Search Results (4,403)

Crab traps are commonly used tools in both commercial and recreational fisheries to capture crabs through baited enclosures. However, the existing conventional designs often suffer from reduced catch efficiency, high bycatch rates, and rapid bait deterioration, which undermine their effectiveness and environmental sustainability. This study evaluated the effectiveness of an innovative crab trap setup incorporating an attractor device, designed not only to enhance crab catch rates but also to serve as a localized laboratory simulator for Outcome-Based Education (OBE) in Industrial Arts. Utilizing a developmental research design, this study was conducted in Barangay Caloc-an, Magallanes, Agusan del Norte. The research involved commercial and recreational crab fishermen, as well as electrical and electronics experts, to assist in setting up and evaluating the innovative crab trap device. The key variables examined included the type of attractor device used, the dispersal rate of the liquid bait, and the trap’s overall effectiveness in capturing crabs. Four different bait dispersal intervals were tested: 40 min, 30 min, 10 min, and 30 s. Results showed that shorter dispersal intervals significantly increased catch rates, with the 30 s interval yielding the highest and most consistent results. The developmental research framework enabled iterative testing and refinement of the trap system, allowing for continuous improvement of its components. Importantly, this study’s broader educational aim was to provide students with a practical, culturally relevant, and outcome-focused learning experience, where technical skills and scientific inquiry are applied in real-world contexts. The crab trap device served not only as a fishing tool but also as a simulated laboratory apparatus for Industrial Arts instruction, fostering skill development and engagement. Overall, this study contributes valuable insights into both fisheries management and educational innovation, demonstrating that a well-designed crab trap device can support more effective and sustainable fishing practices while also enhancing Industrial Arts education through hands-on, localized learning experiences. Full article

The second-hand Battery Electric Vehicle (BEV) market in Italy is affected by substantial information asymmetry, particularly with regard to battery State of Health (SOH), residual value, and expected maintenance costs. This lack of transparency limits consumer confidence and reduces the potential of used BEVs to support a broader and more inclusive electric mobility transition. In this study, a data-driven decision-support framework is developed to improve the evaluation of second-hand BEVs in the Italian market. The proposed approach combines market data collected from major online platforms with historical price reconstruction and an assessment of the information asymmetries that limit user confidence in the second-hand BEV market. It also incorporates a semi-empirical SOH estimation model based on observable vehicle characteristics. The results reveal a consistent depreciation gap between BEVs and comparable internal combustion engine vehicles across different market segments and indicate that battery-related uncertainty appears to be one of the factors associated with consumer hesitation. The framework shows that combining non-invasive battery-health estimation with maintenance-related information can support a more objective assessment of used electric vehicles. Overall, the study demonstrates the potential of integrated digital and engineering-based tools to reduce uncertainty and enhance transparency in the second-hand BEV market. Full article

The development of sustainable tourism requires the use of planning methods that combine environmental protection with inclusive access to nature-based destinations. This article presents a macro-level spatial decision-support framework for planning service infrastructure for specialized off-road electric wheelchairs in mountain destinations. The proposed framework combines predefined static vehicle-related constraints, Geographic Information System (GIS) analysis using QGIS and OpenStreetMap data, and Multi-Criteria Decision Analysis (MCDA). The spatial filtering stage evaluates terrain feasibility using an adopted maximum longitudinal slope threshold and minimum path-width requirement. The location–allocation stage combines Simple Additive Weighting (SAW) with a spatial-dispersion procedure to identify service hubs that are both suitable and regionally distributed. The method is not a dynamic engineering model of vehicle performance, but a GIS-MCDA planning tool for preliminary regional infrastructure siting under predefined operational constraints. Full article

Low-voltage electrical contacts are core components of power distribution systems, renewable energy installations, and industrial automation equipment. The electric arc generated during contact switching is the primary cause of contact erosion, material transfer, and equipment failure, posing significant threats to system reliability and operational safety. The accurate prediction of arc parameters is hindered by two challenges: the high scatter in available data undermines empirical models, and purely data-driven approaches risk physically implausible results. To address this, a Gaussian Mixture-enhanced Bayesian-optimized Physics-Informed Neural Network (GB-PINN) is proposed. Three core contributions are made: (1) High-fidelity MHD simulation foundation: A magnetohydrodynamic (MHD) multi-physics coupling model of the contact arc was constructed and validated against experiments, showing high fidelity with only 1.63% error in arc duration and 1.82% in arc energy. A multivariate simulation dataset was generated by varying key contact parameters based on this validated model. (2) GMM-based data augmentation: The measured and simulated data were modeled and sampled via Gaussian Mixture Model (GMM) to enrich the dataset while preserving physical consistency. (3) BOHB-optimized PINN prediction: The Bayesian Optimization and Hyperband (BOHB) algorithm was employed to optimize the PINN hyperparameters, enhancing training efficiency and predictive accuracy. Experimental results demonstrated that the proposed GB-PINN achieved superior performance in predicting arc duration and energy, with mean absolute errors (MAE) of 0.079 ms and 0.624 mJ, root mean square errors (RMSE) of 0.099 ms and 0.774 mJ, and coefficients of determination (R²) of 0.980 and 0.979, significantly outperforming grey model (GM (1, N)), long short-term memory (LSTM), and Transformer models. As a physics-informed data-driven tool, GB-PINN enables high-precision arc prediction, providing reliable support for electrical contact design. Full article

The adoption of lithium-ion batteries (LIBs) as secondary rechargeable batteries across many industries, including consumer electronics, electromobility, industrial tools, and electrical energy storage, is on the rise. As lithium-ion batteries approach the end of their life, there is a need to assess them for the possibility of a secondary application or reuse for a less demanding application. The extra connections of individual cells, BMS, temperature sensors, and other components to form a compact battery pack pose a challenge for second-life assessment, which usually prefers to separate individual cells for testing before discarding very bad cells for recycling and grading cells with substantive capacity based on their remaining capacity. This is a high cost for the second-life assessment. This work seeks to investigate an approach that avoids dismantling the battery pack into individual modules, cells, and BMS by including a BMS feature that allows the capacity and power ratings to be rescaled onboard after its first use. A set of cells with different chemistries was used in this work: a nickel–cobalt–aluminium oxide cathode with a silicon-doped graphite anode (NCA-GS), a nickel–cobalt–aluminium oxide cathode and graphite, and a lithium–nickel–manganese–cobalt oxide (NMC) cathode with a graphite anode (NMC-G) with various ageing states and behaviours. Their internal resistance and capacity at the beginning and end of life were compared. The scaling factor was obtained by finding the square root of the ratio of the internal resistance at EOL to that at BOL. With the current obtained by multiplying the cycling current rate by the rescaling factor, the surface temperature profile of the aged cells during cycling became the same as the temperature at the beginning of life. The relaxation voltage after discharge to 0% SOC and charge to 100% SOC was used to set the low and high cut-off voltages, respectively. This contributed significantly to reduced ageing and to a lower temperature rise in the spent cells. This set the stage for rescaling or derating battery systems without separating the individual cells, which is a huge cost for second-life use of lithium-ion batteries. BMS can be designed with configurable voltage and current limits, so that when repurposed for a second life, only a simple configuration or firmware update may be necessary. Full article

This study investigates the cross-border consequences of large-scale military attacks on Ukraine’s critical energy infrastructure and their implications for European energy resilience. Unlike prior research focused primarily on national-level disruption, this paper conceptualizes wartime infrastructure destruction as a source of systemic spillover risk within interconnected electricity systems. We develop an analytical framework integrating three dimensions: shock probability, structural vulnerability, and recovery capacity. Using evidence from 2022–2026 and comparative assessment of selected European Network of Transmission System Operators for Electricity (ENTSO-E) countries, we identify substantial asymmetries in exposure and resilience. Moldova appears highly vulnerable due to structural dependence and limited flexibility, whereas Poland demonstrates stronger resilience supported by diversification and institutional capacity. The findings show that shocks originating in Ukraine propagate through electricity trade flows, balancing constraints, and price volatility. The results highlight that large-scale attacks on the energy system threaten not only immediate regional security but also the long-term energy sustainability of the interconnected European network. The paper contributes to the literature by linking war-induced infrastructure damage with sustainable energy governance and by proposing resilience tools such as digital twins and blockchain coordination. The results are relevant for policymakers, transmission operators, and crisis management institutions across Europe. Full article

Deep excavation dewatering is an energy-intensive groundwater control process in underground engineering, especially under strong recharge and heterogeneous hydrogeological conditions. Conventional dewatering designs often rely on conservative pumping schemes to ensure the required drawdown, which may generate redundant groundwater extraction, unnecessary electricity consumption, additional carbon emissions, and excessive drawdown-induced settlement. To address this problem, this study develops a coupled improved genetic algorithm and MODFLOW optimization model, termed IGA-M, for dewatering well-group operation under engineering safety constraints. The purpose of the proposed model is not to reduce pumping arbitrarily, but to identify and eliminate redundant pumping while satisfying prescribed requirements for target water levels, settlement control, and hydraulic-gradient safety. Through the FloPy interface, the Improved Genetic Algorithm is dynamically linked with MODFLOW to establish a closed-loop simulation-optimization framework. In each optimization iteration, candidate well operation schemes are automatically transferred to MODFLOW, and the simulated hydraulic heads and settlement responses are returned to evaluate the objective function and safety constraints. In this framework, groundwater extraction, electricity consumption, carbon emissions, and land subsidence are treated as physically linked performance indicators of the optimized dewatering scheme. Validation using an idealized case shows that, under the same safety requirements, the IGA-M model reduces redundant hydraulic loading compared with the traditional uniformly distributed pumping method. By removing redundant pumping beyond the safety requirement, the optimized scheme reduced groundwater extraction by 62.7%, which was accompanied by a 44.9% decrease in both carbon emissions and comprehensive costs, as well as a 57.7% reduction in settlement at observation points. In a practical high-permeability deep excavation adjacent to the Yellow River, the model achieved well-group flow regulation under strong recharge conditions. Compared with the traditional scheme, it eliminated approximately 661,000 m³ of redundant groundwater extraction, corresponding to a 17.7% decrease, and consequently saved 26,800 kWh of electricity and reduced CO₂ emissions by nearly 16,000 kg during the dewatering period. These results demonstrate that the proposed IGA-M framework can transform MODFLOW from a post-design verification tool into an active optimization engine for dewatering design. It provides a physically based decision-support method for reducing redundant pumping and improving energy efficiency while maintaining engineering safety. Full article

Cities preparing Sustainable Urban Mobility Plans (SUMPs) increasingly require practical tools capable of merging diverse mobility datasets and transforming them into planning-relevant indicators. This article introduces PRIORITY (Platform for the tRansition to sustaInable zerO-caRbon mobilITY), a prototype platform designed to support mobility analysis and decision-making in urban contexts. The platform integrates Floating Car Data, GTFS feeds describing public transport supply, and detailed land-use and zoning information. By relying on these heterogeneous data streams, PRIORITY generates indicators such as travel and stop times, trip distances, trip volumes, energy consumption, pollutant emissions, external costs, and electric-vehicle charging behavior. The platform is organized into two main components: a back end and a front end. The back end, which constitutes the operational core, manages all collected data and ensures their structured storage in a shared database capable of handling large volumes of information on urban form, individual mobility patterns, public transport services, and modeling outcomes. The front end provides an intuitive and versatile interface that dynamically presents the outputs generated by the platform’s analytical and modeling processes. A case application for the Metropolitan City of Rome (Italy) illustrates the operational use of the prototype and shows how PRIORITY can support transparent and reproducible evaluations during the preparation and monitoring of SUMPs. The demonstrated workflow highlights the prototype’s value for public authorities and planners seeking data-informed approaches to urban mobility assessment and decarbonization strategies. Full article

Background and Objectives: Brachial plexus birth injury (BPBI) is a peripheral nerve injury occurring during birth that may result in upper-extremity weakness and functional impairment. This systematic review aimed to evaluate the effects of neuromuscular electrical stimulation (NMES) on motor function, muscle strength, range of motion, and upper-extremity function in children with BPBI. Materials and Methods: This systematic review was conducted according to PRISMA guidelines and registered in PROSPERO. PubMed, CINAHL, Scopus, Web of Science, PEDro, and the Cochrane Library were searched from inception to 5 May 2026. Only randomized controlled trials were included. Methodological quality was assessed using the PEDro scale, and risk of bias was evaluated using the RoB 2 tool. Results: Seven randomized controlled trials involving 197 participants were included. Several studies reported improvements in shoulder abduction, elbow flexion, wrist extension, muscle strength, and motor function following NMES compared with conventional therapy. The combination of NMES and constraint-induced movement therapy demonstrated favorable outcomes in functional performance. However, substantial heterogeneity was observed across studies regarding participant characteristics, NMES parameters, treatment duration, and outcome measures. The certainty of evidence ranged from low to very low. Conclusions: Current evidence suggests that NMES may serve as a potential adjunct to conventional rehabilitation in children with BPBI. However, given the low to very low certainty of the evidence, high risk of bias, and substantial clinical and methodological heterogeneity among the included studies, definitive clinical recommendations cannot currently be made. Future well-designed randomized controlled trials using standardized protocols, consistent outcome measures, and longer follow-up periods are warranted. Full article

Electroretinography (ERG) is a non-invasive technique used to assess retinal function via electrical responses to light stimuli. We established baseline ERG parameters and a standardized recording protocol for collared scops owls (Otus lettia). Twelve eyes of six owls were evaluated. In addition to the pre-release assessment, ocular reflex tests and basic ophthalmic examinations were performed before the induction of anesthesia. Routine radiographic and hematological examinations were performed under general anesthesia, followed by ERG recordings. Under scotopic –20 dB conditions, the a-wave amplitude was 1.78 ± 0.53 μV (implicit time: 37.83 ± 5.52 ms), and the b-wave was 41.59 ± 10.71 μV (100.88 ± 10.9 ms). For scotopic 0 dB mixed responses, the a-wave amplitude was 27.98 ± 5.9 μV (27.64 ± 2.71 ms), and that of the b-wave was 175.51 ± 13.82 μV (97.02 ± 7.01 ms). Under photopic conditions, the a-wave and b-wave amplitudes were 2.88 ± 2.06 μV (28.67 ± 2.77 ms) and 25.53 ± 10.61 μV (77.78 ± 16.18 ms). To the best of our knowledge, this is the first study to establish species-specific baseline ERG parameters for collared scops owls. These findings provide a valuable tool for assessing retinal function in raptors and may serve as a baseline framework for ERG evaluation in other avian species. Full article

This study uses and compares stationary and non-stationary Generalised Extreme Value Distribution (GEVD) to model the behaviour of nitrogen dioxide (NO₂) emission maxima from each of 13 Eskom’s coal-fuelled power stations. The pollutant is modelled to facilitate monitoring and regulation in order to protect public health and the environment. The Maximum Likelihood Estimate (MLE) and Generalised Maximum Likelihood Estimate (GMLE) parameter estimation methods are used and compared in finding the best-fitting model per power station. The results show that a non-stationary model with time-dependent location and/or scale parameter(s) produced the best fit for ten of the power stations, while a stationary model gave the best fit for three, as confirmed by the diagnostic tools. Future extremely high NO₂ emissions were estimated by making use of the 40 and 100 quarter return levels based on the best-fitting models. This study shows how stationarity may not hold for all NO₂ emission data from Eskom’s coal-fired power stations. Modelling data using time-dependent non-stationary GEVD models can be useful, especially in identifying and predicting trends or patterns in worsening high NO₂ emissions with time. This modelling approach is important in providing information for planning and policy formulation of extreme emissions from coal-fired electricity-generating power stations at Eskom (South Africa). Full article

As a significant branch of nanotechnology, carbon-based nanomaterials (CNMs) have garnered extensive attention for their broad application potential in agriculture, attributed to their unique structural and physicochemical properties. They are considered one of the important tools for promoting sustainable agricultural development. Among them, carbon nanotubes (CNTs), owing to their excellent mechanical properties, electrical characteristics, and high specific surface area, have recently attracted considerable interest in plant growth regulation and the development of agricultural inputs. This article systematically reviews the research progress of CNMs, especially CNTs, in agriculture. Firstly, it outlines the structural characteristics and physicochemical properties of different types of CNMs. Subsequently, from a plant physiological perspective, it focuses on analyzing their mechanisms of action in nutrient uptake, photosynthesis regulation, and antioxidant defense. Based on this, it summarizes the application progress of CNMs in plant growth promotion, nano-pesticide and fertilizer delivery, and precision agriculture sensing. Furthermore, this article emphasizes the dose-dependent biphasic effect (hormesis) of CNMs on plants: at relatively low, system-specific doses, they can promote growth and enhance stress resistance, whereas at higher or supra-optimal doses, they may induce oxidative stress, cellular damage, and photosynthesis inhibition. However, significant variations in responses exist depending on the material type, physicochemical properties, and plant species, and a unified understanding of the underlying mechanisms has not yet been established. Finally, this article discusses green synthesis strategies for CNMs and their potential ecological risks and points out that future research should focus on key issues such as precise dose regulation, long-term environmental behavior, and multi-scale mechanism analysis. This review aims to provide a systematic reference for understanding CNM–plant interactions and their safe application in agriculture. Full article

Physical energy-based therapies are non-invasive adjunctive interventions that deliver mechanical, electromagnetic, light, or radiofrequency/thermal energy to tissues with the aim of reducing symptoms and improving tolerance of active rehabilitation. Osteoarthritis (OA) is a heterogeneous whole-joint disorder in which cartilage degeneration, subchondral bone remodeling, synovitis, peri-articular tissue dysfunction, neuromuscular impairment, and pain sensitization may interact to produce pain, stiffness, and activity restriction. As conservative therapy for OA, education, progressive therapeutic exercise, weight management when indicated, and self-management remain the core of care. Nevertheless, some patients cannot fully participate in exercise because of pain, fear of movement, load intolerance, comorbidity, or limited access to supervised rehabilitation. This narrative review synthesizes evidence published mainly between 2016 and 2026 for extracorporeal shock wave therapy (ESWT), photobiomodulation/low-level laser therapy (PBMT/LLLT), pulsed electromagnetic field therapy (PEMF), transfer energy capacitive and resistive/capacitive–resistive electric transfer (TECAR/CRET) therapy, body weight support and aquatic unloading strategies, and mechanosonic vibration therapies. The available literature suggests that ESWT and PBMT/LLLT may provide short- to mid-term pain and function benefits in selected patients with knee OA when parameters are aligned with evidence-supported dosing windows. PEMF and vibration therapies show promising but less consistent effects because protocols, devices, sham conditions, and populations vary. TECAR/CRET and unloading approaches are best interpreted as enabling tools that may reduce guarding, improve walking tolerance, or increase the quality of therapeutic exercise, rather than stand-alone disease-modifying treatments. Current national and society guidelines consistently prioritize exercise, education, and weight management; most of the modalities reviewed here are absent from guidelines or are supported only indirectly, which justifies cautious wording and individualized use. A practical application model is, therefore, time-limited and goal-oriented: identify the barrier to rehabilitation, select a modality with a plausible mechanism and published protocol, monitor pain and functional response, and discontinue the modality if it does not improve participation in active care. Full article

With the rapid growth of transportation electrification and smart energy systems, the reliable operation of electric vehicle (EV) charging infrastructure has become an important issue for sustainable transport, since charging faults may interrupt service and shorten equipment lifetime. However, practical charging environments are often characterized by heterogeneous operating conditions and severely imbalanced fault distributions, which limit the effectiveness of conventional fault diagnosis methods. To address these challenges, this study proposes a lightweight Proto-Contrastive Discriminative Learning (PCDL) framework for intelligent fault diagnosis in EV charging systems. The proposed method combines supervised contrastive learning with a prototype-distance discrimination mechanism to improve the identification of rare abnormal states under long-tailed data conditions. Heterogeneous charging features, including discrete control signals and continuous total harmonic distortion (THD) indicators, are projected into a discriminative embedding space, while anomaly detection is performed according to the relative distances between samples and class prototypes. Experimental results on a publicly available EV charging-pile monitoring dataset, containing 122,144 samples with four discrete control/safety features and two THD-based power-quality features, demonstrate that the proposed framework maintains stable detection performance under imbalance ratios of 1:1, 1:10, and 1:100. Under the most challenging 1:100 condition, the proposed method achieves an F1-score of 84.21%, representing a 29.08% improvement over the strongest baseline method. In addition, the framework requires only approximately 11 KB of memory and maintains CPU inference latency below 6.3 ms, demonstrating strong potential for real-time deployment on resource-constrained edge devices. These results suggest that the proposed framework can provide a lightweight diagnostic tool for practical charging stations and support safer and more reliable EV charging operation. Full article

Continuous industrial material-handling systems are operationally and energy-intensive technological structures in which downtime affecting one equipment group can reduce the availability of the entire production chain. This study develops a probabilistic framework for assessing downtime impacts when detailed historical event-level downtime records are available, but complete technical and economic equipment parameters are missing. The analysis is based on 6605 downtime records for conveyors, excavators and stackers observed between 2017 and 2025. Historical downtime records were combined with interval-based assumptions for power demand, load factor, handling capacity, electricity price and commodity value, and were propagated through a Monte Carlo simulation with 10,000 iterations. The results revealed a strong concentration of downtime burden. The combination of P–Conveyor–Material Collapse accounted for 32.58% of total downtime, while the top five equipment–fault combinations explained 67.86% of cumulative downtime. At the system level, the median modelled energy-service unavailability reached approximately 4339 MWh, the median production-loss equivalent reached approximately 9279 kt, and the median total economic loss was approximately EUR 209.5 million. The proposed Energy–Economic Impact Index integrated event frequency, downtime severity, energy-service unavailability and economic loss into a single maintenance-prioritisation indicator. The highest-ranked maintenance target was P–Conveyor–Material Collapse, confirming that maintenance priorities should be determined by combined operational, energy-related and economic consequences rather than by event frequency alone. The study demonstrates that historical downtime records can be transformed into a probabilistic decision-support tool for risk-based maintenance planning in industrial systems with incomplete technical and economic data. Full article