Science, Engineering and Technology
type: channel
description: SCIENCE, ENGINEERING AND TECHNOLOGY ISSN: 2744-2527 (Online); ISSN: 2831-1043 (Print) Science, Engineering and Technology is an open-access journal employing a double-blind peer review process and publishing scholarly research with a primary focus on engineering, applied sciences, advanced technologies and applications. The journal’s primary areas of focus include: Computer Science and ICT | Traffic and Transportation Engineering | Mechanical and Materials Engineering | Electrical and Electronic Engineering | Civil and Structural Engineering | Architecture and Planning | Industrial Engineering and Management | Logistics, Distribution, and Warehousing | Environmental Science and Engineering | Applied Mathematics and Statistics | Engineering and Learning Technologies | Interdisciplinary Applications | Multidisciplinary (SET). Call for Papers Authors are invited to submit original research articles, review articles, and case studies that align with the journal’s scope. Submissions are accepted on a continuous basis and are subject to the journal’s standard peer review process. Thanks to our sponsors, the article processing charge (APC) for submissions made by the end of April 2026 will be reduced by 50%, from 500 USD to 250 USD. The APC is applied only after an article has been formally accepted for publication. Author Guidelines - Submit an Article - Find out more Join Our Reviewer Community Are you passionate about staying updated with the latest research and scholarly articles in your field? Do you have insights and expertise you're eager to share? If so, we invite you to join our community of reviewers! By providing constructive feedback on submissions, you play a vital role in shaping the direction and quality of the articles we publish.Reviewers receive a certificate upon completing their review, as well as an APC discount and other benefits. Register as Reviewer - List of Reviewers - Find out more Follow us on LinkedIn Join our academic community and stay inform…
publisher: O.D. IMS Vogosca
language: en
publication name: Science, Engineering and Technology
issn: 2831-1043
copyright: Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website, social networking sites, etc).
items: Two-Phase Intercritical Annealing AR400 Steel: Effects on Phase Dispersion and Impact Resistance for Mining Applications
items: Spare Parts and Material Management in Electric Vehicle Maintenance: A Multidisciplinary Review
items: Advances in the Development, Processing, and Application of Locally Sourced Clay-based Refractory Materials for Furnace Application in Nigeria
items: A Comparative Case Study of Two Pedagogical Approaches in Web Development Education: From a Traditional Java Environment to a Modern Ruby on Rails Ecosystem
items: Comprehensive Evaluation of Waste-Derived Fuels As Sustainable Alternatives in Cement Production
items: Optimization of Operational Parameters to Minimize Cutting Energy during Kenaf Harvesting
items: Tailoring Heat Treatment Strategies to Enhance Corrosion Resistance and Mechanical Performance of Low-Carbon Steel in Marine Environment
items: Goldmine Tailings Stabilization using Quarry Dust and Coal Ash for Pavement Applications - A Review
items: Integrated Experimental and Analytical Investigation of Reverse Osmosis Desalination Systems
items: Suitability Assessment of Organic Carbon Additives in the Carburization of Low Carbon Steel (AISI 1020) for Engineering Applications
items: Modeling A Reverse Osmosis Desalination Plant: A Practical Framework Using Wave Software
items: A Survey on Blockchain-driven Music Industry: Trends, Gaps, and Future Directions
items: An Overview on Blockchain-based Social Media
items: Optimal Placement and Sizing of Renewable Distributed Generators for Power Loss Reduction in Microgrid using Swarm Intelligence and Bio-inspired Algorithms
items: GFEA: Leader Election Algorithm for Choosing a GroupDecision Support System Facilitator
items: Fine-tuning AraGPT2 for Hierarchical Arabic Text Classification
items: Diagnosis of Multiple Open-circuit and Short-circuit Faults in Three-phase Multicellular Inverter Based on Sliding Mode Observer
items: Odour, Colour and Turbidity Removal from Selected Industrial Wastewater Using Electrocoagulation Process
items: Thermo-Mechanical Assessment of Bio-based Insulating Material Using Phase Change Materials and Date Palm Fibers
items: Effect of Soil Salinity on Thermal Behavior of Sandy Soils Under a Sahara Climate
items: Malware Detection Using a Random Forest Method Trained on a Balanced Synthetic Dataset
items: Assessment of Refuse Dumpsite Impact on Groundwater Quality: A Case Study from Awotan, Ibadan, Oyo State, Nigeria
items: Investigation of the Properties of Waste Expanded Polystyrene (EPS) Modified Bitumen
items: Performance Analysis of Fractionalized Order PID Controller-based on Metaheuristic Optimisation Algorithms for Vehicle Cruise Control Systems
items: Identification of Plasma Proteins Associated with Alzheimer's Disease Using Feature Selection Techniques and Machine Learning Algorithms
items: Uncertainty Quantification and Sensitivity Analysis of Concrete Structure Using Multi-Linear Regression Technique
items: Towards Sustainability: Tracking Carbon Footprint Trends at Ezemvelo KZN Wildlife
items: A Computational Model of Attention-Guided Visual Learning in a High-Performance Computing Software System
items: The Advanced Actuarial Data Science Based AI-Driven Solutions for Automated Loss Reserving Under IFRS 17 in Non-Life Insurance
items: A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data

Two-Phase Intercritical Annealing AR400 Steel: Effects on Phase Dispersion and Impact Resistance for Mining Applications
type: item
description: Low-carbon AR400 abrasion-resistant steels are utilised as wear packages prolonging the production life of ground-engaging tools on earth-moving machines in South Africa's mining sector. While AR400 steel is renowned for its tribological properties and wear resistance, components frequently fail prematurely due to inadequate impact absorption, necessitating premature removal from the heavy-duty production environment. This study investigates a two-phase intercritical annealing process to enhance the ductility and impact energy absorption of AR400 steel. Phase 1 involves heat treating the specimens at 1000°C for 25 minutes, then oil quenching. Phase 2 reheats the specimens to 750°C for 20 minutes, followed by cool water quenching. The efficacy of this treatment was evaluated by comparing the mechanical properties, phase dispersions, and general grain morphology of the heat-treated specimens with those of the untreated control specimens. Microstructural analysis using optical and scanning electron microscopy revealed a transformation from the martensitic phase dispersion in the control specimens, typically associated with AR400, to a primarily bainitic microstructure in the intercritically annealed samples. This change improved the impact energy absorption and ductility of the 20 mm specimen by 200%. However, the results were inconsistent across material thickness. The 12 mm and 16 mm specimens showed a decrease in impact resistivity and approximately 40% reduction in material hardness and strength, irrespective of material thickness, compromising wear resistance. The results demonstrate that while the proposed two-phase intercritical annealing heat treatment can enhance impact properties, the concurrent reduction in hardness limits its practical application in industries requiring wear and impact resistance.
subject: Research Article
  topic: section
subject: Mechanical and Materials Engineering
  topic: category
subject: Civil and Structural Engineering
  topic: category
subject: Phase Dispersion
  topic: keywords
subject: Intercritical Annealing
  topic: keywords
subject: Oil-Quenching
  topic: keywords
subject: Brinell Hardness
  topic: keywords
creator: Witske Meyer
creator: Tamba Jamiru
creator: Lodewyk Willem Beneke
creator: Taoreed Adesola Adegbola
rights: Copyright (c) 2026 Witske Meyer, Tamba Jamiru, Lodewyk Willem Beneke, Taoreed Adesola Adegbola https://creativecommons.org/licenses/by/4.0
date: 2026-01-14
publication date: 2026-01-14
doi: 10.54327/set2026/v6.i1.319

Spare Parts and Material Management in Electric Vehicle Maintenance: A Multidisciplinary Review
type: item
description: Maintenance and management of spare parts for electric vehicles requires a specific approach due to high-voltage components, technological complexity, and limited availability of specialized parts. Key challenges include optimizing inventory, monitoring the life of battery systems, ensuring compatibility of parts across different vehicle models, and organizing efficient procurement, storage, and distribution. Of particular importance are safety protocols when working with high voltages, the use of specialized technical and protective materials, and environmentally friendly waste management and battery recycling. Digital solutions, including ERP and CMMS systems, IoT sensors, remote diagnostics, and predictive analytics based on artificial intelligence, enable better maintenance planning, cost reduction, and increased vehicle reliability. The paper provides an analysis of the technical, safety, environmental, and economic aspects of electric vehicle maintenance, with an emphasis on the digitalization and optimization of logistics processes. Based on the analysis, recommendations are proposed for improving the maintenance system, including the integration of advanced technologies, process standardization, and strengthening the education of service personnel. In addition, the paper identifies key research questions and outlines directions for future research—particularly in areas such as the digital integration of spare parts logistics, the environmental impact of material uses and disposal, and the role of artificial intelligence in predictive maintenance strategies. The findings suggest that the greatest improvement potential lies in combining predictive maintenance, sustainable practices, and operational cost reduction, thereby contributing to the long-term reliability and competitiveness of electric mobility.
subject: Review Article
  topic: section
subject: Electrical and Electronic Engineering
  topic: category
subject: Industrial Engineering and Management
  topic: category
subject: Logistics, Distribution and Warehousing
  topic: category
subject: Environmental Sciences and Engineering
  topic: category
subject: Interdisciplinary Applications
  topic: category
subject: Multidisciplinary (SET)
  topic: category
subject: electric vehicles (EV)
  topic: keywords
subject: spare parts
  topic: keywords
subject: inventory management
  topic: keywords
subject: digitalization of processes
  topic: keywords
subject: vehicle maintenance
  topic: keywords
subject: battery systems
  topic: keywords
creator: Fuad Klisura
creator: Marinko Aleksić
creator: Muhamed Sarvan
rights: Copyright (c) 2025 Fuad Klisura, Marinko Aleksić, Muhamed Sarvan https://creativecommons.org/licenses/by/4.0
date: 2025-10-31
publication date: 2025-10-31
starting page: 209
ending page: 227
doi: 10.54327/set2025/v5.i2.315

Advances in the Development, Processing, and Application of Locally Sourced Clay-based Refractory Materials for Furnace Application in Nigeria
type: item
description: Refractories are ceramic materials that are used in high-temperature applications, often above 1100oC. These materials find applications in reactors, kilns, ovens, and furnace linings. The need for refractory materials is consistently increasing to accommodate the ongoing development of different industries and factories. Local clays are currently under investigation to address the insufficient supply of refractory materials. Although the geo-morphology of the various locations where deposits of clay are found has been examined, comparatively little work has been done to fully evaluate these deposits to ascertain their suitability for use. This paper includes a review of the refractories produced from locally sourced clay deposits in Nigeria, followed by an evaluation of their usability for furnace lining. Generally, most of the findings reiterate the fact that the local clay has excellent properties for refractories; however, the local manufacturing of refractories in Nigeria has been very low, hence the nation still depends largely on imported refractories. Adequate funding for the local manufacturing of refractories would be needed to access and explore about 8 billion tonnes of clay deposits across Nigeria, thereby reducing imports and maximizing local production.
subject: Review Article
  topic: section
subject: Mechanical and Materials Engineering
  topic: category
subject: Refractories
  topic: keywords
subject: Local clay
  topic: keywords
subject: Furnace lining
  topic: keywords
subject: Physical properties
  topic: keywords
subject: Chemical properties
  topic: keywords
creator: Oryina Mbaadega Injor
creator: Emmanuel Rotimi Sadiku
creator: Moipone Linda Teffo
creator: Munyadziwa Mercy Ramakokovhu
creator: Victor Ugbetan Agbogo
creator: Williams Kehinde Kupolati
rights: Copyright (c) 2025 Oryina Mbaadega Injor, Emmanuel Rotimi Sadiku, Moipone Linda Teffo, Munyadziwa Mercy Ramakokovhu, Victor Ugbetan Agbogo, Williams Kehinde Kupolati https://creativecommons.org/licenses/by/4.0
date: 2025-10-01
publication date: 2025-10-01
starting page: 193
ending page: 208
doi: 10.54327/set2025/v5.i2.300

A Comparative Case Study of Two Pedagogical Approaches in Web Development Education: From a Traditional Java Environment to a Modern Ruby on Rails Ecosystem
type: item
description: This study presents a comparative case study of the evolution of a software development course at Kindai University. We analyze two distinct pedagogical ecosystems: a traditional course based on Java Servlet/JSP with a local integrated development environment (IDE), and its subsequent iteration, a modern course employing the Ruby on Rails framework (a Web Application Framework, or WAF), Git for version control, a cloud-based IDE, and Platform as a Service (PaaS) for deployment. This study was not a controlled experiment isolating the effects of a WAF but rather an exploratory analysis of how a shift in the entire toolchain impacted student outcomes and perceptions. Quantitative analyses of student projects over three years for each course revealed that the modern Ruby-based ecosystem resulted in applications with approximately 50% more screens and screen transitions, despite requiring approximately 40% less source code. Furthermore, student surveys indicated significantly higher comprehension and interest in the modern courses. However, the number of data models and user stories remained consistent, suggesting that upstream design thinking was less affected by the technology stack. These findings suggest that adopting a modern, integrated development ecosystem can foster a more productive and engaging learning experience. We conclude by discussing the implications of these findings for curriculum design, emphasizing the value of incorporating contemporary, industry-aligned toolchains into software engineering education, while acknowledging that the observed benefits stem from the synergistic effect of multiple technologies rather than from a single component.
subject: Research Article
  topic: section
subject: Computer Science and ICT
  topic: category
subject: Engineering and Learning Technologies
  topic: category
subject: Interdisciplinary Applications
  topic: category
subject: Computer Science Education
  topic: keywords
subject: Software Engineering Curriculum
  topic: keywords
subject: Web Application Frameworks (WAF)
  topic: keywords
subject: Software Development Toolchain
  topic: keywords
subject: Comparative Case Study
  topic: keywords
creator: Keiichi Takahashi
rights: Copyright (c) 2025 Keiichi Takahashi https://creativecommons.org/licenses/by/4.0
date: 2025-10-10
publication date: 2025-10-10
starting page: 20
ending page: 28
doi: 10.54327/set2025/v5.i2.305

Comprehensive Evaluation of Waste-Derived Fuels As Sustainable Alternatives in Cement Production
type: item
description: The cement industry accounts for approximately 7–8% of global carbon dioxide (CO2) emissions, primarily due to the energy-intensive clinker production process and reliance on fossil fuels. The environmental impact of this industry is particularly evident in the release of greenhouse gas (GHG) emissions. Therefore, the industry is exploring ways to reduce its energy costs and reliance on traditional fuels and mitigate environmental concerns by using waste-derived materials as a fuel substitute for cement production. In response to increasing environmental pressures, the substitution of fossil fuels with alternative fuels (AF) such as refuse-derived fuel (RDF), biomass, sewage sludge (SS), and used tires has emerged as a viable decarbonization strategy. This paper aims to provide a comprehensive analysis of AFs within the cement industry by reviewing previous studies, focusing on their GHG emissions and the technical, environmental, and economic implications of AFs adoption in cement kilns. A structured literature analysis was employed to evaluate fuel types, heating values, thermal substitution rates, combustion stability, and their effects on clinker quality. Data trends indicate that thermal substitution rates exceeding 80% are achievable with RDF and tire-derived fuels under optimized conditions, while biomass and SS require pretreatment for stable combustion. Environmental assessments report up to 30% reduction in CO₂ emissions and significant decreases in SOx and NOx with proper blending. The review also highlights key gaps in regional adoption and long-term performance evaluations. It concludes by recommending targeted policy support, plant-specific feasibility assessments, and integrated LCA-MCDM frameworks to scale the sustainable use of Afs.
subject: Review Article
  topic: section
subject: Civil and Structural Engineering
  topic: category
subject: Industrial Engineering and Management
  topic: category
subject: Environmental Sciences and Engineering
  topic: category
subject: Alternative fuels
  topic: keywords
subject: Cement industry
  topic: keywords
subject: Cement kiln
  topic: keywords
subject: Cement production
  topic: keywords
subject: Waste-derived fuels emission
  topic: keywords
creator: Oluwafemi Ezekiel Ige
creator: Musasa Kabeya
rights: Copyright (c) 2025 Oluwafemi Ezekiel Ige, Musasa Kabeya https://creativecommons.org/licenses/by/4.0
date: 2025-10-01
publication date: 2025-10-01
starting page: 149
ending page: 170
doi: 10.54327/set2025/v5.i2.287

Optimization of Operational Parameters to Minimize Cutting Energy during Kenaf Harvesting
type: item
description: This study evaluated the cutting and specific cutting energy of different kenaf varieties at different maturity stages during mechanical harvesting, while evaluating an improved kenaf harvester. The effects of operation parameters (related to the crop's biological properties; crop varieties "Cuba 108,” "Ifeken 400,” and "Ifeken Di 400"; and crop maturity: 10 to 16 weeks after planting) on the machine performance were optimized in a 3 × 4 factorial experiment using randomized response surface methodology (optimal custom design) to optimize the energy consumption. The results showed that the cutting energy required for harvesting kenaf increased from 1.8 to 3.3 joules as the crop matured from 10 to 16 weeks after planting (WAP). Among the tested crop varieties, "Ifeken 400" consistently required the highest cutting energy, followed by "Cuba 108", while "Ifeken Di 400" required the least energy. The specific cutting energy increased from 5000 to 11661 J.m⁻² as the crop matured, indicating differences in energy demand among the kenaf cultivars. These findings imply that the physical and mechanical properties of kenaf directly affect its stiffness. The stiffness factor of the crop is determined by crop maturity (weeks after planting) and crop variety. Therefore, the proper estimation of these operational parameters is vital for increasing the energy efficiency during the harvesting of kenaf.
subject: Research Article
  topic: section
subject: Mechanical and Materials Engineering
  topic: category
subject: Industrial Engineering and Management
  topic: category
subject: Interdisciplinary Applications
  topic: category
subject: Crop parameters
  topic: keywords
subject: Crop maturity
  topic: keywords
subject: Crop varieties
  topic: keywords
subject: Cutting energy
  topic: keywords
subject: Specific cutting energy
  topic: keywords
creator: Thomas Adedayo Ayorinde
creator: Tamba Jamiru
creator: Peet Le Roux
creator: Oseni Kehinde Owolarafe
rights: Copyright (c) 2025 Thomas Adedayo Ayorinde, Tamba Jamiru; Peet Le Roux; Oseni Kehinde Owolarafe https://creativecommons.org/licenses/by/4.0
date: 2025-09-04
publication date: 2025-09-04
starting page: 10
ending page: 19
doi: 10.54327/set2025/v5.i2.285

Tailoring Heat Treatment Strategies to Enhance Corrosion Resistance and Mechanical Performance of Low-Carbon Steel in Marine Environment
type: item
description: This study examines the impact of various heat-treatment processes on the corrosion resistance and mechanical properties of low-carbon steel, specifically for maritime applications. The carbon steel samples were evaluated at 750°C and subsequently cooled using two methods: normalizing and quenching. Alterations in the microstructure, hardness, and corrosion resistance were examined using contemporary techniques such as X-ray diffractometry (XRD), optical microscopy, wear testing, and electrochemical analysis in simulated seawater. The primary findings indicate that quenching enhances the hardness (from 170 to 404 HV) and corrosion resistance of steel by producing martensite, with a corrosion rate of 8.44 × 10⁻⁵ mm/year; however, this process also increases the brittleness of the steel. The process of normalizing results in a microstructure characterized by ductility, comprising both ferrite and pearlite phases. This structure exhibited a moderate level of resistance to corrosion, with a corrosion rate of 0.001018 mm/year. Conversely, the integration of normalizing with quenching results in a well-balanced microstructure, enhancing the material's toughness and corrosion resistance, with a lowest corrosion rate of 6.12 × 10⁻⁵ mm/year. Testing under saline conditions revealed that the quenched samples exhibited superior performance, highlighting the significance of cooling rates in enhancing material properties. This study demonstrates the importance of heat treatment in reducing the damage caused by saltwater corrosion of carbon steel. It also provides useful information on how to ensure that marine infrastructure is more durable and functions better.
subject: Research Article
  topic: section
subject: Mechanical and Materials Engineering
  topic: category
subject: Environmental Sciences and Engineering
  topic: category
subject: Heat Treatment
  topic: keywords
subject: Low-carbon steel
  topic: keywords
subject: Marine Environment
  topic: keywords
subject: Microstructure Evolution
  topic: keywords
subject: Mechanical Properties
  topic: keywords
creator: Basiru Philip Aramide
creator: Tamba Jamiru
creator: Taoreed Adesola Adegbola
creator: Abimbola Patricia Idowu Popoola
rights: Copyright (c) 2025 Basiru Philip Aramide, Tamba Jamiru, Taoreed Adesola Adegbola, Abimbola Patricia Idowu Popoola https://creativecommons.org/licenses/by/4.0
date: 2025-08-01
publication date: 2025-08-01
starting page: 1
ending page: 9
doi: 10.54327/set2025/v5.i2.284

Goldmine Tailings Stabilization using Quarry Dust and Coal Ash for Pavement Applications - A Review
type: item
description: The quest to improve the properties of soil and other construction materials by incorporating industrial and agricultural wastes is a growing concern for the construction industry. Many researchers have recently focused on employing waste materials as stabilizers due to their good pozzolanic interactions with soil particles. Its significance in civil engineering projects such as foundations in buildings and pavement construction cannot be overemphasized. The construction of pavements usually involves using large quantities of natural earth/aggregate materials, often mixed with conventional stabilizers (cement, lime, and bitumen). There is already a shortage of natural aggregate materials in many developing nations. However, a significant quantity of waste, such as mining tailings, is generated by mining industries yearly. In contrast, the disposal of these wastes is not only expensive but has also resulted in various ecological and environmental problems. The literature has already explored methods of stabilization and solidification of mining tailings employing conventional agents. However, the use of traditional stabilizers indicates an important source of contamination for the environment. Therefore, alternative stabilizing materials are needed. The approach to this literature review included a systematic procedure for locating, choosing, and evaluating sources. The logic for the sources’ selection prioritized contemporary, peer-reviewed studies that particularly address the geotechnical properties of industrial waste products in road construction. This study reviews the geotechnical properties of industrial waste products, such as goldmine tailings, quarry dust, and coal ash and the technical benefits of using them for pavement construction.
subject: Review Article
  topic: section
subject: Civil and Structural Engineering
  topic: category
subject: Environmental Sciences and Engineering
  topic: category
subject: Interdisciplinary Applications
  topic: category
subject: Multidisciplinary (SET)
  topic: category
subject: stabilization
  topic: keywords
subject: pavement
  topic: keywords
subject: goldmine tailings
  topic: keywords
subject: quarry dust
  topic: keywords
subject: coal ash
  topic: keywords
subject: geotechnical properties
  topic: keywords
creator: Jacob .O Ikotun
creator: Gbenga Emmanuel Aderinto
creator: Valentine Katte
creator: Adewumi John Babafemi
rights: Copyright (c) 2025 Jacob Olumuyiwa Ikotun, Gbenga Emmanuel Aderinto, Valentine Yato Katte, Adewumi John Babafemi https://creativecommons.org/licenses/by/4.0
date: 2025-10-07
publication date: 2025-10-07
starting page: 171
ending page: 192
doi: 10.54327/set2025/v5.i2.220

Integrated Experimental and Analytical Investigation of Reverse Osmosis Desalination Systems
type: item
description: Water scarcity is a growing global issue, necessitating innovative and sustainable solutions for freshwater generation. Among the available technologies, reverse osmosis (RO) has become the primary method for seawater desalination due to its effective salt rejection and high energy efficiency. This study presents an integrated experimental and analytical investigation of a full-scale seawater reverse osmosis (SWRO) plant with a 2 MLD capacity at the Victoria and Alfred (V&A) Waterfront in Cape Town, South Africa. Operational data were collected over six months, including feedwater temperature (13.66 –16.78 °C), pressure (50–60 bar), total dissolved solids (32,883–38,387 mg/L), and pH (6.19–7.89). The plant consistently produced high-quality permeate with TDS around 500 mg/L, achieving a 31% recovery rate at an average energy consumption of 3 kWh/m³. Machine learning models, specifically multiple linear regression and decision trees, were used to predict RO performance and to explore the relationships between operational parameters. Results show that higher feed pressure improves permeate flux but raises energy use, increased feedwater temperature boosts flux and slightly reduces energy consumption, while deviations from near-neutral pH negatively impact product quality and efficiency. The novelty of this work lies in combining real plant operational data with predictive analytics to establish parameter-based performance relationships and identify optimal operating ranges (e.g., feed pressure ~52–55 bar, pH ~7). These insights provide a strong foundation for optimizing desalination processes, improving membrane efficiency, and guiding the design and operation of future RO desalination projects.
subject: Research Article
  topic: section
subject: Mechanical and Materials Engineering
  topic: category
subject: Industrial Engineering and Management
  topic: category
subject: Environmental Sciences and Engineering
  topic: category
subject: Interdisciplinary Applications
  topic: category
subject: Desalination
  topic: keywords
subject: Seawater
  topic: keywords
subject: Reverse Osmosis
  topic: keywords
subject: Operational Parameters
  topic: keywords
creator: Olufisayo Emmanuel Ojo
creator: Olanrewaju Akanni Oludolapo
rights: Copyright (c) 2025 Olufisayo Emmanuel Ojo, Olanrewaju Akanni Oludolapo https://creativecommons.org/licenses/by/4.0
date: 2025-12-02
publication date: 2025-12-02
doi: 10.54327/set2026/v6.i1.283

Suitability Assessment of Organic Carbon Additives in the Carburization of Low Carbon Steel (AISI 1020) for Engineering Applications
type: item
description: The quest to enhance the mechanical properties of low-carbon steel (LCS) has stimulated the exploration of diverse carburization techniques, with growing attention on organic additives derived from agricultural wastes as sustainable alternatives to conventional materials. This study investigated sheanut shell (SNS) and eggshell (ES) ash as eco-friendly carburizing agents for AISI 1020 steel to improve its performance for engineering applications. The objective was to evaluate their potential in enhancing hardness, strength, impact resistance, and microstructural properties of LCS. Experimental analysis compared carburized and un-carburized (UC) samples, focusing on hardness, tensile strength, impact energy, and microstructural features. The findings showed that carburization significantly increased hardness, with carburized LCS reaching 513 HB compared to 398 HB for UC LCS. However, UC LCS exhibited higher yield strength (221.3 N/mm²) and ultimate tensile strength (241.1 N/mm²), whereas carburized LCS absorbed more fracture energy (63.72 J), reflecting a trade-off between hardness and tensile strength. Microstructural examination revealed improved surface morphology, metallurgical bonding, and higher pearlite concentration due to carbon diffusion, while energy dispersive spectroscopy confirmed elevated carbon content in carburized samples. Structural analysis further identified both crystalline and amorphous carbon phases. The study concludes that SNS and ES ash are effective sustainable carburizing additives capable of enhancing surface properties of LCS, making the material suitable for high-strength and wear-resistant applications. It recommends the wider adoption of these agro-waste additives in industrial carburization processes to reduce reliance on costly conventional materials while promoting sustainable engineering practices.
subject: Research Article
  topic: section
subject: Mechanical and Materials Engineering
  topic: category
subject: Carburization process
  topic: keywords
subject: Low carbon steel
  topic: keywords
subject: Organic wastes
  topic: keywords
subject: Mechanical testing
  topic: keywords
subject: Microstructural analysis
  topic: keywords
creator: Mathew Olurotimi Adeoti
creator: Tamba Jamiru
creator: Taoreed Adesola Adegbola
creator: Lodewyk Willem Beneke
rights: Copyright (c) 2025 Mathew Olurotimi Adeoti, Tamba Jamiru, Taoreed Adesola Adegbola, Lodewyk Willem Beneke https://creativecommons.org/licenses/by/4.0
date: 2025-09-03
publication date: 2025-09-03
starting page: 44
ending page: 52
doi: 10.54327/set2025/v5.i2.277

Modeling A Reverse Osmosis Desalination Plant: A Practical Framework Using Wave Software
type: item
description: Seawater desalination is a highly successful and effective method of obtaining fresh water from saline water sources. Reverse osmosis (RO) is a key and pivotal technology in seawater desalination as it produces high-quality freshwater from seawater with low energy consumption, in comparison to alternative technologies. However, the practical modelling of a comprehensive full-scale RO system is challenging due to fluctuating operating conditions stemming from seasonal variations and progressive fouling of the membrane during prolonged filtration operation. This study presents a comprehensive modeling framework for a seawater reverse osmosis (SWRO) desalination plant using DuPont’s Water Application Value Engine (WAVE) software. The modeled system integrates ultrafiltration (UF) for pretreatment and ion exchange (IX) polishing for post-treatment, which reflects the actual operational structure of the Victoria & Alfred Waterfront desalination plant in Cape Town, South Africa. The model simulates the hydraulic and separation performance under steady-state conditions, using plant-specific data for feed salinity, pressure, flow rates, and membrane configuration. Results demonstrate the WAVE model’s capability to accurately predict key performance parameters, including permeate flow, energy consumption, recovery rate, and total dissolved solids (TDS) removal. Simulated results indicate improved recovery (45.7% vs. 31%) and reduced specific energy consumption (5.91 kWh/m³ vs. 6.58 kWh/m³) compared to actual plant data. The study validates the model's predictive accuracy and highlights its application in optimizing system design, minimizing operational costs, and guiding future desalination infrastructure development under varying operational conditions.
subject: Research Article
  topic: section
subject: Mechanical and Materials Engineering
  topic: category
subject: Industrial Engineering and Management
  topic: category
subject: Environmental Sciences and Engineering
  topic: category
subject: WAVE
  topic: keywords
subject: Ultrafiltration
  topic: keywords
subject: Pretreatment
  topic: keywords
subject: Reverse Osmosis
  topic: keywords
subject: Desalination
  topic: keywords
subject: Seawater
  topic: keywords
subject: RO membrane
  topic: keywords
subject: RO Model
  topic: keywords
creator: Olufisayo Emmanuel Ojo
creator: Olanrewaju Akanni Oludolapo
rights: Copyright (c) 2025 Olufisayo E. Ojo, Olanrewaju A. Oludolapo https://creativecommons.org/licenses/by/4.0
date: 2025-09-24
publication date: 2025-09-24
starting page: 29
ending page: 43
doi: 10.54327/set2025/v5.i2.273

A Survey on Blockchain-driven Music Industry: Trends, Gaps, and Future Directions
type: item
description: The music industry is a vast field that has different stakeholders, such as artists, publishers, promoters, etc., for the creation, distribution, promotion, and monetization of music. Blockchains can help the music industry maintain the legal and ethical aspects of music creation, distribution, and incentivization while also preventing frauds owing to their intrinsic security attributes. We oversee various blockchain-driven music industry systems, where we comprehend 11 functions of blockchain-driven music industry perception and inspect them comprehensively towards music industry- and blockchain-linked attributions. We lumped a precursory sample of 89 resources by selecting the reports for filtering benchmarks looked up from E-libraries by applying a descriptive and persistent narrative synthesis-driven quality analysis methodology to identify trends, gaps, strengths, and weaknesses. Founded on the overview, in the blockchain-driven music industry, blockchain can pave the path for blockchain-based musical platforms (D1), decentralized music apps (D2), author attribution, monetization, and royalty payments (D3), preventing ticketing frauds (D4), music recommendation (D5), piracy prevention (D6), digital rights management systems (D7), music supply chain automation (D8), metadata optimization and tracking (D9), disintermediation (D10), and licensing (D11). Comprehensive inspection exposes that in the blockchain-driven music industry, 28.2% draw upon digital rights management (D7), 79.4% draw upon traditional blockchain, and 12.9% draw upon PoS/PoW consensus, drawing the hypothesis that there exists a trend toward reducing third-party reliance and improving revenue transparency and rights for artists. Another hypothesis is that there are gaps such as lack of practical implementation, lack of experimental validation under quantum attacks, and lack of focus for music ticketing fraud prevention, music recommendation, music supply chain automation, and metadata optimizati…
subject: Review Article
  topic: section
subject: Computer Science and ICT
  topic: category
subject: Electrical and Electronic Engineering
  topic: category
subject: Music industry
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Piracy
  topic: keywords
subject: Monetization
  topic: keywords
subject: Music copyright
  topic: keywords
creator: Patikiri Arachchige Don Shehan Nilmantha Wijesekara
rights: Copyright (c) 2025 Patikiri Arachchige Don Shehan Nilmantha Wijesekara https://creativecommons.org/licenses/by/4.0
date: 2025-05-05
publication date: 2025-05-05
starting page: 116
ending page: 148
doi: 10.54327/set2025/v5.i2.223

An Overview on Blockchain-based Social Media
type: item
description: Social Media (SM) platforms allow users to create and share multimedia content characterized by interaction among the users through profile creation, messaging systems, communities, etc. Blockchains can improve the security and ethical aspects of SM due to their innate security characteristics. We overlook countless blockchain-based SM schemes, where we perceive 9 duties of blockchain-based SM abstraction and dissect them intensively, rooted in SM- and blockchain-linked traits in contrast to existing reviews that do not review in broad scope and lack a critical analysis to show ways to focus on practical implementation. We heaped a preparatory sample of 93 works by sieving the studies for leaching rules and delving into E-repositories by employing a mixed-method systematic review with a narrative synthesis and quality assessment approach. Built upon the scrutiny, in blockchain-based SM, blockchain can assist by providing social media platforms (G1), social DApps (G2), copyright protection (G3), harassment prevention (G4), ensuring privacy and security (G5), fake/vicious news prevention (G6), data/user behavior/event processing and analysis (G7), proper incentivization (G8), and user migration (G9). Critical analysis uncovers that from blockchain-based social media, an overall 40% harness 20% from G6 and G7 each, 82.5% harness conventional blockchain, and 22.5% harness DPoS consensus. Next, we critically analyze the strengths and weaknesses of the reviewed works, focusing on performance and characteristics. Furthermore, we identified lack of empirical validation, under-exploration of ethical concerns and biases in detection models, and non-assessment of decentralization risks as gaps in the study. Eventually, we reveal the opportunities and discomforts of the abstraction of blockchain-based SM, state the study’s limitations, and then bestow solutions to resist them with future directions emphasizing practical implications.
subject: Review Article
  topic: section
subject: Computer Science and ICT
  topic: category
subject: Interdisciplinary Applications
  topic: category
subject: security
  topic: keywords
subject: blockchain
  topic: keywords
subject: social media
  topic: keywords
subject: blockchain-grounded social media
  topic: keywords
subject: social media ethics
  topic: keywords
creator: Patikiri Arachchige Don Shehan Nilmantha Wijesekara
rights: Copyright (c) 2025 Patikiri Arachchige Don Shehan Nilmantha Wijesekara https://creativecommons.org/licenses/by/4.0
date: 2025-03-20
publication date: 2025-03-20
starting page: 213
ending page: 247
doi: 10.54327/set2025/v5.i1.222

Optimal Placement and Sizing of Renewable Distributed Generators for Power Loss Reduction in Microgrid using Swarm Intelligence and Bio-inspired Algorithms
type: item
description: To responsibly fulfill the world's expanding electrical energy needs, renewable energy sources are now essential. Future energy policies must include these sources—like solar and wind energy—because they lower carbon emissions and save the environment. The optimal location and sizing of renewable distributed generators (OLSRDG) in the microgrid are determined in this study by applying one of the universal bio-inspired techniques and one of the swarms’ algorithms. With lower power losses, an improved voltage profile, increased dependability, and stability, the goal is to improve energy efficiency and lessen reliance on the main grid while also enhancing the grid's overall performance and stability. The acquired results are promising and show the efficacy and resilience of the suggested technique in solving OLSRDG problems compared to recently published results. The results showed that the optimization process led to loss reduction, with the percentage of power loss reduction ranging from 45.387% to 73.89% using the PSO. While the percentage of loss reduction using the BAT ranged from 51.78% to 71.57%.
subject: Research Article
  topic: section
subject: Electrical and Electronic Engineering
  topic: category
subject: bat algorithm
  topic: keywords
subject: distributed generators
  topic: keywords
subject: micro-grids
  topic: keywords
subject: optimal placement and sizing
  topic: keywords
subject: particle swarm optimization
  topic: keywords
subject: renewable energy sources
  topic: keywords
creator: Nabil MEZHOUD
creator: Ahmed Bahri
creator: Bilel Ayachi
creator: Farouk Boukhenoufa
creator: Lakhdar Bouras
rights: Copyright (c) 2025 Nabil MEZHOUD, Ahmed Bahri, Bilel Ayachi, Farouk Boukhenoufa, Lakhdar Bouras https://creativecommons.org/licenses/by/4.0
date: 2025-03-19
publication date: 2025-03-19
starting page: 163
ending page: 176
doi: 10.54327/set2025/v5.i1.208

GFEA: Leader Election Algorithm for Choosing a GroupDecision Support System Facilitator
type: item
description: Group decision support systems (GDSSs) are computer-assisted collaborative work software that facilitates group meetings asynchronously and from different locations. Even so, collaborative work in GDSS demands coordination provided by a single controlling entity known as the GDSS facilitator. However, the problem of electing a GDSS Facilitator hasn’t been treated enough in the literature, and it is often neglected. Despite that, the large number of responsibilities assigned to the facilitator makes his role crucial to the effectiveness of the group meeting. Thus, the authors focused on finding an appropriate approach for electing the facilitator. The similarities between the problematics of electing a GDSS facilitator and a distributed system leader led the authors to consider applying a distributed election algorithm for electing a GDSS facilitator. Nonetheless, current algorithms only consider computer criteria and lack a formal weighting method. Consequently, we proposed a new distributed election algorithm called GFEA (GDSS Facilitator Election Algorithm) that is designed to choose a facilitator within a GDSS. This algorithm selects a facilitator among a set of decision-makers based on multiple election criteria weighted using an objective weighting method called MEREC. A backup leader is reserved to replace the leader if he fails, and a new tie-breaking mechanism is proposed. Moreover, the initiator failure is handled. By adopting distributed system leader election principles, GFEA provides a robust solution for a decisive GDSS challenge.
subject: Research Article
  topic: section
subject: Computer Science and ICT
  topic: category
subject: Industrial Engineering and Management
  topic: category
subject: Interdisciplinary Applications
  topic: category
subject: Leader Election
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: GDSS
  topic: keywords
subject: Facilitator
  topic: keywords
subject: Multi-criteria
  topic: keywords
creator: Sabir Mohammedi Taieb
creator: Mohamed Adnane Laredj
rights: Copyright (c) 2025 Sabir Mohammedi Taieb, Mohamed Adnane Laredj https://creativecommons.org/licenses/by/4.0
date: 2025-05-15
publication date: 2025-05-15
starting page: 53
ending page: 74
doi: 10.54327/set2025/v5.i2.216

Fine-tuning AraGPT2 for Hierarchical Arabic Text Classification
type: item
description: Text classification consists in attributing a text to its corresponding category. It is a crucial task in natural language processing (NLP), with applications spanning content recommendation, spam detection, sentiment analysis, and topic categorization. While significant advancements have been made in text classification for widely spoken languages, Arabic remains underrepresented despite its large and diverse speaker base. Another challenge is that, unlike flat classification, hierarchical text classification involves categorizing texts into a multi-level taxonomy. This adds layers of complexity, particularly in distinguishing between closely related categories within the same super-class. To tackle these challenges, we propose a novel approach using AraGPT2, a variant of the Generative Pre-trained Transformer 2 (GPT-2) model adapted specifically for Arabic. Fine-tuning AraGPT2 for hierarchical text classification leverages the model's pre-existing linguistic knowledge and adapts it to recognize and classify Arabic text according to hierarchical structures. Fine-tuning, in this context, refers to the process of training a pre-trained model on a specific task or dataset to improve its performance on that task. Our experiments and comparative study demonstrate the efficiency of our solution. The fine-tuned AraGPT2 classifier achieves a hierarchical HF score of 80.64%, outperforming the machine learning-based classifier, which scores 41.90%.
subject: Research Article
  topic: section
subject: Computer Science and ICT
  topic: category
subject: Arabic Text Classification
  topic: keywords
subject: Hierarchical Classification
  topic: keywords
subject: AraGPT2
  topic: keywords
subject: GPT-2 Fine-tuning
  topic: keywords
subject: Generative Pre-trained Transformer
  topic: keywords
subject: Large Language Model
  topic: keywords
creator: Djelloul BOUCHIHA
creator: Abdelghani BOUZIANE
creator: Noureddine DOUMI
creator: Benamar HAMZAOUI
rights: Copyright (c) 2025 Djelloul BOUCHIHA, Abdelghani BOUZIANE, Noureddine DOUMI, Benamar HAMZAOUI https://creativecommons.org/licenses/by/4.0
date: 2025-03-17
publication date: 2025-03-17
starting page: 55
ending page: 65
doi: 10.54327/set2025/v5.i1.224

Diagnosis of Multiple Open-circuit and Short-circuit Faults in Three-phase Multicellular Inverter Based on Sliding Mode Observer
type: item
description: The reliability and security of multicellular converters have become crucial tools for safeguarding electrical power conversion and ensuring the continuity of electrical drives. This concern has always been paramount in numerous industrial applications. Ensuring the reliability, continuity, and robustness of the three-phase multicellular inverter critically depends on accurately diagnosing faults in insulated gate bipolar transistor (IGBT) switches; these failures carry both technical and economic consequences for electrical system conversion. Therefore, detecting and diagnosing faults is crucial to preserving converters against these potential issues. This study aims to investigate the operational behavior of the three-phase multicellular inverter under normal and faulty conditions, more precisely focusing on open-circuit and short-circuit faults in converter switches. To achieve this objective, the paper introduces a fault diagnosis technique based on a sliding mode observer for power switches in the three-phase multicellular inverter. The research is divided into two main sections. The initial segment concentrates on the aspects of sliding mode control, aiming to attain regulated output voltages, output currents, and floating capacitor voltage. This control strategy is essential for maintaining a stable and consistent operation of the inverter. The second segment focuses on fault diagnosis, analyzing the impact of a defective three-phase multicellular inverter on the overall functionality of the electrical system. The performance of the proposed algorithm is assessed and validated through simulations in the MATLAB/Simulink environment.
subject: Research Article
  topic: section
subject: Electrical and Electronic Engineering
  topic: category
subject: Three-phases multicellular inverter
  topic: keywords
subject: faults diagnosis
  topic: keywords
subject: open-circuit fault
  topic: keywords
subject: short-circuit fault
  topic: keywords
subject: sliding mode observer
  topic: keywords
creator: Fatma Khater
creator: Abderrezak Aibeche
creator: Sid Ali Fellag
creator: Mohamed Zinelabidine Doghmane
creator: Hamza Akroum
rights: Copyright (c) 2025 Fatma Khater, Abderrezak Aibeche, Sid Ali Fellag, Mohamed Zinelabidine Doghmane, Dr. H. Akroum https://creativecommons.org/licenses/by/4.0
date: 2025-03-17
publication date: 2025-03-17
starting page: 137
ending page: 153
doi: 10.54327/set2025/v5.i1.237

Odour, Colour and Turbidity Removal from Selected Industrial Wastewater Using Electrocoagulation Process
type: item
description: Electrocoagulation (EC) is an efficient electrochemical method for treating water using electric charges to destabilize and coagulate pollutants. In this study, a bipolar electrocoagulation reactor with aluminum electrodes was used to treat selected industrial wastewater. Key parameters, including odor, turbidity, Color, and other physicochemical parameters, were analyzed to evaluate the performance of the reactor. Focusing on textile wastewater and two types of cassava wastewater, fufu and starch, this study assessed Odor and turbidity removal using aluminum electrodes. Textile wastewater was used to examine Color removal. The operational parameters—voltage (10V–40V), temperature (30°C–38°C), and operating time (15 minutes to 1 hour)—were systematically varied to optimize the performance. The reactor significantly improved turbidity and color removal, with moderate odor reduction. This study highlights the strong capability of the EC process in reducing turbidity using aluminum electrodes, despite challenges in Odor reduction. The electrocoagulation process effectively removed color, BOD, COD, TSS, and TDS from the wastewater. Voltage adjustments and electrolysis time are critical in optimizing pollutant removal and aligning with regulatory standards for industrial wastewater discharge. Despite its overall effectiveness, the challenges of achieving complete odor and BOD removal highlight areas for further research. This study highlights the potential of EC as a sustainable solution for industrial wastewater treatment.
subject: Research Article
  topic: section
subject: Civil and Structural Engineering
  topic: category
subject: Environmental Sciences and Engineering
  topic: category
subject: Multidisciplinary (SET)
  topic: category
subject: Electrocoagulation
  topic: keywords
subject: Textile Wastewater
  topic: keywords
subject: Cassava Wastewater
  topic: keywords
subject: Removal Efficiency
  topic: keywords
subject: Wastewater Treatment
  topic: keywords
creator: Peter Abiodun Akinlabi
creator: Titilayo Elestina Adepoju
creator: Chibueze Godwin Achi
creator: Akinwale Oladotun Coker
rights: Copyright (c) 2025 Engr Dr Chibueze Achi, Engr Peter Akinlabi, Titilayo Adepoju, Prof Akinwale Coker https://creativecommons.org/licenses/by/4.0
date: 2025-01-10
publication date: 2025-01-10
starting page: 77
ending page: 97
doi: 10.54327/set2025/v5.i1.153

Thermo-Mechanical Assessment of Bio-based Insulating Material Using Phase Change Materials and Date Palm Fibers
type: item
description: To mitigate extreme temperature fluctuations in arid southern Algerian cities, developing an internal thermal environment that is either independent of or only marginally influenced by external conditions is a viable solution. In this study, we successfully created a eutectic mixture of animal and plant fatty acids, excluding petroleum sources, consisting of 70% myristic acid (MA) and 30% stearic acid (SA). This phase change material (PCM) was then impregnated into date palm fiber waste (DPF) using a vacuum technique. The melting points of both the eutectic mixture and the impregnated date palm fiber were measured at 35°C and 34.5°C, respectively. Clay bricks, which are widely used in Algerian construction, were prepared with 75% dune sand and 10% lime. Date palm fibers impregnated with PCM were added in varying proportions (0.5%, 1%, 1.5%, 2%) to test the mechanical and thermal properties of the bricks. The results showed an improvement in thermal insulation, with a reduction in thermal conductivity by 11% for bricks containing 2% impregnated palm fibers. The compressive strength of these bricks remained within acceptable limits, regardless of whether the PCM was in a solid or liquid state. Numerical simulations showed that adding MA-SA/DPF to clay bricks contributed to a 30% reduction in outward heat flow in winter and a 25% reduction in inward heat flow in summer. This, in turn, resulted in a corresponding decrease in energy consumption.
subject: Research Article
  topic: section
subject: Civil and Structural Engineering
  topic: category
subject: Environmental Sciences and Engineering
  topic: category
subject: Multidisciplinary (SET)
  topic: category
subject: Phase change materials
  topic: keywords
subject: Brick
  topic: keywords
subject: Thermal conductivity
  topic: keywords
subject: Date palm fiber
  topic: keywords
subject: Compressive strength
  topic: keywords
creator: Mahi Eddine Brahimi
creator: Mustapha Maliki
creator: Nadia Laredj
creator: Hanifi Missoum
creator: Miloud Sardou
rights: Copyright (c) 2025 Mahi Eddine Brahimi, Mustapha Maliki, Nadia Laredj, Hanifi Missoum, Miloud Sardou https://creativecommons.org/licenses/by/4.0
date: 2025-03-04
publication date: 2025-03-04
starting page: 66
ending page: 76
doi: 10.54327/set2025/v5.i1.209

Effect of Soil Salinity on Thermal Behavior of Sandy Soils Under a Sahara Climate
type: item
description: Soil salinity represents a harmful environmental problem that occurs either naturally or as a consequence of ineffective management practices by humans, especially in arid regions. Assessing ground temperature profile represents an important indicator for evaluating the performance of geothermal systems, used in air conditioning of building. This paper proposes a conceptual and numerical model for predicting the hydrothermal behavior of unsaturated soils. The effect of soil salinity was investigated in this research. Two soil types (sandy and sandy loam) were studied under three salinity levels. Moreover, the arid meteorological conditions of Sahara Desert were considered in the developed model. According to the results, it appears that sandy soils exhibit optimal thermal behavior at a moderate salinity concentration of C = 0.1 M, with increased surface temperatures during warmer periods and better heat conservation in colder conditions. Conversely, sandy loam soils respond most effectively at a higher salinity concentration of C = 0.2 M, particularly excelling in heat retention during the summer months. These results emphasize the intricate interaction between soil composition and salinity in regulating temperature patterns, providing important knowledge for enhancing soil management practices that are customized to particular soil types and environmental factors, with potential implications for geothermal applications.
subject: Research Article
  topic: section
subject: Civil and Structural Engineering
  topic: category
subject: Environmental Sciences and Engineering
  topic: category
subject: salinity
  topic: keywords
subject: thermal conductivity
  topic: keywords
subject: unsaturated soil
  topic: keywords
subject: heat transfer
  topic: keywords
subject: salt concentration
  topic: keywords
creator: Djihad Bennaceur
creator: Nadia Laredj
creator: Mustapha Maliki
creator: Hanifi Missoum
rights: Copyright (c) 2025 Djihad Bennaceur, Nadia Laredj, Mustapha Maliki, Hanifi Missoum https://creativecommons.org/licenses/by/4.0
date: 2025-01-17
publication date: 2025-01-17
starting page: 126
ending page: 136
doi: 10.54327/set2025/v5.i1.212

Malware Detection Using a Random Forest Method Trained on a Balanced Synthetic Dataset
type: item
description: The accuracy of malware detection is closely related to the available datasets, which are often small and imbalanced. To overcome these challenges, this study proposed a new method that creates synthetic malware data and increases the size and balance by generating several data sets with a flow-based model. Subsequently, a random forest classifier is fitted on this augmented dataset. This study aimed to analyze the generation of synthetic data based on flow-based models and the impact of synthetic data generation on the performance of a random forest for malware detection. A flow-based model was used to generate a balanced synthetic dataset based on the CICMalDroid2020 dataset. The generated data was used for feature selection and engineering to optimize the Random Forest model. The experimental results demonstrate the effectiveness of the proposed approach. The flow-based model generated an additional 13,402 samples, massively increasing the dataset size, even though the original dataset had only 11,598 data entries. After training on the synthetic augmented dataset, the Random Forest model achieved better performance compared to the original dataset evaluation with metrics precision (93%), recall (100%), balanced precision (96%), and the F1 score (91%). The results show that flow-based model-generated synthetic data can significantly enhance malware detection capabilities.
subject: Research Article
  topic: section
subject: Computer Science and ICT
  topic: category
subject: malware detection
  topic: keywords
subject: accuracy
  topic: keywords
subject: random forest
  topic: keywords
subject: flow-based model
  topic: keywords
subject: balanced dataset
  topic: keywords
subject: synthetic dataset
  topic: keywords
creator: Neo Onica Matsobane
creator: Sello Mokwena
rights: Copyright (c) 2025 Neo Onica Matsobane, Sello Mokwena https://creativecommons.org/licenses/by/4.0
date: 2025-03-28
publication date: 2025-03-28
starting page: 203
ending page: 212
doi: 10.54327/set2025/v5.i1.167

Assessment of Refuse Dumpsite Impact on Groundwater Quality: A Case Study from Awotan, Ibadan, Oyo State, Nigeria
type: item
description: Groundwater, an essential freshwater resource, is critical to life and supports various industries. Unrestrained urban expansion and inefficient waste management practices endanger groundwater quality. This research examines the groundwater quality and impacts of the Refuse Waste Dumpsite located at Awotan, Ibadan. Additionally, this study provides a framework for assessing dumpsite impacts on groundwater, which can be applied to similar urban contexts worldwide, contributing to global efforts to ensure safe water quality. Samples of residential Well water near the dumpsite were collected to determine their physical, chemical, and bacteriological characteristics. The physical and chemical parameters investigated include Temperature, pH, Electrical conductivity, Nitrate, and Chloride, which were determined using the standard analytical methods. In contrast, bacteriological parameters such as Total Coliform and E.Coli Count were determined. Trace metals such as Pb and Fe were also determined to ascertain the relationship between pollutant levels and distances from the dumpsite and to evaluate compliance with WHO and NSDWQ water quality standards. Regression analysis revealed a strong correlation (R = 0.999, R² = 0.998) between dumpsite distance and chemical water quality, but the results were not statistically significant (p = 0.066). Chemical parameters like alkalinity (p = 0.484) and nitrate (p = 0.338) showed no significant impact from distance. For bacteriological quality, the model was statistically significant (p = 0.009) with an R² of 0.994. Total coliform (p = 0.055) was nearly significant, indicating potential distance-related bacterial contamination. The findings underscore the pressing need for sustainable waste management to safeguard groundwater resources.
subject: Case Study
  topic: section
subject: Environmental Sciences and Engineering
  topic: category
subject: Leachates
  topic: keywords
subject: Landfill
  topic: keywords
subject: Water Pollution
  topic: keywords
subject: Waste Management
  topic: keywords
subject: Water Quality Assessment
  topic: keywords
subject: Sustainable Development Goals (SDGs)
  topic: keywords
creator: Ibrahim Adetayo Lawal
creator: Chibueze Godwin Achi
creator: Akinwale Oladotun Coker
rights: Copyright (c) 2024 Ibrahim Adetayo Lawal, Chibueze Godwin Achi, Akinwale Oladotun Coker https://creativecommons.org/licenses/by/4.0
date: 2024-12-14
publication date: 2024-12-14
starting page: 286
ending page: 303
doi: 10.54327/set2025/v5.i1.161

Investigation of the Properties of Waste Expanded Polystyrene (EPS) Modified Bitumen
type: item
description: The "white pollution" caused by the indiscriminate disposal of waste-expanded polystyrene (EPS) packaging foams in Nigeria not only poses a serious environmental threat but also results in significant waste of resources. Again, the poor waste management practices adopted in most of the urban and rural areas in Nigeria also compounded this situation, coupled with the known fact that polymeric materials generally do not decompose (i.e., are non-biodegradable) easily, thereby making them serve as a threat to the surrounding environment. Given resolving this problem, these discarded EPS packaging foams were used in the modification of 60/70 bitumen in this study, as it has been reported that polymer modifiers are known as a possible solution for improving road life in the face of increasing and heavy traffic loading when suitably incorporated into the virgin bitumen. The binder physical tests were carried out on the virgin bitumen and the hot mix waste EPS-modified bitumen samples at modifier contents of 2.5, 5.0, 7.5, 10.0, 12.5, and 15.0%. The results from the tests showed that the best-improved properties were achieved at 5.0 % EPS modifier content with increased softening point (53°C), flash point (275°C), and decreased penetration (50 dmm), while its computed penetration index (-0.48) value indicated that this blend falls within the category of the most acceptable road bitumen. Waste EPS is a suitable bitumen modifier, which enhanced its performance and could equally solve the problem of “white pollution” in Nigeria.
subject: Research Article
  topic: section
subject: Civil and Structural Engineering
  topic: category
subject: Environmental Sciences and Engineering
  topic: category
subject: Bitumen
  topic: keywords
subject: Polymer Modified Bitumen
  topic: keywords
subject: Waste Expanded Polystyrene (EPS)
  topic: keywords
subject: Test
  topic: keywords
creator: Ganiyu Abolore LAWAL
creator: Bamidele Ibikunolu Olugbemi DAHUNSI
rights: Copyright (c) 2025 Ganiyu Abolore LAWAL, Professor B. I. O. Dahunsi https://creativecommons.org/licenses/by/4.0
date: 2025-02-25
publication date: 2025-02-25
starting page: 45
ending page: 54
doi: 10.54327/set2025/v5.i1.221

Performance Analysis of Fractionalized Order PID Controller-based on Metaheuristic Optimisation Algorithms for Vehicle Cruise Control Systems
type: item
description: Recently, automotive manufacturers have prioritized cruise control systems and controllers, recognizing them as essential components requiring precise and adaptable designs to keep up with technological advancements. The motion of vehicles is inherently complex and variable, leading to significant non-linearity within the cruise control system (CCS). Due to this non-linearity, conventional PID controllers often perform suboptimally under varying conditions. This research introduces a fractionalized-order PID (FrOPID) controller, which has an extra parameter that makes regular PID controllers work better. A comparative analysis is conducted between classical PID controllers and FrOPID controllers optimized using three metaheuristic algorithms: Harris Hawks Optimization (HHO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The evaluation is carried out using a linearized model of the vehicle cruise control system (VCCS). The results demonstrate that fractionalized-order PID controllers significantly outperform conventional PID controllers, particularly regarding rise time and settling time. Among the designs that were considered, the one that combines HHO and FrOPID works the best at finding a balance between responsiveness and stability. It is also the most durable and flexible, able to adapt to changes in vehicle mass and environmental conditions. This highlights the effectiveness of fractionalized-order controllers in managing the dynamic behavior of vehicles.
subject: Research Article
  topic: section
subject: Mechanical and Materials Engineering
  topic: category
subject: Electrical and Electronic Engineering
  topic: category
subject: Industrial Engineering and Management
  topic: category
subject: Vehicle cruise control system (VCCS)
  topic: keywords
subject: Fractionalized order PID controller
  topic: keywords
subject: Optimization methods
  topic: keywords
subject: PID controller
  topic: keywords
subject: Robustness analysis
  topic: keywords
creator: Abdelhakim Idir
creator: Abderrahim Zemmit
creator: Khatir Khettab
creator: Mokhtar Nesri
creator: Sifelislam Guedida
creator: Laurent Canale
rights: Copyright (c) 2024 Abdelhakim Idir, Abderrahim Zemmit, Khatir Khettab, Mokhtar Nesri, Sifelislam Guedida, Laurent Canale https://creativecommons.org/licenses/by/4.0
date: 2024-03-18
publication date: 2024-03-18
starting page: 1
ending page: 14
doi: 10.54327/set2025/v5.i1.241

Identification of Plasma Proteins Associated with Alzheimer's Disease Using Feature Selection Techniques and Machine Learning Algorithms
type: item
description: Alzheimer’s disease (AD) is a chronic, progressive neurodegenerative disorder that typically affects elderly individuals. Detecting Alzheimer’s using plasma proteins is a critical step toward improving treatment results for this disease. This study aims to use computational algorithms to explore the relationship between plasma proteins and AD progression by identifying a panel of plasma proteins that can serve as biomarkers for tracking and diagnosing AD. We applied two feature selection methods, Sequential Backward Feature Selection (SBFS) and Analysis of Variance (ANOVA) to extract significant proteins from a dataset of 146 proteins. The data was collected from the plasma of 566 individuals, comprising both Alzheimer’s patients and healthy controls. The SBFS technique generated all possible combinations of protein groups from the 146 proteins, which were then trained and tested using five machine learning models: Decision Tree, Random Forest, Extremely Randomized Trees, Extreme Gradient Boosting, and Adaptive Boosting. Subsequently, ANOVA was applied to refine and reduce the selected panel size. Finally, we used XGBoost and AdaBoost models to validate the final panel. The findings introduce a plasma protein panel consisting of A2Macro, BNP, BTC, PPP, and PYY proteins for diagnosing AD. This panel achieved a sensitivity of 88.88%, a specificity of 66.66%, and an AUC of 0.85. These results demonstrate that plasma protein biomarkers can facilitate timely interventions, potentially slowing disease progression and improving patient outcomes. This non-invasive and affordable diagnostic method has the potential to make Alzheimer’s screening accessible to a broader population.
subject: Research Article
  topic: section
subject: Interdisciplinary Applications
  topic: category
subject: Multidisciplinary (SET)
  topic: category
subject: Alzheimer’s disease
  topic: keywords
subject: ANOVA
  topic: keywords
subject: blood biomarker
  topic: keywords
subject: Feature selection
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Plasma proteins
  topic: keywords
creator: Zakaria Mokadem
creator: Mohamed Djerioui
creator: Bilal Attallah
creator: Youcef Brik
rights: Copyright (c) 2025 Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik https://creativecommons.org/licenses/by/4.0
date: 2025-02-06
publication date: 2025-02-06
starting page: 192
ending page: 202
doi: 10.54327/set2025/v5.i1.189

Uncertainty Quantification and Sensitivity Analysis of Concrete Structure Using Multi-Linear Regression Technique
type: item
description: The dynamic analysis of structures with uncertain parameters presents an attractive field of structural health monitoring in many cases of technological interest. In the dynamic analysis of hydraulic structures, such as existing dams, modeling assumptions, resulting inaccuracies, and changes in seismic loading are typically the main sources of uncertainties. Many hydraulic structures of concrete can be subjected to seismic loads. However, it is necessary to take haphazard or random phenomena as crucial considerations when assessing the security of these structures or planning new ones. This paper shows computational analysis for the characterization of the behavior of a concrete gravity dam under seismic loads, which are considered sources of uncertainties. The multi-linear regression methodology was performed and applied to evaluate the dynamic response of the considered structure. Numerous nonlinear time history analyses based on Latin Hypercube Sampling were realized to investigate the effect of uncertain parameters on the dynamic response. These analyses were applied to two types of seismic actions, the near and far earthquakes, which act on a concrete gravity dam. Then, a sensitivity analysis was used for each random variable to quantify its risk and clarify its influence on the dynamic behavior of the dam. Results divulge that for near-fault cases, major variables affecting the global sensitivity across all limit states are the Young’s modulus of soil and concrete. On the other hand, for far-fault cases, the important variables influencing the global sensitivity index include the compressive strength of concrete, Young’s modulus of soil, and cohesion.
subject: Research Article
  topic: section
subject: Civil and Structural Engineering
  topic: category
subject: Damage Assessment
  topic: keywords
subject: Multi-Linear Regression Technique
  topic: keywords
subject: Probabilistic model
  topic: keywords
subject: Sensitivity Analysis
  topic: keywords
subject: Concrete structure
  topic: keywords
creator: Abdelhamid Hebbouche
creator: Salim Bennoud
creator: Ayoub Zeroual
rights: Copyright (c) 2025 Abdelhamid Hebbouche, Salim Bennoud, Ayoub Zeroual https://creativecommons.org/licenses/by/4.0
date: 2025-01-06
publication date: 2025-01-06
starting page: 15
ending page: 28
doi: 10.54327/set2025/v5.i1.205

Towards Sustainability: Tracking Carbon Footprint Trends at Ezemvelo KZN Wildlife
type: item
description: Carbon footprint assessment is important to combat global warming and promote sustainability. Globally, organizations committed to biodiversity conservation are essential for maintaining ecosystems and the people who inhabit them. Nonetheless, these organizations produce a carbon footprint due to their operating operations. Hence, this study aimed to assess the specific carbon footprint of the Ezemvelo KZN Wildlife (EKZNW) to improve its understanding of its environmental implications and encourage sustainable behaviors within its particular missions. Using the greenhouse gas protocol corporate accounting and reporting standard as a guide, the study methodology examines greenhouse gas emissions from direct (Scope 1) and indirect (Scopes 2 and 3) sources related to Ezemvelo wildlife activities over five years (2014/2015--2018/2019). The results show that 34,016.62 tons of carbon dioxide equivalent (tCO2e) are emitted on average each year. The majority of these emissions are caused by Scope 2 electricity consumption, which accounts for 23,475.82 tCO2e, and Scope 1 emissions, which account for 7,826.20 tCO2e. Furthermore, there was a noticeable difference in emissions between the reserves, with the Imfolozi Game Reserve having the highest emissions. The findings of this study direct EKZNW toward ecologically conscious behaviors by acting as a catalyst for educated decision-making. The insight gained paves the way for proactive steps to lower carbon emissions, coordinating conservation efforts with more general goals of sustainability and climate resilience.
subject: Research Article
  topic: section
subject: Environmental Sciences and Engineering
  topic: category
subject: Climate change
  topic: keywords
subject: sustainability
  topic: keywords
subject: GHG protocol
  topic: keywords
subject: emission sources
  topic: keywords
subject: Paris agreements
  topic: keywords
creator: Kgaphudi Wendy Madiope
creator: Jacob Adedayo Adedeji
creator: Sebataolo Rahlao
rights: Copyright (c) 2025 Kgaphudi Wendy Madiope, Jacob Adedayo Adedeji, Sebataolo Rahlao https://creativecommons.org/licenses/by/4.0
date: 2025-01-15
publication date: 2025-01-15
starting page: 29
ending page: 44
doi: 10.54327/set2025/v5.i1.206

A Computational Model of Attention-Guided Visual Learning in a High-Performance Computing Software System
type: item
description: This research investigates transformer architectures in high-performance computing (HPC) software systems for attention-guided visual learning (AGVL). The study focuses on the effects of environmental factors and non-contextual stimuli on cognitive control. It reveals how attention increases responses to attentive stimuli, thereby normalizing activity across the population. Transformer blocks use parallelism and less localized attention than current or convolutional models. The study investigates the use of transformer topologies to enhance language modeling, focusing on attention-guided learning and attention-modulated Hebbian plasticity. The model includes an all-attention layer with embedded input vectors, non-contextual vectors containing generic task-relevant information, and self-attentional and feedforward layers. The work employs relative two-dimensional positional encoding to address the challenge of encoding two-dimensional data such as photographs. The feature-similarity gain model proposes that attention multiplicatively strengthens neuronal responses based on how similar their feature tuning is to the attended input. The attention-guided learning approach rewards learning with neural attentional response gain, which the network modifies via gradient descent to achieve the projected objective outputs. The study discovered that supervised error backpropagation and the attention-modulated Hebbian rule outperformed the weight gain rule on MNIST; however, concentration differed.
subject: Research Article
  topic: section
subject: Computer Science and ICT
  topic: category
subject: Computational Model
  topic: keywords
subject: Attention-Guided Visual Learning
  topic: keywords
subject: High-Performance Computing
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Computer Vision
  topic: keywords
creator: Alice Ahmed
creator: Md. Tanim Hossain
rights: Copyright (c) 2024 Alice Ahmed, Md. Tanim Hossain https://creativecommons.org/licenses/by/4.0
date: 2024-12-25
publication date: 2024-12-25
starting page: 106
ending page: 115
doi: 10.54327/set2025/v5.i1.245

The Advanced Actuarial Data Science Based AI-Driven Solutions for Automated Loss Reserving Under IFRS 17 in Non-Life Insurance
type: item
description: This study introduces an AI-driven Automated Actuarial Loss Reserving Model (AALRM) designed to meet IFRS 17 standards for non-life insurance. The model leverages advanced machine learning techniques to improve accuracy, efficiency, and adaptability in loss reserves, with a specific focus on inflation-adjusted frequency-severity modeling. A unique aspect of this research is the integration of bancassurance services, enabling automated management for both microfinance and car insurance on a unified platform. This includes a no-claims bonus system that categorizes policyholders into four tiers—base, variable, final, and high-bonus—resulting in more precise risk assessments and enhanced customer retention. Among eight evaluated machine learning algorithms, the Random Forest (RANGER) outperformed others for estimating Aggregate Comprehensive Automated Actuarial Loss Reserves (ACAALR). The model’s effectiveness was validated through stress tests, scenario analyses, and comparisons with traditional methods like the Chain Ladder. Additionally, the study introduces a novel Robust Automated Actuarial Loss Reserve Margin (RAALRM) with adaptive bounds, addressing traditional limitations in reserve margin calculations. This AI-integrated approach significantly improves predictive accuracy, operational efficiency, and strategic decision-making, offering a scalable solution for the insurance industry.
subject: Research Article
  topic: section
subject: Applied Mathematics and Statistics
  topic: category
subject: Multidisciplinary (SET)
  topic: category
subject: Actuarial Data Science
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Data Analytics
  topic: keywords
subject: Automated Actuarial Loss Reserves
  topic: keywords
subject: Actuaries
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Brighton Mahohoho
creator: Charles Chimedza
creator: Florance Matarise
creator: Sheunesu Munyira
rights: Copyright (c) 2025 Brighton Mahohoho, Charles Chimedza, Florance Matarise, Sheunesu Munyira https://creativecommons.org/licenses/by/4.0
date: 2025-05-05
publication date: 2025-05-05
starting page: 75
ending page: 115
doi: 10.54327/set2025/v5.i2.210

A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data
type: item
description: Alzheimer’s disease (AD) is a gradient degeneration of essential cognitive activities such as memory, thinking, and cognition. AD mainly affects elderly individuals and is recognized as the most common cause of dementia. This study investigates the predictive performance of nine supervised machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, Gaussian Naïve Bayes, Multi-Layer Perceptron, eXtreme Gradient Boost, and Gradient Boosting—using neuropsychological assessment data. We applied two classification techniques—binary and multiclass—to classify 1761 subjects into three categories: cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD). Binary classification tasks focused on CNvsAD and CNvsMCI subsets, while multiclass classification used the full dataset (TriClass). Hyperparameter tuning was performed to optimize model performance. The results indicate that ensemble learning models, particularly Gradient Boosting (GB) and Random Forest (RF), exhibited superior accuracy compared to other algorithms. Most models for the CNvsAD subset achieved the highest accuracy (97.74%), while GB achieved the best performance (94.98%) for the CNvsMCI subset. For multiclass classification, RF achieved the highest accuracy at 84.70%. These findings highlight the robustness and efficiency of ensemble learning algorithms, especially in handling complex, non-linear data structures. This study underscores the potential of RF and GB as reliable tools for early detection and classification of Alzheimer’s disease using neuropsychological data.
subject: Research Article
  topic: section
subject: Computer Science and ICT
  topic: category
subject: Interdisciplinary Applications
  topic: category
subject: Alzheimer’s disease
  topic: keywords
subject: Dementia
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Classification
  topic: keywords
subject: Neuropsychological assessment
  topic: keywords
creator: Zakaria Mokadem
creator: Mohamed Djerioui
creator: Bilal Attallah
creator: Youcef Brik
rights: Copyright (c) 2024 Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik https://creativecommons.org/licenses/by/4.0
date: 2024-12-23
publication date: 2024-12-23
starting page: 177
ending page: 191
doi: 10.54327/set2025/v5.i1.182