Eng, Volume 7, Issue 2 (February 2026) – 45 articles

Menstruation, a biological phenomenon experienced by more than half of the global population, remains stigmatized and poorly addressed in the context of research and public discourse. One overlooked issue is that of “period pollution,” the waste generated by millions of feminine hygiene pads (menstrual pads) that end up in landfills or the environment. Simultaneously, Japanese knotweed (Reynoutria japonica), a non-native invasive plant which disrupts native species, leads to the disruption of ecological systems. This experimental study assesses the Japanese knotweed plant for its potential to serve as the absorbent core in a sustainable menstrual pad, helping to address both environmental challenges in tandem. As control groups, commercial pads (Natracare and Saathi) were tested for their performance as absorbent materials, as defined by the absorbency ratio (AR) test. All preliminary studies were done using normal saline solutions dyed with red food coloring. Saathi pads demonstrated significantly higher levels of AR compared to Natracare and knotweed pads due to the presence of superabsorbent polymers, making it an unreliable benchmark. Because Japanese knotweed is composed of cellulosic fibers that absorb water through hydrogen bonding to hydroxyl groups and capillary imbibition within porous fiber networks, lignin removal via alkaline processing was employed to enhance absorbency prior to experimental testing. The inner lumen of the knotweed was selected and delignified using a sodium hydroxide bath, later being shaped into an absorbent core akin to the measurements of the commercial pads and inserted into Natracare shells for proof-of-concept testing. Although knotweed-based pads exhibited lower AR values than Natracare, the testing places the knotweed prototype at approximately 40% of the fluid capacity, indicating a strong starting point for a natural fiber. To further evaluate the processing feasibility of Japanese knotweed beyond laboratory-scale pad prototyping, Japanese knotweed biomass was subjected to conventional Kraft pulping, which helps to remove lignin and increase absorbency. The Kraft pulping produced a moderately delignified brown pulp with a Kappa number of 20. Due to limiting factors, the absorbency of the pulp was not tested. However, the pulp’s fiber dimensions were comparable to hardwood pulps that are commonly used in absorbent applications, suggesting feasibility for future development into bleached fluff pulp and sustainable menstrual hygiene products. Full article

This study numerically investigates the hydrothermal behaviour of a Jeffrey nanofluid with relevance to maritime thermal systems. The coupled nonlinear governing equations for momentum, heat, and mass transport are solved using a shooting technique that accounts for magnetohydrodynamic effects, Darcy porous-media resistance, viscous dissipation, and spatially varying internal heat generation. Variable thermophysical properties, including temperature-dependent viscosity and density, are also considered. The results reveal that porous resistance, fluid elasticity, and thermophysical variations significantly influence velocity, temperature, and concentration fields. The combined effects of porous drag and variable properties markedly alter the characteristics of heat and mass transfer. These findings provide insights into thermal and mass-transport performance, including skin friction, heat transfer, and concentration distributions, which are critical metrics for porous heat exchangers and nanofluid-based maritime coatings. Here, maritime relevance is represented via a generalised porous nanofluid model rather than a specific material. Among the key findings, increasing the slip velocity factor can reduce the surface skin-friction coefficient by approximately 48.7%, while the heat-transfer rate increases by nearly 27.1%, accompanied by a decrease of about 18.9% in the Sherwood number. Conversely, raising the density factor enhances the skin friction coefficient by roughly 103.8% and also augments the heat and mass transfer rates by about 61.3% and 106.1%, respectively. Likewise, at zero relaxation–retardation ratio, the flow reduces to the Newtonian case. Increasing this factor reduces the local Nusselt number by about 1.45%, indicating a slight weakening of heat transfer due to elastic effects. Furthermore, the reliability of the current numerical framework is established through a dual-validation approach, including an analytical assessment of limiting cases and a rigorous comparison with established data from the literature. Full article

Traditional underground space evaluation systems often employ 2D GIS methods to represent 3D information, leading to issues such as the loss of 3D spatial data and insufficient resolution in depth. To address the practical needs and methodological steps of 3D geological suitability evaluation for underground space (3D UGEE) development, this study adopts an integrated secondary development approach to design and implement a software system capable of conducting quantitative geological suitability evaluation in three dimensions using multivariate data. The system incorporates the latest methods and achievements in 3D UGEE, featuring functional modules such as multidimensional data conversion, 3D statistical analysis, 3D spatial distance analysis, and 3D comprehensive evaluation, which enable the integration and analytical assessment of multivariate geoscientific data. In comparison with existing 3D-UGEE systems, the proposed 3D-UGEE system integrates a broader range of functional modules, conducts in-depth integration and mining of multi-source geological data, boasts robust 3D graphical display and interactive capabilities, and achieves more efficient operational performance. This study elaborates on the system’s overall architecture, development approach, and the design and implementation processes of its functional modules. Application results from a case study in Qingdao demonstrate that the system not only provides a suite of 3D spatial analysis and comprehensive evaluation tools for integrating multivariate geoscientific data but also offers robust support for enhancing 3D UGEE practices. Full article

Carbon fiber-reinforced plastics (CFRP) are widely used in deployable space structures due to their strength-to-weight ratio, yet their inherent brittleness and limited damage tolerance constrain their performance under large deformation. This study reports a new concept, the Kevlar composite deployable lenticular tube (CDLT), for improved toughness and reliable stowability. The buckling response of Kevlar CDLT under axial compression and torsion was characterized, and its stowability was verified through experiments and finite element analysis (FEA). Axial compression studies show that the load–displacement curve transitions from linear elastic to nonlinear deformation at the critical buckling load; meanwhile, local stress magnification occurs in the central arc region. Damage analysis further reveals that buckling instantaneously induces localized wrinkling and matrix failure. Torsional analysis shows that the CDLT exhibits an initially linear torque–twist response, governed by shear stiffness. However, once the critical torque is exceeded, torque decreases sharply due to localized collapse and overall buckling. Moreover, the outermost layers bear the highest stresses, whereas the inner layers remain comparatively uniform and less stressed. Furthermore, the influence of different layup sequences, ply numbers, and total thickness on the load-bearing capacities of CDLT was investigated, ultimately determining the optimal layup scheme. Finally, the stowability analysis demonstrates that the Kevlar CDLT, configured as a six-ply laminate with a total thickness of 0.72 mm, achieves an optimal balance between stiffness and flexibility. In this comparison, both the Kevlar and CFRP CDLTs employ identical lenticular cross-sectional geometries, fully consistent boundary conditions, the same overall laminate thickness (0.72 mm), and an identical stacking sequence of [45°/−45°/90°/90°/45°/−45°], with the material properties being the only variable. Under these strictly controlled conditions, the coiling torque of the Kevlar CDLT is reduced by at least 48% relative to that of the CFRP CDLT. This study preliminarily verifies the load-bearing capacity and stowability of novel Kevlar CDLTs, providing valuable guidance for the design of deployable space structures. Full article

This study presents an integrated approach combining environmental risk assessment and experimental performance evaluation for asphalt production plants incorporating reclaimed asphalt pavement (RAP). Unlike previous studies, which focus separately on mechanical performance or environmental impact, our methodology applies a semi-quantitative Environmental Impact Score (EIS), calculated using legal requirements (L), pollutant characteristics (P), and control measure effectiveness (C). The EIS framework is based on ISO 14001 and ISO 31000 principles. The results indicate that significant impacts are mainly associated with high-temperature processes and hazardous materials, while mitigation measures effectively reduce residual risks. The experimental investigation compared conventional asphalt mixtures with mixtures containing 9.71% RAP across different bitumen contents. Key quantitative findings include a 3-point increase in EIS for RAP mixtures due to higher volatile organic compound (VOC) emissions and a 3–8% improvement in Marshall stability and stiffness at lower bitumen contents (3.8–4.2%). The results demonstrate that RAP can enhance mechanical performance while supporting circular economy objectives, provided that environmental risks are actively managed through process control and mitigation measures. This work highlights the novel integration of quantitative environmental scoring with laboratory validation, providing a reproducible framework for sustainable and risk-informed asphalt production. Full article

The reliability and efficiency of induction motors in Industry 4.0 environments critically depend on advanced fault detection systems capable of real-time monitoring and diagnosis. This paper presents a novel deep learning approach combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for automated detection and classification of inter-turn short-circuit faults in three-phase induction motors. Our methodology processes three-phase current signals through a sophisticated CNN-LSTM architecture that extracts both spatial and temporal fault patterns. The proposed system classifies seven distinct motor conditions: healthy operation, three levels of high-impedance faults (HI-1 to HI-3), and three levels of low-impedance faults (LI-1 to LI-3). Experimental validation demonstrates exceptional performance, with the CNN-LSTM model achieving 97.2% accuracy, significantly outperforming traditional machine learning approaches, including SVM (66.3%), Random Forest (67.4%), and KNN (78.1%). The system provides real-time fault classification with inference times under 3 ms, making it suitable for continuous monitoring in smart manufacturing environments. Full article

This study examines a top-chord-free open-web steel-truss composite floor in which the concrete slab functionally replaces the traditional top chord and works jointly with vertical square-tube web members and a square-tube bottom chord. Two scaled specimens—with and without concrete infill in the end shear-bending blocks—were fabricated and tested under static loading. The load–deflection response delineates three stages: elastic, elastic–plastic, and failure. Tests show that infilling the shear-bending blocks does not enhance global mechanical performance. In the elastic range, the mid-span open-web section satisfies the plane-section assumption with a linear strain profile, whereas the solid-web section exhibits a bilinear distribution. A validated ANSYS finite-element model reproduces the measured responses and supports a parametric study showing that span-to-depth ratio, opening-to-span ratio, slab (flange) thickness, and width-to-span ratio significantly affect ultimate capacity and deflection. Design recommendations are proposed: span-to-depth ratios of 11–14 and opening-to-span ratios of 0.04–0.07. An equivalent-stiffness-based simplified linear-elastic deflection formula with a reduction factor is derived, which accurately tracks deflection evolution and enables serviceability-driven selection of web spacing and overall structural depth. Full article

Temperature prediction for partially shaded photovoltaic (PV) modules is essential for ensuring the stability and safety of PV systems. However, existing methods suffer from high computational complexity, limiting their applicability in engineering practice. Aimed at a real-time and portable algorithm that can be embedded in mobile devices for intelligent monitoring of PV stations, a simple and fast method is designed in this work for estimating the thermal behavior of PV modules under partial shading conditions. To the best of our knowledge, this is the first work in this field that achieves computational simplicity without relying on professional commercial software. The experimental results validate the accuracy of the proposed method in comparison with the multiphysics model (which is widely regarded as the benchmark in this field) while significantly improving computational efficiency. Simulations are conducted to explore the effects of shading proportions and environmental conditions. Shading proportions ranging from 6% to 90% are prone to promoting the development of hotspots under conditions that involve partial shading of an individual cell. Higher irradiance, a higher ambient temperature and a lower wind speed result in a higher temperature of the PV module. Full article

Reservoir water-level fluctuations periodically alter the physical and mechanical properties of accumulation deposits in the bank slope zone, potentially triggering geological hazards such as collapses and landslides. This study developed an original laboratory mechanical testing system to systematically investigate the evolution of deformation and shear strength parameters in these accumulation deposits throughout the reservoir operation period. Tests conducted on the accumulation deposits in the Baijiabao bank slope demonstrate that under the coupled effects of anisotropic stress and cyclic wet–dry conditions, the compression modulus, cohesion, and internal friction angle decrease significantly, by 10.6%, 11.4%, and 13.2%, respectively. As the number of wet–dry cycles increases, the rate of reduction in these parameters gradually diminishes. Between the second and fourth cycles, the decreases in compression modulus, cohesion, and internal friction angle were 9.7%, 8.6%, and 6.9%, respectively. Beyond the eighth cycle, the values of these parameters stabilize with minimal further change. Full article

This study presents an integrated engineering methodology aligned with the planning phase of the ISO 50001:2018 (Energy Management Systems—Requirements with Guidance for Use. International Organization for Standardization (ISO): Geneva, Switzerland, 2018) energy management standard for the design, sizing, and assessment of a solar thermal system applied to domestic hot water production in a medium-scale hotel building. The proposed framework focuses on the energy review stage of ISO 50001, incorporating site-specific climatic assessment, spatial layout optimization, structural feasibility analysis, and energy performance evaluation to support informed technology selection and system viability. Thermal performance is assessed using real operational data from the case study, complemented by a data-driven multivariable regression-based energy performance indicator (EnPI) that relates electricity consumption to cooling degree days and room occupancy. This regression model, developed in accordance with ISO 50001 recommendations, enables transparent monitoring of energy performance under real operating conditions without relying on black-box predictive techniques. Material selection criteria for absorber plates, heat-transfer components, transparent covers, and insulation layers are discussed to support both initial efficiency and performance stability under site-specific climatic conditions. In addition, an indicative and qualitative analysis of material-dependent performance evolution is introduced to support comparative decision-making, without implying quantitative lifetime prediction. Structural feasibility of the collector support system is examined through finite-element simulations under combined gravitational and wind loads, providing illustrative verification of stress distribution under representative operating conditions. The installed system delivers an annual thermal energy contribution of 8468 kWh, resulting in an estimated reduction of 7.79 t of CO₂ emissions per year. Economic indicators suggest a short payback period and a favorable internal rate of return, which should be interpreted as order-of-magnitude estimates within the planning scope of the methodology. Overall, the proposed methodology provides a replicable and multidisciplinary planning-phase framework aligned with ISO 50001 for the design and assessment of solar thermal systems in medium-scale buildings under real operating conditions. Full article

Traditional single-ended traveling wave fault location is sensitive to velocity uncertainty, complex topologies, and variations in the equivalent impedance of converter stations. This paper proposes a fault distance calibration method based on the fusion of traveling wave redundant information and inverse weighting: multiple sets of initial distance estimates are formed using wave fronts arrival times measured at multiple terminals. These estimates are then calibrated through inverse weighting fusion according to the error sensitivity of each redundant observation, thereby suppressing errors caused by wave velocity deviations and structural inhomogeneities. Simulation verification using PSCAD/EMTDC for a four-terminal VSC-MTDC loop network demonstrates that this method reduces dependence on precise wave velocity measurements while enhancing the accuracy and robustness of DC loop network fault location. Full article

In this study, we present an extension of the Takagi–Sugeno fuzzy inference system (TS-FIS) framework based on prototypical fuzzy similarity (PFS) for defect detection in concrete. The key novelty lies in integrating the PFS mechanism into the TS-FIS+ANFIS architecture, thus enabling a hybrid rule–activation mechanism, bringing together fuzzy interpretability with data-driven similarity learning. To describe the ultrasonic concrete defect scenario, a high-fidelity finite element method (FEM) model that combines solid mechanics with fluid acoustics has been developed. From this numerical model, a synthetic dataset of about 36.8 million samples has been generated. The performance of the proposed TS-FIS+ANFIS+PFS classification system has been compared with that of a conventional FIS+ANFIS model, its particle-swarm-optimized (PSO) version and a Decision Tree (DT) classifier. The proposed model achieved the best performance, with a classification accuracy of 85.4% and an inference time of approximately 0.2 ms per sample. In contrast, the conventional, the PSO and the DT classifiers yielded accuracies of 60.5%, 62.0%, and 76.0%, respectively. These results confirm that PFS improves sensitivity and alleviates the computational effort, representing a potential candidate toward the realization of a defect abacus for concrete, an atlas conceived as a systematic collection of defect configurations associated with specific ultrasonic responses. Full article

The rapid rise in the use of Liquefied Petroleum Gas (LPG) as an alternative vehicle fuel in Ghana presents both opportunities and risks within the national energy transition agenda. This study investigates LPG safety as well as environmental and regulatory implications using a multi-method quantitative approach that combines structured survey data, exploratory multivariate analysis (MCA), and machine learning classification (Random Forest) to uncover emerging associations and patterns in LPG safety practices. Primary data were obtained from 384 respondents, including vehicle operators, auto-technicians, regulatory officials, and LPG station attendants across five major transport zones: Kejetia, Asafo, Ahodwo, Bantama, and Suame Magazine. The MCA identified four distinct behavioural and safety profiles—At-Risk, Proactive Safety, Compliant and Equipped, and Formal and Reported—reflecting diverse compliance and risk patterns across socio-occupational groups. The Random Forest classifier achieved a predictive accuracy of 96.5% based on cross-validated performance. Sensitivity and specificity values were high, indicating reliable discrimination among incident types. To reduce the risk of overfitting, k-fold cross-validation and monitored error convergence were performed across increasing numbers of trees. While the model shows strong predictive capability, we present these results cautiously and emphasize observed associations and emerging patterns rather than definitive predictive conclusions. The findings reveal that while economic motivations underpin LPG adoption, weak institutional enforcement and widespread informal installations heighten safety vulnerabilities. Comparisons with sub-Saharan and Asian contexts underscore the need for a structured regulatory framework, mandatory certification of installers, and periodic vehicle inspections. The study contributes to the broader discourse on informal energy transitions in developing economies by demonstrating how technical and behavioural determinants interact within weak regulatory systems. Policy recommendations emphasize the integration of data-driven risk assessment tools into regulatory oversight to enhance vehicular LPG safety and sustainability. Full article

N₂O emissions exacerbate the greenhouse effect, urgently demanding advances in abatement technologies. Catalytic decomposition of N₂O over cobalt-based oxides with alkali metal promoters remains challenging because these catalysts are used in pelletized form, limiting their activity to a narrow outer-shell region due to internal diffusion limitations. However, research efforts continue to focus on enhancing Co–alkali metal contact on unsupported powder samples under inert conditions, even though, under industrial conditions, catalysts are exposed to inhibitory components of waste gases and N₂O, and the powder form is unsuitable for practical application. This study aims at testing N₂O decomposition over catalysts with a Co₃O₄-Cs active phase supported on a ceramic foam. For this purpose, we characterized these catalysts by H₂ temperature-programmed reduction, H₂O and NO temperature-programmed desorption, atomic absorption spectroscopy, and X-ray diffraction and assessed their catalytic performance under an inert-gas atmosphere and with O₂, water vapor, and NO to simulate industrial conditions. Using a pseudo-homogeneous, one-dimensional model of an ideal plug flow reactor in an isothermal regime, the simulation calculations for a full-scale catalytic reactor for N₂O abatement in waste gas from HNO₃ production were performed. The Cs₂CO₃ precursor significantly enhanced catalyst reducibility and electron transferability, increasing N₂O decomposition efficiency in inert gas, but its high hygroscopicity decreased resistance to water vapor and NO, overriding its advantages under industrial conditions. Conversely, glycerol-assisted impregnation enhanced catalyst performance regardless of Cs precursor. These foam-supported catalysts offered several other advantages, including lower pressure drop and lower active phase loading with matching catalytic activity. Based on our findings, depositing Cs₂CO₃ on ceramic foam through glycerol-assisted impregnation may facilitate catalytic N₂O decomposition at the industrial level and, therefore, promote environmental sustainability by reducing N₂O emissions. Full article

The high dependence on fossil fuels for energy supply in hospitals compromises their operational sustainability, increases costs, and contributes significantly to polluting emissions. This study evaluates the technical, economic, and environmental feasibility of integrating photovoltaic and solar thermal systems in a hospital located in a tropical Caribbean environment, characterized by continuous operation and high energy demand. The methodology combines advanced simulation using PVsyst for the photovoltaic subsystem and the f-chart method for the solar thermal system, using real data on electricity and domestic hot water demand. The proposed system achieves an installed photovoltaic power of close to 390 kWp, with an annual production of around 0.7 GWh and an average performance ratio of 0.80, demonstrating high technical performance. The solar thermal subsystem covers approximately two-thirds of the annual domestic hot water demand, supported by thermal storage suitable for hospital operation. From an economic standpoint, the total estimated investment is recovered in less than 10 years, with a positive net present value, confirming the system’s profitability over its useful life. In environmental terms, hybrid integration avoids more than 400 t of CO₂ per year, contributing significantly to the decarbonization of the health sector and the strengthening of energy security. The results obtained demonstrate that photovoltaic–thermal integration in tropical hospitals is technically and economically viable and constitutes a replicable solution for regions with high solar radiation and energy vulnerability. This research provides a comprehensive and reproducible methodological framework that can support sustainable energy planning and the design of public policies aimed at low-emission healthcare infrastructure. Full article

To meet the requirements of high-efficiency thermal management without external power in long-distance and distributed multi-heat source scenarios, this paper proposes a dual compensation chamber multi-evaporator loop heat pipe system (DCCME-LHP). The system uses a capillary pump to provide capillary driving force, and through the step-by-step advancement of multiple condenser-evaporator combination, it achieves heat transfer and long-distance transportation among multi-heat sources. The experimental system investigates the effects of working fluid charge ratio, time interval, and heat load on the system’s hydrodynamic stability and heat transfer limit. The results show the optimal comprehensive performance of startup and steady state can be achieved with the charge ratio of 75% and a time interval of 8–10 min. The system operates stably under a total heat load of 270 W (90 W for the capillary pump and 60 W for each of the three evaporators). When the heat load of a single-stage evaporator rises to 70 W, the system enters the operation failure zone, and the steady-state temperature plateau jumps. This study provides a theoretical basis and experimental support for the design and stable operation strategy of long-distance multi-heat source thermal control systems. Full article

Stable circumstances and an improved voltage profile need power compensators integrated with energy storage elements in AC power systems. The control of these compensators is of paramount importance for obtaining high accuracy, reliability, and better system dynamics, which involves careful controller design considerations and small-signal analysis. This paper focuses on the use of a static synchronous compensator (STATCOM) and supercapacitor energy storage system (SCESS) for achieving voltage stability, grid support, and better system dynamics. After the primary load is shifted to the grid, real power assistance is promptly injected into the AC grid to enhance the DC-link voltage, as well as the grid voltage, and reduce supply current from the grid using a vector control technique. The SCESS is handled with the help of a bidirectional DC–DC converter, which facilitates charging and discharging during boost and buck operations, respectively. Using small-signal modeling, the stable system is designed to obtain a reliable and stable output, which is confirmed by the systematic simulations and experiments. Full article

This study presents a low-cost wireless sensor network (WSN) integrated with an Internet of Things (IoT) platform for continuous monitoring of agro-environmental parameters relevant to rice harvest decision support. Solar-powered sensor nodes equipped with temperature-humidity (DHT22) and light intensity (BH1750) sensors were deployed in a Pathum Thani 1 rice field in Si Prachan, Suphan Buri province, Thailand. Environmental data were recorded hourly from June to September 2025 and transmitted wirelessly to a cloud-based dashboard for real-time visualization. Growing Degree Days (GDD) were calculated from measured air temperature using a literature-based base temperature, and cumulative GDD (CGDD) was used to track rice growth progression across vegetative, reproductive, and grain-filling stages. The system demonstrated stable long-term operation and continuous data acquisition under field conditions. Observed CGDD trends were consistent with reported growth-stage thresholds for the studied rice variety, while measured light intensities ranged from 36,900 to 37,810 lx, relative humidity remained consistently high throughout the season, and air temperatures varied between daily minima of 23.5–25.2 °C and maxima near 35.4 °C, which are suitable for rice photosynthesis and development. The seasonal CGDD increased linearly to 580.3, 1189.9, 1593.7, and 2385.7 °C by the end of June, July, August, and September, respectively, exhibiting a strong linear relationship with days after 1 June 2025 (R² = 0.9999), which confirms stable thermal accumulation throughout the growing season. Full article

Smart suspensions have significantly improved passenger comfort and vehicle stability compared to their passive counterparts. This manuscript explores the integration of artificial intelligence (AI) into hybrid suspension control systems to enhance vehicle stability and ride comfort under conditions where suspended mass changes. A one-quarter-vehicle model is employed to simulate and evaluate the performance of a hybrid control strategy, which combines skyhook and groundhook methods using a dynamic weighting factor ($\alpha$). This investigation considers an everyday situation where the sprung mass of a vehicle changes considerably when passengers enter or exit the automobile, impacting the suspension performance. Reinforcement learning techniques are utilized to optimize $\alpha$, achieving an acceptable balance between passenger comfort and vehicle stability. Simulation results show significant improvements in the dynamic response of the sprung mass compared to traditional passive systems, while keeping vehicle stability. Although improvements in road holding are incremental, simulation effort validates the AI-based hybrid system’s potential for refinement and practical application. Validation in MATLAB-Simulink demonstrates the system’s adaptability to varying road conditions and load distributions. The findings highlight the transformative role of AI in suspension control, paving the way for real-time implementation, advanced algorithms, and integration into full-vehicle models. This study contributes to the ongoing development of intelligent suspension systems toward vehicle performance advancement by improving passenger comfort and road holding. Full article

In energy-harvesting storage systems, in order to guarantee the correct operation and integration of its parts into the system, different power converters must be used. Using several stages increases energy processing and therefore decreases the overall efficiency of the system. In this paper, an integrated multi-port converter with galvanic isolation is proposed. It allows the transfer of energy between the solar panel, the battery, and the user using the fewest possible stages, thus maximizing efficiency. Operating in three modes depending on the battery’s state of charge, solar radiation and load conditions, the converter can conduct electric power between its ports. The proposal was validated in a 1 kW prototype performing the different modes of operation. It should be noted that a PV emulator (ETS150X5.6C-PVF) was used in the experimental setup; by means of this device, conditions such as solar irradiance and temperature, which affect the energy generation of PV panels, were controlled. In addition, the transformer employed in the prototype implementation was handmade; therefore, its design could be improved to obtain better performance. The experimental results show efficiencies exceeding 94%, and an analysis of the distribution of losses in the circuit was carried out. Also, a comparison with previous proposals is presented, showing competitive features. Full article

The construction industry is central to the socio-economic and infrastructural advancement of developing countries; however, it continues to face persistent performance challenges, most notably recurrent cost overruns. While systematic cost risk management is recognized as a critical approach to improving project outcomes, its adoption across the industry remains limited. This study seeks to identify and rank the critical obstacles that hinder contractors from integrating systematic cost risk management into building construction projects. A comprehensive methodology was employed, including an in-depth literature review and three rounds of Delphi. The Relative Importance Index (RII) was used to evaluate the severity of the identified barriers, and Holm-corrected Spearman’s rank correlation analysis was applied to examine the relationships among them. The findings reveal that the most influential barriers include the absence of structured risk management frameworks within organizations, insufficient top management support, the lack of collaborative risk management mechanisms among stakeholders, limited technical knowledge and skills in risk management, and inadequate client support. The strong positive correlations among these barriers highlight their interdependent nature and underscore the systemic challenges facing contractors. This study contributes to the broader field of civil and structural engineering by providing evidence-based insights that can support the development of targeted strategies to enhance cost risk management practices in developing-country construction environments. Full article

The current research first investigates the flow in the fractional order of a vertical artery with atherosclerosis using a Casson-based penta-hybrid nanofluid. Gold (Au), copper (Cu), silver (Ag), magnesium oxide (MgO), and alumina (Al₂O₃) nanoparticles are dispersed in blood to make the hybrid nanofluid. It is assumed that the flow is very pulsatile. The mathematical model is constructed by using differential forms of the conservation laws of mass, momentum, energy, and irreversibility analysis. By applying the mild stenosis approximation, the governing equations are transformed into dimensionless form. To generalize the classical model to its fractional counterpart, the Caputo–Fabrizio fractional derivative (C-FFD) is employed. Closed-form solutions for the velocity and temperature fields are realized by the joint application of the Laplace and Hankel transforms. The impact of essential physical parameters on velocity, temperature, and entropy generation is displayed through figures. The physical significance of enhanced thermal characteristics is shown, emphasizing their potential relevance to thermal regulation, targeted drug delivery, and minimization of irreversible energy losses in biomedical flow systems. The velocity profile elevates with the increase in the Casson parameter, while the temperature drops as the fractional-order parameter rises. Entropy generation is observed to amplify with the increasing values of the thermodynamic parameter in question, whereas an opposite tendency is seen for the Bejan number. The Bejan number decreases as the control parameter becomes higher. The novelty of the present investigation lies in the simultaneous incorporation of Caputo–Fabrizio fractional dynamics, penta-hybrid nanoparticle suspension, and entropy generation analysis in a stenosed arterial configuration. Unlike existing fractional Casson blood flow models that primarily focus on single or hybrid nanofluids, the present framework highlights the synergistic enhancement of thermal transport and irreversibility control achieved through penta-hybrid nanoparticles, which may be relevant for advanced biomedical and targeted therapeutic applications. Full article

For efficient grid operation and energy management, accurate forecasting of solar radiation is essential. The unpredictable nature of weather makes this task challenging to accomplish. Existing forecasting models fail to deliver accurate results under these conditions, which results in decreased operational efficiency for renewable energy systems. We are proposing a novel methodology that combines feature engineering, machine learning, and Bayesian Optimization (BO) to obtain optimal performance. First, time frequency characteristics are extracted using a Fast Fourier Transform (FFT)-based feature engineering approach to capture dominant patterns from meteorological data. The FFT features reveal essential periodic patterns, which describe solar irradiance and its associated variables, enabling models to perform better over different time periods. The model hyperparameter tuning process, which uses Bayesian Optimization, improves prediction results. Model performance is evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². The results show clear improvements across Random Forest (RF), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) models, with the MLP model achieving the strongest overall performance. Specifically, the MLP achieved an R² value of 0.92, with MAE and RMSE values of 1.78 and 2.75, respectively. The proposed method also demonstrates robustness under varying weather conditions and time-series cross-validation (TSCV). Overall, the combined effects of frequency-domain feature engineering and Bayesian Optimization enable robust and adaptive forecasting of solar radiation resources. Full article

This study proposes a novel discrete element modelling (DEM) framework for investigating the fracture behavior of jointed rock masses, with particular emphasis on rock specimens containing a single pre-existing joint. The proposed method explicitly represents joint geometry through constrained mesh generation, enabling accurate simulation of joint-controlled fracture processes without introducing additional joint constitutive models. The reliability of the numerical approach is validated by comparison with laboratory uniaxial compression tests on rock specimens containing a single closed joint. On this basis, the influences of joint dip angle, length and width on the mechanical response and fracture evolution of single-jointed rock specimens are systematically examined. The results demonstrate that the proposed method can effectively capture stress–strain behavior, crack initiation, propagation, and coalescence governed by joint geometry. Owing to its high computational efficiency, straightforward implementation, and accurate geometric representation, the proposed framework shows strong potential for simulating brittle fracture processes in jointed rock masses. Full article

In most AFM nanoindentation experiments on soft biological samples, classical contact mechanics models, such as Hertz or Sneddon’s equations, are commonly employed to determine the Young’s modulus. However, biological materials are inherently heterogeneous, and their mechanical properties often depend on the indentation depth. In this work, we present a novel and simple approach to quantify how the apparent modulus varies with increasing indentation depth. The method is based on the general indentation equation for axisymmetric indenters combined with a straightforward polynomial fitting of the force–indentation data. The proposed approach offers significant advantages, as it greatly simplifies the fitting process without requiring any advanced algorithms, while maintaining high accuracy. In addition, it is shown that the depth-dependent mechanical properties of cells can be described by a simple law, $E\left(h\right)=C_d/h+E_l$ , where $E_l$ is the limiting value of the apparent modulus at large indentations, and $C_d/h$ represents the depth-dependent contribution dominant at the initial stages of the indentation process. Here, $C_d$ is a positive stiffness coefficient, and $h$ is the indentation depth. This is a very important result, indicating that by using the pair of coefficients $C_d$ and $E_l$, we can fully describe the mechanical properties of cells, capturing their depth-dependent mechanical behavior. Experiments on fibroblasts and H4 human glioma cells confirm the accuracy of this equation. The proposed methods provide an accessible and reliable framework for nanoscale mechanical characterization, offering insights into the depth-dependent elasticity of heterogeneous soft materials and revealing mechanical patterns in biological samples. Full article

Vans (light commercial vehicles) account for only about 11% of the European light-duty vehicle fleet. However, they are mostly used in urban delivery and service operations where frequent stop-and-go driving increases tyre abrasion. Furthermore, their annual mileage is on average more than 70% higher than that of passenger cars. For these reasons, vans are estimated to generate tyre wear emissions that are at least 2.5 times higher than those of passenger cars on a per-vehicle basis, and therefore make a disproportionate contribution to microplastic pollution in cities. The Euro 7 pollutant emission standards introduce, for the first time, regulatory limits on tyre abrasion for passenger car tyres (C1 class) from 2028 and for light-commercial-vehicle tyres (C2 class) from 2030, building on United Nations (UN) tyre testing procedures developed under UN Regulation 117. While two candidate test methods (a real-world method and a laboratory method) have been agreed on for C1 tyres, no equivalent standard exists yet for C2 tyres, and very few experimental data have been published so far. In this study, we adapt the C1 real-world-based method to winter C2 tyres (snow three-peak mountain snowflake, 3PMSF) fitted to vans, and we discuss the practical and regulatory challenges encountered. The resulting abrasion rate and abrasion level indices provide first experimental emission factors for C2 tyres and can inform the ongoing development of regulatory test procedures and limit values for van tyres. We also develop an experimental and analytical framework to relate abrasion measurements to tyre service life (mileage potential). Full article

Accurate early-stage assessment of building energy and carbon performance is essential for informed sustainable design yet remains challenging due to limited design detail and simulation effort. This study presents a Building Information Modeling–Machine Learning (BIM-ML) framework for predicting office building energy and carbon performance at early design stages using simulation-based datasets. A reduced-factorial Design of Experiments (DOE) generated 210 parametric office building models for Orlando, Florida (ASHRAE Climate Zone 2A), complemented by additional climate scenarios. Systematic variations in geometry, envelope, building systems, and operational schedules produced a dataset with 14 independent variables and five performance indicators: Energy Use Intensity, Operational Energy, Operational Carbon, Embodied Carbon, and Total Carbon. Four regression methods—Linear Regression, Model Tree (M5P), Sequential Minimal Optimization Regression, and Random Forest—were trained and evaluated using 10-fold cross-validation. Random Forest showed the strongest overall predictive performance. Feature-importance analysis identified HVAC system type, Window-to-Wall Ratio, and operational schedule as the most influential parameters, while geometric factors had lower impact. Cross-climate analysis and validation with measured data from two university office buildings indicate that the framework is adaptable and generalizable, supporting reliable early-stage evaluation of energy and carbon performance. Full article

To comprehensively evaluate the thermal risk parameters of the HMX synthesis process via the acetic anhydride method, we systematically investigated the safety of raw materials, ingredient mixing, nitration, and crystal transformation processes using DSC, ARC, and reaction calorimetry, which enabled the optimization of feeding strategies based on the exothermic characteristics observed during both ingredient mixing and nitration. Results indicate that the decomposition temperatures of raw materials and products are all above 200 °C, showing excellent thermal stability. Thus, multi-batch feeding is preferred for reaction material preparation. For the nitration process, continuous and stable feeding must be guaranteed during the feeding stage. During nitration, the temperature relationship satisfies T_p < MTSR < MTT < T_D24, wherein the risk of secondary decomposition and overflow is low. Additionally, both the nitration filtrate and crystal transformation filtrate exhibit low thermal hazards. These collective findings indicate that the acetic anhydride-based HMX synthesis process maintains relatively safe operational characteristics under standard processing conditions. Full article

This study introduces a hybrid methodological approach for personalised clinical decision support, integrating SHACL-based deterministic constraints with Bayesian probabilistic models. The primary goal is to validate the model and demonstrate the benefits of combining encoded clinical knowledge with probabilistic uncertainties in managing complex therapeutic scenarios. The framework was applied to recurrent urinary tract infections (UTIs) in postmenopausal patients, a clinical context marked by high frequency, treatment challenges, and potential conflicts among therapeutic guidelines. Realistic simulated case studies were developed, encompassing both simple clinical profiles and complex situations, such as patients with antibiotic resistance. Each profile was modelled in RDF/Turtle, enabling semantic representation of clinical features and therapeutic rules. The system automatically calculates success and failure probabilities for different therapeutic scenarios, dynamically adapting them based on follow-up data. This allows clinicians to assess not only the initial therapy choice (Case study no. 1) but also the potential addition of supplementary interventions during treatment (Case study no. 2). Results highlight that the proposed hybrid SHACL–Bayesian framework enables tightly coupled deterministic–probabilistic reasoning, where SHACL constraints define the admissible clinical decisions and Bayesian inference operates within this validated space. Compared to deterministic or probabilistic approaches, the combined framework more effectively handles uncertainty, guideline conflicts, and temporal updates. The scientific contribution lies in showing that this integration enhances decision support for recurrent UTIs in postmenopausal patients, providing clinically consistent, transparent, and adaptive therapeutic recommendations aligned with the patient’s evolving condition. Full article

The objective of this research is to enable the engineered manufacturing of sewn and embroidered e-textiles. It is achieved by conducting sewability assessments of commercially available conductive yarns and providing optimal sewing parameters to ensure electrical performance and mechanical suitability. Our approach includes yarn sampling, measurements, sewing experiments, statistical modeling, and performance tests of sewn sensors. We have scrutinized a range of conductive yarns with different formation mechanisms and electrical conductivities. Highly conductive, flexible, and fine count yarns are of particular interest in this proposed research. The physical properties of selected conductive yarns have been characterized and sewing experiments have been followed to evaluate the machine sewability of these conductive yarns under diverse sewing conditions. Using multiple logistic regressions and machine learning, these empirical observations are generalized and sewability models are established. Full article