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 their interdisciplinary and multidisciplinary applications. The journal supports both disciplinary and cross-disciplinary research and covers the following core and related areas: Mechanical and Materials Engineering | Electrical and Electronic Engineering | Civil and Structural Engineering | Traffic and Transportation Engineering | Industrial Engineering and Management | Environmental Science and Engineering | Logistics, Distribution, and Warehousing | Architecture and Planning | Computer Science and ICT | 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 manuscripts submitted by the end of July 2026 is reduced by 50% (from 500 EUR to 250 EUR). The APC is charged only after an article has been formally accepted for publication. Author Guidelines - Submit an Article - Call for Papers: Special Issue - 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 informed about the latest research and journal updates by following us on Linkedin.
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Development of an Adaptive Multi-Criteria Optimization Framework Using the PSI Method: Validation via Thermal Insulation Selection
Multi-criteria optimization is primarily utilized to rank various alternatives and project types. However, the question arises: is it possible to use the multi-criteria optimization process for something more than that, in terms of determining the actual set of optimal criteria and creating optimal paths of their changes according to desired constraints? In this regard, it is necessary to find a connection between the output provided by multi-criteria optimization method itself and a hypothetical alternative that represents the current optimal alternative, referred to as a synthetic profile or transit optimum, which does not represent any initial alternative in the optimization matrix. Such an actual transit alternative is formed by the mutual influence of all factors in the decision matrix, but also depends on the multi-criteria optimization process itself. This requires the creation of a mathematical feedback loop between the optimization process and the redistribution of all alternatives according to the criteria, to create a new transit optimal set of criteria. As a representative method for multi-criteria optimization, the PSI (Preference Selection Index) method was selected because of its advantages in terms of its integrated procedure for calculating criteria weights and its objectivity in categorization. The proposed adaptive PSI framework introduces a synthetic profile that enables iterative feedback and two-stage optimization, transforming the classical static ranking into a dynamic adaptive process with a clear methodological advancement over existing MCDM techniques. The overall concept was tested on the optimization of thermal insulation thickness for walls under the conditions of Bosnia and Herzegovina.
Copyright (c) 2026 Srđan Vasković, Petar Gvero, Milovan Kotur, Đorđe Vojinović, Ljubiša Preradović, Danijela Kardaš-Ančić, Milan Pupčević https://creativecommons.org/licenses/by/4.0
Analysis of Vibro-Impact System with Ideal Excitation and Various Friction Models
In this paper, the dynamic analysis of a vibro-impact system with ideal excitation and various friction models is performed. A physical model of an oscillating mechanical system with possible impact occurrence is presented and the corresponding mathematical model is derived by using Lagrange’s equations of motion. To describe interaction between impact element and environment, three different friction models are considered: Coulomb, viscous and Coulomb-Stribeck model. Newton’s impact law with a coefficient of restitution is employed to describe relationship between pre-impact and post-impact velocities. The dynamic behavior of the vibro-impact system under the ideal excitation, where the system does not influence the excitation source, is investigated for each friction model by numerically solving the governing equations. The results of numerical analysis are presented through amplitude-frequency diagrams, displacement-time responses and phase portraits. The main objective is to determine the influence of different friction models on amplitude-frequency diagrams, particularly on the regions exhibiting impact and non-impact behavior. For parameter regions with multiple coexisting solutions, basins of attraction are constructed to illustrate the dependence of the system regime on initial conditions.
Copyright (c) 2026 Kanita Lemeš, Džanko Hajradinović, Zlata Jelačić https://creativecommons.org/licenses/by/4.0
Multi-Criteria Evaluation of Key Performance Indicators in Pharmaceutical Supply Chains
Pharmaceutical Supply Chains (PSCs) represent one of the key segments of modern healthcare systems, given their direct impact on patient safety, therapy availability, and healthcare sustainability. The complexity of these chains stems from stringent logistical and regulatory requirements and the necessity of maintaining product quality and safety, as well as demand uncertainty and global supply disruptions. Effective management of PSCs requires systematic performance monitoring, the identification of key indicators, and decision-making based on objective measures. This paper identifies and describes key performance indicators (KPIs), including strategic collaboration, supply chain security, service quality, sustainability, costs, flexibility, resilience, risk management, and visibility. Using the Analytic Hierarchy Process (AHP), a multi-criteria decision-making method, these indicators were ranked. Safety, resilience, and service quality were found to be the most important for supply chain operations. This study contributes to the theoretical understanding of KPIs in PSCs, identifies a set of relevant indicators, determines their significance through the AHP method, and provides practical guidelines for managers regarding priorities in KPI management and monitoring.
Copyright (c) 2026 Miloš Veljović, Teodora Jorgaćević, Snežana Tadić, Mladen Krstić, Vasilije Perović https://creativecommons.org/licenses/by/4.0
AI-Driven Conversational Interfaces in IoT Ecosystems: Systematic Review of User Acceptance Models for Chatbots
Digital communication based on artificial intelligence and chatbots is rapidly expanding in Internet-of-Things (IoT) environments. User interaction with chatbots is being integrated into various systems, such as smart homes and connected devices. Adoption is determined by acceptance, influenced by psychological and technical factors that vary across different IoT use cases. While the Technology Acceptance Model (TAM) has been applied to diverse technologies and contexts, limited research addresses its application specifically to chatbots in IoT ecosystems. This study aims to systematically identify key acceptance factors and propose an integrated framework extending traditional TAM with IoT-specific variables. A systematic literature review (SLR) of peer-reviewed articles was conducted across six academic databases (October 2024 to December 2025), covering the last five years and yielding 11 studies meeting all inclusion criteria. Results reveal five critical acceptance factor categories: cognitive and affective factors, empathy and personalization, reliability and privacy, transparency and data management and social influence. The study's novel contribution is the integration of IoT-specific considerations including device interconnectivity, context-awareness, and data sensitivity with traditional TAM constructs, thereby addressing a significant gap in chatbot acceptance within connected device environments. Further research should validate this framework and explore long-term intentions.
Copyright (c) 2026 Veit Schlaus https://creativecommons.org/licenses/by/4.0
Cultural Heritage Management Challenges: Historical Village of Huta Siallagan in Indonesia
Effective cultural heritage preservation requires a holistic approach, guided by management that integrates governance, planning (including disaster prevention, risk management, promotion, education, community involvement, awareness-raising, and sustainable tourism), financing, and implementation. Despite existing frameworks, many historical sites face conservation challenges, often stemming from gaps in planning and management. This study aims to define the cultural heritage management components for Huta Siallagan, a historical village in Indonesia, located on an island within the UNESCO Global Geopark of Lake Toba. The village has preserved its unique Batak cultural heritage retaining its natural setting, site layout, building structures, while intangible practices continue to be actively maintained. The study examined current legal status of the site through national and international legislative frameworks as well as its overall condition. Finding indicate that gaps remain in planning and management frameworks. The study concludes with recommendations for multi-level strategies and heritage management components to strengthen existing conditions and enhance sustainable development.
Copyright (c) 2026 JM Vivi Violina; Lana Kudumovic https://creativecommons.org/licenses/by/4.0
Experimental Study of the Combined Effects of Operating Parameters of a Diesel Engine under Increasing Exhaust Backpressure Conditions
Diesel engines remain widely used in transportation and power generation due to their high efficiency and durability. However, their combustion behaviour, performance, and emissions are strongly influenced by operating conditions such as exhaust backpressure (EBP). This study experimentally investigates the combined effects of compression ratio (CR), engine load, and EBP on combustion characteristics, thermal performance, heat transfer, and emissions of a diesel engine operating at 1 500 rpm. Results show that moderate CR values (14–16) and engine loads of 50–75% enhance in-cylinder pressure, heat release rate, and combustion efficiency. In contrast, excessive CR (18) and high EBP (60 kPa) increase in-cylinder temperature and residual gas fraction (RGF), leading to reduced heat release rates and longer ignition delay. The highest brake thermal efficiency (approximately 33%) is achieved at moderate load and low EBP, whereas high CR and load under elevated EBP reduce efficiency and increase brake-specific fuel consumption. Heat transfer analysis indicates that excessive CR and load significantly raise thermal loads, emphasizing the need for accurate predictive correlations in engine design. Emissions analysis reveals minimal CO and HC at moderate conditions, while NOx and smoke increase under extreme operating regimes. Overall, the findings identify an optimal operating window (CR 14–16, 50–75% load, EBP ≤45 kPa) and provide practical guidance for diesel engine under backpressure constraints, contributing to improved efficiency and emissions control.
Copyright (c) 2026 Nhlanhla Khanyi, Riaan Stopforth https://creativecommons.org/licenses/by/4.0
Application of “WHAT–IF” Analysis in Risk Assessment During Rescue Operations at Traffic Accidents
Technical rescue in traffic accidents represents one of the most dynamic and high-risk interventions for emergency services. This study aimed to apply the WHAT–IF analysis to systematic risk assessment during these operations, with particular focus on hydraulic equipment. Through a structured expert assessment methodology that included the Brainstorming and Delphi methods, 10 key adverse event scenarios were identified. The identified scenarios covered risks related to vehicle instability, fire, electric shock in electric/hybrid electric vehicles, equipment failure, and coordination errors. These were evaluated using three-level scales for probability and consequences. The assessments were integrated into a 3×3 risk matrix, allowing categorization into low, medium, high, and critical risk levels. The results showed that by implementing targeted treatment measures — including the standardization of stabilization procedures, Lockout/Tagout procedures for electric vehicles, the appointment of a safety officer, and the use of thermal imaging equipment — initially high and critical risks can be significantly reduced. Statistical analysis using the Wilcoxon signed-rank test confirmed that this reduction was statistically significant (p < 0.01), with a large effect size (r = 0.59), demonstrating a shift of risks into an acceptable zone in accordance with the ALARP (As Low as Reasonably Practicable) principle. It is concluded that the WHAT–IF analysis provides a clear, operational, and adaptable framework for improving the safety of rescuers and casualties, offering directly applicable guidelines for procedural and technological interventions, as detailed in the attached tabular manuals.
Copyright (c) 2026 Teodora Kovačević, Marinko Aleksić, Fuad Klisura, Luka Maljević https://creativecommons.org/licenses/by/4.0
Neuro-Symbolic Control with Large Language Models for Language-Guided Spatial Tasks
Although large language models (LLMs) have recently become effective tools for language-conditioned control in embodied systems, instability, slow convergence, and hallucinated actions continue to limit their direct application to continuous control. A modular neuro-symbolic control framework that distinguishes between low-level motion execution and high-level semantic reasoning is proposed in this work. While a lightweight neural delta controller performs bounded, incremental actions in continuous space, a locally deployed LLM interprets symbolic tasks. We assess the suggested method in a planar manipulation setting with spatial relations between objects specified by language. Numerous tasks and local language models, such as Mistral, Phi, and LLaMA-3.2, are used in extensive experiments to compare LLM-only control, neural-only control, and the suggested LLM+Deep Learning (LLM+DL) framework. In comparison to LLM-only baselines, the results show that the neuro-symbolic integration consistently increases both success rate and efficiency, achieving average step reductions exceeding 70% and speedups of up to 8.83x while remaining robust to language model quality. The suggested framework enhances interpretability, stability, and generalization without any need of reinforcement learning or costly rollouts by controlling the LLM to symbolic outputs and allocating uninterpreted execution to a neural controller trained on artificial geometric data. These outputs show empirically that neuro-symbolic decomposition offers a scalable and principled way to integrate language understanding with ongoing control, this approach promotes the creation of dependable and effective language-guided embodied systems.
Copyright (c) 2025 Momina Liaqat Ali, Muhammad Abid, Muhammad Saqlain, Jose M. Merigo https://creativecommons.org/licenses/by/4.0
Green Synthesis of Tin Oxide (SnO₂) Nanoparticles from Senna alata Leaves Extract
The plant-mediated synthesis of metal oxide nanoparticles is an eco-friendly and sustainable approach for hazardous chemical- and energy-expending protocols. Chemically synthesized methods often use toxic reagents and are biological hazards, whereas physical methods require special equipment and may lead to the loss of control of the particles. In this work, fresh Senna alata leaf extract was used to synthesize SnO₂ nanoparticles via a green route as reducing, stabilizing, and capping agents. A metal precursor (1.0 M tin chloride) was utilized. The prepared nanoparticles were characterized via X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and ultraviolet‒visible (UV‒Vis) spectroscopy to investigate their structural, morphological and optical properties. As derived from the XRD measurements, a major peak was detected for the (002) plane, and the average crystallite size was 8.25 nm, with a d-spacing value of approximately 0.2575 Å, corresponding to nanocrystalline SnO₂, which confirmed its successful formation. The FTIR analyses revealed characteristic molecular vibrations related to structures of SnO₂. Scanning and transmission electron microscopic evaluations revealed crystallites with distinctive morphologies of SnO₂ nanoparticles. The absorption edge is approximately 300 nm, and the bandgap energy is estimated to be 4.57 eV via optical characterization. The preferred (002) orientation, nanoscale crystallinity and wide bandgap make the synthesized SnO₂ nanoparticles multifunctional. These properties render them excellent candidates for optoelectronic and environmental sensors, energy storage applications, and anticorrosion coatings, providing a versatile architecture for designing next-generation nanotechnologies.
Copyright (c) 2026 Oludele Adegboyega , Israel Adewoye Adegboyega, Jacob Adedayo Adedeji https://creativecommons.org/licenses/by/4.0
Two-Phase Intercritical Annealing AR400 Steel: Effects on Phase Dispersion and Impact Resistance for Mining Applications
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.
Copyright (c) 2026 Witske Meyer, Tamba Jamiru, Lodewyk Willem Beneke, Taoreed Adesola Adegbola https://creativecommons.org/licenses/by/4.0
Spare Parts and Material Management in Electric Vehicle Maintenance: A Multidisciplinary Review
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.
Copyright (c) 2025 Fuad Klisura, Marinko Aleksić, Muhamed Sarvan https://creativecommons.org/licenses/by/4.0
Advances in the Development, Processing, and Application of Locally Sourced Clay-based Refractory Materials for Furnace Application in Nigeria
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.
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
Optimised Feature Selection for Mobile Edge DDoS Detection Using Quasi-Opposite Firefly Algorithm
Mobile edge computing (MEC) reduces latency for delay-sensitive applications by bringing computations closer to end users. However, this technology is vulnerable to security threats, notably Distributed Denial of Service (DDoS) attacks. DDoS attacks are characterised by distributed malicious nodes that flood the target with data packets, causing system unavailability or performance degradation. This contradicts the objective of the MEC, which is to reduce delay and latency. To address this, we propose a feature selection technique to improve the detection of DDoS attacks in MEC using machine learning techniques. The proposed approach employs quasi-opposite-based learning (QOBL), a concept often utilised in differential evolution algorithms, to modify the Firefly Algorithm (FA) to form a Quasi-Opposite Firefly Algorithm (QOFA) to optimise feature selection. FA excels at navigating complex feature spaces for global optimisation but suffers from premature convergence to local optima. QOBL mitigates this by guiding FA toward local solutions, improving efficiency and detection accuracy. By selecting only the most relevant features, the QOFA reduces computational complexity while maintaining robust performance. Simulations in MATLAB demonstrated that QOFA outperformed traditional FA, achieving a higher detection accuracy (up to 95%). This approach enhances the efficiency of machine learning models for DDoS detection in MEC, ensuring a reliable and low-latency network performance that is critical for real-time applications.
Distributed Denial of Service attack (DDoS)
Copyright (c) 2026 Sekgoari Semaka Mapunya, Mthulisi Velempini https://creativecommons.org/licenses/by/4.0
A Comparative Case Study of Two Pedagogical Approaches in Web Development Education: From a Traditional Java Environment to a Modern Ruby on Rails Ecosystem
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.
Copyright (c) 2025 Keiichi Takahashi https://creativecommons.org/licenses/by/4.0
Comprehensive Evaluation of Waste-Derived Fuels As Sustainable Alternatives in Cement Production
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.
Copyright (c) 2025 Oluwafemi Ezekiel Ige, Musasa Kabeya https://creativecommons.org/licenses/by/4.0
Optimization of Operational Parameters to Minimize Cutting Energy during Kenaf Harvesting
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.
Copyright (c) 2025 Thomas Adedayo Ayorinde, Tamba Jamiru; Peet Le Roux; Oseni Kehinde Owolarafe https://creativecommons.org/licenses/by/4.0
Tailoring Heat Treatment Strategies to Enhance Corrosion Resistance and Mechanical Performance of Low-Carbon Steel in Marine Environment
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.
Copyright (c) 2025 Basiru Philip Aramide, Tamba Jamiru, Taoreed Adesola Adegbola, Abimbola Patricia Idowu Popoola https://creativecommons.org/licenses/by/4.0
Goldmine Tailings Stabilization using Quarry Dust and Coal Ash for Pavement Applications - A Review
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.
Copyright (c) 2025 Jacob Olumuyiwa Ikotun, Gbenga Emmanuel Aderinto, Valentine Yato Katte, Adewumi John Babafemi https://creativecommons.org/licenses/by/4.0
Integrated Experimental and Analytical Investigation of Reverse Osmosis Desalination Systems
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.
Copyright (c) 2025 Olufisayo Emmanuel Ojo, Olanrewaju Akanni Oludolapo https://creativecommons.org/licenses/by/4.0
Suitability Assessment of Organic Carbon Additives in the Carburization of Low Carbon Steel (AISI 1020) for Engineering Applications
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.
Copyright (c) 2025 Mathew Olurotimi Adeoti, Tamba Jamiru, Taoreed Adesola Adegbola, Lodewyk Willem Beneke https://creativecommons.org/licenses/by/4.0
Modeling A Reverse Osmosis Desalination Plant: A Practical Framework Using Wave Software
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.
Copyright (c) 2025 Olufisayo E. Ojo, Olanrewaju A. Oludolapo https://creativecommons.org/licenses/by/4.0
A Survey on Blockchain-driven Music Industry: Trends, Gaps, and Future Directions
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 optimization and tracking. At last, we announce the capabilities and adversities to the perception of the blockchain-driven music industry and then contribute propositions to impede them along with future directions to cater to the gaps identified.
Patikiri Arachchige Don Shehan Nilmantha Wijesekara
Copyright (c) 2025 Patikiri Arachchige Don Shehan Nilmantha Wijesekara https://creativecommons.org/licenses/by/4.0
An Overview on Blockchain-based Social Media
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.
Optimal Placement and Sizing of Renewable Distributed Generators for Power Loss Reduction in Microgrid using Swarm Intelligence and Bio-inspired Algorithms
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%.
Copyright (c) 2025 Nabil MEZHOUD, Ahmed Bahri, Bilel Ayachi, Farouk Boukhenoufa, Lakhdar Bouras https://creativecommons.org/licenses/by/4.0
GFEA: Leader Election Algorithm for Choosing a GroupDecision Support System Facilitator
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.
Copyright (c) 2025 Sabir Mohammedi Taieb, Mohamed Adnane Laredj https://creativecommons.org/licenses/by/4.0
Fine-tuning AraGPT2 for Hierarchical Arabic Text Classification
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%.
Copyright (c) 2025 Djelloul BOUCHIHA, Abdelghani BOUZIANE, Noureddine DOUMI, Benamar HAMZAOUI https://creativecommons.org/licenses/by/4.0
Diagnosis of Multiple Open-circuit and Short-circuit Faults in Three-phase Multicellular Inverter Based on Sliding Mode Observer
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.
Copyright (c) 2025 Fatma Khater, Abderrezak Aibeche, Sid Ali Fellag, Mohamed Zinelabidine Doghmane, Dr. H. Akroum https://creativecommons.org/licenses/by/4.0
Odour, Colour and Turbidity Removal from Selected Industrial Wastewater Using Electrocoagulation Process
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.
Copyright (c) 2025 Engr Dr Chibueze Achi, Engr Peter Akinlabi, Titilayo Adepoju, Prof Akinwale Coker https://creativecommons.org/licenses/by/4.0
Thermo-Mechanical Assessment of Bio-based Insulating Material Using Phase Change Materials and Date Palm Fibers
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.
Copyright (c) 2025 Mahi Eddine Brahimi, Mustapha Maliki, Nadia Laredj, Hanifi Missoum, Miloud Sardou https://creativecommons.org/licenses/by/4.0
Effect of Soil Salinity on Thermal Behavior of Sandy Soils Under a Sahara Climate
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.
Copyright (c) 2025 Djihad Bennaceur, Nadia Laredj, Mustapha Maliki, Hanifi Missoum https://creativecommons.org/licenses/by/4.0