Engineering Commons^™

Ai In Society: A Regulatory Framework For Responsible Integration, Ziad Doughan, Sari Itani, Hadi Al Mubasher

BAU Journal - Science and Technology

This review paper studies the influence of Artificial Intelligence (AI) and Machine Learning (ML) on society in various categories in detail. AI and ML have developed rapidly in the past two decades, thus changing our lifestyles. These developments have various positive and negative impacts on society. This paper explores the many societal impacts of AI and ML, in economics, social aspects, ethics, and policy, shedding light on the opportunities and challenges that arise. An interdisciplinary insight is capable of understanding the challenges society faces when it uses AI and ML, locking opportunities that lie ahead and identifying promising paths towards …

Electronic Structure With Rovibrational Calculation For The Molecule Ali, Including The Spin-Orbit Coupling Effect, Hana Abdelnabi, Ghassan Younes, Dunia Houlla, Mahmoud Korek

BAU Journal - Science and Technology

The adiabatic potential energy curves for the lowest singlet and triplet electronic states in the representations 2S+1Λ+/- and Ω(+/-) (with and without spin-orbit coupling) of the molecule AlI have been computed by using the complete active space self-consistent field (CASSCF) with multireference configuration interaction (MRCI+Q) method. The spectroscopic constants ωe, Re, Be, and Te have been calculated. Using the canonical quantum mechanics approach, the rovibrational constants Ev, Bv, Dv, Rmin, and Rmax were calculated for the low-lying electronic states with and without spin-orbit coupling. A comparison between our calculated values and those available in the literature reveals excellent agreement.

Comparative Finite Element Method-Based Optimisation Of Coir Fibre Laminates: Aluminium Skins Versus Carbon Cloth Skins, Muhamed Swaleh Ahmed, Bernard Wamuti Ikua, Abel Nyakundi Mayaka

Mansoura Engineering Journal

Natural fibre composites offer a sustainable alternative to conventional composite materials. Their mechanical efficiency is influenced by laminate structure and skin material selection. This study presents a comparative finite element analysis and optimisation of coir fibre-reinforced laminates employing either aluminium skins or carbon cloth skins. COMSOL Multiphysics was used to determine their mechanical response. Optimisation was performed using the BOBYQA algorithm to enhance load sharing between the coir core and skin materials. Results indicated that aluminium-skinned laminates achieve superior compressive and flexural performance, achieving a peak compressive strength of 210 MPa and sustaining a flexural load of 0.65 kN at …

Elli’S Liquid Flow In A Wavy Channel With Slip Effect, Wall Properties, And Heat Transfer, P. Devaki, Yatin Sood

Mansoura Engineering Journal

The dual behavior of Elli’s fluid motivated us to work on it. The fluid behaves as both Newtonian/ non-Newtonian based on the low and high shear rates, respectively. The paper focused on the flow of Ellis fluid in a peristaltic channel with wall properties and heat transfer. The channel is symmetric in nature, and slip conditions are considered near the elastic walls. The governing equations of the flow are solved analytically using suitable boundary conditions, which yield velocity and temperature functions. The novelty of the paper is to analyze the nature of Newtonian/non-Newtonian fluids under the same conditions so that …

Investigation Of Factors Influencing Electric Vehicle Adoption In Indonesia: Ev Owners’ Perspectives, Desrina Yusi Irawatia, Nur Aini Masruroh, Nur Mayke Eka Normasari

ASEAN Journal on Science and Technology for Development

Electric vehicle (EV) uptake in Indonesia remains markedly below policy benchmarks. This study applies the Unified Theory of Acceptance and Use of Technology version 3 (UTAUT3), an extension of UTAUT2 that incorporates personal innovativeness as an additional construct to examine its impact on both behavioral intention and actual EV adoption within the Indonesian context. Unlike studies that typically survey the general public, this study focuses on actual EV users and owners, providing more representative and responsive insights into real-world EV usage. A total of 208 respondents participated, with 135 from the Jabodetabek area and 73 from Surabaya. The UTAUT3 framework …

A Deep Neural Approach To Network-Based Obfuscated Malware, Meera Parmar, Sunil Gautam

ASEAN Journal on Science and Technology for Development

The rising prevalence of obfuscated malware poses a critical threat to network security, undermining traditional detection methods and jeopardizing data integrity and system reliability in an increasingly connected world. This growing danger highlights the urgent need for advanced solutions to protect against evolving cyber risks. This research intro-duces a novel framework to enhance malware detection, employing a hybrid architecture that integrates spatial and temporal analysis with attention mechanisms. The approach leverages a large dataset subsample, focusing on key feature selection and augmentation to improve robustness against evasion techniques. This innovative framework offers a significant advancement in identifying malicious network traffic, …

Captive Model Test For Hydrodynamic Derivatives Of Ship Maneuvering In Regular Head Waves, Pin-Yuan Huang, Zih-Yao Lin, Tsung-Yueh Lin, Fu-En Lee, Yan-Wei Lai

Journal of Marine Science and Technology–Taiwan

The motion of ship in wave can be discussed in seakeeping and maneuvering. In model tests, the former is found by the motion response of waves while the latter requires captive model test to acquire hydrodynamic derivatives. The hydrodynamic derivatives in waves are discovered to be different from those in calm water. To understand better the behavior of maneuverability in waves, maneuvering tests in a wide range of waves are performed in this study. This study presents an experimental investigation into the maneuvering behavior of a container ship in regular waves, covering a wide range of wavelengths from half to …

Impact Of In-Cabin Human Machine Interface Designs On Passenger Trust In Urban Air Mobility, Ricole A. Johnson, Erika E. Gallegos

Journal of Aviation/Aerospace Education & Research

Urban Air Mobility (UAM) presents a promising solution for alleviating urban traffic congestion and enhancing transportation efficiency. However, the widespread acceptance of UAM, particularly sustained ridership of autonomous passenger air vehicles (PAVs), is contingent upon addressing critical human factors for building passenger trust, especially in the absence of a human pilot. This paper evaluates how different in-cabin human-machine interface (HMI) designs affect passenger trust in the aircraft and in-cabin display, situation awareness, and pilot preferences. Forty participants, equally split between early and late technology adopters, completed two simulated flights: a baseline flight with no HMI and a second flight featuring …

The Role Of User Experience In Virtual Reality Flight Training: A Comparison With Pc-Based Simulation, Tianxin Zhang, Christina M. Frederick, Barbara Chaparro

Journal of Aviation/Aerospace Education & Research

The current project explores how user experience (UX) in virtual reality (VR) flight simulation influences both cognitive learning outcomes and procedural skill acquisition when compared to traditional desktop-based simulation.. Using a quasi-transfer of training design, 48 student pilots were randomly assigned to VR, Desktop, or Control groups and trained on the Chandelle maneuver. UX was measured with the User Experience Questionnaire (UEQ), while training effectiveness was evaluated through a post-training knowledge test and recorded maneuver performance on a high-fidelity flight training device. Results indicated that the VR group reported significantly higher UX scores in Attractiveness, Stimulation, and Novelty, and showed …

A Dual-Domain Face Forgery And Deepfake Detection Framework, Neha Pradyumna Bora, Pradyumna Mulchand Bora, Rushikesh Sanjay Kumavat, Raunak Manoj Gangwal, Prit Sandesh Jain, Hardik Vijay Ostwal

Mansoura Engineering Journal

Concerns about media authenticity, privacy, and information security arising from rapid advances in deepfake technology have made it increasingly important to detect manipulated facial content reliably. The primary goal of our work was to develop a deepfake detection model with high generalization across multiple datasets. To achieve this, we developed a hybrid detection approach that combines spatial visual texture analysis and frequency-domain analysis to detect manipulated facial images. Our approach includes convolutional spatial feature extraction from facial images and frequency—domain representations of the same images obtained by applying the fast Fourier transform. To help alleviate concerns about overreliance on frequency—domain …

A Comprehensive Statistical And Regional Analysis Of Lng-Powered Marine Vessels On Ghg Mitigation Strategies Considering Existing Bunkering Stations And Gwp Values, Canberk Hazar, Onur Yuksel, Murat Bayraktar

Journal of Marine Science and Technology–Taiwan

This study aims to quantify and analyse emissions from marine vessels that can operate on liquefied natural gas (LNG) but continue to use conventional fuels, largely due to the limited availability of LNG bunkering stations (BSs) over long distances. Four regions have been identified as having high concentrations of LNG-fueled vessels but limited access to operational BSs: the West Coast of the United States of America (USA), South Africa–Good Hope–Madagascar, Northwest Africa, and Brazil. This selection is based on the geographical distribution of these ships and the existing infrastructure. Hourly greenhouse gas (GHG) emissions have been calculated by considering the …

Nahb Production Home Competition As A Learning Tool Towards Incorporating Business Skills In Construction Management Programs, George Berghorn, Chandler Jones, M.G. Matt Syal

The Professional Constructor

There is a growing realization in the construction industry that the traditional focus of Construction Management (CM) education on technical competencies (such as scheduling, estimating, and construction methods) is insufficient in preparing graduates to meet the strategic and leadership demands of the modern construction profession. A successful construction company requires more than project execution; it demands expertise in business development, financial management, marketing, risk management, and organizational leadership. The disparity between the technical and business aspects of CM education has produced graduates who may succeed in project management yet find it challenging to engage with comprehensive business operations.

This study …

Unveiling Microplastic Removal And Characteristics In Wastewater From Two Municipal Wastewater Treatment Facilities In Indonesia, Nurul Setiadewi, Prayatni Soewondo, Cynthia Henny

Applied Environmental Research

Wastewater treatment plants (WWTPs) are considered an entrance pathways for microplastic (MP) pollution in aquatic environments. This study reveals the removal and characteristics of MPs in wastewater from two municipal WWTPs in Indonesia. The influent contained 17.1 ± 5.65 particles L-1 (WWTP A) and 15.45 ± 4.31 particles L-1 (WWTP B), whereas the effluent contained 1.41 ± 0.01 and 1.5 ± 0.16 particles L-1. The removal efficiency was 91.75% for WWTP A and 90.32% for WWTP B, with no statistically significant difference (p > 0.05). WWTP A employed advanced treatment units, whereas WWTP B used a conventional pond-based system. MPs were …

Assessment Of The Influence Of Human Activities On The Occurrence Of Forest Fires In Thailand Via Multiple Linear Regression (Mlr), Sittipong Ruktamatakul, Jirarat Insuk, Benjaporn Pinwongpet, Sarisa Ruktametakul, Pornpis Yimprayoon

Applied Environmental Research

Forest fires represent one of the most critical environmental challenges in Thailand, with impacts varying depending on forest type, fuel characteristics, terrain conditions, fire intensity, and the frequency of fire occurrence on the same landscape. While forest fires can contribute to ecosystem degradation, biodiversity loss, and the depletion of natural resources, such effects are not uniformly severe across all forest ecosystems. Understanding the human-induced factors contributing to forest fire occurrence is crucial for developing effective prevention strategies and promoting sustainable forest management. This study aimed to identify the anthropogenic factors influencing forest fire areas in Thailand via multiple linear regression …

Effect Of Chromium Doping On The Uv- And Sunlight-Driven Photocatalytic Performance Of Srtio3, Ro’Ikhatul Jannah, Dianisa Khoirum Sandi, Fahru Nurosyid, Risa Suryana, Didier Fasquelle, Yofentina Iriani

Applied Environmental Research

Chromium (Cr)-doped strontium titanate (SrTiO3, STO) photocatalysts with compositions of SrTi1-xCrxO3 (x = 0, 5, and 10%) were prepared via the coprecipitation method. This study aimed to investigate the effects of Cr doping on structural, morphological, and optical properties. Furthermore, this work aimed to examine the photocatalytic performance of pure and Cr-doped STO against methylene blue (MB) degradation under ultraviolet (UV) and sunlight exposure. X-ray diffraction confirmed the formation of the cubic STO phase and the insertion of the Cr dopant in the STO structures. Furthermore, Cr doping reduced the lattice constant, crystallite size, and average particle size and narrowed …

Wings Of Perception: Investigating Customer Sentiments In Indian Aviation Sector, Yashodhan Karulkar, Kaiwan Vaghchhipawala, Aditya Trivedi, Anushree Talekar

Journal of International Technology and Information Management

India's aviation sector, a key contributor to the nation's economy, has experienced rapid growth, supporting nearly 7.5 million jobs and contributing approximately $30 billion annually to the GDP (Gross Domestic Product). The growth, driven by increased demand for air travel and government incentives, has resulted in more competition among carriers. In the competitive market, it is necessary to understand customer preferences to enhance the quality of service and maintain a competitive edge. The dissemination of customer opinions on social media and review platforms offers airlines the opportunity to access passenger views. However, extracting useful information from this unstructured data is …

Business Process Redesign For Reducing Undelivered Product Return Losses In E-Commerce – An Explainable Ai Approach, Venkataraghavan Krishnaswamy, Deepa R, Himanshu Sharma

Journal of International Technology and Information Management

Product returns in e-commerce affect the profitability of the e-tailer. We adopt a two-stage approach to reduce undelivered product returns in an e-commerce firm. First, we develop and compare machine learning techniques—logistic regression, decision trees, Naïve Bayes, random forest, adaptive boosting, gradient boosting, stochastic gradient boosting, and deep neural networks—on their ability to predict undelivered returns. Next, we use explainable methods, such as relative importance and Shapley values, to develop insights from the best-performing machine learning model. Finally, we use these insights and the predictive model to redesign the firm’s order fulfillment and return processes. A Post-implementation evaluation of the …

Construction Industry Preparedness For Generation Z And Millennials: Aligning Industry Characteristics With Workforce Job Preferences, Ameenullah Amiri, Dhaval R. Gajjar, Jason D. Lucas

The Professional Constructor

The construction industry in the United States suffers from workforce shortages, partly due to younger generations being hired more slowly than baby boomers retire. Generation Z’s (Gen Z) lack of interest in joining the construction industry, coupled with the retirement of older workers, has amplified the problem. This paper investigates existing literature to identify the work and career preferences of Gen Z and Millennials and determine where the industry may focus efforts to attract these two generations. Through a systematic literature review, this study identified overlaps and gaps between the current construction industry workforce’s perceptions of the industry and the …

Code-Net++: An Attention-Guided Deep Learning Framework With Grad-Cam-Based Explainability For Covid-19 Detection Using Chest X-Ray Images, Fareesa Amina, Dr Krishnanaik Vankdoth

Mansoura Engineering Journal

Chest radiograph imaging has emerged as a practical and scalable diagnostic modality for respiratory diseases, including COVID-19. However, accurate discrimination of COVID-19 manifestations from other pulmonary abnormalities remains challenging because of low contrast, imaging noise, and overlapping radiographic patterns. This work presents CODE-NET++, an enhanced attention-guided deep learning framework with Grad-CAM-based explainability for reliable COVID-19 detection using chest X-ray images. The proposed framework integrates adaptive trilateral filtering for image enhancement, Reverse Edge Attention Network (RE-Net) for lesion-aware segmentation, and an Enhanced LinkNet architecture with dilated convolutions for multiscale feature extraction and classification. Grad-CAM-based explainable artificial intelligence visualization is incorporated to …

The Effect Of Second-Order Slip In Hydromagnetic Eyring-Powell Flow: A Bvp4c Study, Ch. Maheswari, B. Naga Lakshmi, G. Dharmaiah, R. Mohana Ramana, B. Reddappa

Mansoura Engineering Journal

The steady-state Eyring–Powell boundary layer fluid flow past a moving surface is examined, considering various parameter implications. These include the wall mass transfer parameter (0.5  C   2.0), the magnetic field parameter (5  Mn 20), the velocity ratio parameter (0.1  λ1  0.7), Eyring-Powell fluid parameters (0.5 K 2 & 0.1 E 0.4), as well as the first-order velocity slip (0.1 γ 1.1) and second-order velocity slip (0.1 δ  0.4). By employing appropriate similarity transformations, the PDEs are reduced to a set of non-linear ODEs, which are subsequently solved using the bvp4c numerical technique and, an implication of key factors on shear …

A Hybrid Object Detection And Temporal Attention Framework For Intelligent Video Surveillance, Sudheer Reddy Bandi, Eswari Vanaparthi, Yakshini Edapalli, Roshan Kavuri

Mansoura Engineering Journal

The rapid growth of urban areas has greatly heightened the need for smart video surveillance systems that can automatically process extensive amounts of CCTV footage. Traditional surveillance methods largely depend on human monitoring, which is not only inefficient but also susceptible to human mistakes, especially in intricate and crowded environments. To tackle these issues, this paper introduces a combined object detection and temporal attention for intelligent video surveillance that concurrently analyzes spatial and temporal data from video streams. The proposed system analyzes real-time CCTV footage utilising a multi-pathway frame extraction technique that includes slow, fast, and full-frame sampling to capture …

Extraction And Characterization Of Fresh And Dehydrated Cactus (Opuntia Ficus Indica) Polysaccharide For Hydrogel Preparation, Aye Thwe Thwesoe, May Myat Khine

ASEAN Journal on Science and Technology for Development

In this research, extraction of polysaccharide compounds from Cactus (Opuntia Ficus Indica) leaves in both fresh and dehydrated condition by solvent precipitation method using three types of water, acid (HCL) and NaOH. Before extraction, the physicochemical properties were examined to determine optimum yield (%) of extracted polysaccharide by optimization of Box-Behnken Design (BBD) of response surface methodology (RSM). The polysaccharide-based acrylamide hydrogel was prepared by free radical polymerization. The functional and structural characterization was done by FTIR, XRD and SEM for examination of extracted polysaccharide as raw polymer backbone in hydrogel preparation and prepared hydrogel as adsorbent for metal removal. …

Beyond The Binary: Navigating Ai’S Role In Palliative Ethics-A Review, Reebu Sara Varghese, Tijo Cherian

ASEAN Journal on Science and Technology for Development

Palliative care is being influenced by artificial intelligence, especially when it comes to data-related aspects. In this context, care can be enhanced in terms of the quality of its results and the level of its efficiency, particularly with the help of technological tools such as artificial intelligence, which is capable of managing different types of information in the field of health care. Such characteristics have the potential to improve the quality of patient care while at the same time reducing the workload of healthcare professionals in palliative care. However, there are considerable ethical issues that need to be addressed with …

Ai-Assisted Hybrid Ga–Pso Channel Allocation Under 3gpp Tr 38.901 Uma For Efficient 5g Radio Resource Management, Sharada Narsingrao Ohatkar

ASEAN Journal on Science and Technology for Development

The increased traffic and heterogeneity in the 5G/B5G network require efficient radio resource management (RRM). However, the existing methods, such as GA and PSO, have poor convergence speed and require proper initial conditions. Additionally, learning-based methods have high computational complexity. Hence, in this paper, a novel AI-assisted Hybrid Channel Allocation (AI–HCA) framework is proposed by using a support vector regression (SVR)-based predictive initialization method and GA-PSO optimization. Simulation results using the 3GPP UMa channel model show that the proposed method has a 28% reduction in call blocking probability (CBP), a 15-25% enhancement in spectral efficiency (SE), and a 18-25% enhancement …

A Hybrid Geospatial And Remote Sensing Methodology For Drought Vulnerability Assessment In Semi-Arid Ecosystems, Kaifi Fakhir Chomani

Mansoura Engineering Journal

The Kurdistan Region of Iraq (KRI) faced significant drought challenges due to global and environmental changes, necessitating drought assessments. Advanced techniques of remote sensing, Geographic Information Systems (GIS), and Analytic Hierarchy Process (AHP) were combined in this research to perform drought vulnerability zonation for KRI. Average annual rainfall, Average number of rainy days, Average annual temperature, slope, elevation, normalised difference water index (NDWI), normalised difference vegetation index (NDVI), land surface temperature (LST), and temperature condition index (TCI) were selected as contributing parameters for drought vulnerability assessments. The considered parameters were weighted using pairwise comparison, and thematic maps were created to …

A Machine Learning–Based Framework For Traffic Classification And Threshold-Based Load Management In 5g Network Slicing, Safi Ibrahim, Younis S. Younis, Kamal S. Hamza, Mohamed M. Ashour

Mansoura Engineering Journal

This study proposes a machine learning–based framework that applies machine learning techniques to improve the efficiency of 5G network slicing through automated traffic classification and threshold-based load management . The proposed model optimizes resource allocation among the three standardized 5G slice types: enhanced Mobile Broadband (eMBB), ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC). Two supervised learning algorithms—K-Nearest Neighbors (KNN) and Support Vector Machine (SVM)—are trained using Quality of Service (QoS) parameters such as packet delay, loss rate, and Quality Class Identifier (QCI). Experimental evaluations were conducted on two large-scale datasets containing over 400,000 traffic instances, demonstrating that the …

A Hybrid Geoid Model For The Perlis Region, Malaysia Based On Cadastral Reference Marks And A Geometric Approach, Muhammad Aiman Haiqal Abdul Malek, Muhammad Faiz Pa’Suya, Ramazan Alpay Abbak, Ami Hassan Md Din, Noorfatekah Talib

Mansoura Engineering Journal

This study presents the development of a high-resolution local geoid model for the Perlis region, Malaysia, using a geometric approach that integrates GNSS-levelling benchmarks with Cadastral Reference Mark (CRM) data. A total of 38 GNSS-levelling benchmarks were used as reference points, while 3,725 CRMs were incorporated to significantly enhance spatial coverage for geoid modelling. Orthometric heights at the CRM points were first determined by transferring heights from the reference benchmarks using gravimetric geoid information. Subsequently, geometric geoid heights were computed from the differences between ellipsoidal and orthometric heights. Five interpolation techniques were evaluated to generate the geometric geoid surface, namely …

Effect Of Two Cut Off Rows On Seepage Underneath Hy-Draulic Structures Using Weak Form Differential Quadra-Ture Element Method: A Theoretical Approach, Esraa Ahmed, El-Masry A. A, Hossam A.A. Abdel Gawad, Ahmed Elhamrawy

Mansoura Engineering Journal

Investigating confined seepage underneath hydraulic structures is crucial to ensure the safety of these structures. The Weak Form Quadrature Element Method (WFQEM) is the basis of this paper, which explores the behavior of confined flow. The seepage flow governing equation is solved numerically to estimate the uplift pressure underneath the hydraulic structures and the exit gradient.The Gauss–Lobatto–Legendre (GLL) type is used as nodal and integration points. Several models were solved to validate the applied numerical method. The obtained results are compared with theoretical and preceding numerical solutions mentioned in the literature. The system of equations generated from applying the WFQEM …

Reliability-Oriented Spatiotemporal Machine Learning For High-Impact Power Outage Event Prediction, Marwa Gamal

Mansoura Engineering Journal

Power outages have become an increasing concern for modern power systems due to their impact on infrastructure reliability, economic activities, and public safety. The growing frequency of extreme weather events and the rising demand for electricity have made it more difficult to anticipate high-impact outage events. One of the main challenges in this context is the complex interaction between temporal patterns and geographic variations, which traditional methods often fail to capture effectively. This study develops a machine learning framework that combines temporal characteristics with geographic information to improve the prediction of high-impact power outages. Temporal features such as seasonal patterns, …

Optimal Design Of Concrete Canal Section For Minimizing Overall Costs Using Artificial Ecosystem Optimization, Aya M. Elkhouly, Hamdy A. El-Ghandour, Tharwat Sarhan, Mahmoud E. Abd-Elmaboud

Mansoura Engineering Journal

The optimal design of concrete-lined canals represents a critical challenge in hydraulic engineering, requiring simultaneous minimization of construction and operational costs while satisfying hydraulic performance constraints. This study presents a novel optimization framework that utilizes the Artificial Ecosystem-based Optimization (AEO) algorithm for determining optimal canal cross-sectional dimensions. The proposed methodology comprehensively considers excavation costs, lining expenses, and water losses due to seepage and evaporation while ensuring hydraulic efficiency through velocity and Froude number constraints. The AEO algorithm mimics natural ecosystem processes through production, consumption, and decomposition phases to efficiently explore the design space and converge to global optima. The framework …