Search Results (13,421)

This study proposes a knee joint motion detection method based on overlapping spectrum demodulation using fiber Bragg grating (FBG) technology. A flexible FBG encapsulated with polydimethylsiloxane (PDMS) is attached to the joint surface. Axial strain in the FBG sensor is generated due to the bending and extension movements of the joint, which leads to a central reflection wavelength shift of the FBG sensor. The overlapping spectrum between the FBG reflection and the output of a tunable fiber laser is related to the wavelength shift of the FBG. The variation is expressed as the changes in reflected optical power received by an optical power meter. It transforms complex spectral analysis into intuitive optical power measurement for demodulating the reflected wavelength of the FBG sensor. The relationship between the optical power of the overlapping spectrum and wavelength shift of the FBG induced by joint motion is theoretically and experimentally analyzed. The real-time demodulation of joint motion is realized based on this relationship. Experimental results demonstrate that the system exhibits good repeatability in monitoring knee joint motion. The performance and practical potential of the system are evaluated through a quantitative comparison with existing techniques and an analysis of its current limitations. Full article

With the rapid expansion of global photovoltaic (PV) installed capacity, hot spot defects have become a major hidden danger that reduces power generation efficiency and threatens the safe and stable operation of PV stations. Unmanned aerial vehicle (UAV) infrared remote sensing is a key technology for the efficient intelligent monitoring of large-scale PV stations. However, detecting tiny hotspots in such infrared images poses severe challenges. Most of these defects are ultra-small targets with extremely low pixel size and weak contrast, which are easily submerged by complex background noise, leading to prominent issues including low detection accuracy and high miss rates. To address these issues, we propose a lightweight detection network based on YOLO11n, named PHSNet, for PV hotspot detection in UAV infrared images. Its core designs include the dynamic convolution integrated C3k2 (Dy-C3k2) for small target feature enhancement, context-guided downsampling (CG-Down) to alleviate feature loss during downsampling, optimized detection layers, and a lightweight shared deconvolutional detection head (LSDECD) for small target adaptation in low-altitude aerial scenes, forming a full-link optimization architecture for tiny target feature perception. Experiments on a dedicated dataset (4025 images, 25,181 annotations, 92% targets < 20 pixels) show that PHSNet achieves 0.73 AP₅₀ and 0.315 AP, surpassing YOLO11n by 0.1 in AP₅₀ and 0.058 in AP, respectively. With only 1.8 M parameters and 98.8 FPS, it outperforms mainstream lightweight models, including YOLOv8n and RT-DETR-R18, strikes a superior accuracy–efficiency balance, and provides an efficient solution for real-time intelligent monitoring and edge deployment of PV stations. Full article

Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, with growing applications in pediatric cardiology. AI techniques, particularly machine learning and deep learning, enable the analysis of complex and heterogeneous data, supporting diagnosis, risk stratification, and clinical decision-making. This paper provides an overview of current AI applications in this field, discusses existing challenges, and explores future perspectives. In pediatric cardiology, AI has shown promising results across multiple domains. In electrocardiography, AI algorithms improve diagnostic accuracy and enable early detection of cardiac conditions, even in asymptomatic patients, while facilitating telecardiology-based care pathways. In cardiac auscultation, AI-assisted digital stethoscopes enhance the distinction between innocent and pathological murmurs, supporting primary care physicians and optimizing referral to pediatric cardiologic centers. Multimodality imaging represents one of the most advanced areas of AI applications. In echocardiography, magnetic resonance and computed tomography, AI improves image acquisition, view classification, and automated quantification, contributing to more standardized and reproducible assessments. Additionally, emerging technologies such as virtual reality, integrated with AI, offer innovative tools for education, surgical planning, and patient-specific modelling. Despite these advances, several limitations remain, including limited availability of large pediatric datasets, challenges in model generalizability and issues related to interpretability and integration into clinical workflows. In conclusion, AI represents a powerful complementary tool in pediatric cardiology, with the potential to improve diagnostic accuracy, optimize healthcare resources and support the transition toward precision medicine. Full article

Diamond nitrogen-vacancy (NV) centers are regarded as promising microwave sensors owing to their excellent magnetic sensitivity, stability, and environmental compatibility. However, traditional confocal test platforms based on diamond NV centers are bulky, which limits their practical applications. In this paper, a fiber-coupled compact NV microwave magnetometer is designed that employs the continuous heterodyne measurement method and a fast Fourier transform to measure multiple microwave fields. We integrated the laser excitation module, microwave antenna module, and fluorescence collection module into a single unit, reducing the volume of the magnetometer to 13 cubic centimeters. By adjusting the frequency and power of the measured microwave signals, the applicability of the device under different frequency and power conditions was verified. Experimental tests show that the microwave magnetometer can simultaneously detect multiple microwave fields with different frequencies and power levels, achieving a frequency resolution on the order of millihertz (mHz) and a microwave detection sensitivity of 0.385 nT/Hz^1/2. These results demonstrate the magnetometer’s multi-microwave-field measurement capability, making it highly promising for applications such as microwave anomaly localization and medical diagnosis. Full article

Amid the ongoing acceleration of climate change over recent decades, extreme precipitation events have become more frequent and intense on a global scale, triggering severe natural hazards and considerable socioeconomic damage. Nevertheless, how extreme precipitation has evolved at the national level over long time spans, and what role atmosphere–ocean teleconnections play in driving regional differences, remains insufficiently explored. This study addresses that knowledge gap by conducting a comprehensive assessment of eight ETCCDI-based extreme precipitation indices (PRCPTOT, CWD, R20, R95p, R99p, RX1day, RX5day, and SDII) across six climatic sub-regions of China (Northeast, North, East, Central South, Northwest, and Southwest) over 1960–2020, drawing on daily records from 695 quality-controlled meteorological stations. Key atmospheric and oceanic circulation drivers were further diagnosed and their joint influence was quantified via multiple wavelet coherence (MWC). The analysis shows that five of the eight indices (CWD, R95p, R99p, RX1day, and RX5day) underwent statistically significant fluctuating changes (p < 0.05) throughout the 61-year record. Seven indices, all except CWD, demonstrated upward tendencies, with mutation points clustering after 2010, most notably between 2011 and 2016. Wavelet power spectra indicates elevated energy concentrations at multiple time scales, although only CWD exhibited a statistically significant periodicity of approximately 8–10 a (p < 0.05 against red noise). In terms of spatial patterns, index magnitudes generally increased along a northwest-to-southeast gradient. Stations registering significant upward shifts were concentrated in East and Central South China, whereas significant downward shifts appeared mainly in North China and the northern portion of East China. An altitude-dependent pattern was also detected: CWD rose with elevation, while the remaining indices declined sharply below 1288 m, fluctuated in the 1288–2090 m band, and dropped again above 2090 m. Wavelet coherence analysis uncovered significant resonance between extreme precipitation and four circulation indices—SCSMMI, WPSHI, PNA, and NAO. MWC further identified three driver combinations—ENSO-PNA, SCSMMI-WPSHI, and ENSO-NAO-EASMI—as the most influential, acting both individually and synergistically. These results furnish an empirical basis for forecasting, preventing, and managing precipitation-related disasters across China under future climate scenarios. Full article

Given the complex operating conditions and latent faults exhibited by electric vehicle charging infrastructure amid massive order volumes, traditional monitoring methods based on thresholds or single statistical metrics struggle to detect dynamic, time-varying anomalies. This paper proposes a method for identifying and tracing the operational status of charging facilities based on the k-shape time-series clustering algorithm. This method directly uses charging current time series as the research object, eliminating the cumbersome manual feature extraction process. By utilizing a shape-based distance (SBD) metric strategy, it overcomes common time-series data issues such as phase shifts and amplitude scaling while preserving the integrity of the time dimension. Through iterative calculation of cluster centroids, the algorithm successfully and adaptively classifies massive amounts of data into typical clusters such as “standard charging,” “deep oscillation,” and “power-limited.” Based on the clustering results, this paper further constructs a “shape-operating condition” mapping mechanism. Combined with a Bayesian posterior probability model, this enables the localization of high-risk “vehicle-charger” combinations statistically associated with abnormal waveforms. Empirical studies demonstrate that this method can effectively identify equipment performance degradation at the micro-level of waveforms and provide prioritized inspection clues for the intelligent operation and maintenance of charging networks. Full article

Unmanned Aerial Vehicle (UAV) clusters have broad applications in agricultural detection, traffic control, and disaster rescue, where navigation and positioning serve as the core technology. However, satellite navigation fails to meet the requirements of region-wide navigation due to the urban canyon effect. Although the China Satellite Network (CSN) boasts advantages such as high landing power and low latency, it can only achieve single-link communication. Consequently, exploring how to realize cooperative positioning via UAV clusters has become an urgent research need. In this paper, an Unmanned Aerial Vehicle Cluster Communication–Navigation Integrated Cooperative Positioning (UCNCP) algorithm is proposed. This algorithm combines the communication and navigation characteristics of the CSN, establishes a single pseudorange measurement model and cluster geometric topology, and constructs an architecture for cooperative positioning based on UAV cluster pseudorange measurements and inter-UAV ranging data, thereby achieving reliable navigation and positioning of UAV clusters. Comparative experiments between the proposed method and other low-orbit satellite positioning methods demonstrate that the UCNCP algorithm exhibits higher positioning stability. When abrupt changes occur in navigation information, it can effectively mitigate the impact of abrupt change errors on positioning accuracy, improving the positioning stability of UAV clusters by more than 30%. Full article

AC microgrids with high penetration of inverter-based distributed energy resources (IBDERs) introduce major protection challenges due to reduced fault current levels, bidirectional power flows, and control-dependent fault behavior. Under these conditions, short-circuit current calculation and relay protection coordination become tightly coupled, since inaccurate fault modeling directly degrades relay sensitivity and selectivity. This review presents a protection-oriented assessment of state-of-the-art short-circuit calculation and relay protection strategies for AC microgrids. The analysis shows that conventional IEC-based fault models and static overcurrent protection schemes are insufficient for inverter-dominated networks. Generalized Δ-circuit–based modeling framework is identified as the most suitable foundation for microgrid fault analysis, as they enable inverter-aware phasor-domain representation and support both grid-connected and islanded operation. In addition, adaptive relay coordination approaches that incorporate time-varying IBDER participation and fault ride-through behavior demonstrate improved coordination robustness compared to conventional fixed settings, although their practical deployment remains constrained by network topology and communication requirements. Simulation results obtained on a representative microgrid case study confirm that the combined application of protection-oriented short-circuit modeling and adaptive relay coordination significantly improves fault detection reliability and coordination performance. The findings highlight the necessity of jointly addressing fault modeling and protection design to ensure reliable operation of inverter-dominated AC microgrids. Full article

Capacitive sensing offers a low-power, non-optical route for automated insect monitoring, but architecture-level benchmarking under shared geometry remains limited. Rather than presenting a general framework, this study proposed a configuration-specific laboratory benchmark comparing four sigma-delta and charge-transfers in a 6 mm dual-arc conduit at 25 °C, targeting six adult terrestrial arthropod species spanning a 25-fold range of the body cross-sectional area. Static measurements showed a strong linear relationship between ΔC_static and body cross-sectional area (17.96 fF/mm², r = 0.995), supporting first-pass conduit sizing and detectability screening. In contrast, transit amplitudes were not monotonic with body size because posture, motion, and gap occupancy affected waveform shape. Under chamber conditions, static sensitivity degraded by less than 3.2% across all architectures from RH 40% to 80%. However, under the deployment-oriented noise model, SNR_FR degradation was substantially higher for charge-transfer devices (64.8–66.8%) than for Σ–Δ devices (≤35.5%), because the composite noise floor amplifies the effect of humidity-induced baseline drift. These results generated a conduit-specific reference dataset for preliminary capacitance-to-digital converter (CDC) selection within the tested 6 mm dual-arc geometry. In addition, the experimental validation focused on laboratory baseline noise characterization, long-term drift, and trap-integrated testing in temperature-controlled environments and natural-locomotion trials, providing critical information on configuration-specific architectures and body-size-scaling reference. This study serves as an initial step toward real-world capacitive insect sensing. Future studies will investigate additional conduit geometries and insect species to improve the robustness of the proposed framework. Full article

Partial discharge (PD) detection effectively identifies insulation defects in power equipment. Radio frequency (RF) methods for PD detection offer promising advantages due to their non-invasive measurement capability and ability to locate discharge sources. However, microstrip antennas used as RF sensors for PD detection suffer from narrow bandwidth and limited installation flexibility. To address these limitations, this paper presents a novel flexible microstrip antenna design. By incorporating a partial ground plane and oblique-cut meandering techniques and optimizing the structural parameters using an improved whale optimization algorithm (I-WOA), the operating bandwidth is expanded from 0.612–0.625 GHz to 0.346–2.0 GHz, while the overall size is reduced to 75.3% of its original dimensions. The antenna’s performance was validated through GTEM cell measurements and PD calibration pulse tests, confirming its suitability for RF detection of PD in power equipment such as transformers and cable joints. Notably, when the antenna was conformally wrapped around a cable joint, the response amplitude increased by 14%. This study contributes to the development of a low-cost, broadband, and flexibly installable RF sensor for partial discharge detection. Full article

Acorns (Quercus spp.) are an underutilized forest resource with recognized nutritional and bioactive potential, making them promising candidates for the development of sustainable plant-based functional foods. This study aimed to valorize acorns through the formulation of two novel acorn-based products, a plant-based beverage, and a pudding, and to assess their nutritional properties, sensory acceptability, and, for the beverage, refrigerated shelf-life stability. The beverage was optimized as a neutral-flavored milk alternative, using sodium alginate as a natural clean-label stabilizer to enhance emulsion stability and physicochemical properties. The final formulation exhibited low energy density and a lipid profile rich in monounsaturated fatty acids, contributing to its nutritional and functional value. Throughout 63 days of storage at 4 °C, sodium alginate effectively prevented phase separation and supported the retention of antioxidant capacity, as evidenced by stable ferric reducing antioxidant power (FRAP) and total phenolic content, although ABTS radical scavenging activity declined over time. No microbial growth was detected during storage, confirming the adequacy of the applied thermal treatment and aseptic filling procedures applied. The acorn-based pudding, developed by adapting a traditional egg-based recipe, functioned as a proof of concept illustrating the technological versatility of acorns across distinct plant-based matrices, exhibiting a nutritional profile comparable to commercial counterparts and high consumer acceptability. Overall, this work demonstrates the technological feasibility and versatility of incorporating acorns into plant-based food matrices, supporting their potential as sustainable ingredients for the development of innovative value-added foods and contributing to the valorization of forest resources. Full article

Hyperspectral image change detection (HSI-CD) is becoming increasingly important in understanding how the Earth’s surface evolves over time, from monitoring ecosystems to tracking urban expansion. Unlike traditional pixel-based or hand-crafted approaches, deep learning models can automatically learn powerful spectral–spatial features, making them especially effective for this task. In this review, we bring together recent advances in deep learning for HSI-CD, combining a meta-analysis of the literature with an overview of the main model families and training strategies. We cover supervised, semi-supervised, and unsupervised methods, as well as newer directions such as transfer learning, self-supervised frameworks, and hybrid designs that blend CNNs, transformers, and graph neural networks. We also discuss benchmark datasets, evaluation protocols, and case studies that show how these methods perform in practice. Beyond summarizing the current progress, the review highlights ongoing gaps, such as limited labeled data, generalization across sensors, computational efficiency, and the need for interpretability, and points to emerging opportunities for future work. Our goal is to provide both a snapshot of the current state of the field and a road map for advancing deep learning-based HSI-CD. Full article

A linear frequency modulation (LFM) signal is widely used in radar systems. However, its inherently high autocorrelation sidelobes can degrade weak-target detection, while amplitude and phase distortions caused by transmitter systems may further elevate sidelobe levels. To address these issues, a joint pre-compensation and windowing optimization framework is proposed for a transmitter-distorted LFM signal. First, a regularized pre-compensation filter with gain constraints is constructed to compensate for transmitter-induced distortions and restore the waveform. Considering that the system frequency response is difficult to estimate accurately in practice, amplitude and phase perturbations are introduced, and a pre-compensation filter under perturbation is derived to improve robustness. To overcome the limited flexibility of fixed windows, a parameterized cosine-series window is employed, and the firefly algorithm is employed to jointly optimize the window coefficients and width, achieving a better trade-off among peak sidelobe ratio, integral sidelobe ratio, main lobe width, and peak-to-average power ratio. Simulation results demonstrate that the proposed method compensates transmitter distortions, significantly suppresses autocorrelation sidelobes, and maintains favorable performance under perturbations. Full article

Background: Solar energy provides a sustainable and environmentally friendly alternative to fossil fuels, and photovoltaic (PV) systems are increasingly deployed worldwide. However, their operational reliability is often compromised by various fault conditions, which reduce power output and shorten system lifespan. Although automated image-based deep learning methods have shown promise for PV fault classification, their performance is often limited by severe class imbalance and subtle, low-contrast defect patterns. This study aims to address these challenges by proposing an improved deep learning framework for robust PV fault classification. Method: An attention-enhanced convolutional neural network framework, termed AEConvNeXt, is proposed for PV fault classification. The model is built on a ConvNeXt-Tiny backbone and incorporates a dropout-regularized Convolutional Block Attention Module (CBAM) to enhance localized feature refinement. To further improve learning under imbalanced data conditions, a hybrid loss function combining Cross-Entropy Loss and Focal Loss is employed. Results: Experimental evaluations demonstrate that AEConvNeXt achieves an overall accuracy of 94.37% and a macro F1-score of 94.43%, outperforming the strongest baseline model, ResNet-50, by more than 3%. Grad-CAM visualizations further confirm that the model effectively focuses on fault-relevant regions, improving interpretability. The proposed framework also shows consistent and robust performance across all six PV fault categories under varying conditions. Conclusions: The proposed AEConvNeXt framework provides an accurate and explainable solution for real-time PV fault detection, effectively addressing class imbalance and improving minority fault recognition. Full article

Conventional truck emission accounting methods based on average activity levels and static emission factors are increasingly inadequate for dynamic regulation and policy comparison at high spatiotemporal resolution. This review synthesizes recent progress in dynamic truck emission estimation from four perspectives: multi-source data support, key feature extraction, physics-constrained emission modeling, and governance-oriented applications. The literature was collected from Web of Science Core Collection and ScienceDirect for the period 2014–2026, supplemented by backward reference checking, and was analyzed through a progressive framework linking data, features, models, and governance tasks. Unlike previous reviews that usually discuss emission inventories, conventional emission models, or data-driven prediction methods separately, this review highlights an integrated governance-oriented chain that connects multi-source data fusion, mechanism-related feature construction, physics-constrained modeling, and environmental management applications. Existing studies suggest that multi-source data, including GPS trajectories, on-board diagnostics (OBDs), on-board monitoring (OBM), portable emissions measurement system (PEMS) measurements, traffic flow monitoring, and road network attributes, provide an important basis for representing real-world operating processes. Meanwhile, key features have expanded from surface-level variables such as vehicle velocity to mechanism-related factors, including payload, road grade, engine operating conditions, vehicle-specific power, and roadway context. Truck emission modeling has also evolved from unconstrained or weakly constrained approaches toward frameworks that place greater emphasis on physical consistency, interpretability, and result credibility. In parallel, application scenarios have extended from emission quantification to high-emission vehicle identification, dynamic inventory development, hotspot detection, policy comparison, and transport optimization. These developments can support policymakers, transportation planners, and environmental agencies in moving from aggregate emission accounting toward targeted and process-based truck emission governance. Current research, however, still faces challenges related to data consistency, model generalizability, uncertainty propagation, and real-time application. Future work should focus on standardized datasets, hybrid AI–physics modeling frameworks, uncertainty-aware validation, real-time deployment in intelligent transportation systems, and improved links between dynamic estimation and practical environmental management. Full article