variables to enhance the understanding of their influence on structural integrity. Our methodology involved analyzing the dataset to identify patterns of bridge deterioration, focusing on the role of environmental factors as critical determinants of structural health. We employed machine learning algorithms to forecast deterioration over a 30-day period, utilizing forecast scores as a primary outcome measure. The results showed a significant correlation between environmental conditions and bridge degradation, with higher average degradation scores in structures exposed to increased temperatures (mean 25.186°C), humidity (mean 59.836%), and wind speeds (mean 7.602 m/s). Bridges classified under "Severe" damage exhibited notably higher forecast scores, indicating accelerated deterioration compared to those with "Minor" or "No Damage." These findings suggest that integrating environmental data into predictive models can significantly enhance the accuracy of deterioration forecasts, aiding in the prioritization of maintenance activities. Our findings suggest that predictive models incorporating environmental variables offer a robust framework for infrastructure management. This approach facilitates data-driven decision-making, enabling more effective maintenance scheduling and resource allocation. Ultimately, the integration of environmental monitoring into bridge maintenance strategies is crucial for optimizing infrastructure resilience and longevity in the face of evolving environmental challenges.
Copyright (c) 2025 Amar Alhamd Mohammed Yasser, H.M.A. Asfoor, K K Igorevich https://creativecommons.org/licenses/by/4.0
Integration of Titanium Alloys in Robotic Design and the Rise of Medical Robotics
The fusion of advanced materials with modern robotics creates excellent results in robotic systems, increasingly applied in industrial, aerospace, and medical applications. Among such materials titanium alloys have found wide acceptance due to high specific strengths with attributes that include resistance to corrosion, biocompatibility, thermal stability—thus ideal for surgical robots, mobile robots as well as precision robots. The main problems are cost, machinability and weldability plus low-temperature brittleness. Meanwhile, innovation in medical robotics has raised the bar wherein the current scope of implementation includes surgical support, sterilization support and ancillary medication support together with vital sign monitoring and remote procedures highly necessitated because of COVID-19. Exemplified by da Vinci robot systems that productively combine titanium parts with state-of-art technologies giving a chance for minimally invasive surgery extremely accurate and safe operations. This paper will review the major titanium alloys which currently find application in making robots, criteria relevant to material selection and development cum application of medical robots besides having technical cum economic cum ethical challenges. It also discusses future trajectories in AI, VR/AR, and surgical automation with a note that the right choice of alloys and further innovations are key to making possible the subsequent generation of high-performance robotic systems.
Copyright (c) 2025 Noah Mohammed Saleh, Mehdi Qahraman Fakhruldin, Raed A. Hasan, Ali Mohammed Saleh, Muhaiman faleh hamad, Khalil F. Yasin, Muqdad Daham Jasim https://creativecommons.org/licenses/by/4.0
Energy-Efficient Hyperparameter Optimization in Machine Learning Using Coati Optimization Algorithm (CMRLCCOA)
Increasing demand for energy-efficient machine learning models requires optimization strategies that minimize computing costs and increase model performance. We use the Coati Optimization Algorithm to optimize SVM classifier hyperparameters. Lévy Flight explores, Sine Chaotic Mapping initializes populations, and Convex Lens Imaging Reverse Learning accurately searches. Iris dataset model accuracy and energy consumption improved with the method. CMRLCCOA balances computational economy and model accuracy, making it suited for energy-efficient machine learning. CMRLCCOA-based SVM hyperparameter optimization improves exploration and exploitation through Sine Chaotic Mapping, Lévy Flight, and Convex Lens Imaging Reverse Learning. Machine learning models in resource-constrained situations must maximize accuracy and minimize energy use. We found that CMRLCCOA fits these standards. This study demonstrates that CMRLCCOA helps machine learning conserve energy while preserving model accuracy. CMRLCCOA maximizes energy-efficient hyperparameter optimization by improving functional and non-functional SVM hyperparameters.
Copyright (c) 2025 Yasmin Makki Mohialden, Saba Abdulbaqi Salman, Ahmed Hussein Ali https://creativecommons.org/licenses/by/4.0
A comparative analysis of deep learning models used in early detection of breast cancer
Breast cancer is a type of cancer that begins in the cells of the breast. It can occur in both men and women, but it is more common in women. The cancer usually develops in the milk ducts or milk-producing glands. Possible symptoms include breast lumps, changes in shape or size, or unusual discharge. Early detection and appropriate treatment are key to improved outcomes. Breast cancer is a major challenge in the healthcare sector, and finding ways to detect it early is crucial, as early diagnosis offers great hope for treatment and cure. In this study, we present a comparative analysis of four prominent deep learning models, including VGG16, ResNet50, InceptionV3, and CNN from scratch, on a specific classification task. The models were evaluated using several metrics, including accuracy, sensitivity, specificity, AUC (Area Under the Curve), precision, recall, false positive rate (FPR), and false negative rate (FNR). The results demonstrate that the ResNet50 model outperforms the others in almost all evaluation metrics, achieving the highest accuracy (94.8%), sensitivity (94.5%), specificity (95.0%), and AUC (0.98). The VGG16 model also performs well, showing promising results with an accuracy of 93.5% and an AUC of 0.97. The study highlights the effectiveness of ResNet50 in image classification tasks, providing insights into the strengths and weaknesses of each model.
Copyright (c) 2025 Wisam K. Jummar , Osama Khamees Ali , Hadeel M. Saleh https://creativecommons.org/licenses/by/4.0
Investigating Factors Affecting AI Adoption in Higher Education Using Structural Equation Modelling (SEM)
Artificial Intelligence (AI) can influence teaching, learning, and management in higher education. The use of AI requires effective socio-technical factors at different levels of use and implementation in all organizational, professional, and ethical contexts. The paper initiates thus a synthesis between the Theory of Planned Behavior and the Technology Acceptance Model, investigating major factors that determine the use of AI in institutions of higher education. The two theories provide an understanding that is both behavioral and technological in nature concerning the factors that determine AI adoption. Factors like easy use, support, institutional awareness at AI being, technical competency, ethical and legal barriers, and perceived importance were considered. Components of the TPB that reflect how users make decisions, such as subjective norms, attitudes, and perceived behavioral control, are also incorporated. In the quantitative part of the study, researchers used standardized surveys to question faculty students and administrative staff from multiple institutions. To study statistically the relationships between the indicated variables multiple regression and SEM were run on simulated data. From the result it is the imperative need of the hour for organizations to address ethical issues invest in digital literacy and implement explicit rules related to AI. This study will also be useful for future legislators’ administrators and AI developers who seek to advance the integration of ethical and sustainable AI in higher education.
Copyright (c) 2025 Waleed A Hammood https://creativecommons.org/licenses/by/4.0
Energy-Efficient Task Offloading and Resource Allocation in Mobile Cloud Computing Using Edge-AI and Network Virtualization
In the emerging landscape of Mobile Cloud Computing, energy efficiency and resource optimization are very vital challenges because most of the cloud and edge resources are increasing the execution of tasks in mobile devices. The focus of this paper is to propose a new energy-efficient task offloading and resource allocation framework in Edge-AI enabled network virtualization for dynamic management of computational tasks in mobile cloud environments. The framework allows real-time decisions of task offloading by comparing energy consumption of local execution versus edge processing and further looks at the received performance gains in executing that way. Then, based on the savings in energy and availability of the edge resources, it grades tasks for offloading. Network virtualization optimizes edge resource use by allocating resources depending on demand from tasks, leading to a reduction in latency for increased processing efficiency. The simulation results proved that our approach could really cut down energy consumption on mobile devices, with low latency and high rates of task success, better than cloud-only offloading through static edge computing methods and traditional dynamic programming.
Copyright (c) 2025 Raed A. Hasan, Khalil Yasin, Parween R. Kareem, Ali Mohammed Salih, Husniyah Jasim, Mohamed Arif Ameedudeen, Mohammad Abdullah Abbas, Ali Rachini, Aaron Mogeni oirere https://creativecommons.org/licenses/by/4.0
Advancements and Performance of Evaporative Cooling Technologies: Applications, Benefits, and Future Prospects
Evaporative cooling is a widely adopted technology for various applications, including industrial processes, HVAC systems, building cooling, and microclimate regulation. It is known for its cost-effectiveness, energy efficiency, and environmental friendliness compared to conventional refrigerants. With buildings accounting for a significant portion of global energy use, enhancing cooling technology efficiency is critical. This review explores recent advancements in evaporative cooling technologies, particularly those involving desiccants, membranes, and hybrid systems, such as air-mediated indirect evaporative cooling (AMIEC) and water-mediated indirect evaporative cooling (WMIEC). These innovations address traditional challenges like maintenance, efficiency fluctuations, and ambient condition dependencies, while offering sustainable alternatives to ozone-depleting refrigerants. The review delves into the principles and classifications of evaporative cooling, detailing both direct and indirect methods using air and water as cooling media. Factors influencing the efficiency and cost of these systems, such as materials for water evaporation interfaces and design improvements, are discussed. Enhanced evaporative cooling techniques, including desiccant sorption and membrane-assisted cooling, are highlighted for their potential to improve performance in humid environments. Furthermore, the review examines the performance metrics of evaporative coolers, such as cooling capacity, energy efficiency ratios (EER), and effectiveness. Case studies and performance analyses of modified evaporative coolers demonstrate significant energy savings and increased efficiency. Despite challenges like high humidity sensitivity and maintenance needs, ongoing research and development are paving the way for more robust and efficient designs. The integration of evaporative cooling with other technologies, such as vapor compression and solar energy, holds promise for future advancements. In conclusion, evaporative cooling represents a sustainable and efficient alternative to traditional cooling methods, with significant potential for reducing global energy consumption and environmental impact. Continued innovation and addressing current limitations will enhance its applicability and effectiveness, positioning evaporative cooling as a key technology in the future of energy-efficient cooling solutions.
Copyright (c) 2025 Raed Abdulkareem Hasan, Mahmod A. Abdulqader , Azil Bahari Alias , Noor A. Hussein , Omer Khalil Ahmed , Jafar Keighobadi , Ali Mohammed Saleh , Zeyad K. Hamad , Noah Mohammed Saleh , Musaria Karim Mahmood https://creativecommons.org/licenses/by/4.0
Analysis of Electric Field Influence on Heat and Mass Transfer in Non-Newtonian Fluid Flow over a Non-Uniform Surface
Electric field-non-Newtonian fluid flow over non-uniform surfaces has attracted significant interest because of its application in many industrial and biomedical systems. In contrast to Newtonian fluids, which exhibit a constant viscosity under different shear conditions, non-Newtonian fluids, including suspensions, biological fluids, and solutions based on polymers, display viscosity that varies with the applied shear rate, leading to more intricate flow dynamics. The behavior of these fluids becomes even more complex when they pass through wavy or uneven geometries because surface characteristics affect transport efficiency, boundary layer formation, and flow stability. By creating electrohydrodynamic forces from charge interactions and fluid dipole motion, the application of an external electric field adds even more complexity. To properly characterize these behaviors, coupled, nonlinear equations governing species transport, fluid motion, thermal energy distribution, and electric interactions must be solved. Developing accurate and effective designs for drug delivery systems, advanced thermal management technologies, and microfluidic devices requires a thorough understanding of the interactions between surface topology, fluid rheology, and electric field effects.
Copyright (c) 2025 Ayad Ibrahim https://creativecommons.org/licenses/by/4.0
The Role of Passive Techniques in Modern Sustainable Architecture
Passive systems enabled the path towards energy efficient buildings, which further sustainably met both existing and future needs to halt the rapid rise in energy consumption on a global scale. Thus, passive strategies have been the subject of this research, which aims to analyze their contemporary and historical uses in various types of architecture, where architecture then be references and example to integrate with modernity. Also, strategies as building orientation, natural ventilation, daylighting, thermal mass, and envelope were discussed. Beside challenges building passive systems, which rely on climate contexts and their ineffective application if used exclusively, were also brought into consideration. By means of analyses on case studies and context of trends emerged from passive system applications, effort was made to ensure clear understanding about the issues related to passive strategies integration have been followed by the possibility practicing passive systems integration with active ones in a dominant role when in need for energy savings and comfort. Suggestions for future research directions and communities in the field of passive systems application set around innovative materials, community driven design, encouraging passive integration systems in urban resilience frameworks.
Copyright (c) 2024 Bara’ Al-Mistarehi, Guma Ali, Wamusi Robert, Habib Hassan https://creativecommons.org/licenses/by/4.0
Time Evolution of Quantum Wave Packets: A Numerical Approach Using Schrödinger’s Equation
The advancement of quantum wave parcels plays a principal part in understanding the behavior of quantum particles over time This study numerically reenacts the time dependent Schrödinger condition to analyze how wave parcels change under diverse introductory conditions by changing parameters such as beginning position wave number and parcel width we investigate the impacts on likelihood thickness dissemination The results outline key quantum wonders counting wave bundle spreading position vulnerability and obstructions designs The discoveries contribute to a more profound understanding of quantum mechanics and can be connected to areas such as quantum optics and molecule physics.
Copyright (c) 2024 Ioannis Adamopoulos, Antonios Valamontes, Niki Syrou https://creativecommons.org/licenses/by/4.0
Using of Computational Fluid Dynamics (CFD) and Machine Learning (ML) for predictive modelling in fluid dynamic
Fluid dynamics predictive modeling for multiphase flow and heat exchanger optimization is undergoing a paradigm shift with the advent of deep learning techniques. The five different approaches under this framework are each concerned with the challenges of predictive modeling. At the heart of this is the ability for a hybrid integration of Computational Fluid Dynamics (CFD) and Machine Learning (ML) where both can coexist without interfering with each other allowing iterative refinement till convergence. When errors occur in the solution, the framework employs adaptive mesh refinement (AMR) to modify the mesh dynamically so that improved spatial accuracy and flexibilities are maintained for multiphase flow models. Moreover, the Machine learning bolstered Lattice Boltzmann Method (LBM), not only makes it superfast but also improves accuracy. An innovation aspect of the framework is its data-driven calibration and validation approach in which statistical parameters within each individual model are updated in real-time utilizing real-world data so that predictions remain tightly coupled with experimental results. To achieve a reasonable computation speed, parallelized algorithm is used in large simulations, which works by dividing the whole domain into independent pieces for parallel executions. A qualitative performance comparison is shown in tables and charts, demonstrating the advantage of the framework over others in accuracy, processing efficiency, scalability, flexibility stability and real-world applicability. The iterative and adaptive nature of the framework allows constant improvements to be made, which is ideal for modelling complex multiphase flows. This complete, state-of-the-art approach provides robust new tool for predictive modeling scientists and engineers alike and significant future cross-industry applicability will cement its status as an all-in-one CFDS solution.
Hybrid CFD and Machine Learning Framework
Copyright (c) 2024 Baqer A Hakim https://creativecommons.org/licenses/by/4.0
Smart Building Systems: A Confluence of Architecture and Technology
Architecture is constantly evolving as its relationship with technology grows. Smart building systems are the combination of the architectural and technological dimensions of a structure. These are networked architectures which rely on Internet of Things (IoT) devices, artificial intelligence (AI) and automation to optimize the operational performance, sustainability, and occupant experience of a building. This paper discusses the emergence and advancements of smart building systems, their characteristics and technological architecture, and their contribution to energy efficiency and sustainability. It also addresses the challenges of interoperability, cybersecurity, and user interface design in smart buildings, helping to understand the trends and developments of smart architecture and their impact on the future of urban centers. Moreover, case studies illustrate the innovative design and best practices of an intelligent building system or a set of networked buildings to transform the built environment with smart technologies.
Copyright (c) 2025 Omar G. Ahmed https://creativecommons.org/licenses/by/4.0
Sustainable Design Strategies in Architectural Engineering: A Comprehensive Review
Sustainable design is at the heart of architectural engineering. The reason is that it contributes to the reduction of the negative human impact on the environment and brings improvements to human conditions as well. The proposed research is devoted to sustainable architecture with the main focus on its principles, strategies, and advanced technologies. Energy efficiency, material choice, and life cycle assessments are among the core aspects of such architecture. Through the extensive literature review, the study will promote the understanding of the integration of sustainability in architecture educational programs, regulatory policies’ effects, economic aspects, and the social advantages of using sustainable design. The overview of successful projects worldwide will deliver practical recommendations for construction and design specialists. The importance of taking care of an integrated, holistic approach to solve issues and overcome barriers on the way to sustainability will be proven as well. Finally, predicted developments will be discussed together with new technologies to follow in order to secure the future of sustainable architecture.
Copyright (c) 2025 Amar Alhamd Mohammed Yasser https://creativecommons.org/licenses/by/4.0
Flood-proof Architectural Solutions for Urban Environments: A Review
Uban flooding is an ongoing problem due to climate change and increased urbanization. This paper aims to research solutions on flood-proof architecture, and flood-proofing techniques that can lessen the impact of urban flooding. Based on an intensive literature review and case-based analysis, solutions were proposed. This includes design principles, elevated structures, water-resistant construction materials, and green design plans. Modern solutions that focus on technology were also discussed, such as amphibious structures and hydraulic barriers. These architectural solutions not only contribute to flood-proofing but also increase resilience and ensures functionality in our urban spaces with aesthetic value. Ultimately, architectural practices must address environmental issues in mind to promote healthy urbanization.
Copyright (c) 2023 Daad Fatehelrahman Yassin Mustafa https://creativecommons.org/licenses/by/4.0
Daylighting in High-Rise Office Buildings: A Comprehensive Review
This article is a detailed review of the significance of daylighting in high-rise office buildings and its importance to the energy performance, visual comfort and productivity of building occupants. Due to increased urbanization globally, high-rise buildings are becoming prevalent in cities, the need for sustainable design approaches, maximize the use of daylight, eliminating glare and increase productivity. Historical background and overview of day lighting systems are discussed, as well as the current techniques and new technologies for passive and active daylight systems. The article also treats some daylighting problems related to buildings, such as glare, regulatory compliance, daylight deprivation, non-uniform daylight distribution. Case studies of projects implemented with exceptional daylighting are introduced. Significant findings on integrating new technologies, self-sufficient buildings, adaptive shade, and the impact of layout in a space to achieve the desired daylighting were also discussed. The potential impact of green rating systems such as LEED and WELL on sustainable daylighting is also highlighted in the review. The future of day lighting with innovative dynamic materials such as electrochromic glass, and climate responsive controls are also discussed to provide a balance between energy savings, productivity and aesthetics.
Copyright (c) 2024 Daad Fatehelrahman Yassin Mustafa https://creativecommons.org/licenses/by/4.0
Innovative Techniques for Enhancing Building Energy Efficiency: A Comprehensive Review
This review paper provides a literature review on the state of the art, recent development of energy efficiency for smart building with findings published in the past five years. And it shows how growing Building Energy Efficiency (BEE) and decreasing energy usage innovative approaches and technologies are being used. Common methods for assessing and forecasting energy utilization are presented, and methods for adapting the internal configuration of a building to supply energy efficient performance are addressed. Particular emphasis is placed on the control of luminaires because their efficiency greatly determines energy consumption. The review looks at diverse energy conserving strategies such as the energy application forecasts, IoT facilities in smart buildings, innovations in light management systems and employing of quad code services informing occupants of energy consumption rates. Further, the emulation of various methods of retrofitting interior designs is also discussed with a focus on the aspect of energy efficiency. This paper also discusses the effectiveness of applying these methods under different circumstances, given particularity of the building design or operation, and environmental contexts. Based on a synthesis of data from the latest studies, this review provides recommendations for implementing energy-efficient solutions following specific construction requirements. The result provides an addition on the existing knowledge regarding the implementation of sustainable building practices in conforming with worldwide energy standard. By so doing the paper highlights the importance of technological inventions and design techniques which contributes to the paper’s purpose of being a useful tool for researchers, architects, and policymakers in the provision of energy-efficient buildings in the future.
Copyright (c) 2023 Linda Aziz Jawad Alfayyadh https://creativecommons.org/licenses/by/4.0
Advances in Concrete Technology Based on New Materials and Applications
Due to the existence of new innovative construction technologies, material, and equipment's, civil engineering practice has been enhanced profoundly; despite the concrete as the most popular structural framework. Due to the changes taking place in the global economy and the rising expectations laid down on people, construction of civil engineering structures has become inevitable to meet the requirement of modern civilization. This expansion does not only enhance the progression of infrastructure, but also the improvement in the construction business. To be also more precise, it is easy to understand that concrete being one of the key components in construction is a direct reflection of the civil structures quality. New concrete materials are not just an improvement on the conventional concrete material but can be considered an entirely different entity in terms of its mechanical properties and functional life. These improvements are realised through the adaptation of novel additives, improved manufacturing processes and ultilisation of environment friendly materials. Hence there is added strength, better resistance to the vagaries of the environment and less maintenance than normal concrete while being more satisfactory for complicated designing. This present paper aims at presenting a survey of the current trends in concrete technology, and major concentration will be made on exploring how the new materials in concrete are useful in the enhancement of civil engineering. This research shows the possibility for such materials to revolutionize construction techniques and technologies by focusing on their mechanical characteristics, structural benefits, and durability. Additionally, the paper considers how these developments impact the concrete industry and the practical implications and realisation of new concrete technologies and durable construction. It is believed that this review will be useful for researchers and practitioners in the field and contribute to the expanded understanding of the possibilities and risks connected with the use of advanced concrete materials in civil engineering.
Copyright (c) 2024 Muhannad Muhsin https://creativecommons.org/licenses/by/4.0
Structural Efficiency of Non-Prismatic Reinforced Concrete Beams: A Comprehensive Review
The use of non-prismatic reinforced concrete beams in construction and architecture has come as an innovative evolution to shape the future of structural design in relationship to construction material efficiency, economy, and versatility. Several structural, architectural, and economic advantages can be identified in the use of non-prismatic beams compared to conventional prismatic forms. In this review, distinct types of non-prismatic beams such as tapered, hunched, and segmented are presented, and their specific advantages and areas of applications in contemporary constructions, such as bridges and retrofitted infrastructures, are discussed. Alongside, highlights of advanced novel materials as well as design developments, such as glass and carbon fiber reinforced polymers or digital design technologies, which further enhance the structural, aesthetic, and sustainability performance of the non-prismatic components, are provided. Ultimately, challenges that non-prismatic beams currently face such as optimizing loads, enhancing cost-effectiveness, and reducing potentially excessive structural depth are discussed, as well as expectations for future developments in their design and applications. The potential of non-prismatic beams in providing a more sustainable, innovating, and contemporary evolution in construction practices is recognized.
Copyright (c) 2024 Ahmed Majid Imran, Muhammed J.Awad https://creativecommons.org/licenses/by/4.0
Heat Transfer and Integration of Heat Pipes: Advances, Applications, and Future Directions
Heat pipes are essential tools in managing heat effectively, offering remarkable efficiency by using phase change and capillary action to transfer heat. They have become indispensable in various fields, from cooling electronic devices to improving renewable energy systems and advancing aerospace technology. This paper explores the development and types of heat pipes, ranging from traditional thermosyphons to more sophisticated designs like loop and flat heat pipes. It delves into their materials, manufacturing methods, and how they are integrated into systems to maximize performance and reliability. Challenges such as overcoming thermal resistance, enhancing material durability, and ensuring functionality under extreme conditions are discussed. The role of modern techniques, like computational simulations and machine learning, in optimizing heat pipe design is also examined. By addressing these aspects, this study highlights the promise of heat pipes in creating efficient and sustainable solutions to meet today’s energy and environmental demands.
Copyright (c) 2024 Salwa Bouadila https://creativecommons.org/licenses/by/4.0
Resilience in Civil Infrastructure: Designing for Natural Disasters
Climate change has directly affected the increasing intensity and frequency of natural disasters, hence resilient civil infrastructure is essential to climate changes. Within this paper, the principles and approaches to resilient infrastructure systems will be defined. A closer look will be taken to examine suitability in modularity, redundancy, adaptability and community engagement. The learning from analyzed failures of the past, risk assessment approaches, state-of-the-art innovations such as digital twins, self-healing materials, among others, can be synthesized and can help to promote infrastructure systems' robustness and sustainability. The retrofitting of predominate existing infrastructure systems along with integration of living systems are among the most effective, efficient, low-cost and environmentally compatible methods to adopt and develop further towards zero-loss structures. Inclusive processes help to prove that it is possible treating resilience planning fairly to every community member. Most of the discussed and further outlined processes focus on equity to protect both climate vulnerable and naturally vulnerable areas and the communities living those. By collating state-of-the-art technology, advancements, progressions and future goals, this paper aims to help with one of the many scientific approaches of producing more resilient, adaptable to change infrastructure systems in an environmentally aggressive future.
Copyright (c) 2023 Ahmed Majid Imran https://creativecommons.org/licenses/by/4.0
Thermal Performance of Twisted Heated Duct for Different Twisting Angles
In recent years, the focus of research is mainly on the twisted pipe and ducts, as it can significantly improve heat transfer characteristics of thermal exchangers in compact and highly effective heating systems. The conducted research seeks the impact of different twisting angles (90°, 180° and 360°) on surface temperature and Nusselt number, which allow describing effectiveness of heat transfer by convection. The analysis reveals that twisted ducts generate the secondary flows that displace the thermal boundary layer and result in higher heat transfer rates. Even 90° of twisting offer an impressive enhancement when compared to plain ducts, while 180° and 360° of twisting lead to the best results.
Copyright (c) 2024 Adeel H. Suhail https://creativecommons.org/licenses/by/4.0
Heat Transfer Enhancement Inside a Duct Using Turbulators, Vortex Generators, and Baffles
This research investigates sophisticated methods for augmenting heat transmission in duct systems, emphasizing the utilization of passive devices such vortex generators, turbulators, and baffles. These devices enhance turbulence, facilitate fluid mixing, and optimize heat transfer efficiency while minimizing pressure drop penalties through the manipulation of fluid flow dynamics. A thorough computational and experimental study was performed to assess the thermal and hydraulic efficiency of several designs, such as ribbed turbulators, twisted tape inserts, and delta-wing vortex generators. The findings indicate that optimum combinations of these devices markedly improve thermal performance, rendering them appropriate for use in small heat exchangers, automobile radiators, and industrial cooling systems. The results offer significant insights for the design of high-efficiency heat transfer systems that reconcile thermal improvement with pressure drop factors.
Copyright (c) 2024 Farqad T. Najim https://creativecommons.org/licenses/by/4.0
Advances in High-Performance Concrete: A Comprehensive Review of Materials, Design, and Applications
High-performance concrete (HPC) represents a significant advancement in modern construction, offering enhanced strength, durability, and workability compared to conventional concrete. This paper reviews recent developments in HPC, including the use of supplementary cementitious materials (SCMs), recycled aggregates, and advanced admixtures to improve mechanical properties and sustainability. Technological innovations, such as computer-aided design tools and performance-based specifications, have enabled precise mix designs tailored to specific applications. Applications of HPC span diverse sectors, including bridges, high-rise buildings, pavements, and marine structures, leveraging its superior properties for durability and efficiency. Future trends emphasize sustainability through recycled materials, nanotechnology, and smart sensors, ensuring HPC remains adaptable to evolving industry needs. This review highlights the potential of HPC as a versatile and sustainable material for next-generation infrastructure.
Copyright (c) 2023 Hawwa Hussain Mohammed Ali https://creativecommons.org/licenses/by/4.0
Enhancing the Performance of Solar Air Heating Systems: Techniques and Innovations
Recently, tubular solar stills are being intensively studied as a feasible and effective technique for solar desalination. This innovative technological framework is fundamentally based on the sustainable and eco-friendly paradigm that promotes environmental consideration and resource efficiency. This comprehensive review has highlighted the series of recent improvements in essential design innovations, the performance of advanced materials, and the application of efficiency optimization techniques. For instance, the use of Phase Change Materials (PCMs), nanocomposites, multi-staging boiling systems, and implementation of structural modifications such as cascading tubular designs and various geometrical alterations have underlined the crucial importance of enhancing thermal efficiency and maximizing daily water productivity. The results obtained from multiple studies show that these pivotal innovations have the potential to be highly disseminated across different regions, thus making significant strides towards overcoming the persistent issue of freshwater scarcity. Additionally, they can be applied in a scalable and practical approach. Future directions point towards the integration of artificial intelligence and the use of hybrid renewable energy systems to further optimize performance, adaptability, and efficiency, allowing for an insightful approach to addressing water shortages effectively.
Copyright (c) 2023 Sadiq B. T. Al-Omari https://creativecommons.org/licenses/by/4.0
Electric Vehicles in the 21st Century: Historical Evolution, Environmental Impact, and Safety Challenges for Sustainable Mobility
Electric vehicles are currently one of the most promising and relevant alternative modes of transport that help significantly reduce dependence on fossil energy sources such as petroleum products, as well as reduce emissions of harmful substances into the atmosphere, which in turn has a positive effect on the environment. The article examines in detail the history of the emergence and evolution of electric vehicles, from their first models to modern high-tech solutions. It also analyzes the reasons why electric vehicles began to displace cars with internal combustion engines at the beginning of the 20th century, including technological, economic and environmental factors that influenced consumer choice. In addition, the article highlights the reasons for the growing popularity of electric vehicles in our time, including increased awareness of environmental issues and climate change, as well as the development of charging infrastructure and support for electric vehicles. However, along with the benefits, the use of electric vehicles is also associated with certain dangers and risks that require careful study and the development of appropriate norms and regulations. This article focuses on key safety aspects, including the operation, maintenance and disposal of electric vehicles, as well as the environmental sustainability of their production and use. In this way, the article provides a comprehensive analysis of current trends in the field of electric mobility, highlighting both its positive aspects and potential challenges that need to be addressed to achieve a sustainable future.
Copyright (c) 2024 Andrei Korneev, Yitong Niu, Amer Ibrahim https://creativecommons.org/licenses/by/4.0
Advanced Hybrid Mask Convolutional Neural Network with Backpropagation Optimization for Precise Sensor Node Classification in Wireless Body Area Networks
Wireless Body Area Networks (WBANs) are crucial in continuous health monitoring, fitness tracking, and other applications where a real-time collection of physiological data is needed by sensors worn on the body. This is important in WBAN to achieve reliable data transmission, energy efficiency, and overall system performance. Still WBANs present several challenges: for example, the data that is being collected is heterogeneous since it originates from diverse IMDs measuring different bio-physiological signals; these can be also quite noisy because of motion artifacts when moving; as well high limitations when it comes to energy, bandwidth or storage push for low-complexity methods rather than standard deep learning techniques. Common Convolutional Neural Networks) CNNs( are successfully utilized for spatial information extraction but they cannot catch temporal dependencies well and also, WBAN sensor data has a noisy and multi-modal structure which acts as an additional challenge for traditional CNNs. These limitations emphasize the need of a flexible, fast and precise classification model based on the specific needs of WBAN applications. To overcome these challenges, this paper presents a novel hybrid neural network architecture consisting of combined 2D and 3D convolutions for spatial-temporal feature extraction along with masked convolution layers to provide an ability to adaptively ignore uninterested parts of the data. The model aims at achieving a high classification performance while also balancing with the system computational efficiency, perfect for its ipsi deployment on resource constrained WBAN devices. Then, we apply further backpropagation optimization measures such as adaptive learning rate scheduling and gradient clipping, to improve the stability of training speed and reduce latency which in return finds its way into supporting real-time processing capabilities of the model. By using each of these components, the model is able to deal with the multi-dimension aspects and high noise level nature of WBANs without excessive computation resources [18]. The Hybrid Masked CNN model is shown to out-perform existing approaches without such masking, yielding substantially higher performance in terms of accuracy, precision, recall and F1-score across all metrics defined for the application as compared to traditional 2DCNNs, 3DCNNs and other hybrid models. Consequently, the latency of the model is significantly decreased as well which confirms its applicability to real-time WBAN applications. The obtained results confirm the efficacy of features from hybrid architectures with masked convolutions along with optimization in training techniques for WBAN sensor node classification. The results of this paper improve WBAN technology by providing a solid and scalable solution which can be implemented when more reliability and flexibility are required from such systems in applications like healthcare, fitness or any other field.
Hybrid Convolutional Neural Network (CNN)
Copyright (c) 2024 Israa Ibraheem Al Barazanchi, Wahidah Hashim, Reema Thabit, Noor Al-Huda K. Hussein https://creativecommons.org/licenses/by/4.0
Advanced Composite Materials for Sustainable Construction: Innovations in Civil Engineering Applications
Modern electrical grids are incredibly complex and changing rapidly due to integrating more renewable energy as well as the varying demand loads. Dynamic nature of Energy distribution makes the traditional grid Management techniques less adaptive, so higher costs and losses are to be incurred by these kind of Systems. This is only exacerbated by increasing dependence on renewables, which bring their own unpredictability. Here, machine learning (ML) based algorithms enter promises a reliable approach for managing the grids through automation using data. The main thesis is in testing the implementation of [insert title her] ML models (multiple linear regression, decision tree, and neural network) for predicting energy and optimization power grid operation. The findings also indicated considerable enhancements, such as approximatively 95 in penetration rates of with neural networks achieving over 95% accuracy in consumption predictions, a reduction in energy losses by up to 20%, and stability enhancements of approximately 15% during peak and stress periods. Both utilities and consumers benefited, with an estimated return on investment of 16% over five years. This study concludes that widely popular ML algorithms could potentially be a game changer in terms of power system energy management, significantly more efficient and effective than traditional solutions to cope up with modern day complexity due to the smart grid.
Copyright (c) 2024 Noor Al-Huda K.Hussein, Sura Khalaf, Krunalkumar D. Shah https://creativecommons.org/licenses/by/4.0
Optimizing Energy Efficiency in Smart Grids Using Machine Learning Algorithms: A Case Study in Electrical Engineering
Today's electrical grids are growing exponentially more complex and 21st century trends, such as the addition of renewable energy sources and increased demand elasticity, challenge traditional approaches to promoting greater efficiencies in how we consume power. This is where traditional management techniques seem to fail and results in more energy losses & therefore resources being spent on operations. The increasing penetration of renewable sources with its inherent variability, machine learning (ML), as a data-driven intelligent approach is considered to be able to control the grid. In conclusion, this paper addresses how ML techniques such as regression models, decision trees and neural networks could increase prediction accuracy, balance distribution of loads classes and improve stability for smart grids. Findings from the study show that along hospitality customers' backgrounds, machine learning algorithms can provide energy consumption predictions with better accuracy than neural networks (over 95% and for both a reduction up to 20 % on total losses in energy), coupled with additional stability requirements during peak hours which would demand an allocation between15%. Utility and consumer savings from these advances equated to 16% ROI in year-one costs by third-party renewable developers, providing a significant cost reduction These results imply that ML has the potential to revolutionize how energy management is handled in smart grids, offering a swift and cheap resolution to issues surrounding modern power system. Basically, ML has great potential in resolving grid management issues such as demand prediction, load balancing and system stability. Yet the challenges of quality data, transparency in model explanation and security stand between the promise on this new scale; additional research is needed here.
Copyright (c) 2023 Noor Al-Huda K.Hussein, M'bark Abouessaouab https://creativecommons.org/licenses/by/4.0