Eng, Volume 7, Issue 5 (May 2026) – 63 articles

Concrete compressive strength is critical to structural safety, durability, and material cost. Conventional machine learning models are often limited in capturing complex nonlinear dependencies and generalizing. To address this, a residual fusion framework is proposed that combines a least squares support vector machine (LSSVM) optimized by DE with multi-level residual structure bagged decision trees (TreeBagger) and least squares boosting (LSBoost). DE-tuned LSSVM hyperparameters are followed by a multi-level residual scheme that compensates errors layer by layer, with LSBoost performing adaptive nonlinear fusion. Experiments under varied splits, ablation, and multiple seeds show the model outperforms traditional single and ensemble methods in accuracy, generalization, and stability. The ablation attributes the improvements to complementary residual mechanisms and the fusion architecture, rather than simply adding learners. Across multiple runs, an average coefficient of determination (R²) of 0.9490, a mean absolute error (MAE) of 3.7873 MPa, a root mean square error (RMSE) of 2.4998 MPa, and an R² standard deviation of 0.0029 were obtained, confirming stability. Shapley additive explanations (SHAP) analysis further reveals that age and water–cement parameters dominate, with patterns consistent with hydration and water–binder theory. The proposed framework thus offers high accuracy, physical interpretability, and engineering applicability. Full article

The article is devoted to the study of the properties of the obtained heat-insulating refractory materials, based on fireclay scrap of various fractions (2.5 mm, 1.0 mm, 0.5 mm, and 0.1 mm) using a complex of mineral and oxide additives. The fillers used were titanium dioxide powder and silicon production wastes, which included microsilica powder, aluminum oxide, zinc oxide, zirconium oxide, chromium oxide, iron oxide, cement, lime, and baking soda. The choice of these fillers was due to the fact that they initially have corrosion resistance. Liquid glass acted as a binder. The resulting thermal barrier material was tested to determine its physical and mechanical properties, namely, thermal conductivity, porosity, compressive strength, and microstructure. According to the obtained results for the physical and mechanical properties, the secondary refractory material had properties close to GOST. So, according to GOST 12170-2021, the thermal conductivity values of the obtained materials were included in the 0.03–15.0 W/(m·K) range. The porosity values of the obtained samples complied with GOST 2409-2014 and were not more than 30%. The maximum compressive strength was 171.31 kgf/mm². The microstructure of the material of the obtained samples was very porous, and the pores were evenly distributed throughout the volume, which is extremely important for heat-insulating materials. A distinctive feature of the technology was the absence of a high-temperature firing stage: the required physical and mechanical properties of the material were achieved when heated to 180–300 °C with subsequent slow cooling in the furnace, which significantly reduces energy consumption compared to traditional refractory technologies. The use of waste from the production of chamotte scrap and microsilica will help to reduce negative impacts on the environment, save natural resources, and expand the raw material base. Full article

Ultraviolet (UV) disinfection is widely used in water treatment; however, its effectiveness strongly depends on water optical quality (e.g., turbidity, total dissolved solids, and UV transmittance, UVT). This study investigates an integrated RO–UV scheme in which reverse osmosis (RO) pretreatment improves UVT and thereby increases the effective UV dose available for microbial inactivation. First, UV-only reactor performance is characterized using literature data to fit an intensity-specific dose response relationship. The RO contribution is then incorporated at the process level through a UVT based coupling and evaluated using deterministic low/central/high scenarios (p05/p50/p95) constructed from assumed input ranges. Finally, a multi-objective optimization solved with the Grey Wolf Optimizer (GWO) is used to identify operating conditions that maximize predicted bacterial log-inactivation while limiting a UV-equivalent energy proxy based on nominal UV dose. Across the investigated flow-rate and intensity ranges, RO pretreatment yields a systematic increase in effective dose (median gain $\approx 6.8\%$) and a corresponding improvement in predicted inactivation, with the marginal benefit depending on the dose response regime. Full article

This study establishes an integrated qualification workflow combining mechanical testing, microstructural characterization, and statistically defined eddy current testing (ECT) on the same material heats to provide a coherent and traceable material qualification methodology. Forged 316 and rolled 304L were fully annealed and subsequently subjected to a 700 °C/1 h low-temperature stress-relief (recovery) treatment. Room-temperature tensile testing and Charpy impact testing at room and cryogenic temperatures were performed alongside optical and electron microscopy to quantify grain size, δ-ferrite content, and representative fracture morphology under the investigated conditions. ECT responses were evaluated using a statistically defined threshold (T = μ + 3σ) as a decision criterion for indication screening under assumed noise conditions and calibrated near-surface inspection sensitivity. The tested specimens showed stable measured mechanical responses, the examined fracture surfaces were consistent with predominantly ductile fracture behavior, and no reportable ECT indications were observed above the adopted threshold. The proposed framework provides a reproducible and scalable strategy for reducing uncertainty in material qualification and strengthening integration between destructive and non-destructive evaluation in stainless steel applications. Full article

The wettability of dust is fundamental to its dispersion and control in mining operations. Current research, however, focuses largely on isolated properties, leaving the synergistic mechanisms of multi-scale factors-such as particle size, morphology, and surface chemistry-poorly understood. This study integrates field measurements, laboratory characterization, and theoretical analysis to investigate the spatial distribution and wetting behavior of dust in fully mechanized mining faces. The results show that respirable dust preferentially accumulated in mechanically disturbed and personnel-exposure zones. At the shearer operator’s station, respirable dust concentrations reached 328.6 mg/m³ in Mine A and 278.4 mg/m³ in Mine B, which were 1.8 and 1.6 times higher than those at the shearer cutting point, respectively. Mine A dust also showed poorer wettability, with a higher water contact angle of 148.9° ± 2.1° compared with 134.7° ± 1.8° for Mine B, mainly due to its larger agglomerates, rougher surface morphology, and more hydrophobic surface chemistry. Accordingly, targeted development pathways for spray and foam technologies are outlined, including compound wetting agents and micro-nano enhanced foaming systems. The integrated multi-scale framework linking concentration, particle size, morphology, surface chemistry, and wettability provide an application-oriented basis for understanding coal mine dust behavior and for supporting more precise and intelligent dust-control strategies. Full article

The sustainable management of dredged sediments poses a major environmental and economic challenge, particularly in Morocco, where large quantities are annually discarded as waste. Contributing to resource efficiency and circular economy objectives, this study represents the first systematic application research of Moroccan Mehdia Harbor sediments in mortar formulations. Three substitution strategies were investigated at substitution rates of 5–30%: (i) replacement of cement with fine sediments (series MA); (ii) replacement of sand with intermediate sediments (series MB); (iii) replacement of sand with sandy sediments (series MC). Mechanical testing at 28 days showed that both compressive and flexural strengths remained comparable to the reference mortar for substitution levels up to 10–15%, depending on sediment type. Beyond these limits, a marked strength reduction was observed, particularly for cement replacement with fine, clay-rich sediments. Mortars incorporating sandy sediments (MC) exhibited the best performance, maintaining over 80% of the reference compressive strength up to 15%. Leaching tests confirmed the environmental stability of all formulations, which remained within the “inert” waste classification up to 15% substitution. These findings demonstrate that dredged sediment incorporation in mortar is both technically and environmentally feasible for non-structural applications, promoting sustainable materials within a circular economy framework. Full article

The electrification of field service fleets introduces complex constraints: shift limits, overtime fairness, and battery–range feasibility. This paper proposes the Multi-Shift Single Electric Vehicle Routing Problem under Possibilistic Uncertainty (MS-SEVRP-PU), a formulation focused on a single-vehicle multi-shift planning unit and capturing imprecise travel/service times and state-of-charge dynamics. Travel durations and energy consumption are modelled as triangular fuzzy numbers to reflect expert knowledge when probabilistic data is limited. A closed-form credibility function evaluates overtime risk, while an Ordered Weighted Averaging (OWA) aggregation of per-shift risks ensures fairness by discouraging systematic overload on specific shifts. To solve this multi-objective problem, we develop a Pareto-Conditioned Transformer with risk-aware and battery-conscious large neighbourhood search (PCT-RABLNS), combining a preference-conditioned attention policy with targeted local search. Computational experiments on calibrated municipal maintenance case studies indicate that PCT-RABLNS improves hypervolume by 2–5% over strong baselines and reduces maximum shift overtime risk by 15–25%, with a marginal makespan overhead of only 1–3%. The results demonstrate that the proposed framework is a promising decision-support approach for energy-aware, risk-fair, and operationally compliant planning of single-vehicle, multi-shift electric service operations, jointly integrating multi-shift routing, fuzzy uncertainty, and preference-conditioned reinforcement learning. The paper also discusses how the framework can be extended to multi-vehicle settings. Full article

Reliable potato quality monitoring during postharvest handling requires compact sensing systems that can acquire chemically relevant information while operating on irregular tuber surfaces. In this study, a Flexible Spectral Sensing Gripper (FSSG) was developed by integrating a low-cost 12-channel visible/near-infrared (Vis/NIR) spectral sensor array, electronic components, and an ESP32-S microcontroller onto a flexible printed circuit (FPC) substrate encapsulated with PDMS. By embedding the sensing units into the grasping interface, the FSSG enables conformal, multi-point spectral acquisition during potato handling, reducing optical-coupling uncertainty associated with unstable contact. Spectral reflectance data were collected from potato tubers, and dry matter content (DMC) and starch content (SC) were determined by standard chemical analysis as reference values. Multiple linear regression (MLR) and partial least squares regression (PLSR) models were compared under Norm, SNV, MSC, SNV-Norm, and MSC-Norm preprocessing conditions, and support vector machine (SVM) classification was used to distinguish healthy and artificially induced deteriorated samples. Normalization combined with MLR provided the best performance among the evaluated regression approaches, achieving cross-validation coefficients of determination ($R_{CV}^2$) of 0.847 and 0.817 and RPD values of 2.557 and 2.345 for DMC and SC, respectively. The SVM model achieved 98.67% accuracy for healthy versus artificially induced deteriorated potato samples. Overall, the FSSG demonstrates the value of combining gripper-integrated spectral sensing with interpretable chemometric modeling for potato quality screening. The FSSG enables real-time non-destructive quality prediction and disease-detected classification of potatoes, improves sorting accuracy and production efficiency, and provides general sensing solutions for controlled-environment agriculture, cold-chain logistics, and value-added processing of agricultural products. Full article

Linear assets (LAs), such as conveyor systems, road networks, pipelines, and power transmission lines, are typically maintained through localized, segment-based interventions. While such approaches effectively address spatially heterogeneous degradation, they often neglect the system-level consequences of repeated local actions. In particular, improvements in segment condition may be accompanied by increased structural complexity, leading to reduced reliability and higher lifecycle costs. This paper proposes a unified engineering framework that integrates segment-level condition assessment with system-level structural effects. The framework is based on a dual representation of asset condition, distinguishing between material state (MS) and structural state (SS), which correspond to material aging (MA) and structural aging (SA), respectively. A key contribution is the introduction of the fragmentation penalty (FP), capturing the negative impact of increasing segmentation and interface density on system performance. The framework incorporates multi-threshold decision logic, enabling differentiation between operational, refurbishment, and replacement regimes, and interprets maintenance actions as transformations affecting both condition and structure. A formal model is developed to represent the asset as a dynamic system of segments and interfaces. It provides a basis for future empirical calibration and structure-aware optimization. Although the model is developed using conveyor belt loops as a reference case, its broader relevance is discussed for other classes of linear assets with repeated local intervention and evolving structural heterogeneity. A simple worked example is included to demonstrate the operational meaning of the proposed fragmentation-aware perspective. The results show that maintenance decisions may change when structural side effects are considered together with local condition improvement, and they provide a basis for future empirical calibration and structure-aware optimization of maintenance strategies. Full article

Heat generated during photopolymerization of resin-based composites from both the exothermic reaction of the material and the irradiance of light-curing units poses a risk to pulp vitality, especially in deep restorations. This study aimed to evaluate temperature variation (ΔT) during the photopolymerization of different resin composites, considering material type, shade, increment thickness, and light-curing unit output. An in vitro experimental study with a factorial design was conducted. Specimens were prepared using 2.0 mm and 4.0 mm increments from conventional (nanohybrid), bulk-fill, and flowable resin composites in different shades (BW, A1, A3, A4, and XB) and different light-curing unit output (100% and 50% battery charge). ΔT was measured using a type K thermocouple (Omega Engineering, Norwalk, CT, USA) positioned at the center of each increment. Data were analyzed using four-way analysis of variance (ANOVA) (α = 0.05). All groups demonstrated a statistically significant temperature increase (p < 0.05), with ΔT values ranging from 3.24 °C to 18.18 °C. Composite type significantly influenced ΔT (p < 0.001), with flowable composites showing the highest temperature rise, followed by bulk-fill and conventional composites. Increment thickness also had a significant effect (p = 0.008), with 4.0 mm increments producing greater temperature increases. Shade significantly affected ΔT (p < 0.001), with the XB shade exhibiting the highest values. Additionally, higher light-curing output (100%) resulted in significantly greater temperature increases compared to 50% output (p < 0.001). Photopolymerization temperature rise is influenced by multiple interacting factors. The combination of flowable composites, darker shades, thicker increments, and higher curing output may increase thermal risk. These findings should be considered when optimizing clinical protocols to minimize potential pulpal damage. Full article

Supercapacitors offer high power density; however, improving their energy density requires enlarging the active surface area and optimizing ion transport pathways. In this study, a Ti₃C₂T_x MXene@ZnO composite electrode was fabricated to suppress the restacking of MXene layers and enhance the specific surface area. Ti₃C₂T_x MXene was synthesized, followed by ZnO incorporation using a simple precipitation process. The introduction of ZnO effectively stabilized the layered MXene structure and promoted pore formation. BET analysis revealed that the composite synthesized for 2 h exhibited the largest specific surface area of 43.639 m² g⁻¹, indicating the most effective pore structure development. Electrochemical evaluation as a supercapacitor electrode demonstrated that the 2 h composite achieved the highest specific capacitance of 139.0 F g⁻¹ and the longest discharge time of 172.6 s. These improvements are attributed to the expanded pore structure and increased electrochemically active surface area induced by ZnO incorporation. Overall, the Ti₃C₂T_x MXene@ZnO composite exhibits enhanced structural stability and ion transport properties, demonstrating its strong potential and stable electrode material for advanced energy storage applications. Full article

Prestress loss and bi-directional prestress effects are critical design parameters that determine the bearing capacity of large-span U-shaped aqueducts. Based on a 42 m span simply supported U-shaped aqueduct, the pipeline friction coefficients were tested through least-squares fitting and validated against a finite element analysis model. The results revealed pipeline friction induced 4.82–5.08% longitudinal and 35.84–39.23% circumferential prestress loss, with 12-month post-tensioning monitoring showing 9.84% (longitudinal) and 3.15% (circumferential) long-term loss. Maximum concrete compressive stresses reached 5.83 MPa (inner wall) and 7.14 MPa (outer wall) under empty groove conditions. Six prestress tensioning sequences were numerically compared to identify the optimal “both ends to center” circumferential tensioning scheme. The prestressed tendon layout was optimized by increasing circumferential tendon spacing from 40 cm to 60 cm while maintaining global compression. This research provides a systematic framework for prestress optimization in curved concrete structures. Full article

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder for which delayed recognition may limit timely clinical management. This study investigates a reproducible computer-aided screening approach based on facial motion analysis from standard RGB video recorded during the diadochokinetic /pataka/ task. Facial landmarks were extracted using a face-mesh model and mapped into spherical coordinates to represent facial motion trajectories. Coordinated facial behavior was characterized through pairwise Pearson correlation matrices computed between landmark trajectories, yielding correlation-based descriptors of inter-region motion patterns. We compared a domain-informed Manual-24 reference configuration with data-driven feature-selection strategies (ElasticNet and mRMR) under a leakage-aware nested cross-validation design using the Toronto NeuroFace dataset. Performance was reported as mean ± standard deviation across outer folds, with sensitivity emphasized because of its relevance for screening-oriented applications. The primary configuration (mRMR, $k=3$, $\varphi$ + kNN) achieved 61.11 ± 19.24% accuracy, 61.11 ± 9.62% sensitivity, and 61.11 ± 34.70% specificity. These results suggest that correlation-derived coordination patterns contain discriminative information for ALS/HC separation, although fold-level variability indicates that performance should be interpreted cautiously. Task-aligned comparisons with prior /pataka/-based studies highlight the influence of sensing modality, evaluation level, and uncertainty reporting on apparent performance. Overall, correlation-based facial motion descriptors combined with leakage-aware feature selection provide a transparent proof-of-concept framework for RGB video-based ALS screening, motivating validation on larger cohorts and independent datasets. Full article

This research utilizes stereolithography (SLA) technology to analyze the mechanical properties of the fabricated parts. SLA operates by precisely hardening liquid resin layer by layer with a focused ultraviolet (UV) light, enabling the creation of precise shapes and intricate details. Plant-based resins are becoming increasingly popular as alternatives to conventional polymer resins. However, the mechanical performance of SLA-printed parts made from bio-based materials can vary significantly depending on the printing parameters. To achieve acceptable performance, the optimization of the printing parameters is crucial. This study investigates the impact of print parameters on the mechanical and morphological characteristics through the use of L27 Taguchi’s orthogonal array. For this purpose, a combination of the most influential controlled parameters, including layer thickness, exposure time, bottom layer count, bottom exposure time, lifting distance, lifting speed, and print orientation, was assessed. The mechanical properties of the samples were evaluated after washing and UV curing. The optimal parameter combination was identified using the signal-to-noise (S/N) ratio, and analysis of variance (ANOVA) identified the significant parameters affecting the mechanical properties. The findings confirmed by the morphology analysis revealed that layer thickness, followed by bottom exposure time and exposure time, strongly influenced interlayer bonding and mechanical performance. Full article

The quality of electrical power on distribution networks depends heavily on the performance of the harmonic filtering method implemented according to the operating conditions of the system under study. This article proposes a new experimental approach that allows us to determine the dynamic behavior of the harmonic signature of the nonlinear load connected to the electrical network. The ultimate goal is to propose a new law for extracting the reference currents required during the overall online harmonic filtering process using a hybrid filter. This offers advantages in robustness and accuracy during variations in the electrical network compared to the classical methods used in the current literature. The methodological approach consists of selecting several nonlinear loads according to specific profiles and technical characteristics, then experimentally analyzing their harmonic signatures over time to obtain new models for extracting reference currents that will ultimately be faster to implement. In a decentralized global harmonic filtering strategy using a TLC adaptive hybrid filter compliant with the IEEE-519-2022 standard, the results obtained offer THD (total harmonic distortion) values of 1.88%, 3.29%, and 2.78% in three-phase currents, with a reduced DC voltage consumption of 105 V for the inverter. This contrasts with similar models in the current literature, which require input DC voltages exceeding 850 V for identical performance. This work therefore represents a major contribution to new models for extracting experimentally obtained reference currents, enabling the optimization of power quality on electrical networks through the use of a hybrid filter. Full article

Biomechanics sits at the interface of engineering and medical sciences, offering essential insight into how tissues, organs, and biological systems respond to mechanical loading. This review brings together recent advances in musculoskeletal and cardiovascular biomechanics, illustrating how experimental techniques, computational modeling, and multiscale analysis are used to characterize load transfer, tissue deformation, fatigue, and injury mechanisms. In musculoskeletal applications, predictive simulations, wearable sensing technologies, and neuromechanical assessment tools support improved injury prevention, rehabilitation planning, and assistive device development. In the cardiovascular domain, patient-specific modeling, fluid–structure interaction analyses, and advanced imaging approaches clarify how hemodynamics, vessel wall mechanics, and device–tissue interactions influence disease progression, implant performance, and therapeutic outcomes. Emerging technologies including artificial intelligence, machine learning, digital twin frameworks, biofabrication, soft robotics, and self-powered sensing are enabling data-driven, real-time, and personalized interventions that connect mechanistic understanding with clinical practice. Despite these advances, challenges remain in accounting for individual variability, integrating multiscale data, and translating computational predictions into clinically validated solutions. By emphasizing interdisciplinary strategies that unite biomechanics, computational analytics, and innovative device engineering, this review outlines a pathway toward predictive, patient-centered healthcare and next-generation therapeutic and rehabilitation solutions. Full article

Lead-free perovskite solar cells have become promising materials in the solar energy field; however, there are some constraints limiting their efficiency, like unfavorable band alignment, high defect densities, and inefficient charge extraction. Cs₃Sb₂I₉ is a lead-free material that has excellent stability, but its experimentally reported efficiencies remain low (<4%). Therefore, Cs₃Sb₂I₉ device performance was investigated using the one-dimensional Solar Cell Capacitance Simulator (SCAPS-1D), where the planar n–i–p structure was analyzed, focusing on its band alignment, transport layers, and key device parameters. The optimized device achieved a power conversion efficiency (PCE) of 13.62%, an open circuit voltage (Voc) of 1.37 V, a short circuit current density (Jsc) of 11.77 mA/cm², and a fill factor (FF) of 84.15% with a 180 nm PCBM electron transport layer, a 150 nm Cu₂O hole transport layer, and a 500 nm absorber thickness. This study advances the development of efficient lead-free perovskite solar cells, promoting sustainable and clean energy. Full article

Landslides are widespread and highly destructive geological hazards that pose serious threats to infrastructure and densely populated areas. Conducting scientific and accurate predictions of landslide spatial distribution is therefore of great practical importance for supporting landslide prevention, risk management, and the reduction in casualties and economic losses. Landslides are driven by multiple variables, including elevation, road distance, river distance, slope and land use, with complex nonlinear interactions that traditional linear models cannot accurately capture. This study adopts a Categorical Boosting model (CatBoost) as the base prediction model, which demonstrates strong performance in capturing interactions among multiple variables and achieves relatively robust landslide spatial distribution predictions without complex feature engineering. However, CatBoost is highly sensitive to hyperparameters and difficult to manually optimize. Based on the Nutcracker Optimization Algorithm (NOA), which features an efficient search strategy, a multi-level improved Nutcracker Optimization Algorithm (COLNOA) is proposed to optimize its hyperparameters. The proposed algorithm integrates Circle Chaotic Mapping into the initial population construction of the NOA to generate two distinct populations and enables information exchange between them during the evolutionary process, thereby enhancing global search capability. In addition, Opposition-Based Learning and lateral mutation strategies are introduced to update inferior individuals in each iteration, improving their search capability. Based on these improvements, a COLNOA-CatBoost prediction model is developed. The proposed model is applied to a case study in Wanzhou District, Chongqing, China. The results show that the proposed model achieves a recall of 0.863, an F1-score of 0.860, and an accuracy of 0.865, outperforming baseline models such as decision trees. Compared with the original CatBoost model, recall, F1-score, and accuracy are improved by 34.8%, 35.0%, and 35.1%, respectively. The spatial prediction results indicate that high-risk landslide areas in Wanzhou District are mainly concentrated in regions such as Zouma Town, medium-risk areas in Xintian Town, low-risk areas in Fenshui Town, and very low-risk areas in Longju Town. Further analysis of terrain and landforms indicates that the high-risk areas for landslides in Wanzhou District are mainly related to steep slopes, deep river valleys, exposed or cut slopes at the foot of the slope, runoff convergence, and road excavation slopes. The extremely low and low-risk areas are mostly distributed in the middle and low mountain and hilly areas with relatively flat terrain, weak river cutting and engineering disturbance. This is consistent with the previous correlation analysis that the number of landslides increases with increasing slope and decreases with increasing elevation, distance from rivers, and distance from roads. Overall, the proposed model provides an effective approach for landslide spatial distribution prediction. Full article

This study investigates the use of mechanical grinding to activate coal bottom ash (CBA) as a low-carbon supplementary cementitious material. Two CBA powders with different fineness levels (75 μm and 45 μm, denoted as SCBA and GCBA) were used to replace 10–50% of cement in mortar specimens. Performance was evaluated through ISO-standard strength tests and the activity index, while micro-analytical techniques characterized the hydration mechanism. The results show that this grinding treatment significantly enhanced pozzolanic activity; at 28 days, the compressive strength of the mixture with 30% GCBA replacement reached 30.6 MPa, which was 44% higher than that of the corresponding SCBA mixture (21.2 MPa). Microstructural analysis confirmed the consumption of portlandite (CH) and the predominant formation of interwoven C-S-H gels and ettringite (AFt), along with residual quartz, calcite, and mullite. These products refine the pore structure and densify the interfacial transition zone. Economic and environmental analysis reveals that CBA substitution reduces raw material costs by 120 CNY/ton and carbon emissions by approximately 261.3 kg CO₂/t. Based on the balance of mechanical integrity and environmental benefits, mechanical grinding of CBA to 45 μm at a 30% cement replacement level is proposed as a promising approach for producing low-carbon cementitious materials and for future application in green concrete. Full article

Ensuring fairness in machine learning while maintaining predictive performance remains a fundamental challenge in data science. Most fairness-aware learning approaches rely on fixed penalty scalarization or static multi-objective formulations, which often lead to unstable trade-offs and sensitivity to manually tuned hyperparameters. In this paper, we propose SAFEA (Self-Adaptive Fairness Entropy Algorithm), a novel evolutionary optimization framework that dynamically regulates the fairness–accuracy trade-off using inequality-aware feedback mechanisms. SAFEA introduces two complementary measures: the Fairness Entropy Index (FEI), which captures the dispersion of group-level fairness violations, and the Gini Fairness Index, which quantifies disparity in prediction errors across protected groups. These measures guide an adaptive penalty update rule that autonomously adjusts the fairness coefficient during the evolutionary search process, eliminating the need for manual tuning. Theoretical analysis establishes boundedness and stability of the adaptive penalty under mild assumptions and discusses convergence properties under Lipschitz-continuous objectives. Experimental evaluation on benchmark datasets (Adult Income, COMPAS, and German Credit) demonstrates that SAFEA improves hypervolume by up to 12.4% compared to NSGA-II fairness formulations, reduces demographic parity difference by 18–25% relative to static penalty evolutionary methods, and achieves up to 3.1% higher F1-score than adversarial debiasing approaches while maintaining competitive accuracy. These results indicate that entropy-guided adaptive regulation leads to smoother fairness convergence and better Pareto front coverage. The proposed framework bridges inequality theory and evolutionary multi-objective optimization, providing a scalable and effective solution for fairness-aware learning in high-stakes applications. Full article

Mechanical bar screens serve as the initial treatment stage used to catch insoluble and coarse debris from wastewater flow. They are essential equipment that ensures the protection and efficient operation of downstream facilities. Different parameters affect the performance of bar screens, including particle size, flow hydraulics, and screen design. Most previous studies have primarily focused on bar screens with rectangular bars and single-phase flow. However, investigating different bar shapes with the presence of waste debris as a second phase is crucial for achieving the optimal design and accurately predicting a bar screen’s efficiency. Therefore, four bar cross-section shapes were examined using 3D simulations of two-phase flow (water and particles). The discrete phase model (DPM) in ANSYS Fluent CFD software was used to represent waste particles in a Lagrangian framework and to evaluate their retention efficiency. The numerical results, validated by a previous study of a wastewater bar screen, indicate that the traditional rectangular bar shape traps a higher rate of debris but results in higher pressure losses. Alternative bar shapes, such as rounded, streamlined, and teardrop cross-sections, have been studied for design improvements. The improved teardrop shape presents significant effectiveness, offering a better balance between pressure loss reduction and enhanced particle separation efficiency. Based on this study, further investigations coupling CFD techniques with the particle tracking method can be carried out for the optimal design of other filtration equipment. Full article

The possibility of securing textual image data sharing exponentially strengthens when it harnesses the potential of cryptography as well as deep learning methods. A review of the existing literature showcases some interesting and productive initiatives; however, they are noted with issues, viz., increased reconstruction error, weak generation of pseudorandom keys, static threshold-based validation, etc. All these issues lead to suboptimal data integrity as well as confidentiality, which is a leading gap in research on neural optimised-based solutions. Therefore, the proposed system introduces an innovative Twister Pseudo Random and Barzilai–Borwein Gradient Autoencoder Neural Network (TPR-BBGAN) for secure textual image data sharing. The model introduces various novel operations, viz., feature extraction using fuzzy batch-normalised preprocessing, key extraction using the Barzilai–Borwein method, an autoencoder, and Mersenne Twister. The TPR-BBGAN determines the optimal threshold dynamically, contributing to a reduction in the reconstruction error while convergence performance is boosted. The experimental outcome shows that the TPR-BBGAN achieves a 12–20% enhancement in data confidentiality, a 6–17% enhancement in data integrity, a 30–46% reduction in bit-error rate, and a 6–20% increase in the Peak Signal-to-Noise Ratio (PSNR) in contrast to existing models. Full article

The growing use of electric vehicles (EVs) requires fast charging solutions capable of delivering high power levels with greater efficiency and less impact on the power grid. This article presents the design and simulation of a Level 3 fast direct current (DC) charger for electric vehicles based on an LLC resonant DC-DC converter. The proposed architecture incorporates an isolated LLC resonant converter, selected for its soft switching capability, low switching losses, and reduced electromagnetic interference (EMI). The main contribution of this work is the design and simulation of a 50 kW LLC resonant converter developed specifically for a Level 3 DC fast charger for electric vehicles, a power level that, to the authors’ knowledge, has not been previously described in the current scientific literature using this topology. For the proposed converter, it has been proposed to use commercially available wide bandgap (WBG) semiconductor devices specifically made of silicon carbide (SiC). This allows for high switching frequency operation, lower conduction and switching losses, and higher power density. The key design parameters, component selection, and operating principles are analyzed in detail. Simulation results demonstrate high conversion efficiency, reduced switching stress, and stable operation under fast charging conditions, validating the suitability of the LLC topology for high-power electric vehicle charging applications. The proposed system offers a scalable and efficient solution that can contribute to the development of compact, grid-compatible DC fast charging stations, supporting the growing demand for electromobility infrastructure. Full article

This study presents the design of a model-free control scheme for the load-following operation of a Pressurized Heavy Water Reactor (PHWR), which is a 70th-order Multi-Input Multi-Output, strongly coupled and open-loop unstable energy-generating system. To design a model-free controller for a PHWR, we propose a novel cross-coupled conditional error surface and a modified funnel algorithm-based funnel control scheme. The proposed control scheme reduces the effects of inter-zonal coupling and improves the steady-state accuracy without degrading the transient performance. The proposed control scheme is of low complexity and does not require system dynamics for controller design. A closed-loop stability analysis is carried out to ensure the boundedness of the closed-loop system trajectories. Furthermore, the proposed control scheme is evaluated by simulating different scenarios, which demonstrate its effectiveness. Full article

Manufacturing high-performance turbomachinery blades remains one of the most demanding challenges in aerospace and energy engineering, requiring tight control over microstructure, geometry, and cooling architectures. Despite rapid progress in casting, machining, and additive manufacturing, the field lacks a structured classification that links process capabilities with blade functional requirements and future design trends. This review addresses that gap by introducing a new classification scheme for turbomachinery blade manufacturing technologies, organized into three complementary domains: (i) foundational fabrication routes (casting, forging, precision machining); (ii) advanced and hybrid processes (powder-bed fusion, directed-energy deposition, additive–subtractive systems, laser repair); and (iii) digital and intelligent manufacturing enablers (in situ monitoring, AI-driven process control, digital twins, and automated inspection). Within each class, the review maps process parameters to resulting structural performance, defect modes, cost drivers, and certification challenges. Special emphasis is placed on the manufacturing implications of emerging blade architectures, such as intricate internal cooling channels, gradient materials, and bio-inspired aerodynamic profiles. By consolidating disparate techniques into a structured taxonomy, this paper clarifies current limitations, identifies cross-technology synergies, and outlines priority research directions for achieving next-generation turbomachinery blade manufacturing. Full article

The accurate temperature forecasting system provides essential benefits for managing outdoor activities, controlling electricity consumption, and ensuring public health and safety in areas with extreme heat. The researchers developed a hybrid Long Short-Term Memory–Convolutional Neural Network (LSTM–CNN) model that uses daily time-series data from Makkah, Saudi Arabia, to enhance short-term temperature prediction results. The forecasting task is defined as daily multi-step prediction, generating 1-day, 3-day, and 6-day ahead temperature forecasts. The proposed model combines LSTM networks to capture long-term temporal dependencies and CNN to extract short-term variations. The system uses temporal features, lag features, and rolling statistical features to improve data representation, while Genetic Algorithm (GA) optimization handles the selection of model hyperparameters. The framework uses ten-fold cross-validation to test its performance, ensuring consistent performance across all testing scenarios. The results demonstrate strong predictive accuracy, with the GA-optimized model achieving a Mean Absolute Error (MAE) of 0.55 °C for 1-day forecasts and 1.28 °C for 6-day forecasts, with R² values reaching up to 0.98. The proposed model outperformed Autoregressive Integrated Moving Average (ARIMA), LSTM, and Transformer models during benchmark tests, providing better forecasting results across various time intervals. These findings indicate that the proposed model demonstrates accurate and reliable temperature forecasting performance for arid to semi-arid climatic conditions. Full article

This study presents the development of predictive models for the mechanical properties of a cement-stabilized base layer incorporating waste rubber using artificial neural networks. The considered input parameters included ultrasonic pulse velocity (UPV), compressive strength (f_c), rubber content (mass %), cement content (mass %) and curing duration (days). The models were employed to predict indirect tensile strength (f_t) and the static modulus of elasticity (E_st). A total of ten neural network models were developed and systematically evaluated. The results indicate that UPV is a highly relevant parameter, as its importance remains constant across all models (0.439–0.497 for f_t and 0.167–0.225 for E_st prediction), reflecting its stable contribution to predictions, while curing duration exerts a particularly significant effect on E_st (0.236–0.621). The significance of the remaining input parameters varies depending on their combination within each model, highlighting the critical role of selecting an appropriate set of input variables. Statistical analysis demonstrates that all models exhibit a high level of reliability, confirming the suitability of neural networks for accurately predicting the mechanical behaviour of cement-based materials containing waste rubber. High need for standardisation of such models is highlighted. Full article

Ultra-deep marine carbonate cavity reservoirs in Northwest China are characterized by strong heterogeneity and complex geometries. Conventional seismic inversion methods generate over-smoothed results, blur geological boundaries, and suffer from severe non-uniqueness, making it difficult to accurately identify low-impedance cavity anomalies. To tackle these problems, this study develops a shape-constrained multi-trace seismic inversion method based on the Mumford–Shah functional. To meet the computational requirements, a multi-trace inversion framework is adopted, and the Ambrosio–Tortorelli approximation is introduced to convert the non-smooth Mumford–Shah functional into a solvable smooth form. The proposed method realizes the joint inversion of acoustic impedance and geological interfaces, implicitly encourages piecewise constant regions and preserves sharp geological boundaries, and effectively mitigates inversion non-uniqueness. Numerical experiments and field applications validate that the method delivers high lateral resolution and boundary accuracy even with limited prior information and reliably delineates discrete “string-bead” cavity geometries with high consistency to drilling and logging data, providing a robust solution for fine characterization of complex carbonate cavity reservoirs. Full article

Hidden waterlogging of subsurface soils may develop without clear external signs, while still deteriorating the hydro-physical state of foundation soils. This proof-of-concept study demonstrates a compact monitoring and interpretation workflow for identifying such zones through moisture profiling and subsequent engineering interpretation using the liquidity index (${\mathrm{I}}_{\mathrm{L}}$) for cohesive soils and the saturation ratio (${\mathrm{S}}_{\mathrm{r}}$) for non-cohesive soils. The developed prototype comprises a modular immersion probe, Arduino-based transmitter and receiver units, 433 MHz ASK wireless communication, and data logging. Using geotechnical survey data from a representative site in Astana, a baseline hydro-physical state and an intentionally constructed synthetic risk-waterlogging scenario were analyzed through vertical profiles and horizontal interpolation maps. Under the baseline state, moisture content varied mainly from about 6 to 23%, while most ${\mathrm{I}}_{\mathrm{L}}$ and ${\mathrm{S}}_{\mathrm{r}}$ values remained within the normal zone. In the synthetic scenario, the response was much stronger in cohesive soils, where ${\mathrm{I}}_{\mathrm{L}}$ increased from about −0.55 to 1.8, whereas ${\mathrm{S}}_{\mathrm{r}}$ in non-cohesive soils changed only slightly. The Welch’s t-test indicated significant scenario-related changes for ${\mathrm{I}}_{\mathrm{L}}$ (p-value of 1.095 × 10⁻¹⁹) but not for ${\mathrm{S}}_{\mathrm{r}}$ (p-value of 0.147). The results show the methodological potential of the proposed workflow for engineeringly interpretable zoning of hidden waterlogging; however, site-specific calibration, metrological characterization, and field validation are still required before practical deployment. Full article