Journal of Science and Technology is an Open Access Peer-Reviewed Journal Published Monthly by Faculty of Engineering and Computing – University of Science & Technology - Aden - Yemen. The journal welcomes articles that contribute to a wide spectrum coverage of science and technology. Originality, high quality and significance of the scientific content are essentially considered. Online ISSN: 2410-5163 Print ISSN: 1607-2073
The Physical, Hydraulic, and Mineralogical Characterization study of the Traditional Qadhad Material of the Cisterns of Aden
Qadadh is a traditional building material in Yemeni architecture. It has been used for centuries to dams, cisterns, channels, and other water structures due to its high resistance to leakage and moistness for a long-term durability. This research aims to characterize and analyze the properties of Qadadh based on specimens taken from historic Aden cisterns for the purpose of a reproduction practice of the material. Comprehensive physical (bulk density, open porosity, water absorption, capillary absorption), hydraulic (permeability, hydraulic indicators) and mineralogical tests (XRD X-ray diffraction, XRF X-ray fluorescence, and SEM-EDS scanning electron microscopy with energy-dispersive X-ray spectroscopy) were conducted based on international specification. The findings indicated for the bulk density of Qadadh in Aden cisterns to be 1690 kg/m³, with open porosity and water absorption at 25.04% and 14.89%, respectively. The permeability coefficient was measured at approximately 17.6×10⁻⁹ m/s. These results may refer to its efficiency to prevent water leakage and a good compatibility between density and durability. Results of mineralogical analyses (XRD, XRF) revealed the mineral and chemical composition of the Qadadh. Moreover, mineralogical images of SEM-EDS highlighted the dense structure of Qadadh which may be attributed to calcium-silicate-hydrate C-S-H and calcium- alumino-hydrate C-A-H gel formations which enhanced its waterproofing capabilities over extended periods in coastal environments.
Traditional building materials, Waterproofing material, Characterization, XRF ، XRD ، SEM
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A Secure Web-Based School Management System Using Role-Based Access: Case Study from Nigeria
The presence of manual and disjointed digital systems in secondary schools across Nigeria leads to inefficiencies in the processes of student registration, grade management, and ID card distribution. This article presents a secure web-based School Management System (SMS) that was created with Node.js, Express.js, MySQL, and Bcrypt for Imamu Hafsin Model International School in Kano. Among its main features are easy login recovery, a tiered role-based access control system, automated ID card creation, and demographic analysis tools. The system has undergone field testing and has shown improved performance compared to Fedena and OpenSIS in areas such as security, access control, and localization. This development enhances administrative effectiveness within resource-limited settings.
Academic Data Analytics; Nigerian Secondary Education; Node.js; Role-Based Access Control (RBAC); School Management System (SMS)
The New Two-Parameter Exponential-Gamma-Based Distribution: Properties and Application to Non-Communicable Disease Data
This study introduces a novel lifetime distribution, termed the New Two-Parameter Exponential–Gamma (NTPEG) distribution, developed through an additive mixture framework to enhance flexibility in modeling lifetime and survival data. Fundamental structural properties, including moments, hazard rate behavior, and entropy, are derived and discussed. Parameters of the model are estimated via the maximum likelihood method, and simulation studies demonstrate the reliability and efficiency of the estimators. The practical utility of the NTPEG model is illustrated using survival data on non-communicable diseases, particularly among acute bone cancer patients. Comparative analyses with existing extensions of the Lindley distribution reveal that the proposed model provides superior fit and flexibility for complex real-world data. Overall, the NTPEG distribution represents a robust and versatile tool for researchers and practitioners in survival and reliability analysis.
The Factors Influencing the Selection and Formation of Architectural Concepts
Although the design concept is the core of the architectural process, its formulation requires a series of complex decisions influenced by several factors that influence its generation, development, and transformation into creative architectural solutions. The problem lies in the lack of a clear understanding of the factors influencing the concept and the lack of knowledge of the strength of each factor's influence. The research seeks to analyze these factors and determine which ones play the most prominent role in shaping the concept. It uses a methodology that combines quantitative and qualitative analysis. A theoretical framework for the factors was constructed, followed by a questionnaire that was distributed to the sample. The results showed that functional factors occupy the forefront in terms of influence with a percentage of 13.39%, indicating a growing awareness of the role of functional efficiency. It was followed by factors related to the user and client with a percentage of 12.88%, reflecting the growing interest in meeting human needs and requirements in architectural spaces. Both aesthetic and technological factors received similar weights (12.66%, 12.76%), indicating a balance between artistic, aesthetic, and technical expertise in design. The study recommended the need to enhance the use of smart technologies as a tool for developing design concepts, as well as supporting the trend toward localizing design by invoking elements of local identity and the climatic environment.
Architectural concept, architectural function, design process, architectural design.
Design of Solar Photovoltaic Energy System to Power a 3 HP Motorised Cassava Shredding Machine in Umudike, Abia State, Nigeria
Abstract— This study presents the finalized design and simulated performance of an autonomous Solar Photovoltaic (SPV) system specifically engineered to power a 3 hp motorized cassava shredding machine at the Ahia Ndoro market in Umudike, Abia State, Nigeria. The machine, which incorporates an electric motor, an internal combustion engine, and a manual winding mechanism, has a maximum input capacity of 16 kg of cassava. The system was designed to meet a total daily energy requirement of 18.7 kWh, corresponding to a maximum operational power output of 2.3 kW. The final component specification requires an array of 20 solar panels (rated at 435 W each), a total of 12 batteries (each with 250 Ah capacity), and an inverter with an input power of 3 kVA. For system integrity, the maximum DC parameters were calculated to be 173 A at 24 V, while the maximum AC parameters were 10.13 A at 230 V. The solar charge controller was specified with an amperage of 97 A. Performance simulations confirmed the system's robust operational capacity: the minimum monthly energy provision of 23.3 kWh per day (recorded in August, the month of lowest solar radiation) comfortably exceeds the required daily energy of 18.7 kWh. The maximum daily production reached 218 kWh per day, yielding an annual average Solar Performance Ratio (SPR) of 75%. Consequently, the proposed SPV system is confirmed to satisfy and secure the energy demands of the Abacha shredding machine, offering a reliable and sustainable power solution.
solar energy design, cassava shredding machine, actual output energy, theoretical output energy, solar radiation, performance ratio.
Core Requirements for Developing a Successful BIM Execution Plan in Yemen: A Quantitative Study
Building Information Modelling (BIM) is a disruptive technology that has the ability to greatly improve the efficiency and effectiveness of construction projects. However, its implementation in impoverished nations like Yemen is hampered by significant barriers, most notably a lack of a defined and contextualised implementation plan. This study fills a major need by identifying and prioritising the key elements for creating a successful BIM Execution Plan (BEP) specific to the Yemeni environment. A structured questionnaire was distributed to a targeted sample of 56 specialists, including academics (30.4%), project managers (23.2%), and consultants/engineers (46.4%).Because of the scarcity of professionals with prior experience in the technology, the snowball sampling technique was used to pick the sample. The study indicated that BIM adoption in Yemen remains in its early stages. It successfully identified 10 major implementation needs and analysed their possibility, with experts citing defining BIM objectives, forming the implementation team and defining BIM utilisation as the most crucial precondition for effective adoption. This study provides a tested, prioritised checklist that will serve as a core roadmap for Yemeni construction enterprises. By concentrating on these critical first steps, businesses may streamline the implementation process and efficiently overcome the initial barriers to BIM adoption.
Building Information Modeling (BIM), BIM Execution Plan (BEP), Requirements, Quantitative, Yemen Construction Industry (YCI).
Leveraging Artificial Intelligence And Statistical Methods For Evaluating Geotechnical Properties Of Lateritic Soil In Road Construction: A Case Study On The Umuahia-Ikot Ekpene Road
Geotechnical properties of lateritic soils are critical for road construction, influencing subgrade stability and pavement performance. This study investigates these properties along the Umuahia–Ikot Ekpene Road, with a focus on predictive modeling and variable importance. Four locations were sampled at depths of 150 mm, 300 mm, and 450 mm. Key properties, including moisture content, particle size distribution, Atterberg limits, compaction characteristics, and California Bearing Ratio (CBR), were analyzed. Moisture content ranged from 10.8% to 13.0%, with Locations A (12.2%) and C (12.8%) exhibiting the highest values. All soils were classified as well-graded, with gravel content varying from 12.5% to 16.0%. Atterberg limits indicated medium plasticity, with plasticity index values between 12% and 14%. Maximum dry density (MDD) ranged from 1795 to 1850 kg/m³, Optimum Moisture Content (OMC) from 10.8% to 12.1%, and degree of compaction from 95% to 99%. Unsoaked CBR values were 24-31%, and soaked CBR values were 17-23%. Lasso Regression outperformed other models (Linear regression, ridge regression, decision tree regressor, gradient boosting regressor, support vector regressor and random forest regressor) in predicting CBR, achieving R² values of 0.86 (unsoaked) and 0.98 (soaked). Feature importance analysis highlighted the key soil properties driving CBR variations while sensitivity analysis reveals that MDD (kg/m³) has a significantly greater impact on the predicted soaked CBR values compared to moisture content (%). The results suggest that these lateritic soils possess moderate strength, making them potentially suitable for subgrade materials. However, stabilization is likely required for sub-base or base layers in road construction.
Developing an Urban Framework to Rehabilitate the Russeifa Watercourse as a Blue-Green Corridor in Jordan
Watercourses are one of the most vital elements in cities, which are essentially the core of city formation. Over time, with increased urbanization and industrialization, many watercourses faced difficulties in preserving their natural form, resulting in the loss of many of their characteristics. This transformation has led to a clear degradation of ecosystems and increased pollution and health risks. Many countries tend to undertake projects to rehabilitate watercourses that have degraded, aiming to achieve environmental, as well as socio-economic benefits. Russeifa city is one of the most densely populated cities in Jordan and the Arab world. The watercourse passing through it is also a source of pollution that poses risks to the city and its residents. However, the study aims to present a suggested plan to rehabilitate the watercourse as a blue-green corridor to enhance the water and greenery in the city. Thus, the study relied on previous studies, in addition to interviews with officials and residents, to understand their connection to the river and the problems they suffer from. It turned out that the watercourse is currently used only as a landfill. Furthermore, the study relies on a study of successful case studies to extract suitable mechanisms that fit the Russeifa watercourse rehabilitation. In the end, the study shows that it is necessary to set logical and achievable goals for adopting the rehabilitation strategy. In addition to activating the role of citizens in development and rehabilitation projects, they are an essential element in the long-term success. Moreover, this study contributes by proposing an urban framework for transforming degraded watercourses into blue-green corridors, offering both theoretical insights and practical guidelines applicable to similar urban contexts in Jordan and beyond.
Predictive Modeling for University Major Selection: An AI-Driven Solution Using Arab Graduate Data
Choosing an appropriate university major is crucial for students’ academic and career success. This study presents an AI-driven recommendation system using supervised machine learning, specifically the Random Forest algorithm, to support major selection based on academic performance (GPA, entrance exam scores) and labor market relevance (major-specific employment rates). The system was trained on 1000 student records from the "Arab University Graduate Data Set" and received 97% accuracy, with employment rate and GPAs appearing as the most effective predictions. Unlike previous studies focused on developed countries, this research emphasizes AI ability in the environment with limited resources as Yemeni universities. It provides a scalable solution to coordinate educational alternatives with labor market needs. Future work will integrate personal interests and socio-economic factors to increase privatization.
AI in education, Random Forest, major selection, Yemen, labor market alignment
Lighting System Based on the Human Presence for Daily Applications
The scope of this project is to use embedded systems to automate light switching and light adjustment based on human presence. Therefore, this system can be implemented in any closed place to control the light status and intensity. This system will detect the human presence in a closed workplace by counting the number of humans inside the place. The counting process will be applied by using two distance sensors in each door. The triggering of the two sensors will specify if the human is going in or going out. After detecting human presence, the light intensity will be adjusted according to the light intensity in the place. Light intensity will be adjusted automatically either using a variable resistor and stepper motor or using fixed voltage lines with different values of resistance for each one, which will give different light intensities to each line. Furthermore, this system is able to be controlled manually through an Android app.
Lighting System, Human Presence, Daily Applications
An Optimized Hybrid Intelligent System for High-Accuracy Dewpoint Pressure Estimation
Dewpoint pressure (DPP) is a critical property of gas condensate reservoir development. Accurately estimating this property remains a significant challenge. Existing empirical correlations and iterative methods lack sufficient accuracy due to complexity and computational intensity. However, despite their utilization involving complex computations, they have not achieved sufficient accuracy. Several individual intelligent systems have been utilized to predict this property with good accuracy, but the application of hybrid models is less common. Therefore, this study proposes two hybrid intelligent models—Particle Swarm Optimization combined with Neural Networks (PSONN) and Neuro-Fuzzy (NFuzzy)—to enhance prediction accuracy of dewpoint pressure. Approximately 860 collected data points were used to develop these hybrid models. Inputs such as temperature (T), hydrocarbon composition, specific gravity, and molecular weight of heptane plus were utilized to predict the dewpoint pressure. In this study, the performance of both intelligent hybrid systems is compared to the most widely published Artificial Intelligence (AI) models. Based on statistical error analysis results, the new hybrid models outperform the published models. The results confirm that the PSONN hybrid model achieved the best performance with an absolute percent relative error (APRE) of 2.47%.
Artificial intelligence; Hybrid models; Neuro-Fuzzy; PSONN, Dewpoint Pressure
A Real-Time Gender and Age Prediction System Based on Facial Images Using Convolutional Neural Networks
Real-time gender and age prediction based on facial images has become essential in applications such as surveillance, demographic analytics, and human-computer interaction. However, existing systems often struggle with challenges like inconsistent lighting, occlusions, pose variations, and dataset imbalances, which reduce accuracy and hinder deployment on resource-constrained devices. This research introduces a real-time gender and age prediction system that leverages Convolutional Neural Networks (CNNs), utilizing fine-tuned ResNet-34 for gender classification and a modified VGG-16 for age estimation. A robust preprocessing pipeline, integrating image normalization, histogram equalization, data augmentation, and synthetic data generation, enhances model generalization across diverse datasets. Efficient face detection techniques, combined with lightweight model architectures, enable the system to achieve inference speeds below 500 milliseconds per frame without GPU acceleration. Experimental evaluations across IMDB-WIKI, UTKFace, MORPH II, and Adience datasets revealed gender classification accuracies exceeding 94% and age prediction one-off accuracies above 90%. The proposed system demonstrates a scalable and efficient solution for real-time facial attribute recognition in dynamic and resource-limited environments.
Gender Classification, Age Estimation, Convolutional Neural Networks, Real-Time Facial Recognition, Data Augmentation, Cross-Dataset Generalization
Artificial Intelligence for Enhanced Cybersecurity: A Comprehensive Review and Future Directions
The complexity and increasing rate of cyberattacks have become a formidable hurdle to people, institutions, and infrastructure vital sectors in all continents. Conventional signature-based defenses to cybersecurity threats, which are usually backreactions by nature, can hardly keep up with trends in threats that are constantly shifting. Artificial Intelligence (AI), including Machine Learning (ML) and Deep Learning (DL), is the new paradigm enabling completely new opportunities to enhance cybersecurity mechanisms. This paper is an in-depth overview of how AI can be used to enhance cybersecurity, the various uses and applications, techniques, and all other future possibilities. We also get into the various aspects of AI being used in cybersecurity, which includes intrusion detection, malware analysis, security orchestration, threat intelligence, and vulnerability management. Next, we address the challenges and limitations of the implementation of AI in cybersecurity, that is, the data privacy problems, adversarial trained AI attacks, and explainability. Lastly, we point out the emergent lines of future research with the most potential, such as blockchain + AI, further development of Explainable AI (XAI), and combining human interaction with AI. The review is intended to be a useful tool for both researchers, practitioners, and policymakers in order to learn more about AI and how it can be used to develop more proactive and resilient cybersecurity.
Artificial Intelligence, Cybersecurity, Machine Learning, Deep Learning
DELINEATION OF GEOLOGICAL BOUNDARY IN IDI-EMI AND ENVIRONS, SOUTHWESTERN NIGERIA USING ELECTRICAL RESISTIVITY METHOD
Abstract— Old geological maps in areas with complex rock formations often lack accuracy, risking misinformation, especially where lithological boundaries are subtle. This study integrates Vertical Electrical Sounding (VES) and 2D electrical imaging to improve subsurface mapping in the Idi-Emi area, southwestern Nigeria. The approach successfully identified three terrain types—sedimentary, transition, and basement complex—each with distinct resistivity and layer thickness ranges. The topmost geoelectric layers showed resistivities of 42–634 Ωm and thicknesses of 0.6–1.9 m across these terrains. New geological boundaries were delineated, refining the existing map and aligning with the Dahomey Basin framework. The enhanced geological model provides critical insights for groundwater aquifer delineation, hydrogeological exploration, and controlling illegal mining activities in the region. This integrated geophysical method demonstrates a valuable approach for accurate subsurface characterisation in geologically complex terrains.
Efficient Simulation of Water Network Remodeling Using the Virtual Distortion Method: A Novel Hydraulic Reanalysis Framework
Efficient and adaptive hydraulic modeling is increasingly essential for managing modern water distribution networks (WDNs), especially in the face of urbanization and infrastructure aging. This paper introduces the application of the Virtual Distortion Method (VDM)—originally developed for structural systems—to simulate remodeling scenarios in WDNs. By imposing localized virtual distortions to represent topological changes such as pipe blockages, the method allows accurate approximation of flow redistribution and pressure head without full system recomputation. A numerical case study confirms the accuracy of VDM in replicating network behavior, achieving over 95% consistency with conventional full-system recalculations while reducing computational time by more than 80%. The approach is easily integrable with standard tools such as EPANET or MATLAB and presents a scalable, explainable, and computationally efficient alternative for network redesign, emergency planning, and real-time simulation. In addition, the framework can be extended and hybridized with reduced-order models, physics-informed neural networks (PINNs), and graph neural networks (GNNs) to further enhance scalability and adaptability.
Virtual Distortion Method (VDM), Water Distribution Networks, Computational Efficiency,
A Comparative Study of Distance Measures for 2DPCA-Based Face Recognition
Face recognition is considered a primary technology in biometric systems. Despite its merits, the systems suffer because of the various facial variations. Two-Dimensional Principal Component Analysis (2DPCA) limits these variations by preserving facial spatial information and, at the same time, improves the computational efficiency. Those features give it an advantage in different applications, not only in face recognition but also in artificial intelligence (AI) applications. However, the distance methods used for classifications associated with 2DPCA are a significant issue. This paper explores and modifies various distance measures defined in the literature of the PCA approach for face recognition. These fourteen-distance metrics are modified and compared to the standard Euclidean distance on well-known face databases, namely the ORL, AR, and LFW databases. The experiments on these databases, which have diverse variations in facial images, manifest the superiority of the correlation coefficient-based distance method. Additionally, the results also show remarkable performance of angle-based and Canberra distance methods. These results clarify the importance of this work in enhancing distance metrics within 2DPCA algorithms to enhance the accuracy and robustness of both traditional and AI-driven recognition systems.
Two-dimensional PCA, Face recognition, Classification Methods
Evaluating the Prediction Performance of Random Forest in Classification of Carbonate Lithology
Accurate lithology prediction in carbonate reservoirs is essential for hydrocarbon exploration but remains challenging due to their complex heterogeneity. Traditional methods (e.g., seismic and well-log analysis) often fail to capture subtle lithological variations, while machine learning approaches such as Random Forest (RF) remain underexplored for carbonates. Previous research has not sufficiently compared Random Forest with advanced models such as XGBoost and deep learning approaches, nor provided detailed feature importance analyses specific to carbonate lithology classification. This study employs a dataset comprising 4,624 samples characterized by ten petrophysical properties to evaluate the classification performance of RF. Our optimized RF framework demonstrates superior accuracy while reducing dependence on costly core sampling, thereby improving the precision of carbonate reservoir models.
Carbonate lithology prediction, Random Forest, machine learning in geoscience, reservoir characterization, feature importance
DESIGN OF SOLAR PHOTOVOLTAIC SYSTEM FOR UNDERGROUND IRRIGATION PUMPING WATER FOR THE AGRICULTURAL FARMLAND IN NATIONAL ROOT CROPS RESEARCH INSTITUTE, UMUDIKE (NRCRI), ABIA STATE, NIGERIA.
This study addresses the critical challenge of water scarcity and high energy costs impacting agricultural productivity in regions like Abia State, Nigeria, by proposing a sustainable solar photovoltaic (SPV) irrigation pumping system. The objective was to design and evaluate an SPV system for underground irrigation at the National Root Crops Research Institute (NRCRI), Umudike, and ensure a reliable water supply for its 5-hectare farmland. The methodology involved determining pump energy demand (36.3 kWh/day), applying three distinct SPV component sizing methods for cross-validation, and performing a robust stochastic reliability and temperature-adjusted performance analysis using Monte Carlo simulation. Key results indicate that a system with 36 solar panels, 24 batteries, and a 6.0 kVA inverter can consistently meet the daily energy demand. The stochastic analysis demonstrated that while monthly solar deficits can be high in certain periods (e.g., 99.9% in August), even minimal (e.g., 1 day) battery autonomy drastically reduces the probability of energy shortfall. This research provides a practical and resilient framework for off-grid agricultural irrigation in sun-rich developing regions.
solar photovoltaic design, submerged irrigation pump, Agricultural farmland, Performance ratio, Monte Carlo simulation, Economic feasibility
Generalized Trirecurrence of Cartan’s Second Curvature Tensor in Ph-Recurrent Spaces
This paper explores Cartan's second curvature tensor within the framework of generalized recurrent spaces. We derive and analyze the generalized recurrence conditions, and show that satisfies the recurrence relations of the first, second, and third orders. Specifically, we investigate the conditions that lead to the tensor’s behavior in generalized -recurrent spaces, denoted as -G-TR , and establish several theorems regarding the curvature tensor’s covariant derivatives and its relationship with other curvature tensors. Furthermore, we connect these recurrence conditions to the P-Ricci tensor and vector fields in higher-dimensional spaces. We demonstrate the interrelations between these tensors through a series of covariant derivative computations and conclude that the curvature tensor cannot vanish under specific geometric conditions. This study advances the field of differential geometry and its applications in physics by offering a thorough foundation for comprehending the characteristics of Cartan's curvature tensor in generalized recurrent spaces.
Tensor P_jkh^i-generalized recurrent, Generalized Third Recurrent Space, Affinely Connected Spaces, Cartan's Curvature Tensor, Covariant Derivatives, Recurrence Conditions.
AI in E-Learning: Exploring Challenges and LimitationsA Literature Review
This study addresses the critical challenge of water scarcity and high energy costs impacting agricultural productivity in regions like Abia State, Nigeria, by proposing a sustainable solar photovoltaic (SPV) irrigation pumping system. The objective was to design and evaluate an SPV system for underground irrigation at the National Root Crops Research Institute (NRCRI), Umudike, and ensure a reliable water supply for its 5-hectare farmland. The methodology involved determining pump energy demand (36.3 kWh/day), applying three distinct SPV component sizing methods for cross-validation, and performing robust stochastic reliability and temperature-adjusted performance analysis using Monte Carlo simulation. Key results indicate that a system with 36 solar panels, 24 batteries, and a 6.0 kVA inverter can consistently meet the daily energy demand. The stochastic analysis demonstrated that while monthly solar deficits can be high in certain periods (e.g., 99.9% in August), even minimal (e.g., 1 day) battery autonomy drastically reduces the probability of energy shortfall. This research provides a practical and resilient framework for off-grid agricultural irrigation in sun-rich developing regions.
solar photovoltaic design, submerged irrigation pump, agricultural farmland, performance ratio, Monte Carlo simulation, economic feasibility
Cloud Technology and Cybersecurity: A Literature-Based Study on Threats and Safeguards
Cloud computing has fundamentally transformed modern IT by offering scalable, cost-effective services. However, its rapid adoption has introduced critical cybersecurity concerns. This study investigates the impact of cloud technology on information security, focusing on key challenges such as data breaches, unauthorized access, and regulatory compliance. Employing a literature-based approach complemented by a quantitative user survey, it evaluates existing security measures, including encryption, multi-factor authentication, and adherence to frameworks like GDPR and HIPAA. The findings highlight that while cloud computing significantly improves accessibility and efficiency, it also necessitates robust security strategies. The study concludes by recommending enhanced encryption, comprehensive user education, and stringent governance policies to strengthen cloud security and ensure the reliability and trustworthiness of cloud-based systems.
Cloud Computing, Information Security, Data Protection, Cybersecurity, Encryption, Compliance
Cyber Threat Perception in Fragile Banking Environments: A Literature-Based Analysis of the Central Bank of Yemen
This paper presents an empirical synthesis of existing literature on cyber threat perception within fragile-state banking environments, with a focused lens on the Central Bank of Yemen. Through a structured review of 20 scholarly studies—spanning local, regional, and international contexts—the research identifies cognitive, organizational, and contextual factors that influence how banking institutions perceive and respond to cyber threats. The findings reveal critical gaps in cybersecurity awareness, governance structures, and technological infrastructure in Yemen’s financial sector, exacerbated by weak regulatory frameworks and prolonged political instability. This synthesis highlights the urgent need for adaptive, context-sensitive cybersecurity strategies tailored to fragile financial systems. Key recommendations include developing continuous cybersecurity awareness programs, strengthening institutional governance, enhancing leadership engagement, and promoting partnerships with cybersecurity organizations to improve institutional resilience.
Cyber threat perception, Cybersecurity awareness, Banking sector, Central Bank of Yemen, Cyber resilience, Fragile-state financial systems, Governance frameworks.
Assessment of Water Imbalance and Prepaid Water Meter Feasibility in Aden’s First Water Supply Zone
This study addresses severe water imbalance and high Non-Revenue Water (NRW) in Aden's First Water Supply Zone (FWSZ), which serves a projected population of 462,980 by 2025. The methodology involved a comprehensive water balance analysis for the 2023-2025 period and a multi-faceted feasibility study of PWMs. Key findings indicate a significant daily supply-demand gap of −1341.33 m³/d due to insufficient supply and intermittent power. The water balance analysis revealed a staggering NRW of 5,525,788.3 m³/year (57.3% of System Input) for April 2024–March 2025, which encompasses both physical losses and financial losses from uncollected billed water (41.01% of billed consumption). This uncollected revenue results in an estimated annual financial loss of over 1.767 billion Yemeni rials (or USD 751,280), severely compromising the utility's financial viability. The feasibility study confirmed that PWMs are both technically and financially viable; despite a total CAPEX of USD 3.6−5.5 million, the potential annual revenue recovery is strong. Crucially, a phased deployment targeting high-consumption users could achieve a payback period of less than three years. The study strongly recommends a strategic, phased rollout of PWMs, coupled with enhancing pumping power reliability and aggressive NRW management, as the cornerstone for achieving sustainable water services in the FWSZ.
Keywords: Non-Revenue Water (NRW), Water Supply and Demand Management, Prepaid Water Meters (PWMs), Revenue Collection, Aden, (FWSZ)
Studying the Impact of the Internet of Things on Knowledge Management (Case Study: Yemen's Ministry of Communications and Information Technology)
With the development of Internet of Things technologies in recent years, the creation and development of knowledge management systems based on them, as well as the study of this technology's role in knowledge management systems in various organizations, has received significant attention. The Internet of Things has impacted the world over the past decade, and by providing new solutions across various sectors, it has persuaded companies and institutions to benefit from and invest in this field. This research aims to study the role of the Internet of Things in knowledge management in the Yemeni Ministry of Communications and Information Technology. This research is of the applied type and uses a descriptive correlational approach. The statistical population for this study was selected from two statistical groups: five academic experts to validate the questionnaire, and 20 employees of the Yemeni Ministry of Communications and Information Technology to collect data using a non-random, purposeful, and judgmental method. To test the hypotheses, linear and multiple regression methods were used. The Internet of Things has the greatest impact on the knowledge management acquisition process, with an impact factor of 0.250. The Internet of Things also has the least impact, with an impact factor of 0.155, on the knowledge management discovery process. Expanding the services and investments of the Yemeni Ministry of Communications and Information Technology in the field of Internet of Things will increase and improve the performance of this institution.
Internet of Things, Knowledge Management, Organizational Management and Planning, Organizational Performance.
Antibacterial and Antifungal Efficacy of Phyto-synthesized Cobalt Nanoparticles Synthesized Using Aloe vera (Aloe barbadensis Miller) Extract.
Antimicrobial resistance is exponentially increasing because microbes evolve new ways to evade treatment. Therefore, threatening our ability to treat common bacterial and fungal diseases. This research work covers the synthesis of cobalt nanoparticles using extract of Aloe vera, characterization, and antimicrobial activity. Phytochemical screening of aqueous extract of Aloe vera was carried out in accordance with established methods. Co NPs were synthesized using the bottom-up method of synthesis and characterized using FTIR, UV-Vis spectrophotometry, SEM, and XRD. Prominent absorption peaks appeared at 3405 cm⁻¹, which was ascribed to OH vibration; 2923 cm⁻¹, assigned to Sp3 C-H; sp³ 1634 cm⁻¹, assigned to C=O; and the characteristic metal-oxygen peak appeared at 650 cm⁻¹. UV-visible analysis showed wavelengths of maximum absorption (λmax) at about 380 nm and 400 nm. The SEM analysis of Co NPs showed irregular morphology. While the closest crystalline structure was body-centered cubic (BCC) with a size of 10.43 nm. The synthesized Co NPs were employed for antibacterial and antifungal studies against two gram-negative bacteria, Escherichia coli and Salmonella typhi. Two gram-positive bacteria, Staphylococcus aureus and Helicobacter pylori, and two fungi, Aspergillus niger and Candida. The result showed the highest inhibition at 28 mm, 26 mm, 20 mm, and 15 mm for E. coli, S. typhi, H. pylori, and Staph. aureus, respectively. Also, 21 and 20 mm for two fungi, Aspergillus Niger and Candida. Augmentin and ketoconazole were used as bacterial and fungal control drugs. The cytotoxicity (brine shrimp lethality assay) of the plant extract did not pose significant acute toxicity to the brine shrimp, while Co NPs demonstrated a clear concentration-dependent pattern.
Synthesis, Nanoparticles, Antibacterial, Antifungal
Effect of Depreciation Calculation Methods on the Evaluation of Petroleum Projects
The development of oil reserves requires critical decision-making due to the high investment costs involved. Given the substantial investment costs in petroleum projects, these decisions can significantly impact profitability. A key component of these costs is the depreciation, depletion, and amortization (DD&A) of assets over their useful life. This study provides guidelines for managing asset costs in petroleum projects by employing cash flow and financial models. Two cases were analyzed: Case 1 (fixed asset costs) applied S.L. and DDB, while Case 2 (production-linked costs) employed UOP. Although both models yield the same total profit, the cash flow model reflects profits only after several years, whereas the financial model accounts for profit from the first year by distributing the investment cost over the project's estimated life. The study compares the implications of these methods—Straight-Line, Double Declining Balance, and Unit of Production—on profit recognition and investment recovery.
Total profit, DD&A calculation, Cash flow model, Financial model, Petroleum projects
A Robust Hybrid Model Integrating GANs, XGBoost, and Reinforcement Learning (RL)
This study introduces a robust hybrid model that integrates Generative Adversarial Networks (GANs), eXtreme Gradient Boosting (XGBoost), and Reinforcement Learning (RL) to enhance predictive analysis and anomaly detection in ﬁnancial data, speciﬁcally targeting fraud detection and trend forecasting. Leveraging the unique strengths of each component, GANs for generating high-quality synthetic data to address class imbalance, XGBoost for precise prediction models, and RL for dynamic decision-making based on evolving data patterns, this uniﬁed framework oﬀers a novel and ambitious approach to ﬁnancial security. We detail the design, implementation, and comprehensive evaluation of this model using real-world ﬁnancial datasets, demonstrating signiﬁcant improvements in accuracy and decision-making speed within complex economic contexts. The proposed methodology addresses critical challenges such as data imbalance, evolving fraud patterns, and the need for adaptive decision-making, providing a scalable and eﬀective solution for enhanced ﬁnancial security. Experimental results demonstrate that the hybrid model achieves superior performance compared to individual components, with XGBoost achieving % accuracy, RL demonstrating 93.2% accuracy with excellent adaptability, and GANs providing eﬀective data augmentation with 90.35% recall for fraud detection.
GAN, XGBoost, Reinforcement Learning, Hybrid Model
Black Henna Dyes Containing paraphenylenediamine: Assessing the Risks of Exposure on Pruritus, Hematological and Biochemical Parameters
This study investigates the health risks associated with black henna dyes, specifically focusing on the presence and concentration of Paraphenylenediamine (PPD) in commercial products. Henna, historically used as a dye, has evolved to include chemically mixed variants that can cause allergic reactions skin. The research involved analyzing samples from 60 regular users of black henna and 60 control participants who had never used it. In our study most symptoms itching and the dark color urine, The study found hematological indices and changes in kidney function, liver function than group control. The hematological and biomarker may be indicated to toxicity among users, that is probably the health hazards linked to exposure to black henna dyes. The findings underscore the need for awareness regarding the risks associated with these products.
Black Henna dyes; Paraphenylenediamine (PPD); Liver Enzymes; Kidney toxicity; Allergic reactions.
Dimensionality Reduction Techniques in Big Data and Their Impact on E-Learning
With the increasing use of e-learning in various fields, there is a growing need to analyse and process big data generated from student interactions with digital learning systems. This data includes test results, content interactions, and learner behavioural data. High dimensionality in data can hinder analysis using AI and machine learning, necessitating dimensionality reduction to enhance model efficiency and reduce computational complexity. The study examines dimensionality reduction techniques like PCA, LDA, autoencoders, and t-SNE in e-learning. It finds traditional methods effective, but advanced methods like deep autoencoders and hybrid AI models offer superior performance. UMAP outperforms t-SNE for clustering and visualisation tasks.
Dimensionality reduction, e-learning, principal component analysis (PCA), linear discriminant analysis (LDA), autoencoders, and t-SNE
The Role of Developing 5G Networks in Accelerating Digital Transformation: A Case Study of Ericsson
This study examines the role of developing 5G communication networks in accelerating digital transformation by presenting a case study of Ericsson. It reviews the company’s strategies in research and development, standardization, and infrastructure enhancement. The findings demonstrate that adopting 5G technology significantly improves productivity and reduces operational costs across sectors such as manufacturing, transportation, and healthcare, while also boosting competitiveness and generating positive economic impacts, including job creation and an increase in global GDP. The study concludes with practical recommendations, including increasing investments in R&D, strengthening collaborations with regulatory bodies and strategic partners, and continuously updating business models to keep pace with rapid technological advancements.
Communication Networks, Fifth Generation, Digital Transformation, Technology, Innovation, Ericsson