Journal of Engineering Research and Reports

Microstructure–Durability Relationships in Recycled Concrete: A Critical Review of Scanning Electron Microscopy Evidence on Pore Structure, Hydration Products and Interfacial Bonding

HongKang Yue — Tue, 04 Aug 2026 00:00:00 +0000

Recycled aggregate concrete (RAC) offers a route to reducing the extraction of natural aggregate and the landfilling of construction and demolition waste, yet its uptake in durability-critical applications remains constrained by uncertainty over long-term performance. The behaviour of RAC is governed less by bulk mix proportions than by features resolved at the micrometre scale: residual adhered mortar, a heterogeneous pore network, an altered distribution of hydration products, and multiple interfacial transition zones (ITZ) of differing quality. Scanning electron microscopy (SEM), particularly in backscattered electron mode with energy-dispersive X-ray analysis, has become the principal tool for visualising these features, but the evidence it yields is frequently reported descriptively and interpreted in isolation from complementary porosimetry and transport measurements. This review critically synthesises SEM-based evidence on the pore structure, hydration assemblage and interfacial bonding of recycled concrete aggregate (RCA) and RAC, and examines how these microstructural attributes map onto durability under chloride ingress, carbonation, freezing and thawing, and sulfate exposure. The synthesis distinguishes robust and consistent findings, such as the porosity and weakness of the old adhered mortar and its associated interfaces, from claims that remain conditional, such as the quantitative extent of ITZ densification achieved by aggregate pre-treatment. Recurring methodological weaknesses are identified, including the two-dimensional and qualitative nature of much SEM reporting, the reconciliation of pore parameters obtained by different techniques, and the scarcity of long-term field data. Enhancement strategies, notably accelerated and wet carbonation, pozzolanic and nano-modified surface coatings, supplementary cementitious materials, and modified mixing, are evaluated for the strength of their microstructural rationale and the durability gains they deliver. The review concludes that microstructure–durability links in RAC are qualitatively well established but quantitatively under-specified, and it sets out prioritised research needs directed at quantitative, three-dimensional and time-resolved characterisation.

MXene Sheet Size as a Synthesis-encoded Variable: A Critical Review of Processing–Structure–Property Relationships

Francis Mekunye — Fri, 07 Aug 2026 00:00:00 +0000

MXenes are solution-processable two-dimensional carbides and nitrides whose macroscopic performance is often attributed to composition and surface terminations, while the lateral dimensions of individual sheets are treated as a secondary processing detail. This review critically examines the more consequential interpretation that sheet size is a synthesis-encoded structural variable, jointly determined with thickness, edge density, basal-plane defects, oxidation state, intercalants, surface terminations and assembly history. Literature published from the emergence of MXenes in 2011 to 30 May 2026 was selected through transparent searches of accessible scholarly indexes, DOI records, institutional repositories and citation networks. The evidence is strongest for Ti₃C₂Tₓ and shows a recurrent trade-off. Larger, less damaged sheets usually reduce interflake junctions, support liquid-crystalline alignment, increase film conductivity and mechanical integrity, and improve dimensional stability in electromagnetic and electrochemical devices. Smaller sheets provide more edge sites, shorter diffusion distances, greater colloidal accessibility and, in some contexts, stronger photothermal or interfacial activity. These tendencies are not universal because the synthesis routes used to change size simultaneously alter termination chemistry, defect density, oxidation, layer number, ionic residues and film packing. Consequently, many published comparisons cannot isolate a true causal size effect. Hydrofluoric-acid and in situ fluoride etching, electrochemical and hydrothermal routes, Lewis-acid molten-salt chemistry, delamination intensity and post-synthetic fractionation each generate distinctive bundles of structural attributes rather than size alone. A process–structure–assembly–property framework is therefore proposed in which size distributions, rather than single mean values, are interpreted alongside chemistry and orientation. The field now requires matched-batch experiments, harmonised metrology, distribution-aware reporting and application-specific optimisation. Controlling sheet size is not a universal route to ‘better’ MXenes; it is a means of selecting the balance among transport, reactivity, stability and manufacturability required by a particular device architecture.

Research Progress on EPC General Contracting Mode in China

Li Yang, Xin Zhao, Feng Liang, Song Hu, Zhiyuan Tian — Mon, 10 Aug 2026 00:00:00 +0000

Engineering–procurement–construction (EPC) general contracting has become an important instrument in China’s effort to reorganise project delivery around integrated responsibility for design, procurement and construction. Its expansion has been encouraged by national rules that clarify employer requirements, contractor qualifications, integrated project management and responsibility for quality, safety, time and cost. Yet the research field remains fragmented. Domestic studies often isolate contractor capability, prefabricated construction, contractual risk, interface management or digital tools, while studies of Chinese contractors overseas emphasise political, logistics and claims risks. This critical narrative review integrates these strands and evaluates how far the accumulated evidence supports common claims about EPC performance in China. Literature published from 2003 to 1 June 2026 was identified through accessible scholarly indexes, DOI metadata, institutional repositories, official Chinese governmental sources and citation searching. The evidence indicates that EPC’s central advantage is not risk transfer by itself but the potential to coordinate interdependent decisions across the project life cycle. Realisation of that potential depends on clear employer requirements, proportionate risk allocation, mature design and supply-chain capability, effective interface governance, and incentives that reward whole-project optimisation. The strongest China-focused evidence concerns risk identification, governance mechanisms and capability structures; the evidence is weaker for causal improvements in cost, schedule, quality, innovation and life-cycle value. Much of the literature relies on cross-sectional surveys, expert scoring, structural models or single cases, which illuminate mechanisms but provide limited counterfactual evidence. Research on prefabricated buildings and railway projects shows that EPC can reduce fragmentation only when organisational and information interfaces are deliberately managed. Digitalisation, including building information modelling, integrated data environments and artificial-intelligence-assisted contract review, is promising, but its benefits remain contingent on data quality, standardisation and organisational redesign. The review concludes that future progress requires project-level longitudinal datasets, transparent comparison groups, standard performance measures and closer examination of how national rules interact with local implementation and sector-specific conditions.

From the Darkroom to the Algorithm: A Critical Review of Creative Concepts in Post-photographic Image Engineering

Tong Xiao — Thu, 20 Aug 2026 00:00:00 +0000

Photographic practice has been reorganised twice within a single generation. The first reorganisation replaced chemical development with electronic sensing and file-based editing; the second replaced single-exposure capture with multi-frame estimation, learned reconstruction and, most recently, with generative synthesis that requires no optical encounter at all. Commentary on these shifts remains divided between humanities scholarship concerned with indexicality, authorship and evidential authority, and engineering scholarship concerned with reconstruction quality, perceptual metrics and detection performance. The two bodies of work rarely cite one another, and the resulting picture of what has actually changed in creative practice is fragmented. This critical narrative review examines how creative concepts inherited from darkroom and early digital practice have been displaced, preserved or redefined by computational and generative image engineering. Literature was identified through open scholarly indexes and citation searching, appraised for methodological adequacy and conceptual contribution, and synthesised thematically rather than catalogued study by study. Four arguments are developed. Manipulation is a constitutive rather than a deviant feature of photographic culture, so accounts that treat generative imagery as a rupture from an honest analogue past misdescribe the historical record. Computational capture has already relocated many creative decisions from the moment of exposure to statistical priors embedded in processing pipelines, which weakens the assumption that generative synthesis introduced an entirely new condition. Claims that generative systems are creative agents remain poorly specified, and the strongest available evidence concerns changes in the distribution of creative labour rather than machine creativity itself. Perceptual, forensic and provenance approaches to authenticity each fail in complementary ways, and no single approach supports the evidential expectations placed on photographs. Priorities for future work include longitudinal studies of practitioner decision-making, cross-domain evaluation of detection systems, and empirical assessment of provenance infrastructures under realistic distribution conditions.

Secure Artificial Intelligence for Asset Performance in Critical Infrastructure: A Critical Narrative Review of Safety, Operational Excellence and Continuous Quality Improvement

Fri, 28 Aug 2026 00:00:00 +0000

Critical infrastructure operators increasingly deploy artificial intelligence (AI) to detect degradation, predict failures, optimise maintenance, identify cyber anomalies and support operational decisions. These capabilities create a plausible route to higher availability, reliability and quality, yet they also couple asset-performance decisions to data integrity, model uncertainty, software supply chains, human oversight and operational technology security. This critical narrative review examines how secure AI can contribute to asset performance without weakening safety or resilience. Literature spanning predictive maintenance and prognostics, industrial AI, digital twins, industrial cyber-physical security, adversarial machine learning, explainability, uncertainty, machine-learning operations and human factors was critically synthesised. The evidence is strongest for AI as a decision-support layer for condition monitoring, fault diagnosis and targeted maintenance where data provenance is controlled, failure modes are sufficiently represented and recommendations remain bounded by engineering constraints. Evidence for fully autonomous optimisation in high-consequence infrastructure is less mature because benchmark accuracy does not directly establish operational utility, transferability or safe behaviour under distribution shift and hostile manipulation. Digital twins can improve contextual diagnosis and testing, but their value depends on fidelity, synchronisation, governance and protection of the data-model-actuation pathway. Cybersecurity and safety therefore cannot be treated as independent assurance domains: poisoned training data, adversarial inputs, compromised updates or unavailable models can become physical reliability and quality risks. Explainability alone is also insufficient for assurance; uncertainty calibration, abstention, independent validation, auditability and meaningful human authority are required. The synthesis proposes a secure asset-performance loop in which AI recommendations are evaluated against safety, security, service and quality constraints, with outcomes fed back into monitored model and process improvement. The central implication is that asset performance should be maximised as a constrained socio-technical objective rather than as an unconstrained prediction or utilisation metric.

Machine Learning-based Fault Prediction for Smart Electrical Grids in Nigeria: A Critical Narrative Review of Reliability Potential, Evidence Gaps and Deployment Priorities

Sat, 29 Aug 2026 00:00:00 +0000

Nigeria’s electricity system continues to experience reliability challenges arising from transmission and distribution faults, protection coordination difficulties, voltage and frequency deviations, ageing assets, and uneven network observability. Machine learning (ML) is increasingly proposed as a means of identifying abnormal operating states earlier and supporting faster, more selective intervention. This critical narrative review evaluates the extent to which the international fault-analytics literature can support genuine fault prediction and reliability improvement in the Nigerian smart-grid context. Literature published from 1 January 2010 to 22 June 2026 was examined, while older foundational Nigerian evidence was retained where it remained necessary for context. The evidence shows a pronounced terminological and translational gap. Most studies labelled as predictive apply ML after fault inception to detection, classification or localisation, commonly on simulated or synthetically augmented data. Convolutional, recurrent, ensemble, support-vector and hybrid methods often achieve very high test performance in controlled settings, but these results do not establish prospective prediction, field robustness or reduction in outage burden. Nigeria-specific evidence demonstrates technical feasibility for high-impedance-fault diagnosis and voltage-stability classification, yet field-validated prospective models linked to reliability outcomes remain scarce. The strongest near-term opportunity is therefore not autonomous fault anticipation in the abstract, but staged deployment of ML for condition-based risk scoring, abnormal-event recognition, protection support and maintenance prioritisation using utility data streams. Successful translation will require dependable sensing, labelled event histories, topology-aware modelling, uncertainty estimation, cybersecurity, model-drift governance and prospective evaluation against operational endpoints such as outage frequency, interruption duration, energy not supplied and protection misoperations. ML can contribute meaningfully to Nigerian grid reliability, but only when treated as one component of an engineering, data and governance architecture rather than as a substitute for protection design, asset renewal or network reinforcement.

Research Progress in YOLO-Based Road Crack Detection: A Critical Narrative Review of Architectures, Data, Evaluation and Deployment

Kaiyuan Shen, Fanpeng Meng, Song Hu, Lindong Li, Lingchao Chen — Mon, 31 Aug 2026 00:00:00 +0000

Road crack detection is a central computer-vision task in pavement inspection because cracks are visually sparse, geometrically elongated, highly variable in width and topology, and easily confused with shadows, joints, stains and road markings. The You Only Look Once (YOLO) family has become prominent in this domain because it offers an attractive accuracy-speed trade-off for vehicle-mounted, mobile and edge inspection. This critical narrative review evaluates research progress in YOLO-based road and pavement crack detection from 2016 to 26 June 2026, with emphasis on how architectural modifications, task formulation, dataset design and deployment constraints affect the strength of reported evidence. Literature was selected from accessible scholarly indexes and transport/engineering sources, supplemented by citation searching and DOI-level bibliographic verification. The evidence indicates a clear progression from straightforward transfer of generic YOLO detectors towards crack-aware designs that combine multi-scale feature fusion, attention or transformer components, lightweight convolution, revised detection heads, specialised localisation losses and detector-segmenter pipelines. Recent work increasingly addresses edge deployment, illumination variation and background interference. Nevertheless, apparent benchmark gains are difficult to compare because studies differ in datasets, crack taxonomies, train-test partitioning, image resolution, augmentation, hardware and metric definitions. Bounding-box detection also remains an imperfect representation for thin crack morphology, whereas pixel-level segmentation and geometric measurement provide richer maintenance information at higher annotation and computational cost. Cross-region generalisation, uncertainty estimation, reproducible latency measurement, calibration against maintenance-relevant severity and evaluation under adverse weather remain underdeveloped. The strongest direction is therefore not continued accumulation of isolated architectural modules, but standardised, cross-domain evaluation of integrated detection, segmentation and quantification systems under realistic operational constraints. YOLO-based crack detection is technically mature enough for increasingly credible field deployment, yet evidence for robust transfer across roads, sensors and environments remains less mature than within-dataset accuracy results suggest.

Application of AI-based Intelligent Control Methods for Enhancing Product Quality in Glass Manufacturing

Omar Musazade, Stanislav Aghamatov — Tue, 04 Aug 2026 00:00:00 +0000

Aims: This research aims to advance industrial glass manufacturing by implementing a next-generation autonomous control architecture. The study focuses on maximising energy efficiency and product quality by integrating Digital Twin technology, Deep Reinforcement Learning (DRL), and Explainable AI (XAI) to overcome the limitations of legacy control systems.

Study Design: This is an analytical and simulation-based research study focused on the cognitive optimisation of industrial float glass production processes.

Place and Duration of Study: The study was conducted in the Department of "Instrumentation Engineering" (Intellectual Measurement and Control Systems), Azerbaijan State Oil and Industry University (ASOIU), between September 2025 and June 2027.

Methodology: A high-fidelity Digital Twin of a float glass furnace was developed to simulate production dynamics. A DRL-based agent was implemented for real-time furnace regulation, allowing for continuous self-optimisation of thermal zones. The system integrated a Convolutional Neural Network (CNN)-based visual inspection layer for defect detection, complemented by an XAI module that provides transparent, logic-based justifications for autonomous operational adjustments. Theoretical evaluations of hydrogen-natural-gas blending were also conducted to assess sustainability impacts.

Results: The proposed DRL-driven architecture achieved improved thermal regulation, maintaining stability within ±0.5°C compared with traditional Fuzzy-PID models. The combustion strategy, optimised via reinforcement learning, produced a reported reduction in fuel consumption of more than 6% (P < 0.05). The XAI module provided real-time interpretability of system decisions, while the integration of hydrogen-enriched combustion pathways indicated a potential decrease in carbon emissions of approximately 8-10% and maintained high combustion efficiency compared with standard natural gas use.

Conclusion: The transition from localised control to an AI-driven autonomous framework, supported by Digital Twins and Explainable AI, provides a transparent approach to modernising glass manufacturing. These systems may reduce operational risks and environmental impacts, thereby supporting intelligent and sustainable industrial automation.

Modelling of Flash Flood Mitigation through Flow Diversion: A Case Study of the Teesta River Basin, Bangladesh

Eisteuck Ahmed, Mitu Khatun, B. M. Mustasim Billah, Md. Asif Hossen — Wed, 05 Aug 2026 00:00:00 +0000

Flooding remains one of the most devastating natural hazards in Bangladesh, particularly in the northern riverine regions, where transboundary water-management issues exacerbate vulnerability. This study presents a proof-of-concept computational fluid dynamics (CFD) analysis conducted using SOLIDWORKS to model a novel geometric flow-diversion structure for flash-flood mitigation. It evaluates a proof-of-concept hydraulic model designed to mitigate flash-flood damage through flow diversion and velocity reduction in the Teesta River basin. The computational domain for this initial study measures 8.0 m × 2.5 m × 2.0 m. The proposed Flood Flow Mitigation Model employs strategically positioned geometric obstructions to create a diverging-flow effect, resulting in energy dissipation and flow deceleration. The SOLIDWORKS CFD analysis indicates that the system achieves a 20% velocity reduction under baseline conditions (inflow velocity: 1.000 m/s; outflow velocity: 0.800 m/s). Theoretical performance scalability is suggested to be proportional to the system dimensions, although this relationship requires further validation through physical experimentation and parametric testing. The model may address challenges including reduced sediment transport, bank-erosion control, and the provision of additional warning time for vulnerable communities; however, these potential benefits require further investigation through unsteady-flow analysis and field validation. This study contributes to research on flash-flood mitigation in developing countries by presenting a potentially cost-effective and technically feasible approach to flood-risk reduction, subject to detailed economic analysis and site-specific validation. The findings may inform flood-management policy in Bangladesh and other flood-prone regions with similar topographic and hydrological characteristics.

Industrial Sector Decarbonization Pathways for Cement, Iron and Steel, and Chemical Industries in Nigeria

Agnes Oboh, Fidelis Abam, Anthony Obi, Ugwu Hyginus Ubabuike — Fri, 07 Aug 2026 00:00:00 +0000

Nigeria's cement, iron and steel, and chemical industries account for a substantial share of the country's industrial energy use. This study applies the Low Emissions Analysis Platform (LEAP) to project industrial energy demand and greenhouse gas emissions to 2060 under four scenarios: a Baseline reflecting current trends; a Realistic Scenario based on existing policy commitments; a Light-Fossil (LF) pathway; and a Green-Fuel (GF) pathway assuming widespread adoption of carbon capture and storage (CCS), electric arc furnaces, and hydrogen. The three subsectors respond differently to the same assumptions. Cement shows the widest range of outcomes: energy demand falls by 61%, from 60.5 million gigajoules (GJ) in 2015 to 23.55 million GJ in 2060, under the GF pathway, but rises by 522% to 376.78 million GJ under the Realistic Scenario, which assumes continued reliance on conventional clinker production. The chemical sector is the most responsive to fuel switching, with demand falling by 79.6% under GF conditions, compared with a 259% rise in the Baseline. Iron and steel are an outlier: even under GF assumptions, energy demand rises by approximately 13% to 36.60 million GJ by 2060 because electrification and hydrogen-based direct-reduction routes change the source of energy rather than eliminate the need for it. These results indicate that a uniform decarbonisation policy is unlikely to serve Nigeria's industrial sector effectively. Cement decarbonisation depends on CCS deployment and clinker substitution, chemical-sector decarbonisation on electrification and green-hydrogen feedstocks, and iron and steel decarbonisation on a slower, technology-led transition supported by a more reliable grid. The findings provide a sector-differentiated basis for prioritising investment and policy under Nigeria's Energy Transition Plan and its 2060 net-zero target.

Optimal Placement and Sizing of Static Var Compensators on the Nigerian 330 kV Transmission Network: A Comparative Study of Genetic Algorithm, Particle Swarm Optimization, and a Hybrid Approach

Ndubuisi V. Irokwe, Nseobong I. Okpura, Kufre M. Udofia, Jimoh J. Afolayan — Fri, 14 Aug 2026 00:00:00 +0000

This study addresses voltage instability in developing-economy transmission grids by determining the optimal placement and sizing of Static Var Compensators (SVCs) on the Nigerian 330 kV, 50-bus network. Although metaheuristic techniques have been widely applied to FACTS device allocation, most studies rely on standard IEEE test systems, limited optimisation objectives or limited statistical validation on practical transmission networks. To address these limitations, this study applies actual operational data, develops a five-objective optimisation framework that simultaneously minimises the voltage stability index (Lₘₐₓ), active and reactive power losses, generation cost, voltage deviation and SVC installation cost, and compares the genetic algorithm (GA), particle swarm optimisation (PSO) and a hybrid PSO-GA approach. Newton–Raphson load-flow analysis was performed in PSAT/MATLAB, with SVCs modelled as variable shunt susceptances. The optimisation used GA, PSO and a hybrid PSO-GA algorithm comprising 15 PSO iterations followed by 15 GA generations. A normalised weighted-sum fitness function was employed over 20 independent runs, and results were validated using the Wilcoxon signed-rank test (p < 0.05). The base case identified Damaturu, Gombe, Maiduguri, Yola and Kano as northern buses with undervoltage conditions. All methods restored these voltages to acceptable levels, while the hybrid PSO-GA achieved the lowest Lₘₐₓ value (0.311) and the lowest total normalised objective (650). The findings indicate that PSO exploration combined with GA refinement provides balanced reactive-power compensation. The framework remains limited to steady-state analysis and requires contingency, dynamic stability and detailed economic assessment before practical deployment.

Development of A Multiparametric Model Predictive Control System for Temperature Regulation of a Window - Type Air Conditioner

Fri, 14 Aug 2026 00:00:00 +0000

Aims/Objectives: This study aimed to develop a multiparametric Model Predictive Control (mp-MPC) framework for the thermal regulation of a single-zone space conditioned by a window-type air-conditioning (AC) unit. The framework shifts the optimisation burden offline so that an explicit piecewise-affine control law can be synthesised for deployment on low-cost embedded hardware, and its performance is benchmarked against conventional online MPC and reactive baseline controllers under varying ambient temperature scenarios.

Methodology: A lumped-parameter thermal dynamics model derived from the first law of thermodynamics was formulated. A discrete-time state-space mp-MPC model was then designed to regulate supply-air and room temperatures, with ambient temperature incorporated as a measured disturbance. The controller was implemented in MATLAB/Simulink (R2023a) and compared with an online MPC, a Ziegler-Nichols-tuned PID controller, an ON-OFF thermostat, and an open-loop case under three ambient-temperature conditions: constant high temperature (45°C), normal daytime temperature (35°C), and an extreme heatwave ramp (30-50°C). Performance was measured using Mean Square Error (MSE), Integral Square Error (ISE), and Integral Absolute Error (IAE).

Results: Conventional online MPC produced the lowest MSE values (0.250-0.302), representing an 81-97% improvement over mp-MPC. The explicit mp-MPC performed competitively near the nominal design point, with a steady-state error of 0.0018°C at 35°C, but exhibited boundary-switching offsets of up to 3.75°C under non-stationary disturbances. The ON-OFF thermostat showed the weakest regulation, with MSE values up to 118 times higher than those of MPC.

Conclusion: Although mp-MPC remains a computationally attractive option for severely resource-constrained microcontrollers, conventional online MPC is preferable for window-type AC temperature regulation when embedded hardware can support online quadratic programming. The results provide a replicable simulation benchmark for future investigations into the hardware-in-the-loop deployment of explicit predictive control on residential cooling equipment.

Design and Experimental Study of an Indirect Forced Convection PV/T Solar Dryer in a Humid Tropical Zone

Fri, 14 Aug 2026 00:00:00 +0000

Ginger is highly perishable because of its high moisture content, and conventional sun drying is often constrained by variable weather conditions, contamination, and prolonged drying periods. Indirect forced-convection photovoltaic/thermal solar dryers offer a promising approach by simultaneously supplying thermal and electrical energy for controlled drying in humid tropical environments. This study presents the design, construction, and experimental evaluation of an indirect forced-convection photovoltaic/thermal (PV/T) solar dryer for ginger under humid tropical conditions. The system integrates a 150 W PV module, an air-heating section, a drying chamber with three trays, and a fan powered by the photovoltaic component. Experiments were conducted using a 2 kg batch of ginger while monitoring solar irradiance, ambient and collector temperatures, relative humidity, air velocity, sample mass, and drying time. The influence of airflow was assessed at volumetric flow rates of 7, 5, and 3 m³/h, corresponding to air velocities of 2.8, 2.0, and 1.2 m/s, respectively. Lower airflow increased the temperature rise across the PV/T collector and improved thermal performance. At 1.2 m/s, the air-temperature increase between the collector inlet and outlet reached approximately 10°C, the maximum collector-outlet temperature was 54.2°C, the maximum PV-module surface temperature was 62.44°C, and thermal efficiency reached 18.28%. Ginger on the tray nearest the hot-air inlet reached the target moisture content after 380 minutes, whereas the upper tray required 620 minutes. The results show that airflow rate and tray position influenced heat transfer, drying rate, and drying time. The developed PV/T dryer provided simultaneous electrical and thermal energy for forced-convection drying, while the observed differences among trays indicate a need for improved airflow distribution.

An Intelligent Enterprise Asset Management Framework for Railway Maintenance Prioritization: A Machine-learning Proof of Concept Using Benchmark Analogue Data

Adeyemi Adebukunola Ishekwene — Wed, 19 Aug 2026 00:00:00 +0000

Railway networks remain capital-intensive and safety-critical, yet maintenance still relies heavily on reactive and calendar-driven regimes that underexploit the condition data modern systems generate. Published machine-learning applications remain fragmented across asset classes and weakly connected to enterprise decision systems, leaving a gap this study addresses by developing an integrated framework uniting enterprise asset management, predictive analytics, and digital analytics for maintenance prioritisation and decision support. Adopting a quantitative design with a design-science orientation, the framework was implemented across stages spanning data preparation, feature engineering, modelling, evaluation, reliability translation, and decision integration. Two established condition sources supplied the binary classification and degradation estimation tasks. Random forest, extreme gradient boosting, support vector machines, and neural networks were trained with feature scaling, with synthetic minority oversampling applied within training folds for the classification task, then assessed through held-out testing and five-fold resampling, stratified for classification and partitioned at engine level for degradation. Gradient boosting achieved the strongest classification balance, recording an F1-score of 0.737 and a ROC-AUC of 0.981, while the neural network minimised degradation error at a grouped five-fold mean RMSE of 47.25 cycles. Resampling confirmed stability, attribution exposed speed, tool wear, and thermal differential as dominant drivers, and all ten highest-ranked observations were confirmed failures, a precision-at-10 of 1.00. Although benchmark analogues constrain generalisation, the contribution is advanced as a proposed architecture supported by a computational proof of concept, in which predictions are converted into auditable, risk-ranked maintenance priorities pending naturalistic validation on operational railway records.

Health Risk Assessment of Heavy Metals in Groundwater Used for Domestic Purposes in Eket Metropolis, Akwa Ibom State, Nigeria

Samuel Akpan Nta, Erewari Ukoha-Onuoha — Wed, 19 Aug 2026 00:00:00 +0000

Groundwater is the principal source of water for domestic purposes in many parts of Eket Metropolis, Akwa Ibom State, Nigeria, and its quality is therefore relevant to public health. This study presents a health risk assessment of heavy metals in groundwater used for domestic purposes in Eket Metropolis, Akwa Ibom State, Nigeria. Four borehole water samples were collected from four locations. The collected samples were analysed for Pb, Ni, Cr, Mn, Zn, Cu, Cd, and Co. Chronic daily intakes (CDIs) of metals through water ingestion and health risk indices (HRIs) were calculated. Pb, Ni, Cr, Mn, Zn, and Cu were detected at all four sampling locations. The concentrations of Pb, Ni, Cr, Mn, Zn, and Cu ranged from 0.003 - 0.013, 0.002 - 0.005, 0.005 - 0.011, 0.011 - 0.017, 0.031 - 0.036, and 0.010 - 0.019 mg/l, respectively, and their mean concentrations were within the WHO limits. Cd and Co were not detected. The HRIs for Pb in children at borehole locations B2 and B4 warrant concern because the calculated values exceeded unity (1). This indicates a potential health risk to children who use the groundwater for domestic purposes. These findings indicate that borehole water used for domestic purposes at some locations in Eket Metropolis, Akwa Ibom State, Nigeria, is contaminated with Pb and requires further monitoring. Continuous monitoring and additional studies are recommended to ascertain potential long-term effects. Further study is also recommended to assess the seasonal variability of toxic metals in the study area.

Mixed Convection MHD Flow of an Eyring-powell Nanofluid over a Cone with Radiative Heat Transfer and Exponential Internal Heat Generation

Manjula K. M., D. K. Jyoti — Wed, 19 Aug 2026 00:00:00 +0000

The present study investigates the magnetohydrodynamic (MHD) mixed convection flow and heat transfer characteristics of an Eyring-Powell nanofluid over a cone in the presence of nonlinear thermal buoyancy, thermal radiation, Brownian motion, thermophoresis, temperature-dependent heat source, and exponential heat generation. The Buongiorno nanofluid model is employed to account for nanoparticle transport mechanisms. The governing nonlinear partial differential equations describing the flow, thermal, and concentration fields are transformed into a system of coupled nonlinear ordinary differential equations using suitable similarity transformations. The resulting boundary value problem is solved numerically through the shooting technique combined with the adaptive Runge-Kutta-Fehlberg (RKF45) method. The effects of the governing parameters on the velocity, temperature, and nanoparticle concentration distributions, as well as on the skin-friction coefficient, local Nusselt number, and local Sherwood number, are analysed in detail. The results reveal that the mixed convection parameter enhances the fluid velocity, whereas the magnetic field suppresses the flow due to the Lorentz force. The temperature profile increases with increasing thermal radiation, thermophoresis, temperature-dependent heat source, and exponential heat generation parameters, while the nanoparticle concentration decreases with increasing Lewis number and increases with thermophoresis effects. Furthermore, enhanced Brownian motion reduces nanoparticle concentration within the boundary layer. The present findings provide useful insights for the design and optimisation of thermal systems involving non-Newtonian nanofluids in cone-shaped geometries, including heat exchangers, aerospace components, thermal processing equipment, and advanced cooling technologies.

Humanoid Robot Motion Tracking Control Method Integrating Adaptive PID, Kalman Filtering and Feedforward Compensation

Li Jinhui, Zhang Jingyu, Song Changhao, Li Xiangbin — Fri, 21 Aug 2026 00:00:00 +0000

Aims: This study developed and evaluated a humanoid joint-space motion-tracking controller integrating standard Kalman filtering, adaptive proportional-integral-derivative feedback and model-based inverse-dynamics feedforward compensation.

Study Design: A twenty-trial Monte Carlo comparative numerical simulation was conducted against a fixed-gain proportional-derivative baseline under matched plant parameters, reference trajectories, measurement-noise sequences and disturbance sequences.

Place and Duration of Study: The study was conducted at the School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China, from March 2026 to July 2026.

Methodology: A simplified 12-degree-of-freedom humanoid joint-space model was simulated for 500 time steps per trial at a sampling interval of 0.01 s. Each joint state was estimated using a two-state discrete Kalman filter. The PID gains were adjusted online through bounded nonlinear laws, while nominal inverse dynamics supplied inertia, velocity-dependent and gravity feedforward compensation. Tracking performance was assessed using MAE, RMSE, maximum error and 95% confidence intervals.

Results: The mean trial-level MAE decreased from 0.165825 rad to 0.039224 rad, a mean reduction of 76.35% with an approximate 95% CI of [75.91%, 76.78%]. Mean trial-level RMSE decreased from 0.224126 rad to 0.067078 rad, a mean reduction of 70.07% with an approximate 95% CI of [69.81%, 70.33%]. All twelve joints showed lower MAE.

Conclusion: The integrated controller improved joint-angle tracking consistently within the evaluated simplified model. Contact-rich simulation and physical-robot experiments remain necessary for external validation.

Integrating Zero Trust, Risk Management Framework and Defense-in-Depth: A Multi-case Study Analysis of Enterprise Cybersecurity Architectures

Mon, 24 Aug 2026 00:00:00 +0000

Enterprise cybersecurity has shifted from perimeter defence toward continuously verified architectures, yet zero trust, layered defence, and formal risk governance are typically adopted in isolation, producing fragmented estates in which expenditure rises while measurable resilience does not. Existing literature examines each paradigm separately, leaving their interaction at enterprise scale unexamined. This study developed the Integrated Architecture Resilience Model, expressing available control surface as a weighted composite of governance, layering, and verification, and relating it to observed resilience. Two open datasets were analysed, comprising a versioned adversary technique catalogue and a community breach repository, yielding 638 qualifying incidents across financial services, healthcare, government, and critical infrastructure. Regression, configurational analysis, and stratified cross-validation were applied, with coding reliability assessed independently. Threat profiles differed significantly by sector, and 111 of 697 active techniques, representing 15.9 per cent, carried no mapped mitigation, concentrating in discovery. Layering dominated coverage while verification remained weakest. Chance-corrected coding agreement reached 0.683, indicating substantial reliability. The composite explained 35.9 per cent of variance in-sample but failed under cross-validation, establishing that documented coverage and enacted protection are distinct. The study contributes a reproducible diagnostic framework and evidence that control breadth alone does not secure resilience, redirecting attention toward verification depth.

Evaluation of Building Defects and Maintenance Management Practices in Jordanian Public Buildings: Causes, Impacts, and Strategies for Sustainable Improvement

Othman Ahmad Alnsour, Ala’a A. Alrwashdeh, Zakaria Al-Omari — Thu, 27 Aug 2026 00:00:00 +0000

Background: Building defects and inadequate maintenance management can compromise the safety, functionality, serviceability, and long-term performance of public buildings. In Jordan, empirical evidence linking defect assessment with systematic maintenance prioritisation remains limited.

Aim: This study evaluated major building-defect categories in Jordanian public buildings, examined their principal causes and impacts, assessed existing maintenance practices, and developed an integrated framework to support sustainable maintenance management.

Method: A sequential mixed-method approach combined field inspections, a structured questionnaire completed by 111 construction and maintenance professionals, case studies, and stakeholder consultations. Data were analysed using descriptive statistics, Mean Importance Score (MIS), Relative Importance Index (RII), standard deviation, normalised weights, and ranking analysis.

Results: Structural defects were ranked highest (MIS = 4.61; RII = 0.922), followed by water- and moisture-related defects (MIS = 4.55; RII = 0.910) and mechanical, electrical, and plumbing defects (MIS = 4.48; RII = 0.896). Poor workmanship was the highest-ranked cause (MIS = 4.81; RII = 0.962), followed by poor maintenance practices (MIS = 4.76; RII = 0.952) and poor material quality (MIS = 4.72; RII = 0.944). Limited budgets, weak supervision, and non-standardised procedures also contributed to deterioration.

Conclusion: The findings support a shift from predominantly reactive maintenance towards preventive and condition-based approaches. The proposed Building Defect Assessment and Maintenance Management Framework integrates defect identification, condition assessment, prioritisation, strategy selection, implementation, documentation, and performance monitoring to support more systematic lifecycle asset management in Jordanian public buildings.

Enhanced Solar-Integrated Quasi-Z-Source DC–DC Converter for Efficient Electric Vehicle Battery Charging

Mary L. Udoh, Jimoh J. Afolayan, Kufre M. Udofia, Akaninyene B. Obot — Tue, 01 Sep 2026 00:00:00 +0000

The integration of photovoltaic (PV) systems with electric vehicle (EV) charging requires high-gain DC–DC converters that can handle low and variable PV voltages while maintaining efficiency and battery safety. This paper presents the modelling and performance analysis of a solar‑integrated enhanced quasi‑Z‑source DC–DC boost converter (EQZSC) for EV battery charging. The proposed topology incorporates a coupled‑inductor impedance network and an additional voltage‑lift capacitor, thereby achieving a voltage conversion ratio of V₀/V_m = [1+(1+n)D]/[1-(1+n)D]significantly higher than conventional boost and classical quasi‑Z‑source converters. A unified analytical framework is developed, including steady-state analysis, averaged state-space modelling, perturb-and-observe maximum power point tracking (MPPT), and constant-current/constant-voltage (CC–CV) battery charging with hysteresis-based mode transition. Detailed loss and thermal models (semiconductor, magnetic, and junction temperature) are integrated to support a realistic evaluation of efficiency. MATLAB/Simulink simulations are conducted under steady‑state and dynamic irradiance (600–1000 W/m²) conditions, for a PV input range of 60–100 V and a regulated output of 400 V. Key results include a voltage gain of 4.44, peak efficiency of 96.8%, input current ripple below 3%, output voltage ripple below 0.8%, and switch voltage stress limited to 87 V (78% reduction versus conventional boost). Under an irradiance step (600→900 W/m²), the output voltage settles within 25 ms with less than 3% overshoot. The CC–CV charging profile demonstrates state-of-charge (SOC) progression from 10% to 99% in approximately 60 minutes. The proposed converter shows potential as an efficient and thermally stable solution for solar‑powered EV charging infrastructure.

Evaluation of Vernonia amygdalina and Senna alata as Low-cost Plant-based Coagulants and Antibacterial Materials for Water Treatment

Tue, 01 Sep 2026 00:00:00 +0000

Access to safe drinking water remains a critical challenge in many Nigerian communities, where reliance on untreated surface, well and borehole water increases exposure to microbiological and physicochemical contaminants. This study evaluated the phytochemical composition, antibacterial activity and coagulation potential of Vernonia amygdalina (Ewuro-ijebu) and Senna alata (Asunwon) as low-cost plant-based materials for raw-water pre-treatment. Healthy leaves of both plants were collected, shade-dried, milled and extracted separately using ethanol and distilled water. Water samples were obtained from surface water, hand-dug wells and boreholes within the study locality. Qualitative phytochemical screening showed abundant saponins in both plants, with moderate-to-trace levels of flavonoids, alkaloids and carbohydrates; steroids were detected only in S. alata, whereas phlobatannins were detected only in V. amygdalina. Antibacterial susceptibility testing using the agar well diffusion method showed that the ethanolic extracts inhibited five of seven bacterial isolates (71.4%) at 150 mg/mL, whereas the aqueous extracts produced no measurable inhibition. Jar-test evaluation using plant powders produced variable changes in turbidity and other physicochemical properties, while total hardness decreased substantially in treated samples, including a reduction from 350 to 100 mg/L in well water treated with S. alata. Total viable bacterial counts were also substantially reduced in some treated samples following the settling period. These reductions indicate treatment-related decreases in total viable bacterial load but do not, by themselves, establish drinking-water microbiological safety or potability, particularly because residual microorganisms remained after treatment. Accordingly, plant-based treatment should be followed by filtration and an appropriate terminal disinfection step. Based on these findings, a multi-barrier household treatment approach incorporating plant-based coagulation, sedimentation, sand filtration and terminal disinfection is proposed. The findings indicate that V. amygdalina and S. alata have potential as locally available, low-cost pre-treatment materials, although further dosage optimisation, safety assessment and pilot-scale validation are required before household-scale application.