Search Results (78,042)

To meet aviation decarbonization goals, novel electric energy storage systems are required. A promising approach combines a Li-ion battery with a hydrogen proton exchange membrane fuel cell system (PEMFCS) into an electrochemical energy storage and conversion (EC-ESC) system. Proper power management ensures efficiency, reliability and durability. The study investigates EC-ESC performance for regional hybrid electric aircraft under varying degrees of hybridization. By systematically adjusting the power split between the battery and FCS, we quantify its impacts on system sizing, energy efficiency and battery degradation. The results show that a well-balanced power distribution enhances overall efficiency and energy density while extending system lifetime. Full article

Electrical Impedance Tomography (EIT) is a promising non-invasive sensing technique, yet its practical application in resource-constrained environments is often limited by the high computational cost of inverse image reconstruction. To address this challenge, we focus on specific sensing objectives rather than full image recovery. In this study, we propose a lightweight, task-oriented inference framework for object localization in EIT that bypasses the need to solve computationally expensive inverse reconstruction problems. This approach addresses the high computational demands and hardware complexity of conventional iterative methods, which often hinder real-time monitoring in resource-constrained edge computing environments. Training datasets were generated via finite element method (FEM) simulations for Opposite and Adjacent current injection configurations. A feedforward neural network was developed to independently estimate the radial and angular object positions as probability distributions. Our systematic evaluation revealed that the localization performance depends on the injection configuration and model depth; notably, the Opposite method achieved perfect classification accuracy (1.00) for radial estimation with an optimized architecture of four hidden layers, whereas the Adjacent method exhibited higher ambiguity. Results quantitatively evaluated using the Wasserstein distance show that the Opposite configuration produces more localized, unimodal probability distributions than the Adjacent configuration by utilizing current fields that traverse the entire domain. Compared with existing image-based reconstruction methods, including the conventional electrical impedance tomography and diffuse optical tomography reconstruction software (EIDORS ver.3.12), the proposed framework reduced energy consumption from 3.09 to 0.96 Wh, demonstrating an approximately 70% improvement in energy efficiency while maintaining a high localization accuracy without the need for iterative Jacobian updates. This task-oriented framework enables reliable, high-speed, and energy-efficient localization, making it well-suited for low-power EIT applications in mobile and embedded sensor systems. Full article

This paper presents the design, implementation, and experimental validation of a real-time embedded photovoltaic (PV) emulator based on the two-diode model, using a dSPACE DS1103 platform for hardware validation. The proposed system aims to accurately reproduce the electrical behavior of PV modules under varying environmental conditions, including irradiance and temperature variations. The emulator architecture combines a lookup-table-based modelling approach with a programmable DC power source, enabling deterministic real-time execution and efficient implementation. A multi-level control structure is employed, integrating inner-loop regulation, model-based reference generation, and feedback control to ensure accurate tracking of the PV current–voltage (I–V) characteristics. Experimental results demonstrate that the emulator achieves high accuracy, with an approximation error of approximately 1.2% under standard operating conditions. The system exhibits stable dynamic behavior characterized by a time constant of approximately 0.5 s, with performance maintained across different sampling intervals and load conditions. Additional simulations confirm that the two-diode model preserves high accuracy over a temperature range of 15–60 °C, with deviations below 2%. The results highlight that the two-diode model provides an optimal trade-off between modelling accuracy and computational complexity for real-time embedded applications. The proposed emulator offers a flexible and reliable platform for laboratory validation of photovoltaic behavior and provides the foundation for future testing of maximum power point tracking (MPPT) algorithms, power electronic converters, and embedded control strategies under controlled conditions. Full article

To address the challenges faced by virtual power plants (VPPs) in uncertain market environments and complex distribution networks, including strong market coupling, difficulty in multi-resource coordination, and strict safety constraints, this paper proposes a Hierarchical Hybrid Intelligent Framework (H2IF). The proposed framework integrates a market-aware meta-game mechanism, a topology-embedded graph attention coordination method, and a risk-aware soft/hard constraint safety mechanism to achieve economically optimal dispatch of VPPs in complex dynamic scenarios. By explicitly modeling competitive market interactions, the proposed method enhances strategy robustness; by exploiting grid topology priors, it improves multi-agent coordination capability; and by combining differentiable projection with risk-constrained optimization, it jointly ensures operational safety and revenue stability. Simulation results on a modified IEEE 33-bus system demonstrate that H2IF outperforms mainstream deep reinforcement learning methods and rule-based dispatch strategies in overall performance. In the 24 × 300-step testing scenario, H2IF achieves an average single-episode operating cost of 38.23 k$, which is 28.9%, 40.4%, and 26.5% lower than those of MADDPG, SAC, and the rule-based method, respectively, while also yielding the lowest constraint violation level. Ablation studies further verify the effectiveness of each key module in improving profit, reducing operating costs, enhancing tracking performance, and strengthening safety. The results indicate that the proposed method enables coordinated optimization of economy, safety, and robustness for VPP dispatch under uncertain market and operating conditions. Full article

Certain extensions of $F$-controlled self-mappings in metric spaces to the, as called in this manuscript, $F_{\tau \text{'}\tau }$ and modified $*F_{\tau \text{'}\tau }$ controlled self-mappings, which are parameterized by two parameters, are addressed. Those parameters govern the properties of local expansivity, asymptotic nonexpansivity, and contractivity properties of the generated sequences. Also, further generalizations to parameterizations by two real sequences of parameters, which are referred to as $F_{{\left\{{\tau }_j^{\text{'}}\right\}}_{j=0}^{\infty }{\left\{{\tau }_j\right\}}_{j=0}^{\infty }}$-controlled self-mappings, are studied. The main formulated results rely on the asymptotic contractivity and the asymptotic nonexpansivity in metric spaces and some of their relevant properties. In particular, the properties of boundedness of the sequences of distances, as well as those of boundedness of the elements of the sequences themselves, are investigated under asymptotic contractivity or nonexpansivity related to the various types of the above-mentioned $F_{\left(.\right)}$-controlled self-mappings. Also, existence and uniqueness results of fixed points are proved if the metric space is complete, and the resulting Cauchyness properties of sequences and properties of the convergence of such sequences to fixed points are also proved. Finally, two illustrative examples are described if the $F_{\left(.\right)}$-controlled self-mappings are of a cyclic nature when defined using the union of two nonempty closed subsets of the metric space, in the case that those sets intersect, and also in the case when they are disjointed. Full article

In this paper, a novel high-shielding-effectiveness energy-selective surface (HSE–ESS) is proposed. In previous solutions regarding energy-selective surfaces (ESSs) presented in the literature, PIN diodes are usually employed as nonlinear transmission components; however, these diodes may be burnt by powerful high-power microwave (HPM) beams, causing ESSs to lose their shielding effectiveness (SE). To date, no studies have focused on maintaining the SE performance of ESSs after PIN diode failure. To address these limitations, we introduce shape memory alloys (SMAs) into ESS design. The consequences of PIN diode failure are offset by the physical deformation of SMA components caused by high-amplitude-current heating. This characteristic, featuring 30 dB SE, can be defined as high shielding effectiveness (HSE). After completing the design and performing accurate numerical simulations, we fabricated a prototype using PCB technology and characterized it in an anechoic environment, verifying the overall method. In particular, the SMA components proved to be an effective medium for guaranteeing electrical continuity under thermal stress conditions, thus paving the way for their extended adoption in ESSs by substituting or acting as a back-up for PIN diodes. Overall, this approach enhances the reliability and SE of ESSs by adding SMA components. Full article

Soil salinization is one of the primary forms of land degradation in arid and semi-arid regions, severely constraining agricultural production in Xinjiang’s oases. Unmanned aerial vehicle (UAV) imagery provides an effective means for precise monitoring of soil salinization, with image spatial resolution being a key factor affecting assessment accuracy. However, traditional single-scale remote sensing monitoring methods rely solely on spectral and textural features at the leaf scale (0.1 m resolution captures leaf-scale characteristics), neglecting the contribution of multi-scale features (single-row canopy scale and single-membrane-covered area scale (6-row crop canopy)) to soil salinity. For instance, 0.5–1 m reflects single-row canopy scale, while 2 m reflects single-membrane-covered area scale. Therefore, this study developed a multi-scale UAV imagery and machine learning framework to enhance soil electrical conductivity prediction accuracy. This study focuses on oasis cotton fields in Shaya County, Xinjiang. Based on UAV multispectral imagery, we resampled data to generate eight datasets at different spatial resolutions: 0.1, 0.5, 1, 1.5, 2, 2.5, 5, and 10 m. For each resolution, we calculated 21 spectral indices and 48 texture features to construct a feature set. At both single and multispatial scales, spectral indices, texture features, and their spectral-texture fusion features were constructed. Combining these with Backpropagation Neural Network (BPNN), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGBoost) models, a soil EC estimation framework was developed. The impact of three feature combination schemes on cotton field soil conductivity estimation using single-scale UAV imagery was compared. The accuracy of soil EC estimation for cotton fields was compared between multi-spatial scale and single-scale UAV image features. The optimal combination strategy for a multi-spatial scale and multiple features was determined. Results indicate that combining spectral and texture features yields the highest estimation accuracy for cotton field soil electrical conductivity in single-scale analysis. Multi-spatial scale image features outperform single-scale image features in estimating cotton field soil electrical conductivity accuracy. By comparing different feature combinations, when integrating 0.5 m spatial-scale spectra (S1, EVI, DVI, NDVI, Int1, SI) with 0.1 m texture features (RE1_ent, R_cor, RE1_cor, G_hom, B_mea, R_con, NIR_con), the XGBoost model achieved the optimal prediction accuracy ($R^2$ = 0.693, RMSE = 0.515 dS/m), outperforming the methods using multiple features at a single scale. This study developed a novel multi-scale image feature fusion technique to construct a machine learning model. This method describes the image characteristics of soil electrical conductivity at different geographical scales, providing a reference approach for the rapid and accurate prediction of soil electrical conductivity in arid regions. Full article

Realizing high-quality-factor (high-Q) plasmonic resonances in the visible regime is critical for enhancing light-matter interactions and advancing biochemical sensing. However, traditional localized surface plasmon resonances (LSPRs) typically suffer from broad spectral linewidths due to severe radiative damping. In this work, we propose a simple two-dimensional symmetric gold nanohole-array metasurface that supports a symmetry-protected bound state in the continuum (SP-BIC) at normal incidence. By introducing extrinsic symmetry breaking via oblique incidence, this non-radiative dark state is successfully transformed into an observable high-Q quasi-BIC Fano resonance. Cartesian multipole decomposition reveals that this sharp mode ($\lambda \approx 688$ nm) is predominantly driven by a tightly confined Magnetic Dipole (MD) excitation, which drastically suppresses radiative leakage compared to the highly damped Electric Dipole (ED)-dominated LSPR. Consequently, the quasi-BIC mode exhibits an ultra-narrow spectral linewidth ($\mathrm{FWHM}\approx 17.4$ nm). While its bulk sensitivity ($236.9$ nm/RIU) is slightly lower than that of the LSPR mode, the exceptionally sharp resonance yields a remarkably low Limit of Detection (LOD) of $7.35\times {10}^{-3}$ RIU, achieving a nearly five-fold improvement over the traditional LSPR. Furthermore, the quasi-BIC mode maintains an outstanding Figure of Merit (FOM up to ∼19.7 ${\mathrm{RIU}}^{-1}$) across the entire sensing range. By eliminating the need for complex asymmetric nanofabrication, this robust angle-tuned design strategy provides a highly promising platform for the development of high-resolution, low-cost optical biosensors. Full article

Driven by the global agenda of low-carbon urban development, local governments in China have implemented targeted policies requiring new energy vehicle adoption in the ride-hailing industry. This study focuses on a key issue in the development of sustainable smart public transportation systems: the factors affecting the acceptance of electric vehicles (EVs) in ride-hailing services among full-time and part-time drivers. Using 432 valid samples of ride-hailing drivers from Zhangzhou, a third-tier city in China, we compared the basic personal attributes of full-time and part-time drivers. Ordered logit models were developed to explore differences in factors influencing their acceptance of electric ride hailing (ER). Findings reveal: (1) Drivers’ perceived significance of EVs in green transportation is positively associated with their acceptance of ER. (2) Endurance mileage and charging efficiency have no significant effect on acceptance among drivers in underdeveloped cities. (3) Full-time drivers exhibit relatively low concern for subsidy policies, whereas part-time drivers express a pressing need for vehicle purchase subsidies and operational subsidies. (4) Overall, part-time drivers demonstrate higher acceptance of ER than full-time drivers. Based on these findings, this paper offers policy recommendations for governments to enhance ER acceptance among both driver groups. It is important to note that the present study utilizes survey data collected from Zhangzhou. The research conclusions should be treated with caution when applied to other cities, and further studies can be conducted in different regions to verify the results. Full article

Vacuum degree inside vacuum interrupter (VI) deteriorates due to cracks from long-term operation of VI, gas emitted from internal arc heat, leakage through the joint, etc. Partial discharge occurs between the two contacts inside the VI or between the contact and floating shield, which leads to dielectric breakdown and electrical accidents of high voltage apparatus. In this paper, the study on the vacuum degree monitoring of distribution class vacuum interrupter according to non-contact method of coupling capacitor based on partial discharge was performed. In order to monitor the partial discharge between two contacts inside VI with high accuracy, a partial discharge sensing electrode (PDDE) was designed using the 3D finite element method (FEM). In addition, after calculating the internal capacitance according to the structure and size characteristics inside VI, the capacity of the coupling capacitor to detect the signal was calculated. The partial discharge characteristics according to the vacuum degree were analyzed by applying PDDE and a coupling capacitor. As results, it was found that the partial discharge characteristics inside VI differ depending on the voltage type. In addition, it was confirmed that even if VI has the same internal structure and size, the partial discharge characteristics appear differently. Based on the experimental results, we proposed maintenance criteria for VI for each voltage type. Full article

Biochar amendment holds promise for improving saline soils, yet its efficacy is often constrained by the uncertainty of application rates. In this study, a large field trial and associated statistical modeling were conducted to explore the mechanisms by which biochar affects wheat yield in coastal saline soils of northern China. Results showed that biochar application significantly increased soil organic carbon (SOC) content (R²= 0.615, p < 0.001) but induced marked spatial heterogeneity across the field, with the coefficient of variation (CV) reaching 30.2%. Given the difficulty of uniformly applying biochar in the field, subplot-level SOC was used as a proxy for effective biochar distribution. Stepwise regression identified soil electrical conductivity (EC) as the dominant yield constraint (standardized coefficient = −0.69), rather than water and nutrients, and a quadratic relationship was observed between SOC and EC. Structural equation modeling (SEM) further suggested a trade-off: SOC was associated with higher yield through reduced bulk density (BD) (path coefficient = −0.603), whereas high SOC levels were also associated with increased EC under this coastal saline field setting (path coefficient = 0.243), thereby indirectly constraining growth. Consequently, the agronomic response showed a threshold-like transition: the peak wheat yield occurred at an SOC threshold of 13.87 g kg⁻¹ (equivalent to 44.41 t ha⁻¹), which exceeded the point of minimum salinity (11.71 g kg⁻¹, equivalent to ~29.90 t ha⁻¹ biochar). These results suggest that the agronomic benefit of biochar in saline soils depends on maintaining application within an estimated beneficial buffering zone. Full article

Digital electricity meters display readings via digits, but accurate image-based recognition faces a key challenge: the frequent omission of decimal points creates a critical asymmetry between the visual image and its true semantic meaning. To address this visual-semantic asymmetry, we propose an improved YOLOv10n approach incorporating cascaded Visual-Semantic processing. We introduce a Reparameterized Convolution Single-Shot Aggregation (RCSOSA) module and a SimAM attention mechanism to enhance feature extraction, and employ Normalized Wasserstein Distance (NWD) Loss to boost small-target detection. To rectify the visual-semantic asymmetry, we introduce domain-specific format rules based on power industry standards (taking GB/T 17215-2018 as an example) to provide structural constraints for digit recognition. Experimental results show superior performance with 0.870 precision, 0.932 mAP50, and 116 FPS inference speed, outperforming reference models in both precision and efficiency for real-time meter inspection. Full article

Urban roundabouts present significant design challenges for the integration of micro-mobility, yet comparative evidence regarding user behavior remains limited. As cities transition toward sustainable transport networks, understanding the operational needs of different micromobility modes is essential for urban planning. This study investigates the dynamic strategies of micromobility users through a controlled field experiment at a mini-roundabout in Gravina di Catania, Italy. Twenty experienced riders executed crossings using conventional bicycles and electric scooters. Utilizing drone recordings and open-source tracking, the analysis extracted speed, longitudinal acceleration, and path radius across 80 maneuvers. The findings reveal that behavior is highly dependent on vehicle type and geometric deflection. On highly deflected trajectories, e-scooters selected wider radii and achieved up to 15% higher speeds and accelerations than bicycles, whereas on gentler trajectories, they adopted more conservative, tighter lines with intense braking. Bicycles exhibited smaller, less systematic adjustments. These significant kinematic differences indicate that bicycles and e-scooters possess distinct performance envelopes. Treating them as a single legal or design class obscures stability disparities influencing conflict risk. Ultimately, this research provides empirical insights to guide urban planners in redesigning intersections, emphasizing that tailored infrastructure and targeted speed management are critical steps toward safer, truly sustainable urban mobility. Full article

Piezoelectric thin films have been widely used in micro-electromechanical systems (MEMSs), such as sensors, actuators, and resonant devices. Electromechanically driven fractures can severely degrade device performance and reliability. In this work, a phase-field damage model is developed for MEMS piezoelectric thin films under coupled electromechanical loading, incorporating pre-existing defects via an equivalent local fracture toughness. Microcracks and micro-voids arising from manufacturing defects are integrated into the model through an effective local fracture toughness, enabling a unified description of their roles in crack initiation and propagation. The proposed model is implemented in ABAQUS by means of a user-defined element (UEL) subroutine and solved using a staggered scheme. Numerical results show that the level of pre-existing defects, the applied electric potential, and the polarization direction all exert significant effects on fracture behavior. As the defect parameter D_c increases from 0 to 0.10, the reaction force decreases from 87.8 N to 86.3 N, indicating reduced fracture resistance due to manufacturing-induced defects. In addition, the reaction force changes from 90.3 N at −500 V to 86.3 N at +500 V, while it decreases from 102.9 N to 87.1 N as the polarization angle β increases from 0° to 90°. These results demonstrate that pre-existing defects and electromechanical loading jointly govern crack evolution in MEMS piezoelectric thin films. The present study provides a useful numerical tool for fracture analysis, reliability assessment, and structural design of MEMS piezoelectric devices containing manufacturing defects. Full article

Infrared focal plane arrays (IRFPAs) often suffer from spatiotemporal nonuniformity that persists after conventional two-point nonuniformity correction (NUC), especially under temperature drift and time-varying readout conditions. These residuals are typically structured, including column-group striping caused by shared column-end circuits and row-wise baseline/common-mode drift induced by row-scanning paths. We propose a structured, digital-twin-inspired detector-side refinement of two-point NUC that augments the bias term with interpretable low-dimensional components: a static column bias vector capturing group-correlated residuals and a row-related structured term consisting of a static row baseline and a frame-synchronous common-mode component with row-dependent sensitivity, while keeping the two-point gain/offset backbone unchanged. Rather than representing a full system-level digital twin of the infrared payload, the proposed framework serves as a detector-side virtual representation of dominant readout-induced structured residual states that can be estimated and updated from calibration data. Experiments on blackbody calibration data across multiple temperature points demonstrate that the column-related structured component significantly reduces group-wise column residuals, the row-related structured component suppresses time-varying row striping, and the combined method improves both column- and row-direction metrics consistently across temperatures. Full article