Eng

This study aims to experimentally evaluate and compare the electrical–thermal performance of a 20-cell 18650 lithium-ion battery pack cooled by a pure phase change material (PCM) and a PCM/TiO₂ nanoparticle composite to identify an effective passive thermal management approach for EV battery applications. Using a controlled charging–discharging system, thermocouple-based temperature mapping, and systematic tests across multiple C-rates (0.75 C–1.5 C), the study measures the variations in battery temperature, generated heat, and voltage behavior as functions of depth of discharge (DOD) and state of charge (SOC). The results show that the PCM/nanoparticle mixture markedly improves thermal conductivity, reduces peak temperature by approximately 8–10 °C compared with pure PCM, delays thermal saturation at higher C-rates, and enables a wider safe DOD range with reduced voltage sag and lower heat accumulation. Based on the experimental temperature/voltage trends in this study, limit DOD to ≤40–50% at high power (≈1.5 C), ≤50–60% at moderate power (≈1 C), and ≤60–70% at low power (≈0.75 C) (i.e., target SOC windows roughly 60–100% SOC at 1.5 C, 40–100% SOC at 1 C, and 30–100% SOC at 0.75 C), with an absolute practical upper DOD limit of ~70% to avoid frequent deep discharge damage; these limits keep peak temperatures below ~40–45 °C, reduce severe voltage sag near cutoff, and greatly extend cycle life because shallower cycling (e.g., 50% vs. 100% DOD) produces many times more cycles. These improvements enhance battery safety, performance stability, and cycle life, making the nanoparticle-enhanced PCM a practical, compact, and energy-efficient solution for passive battery thermal management in electric vehicles. Full article

The optimal placement of samplers and sensors in water distribution systems (WDSs) and wastewater collection systems (WCSs) is fundamental to effective monitoring, early contamination detection, and system protection. The goal of optimal sensor/sampling placement (OSP) is to maximize the ability to detect, monitor, and track critical variables, such as contaminants or temperature, while maintaining cost-effectiveness and operational efficiency. In practice, OSP problems are inherently multi-objective and typically involve trade-offs between cost minimization, spatial and temporal coverage, detection accuracy, and robustness under uncertainty. This paper presents a comprehensive review of recent single- and multi-objective optimization strategies for source detection and monitoring, drawing on approaches developed in various research fields. The reviewed literature is systematically organized according to problem formulation, objective functions, optimization techniques, and decision-making strategies, paying particular attention to their applicability in real-world WDSs and WCSs. Beyond summarizing existing methods, this review critically examines key methodological assumptions and limitations that hinder practical implementation. These include sparse sensor deployment, budget constraints, and modeling and sensor uncertainty. Finally, the paper identifies open challenges and outlines potential directions for future research aimed at improving the robustness, scalability, and practical relevance of OSP strategies. Full article

In nature, organisms have evolved unique structures that feature low weight, high strength, and damage resistance. The Eurasian eagle-owl serves as a representative example, with specialized feather architectures that enable stable flight in intense and turbulent airflow conditions. Herein, driven by classical design layup guidelines, and inspired by the distinctive fiber architecture of the feather shaft cortex, we propose a regionally tailored layup (RTL) design to enable mass-efficient composite beams with high load-bearing capacity and enhanced damage tolerance. The feather shaft reference lay-up rectangular beam (FSRB) adopts the RTL, and a flange overlap is introduced to preserve the integrity and strength of the flange–web interface; it is then manufactured using inner–outer matched molds in conjunction with vacuum bag molding. Three-point bending shows that the FSRB achieves a flexural strength of 180 MPa and a flexural modulus of 12.1 GPa. Relative to conventional axial (ALRB), Cross-ply (CPRB), single-helix (SLRB), and quasi-isotropic (QLRB) lay-up rectangular beams, the FSRB improves strength by 59.5%, 46.6%, 26.8%, and 21.2%, and increases modulus by 81.7%, 34.7%, 25.1%, and 10.8%, respectively. FEA and SEM observations confirm an RTL architecture in the rectangular beams, characterized by differentiated fiber arrangements in the flange and web. Flanges with an axially dominated layup provide high initial flexural strength and stiffness. The web, formed by a crossed-ply/axial hybrid layup, provides transverse support and redirects crack/delamination growth, thereby promoting progressive failure and enhancing energy dissipation. Overall, this RTL design enables concurrent improvements in load-carrying capacity and damage tolerance. This study offers a design perspective for high-performance load-bearing components. Full article

Rapid urbanization has significantly increased energy demand in buildings, which now represent nearly 30% of global energy use. In India, buildings are built across highly varied climatic conditions, from hot-dry and warm-humid to cold, high-altitude areas, making climate-responsive envelope design essential to enhance thermal performance. Among envelope components, roofs are the most exposed to solar and outdoor thermal loads, playing a key role in managing indoor heat transfer. This study offers a parametric analysis of climate-responsive roof design strategies for India’s five main climatic zones, using transient simulations and statistical evaluation. The effectiveness of insulation placement, insulation material and thickness, and external surface absorptivity was systematically assessed based on roof heat gain and heat loss. Results indicate that over-slab insulation can lower roof heat gain by approximately 15–35% compared to under-slab insulation in warm-humid, hot-dry, composite, and temperate zones. In comparison, under-slab insulation decreases heat loss by about 10% in colder areas. Among insulation materials, 50 mm polyurethane foam (U = 0.433 W/m²·K) consistently outperformed extruded polystyrene and expanded polystyrene, achieving 82–83% reductions in maximum heat gain in cooling-dominated climates and 89% reductions in heat loss in cold regions relative to uninsulated roofs. When combined with a white reflective surface finish (α = 0.26), the total heat transfer reduction increased further to 89–92%. Surface treatments alone cut heat gain by 37–51% in non-cold climates, highlighting their potential as cost-effective retrofit options. Statistical analysis confirmed that dry-bulb temperature is the primary climatic factor influencing roof heat transfer (R² = 0.86–0.98, p < 0.0001), while solar radiation had a weaker effect, especially in optimized roof systems. The findings emphasize the importance of climate-specific roof design and demonstrate that insulation U-value has a greater impact on thermal performance than surface absorptivity, although both are significant. This research offers practical, climate-adjusted guidance for architects, engineers, and policymakers to enhance the thermal performance of roofs in Indian buildings. It supports the development of more resilient, energy-efficient building envelopes. Full article

With the increasing popularity of photovoltaic (PV) equipment in residential and commercial buildings, there is a pressing need for systems that maximize energy efficiency and self-consumption. This paper introduces an integrated management framework for retrofitting existing infrastructures, enabling high photovoltaic (PV) self-consumption in residential buildings through a rule-based control strategy. The framework supports three service classes—defined by user-level Quality of Service (QoS) parameters—and monitors battery voltage along with grid power exchange to coordinate heat pumps, batteries, and hot water cylinders. Experimental deployment in a residential testbed achieved up to 89% PV self-consumption while keeping daily grid usage below 0.5 kWh. Ablation experiments on battery size further demonstrated the approach’s robustness to reduced storage capacities. The use of Commercial-Off-The-Shelf (COTS) components underscores the minimal intrusiveness of this solution, highlighting its potential for seamlessly integrating diverse, vendor-specific equipment into a coordinated control system. Full article

The development of portable electrocardiographic analysis systems necessitates identifying an optimal balance between diagnostic precision and computational efficiency. This research addresses the challenge of optimal feature selection for automated cardiac arrhythmia classification in resource-constrained portable applications. We present a comparative investigation of three distinct feature selection strategies for ECG classification: the MRMR (Minimum Redundancy Maximum Relevance) method, which maximizes relevance while minimizing feature interdependencies; the ReliefF technique, which evaluates discriminative power through proximity analysis in the feature space; and permutation-based importance analysis implemented with neural networks. Utilizing the Large-Scale 12-Lead Electrocardiogram Database for Arrhythmia Study, we construct a hybrid feature space integrating 12 conventional time- and frequency-domain parameters (previously validated and included in the database’s official documentation) with 26 advanced nonlinear descriptors, including the Hurst exponent, DFA scaling parameter, log-absolute correlation measures, mean standard increment from the Poincaré plot, and wavelet entropy. The experimental results demonstrate remarkable convergence among the three paradigms in selecting optimal feature subsets, achieving classification accuracies of 87–89% for four arrhythmia classes using compact configurations of 7–10 features, and 93.57% with an extended 12-parameter set. The 7-feature configuration achieves an 82% complexity reduction compared to the full 38-feature set. Multi-algorithmic analysis confirms the consistent discriminative contribution of the proposed nonlinear descriptors, demonstrating that MRMR, ReliefF, and permutation analyses yield convergent rankings of critical parameters for automated cardiac pathology diagnosis. Full article

Asphaltene–resin–paraffin deposits (ARPDs) represent one of the most complex flow assurance challenges in oil production, particularly under late-stage reservoir development conditions characterized by pressure depletion, temperature gradients, multiphase flow, and compositional changes. Despite extensive industrial experience, ARPD control strategies are often applied empirically, without explicit linkage to the underlying physicochemical mechanisms governing deposit formation. This review presents a comprehensive and mechanism-oriented analysis of ARPD formation and mitigation in a reservoir–wellbore system. The multicomponent composition, structural heterogeneity, and interfacial activity of paraffins, resins, and asphaltenes are examined alongside thermodynamic, hydrodynamic, and operational factors controlling precipitation, transport, adhesion, and deposit growth. Particular attention is paid to the correspondence between ARPD formation stages and applicable prevention or removal technologies. The analysis demonstrates that preventive strategies targeting early-stage physicochemical processes are fundamentally more effective than post-formation removal methods. The mechanisms of inhibitor action—adsorption, desorption, and dissolution—are shown to operate in a complementary manner, while delivery efficiency is strongly influenced by spatial distribution and retention in the formation. Advanced delivery technologies, including microencapsulation and nanocarrier-based systems, provide enhanced control over inhibitor release and persistence under complex reservoir conditions. Overall, this review establishes an integrated framework linking crude oil properties, formation mechanisms, inhibition chemistry, and delivery technologies, providing a rational basis for designing adaptive and efficient ARPD mitigation strategies in modern oil production systems. Full article

Superhydrophilic micro/nano-porous media have extensive applications in electronic thermal management and energy storage systems. Predicting two-phase pressure drop in complex porous structures is of great importance for system design and optimization while remaining highly challenging. This study systematically investigates the two-phase flow resistance characteristics of bi-porous microstructures with multiple particle sizes and porosities under varying boiling regimes. Experimentally, porous samples were fabricated via vacuum sintering using nickel powders and pore-forming agents (CaCl₂), which exhibit superhydrophilicity and enhanced wicking characteristics. A visualized experimental platform was constructed to investigate the impact of pore size combinations, flow velocities, and boiling states on pressure drop. The dataset obtained through multi-factor saturated boiling experiments was further used to derive a semi-empirical model for the two-phase flow pressure drop based on the classic Kozeny-Carman (K-C) and Akagi-Chisholm (A-C) correlations. Results show that the pore size combinations and boiling states have a significant impact on the resistance performance. The proposed model achieves an average prediction deviation below 15.7%, confirming its reliability and its effectiveness as a design framework for optimizing high-capillary-force porous wicks in advanced thermal management systems. Full article

To address stability issues induced by dynamic fracture of weak interlayers in sandstone–mudstone interbedded slopes during blasting excavation, this study investigates the Qingnian Hub diversion channel project of the Ping-Lu Canal through an integrated methodology combining field blasting tests, laboratory dynamic rock experiments, and numerical simulation validation. Field monitoring captured slope dynamic responses, while ultrasonic testing and Split Hopkinson Pressure Bar (SHPB) dynamic splitting tests determined rock mass mechanical parameters. A high-fidelity 3D numerical model developed in ANSYS/LS-DYNA was validated against experimental data, demonstrating reliability with relative errors in peak particle velocity (PPV) below 20% at most monitoring points. Results reveal that increasing interlayer dip angle reduces fracture length along the lower interface while causing internal oblique cracks to initially lengthen and then shorten, with optimal oblique crack development observed at 10–15°. Conversely, greater interlayer spacing first decreases and then stabilizes lower-interface fracture length, whereas oblique crack length peaks at 4.8 m for a 4 m spacing. Based on 25 parametric simulations, a safety criterion using crack-initiation vibration velocity was established, yielding a predictive model dependent on dip angle and spacing. The derived criterion defines a critical vibration velocity range of 5.6–10.0 cm/s for the studied slope configurations. Compared to existing empirical guidelines that rely solely on peak particle velocity, the proposed criterion innovatively incorporates the controlling influence of geological stratigraphic geometry. This study provides theoretical and practical guidance for optimizing blasting parameters and ensuring slope stability in similar engineering contexts. Full article

An HPO (Hunter–Prey Optimizer)-optimized Bidirectional LSTM (HPO-BiLSTM) model is introduced to address the challenges in predicting gas concentration within coal mining working faces. This study aims to adaptively adjust the key hyperparameters (such as learning rate and number of hidden layer units) of the BiLSTM network through intelligent optimization algorithms. While the BiLSTM architecture inherently mitigates gradient vanishing and exploding problems through its gating mechanisms, the proposed HPO method focuses on addressing the inefficiency of manual parameter tuning and the risk of trapping in local optima that traditional methods encounter when dealing with nonlinear and non-stationary gas concentration time series. The experiment utilized the actual methane monitoring data from the 15117 working face of Jishazhuang Coal Mine in Jinzhong City, Shanxi Province (with a sampling interval of 2 min). The proposed HPO-BiLSTM model was compared with baseline models such as LSTM, BiLSTM, GA-BiLSTM, and PSO-BiLSTM in terms of performance. This study systematically compares the performance of LSTM, BiLSTM, and BiLSTM models optimized with GA, PSO, and HPO. Results demonstrate that all optimized models outperform the baselines, with HPO-BiLSTM achieving the best overall performance. It attained the lowest RMSE and highest R² across the training, validation, and test sets, showcasing superior fitting and generalization capabilities. Furthermore, HPO-BiLSTM converged to the lowest loss value (0.00062) in only 15 iterations, demonstrating significantly greater efficiency and stability than both GA-BiLSTM (loss 0.00072, 25 iterations) and PSO-BiLSTM (loss 0.00071, 30 iterations). The experiments confirm that the HPO algorithm effectively configures BiLSTM hyperparameters, mitigates overfitting, and provides a more accurate and robust solution for gas concentration prediction in coal mines. Full article

The power-shift system employed in agricultural multi-range continuously variable transmissions (CVTs) features a complex structure and control logic, presenting significant challenges to the reliability of agricultural machinery. To enable timely detection of faults, constructing an intelligent fault diagnosis classifier to monitor the system’s health status is essential. Typically, fault samples utilized for classifier development originate from ideal bench tests, characterized by uniform patterns and limited diversity, thereby hindering the algorithm’s generalization capability. This study addresses this issue by proposing a generative adversarial network (GAN) model, integrated with a triple loss function and a novel generator architecture, to augment the fault dataset under laboratory conditions. The generator architecture comprises a variational autoencoder module and an oil pressure point attention mechanism, enabling the generation of diverse and fluctuating virtual samples. Building on this augmented dataset, a fault classifier based on one-dimensional ConvNeXt was developed. Experimental results indicate that the classifier achieves an accuracy of 99.73%. While classifier accuracy decreases with increasing noise levels, the GAN-generated dataset provides more comprehensive training, resulting in an accuracy approximately 3% higher than that achieved using the original dataset. Full article

The proliferation of distributed photovoltaic (PV) generation introduces significant operational challenges for distribution networks, including voltage instability and elevated technical losses. While modern PV inverters capable of static synchronous compensator (STATCOM) functionality—forming PV-STATCOM systems—offer a promising solution, their optimal integration remains a complex mixed-integer non-linear programming (MINLP) problem. This paper addresses this gap by proposing a novel hybrid evaluator–optimizer framework for the optimal daily placement and sizing of PV-STATCOM devices. The framework synergistically integrates the metaheuristic crow search algorithm (CSA) for global exploration of discrete device locations with a high-fidelity, multi-period optimal power flow (OPF) model—implemented efficiently in Julia with the Ipopt solver—for continuous operational evaluation and constraint validation. The methodology incorporates realistic 24 h load and solar irradiance profiles. Extensive validation on standard IEEE 33- and 69-bus test systems demonstrates the efficacy of the proposed approach. The results indicate substantial reductions in daily energy losses—by up to 70.4% and 72.9% for the 33- and 69-bus systems, respectively—and corresponding operational costs, outperforming recent state-of-the-art metaheuristic and convex optimization methods reported in the literature. The CSA also exhibits robust convergence and repeatability across multiple independent runs. This work contributes a computationally efficient, open-source planning tool that leverages modern optimization solvers, providing a scalable and effective strategy for enhancing the power quality and economic performance of PV-rich distribution networks. Full article

Securing mine sites is a challenging task due to the complexity of the infrastructure, the variety of physical and digital components, the distribution of assets and machineries, and the large number of stakeholders involved. Given the risks that are present in Tailings Storage Facilities (TSFs), mine operators are seeking technologies to accurately monitor the state of their dams. The latest developments implement evolutive monitoring and responsive risk management systems by adapting accurate Internet of Things technologies, automated mathematical model calculation to continually monitor the structural/geotechnical aspects of TSF, and a portfolio of innovative applications to support decision-making. Within this study, a comprehensive methodology is developed for assessing the environmental sustainability of a smart monitoring solution combining the life cycle assessment (LCA) method with the environmental risk assessment, which quantifies risk reduction potential. The use case scenario is identified based on real industrial data, also aligned with the common characteristics of tailing dams in Europe. Environmental sustainability of the smart monitoring solution is assessed through a cradle-to-grave LCA based on the ReCiPe 2016 (v1.1 Midpoint (H)) method. Monitoring impact alone is reduced primarily by the 40% reduction in monitoring visits, while the results show the environmental improvement of the TSF life cycle by 24% for CO₂-eq., as a step in-line with the EU’s long-term strategy for total decarbonisation in 2050, and Sustainable Development Goal 9 for Industry by the United Nations. Additionally, the 27% freshwater ecotoxicity reduction, 20% human toxicity (cancer) decrease, and the rest of the studied categories indicate an overall footprint improvement for the monitoring solution application on TSFs. The findings demonstrate clearly theoretical, practical and policy implications, not only for the benefit of such solutions for environmental protection, but also for the necessity of integrating risk in sustainability analysis approaches. Full article

This paper presents the development and results of an analytical method for calculating the stability coefficient of a rock mass that is capable of adjusting the face support pressure required for face stability depending on changes in the mass structure at the tunnel face. The analytical relationships presented in this work are based on the simulation of 225 three-dimensional finite element numerical models. The influence of the mass structure at the tunnel face was examined by varying the thickness and position of the layer at the tunnel face, as well as its stiffness and strength parameters. The maximum difference in the values of the main monitored criteria exceeded 85% for such indicators as the area of the surface settlement trough, the area of the zone of negative vertical deformations, the width of the settlement trough, and the maximum value of vertical settlements. This study proposes a practical implementation of the developed analytical method—a stability coefficient of the soil mass was developed to adjust existing analytical relationships when a soil layer with mechanical characteristics differing from the host mass is present at the tunnel face. Full article

Standard maturity methods for concrete monitoring rely primarily on temperature history, often neglecting the influence of internal relative humidity (RH) on hydration kinetics and self-desiccation risks. Continuous in situ monitoring of internal RH remains a challenge due to the high cost, proprietary nature, and lack of reproducibility of existing solutions. This study evaluates a low-cost, open-source embedded sensor array designed to characterize early-age curing behavior through trend-based monitoring—defined here as the evaluation of ensemble consistency and repeatability rather than absolute metrological traceability. The prototype system, based on SHT31 sensors controlled by an ESP32 microcontroller, was embedded in high-performance concrete cylinders (f′c = 45 MPa) to capture the exothermic hydration peak and the equilibration of internal humidity. Results demonstrate that while the sensor encapsulation introduced a geometric disturbance that reduced compressive strength by approximately 25%—a limitation requiring mitigation in structural applications—the system successfully captured reproducible curing transitions. The proposed framework provides an accessible tool for experimental research into internal curing conditions, offering a digital complement to traditional surface-based quality control. Full article

Membrane fouling remains a major obstacle to the performance of the reverse osmosis (RO) desalination processes. Artificial intelligence (AI) is now a promising approach for the reliable modeling of these complex systems. This study evaluates three modeling techniques—multiple linear regression (MLR), artificial neural networks (ANNs), and support vector regression (SVR)—for predicting transmembrane pressure (TMP) at the Boujdour desalination plant, based on five input parameters: temperature, turbidity, pH, conductivity, and feedflow. The analysis is based on an original dataset of 195 daily measurements, and due to the absence of timestamps, the study focuses on state-to-TMP prediction rather than chronological forecasting, with no temporal generalization claimed. Approximately 2000 augmented training samples generated using a conservative SMOGN approach were used for model development, while performance evaluation relied exclusively on 39 independent real test observations. Two modeling strategies were adopted: (i) a minimalist approach based on significant variables identified by an ordinary least squares (OLS) model (pH and conductivity), and (ii) a multivariate approach integrating all parameters to capture non-linear interactions. A rigorous validation framework was put in place to avoid information leakage and ensure the robustness and generalizability of the models. Performance was evaluated using R², RMSE, and MAE metrics, supplemented by robustness and significance analyses including bootstrap confidence intervals, paired statistical comparisons, and interpretability analyses based on permutation importance, partial dependence plots (PDPs), and individual conditional expectation (ICE) curves. The results indicate that the SVR model achieves the best average predictive accuracy among the tested models, albeit with moderate explanatory power. Full article

Container vessels—characterized by high transport work and energy-demanding operating profiles—constitute one of the most emission-significant fleet segments and a strategically important area for implementing and assessing decarbonization initiatives. Responding to the persistent absence of integrated analytical approaches, this paper introduces a unified techno-economic and environmental assessment framework for evaluating green interventions on operating ships. The framework comprises a set of fuel-consumption, environmental performance, and techno-economic metrics and a transparent and globally applicable assessment procedure enabling the consistent comparison of heterogeneous intervention types towards sustainability. The framework is applied to a representative medium-size container vessel to demonstrate its analytical potential and practical relevance. The results of the specific application reveal the systematic trade-offs between environmental and economic performance of green interventions: operational optimization delivers the strongest carbon-intensity improvements and isolated technical retrofits provide favorable economic returns but limited environmental gains, while integrated technical–operational packages achieve the most balanced overall outcomes. Overall, the paper has both a methodological contribution by suggesting a coherent, regulation-aligned assessment structure, as well as a practical decision-support value for ship operators and policymakers. Full article

Thermal energy storage (TES) plays a crucial role in improving the efficiency and reliability of solar thermal systems, particularly when low-cost and readily available materials are desired. This study experimentally investigates the performance of a water–salt thermal energy storage system using sodium chloride (NaCl) at different concentrations in a simple solar collector setup. Experiments were conducted using a laboratory-scale solar thermal energy system under controlled conditions, with water serving as the heat transfer fluid and a fixed flow rate of 15 L/h. The storage medium consisted of water mixed with salt, which was obtained from the Dead Sea before any treatment. In its raw form, this type of salt contains impurities, mainly sand, at a fixed concentration of approximately 1% by weight. The effects of salt concentration on storage temperature, system efficiency, and effective heat capacity were analyzed. The results show that moderate NaCl concentrations improved the average storage temperature by up to 12–18%, increased thermal storage efficiency by approximately 1%, and enhanced the effective specific heat capacity compared to pure water. In contrast, higher salt concentrations resulted in a performance reduction of up to 8–12% due to increased thermal resistance and reduced heat transfer effectiveness. An optimal salt concentration range was identified at which maximum storage efficiency and heat capacity were achieved. These findings demonstrate that common sodium chloride can serve as an effective and economical enhancement material for thermal energy storage when properly optimized. The study provides quantitative evidence and practical insights for the development of low-cost, salt-based thermal energy storage systems for solar thermal applications. This study highlights the importance of concentration optimization and provides practical insights for the development of low-cost, salt-based thermal storage systems for solar energy applications. Full article