Journal of Studies in Science and Engineering

Optimizing Non-Autoclaved Aerated Concrete with Technogenic Wastes: A Study of AOBT and BFW Effects

2025-12-16T02:46:06-07:00

The study aimed to develop an optimised composition for non-autoclaved aerated concrete using technogenic wastes and to evaluate their Effect on key physical and mechanical properties. Antimony ore beneficiation tailings (AOBT, 10-30%) and basalt fibre waste (BFW, 0-4%) were varied according to a two-factor central composite design. Tests were conducted for density, compressive strength, thermal conductivity, and drying shrinkage in accordance with State All-Union Standard procedures. Density ranged from 612 to 740 kg/m³, with the lowest values at 30% AOBT and 2% BFW, due to enhanced gas formation and reduced solid mass. Compressive strength reached 3.8 MPa in the same composition, reflecting a synergistic filler-fibre effect. Thermal conductivity (0.14-0.23 W/m·°C) decreased with increasing AOBT and optimal fibre content, while shrinkage (0.8-2.8 mm/m) was minimised when both components were increased, indicating spatial stabilisation. Regression analysis showed that AOBT primarily affected density and thermal conductivity, and BFW influenced shrinkage. The mixes are designed to comply with the relevant structural-insulating concrete standards for external load-bearing walls in low-rise construction, specifically meeting the requirements for strength class B3.5 as defined by ASTM C90 and ASTM C495. The optimal mix, which achieves a compressive strength of 2.6 to 3.8 MPa, is suitable for load-bearing walls and meets the minimum strength criteria for structural-insulating applications in low-rise buildings, ensuring both adequate mechanical performance and energy efficiency.

A Hybrid CNN-Transformer Framework for Automated Tuberculosis Detection from Chest X-rays

2026-01-17T01:10:29-07:00

Tuberculosis (TB) remains a major global health challenge, demanding rapid and accurate diagnosis to enable timely treatment and reduce disease transmission. Although chest radiography is widely used for TB screening, manual interpretation is often time-consuming, subjective, and constrained by the shortage of experienced radiologists, especially in high-burden, resource-constrained settings. To address these limitations, this study proposes HybridTBNet, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) and Transformers for automated TB detection from chest X-ray images. The model is designed to capture both local pathological patterns and global contextual dependencies through hierarchical feature extraction and attention-based representation learning. A curated multi-source dataset of 6,708 high-quality chest X-ray images, comprising 3,194 TB and 3,514 Normal cases after quality filtering, was used for development and evaluation. A strict patient-wise split was applied to prevent data leakage, resulting in separate training, validation, and held-out test sets; however, minor class imbalance persisted in the final test subset. On the unseen test set, HybridTBNet achieved 98.2% accuracy, 97.1% sensitivity, 98.5% specificity, 94.9% F1-score, 0.991 ROC-AUC, and 0.978 PR-AUC, demonstrating strong potential as an effective automated TB screening support tool.

A Pilot Implementation and Usability Evaluation of a Database-Supported VR/BIM-Based Communication and Simulation System for Healthcare Design

2025-12-12T19:10:10-07:00

This study presents the development and pilot implementation of a Database-Supported VR/BIM-Based Communication and Simulation (DVBCS) system designed to enhance design comprehension, stakeholder engagement, and decision-making in the early planning stages of a disaster hospital in Jordan. The system integrates database-structured BIM models, a real-time visualisation environment built with Unreal Engine, and a semi-immersive VR projection setup to provide interactive walkthroughs and workflow simulations. A pilot usability evaluation was conducted with 10 participants, including five healthcare professionals and 5 members of the architectural and engineering team. Data were collected through structured questionnaires, observations, and guided discussions during the VR sessions. Results indicate that the DVBCS system improved users' understanding of spatial configurations, facilitated clearer communication, and supported more efficient identification of design issues compared with traditional design communication methods. Participants reported high satisfaction with the system's visual clarity, interactivity, and usefulness for collaborative review. However, the study also identified limitations related to hardware costs, required training, and the small sample size. As a pilot study, these findings are preliminary but demonstrate the potential of VR/BIM-supported approaches to strengthen communication and reduce design errors in healthcare planning. Future research should include larger samples and controlled comparisons to validate the system's effectiveness further.

Groundwater Quality and Irrigation Suitability in Carbonate Aquifers of Semi-Arid Regions: A Case Study from the Dammam Aquifer, Southern Iraq

2025-11-15T02:53:24-07:00

Groundwater serves as the primary source of freshwater in arid and semi-arid rural regions due to low precipitation, limited surface water, and growing socio-economic activities. Understanding the hydrogeochemical processes that influence groundwater quality is essential for effective management and long-term sustainability. This study evaluates the hydrogeochemical characteristics of groundwater within the Dammam Formation in the Samawa region of southern Iraq based on data from 30 wells. Laboratory analyses include measuring pH, EC, TDS, total hardness (TH), major cations (Ca²⁺, Mg²⁺, Na⁺, K⁺), and anions (SO₄²⁻, Cl⁻, NO₃⁻, HCO₃⁻). Groundwater suitability for human consumption was assessed using the Groundwater Quality Index (GWQI) according to World Health Organization (WHO) standards. In contrast, irrigation suitability was evaluated using sodium percentage (%Na), sodium adsorption ratio (SAR), permeability index (PI), magnesium hazard (MH), and Wilcox and USSL classification charts. GWQI results showed that 6.67% of samples were "excellent," 6.67% were "good," 66.67% were "poor," 6.67% were "very poor," and 13.33% were "unfit for drinking." Piper diagram analysis indicated 87% of samples in the Ca-Cl field and 13% in the Ca-Mg-Cl field. Wilcox's results revealed 70% of samples were unsuitable for irrigation and 30% of uncertain use. PI results showed 93.33% in class II, suitable for irrigation, and 63.33% with no magnesium hazard. The study recommends desalination with monitoring of sodium, chloride, and sulfate to improve groundwater quality.

A Protocol for Elucidating the Mechanisms of Biosorption: Uptake of Cu(II) and Pb(II) by Viable Pseudomonas aeruginosa Cells

2025-11-01T23:05:08-06:00

This study presents a methodological approach to elucidate the mechanisms underlying the biosorption of Cu(II) and Pb(II) by live Pseudomonas aeruginosa cells. Experiments were conducted under controlled conditions, and kinetic, equilibrium, and desorption analyses were employed to characterise both transport and attachment mechanisms. The biosorption of Pb(II) was best represented by the pseudo-second-order model, while the fit of Cu(II) was best described by both the pseudo-first- and pseudo-second-order models. The diffusion-chemisorption (D-C) model provided additional insight into transport mechanisms. The solid-phase mass transfer index (R_DC) values indicated that intraparticle diffusion played a moderate role in controlling the uptake of both Cu(II) and Pb(II), with a more pronounced effect observed for Pb(II). Equilibrium data conformed best to the Sips isotherm, suggesting heterogeneous surface binding and possible cooperative adsorption. Maximum monolayer capacities were 82.48 mg/g for Cu(II) and 32.50 mg/g for Pb(II). Desorption studies enabled the quantitative attribution of metal binding to distinct interactions, including physisorption, ion exchange, complexation, and intracellular accumulation. Cu(II) biosorption was primarily governed by complexation (51%) and ion exchange (46%), while Pb(II) biosorption was largely driven by ion exchange (64%). The approach formulated in this research offers a practical framework for probing biosorption mechanisms in subsequent investigations.

Cost-effective and Sensitive Anthocyanin-Based Purple Cabbage (Brassica Oleracea var. Capitata L) Extract Biosensor for Diagnostic Spoilage Fish Product

2025-11-05T05:01:26-07:00

Purple cabbage (Brassica oleracea var. capitata f. rubra) contains anthocyanins that exhibit pH-dependent colour changes and can function as visual indicators of food spoilage through their response to volatile basic compounds. This study developed a simple colourimetric indicator label using purple cabbage extract and evaluated its suitability for monitoring milkfish (Chanos chanos) spoilage under different storage temperatures. Extracts were prepared at three levels (30 g, 50 g, and 70 g of cabbage per 100 mL of solvent) and used to fabricate indicator labels, which were characterised using UV–Vis spectroscopy and FTIR to confirm anthocyanin-related spectral features and functional groups. The labels were tested in sealed 100 mL bottles containing 10 g of milkfish, with the indicator positioned 4 cm above the sample to enable headspace exposure without direct contact. Storage was conducted at 15 °C, 25 °C, and 40 °C. Colour changes were recorded at 2, 10, 24, and 32 h and quantified using image analysis. The indicator labels exhibited progressive colour shifts over time, consistent with the accumulation of volatile bases associated with spoilage. Temperature strongly influenced the response rate, with slower changes at 15 °C and faster, more pronounced changes at 25 °C and 40 °C. These findings indicate that the proposed purple-cabbage-based label has potential as a low-cost, non-destructive tool for monitoring milkfish freshness across practical storage conditions.

Enhancing the Mechanical and Moisture Resistance Properties of Plaster of Paris Using Nano-Silica, Glass Fibres, and Polyvinyl Alcohol

2026-01-25T00:49:25-07:00

Plaster of Paris (POP) is widely utilised in construction and artistic applications, yet its limited mechanical strength and sensitivity to moisture remain significant technical challenges. This study investigates the effects of incorporating nanosilica, glass fibres, and polyvinyl alcohol (PVA) on the performance of POP-based composites. Various formulations were evaluated for compressive, flexural, and hardness strengths, as well as water absorption. The experimental protocol distinguished between two immersion conditions: a 2 h immersion period was employed specifically for water absorption testing, while mechanical properties (compressive, flexural, and hardness) were evaluated under wet conditions following a 6 h immersion period. Results demonstrate that integrating nano-silica, glass fibres, and PVA increases mechanical resistance and reduces water uptake. Microscopic analysis indicates that nano-silica particles densify the POP matrix through a microfiller effect. These findings suggest that the modified POP composites exhibit improved performance for short-term moisture exposure, although further research is required to assess long-term durability in aqueous environments.

Optimising Membrane Performance in High-Recovery Seawater Reverse Osmosis Systems to Reduce Biofouling Using Modelling and Simulation

2025-11-29T00:28:24-07:00

The increasing demand for freshwater due to human activities underscores the need for alternative water sources. Desalination of seawater and water reclamation are reliable methods for producing potable water. Among desalination techniques, seawater reverse osmosis (SWRO) is widely adopted due to its lower energy consumption compared to thermal desalination, smaller footprint, reduced waste generation, and consistent performance. Efficient pre-treatment is vital to ensure high-quality feed water and protect RO membranes, which are prone to fouling, scaling, and chemical damage. The HYDRAcapMAX80 ultrafiltration module offers high permeate flux and effectively removes total suspended solids and organic contaminants. A two-pass, two-stage SWRO system was developed, achieving a recovery rate of 45%, salt rejection of 99.1%, and energy consumption of 3.26 kWh/m³. Post-treatment yields permeate water that meets WHO standards, with TDS at 407 ppm, calcium at 6.4 ppm, sodium at 145 ppm, chloride at 204 ppm, fluoride at 0.39 ppm, and a pH of 7.2. System performance was tested under varying salinity and temperature conditions. Higher salinity and lower feedwater temperatures increased TDS and energy consumption, but maintained acceptable performance. This system demonstrates energy-efficient desalination while delivering high-quality drinking water.

An Investigation into the Influence of Silver Nanoparticles on the Refractive Index of AgNO₃-Doped PVP/PVA Nanocomposites via the Refractive Index-Band Gap Energy Paradigm

2025-12-01T04:05:45-07:00

A nanocomposite consisting of a Polyvinylpyrrolidone (PVP) and polyvinyl alcohol (PVA) blend doped with silver nitrate (AgNO₃) was prepared using a combination of chemical reduction and solution casting, with varying silver nanoparticle (Ag-NP) contents. Polymer nanocomposites were prepared by adding AgNO3 (0, 10, 20, and 30 wt.%) to a PVP/PVA host polymeric matrix with a weight ratio containing 60% of (PVP) and 40% of (PVA). The UV-Vis-NIR absorption spectroscopy was employed to examine the optical properties of the prepared films. The impact of various AgNO3 concentrations on the direct and indirect band gap energies was obtained from Tauc's plots, and the refractive index was determined using the optimal formula, as reported in previous studies, which provides an accurate correlation between refractive index and band gap energy. The structural and crystallinity degree were examined by XRD analysis. SEM images showed the nanoparticle formations. The discussion focused on the discovery that the refractive index change increased substantially with increasing salt concentration, consistent with the formation of silver nanoparticles.

Optimal Siting and Capacity of Photovoltaic Distribution Generator in Radial Distribution Networks Using the Salp Swarm Algorithm and Geometric Mean Optimiser

2025-12-23T19:02:03-07:00

This research presents an optimisation framework for determining the optimal location and capacity of photovoltaic distribution generators (PVDGs) in radial distribution networks (RDNs) using two recent algorithms: the Salp Swarm Algorithm (SSA) and the Geometric Mean Optimiser (GMO). Unlike previous studies that treat distributed generation (DG) systems generically, this work offers a detailed modelling approach to PVDG power output and its direct impact on network performance. The framework is tested on both a standard IEEE 33-bus system and a practical Iraqi 66-bus system. Results show significant improvements in network performance after integrating optimal PVDG units, with power losses reduced by 61.334% and 61.571% using SSA and GMO, respectively, in the IEEE 33-bus system, and by 67.660% and 69.781% in the Iraqi 66-bus system. Additionally, the voltage stability index improved by 15.317% and 15.690% in the IEEE system and by 11.568% and 12.689% in the Iraqi system. The GMO algorithm demonstrated superior computational efficiency, converging faster and requiring fewer iterations than SSA. The accuracy and reliability of the results are further validated through comparison with previous work.

Development of Smart Wheelchair to Support Hybrid Learning Accessibility in Smart Classes for People with Disabilities

2026-01-16T22:35:11-07:00

Students with physical disabilities often encounter significant mobility and accessibility challenges in hybrid learning environments, particularly in dynamic classroom settings where conventional wheelchairs lack adaptability and compatibility with hybrid classroom layouts and digital learning activities. This research developed and evaluated a smart wheelchair equipped with sensor-based automated navigation to enhance mobility, independence, and accessibility in hybrid classrooms. The research employed a Research and Development approach using the ADDIE model, integrating prototype development, functional testing, mechanical simulation, and expert validation. The system incorporated a joystick and smartphone-based control, ultrasonic and line sensors for obstacle detection, GPS-assisted positioning, and an auto-parking feature. Mechanical simulation results indicated structural safety with a high safety factor (7.47) and minimal deformation (0.0429 mm). Performance testing demonstrated stable navigation, responsive control with an average latency of 120 ms, and successful auto-parking in 90% of trials. Expert validation involving five specialists showed that the system achieved "highly feasible" ratings across efficiency, durability, aesthetics, precision, and safety indicators. The findings indicate that the proposed smart wheelchair effectively improves independent mobility and supports accessibility in hybrid learning environments. However, further refinement is required to enhance precision and safety performance for broader implementation.

Assessing the Transition toward Industrialized Housing Construction in Jordan

2026-01-17T00:40:56-07:00

The transition toward industrialized housing construction involves shifting from traditional, craft-based practices to standardized, process-oriented, and technology-driven production systems. This study examines the extent to which such a transition is occurring in Jordan’s residential construction sector by assessing the presence of key building industry characteristics and identifying major implementation gaps. A structured questionnaire, based on Lessing’s industrialized construction framework, was used to evaluate eight core characteristics: process planning and control, off-site prefabrication, advanced technical systems and automation, information and communication technology (ICT), stakeholder relationships, supply chain integration, customer focus, and performance measurement. Data were collected from 203 construction professionals representing a wide range of residential sector stakeholders. The results indicate a partial and uneven adoption of industrialized construction practices. While planning, basic ICT usage, and performance measurement show moderate levels of implementation, critical features such as off-site prefabrication, automation, standardized systems, and long-term stakeholder collaboration remain weakly developed. Significant variability among firms highlights a fragmented production environment characterized by reactive logistics and limited supply chain integration. Overall, the findings suggest that Jordan’s housing sector exhibits a semi-industrialized profile, underscoring the need for coordinated policy support, capacity building, and enhanced stakeholder collaboration to advance industrialized housing construction.

Residential Complexes and the Erosion of Natural Identity in Mountainous Cities: The Case of Sulaimani City

2026-01-21T03:12:40-07:00

Rapid urbanisation in mountainous cities has accelerated the tension between the needs of development and conservation of natural identity. The research emphasises the importance of natural features (mountains) and their visibility in a city, which are usually cultural and natural landmarks that preserve the city's original natural identity. It examines how high-rise housing complexes impact the city's natural identity and visual scenery. The primary objective of this study is to evaluate the visual impact of residential building design on natural mountain landscapes (identity) using geographical information system (GIS) viewshed analysis. The results have shown that (88%), that's about (321.81 Ha), the natural context of the Goizha Mountain is interrupted and blocked by high-rise buildings. And only (12%), that is about (45.73 Ha), of the mountain scenery is visible. The study confirms that the disintegration of architectural design (form) from its natural context leads to significant visual distortion, ecological imbalance, a weakened sense of place, and the erosion of the city's natural identity. It highlights that context-sensitive design and height regulation are necessary to preserve the natural identity in mountainous areas.

Real-Time Monitoring of Concrete Strength Evolution Using Embedded Wireless Sensors

2025-12-23T00:22:02-07:00

The maturity technique offers a practical approach for forecasting early-age strength development. This study aimed to evaluate the accuracy of Giatec's SmartRock sensors by comparing their measurements with conventional specimen test results derived from the maturity index. Twelve concrete combinations were evaluated, with compressive strengths ranging from 20 to 45 MPa. The nine concrete mixes were designed with varying proportions of cement, sand, coarse aggregate, water, and admixture, as summarised in Table 1. The cement content ranged from 290 to 430 kg/m³, water-to-cement ratios varied from 0.27 to 0.51, and admixture dosages ranged from 0.5% to 1.2% by weight of cement, depending on the desired strength level (20-63 MPa). Cylinder tests performed at 1, 3, 7, 14, and 28 days provided the data necessary to calibrate the maturity–strength relationships. The resulting correlations between compressive strength and maturity index showed excellent accuracy, with coefficients of determination (R²) ranging from 0.986 to 0.999 across all twelve mixes (20-45 MPa). These results confirm that the SmartRock maturity method reliably predicts early-age strength and offers real-time assessment of in-situ concrete performance. The best-fit Vipulanandan models yielded coefficients of determination (R²) ranging from 0.986 to 0.999, demonstrating excellent agreement between sensor-predicted and laboratory-measured strengths. Predicted early-age strengths (3–7 days) differed from test values by less than 5%, confirming the reliability of the SmartRock system for field applications. The study concludes that the maturity method, when properly calibrated, provides a robust and non-destructive means to predict in-place concrete strength in real time, enabling faster formwork removal and improved construction quality control.

Swarm Intelligence Approach for Optimizing Neural Network Architectures in Medical Data Classification

2026-01-01T18:49:31-07:00

The capability of an Artificial Neural Network (ANN) to achieve accurate classification outcomes depends heavily on its architectural design, particularly the number of hidden layers, nodes, and other hyperparameters. Selecting an optimal architecture remains a crucial yet challenging task for improving network accuracy, especially in medical informatics, where reliability is essential. This paper introduces a novel model, MRFO-MLP, which integrates the Manta Ray Foraging Optimization (MRFO) algorithm with a Multi-layer Perceptron (MLP). MRFO is a swarm-intelligence metaheuristic inspired by the cooperative foraging behaviour of manta rays and is employed to explore potential MLP configurations within the solution space. In the MRFO-MLP model, each candidate solution is represented as a binary vector, which reduces both computational time and implementation complexity. Furthermore, a customised fitness function is designed to evaluate candidate architectures by simultaneously considering network complexity and generalisation error, thereby mitigating the risk of overfitting. The proposed model is evaluated using five medical datasets from the UCI repository. Its performance is compared with several optimisation algorithms, including the Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and traditional machine learning classifiers. Experimental results demonstrate that MRFO-MLP produces efficient and accurate MLP architectures, achieving the best performance on three datasets and the second-best performance on another, highlighting its competitive effectiveness for medical data classification tasks.

A Unifying Paradigm in Predictive Oncology Through Hybrid Voting Ensembles for Optimising Breast Cancer Detection

2025-12-07T15:52:57-07:00

Machine learning (ML) offers substantial promise for improving cancer diagnosis, yet many existing approaches rely on single-classifier models. This study proposes and evaluates a hybrid weighted voting ensemble to optimise predictive accuracy for breast cancer classification by combining a diverse set of ML classifiers. Using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, five base learners were developed: a multi-layer perceptron (MLP), a Classification and Regression Tree (CART), and three support vector machine (SVM) models with linear, radial basis function (RBF), and polynomial kernels. A comparative analysis of individual model performance was conducted against the proposed ensemble. The weighted voting model achieved the best overall performance, with an accuracy of 0.9737, F1-score of 0.9641, and AUC of 0.9952, surpassing the strongest individual classifiers (MLP and SVM-RBF, each with an accuracy of 0.9591). Permutation feature importance highlighted "worst" measurements, such as concave points_worst and radius_worst, as the most informative predictors, consistent with established malignancy indicators. The proposed hybrid voting ensemble provides a robust and reliable framework for breast cancer prediction, demonstrating clear advantages over single-classifier models and supporting more precise decision-making in oncology.

Heart Disease Prediction Using Machine Learning with Greater Cane Rat Algorithm-Based Hyperparameter Tuning

2026-02-11T02:40:44-07:00

Cardiovascular diseases remain a leading global cause of mortality, highlighting the need for reliable and efficient tools for early risk identification. Machine learning (ML) models have shown promise for heart disease prediction; however, their performance can be highly sensitive to hyperparameter selection. Conventional hyperparameter optimisation approaches (e.g., grid search and random search) may be computationally demanding and can explore the search space inefficiently, especially when hyperparameters interact. To address this, this study investigates the Greater Cane Rat Algorithm (GCRA), a nature-inspired metaheuristic, as a hyperparameter optimisation framework for heart disease prediction. GCRA was applied to tune five classifiers, logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (KNN), and decision tree (DT), while naïve Bayes (NB) was evaluated using default settings due to its limited tunable hyperparameters. Experiments were conducted on the UCI Heart Disease dataset under two train–test splits (80/20 and 70/30). Model performance was assessed using accuracy, precision, recall, and F1-score, and results were compared against genetic algorithm (GA) tuning as well as grid search (GS) and random search (RS). Across most models, GCRA improved performance relative to untuned baselines, with the largest gain observed for KNN, where accuracy increased from 0.8033 to 0.8689 under the 80/20 split. Overall, the findings indicate that GCRA can provide an effective and practical hyperparameter optimisation strategy for ML-based heart disease prediction under consistent evaluation settings.

An Efficient Feature Extraction and Image Retrieval Based on Convolutional Neural Networks

2025-12-11T00:35:28-07:00

Image retrieval has become increasingly important in many application domains, including medicine, forensics, biology, astronomy, fashion, and military systems, where large volumes of digital images are generated and stored daily. Consequently, there is a growing need to retrieve images based on their visual content, such as color, texture, and shape, rather than relying on keywords or textual annotations that may be incomplete or unavailable. Content-Based Image Retrieval (CBIR) has been developed to address this challenge. However, designing effective image features remains difficult due to the semantic gap between human perception and machine-level representation. This research proposes a novel hybrid feature extraction and fusion approach for image retrieval, termed Fuse Convolutional Neural Network Models (FCNNM). The proposed method leverages multiple layers of Convolutional Neural Networks (CNNs) to learn discriminative image representations while avoiding the high dimensionality and computational complexity associated with dense layers in traditional Deep CNNs. Instead, the model relies on sequential convolutional and pooling layers. Two CNN architectures with three and four layers, respectively, are designed, and their extracted features are fused to improve retrieval performance. The proposed approach is evaluated using benchmark datasets, namely Corel-1K and Corel-5K. Experimental results demonstrate that fusing 2D CNN 3- and 4-layer models significantly enhances retrieval accuracy, achieving 97% on Corel-1K and 95% on Corel-5K, while maintaining high computational efficiency.

Rehabilitating Heritage Canals in Semi-Arid Cities: A Civil 3D–Based Redesign and Cost Estimation of the Al-Dakair Canal in Basra, Iraq

2025-12-07T15:40:39-07:00

This study redesigned the cross-sections of the Al-Dakair Canal in Basra, southern Iraq, and estimated the earthwork volumes and rehabilitation costs required to restore its hydraulic and heritage functions. The study used Civil 3D to process topographic survey data, generate a TIN surface, redesign the canal cross-sections, and calculate cut and fill volumes using the prismoidal method. The proposed design was checked hydraulically using Manning’s equation and independently compared with ArcMap-based volume calculations. The results showed that most canal sections require excavation, with a total cut volume of 18,800.21 m³ and a total fill volume of 60.33 m³. The proposed cross-section improved the estimated flow capacity to approximately 59.43–60.24 m³/s along the canal. The total preliminary rehabilitation cost was estimated at US$1,274,079 based on local 2025 prices for excavation, transport, levelling, lining, pumping, administrative expenses, and quality control. The comparison between Civil 3D and ArcMap showed close agreement in cut and fill volumes, supporting the consistency of the digital modelling workflow. The study provides an applied framework for using Civil 3D and GIS tools in the rehabilitation planning of heritage canals in semi-arid urban contexts.

Reviving Traditional Streets through Urban Acupuncture: A Comparative Study of El-Nabi Daniel Street in Alexandria, Egypt, and Rainbow Street in Amman, Jordan

2025-12-25T03:02:54-07:00

Urban Acupuncture is an emerging urban strategy that employs small, targeted interventions to improve urban life. This paper examines two case studies: El-Nabi Daniel Street in Alexandria, Egypt, and Rainbow Street in Amman, Jordan. Both sites are culturally rich but economically challenged and have experienced improved spatial quality and community engagement through focused interventions. The main evaluation indicators include spatial, socio-economic, and governance aspects. The implemented actions involved redesigning pavements, installing street furniture, renovating façades, reducing traffic speed, and activating public spaces. The results indicate significant improvements in livability, social interaction, and environmental performance, despite minimal physical change. The approach has proven to be flexible and adaptable to specific urban contexts. Urban Acupuncture is therefore considered an economically affordable and environmentally friendly method for revitalising historical areas with limited financial support.

Driver-Related Factors Affecting Traffic-Sign Letter Height Preferences: A Regression-Based Study in Kut City, Iraq

2025-12-16T15:18:18-07:00

This study investigated driver-related factors affecting preferred traffic-sign letter height in Kut City, Iraq. A questionnaire survey was conducted with drivers of different vehicle types to examine the effects of age, gender, driving experience, and vehicle type on preferred letter height. The collected data were analysed using multiple regression to identify significant predictors of letter-height preference. The results showed that older drivers and heavy-vehicle drivers preferred larger traffic-sign letters. Specifically, the age groups 46–55 years (B = 2.5, p = 0.02) and 56–65 years (B = 4.0, p = 0.001), as well as lorry drivers (B = 2.2, p = 0.005) and bus drivers (B = 1.8, p = 0.02), were positive predictors of preferred letter height. Gender and driving experience were not statistically significant predictors after controlling for the other variables. Model validation was assessed using residual normality checks and multicollinearity diagnostics. The findings suggest that driver age and vehicle type should be considered when refining traffic-sign letter-height guidelines. However, because the study is based on self-reported preferences from one city, further field-based legibility testing is needed before the findings are generalised to wider traffic-sign design standards.

An Ethical AI Approach for Disease Diagnosis Using Prompt-Based History Summarisation

2025-10-28T19:06:15-06:00

Medical history is a cornerstone of accurate diagnosis and effective treatment planning. In light of recent challenges such as the COVID-19 pandemic, the need for efficient, reliable, and secure history-taking methods has become more pressing than ever. Traditional approaches are often time-consuming, prone to human error, and reliant on subjective interpretation. This study proposes an AI-driven solution that automates patient history collection using prompt-based techniques, transforming complex, unstructured data into concise and coherent summaries. Ethical compliance and data privacy are central to the system's design, ensuring the secure handling of sensitive patient information. The integration of artificial intelligence, machine learning, and deep learning enhances both the accuracy of disease prediction and the overall efficiency of healthcare delivery. Through expert validation and responsible implementation, this research highlights the potential of generative AI to support early breast cancer diagnosis and improve the quality of patient care. We evaluated the fluency and semantic alignment of clinical text fields using auxiliary metrics, including ROUGE, BLEU, and BERTScore. Additionally, SHAP-based feature attribution was incorporated to interpret multi-class predictions. Together, these improvements provide a strong semantic foundation for evaluating and filtering decision support in medical AI systems.

Investigating the Crucial Indicators of Green Architecture to Enhance Quality of Life

2025-12-21T03:00:35-07:00

Green architecture has gained increasing attention as an environmentally responsible approach that supports human well-being, sustainable lifestyles, and improved living environments. Although many studies have examined green building principles, the direct relationship between green architecture and quality of life remains insufficiently integrated, as much of the existing literature focuses mainly on technical and environmental performance. This systematic review addresses this gap by identifying and analysing the key indicators of green architecture that contribute to quality of life and by proposing a conceptual framework linking sustainable design with human well-being. Relevant academic literature was systematically reviewed to extract, classify, and compare the most frequently discussed indicators. The findings show that social well-being was the most frequently identified indicator, appearing in 88% of the reviewed studies, followed by community impact and resource management at 84%, and pollution reduction and ecological footprint at 80%. These results indicate that green architecture should be understood not only as a technical response to environmental challenges but also as a holistic strategy for creating healthier, more livable, and socially responsive environments. The study highlights the interrelationships among environmental, social, and resource-based indicators and provides practical guidance for architects, urban developers, and decision-makers seeking to integrate sustainable and human-centred principles into contemporary built environments.