Eng

Based on the global geospatial liquefaction model, this study adopts updated datasets of 30 m depth-averaged shear wave velocity ($V_{S30}$) and groundwater table depth ($wtd$) for the 2008 Wenchuan earthquake. Two models are then established: the local geospatial liquefaction model (LGLM), based solely on regional data, and the Bayesian-updated geospatial liquefaction model (BGLM), using Bayesian recalibration with global prior information and regional updated data. Model performance is evaluated using stratified five-fold cross-validation and spatial blocked cross-validation. The results show that integrating regional recalibration into the Bayesian framework effectively balances global prior information and regional data characteristics, reduces the global model’s systematic biases in regional applications, and provides accurate, stable, and regionally adaptable predictions within the study area. The Bayesian framework quantifies predictive uncertainty and characterizes performance variability. The uncertainty information obtained is more comprehensive and informative than the single-point AUC metric. Key variables for liquefaction prediction can be identified through Bayesian posterior distribution analysis. Regional updated data optimizes parameter estimation and enhances the regional consistency and interpretability of parameters for the Wenchuan earthquake case, providing supportive information for local engineering risk analysis and preliminary assessment. Full article

The direct-quadrature (dq) axis control method is a widely employed approach for off-grid and grid-connected inverters in solar photovoltaic (PV) systems that can regulate active and reactive power control. Conventional fixed-gain dq-axis PI controllers may exhibit degraded transient performance and reduced harmonic suppression capability under highly dynamic operating conditions. This article proposes an innovative control scheme of an inverter-based islanded microgrid consisting of PV generation and battery energy storage systems (BESS) that can deliver stable power sharing and robust voltage regulation even under highly dynamic operating conditions. An improved inverter control method based on an artificial neural network-based proportional integral (ANN-PI) controller is investigated to accurately control the dq-axis approach for the DC-link and voltage control loops. The suggested system was validated under MATLAB/Simulink to prove the effectiveness of the proposed controller. The achieved results indicate that the ANN-PI controller presents a high convergence speed and low overshoot with a low total harmonic distortion (THD) index of 3.9% under resistive and inductive loads, thus meeting the IEEE power quality standards. Full article

Occupational safety is increasingly addressed through engineering-based, proactive risk management approaches that emphasize early identification and mitigation of hazards within industrial systems. In manufacturing environments, the analysis of minor process deviations, near misses, and low-impact operational failures provides critical insights for improving system reliability and safety performance. This paper proposes an integrated engineering framework that combines the 5M model (Environment, Man, Method, Material, Machine) with Source–Detection Matrix analysis to support structured identification, classification, and control of safety-related process deviations. The approach enables systematic root-cause analysis by categorizing contributing factors according to the 5M model, followed by mapping each deviation based on its origin and point of detection within production processes. The methodology was validated through case studies conducted in cosmetics and perfumery manufacturing, involving process-related hazards such as electrical failures and exposure to volatile substances. Validation was also supported by operational data collected over two 6-month periods before (n₁) and after implementation (n₂), based on incident reports, near-miss records, nonconformity reports, and internal audit data (n₁ = 128; n₂ = 95). Quantitative results show improved safety performance, including an increase in Detection at Source Rate from 42% to 74% and a reduction in Minor Incident Frequency from 11 to 5 cases/month. The results demonstrate that integrating causal analysis with detection mapping enhances early-stage identification of process deviations, effectively limiting failure propagation across operational stages and improving overall process safety performance. The proposed framework provides a practical and data-driven tool for improving process reliability, operational safety, and continuous improvement in complex manufacturing environments. Full article

In fecal sludges (FSs) from non-sewered sanitation systems, bound moisture constituted 46–67% of total moisture across all sanitation types investigated, yet the energetic basis for its resistance to removal has not previously been characterized. Existing classifications of moisture fractions lack quantitative binding energy data, leaving the thermodynamic limits of solid–liquid separation undefined for FS. This study investigates the distribution and binding energies of bound moisture fractions in FS obtained from ventilated pit latrines, urine-diverting dehydrating toilets, and septic tank systems. Bound moisture fractions were determined using moisture sorption isotherms, low-temperature convective drying, nuclear magnetic resonance, and thermogravimetric–differential scanning calorimetry analyses. Results show that interstitial moisture constituted 37–50% of total moisture, followed by vicinal (6–14%) and intracellular (3–9%) fractions, with net isosteric heat rising sharply below 20–30% moisture content (w.b.). Evaporation enthalpy exceeded that of bulk water at moisture contents below ~30% (w.b.), consistent with EPS-mediated adsorption and capillary confinement contributing to increased energy requirements for moisture removal and indicating a transition from capillary-controlled to structure-influenced retention. These findings provide a thermodynamic basis for interpreting why conventional mechanical dewatering stalls at a residual moisture content that differs systematically between VIP, UDDT, and septic tank sludges. These insights are relevant for improving FS treatment strategies, particularly in selecting appropriate combinations of dewatering, drying, and pre-treatment processes. Full article

An IoT-based technique is suggested for detecting single-phase earth faults (SEFs) in 6–35 kV cable networks with an isolated neutral. Unlike existing methods based on measuring zero-sequence currents with traditional current transformers, the suggested technique uses a passive magnetically controlled contact (reed switch) placed in the magnetic field of a cable. This enables recording fault currents of 0.5–2.0 A without external power supply and ensures galvanic isolation. The novelty of this technique is the combination of a reed switch current sensor with an IoT platform: instantaneous values of current are measured by the duration of the closed state of the contacts, then the data are transmitted via a radio channel (LoRa 433 MHz, LoRaWAN, or NB-IoT) to a cloud-based SCADA/EMS system for remote monitoring. The amplitude of the current is calculated from the pickup and resetting currents, as well as the duration of the closed state of the contacts; no high-frequency ADC is required. During experimental tests of a prototype with a KEM-5 reed switch and a TZL-10 current transformer, the difference between the calculated and actual protection operation current was no more than 10–5%. Oscillograms confirmed the correct operation of the device when starting, under load, and during an artificial SEF with a current of 1.6 A. The device response time is a fraction of the industrial frequency period, which significantly reduces the emergency mode duration. The suggested system enables decreasing the system average interruption duration index (SAIDI) and the system average interruption frequency index (SAIFI) by selectively disconnecting a damaged section and preventing cascading faults. The use of two independent channels (current transformer and reed switch) increases the reliability of SEF detection and reduces the risk of false operation. Thus, the developed IoT-based technique improves the reliability, safety, and cost-effectiveness of cable network operation. Full article

Two techniques of the Boundary Elements Method (BEM) are coupled for solving piecewise homogeneous elastic problems with body forces. The Domain Superposition Technique (DST), which is an alternative to the classical sub-regions approach, is used to approach the sectorial homogeneities, modeling the domain as a sum of a homogeneous surrounding sector and other complementary ones with different constitutive properties. The Galerkin tensor, with the adoption of a primitive function of the fundamental solution, transforms domain integrals. A classical boundary element matrix system is formed in which, unlike the sub-region idea, no interfaces exist between regions, and compatibility and equilibrium conditions are not imposed. The necessary correlation between the surrounding domain and all sub-domains is performed through the classic boundary element procedure of scanning, in which source points are used as the basis for integration along the boundaries. This research contributes to BEM literature by addressing a specific and non-trivial gap: the simultaneous treatment of body forces and material heterogeneity in a simplified and computationally efficient manner, without resorting to classical sub-region formulations. Full article

This paper proposes an experimental, intelligent optimization approach to improve the thermal cooling performance of an overclocked graphics processing unit (GPU). A closed-loop liquid-cooling system was built and tested utilizing deionized water and a silver (Ag) nanofluid coolant (0.015% vol.) across a variety of microchannel heat sink topologies with varying fin spacing. Key thermal performance indicators, including GPU temperature, coolant outlet temperature, and thermal resistance, were measured at different coolant flow rates. Experiments revealed that raising the flow velocity and decreasing the fin gap considerably enhanced cooling performance, while the Ag nanofluid consistently lowered GPU temperature by 1–3 °C compared to water. An Artificial Neural Network (ANN) surrogate model was constructed and trained using experimental data to support predictive analysis and system optimization, achieving excellent predictive accuracy with low RMSE. The trained ANN model was combined with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to perform multi-objective optimization, aiming to minimize GPU temperature and thermal resistance while improving heat removal. The Pareto-optimal solutions revealed that nanofluid-based cooling offered the best trade-off circumstances, with optimal designs occurring at moderate flow rates and small fin spacing. The ANN-NSGA-II multi-objective optimization results indicated that the best thermal performance of the GPU cooling system was achieved when using Ag nanofluid (0.015 vol.%) as the coolant, with an optimal coolant flow rate in the range of 1.30–1.84 LPM and an optimal fin/channel spacing of 0.57–0.71 mm, producing GPU temperatures of 29.18–29.66 °C, coolant outlet temperatures of 29.06–29.41 °C, and a minimized thermal resistance of 0.0106–0.0152 °C/W; thus, overall, the suggested ANN-NSGA-II framework works well as a practical design tool for improving GPU cooling systems and may be used to other high-heat-flux electronic thermal management applications. Full article

Thin rectangular plates, due to their small thickness relative to length and width and their high strength-to-weight ratio, are widely used in structural elements such as ship hulls, bridge decks, and aircraft wings. They are prone to nonlinear buckling under compressive forces, especially under biaxial in-plane compressive loading with large displacements, where linear theories often fail and membrane stresses complicate analysis. This study aimed to formulate a general mathematical equation for buckling analysis of thin rectangular isotropic plates with large displacements subject to biaxial in-plane forces using the Ritz potential energy functional method, and incorporates both geometric and material nonlinearities. Based on the formulated general equation, a specific equation for an all-round simply supported (SSSS) plate was developed using polynomial displacement shape function to determine the stiffness characteristics. Numerical values for critical buckling and post-buckling loads under biaxial compression for a square plate case were obtained. To validate these results, a comparison with values in the literature was made and the results show high consistency. The uniaxial buckling deviations ranged 0.047–0.10%, while undeformed biaxial buckling coefficients across varying aspect ratios and loading ratios (n = Ny/Nx) showed near-zero differences. From the two studies used for comparison, the maximum deviation is 24.42% and the minimum deviation is 1.12%. This indicates that the new model is adequate. Also, the adequacy of this new equation can be judged based on the simplicity of the formulation, and the closed agreement of the obtained numerical results with established results in the literature. This research enhances theoretical understanding of nonlinear buckling in thin plates and offers practical insights for improving structural reliability and efficiency in civil, mechanical, aerospace, and marine engineering. Therefore, the conclusion is that the model is suitable for buckling and post-buckling analysis of thin rectangular isotropic plates. Full article

High-precision satellite clock offset prediction is a core prerequisite for the BeiDou-3 Global Navigation Satellite System to achieve precise single-point positioning and timing. However, because of space radiation and the physical aging of the clock itself, the operational state of onboard atomic clocks exhibits a high degree of physical heterogeneity and time-varying drift characteristics. Traditional physical models struggle to capture complex nonlinear residuals, while existing deep learning methods often face boundary discontinuities caused by baseline separation when handling long-sequence forecasts. Furthermore, channel crosstalk in multivariate prediction and insufficient sensitivity to dynamic multiscale features limit the robustness of long-term predictions. To address these issues, this paper proposes a clock offset prediction architecture that integrates physically motivated statistical constraints with dynamic adaptive feature learning. Extensive experiments conducted using real BDS-3 precise clock difference products provided by Wuhan University demonstrate that the proposed method effectively mitigates the performance degradation often observed in existing models on heterogeneous satellites during the evaluated period. In the 24-h extrapolation task, the architecture achieved an average root-mean-square error as low as 0.507 ns, significantly improving prediction accuracy. It outperformed mainstream physical models and advanced deep learning baseline algorithms, providing a promising framework with good interpretability for high-precision clock error forecasting under dynamic space weather conditions. Full article

This work demonstrated improvements in the photovoltaic performance metrics of a dye-sensitized solar cell (DSSC) through the application of Eu-doped strontium silicate (Sr₂SiO₄:Eu³⁺), a luminescent downshifting (LDS) material. The material converted underutilized high-energy ultraviolet (UV) photons into lower-energy visible photons for better spectral responsivity in the DSSC. A conventional solid-state technique was applied in the synthesis of the material. Surface morphology was examined by scanning electron microscopy (SEM). Photoluminescence (PL) measurements were conducted to analyze fluorescence emission. The photovoltaic performances of the bare and LDS-enhanced devices were analyzed using photovoltaic current–voltage measurements. Compared to the bare DSSC, the cell containing Sr₂SiO₄:Eu³⁺ LDS phosphor material had an enhancement of 14.8% in the short-circuit current density (J_sc), from 0.243–0.279 mA/cm². The open-circuit voltage (V_oc) yielded an improvement of 10% from 580–638 mV. Maximum power output (P_max) produced a boost of 26.5% from 0.0136–0.0172 mW and the efficiency improvement at 26.6% from 1.09–1.38%. The coefficient of variation was introduced to evaluate device reproducibility. The device with the incorporation of Sr₂SiO₄:Eu³⁺ LDS phosphor depicted a coefficient of variation of 8.5%, suggesting good DSSC reproducibility. Full article

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