Search Results (24,505)

Ultra-wideband (UWB) technology is a crucial facilitator for high-data-rate wireless communication due to its extensive frequency spectrum and low power consumption. Simultaneously, multiple-input multiple-output (MIMO) systems have garnered considerable attention owing to their capability to enhance channel capacity and link dependability. This article discusses the development of small, high-performance MIMO UWB antennas with mutual suppression capabilities to fully use the benefits of both technologies. Additionally, the suggested antenna features a straightforward design and dual-band-notched characteristics. The antenna structure includes two radiating elements measuring 85 × 45 mm². These elements use a rectangular patch provided by a coplanar waveguide (CPW). Double U-shaped slots are incorporated into the rectangular patch to introduce dual-band-notched properties, which help mitigate interference from WiMAX and WLAN communication systems. The antenna is fabricated on a Mylar^® polyester film substrate of 0.3 mm in thickness, with a dielectric constant of 3.2. According to the measurement results, the suggested antenna functions efficiently across the frequency spectrum of 2.29 to 20 GHz, with excellent impedance matching throughout the bandwidth. Furthermore, it provides dual-band-notched coverage at 3.08–3.8 GHz for WiMAX and 4.98–5.89 GHz for WLAN. The antenna exhibits impressive performance, including favorable radiation attributes, consistent gain, and little mutual coupling (less than −20 dB). Additionally, the envelope correlation coefficient (ECC) is extremely low (ECC < 0.01) across the working bandwidth, which indicates excellent UWB MIMO performance. This paper offers an appropriate design methodology for future flexible and compact UWB MIMO systems that can serve as interference-resilient antennas for next-generation wireless applications. Full article

In the electrification of low-altitude aircraft, aviation hybrid power systems have become one of the core research areas in this field due to their significant advantages of low emissions and long endurance. The energy management strategy is an important part of the design of aviation hybrid power systems and has a significant impact on the performance and safety.This paper first develops a 200 kW dual DC-bus series hybrid power system prototype for low-altitude aircraft and its Simulink simulation model; then, it proposes a rule-based energy management strategy that uses the smoothness of the state of charge (SOC) of energy storage batteries as a coordination criterion. The strategy is validated via ground tests, where the battery SOC remains above 30%, the system response time is within 5 s, and the DC-bus voltage fluctuation is within 1%. These results demonstrate the strategy’s feasibility, providing a reference for designing and implementing series hybrid power systems. Full article

With increasing global pressure to decarbonize electricity systems, particularly in regions outside international carbon trading frameworks, it is essential to develop adaptive optimization tools that account for regulatory policies and system-level uncertainty. An emission-constrained power dispatch strategy based on an Adaptive Grouped Fish Migration Optimization (AGFMO) algorithm is proposed. The algorithm incorporates dynamic population grouping, a perturbation-assisted escape strategy from local optima, and a performance-feedback-driven position update rule. These enhancements improve the algorithm’s convergence reliability and global search capacity in complex constrained environments. The proposed method is implemented in Taiwan’s 345 kV transmission system, covering a decadal planning horizon (2023–2033) with scenarios involving varying load demands, wind power integration levels, and carbon tax schemes. Simulation results show that the AGFMO approach achieves greater reductions in total dispatch cost and CO₂ emissions compared with conventional swarm-based techniques, including PSO, GACO, and FMO. Embedding policy parameters directly into the optimization framework enables robustness in real-world grid settings and flexibility for future carbon taxation regimes. The model serves as decision-support tool for emission-sensitive operational planning in power markets with limited access to global carbon trading, contributing to the advanced modeling of control and optimization processes in low-carbon energy systems. Full article

We systematically investigate the combined impact of process variation effects (PVEs), metal gate work function fluctuation (WKF), and random dopant fluctuation (RDF) on the key electrical characteristics of sub-1-nm technology node gate-all-around silicon nanosheet complementary field-effect transistors (GAA Si NS CFETs). Through comprehensive statistical analysis, we reveal that the interplay of these intrinsic and extrinsic sources of variability induces significant fluctuations in the off-state leakage current across both N-/P-FETs in GAA Si NS CFETs. The sensitivity to process-induced variability is found to be particularly pronounced in the P-FETs, primarily due to the enhanced parasitic conduction associated with the bottom nanosheet channel. Given the correlated nature of PVE, WKF, and RDF factors, the statistical sum (RSD) of the fluctuation for each factor is overestimated by less than 50% compared with the simultaneous fluctuations of PVE, WKF, and RDF factors. Furthermore, although the static power dissipation remains relatively small compared to dynamic and short-circuit power components, it exhibits the largest relative fluctuation (approximately 82.1%), posing critical challenges for low-power circuit applications. These findings provide valuable insights into the variability-aware design and optimization of GAA NS CFET device fabrication processes, as well as the development of robust and reliable CFET-based integrated circuits for next-generation technology nodes. Full article

Hydrogen is one of the future green energy sources that could resolve issues related to fossil fuels. The widespread use of hydrogen can be enabled by composite-overwrapped pressure vessels for storage. It offers advantages due to its low weight and improved mechanical performance. However, the safe storage of hydrogen requires continuous monitoring. Combining ultrasonic guided waves with interpretable machine learning provides a powerful tool for structural health monitoring. In this study, we developed a feature extraction approach based on a similarity method that enables interpretability in the proposed machine learning model for damage detection and localization in pressure vessels. Furthermore, a systematic optimization was performed to explore and tune the model’s parameters. This resulting model provides accurate damage localization and is capable of detecting and localizing damage on hydrogen pressure vessels with an average localization error of 2 cm and a classification accuracy of 96.5% when using quantized classification. In contrast, binarized classification yields a higher accuracy of 99.5%, but with a larger localization error of 6 cm. Full article

Electrocardiogram (ECG) and phonocardiogram (PCG) signals are widely used in the early prevention and diagnosis of cardiovascular diseases (CVDs) due to their ability to accurately reflect cardiac conditions from different physiological perspectives and their ease of acquisition. Currently, some studies have explored the joint use of ECG and PCG signals for disease screening, but few studies have considered the trade-off between classification performance and energy consumption in model design. In this study, we propose a multimodal CVDs detection framework based on Spiking Neural Networks (SNNs), which integrates ECG and PCG signals. A differential fusion strategy at the signal level is employed to generate a fused EPCG signal, from which time–frequency features are extracted using the Adaptive Superlets Transform (ASLT). Two separate Spiking Convolutional Neural Network (SCNN) models are then trained on the ECG and EPCG signals, respectively. A confidence-based dynamic decision-level (CDD) fusion strategy is subsequently employed to perform the final classification. The proposed method is validated on the PhysioNet/CinC Challenge 2016 dataset, achieving an accuracy of 89.74%, an AUC of 89.08%, and an energy consumption of 209.6 $\mathsf{\mu }$J. This method not only achieves better balancing performance compared to unimodal signals but also realizes an effective balance between model energy consumption and classification effect, which provides an effective idea for the development of low-power, multimodal medical diagnostic systems. Full article

This paper proposes an automatic power and modulation control (APMC) circuit that can directly detect the degradation of vertical cavity surface emitting laser (VCSEL) diodes by utilizing a novel voltage sensing mechanism, thereby eliminating the need for costly external monitoring photodiodes. Notably, the proposed APMC architecture facilely observes the performance degradation by sampling the voltage values at the upper node of the VCSEL diode during both modulation on and off states. The APC loop can perceive a 25 mV voltage drop that corresponds to a 0.5 mA increase in the threshold current, providing a 4-bit digital switch signal. Thereafter, it is delivered to the VCSEL diode driver to initiate compensation of the bias current. In the AMC loop, a 50 mV voltage drop equivalent to a 1 mA reduction in the modulation current is similarly detected to produce another 4-bit digital code. The proposed APMC IC is designed by using a 180 nm CMOS process and consumes a total power of 18.2 mW from a single 3.3 V supply. Full article

Hydrogen-fueled internal combustion engines present a promising pathway for reducing carbon emissions in urban transportation by allowing for the reuse of existing vehicle platforms while eliminating carbon dioxide emissions from the exhaust. However, operating these engines with lean air–fuel mixtures—necessary to reduce nitrogen oxide emissions and improve thermal efficiency—leads to significant reductions in power output due to the low energy content of hydrogen per unit volume and slower flame propagation. This study investigates whether integrating a mild hybrid electric system, operating at 48 volts, can mitigate the performance losses associated with lean hydrogen combustion in a small passenger vehicle. A complete simulation was carried out using a validated one-dimensional engine model and a full zero-dimensional vehicle model. A Design of Experiments approach was employed to vary the electric motor size (from 1 to 15 kW) and battery capacity (0.5 to 5 kWh) while maintaining a fixed system voltage, optimizing both the component sizing and control strategy. Results showed that the best lean hydrogen hybrid configuration achieved reductions of 18.6% in energy consumption in the New European Driving Cycle and 5.5% in the Worldwide Harmonized Light Vehicles Test Cycle, putting its performance on par with the gasoline hybrid benchmark. On average, the lean H₂ hybrid consumed 41.2 kWh/100 km, nearly matching the 41.0 kWh/100 km of the gasoline P0 configuration. Engine usage analysis demonstrated that the mild hybrid system kept the hydrogen engine operating predominantly within its high-efficiency region. These findings confirm that lean hydrogen combustion, when supported by appropriately scaled mild hybridization, is a viable near-zero-emission solution for urban mobility—delivering competitive efficiency while avoiding tailpipe CO₂ and significantly reducing NO_x emissions, all with reduced reliance on large battery packs. Full article

Plant disease segmentation has achieved significant progress with the help of artificial intelligence. However, deploying high-accuracy segmentation models in resource-limited settings faces three key challenges, as follows: (A) Traditional dense attention mechanisms incur quadratic computational complexity growth (O($n^{2}d$)), rendering them ill-suited for low-power hardware. (B) Naturally sparse spatial distributions and large-scale variations in the lesions on leaves necessitate models that concurrently capture long-range dependencies and local details. (C) Complex backgrounds and variable lighting in field images often induce segmentation errors. To address these challenges, we propose Sparse-MoE-SAM, an efficient framework based on an enhanced Segment Anything Model (SAM). This deep learning framework integrates sparse attention mechanisms with a two-stage mixture of experts (MoE) decoder. The sparse attention dynamically activates key channels aligned with lesion sparsity patterns, reducing self-attention complexity while preserving long-range context. Stage 1 of the MoE decoder performs coarse-grained boundary localization; Stage 2 achieves fine-grained segmentation by leveraging specialized experts within the MoE, significantly enhancing edge discrimination accuracy. The expert repository—comprising standard convolutions, dilated convolutions, and depthwise separable convolutions—dynamically routes features through optimized processing paths based on input texture and lesion morphology. This enables robust segmentation across diverse leaf textures and plant developmental stages. Further, we design a sparse attention-enhanced Atrous Spatial Pyramid Pooling (ASPP) module to capture multi-scale contexts for both extensive lesions and small spots. Evaluations on three heterogeneous datasets (PlantVillage Extended, CVPPP, and our self-collected field images) show that Sparse-MoE-SAM achieves a mean Intersection-over-Union (mIoU) of 94.2%—surpassing standard SAM by 2.5 percentage points—while reducing computational costs by 23.7% compared to the original SAM baseline. The model also demonstrates balanced performance across disease classes and enhanced hardware compatibility. Our work validates that integrating sparse attention with MoE mechanisms sustains accuracy while drastically lowering computational demands, enabling the scalable deployment of plant disease segmentation models on mobile and edge devices. Full article

With the increased power density of internal combustion engines (ICE) and growing demands for lightweight design, the connecting rod big-end bearings are subjected to significant alternating loads. Consequently, the interference–fit interfaces become susceptible to fretting damage, which can markedly shorten engine service life and impair reliability. In the present study, the effects of the big end manufacturing process, bolt preload, and bearing bush interference fit are considered to develop a coupled lubrication–dynamic model of the connecting rod big-end bearing. This model investigates the fretting damage issue in the bearing bush of a marine diesel engine’s connecting rod big end. The results indicate that the relatively low stiffness of the big end is the primary cause of bearing bush fretting damage. Interference fit markedly affects fretting wear on the bush back, whereas the influence of bolt preload is secondary; nevertheless, a decrease in either parameter enlarges the fretting distance. Based on these findings, an optimized design scheme is proposed. Full article

Against the backdrop of accelerating global energy transition, developing high-performance energy-storage systems is crucial for achieving carbon neutrality. Traditional electrode materials are limited by a single densification storage mechanism and low conductivity, struggling to meet demands for high energy/power density and a long cycle life. Carbon/high-entropy alloy nanocomposites provide an innovative solution through multi-component synergistic effects and cross-scale structural design: the “cocktail effect” of high-entropy alloys confers excellent redox activity and structural stability, while the three-dimensional conductive network of the carbon skeleton enhances charge transfer efficiency. Together, they achieve synergistic enhancement via interfacial electron coupling, stress buffering, and dual storage mechanisms. This review systematically analyzes the charge storage/attenuation mechanisms and performance advantages of this composite material in diverse energy-storage devices (lithium-ion batteries, lithium-sulfur batteries, etc.), evaluates the characteristics and limitations of preparation techniques such as mechanical alloying and chemical vapor deposition, identifies five major challenges (including complex and costly synthesis, ambiguous interfacial interaction mechanisms, lagging theoretical research, performance-cost trade-offs, and slow industrialization processes), and prospectively proposes eight research directions (including multi-scale structural regulation and sustainable preparation technologies, etc.). Through interdisciplinary perspectives, this review aims to provide a theoretical foundation for deepening the understanding of carbon/high-entropy alloy composite energy-storage mechanisms and guiding industrial applications, thereby advancing breakthroughs in electrochemical energy-storage technology under the energy transition. Full article

The detection of faint, fast-moving objects such as space debris, in optical data is a major challenge due to their low signal-to-background ratio and short visibility time. This work addresses this issue by implementing the Stack-CNN algorithm, originally designed for offline analysis, on an FPGA-based platform to enable real-time triggering capabilities in constrained space hardware environments. The Stack-CNN combines a stacking method to enhance the signal-to-noise ratio of moving objects across multiple frames with a lightweight convolutional neural network optimized for embedded inference. The FPGA implementation was developed using a Xilinx Zynq Ultrascale+ platform and achieves low-latency, power-efficient inference compatible with CubeSat systems. Performance was evaluated using both a physics-based simulation framework and data acquired during outdoor experimental campaigns. The trigger maintains high detection efficiency for 10 cm-class targets up to 30–40 km distance and reliably detects real satellite tracks with signal levels as low as 1% above background. These results validate the feasibility of on-board real-time debris detection using embedded AI, and demonstrate the robustness of the algorithm under realistic operational conditions. The study was conducted in the context of a broader technology demonstration project, called DISCARD, aimed at increasing space situational awareness capabilities on small platforms. Full article

Antibiotics have dramatically reduced the burden of infectious diseases since their discovery, but the accelerating rise in antimicrobial resistance (AMR) now threatens these gains. AMR was responsible for nearly 5 million deaths in 2023 and continues to undermine the efficacy of existing treatments, particularly in low- and middle-income countries. While efforts to address AMR have focused heavily on antibiotic stewardship and new drug development, vaccines represent a powerful yet underutilized tool for prevention. By reducing the incidence of bacterial infections, vaccines lower antibiotic consumption, interrupt transmission of resistant strains, and minimize the selective pressures that drive resistance. Unlike antibiotics, vaccines offer long-lasting protection, rarely induce resistance, and confer indirect protection through herd immunity. This review examines the global burden and drivers of AMR, highlights the unique advantages of vaccines over antibiotics in mitigating AMR, and surveys the current development pipeline of vaccines targeting key multidrug-resistant bacterial pathogens. Full article

Polymer viscoelasticity is crucial for mechanical performance, but conventional low-frequency methods struggle to isolate viscous loss—a key viscosity indicator. This study introduces a high-frequency ultrasonic method to differentiate the polymer viscous dissipation index by analyzing acoustic phase shifts. We employ ultrasonic phase-shift thermometry to measure localized temperature increases resulting from minute variations in sound velocity during controlled heating. This allows for the quantification of viscous loss, which is then used to distinguish between different polymer formulations. Experimental and simulation results on a series of polyurethane specimens with varying Shore hardness levels demonstrate that decawatt-range (10–20 W) ultrasonic irradiation enables sensitive and precise differentiation. Notably, the Shore A70 polyurethane sample exhibited a significantly higher viscous dissipation index, evidenced by the largest temperature rise (27.5 °C) and the highest proportion of viscous heating to total power dissipation (93.1%) under 17 W acoustic irradiation. While this study focuses on commercially available polymers, the method can be extended to evaluate key performance parameters, such as tensile modulus and glass transition temperature, in polymers fabricated under various processing conditions, thereby offering a powerful tool for material quality assessment. Full article

The thermal discharge from coastal power plants exchanges heat with the surrounding marine environment, potentially affecting the aquatic ecosystem. This study utilizes Landsat-series satellite data from 2008 to 2023 to extract the spatiotemporal distribution characteristics of thermal discharges from the Xiangshan Harbor Guohua Power Plant (GPP) and the Wushashan Power Plant (WPP). Additionally, the study investigates the impact of thermal discharge on local aquatic life by examining the spatiotemporal distribution of chlorophyll-a (Chl-a). The results indicate that (1) the overall area of thermal rise in GPP and WPP shows a decreasing trend. The interannual variation in low thermal rise zones (+1 °C, +2 °C) is substantial, with significant seasonal differences mainly influenced by seasonal sea–air temperature differences, the flow velocity of seawater at the discharge outlet, and water depth. (2) The diffusion of thermal discharge is significantly affected by tides. The area of thermal rise is larger during ebb tide compared to flood tide, and during neap tide compared to mid-tide and spring tide. During the ebb tide of the neap tide period, the total area of thermal rise in WPP is approximately three times that of GPP. (3) There is a significant positive correlation between thermal discharge and concentrations of Chl-a. Thermal discharge has complex impacts on aquatic life, primarily positive. The findings of this study provide important references for analyzing the ecological impacts of thermal discharge from coastal power plants. Full article