Search Results (18,267)

Short-term electricity load forecasting has become increasingly challenging due to growing demand volatility, nonlinear load patterns, and the dynamic penetration of renewable energy sources. Conventional forecasting models often suffer from sensitivity to hyperparameter settings and limited capability in capturing long-term temporal dependencies. To address these issues, this paper proposes a hybrid forecasting framework that integrates an Improved Snow Ablation Optimizer (ISAO) with a Dilated Bidirectional Gated Recurrent Unit (Dilated BiGRU). The proposed ISAO enhances the original Snow Ablation Optimizer through three key strategies to improve performance in high-dimensional optimization problems: (i) a subgroup cooperative mechanism to alleviate cross-dimensional interference, (ii) a learning-automata-based adaptive dimension assignment strategy to dynamically allocate optimization resources, and (iii) a t-distribution-based adaptive step size mechanism to balance global exploration and local exploitation. Extensive experiments on the CEC2017 benchmark suite demonstrate that ISAO achieves superior convergence speed and optimization accuracy, with average rankings of 1.60, 1.77, and 2.03 on 30-, 50-, and 100-dimensional problems, respectively, significantly outperforming the original SAO and several state-of-the-art metaheuristic algorithms. Building upon this optimization capability, ISAO is employed to automatically tune the key hyperparameters of the Dilated BiGRU model. Experiments conducted on the Kaggle electricity load dataset show that the proposed ISAO-Dilated BiGRU model achieves MAE, MAPE, and RMSE values of 20.003, 1.711%, and 25.926, respectively, corresponding to reductions of 16.6%, 15.6%, and 17.7% compared with the baseline model, along with an R² of 0.97841. Comparative results against RNN, LSTM, Random Forest, and the original Dilated BiGRU confirm the robustness and superior long-term dependency modeling capability of the proposed framework. Overall, the proposed ISAO effectively enhances hyperparameter optimization quality and significantly improves the predictive accuracy and stability of the Dilated BiGRU model, providing a reliable and practical solution for short-term electricity load forecasting in modern power systems. Full article

Anaerobic digestion under a Waste-to-Energy (WtE-AD) framework represents a sustainable alternative for managing organic waste and generating bioenergy in developing countries. However, most life cycle assessment (LCA) studies implicitly assume stable operation, overlooking the environmental implications of process instability. In practice, large-scale WtE-AD plants are frequently affected by methanogenic inhibition events that reduce methane production and compromise their technical, economic, and environmental performance. This study—Part 2 of a two-paper series—addresses this gap by quantifying, from a life cycle perspective, the environmental consequences of recurrent methanogenic inhibition events in large-scale WtE-AD systems, complementing the techno-economic analysis presented in Part 1. Large-scale WtE-AD plants were modeled using design equations based on treatment capacity (60–200 t d⁻¹), considering scenarios with up to ten inhibition events over a 25-year operational period. The LCA was conducted in accordance with ISO 14040:14044 standards, defining as the functional unit one ton of co-digested fruit and vegetable residues with meat industry wastes, under an attributional approach with system boundary expansion and evaluating midpoint indicators through the ReCiPe 2016 method. Results show that inhibition events increase greenhouse gas emissions by up to 400% (from 28.1 to 138.6 kg CO₂ eq t⁻¹ of waste treated), while plants with capacities above 125 t d⁻¹ exhibit environmental credits (negative emission balances), demonstrating greater environmental resilience. Electricity substitution from the Mexican grid generated savings of up to 0.624 kg CO₂ eq kWh⁻¹, although the magnitude of the benefits strongly depends on the regional electricity mix. This dependency was further explored through comparative electricity mix scenarios representative of different levels of power sector decarbonization, allowing the sensitivity of WtE-AD environmental performance to regional grid characteristics to be assessed. Compared to landfill disposal (1326 kg CO₂ eq t⁻¹), WtE-AD plants significantly reduce impacts across all assessed categories. By explicitly integrating operational instability into an industrial-scale LCA framework, this work highlights the importance of evaluating methanogenic inhibition events from a life cycle perspective, providing key insights for the design of more sustainable and resilient WtE-AD processes within a Latin American context. Full article

In recent years, the rapid commercialization and widespread adoption of portable and wearable electronic devices have imposed increasingly stringent performance requirements on flexible sensors, including enhanced sensitivity, stability, response speed, comfort, and integration. This trend has driven extensive research and technological advancement in sensor material systems, among which carbon-based materials have emerged as core candidates for high-performance flexible sensors due to their exceptional electrical conductivity, mechanical flexibility, chemical stability, and highly tunable structural features. Meanwhile, new sensing mechanisms and innovative device architectures continue to emerge, demonstrating significant value in real-time health monitoring, early disease detection, and motion-state analysis, thereby expanding the functional boundaries of flexible sensors in the health-care sector. This review focuses on the application progress and future opportunities of carbon-based materials in flexible sensors, systematically summarizing the critical roles and performance-optimization strategies of carbon nanotubes, graphene, carbon fibers, carbon black, and their derivative composites in various sensing systems, including strain and pressure sensing, physiological electrical signal detection, temperature monitoring, and chemical or environmental sensing. In response to the growing demands of modern health-monitoring technologies, this review also examines the practical applications and challenges of flexible sensors—particularly those based on emerging mechanisms and novel structural designs—in areas such as heart-rate tracking, blood-pressure estimation, respiratory monitoring, sweat-component analysis, and epidermal electrophysiological signal acquisition. By synthesizing the current research landscape, technological pathways, and emerging opportunities of carbon-based materials in flexible sensors, and by evaluating the design principles and practical performance of diverse health-monitoring devices, this review aims to provide meaningful reference insights for researchers and support the continued innovation and practical deployment of next-generation flexible sensing technologies. Full article

Many companies in the mining industry include decarbonization of production among their key strategic goals as part of their internal sustainability strategy. This need is driven by a number of factors: stricter regulation in the area of carbon footprint (introduction of carbon taxes, emissions quotas, reporting requirements); sustained growth in demand for electricity and rising market prices; economic feasibility—the need to optimize operating costs and improve energy efficiency. This study provides a comprehensive technical and economic justification for implementing a hybrid power supply system—combining a solar power plant (SPP) and a gas engine power plant (GPP)—at Solidcore Resources’ Varvarinsky hub in Kazakhstan. The methodology includes modeling the energy balance of the real asset (156.9 GWh of annual energy consumption), calculating the output of a 22.6 MW SPP based on local GHI/PR/η parameters, forming and determining the adaptability coefficient Kₐ (proportion of PV in total monthly electricity generation), conducting an economic assessment (NPV, payback period, sensitivity), and inventorying CO₂ emissions under Scope 1–2. The SPP provides approximately 41.3 GWh of electricity generation per year, with an average annual K_a = 0.263; the 40 MW installed capacity of the gas piston power plant covers the residual demand, forming a stable daily and seasonal balance. The project demonstrates a positive NPV (After Tax) = USD 23.65 million with an estimated payback period of 10 years, while the cost of energy in extraction and processing is reduced by almost three times, and the total reduction in CO₂ emissions will be 51%. Thus, hybridization of energy supply systems is a practical compromise between reliability and decarbonization. Determining the adaptability coefficient K_a allows the flexibility of the system to be taken into account, shows how effectively the new energy system uses renewable energy sources, and can be used to optimize the operation of the energy system to achieve the company’s internal sustainable development goals. Full article

Wireless short-range transmission is essential for precise wellbore trajectory control and real-time formation evaluation. Its signal propagation characteristics are influenced by multiple factors, including antenna type, drill collar, mud, and formation resistivity. Most prior studies are based on Magnetic-field Antennas (MFA) and primarily focus on the effects of formation resistivity variations, whereas the investigations on the influence of drill collars and mud resistivity are limited. In this study, a three-dimensional finite-element electromagnetic model of the “antenna–drill collar–mud–formation” system was developed to investigate wireless short-range transmission. The model was used to characterize and compare the electromagnetic field distributions of MFA and Electric-field Antennas (EFA) under in situ conditions. On this basis, a set of parametric sensitivity analyses on transmission performance was performed to quantify the effects of key factors, including drill-collar conductivity and mud resistivity. The results reveal fundamentally different electromagnetic field distributions for the two antenna types: (1) MFA is dominated by localized circumferential magnetic flux loops, whereas EFA transmits signals through axially extended eddy-current channels. (2) The drill collar exerts opposite effects on the two antennas, suppressing signal levels for MFA while significantly enhancing transmission for EFA, resulting in signal amplitudes that are ${10}^3-{10}^5$ times higher. (3) In addition, mud resistivity has little influence on MFA, whereas increasing mud resistivity leads to the pronounced attenuation of EFA signals. These findings provide a quantitative basis for antenna selection and performance optimization in wireless short-range transmission systems under different Logging-While-Drilling (LWD) conditions. Full article

Nanocellulose, a biodegradable and renewable nanomaterial derived from biomass, has emerged as a promising sustainable building block for flexible functional devices due to its renewability, low density, excellent mechanical strength, tunable surface chemistry, and outstanding film-forming capability. This paper provides a critical review of the evaluations and synthesis of recent progress in the manufacturing, functionalization, and incorporation of nanocellulose and its composite materials for electronic devices and electrical systems applications. The paper also highlights the contributions of nanocellulose to performance, durability, and environmental sustainability, along with its potential uses in flexible electrical equipment, energy storage devices, sensors, and conductive components. Furthermore, the review examines the combined effects of nanocellulose with metallic nanoparticles, carbon-based materials, and polymers in developing superior electrically conductive composites. In addition, the article highlights research gaps and suggests future directions for advancing sustainable, high-performance conductive materials. Finally, the paper critically analyzes key challenges such as reliability, interface compatibility, and long-term stability, and proposes strategies to address these limitations. Full article

To address the issues of high electricity costs for industrial loads in enterprise parks, significant peak-valley price differences, and insufficient utilization of renewable energy, a multi-objective capacity optimization method for photovoltaic and energy storage systems has been proposed, incorporating price-based demand response (PDR) and cycle life constraints. Firstly, a multi-objective function was constructed by integrating the aforementioned constraints, aiming to minimize the equivalent annualized comprehensive cost and the energy imbalance rate. Then, to overcome the limitations of the traditional sparrow search algorithm (SSA), such as low convergence speed, limited precision, and the tendency to fall into local optima, an improved SSA was proposed. This improved algorithm was enhanced by the integration of chaotic mapping, adaptive inertia weight, Harris Hawks encircling, and predation strategies. Through these improvements, both the convergence speed and accuracy in solving high-dimensional problems were significantly improved. Finally, a case study was conducted using real load data from an enterprise park in Zhuzhou City. The proposed algorithm achieves a maximum economic benefit improvement of 7.32% over conventional intelligent algorithms while further enhancing power supply reliability. Full article

In order to satisfy the measurement of objects in compact settings, a miniaturized linear inductive encoder with a measurement range of 15 mm is investigated in this paper. The encoder structure integrates a movable part with conductive plates and a stationary part with planar excitation and inductive coils. When a high-frequency alternating current is applied to the excitation coils, a time-varying magnetic field will be generated. Meanwhile, the conductive plates on the movable element will produce an eddy current magnetic field to reduce or boost the magnetic field. As the movable part moves, two-channel amplitude-modulated electrical signals whose amplitudes vary with displacement are obtained. The CORDIC algorithm is utilized to calculate the displacement. The paper describes the structure and working principle of the encoder, presents corresponding finite element simulations of the magnetic field, and introduces a prototype fabricated by PCB technology. Experiments evaluating stability, resolution, and accuracy show that the encoder reaches the measurement accuracy of 12.8 μm within one pitch, and the resolution is 0.7 μm. Importantly, its minimal dimensions (20 mm × 10 mm × 1 mm) enable installation in highly constrained mechanisms. Full article

In this study, ZnO thin films were prepared on the flexible stainless steel (FSS) substrates by the sol–gel method. ZnO nanorods were then hydrothermally grown in the presence of polyvinyl pyrrolidone (PVP) to obtain polymer/nanooxide composites. The microstructure and resistive switching properties of the composites were investigated. X-ray diffraction results confirmed that the PVP-doped ZnO nanorods retained the hexagonal wurtzite structure and had a preferred (002) orientation despite a slight decrease in crystallinity. Surface morphology analysis showed that the addition of PVP resulted in an increase in the nanorod density and a more regular hexagonal structure. Electrical measurements showed a significant improvement in the resistive switching behavior, with a high-resistance state to low-resistance state (HRS/LRS) ratio of 4.67 × 10³. In addition, radiation-tolerant cyclic tests demonstrated that the polymer–oxide hybrid structure effectively buffered irradiation-induced defects, stabilized conductive filament pathways, and preserved switching reliability. These results highlight the potential of PVP-doped ZnO nanorod composites as reliable, flexible, and radiation-tolerant RRAM devices for future aerospace and high-radiation electronics applications. Full article

Non-Pneumatic Tires (NPTs) have been recognized for their advantages, such as low rolling resistance, burst resistance, and lightweight design, which make them highly suitable for application in electric vehicles under complex conditions, including high-frequency starts and stops and high torque. However, the discontinuous spoke support structure has resulted in a significantly higher ground contact pressure distribution compared to traditional pneumatic tires, leading to more severe wear, especially in the contact area where complex stress concentrations have occurred. Currently, the wear behavior mechanisms of NPTs have not been fully clarified, and wear simulation methods that take temperature effects into account are lacking. In this study, a temperature-modified Archard wear equation was integrated into the UMESHMOTION subroutine to achieve real-time updates of the tire surface geometry and simulate the evolution of wear. The modeling approach was validated through experimental testing. The simulation results showed that as the load increased from 100 N to 700 N, the peak ground contact pressure significantly increased, and the contact area gradually expanded, resulting in a notable increase in wear. Additionally, as the slip ratio increased from 2% to 5%, the contact stress and wear area were significantly amplified, leading to an increase in surface roughness and evident local damage. Comparative results indicated that the slip ratio had a more significant impact on wear volume than the load. The study has been conducted from a physical mechanism perspective to verify the dominant role of the slip ratio in the short-term rolling distance of tires, providing a theoretical basis for the structural optimization and wear-resistant design of non-pneumatic tires under complex operating conditions. Full article

To address the trade-off between thermoelectric efficiency in oxide thermoelectric materials used in Aiye Processing Equipment, this study investigates the effect of La doping on the thermoelectric properties of indium oxide (In₂O₃) through experimental characterization and mechanism analysis. The results show that La doping induces synergistic optimization of the electronic structure, lattice dynamics, and defect state of In₂O₃, leading to simultaneous enhancements in thermoelectric and mechanical properties. Specifically, La³⁺ substitution for In³⁺ significantly increases carrier concentration, which, combined with the band convergence-induced elevation of density of states (DOS) near the Fermi level, results in a remarkable improvement in power factor (from the intrinsic enhancement driven by electrical conductivity) while mitigating the reduction in Seebeck coefficient. Meanwhile, lattice distortion caused by ionic radius mismatch and decreased Young’s modulus (due to weakened In-O bonds) jointly enhance phonon scattering and reduce phonon propagation velocity, leading to a significant decrease in lattice thermal conductivity and total thermal conductivity. Consequently, the thermoelectric figure of merit (ZT) of La-doped In₂O₃ increases from 0.055 to 0.358, a six-fold enhancement. Additionally, La doping improves Vickers hardness through three synergistic mechanisms: internal stress from lattice distortion, enhanced interatomic bonding (synergistic reinforcement of ionic and covalent bond components), and dislocation pinning by substitutional defects (La_In). This study demonstrates that La doping achieves the dual regulation of “promoting electrical transport, suppressing thermal conduction, and enhancing mechanical strength” in In₂O₃, breaking the traditional trade-off between thermoelectric and mechanical properties. The findings provide a feasible strategy for the performance optimization of oxide thermoelectrics and lay a foundation for their practical applications in energy conversion systems requiring high efficiency and structural reliability. Full article

In marine hot–humid and salt spray environments, shipborne snap-action limit switches operating under micro-current loads are prone to triggering failures caused by the accumulation of heterogeneous films on electrical contact interfaces, which can induce abnormal behavior in electromechanical systems. To address this issue, this study systematically investigates the failure mechanisms of micro-current limit switches using multimodal diagnostic approaches. The results demonstrate that the migration and accumulation of corrosion products and foreign contaminants within the microswitch unit promote the formation of high-resistance heterogeneous films at the electrical contact interfaces, severely impairing reliable electrical conduction. Electrical contact experiments further reveal that the contact behavior is strongly dependent on the current magnitude. When the current exceeds 2A, arc discharge generated during contact closure can effectively disrupt and remove the heterogeneous films, thereby restoring the electrical functionality of previously failed switches under subsequent micro-current operating conditions. Based on the identified failure mechanism, and inspired by the natural eye-cleaning behavior of crabs, a biomimetic press-and-wipe self-cleaning dual-redundant limit switch design is proposed. The design enables autonomous surface cleaning through controlled reciprocal wiping between the moving and stationary electrical contacts, effectively suppressing the formation and accumulation of high-resistance films at the source. Comparative salt spray and damp heat storage tests demonstrate that the proposed self-cleaning limit switch maintains stable and reliable electrical contact performance in simulated marine environments, significantly improving operational reliability and service life under micro-current loads. This work provides both mechanistic insights and a practical structural solution for enhancing the reliability of electrical contact components operating under low-current conditions in harsh marine environments. Full article

Background/Objectives: Tissue conductivity reflects ionic composition (e.g., sodium), providing critical insights into various diseases. Ultrashort echo time quantitative conductivity mapping (UTE-QCM) offers a method to obtain this information, which is particularly effective for musculoskeletal (MSK) tissues with short T2 relaxation times. The aim of this study is to develop a UTE-QCM framework using ultrashort echo time double echo steady-state (UTE-DESS) and validate its feasibility in the knee. Methods: An ultrashort echo time double echo steady-state (UTE-DESS) sequence was used to acquire S+ and S− images and estimate the transmit radiofrequency field (B1⁺) phase at 3T. The B1⁺ phase was derived by canceling the phase evolution in the free induction decay using these images. This phase data was then processed using two widely used QCM reconstruction methods for comparison: parabolic fitting and an integral-based method. The proposed UTE-QCM framework was validated using a phantom containing three different concentrations of sodium chloride (0%, 0.5%, and 1%). Additionally, three healthy volunteers were recruited to validate UTE-QCM in knee imaging. Results: In both phantom and in vivo experiments, the integral-based QCM demonstrated improved robustness to noise compared to parabolic fitting. In the sodium phantom, the estimated conductivity showed high linearity with sodium concentrations. In the in vivo knee, the generated conductivity maps successfully visualized both long and short T2 tissues. Conclusions: We demonstrated the feasibility of UTE-QCM as a novel quantitative imaging tool targeting short T2 tissues in the MSK system. This technique may facilitate the diagnosis and prognosis of joint disorders. Full article

Growing pressure on electricity grids and the increasing availability of smart meter data have intensified the need for accurate, interpretable, and scalable methods to analyze and forecast household electricity consumption. In this context, this study presents a general, data-agnostic methodology for predicting and classifying household energy consumption. The proposed workflow unifies data preparation, feature engineering, and machine learning techniques (including clustering, classification, regression, and time series forecasting) within a single interpretable pipeline that supports actionable insights. Rather than proposing new prediction algorithms, this work contributes a fully reproducible, end-to-end methodological pipeline that enables the controlled evaluation of the impact of contextual variables, customer segmentation, and cold-start conditions on household energy forecasting. A distinctive aspect of the pipeline is the explicit use of household- and dwelling-level contextual variables to derive customer typologies via clustering and to enrich forecasting models. The models are evaluated for predictive accuracy, reliability under varying conditions, and suitability for operational use. The results show that incorporating contextual variables and clustering significantly improves forecasting accuracy, particularly in cold-start scenarios where no historical consumption data are available. Although numerous public datasets of residential electricity consumption exist, they rarely provide, in an openly accessible form, both detailed load histories and rich contextual attributes, while many are subject to privacy or licensing restrictions. To ensure full reproducibility and to enable controlled experiments where contextual variables can be switched on and off, the experiments are conducted on a synthetically generated dataset that reproduces realistic behavior and seasonal usage patterns. However, the proposed methodology is independent of the specific data source and can be directly applied to any real or synthetic dataset with similar structure. The approach enables applications such as short- and long-term demand forecasting, estimation of household energy costs, and forecasting demand for new customers. These findings demonstrate that the proposed pipeline provides a transparent and effective framework for end-to-end analysis of household electricity consumption. Full article

Shaftless Pump-jet Thrusters (SPTs), which integrate the propulsion motor directly with impellers, provide a compact design and high propulsion efficiency. Despite this, their performance is significantly hampered by eddy current losses in conductive stator sleeves. This study introduces Titanium Matrix Composites (TMC) as superior alternatives to conventional titanium alloys (Ti-6Al-4V, Ti64), leveraging their tailorable anisotropic electromagnetic properties to effectively suppress eddy current losses. Through simulations and experimental validation, the electromagnetic performance of an SPT equipped with a TMC stator sleeve is systematically investigated. Electromagnetic simulations predict a dramatic reduction in eddy current loss of 53.5–79.8% and an improvement in motor efficiency of 5.8–8.5% across the 1500–2900 rpm operational range compared to the Ti64 baseline. Experimental measurements on prototype motors confirm the performance advantage, demonstrating a 3.5–5.7% reduction in input power under equivalent output conditions across the same speed range. After accounting for manufacturing tolerances and control strategies, the refined model demonstrated a markedly improved agreement with the experimental results. This research conclusively establishes TMCs as a high-performance containment sleeve material, which is promising not only for SPTs but also for a broad range of canned motor applications, where an optimal balance between electromagnetic and structural performance is critical. Full article