Search Results (10,037)

Sea ice classification is of fundamental importance for polar monitoring and global climate research. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a frontier technology in polar remote sensing due to its high spatiotemporal resolution and cost-effectiveness. Based on BeiDou System Reflectometry (BDS-R) data acquired from the Fengyun-3E (FY-3E) satellite, this study introduces a classification approach that integrates multi-dimensional sea ice information. A comprehensive feature set was constructed by integrating the Spectral Entropy (SE) of the Normalized Integrated Delay Waveform (NIDW) First-order Differential Curve to characterize the oscillatory complexity of the trailing edge power decay process as a scattering dynamic property, the Root Mean Square height (RMS) to characterize the attenuation magnitude of scattering intensity arising from surface roughness and related factors as a scattering intensity attenuation property, and salinity (S) and L-band brightness temperature (TB) data from SMOS to describe dielectric and radiative properties. These novel features are combined with traditional GNSS-R features. After selecting the optimal feature set via an ablation study, the features were used to train a Random Forest (RF) classifier for sea ice classification. Validated against Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice type products, the proposed method yielded an overall accuracy of 93.86% and a Kappa coefficient of 0.8061. The integration of multi-dimensional features notably improved the identification of Multi-Year Ice (MYI), achieving a Recall of 85.11% and an F1-score of 84.43%. These results indicate that the proposed multi-dimensional feature set provides an effective solution for GNSS-R-based sea ice classification. Full article

This paper proposes a continuous inertia estimation framework for transmission-level power systems with high renewable energy penetration, using a battery energy storage system (BESS) as a controllable small-signal power injection source. The proposed framework integrates BESS-based active power injection, a two-stage signal-smoothing scheme, and a rate-of-change-of-frequency (RoCoF)-based estimation mechanism to enable continuous inertia estimation without relying on major disturbance events. With noise-robust processing and moving-window analysis, the framework can reliably track inertia variations under noisy measurement conditions and diverse operating scenarios. The framework is validated on the IEEE 39-bus system under renewable energy source (RES) penetration levels of 0%, 10%, 20%, and 30%. The estimation error remains within ±3.5% across all scenarios, ranging from 1.26% at 0% RES penetration to 3.43% at 30% penetration. In addition, the estimated inertia closely follows the theoretical decrease from 3.20 s to 2.22 s as RES penetration increases. These results demonstrate the accuracy and robustness of the proposed framework for continuous inertia monitoring in low-inertia power systems with high-RES penetration. Full article

Vision-based structural health monitoring offers a promising alternative to conventional wired sensing systems; however, its adoption is often limited by high hardware costs and computational constraints at sensing nodes. This study presents the design and laboratory validation of a low-cost vision-based system for displacement and strain monitoring using a centralized processing architecture. The proposed system separates image acquisition from computation, where an ESP32-CAM module serves as a lightweight edge node for grayscale image capture and wireless transmission, while computational tasks including displacement tracking, subpixel localization, scale calibration, and strain estimation are performed on a centralized unit. This enables low-cost deployment at USD 60 per node with low power consumption at 1 W. System performance was evaluated through controlled experiments, including a 24 h zero-drift test and quasi-static displacement tests up to 15 μm. Validation against a Linear Variable Differential Transformer (LVDT) shows close agreement, with an absolute error of 2.63 µε and drift within ±2 μm. The system achieves an effective strain range of ±35,000 με. These results demonstrate the potential of low-cost centralized vision-based systems, demonstrating strong potential for practical deployment in structural health monitoring applications. Full article

Monitoring air quality is crucial for understanding and improving public health. There is interest in developing ultra-sensitive, low-power, cost-effective sensors. This work demonstrates that structural modulation of Sn nanoparticles through controlled deposition and oxidation enables a transition from metallic to semiconducting percolative networks, significantly enhancing NO₂ sensing performance at room temperature. The proposed percolation-driven sensing mechanism provides a new framework for understanding charge transport and gas interaction in nanostructured metal oxide systems. The nanoparticles are deposited near the percolation threshold for electrical conduction and, upon exposure to air, consist of a tin core and an amorphous Sn₃O₄ surface. Post-deposition heating in air at 320 °C for two hours forms SnO and Sn₃O₄ on top of the gold electrodes and polycrystalline SnO in the tetragonal litharge phase, known as Romarchite, on the glass between the electrodes. Both as-deposited and heat-treated sensors were capable of detecting NO₂ at room temperature, with a limit of detection in the parts-per-billion range. A percolation model is used to explain their operating currents, in which NO₂ reacts at nanoparticle gaps and intra-grain boundaries to form charge-depletion regions that primarily determine their resistance. Heat treatment has also been found to cause disproportionation of SnO, resulting in tin-rich precipitates and increasing the operating current to the milliampere range. These precipitates, although oxidized on their surfaces when exposed to air, may serve as bridges that reduce the total resistance of the percolating paths. Full article

Monitoring surface deformation at reclaimed airports under construction is crucial for ensuring construction safety. However, significant variations in surface scattering characteristics cause severe decorrelation, limiting the effectiveness of conventional single-polarization Interferometric Synthetic Aperture Radar (InSAR). To address the issue of insufficient coherent pixels, we propose a dual-polarization sequential InSAR technique and compare its performance with traditional Persistent Scatterer Interferometry (PSI) and Distributed Scatterer Interferometry (DSI) at the Dalian Jinzhou Bay International Airport (DJBIA). Using 89 Sentinel-1A dual-polarization (VV-VH) images (August 2022 to October 2025), the results demonstrate that VV and VH polarizations exhibit significant spatial complementarity, highlighting the necessity of multi-polarization data. Further, to address the issue of long-term changes in scattering characteristics, we applied the Sequential Estimation and Total Power-Enhanced Expectation Maximization Inversion (SETP-EMI) method, which dynamically integrates dual-polarization information and performs adaptive phase optimization. This approach significantly enhances monitoring capability in low-coherence areas of the airport under construction, effectively suppressing phase noise, improving interferogram quality, and yielding a more complete and reliable deformation field. Overall, this study systematically validates the SETP-EMI method with dual-polarization information for deformation monitoring at reclaimed airports under construction, providing technical support for engineering safety control and research on reclamation subsidence mechanisms. Full article

Large-scale smart agriculture requires reliable and energy-efficient wireless connectivity to support distributed environmental sensing across wide rural areas. However, existing low-power wide-area network (LPWAN) technologies often face limitations in scalability, reliability, or infrastructure dependency when deployed in large agricultural fields. This study presents the design and experimental evaluation of a hierarchical sensor network architecture that integrates LoRaMESH for multi-hop sensing communication and Wi-Fi HaLow as a sub-GHz backhaul for data aggregation and cloud connectivity. In the proposed system, LoRaMESH forms intra-cluster sensor networks using a lightweight controlled flooding protocol, while Wi-Fi HaLow provides long-range IP-based connectivity between cluster gateways and a central access point. A real-world deployment covering approximately $2.5\mathrm{km}\times 1\mathrm{km}$ of agricultural area was implemented to evaluate the performance of the proposed architecture. Experimental results show that the LoRaMESH network achieves packet delivery ratios above $90\%$ across one to three hops, with average end-to-end delays between $10.6$ s and $13.3$ s. The Wi-Fi HaLow backhaul demonstrates high reliability within short to medium distances, reaching $99.5\%$ packet delivery ratio at 50 m and $89.68\%$ at 200 m. Energy measurements further indicate that the sensor nodes consume only $21.19\mathsf{\mu }\mathrm{A}$ in sleep mode, enabling long-term battery-powered operation suitable for agricultural monitoring applications. These results indicate that the proposed hierarchical architecture is a feasible connectivity option for the tested large-scale agricultural sensing scenario. Because no side-by-side LoRaWAN or NB-IoT benchmark was conducted on the same testbed, the results should be interpreted as a field validation of the proposed architecture rather than as a direct experimental demonstration of superiority over alternative LPWAN systems. Full article

With the advancement of digital hydropower stations, the requirements of real-time, high-precision industrial soft measurement of key power equipment operating status are attracting more and more attention. However, it is difficult to transfer energy to the monitoring sensor in strong electromagnetic environments. In this paper, a high-efficiency, high-power-density magnetic field energy harvester is proposed for monitoring sensors in hydropower stations, which captures the energy from the magnetic flux leakage of a hydroelectric generating set. Efficient magnetic energy capture is achieved by modeling material properties and optimizing the receiver’s magnetic core parameters via a Genetic Algorithm. The theoretical analysis of charging characteristics is given, and a Maximum Power Point Tracking (MPPT) control circuit is proposed, realizing high-efficiency energy conversion. Finally, an experimental planet is built. Under 70–130 Gs power-frequency magnetic fields, the system delivers 2.8–5.1 V open-circuit voltage, 66 mW maximum load power, and 6.5 mW/cm³ power density. Full article

Photovoltaic (PV) power stations are pivotal for the renewable energy transition, yet their operational efficiency is often compromised by defects such as surface dust accumulation and cracks. Traditional manual inspections are labor-intensive and subjective, while conventional monitoring methods struggle with environmental interference. This study proposes YOLO-MSG, a lightweight framework specifically designed for the automated detection of PV module defects during system operation, including normal panels as well as defective conditions such as dusty and cracked panels. The methodology integrates a Multi-Scale Grouped Convolution (MSGC) module for enhanced feature extraction and a Group-Stem Decoupled Head (GSD-Head) to reduce parameter redundancy. Furthermore, a joint optimization strategy involving LAMP and logits-based knowledge distillation is employed to facilitate edge deployment. Experimental results on a specialized PV defect dataset demonstrate that YOLO-MSG achieves a superior balance between detection accuracy and computational cost. Compared to state-of-the-art models like YOLO11 and YOLOv12, YOLO-MSG significantly reduces GFLOPs and parameter count while maintaining highly competitive mean Average Precision (mAP), with improvements of 1.35% in mAP and 2.37% in mAP50-95 over the baseline models. Specifically, the model achieves an average inference speed of 90.30 FPS on the NVIDIA Jetson AGX platform. These findings confirm the algorithm’s industrial viability, providing a robust and efficient solution for the real-time automated maintenance of photovoltaic infrastructures. Full article

Background: Liver biopsy remains the gold standard for staging chronic hepatitis B (CHB), yet it is invasive, costly, and associated with potential complications. There is a critical need for non-invasive, cost-effective biomarkers to monitor disease progression. This study aimed to evaluate the correlation between the Prognostic Nutritional Index (PNI) and Systemic Immune–Inflammatory Index (SII) with histological fibrosis stages and the Histological Activity Index (HAI) in patients with CHB. Methods: This retrospective study analyzed 274 patients diagnosed with CHB (HBsAg positivity > 6 months) who underwent liver biopsy at the University of Health Sciences, Kanuni Sultan Süleyman Training and Research Hospital between February 2016 and February 2022. Histopathological findings were staged using the Ishak fibrosis score and HAI. PNI and SII were calculated from peripheral blood parameters. Statistical discrimination power was assessed using Area Under the Receiver Operating Characteristic (AUROC) curves. Results: The cohort comprised 119 females (43.4%) and 155 males (56.6%), with a mean age of 45.25 ± 11.2 years. Mean values were 55.83 ± 5.33 for PNI and 494.37 ± 336.86 for SII. Fibrosis distribution showed 56.2% at stages F0–F1 and 43.8% at ≥F2. For fibrosis staging, SII demonstrated statistically significant but limited predictive ability for Ishak scores ≥F2, while PNI was significant for identifying advanced fibrosis (≥F4) (p < 0.05). SII showed moderate diagnostic performance for severe inflammation (HAI ≥12; AUROC = 0.848), although this finding should be interpreted cautiously. For lower HAI thresholds (≥6), both PNI and SII demonstrated poor discriminative ability (AUROC 0.5–0.6). Conclusions: Both indices were associated with histological parameters but showed limited overall diagnostic performance. SII appeared relatively better; however, this was descriptively observed without formal statistical comparison. These markers may provide complementary information but should not be used as standalone diagnostic tools. Full article

Accurate measurement of streamflow is fundamental for water resources management, ecological conservation, flash flood early warning, and climate change impact studies. This study presents a proof of concept on the usage of Internet of Things (IoT) for automatic streamflow measurements using commercial off-the-shelf (COTS) hardware. The system is designed, implemented, and experimentally evaluated as a low-cost, solar-powered IoT device tailored to small-order streams and headwater tributaries. At its core is the Hall-effect YF-S201 flow sensor. Although primarily designed for closed-conduit applications, the sensor was tested in a controlled setup where stream water was diverted into a short pipe section, enabling continuous monitoring and calibration. This paper provides details on the design and validation of a low-cost (approximately 24 Euros), solar-powered streamflow measurement system based on a water flow sensor, using wireless communications, and cloud storage based on an ESP32 board, PostgreSQL, and a web interface. The device was tested in a simulated environment. Results indicate the proposed device reliably tracks flow variability, while offering portability, energy autonomy, and cost efficiency, and may serve as a feasible alternative for low-infrastructure, temporary deployments. Full article

Objectives: This study aims to investigate the correlations between changes in skin temperature and surface electromyography (sEMG) parameters during fatigue induced by varying exercise intensities. The study uses infrared thermal imaging and sEMG to explore whether skin temperature fluctuations can indicate muscle fatigue states. Methods: Two static contraction fatigue tests were administered on the right biceps brachii muscle group of 30 healthy male subjects at 30% and 70% MVC (Maximum Voluntary Contraction) intensity levels. Tests were separated by a 5-day interval and continued until complete fatigue was achieved. The left arm served as a control and was not subjected to any load. Infrared thermal imaging was employed to record continuous skin temperature, capturing data from 120 s pre-exercise to 480 s post-exercise commencement at ten frames per second. Concurrently, sEMG parameters (RMS—Root Mean Square, MPF—Mean Power Frequency, and MF—Median Frequency) were synchronously collected at a sampling frequency of 1 kHz. Results: During 70% MVC exercise, skin temperature on the exercised arm consistently decreased, reaching its nadir by the end of the exercise, with a statistically significant divergence from the baseline (p < 0.05). At 30% MVC, skin temperature initially slightly declined before gradually increasing. The control arm’s temperature significantly declined across exercise intensities and during recovery. A significant temporal correlation was observed between skin temperature and sEMG parameters. Conclusions: 1. Variability in skin temperature patterns during muscular fatigue is contingent on the level of exercise intensity. 2. The strong correlation between skin temperature and sEMG parameters suggests that infrared thermal imaging is a promising, rapid technique for monitoring exercise-induced muscle fatigue. Full article

Aiming at the technical pain points of traditional manhole covers with low intelligence high cost and excessive power consumption, this study designs a TENG-based alarm device to enhance the safety and maintenance efficiency of urban infrastructure. The device integrates a water immersion sensor and a displacement sensor enabling real-time status monitoring through a unique TENG mechanism. The solid–liquid mode water immersion sensor detects seepage through the triboelectrification effect. Water droplets contact electrodes on the surface of FEP film and generate electric energy to trigger the detection circuit. The displacement sensor adopts the independent layer mode of TENG and combines with a mechanical tumbler mechanism to realize displacement detection. External force-induced manhole cover displacement drives internal balls to roll and rub against electrodes. Electric energy is then generated to activate the detection circuit. On the basis of the two sensors, an efficient and reliable intelligent alarm system is constructed. The system receives and analyzes displacement and water immersion-sensing signals in real time. It rapidly identifies potential safety hazards including displacement offset water accumulation and leakage. Signal analysis and early warning prompts are completed synchronously. This system provides accurate and real-time data support for public facility monitoring, pipe network operation and maintenance, and regional security in smart cities. It helps achieve early detection and early disposal of hidden dangers and improves the intelligent and refined level of smart city monitoring. Full article

Measurable residual disease (MRD) is the most powerful predictor of relapse and long-term survival in adult acute lymphoblastic leukemia (ALL), consistently outperforming traditional clinical and cytogenetic risk factors. The advent of high-sensitivity next-generation sequencing (NGS) capable of detecting MRD at 10⁻⁶ has transformed monitoring, reclassifying a substantial proportion of patients previously deemed negative by multiparameter flow cytometry (MFC) or quantitative PCR (qPCR), and revealing clinically relevant disease persistence at ultra-low levels. This review synthesizes current MRD detection platforms and their clinical applications across frontline therapy, allogeneic hematopoietic cell transplantation, and relapsed/refractory disease with specific focus on B-ALL. We integrate the 2024 European LeukemiaNet (ELN) and the 2025 US expert panel recommendations, highlighting important differences in preferred methodologies and decision thresholds. Particular attention is given to the emerging role of early deep NGS negativity in guiding transplant deferral among selected standard-risk patients, including some with Ph+ ALL treated with chemotherapy-free regimens, and to the challenges of interpreting persistent low-level positivity (10⁻⁴–10⁻⁶). Despite technological advances, key questions remain: when should deeper detection trigger therapeutic escalation, and how should discordance between modalities, peripheral blood monitoring, and subtype-specific variability be interpreted? Addressing these issues through prospective validation, platform harmonization, and broader global access will be essential to ensure that increasing sensitivity translates into evidence-based, equitable clinical benefit. Full article

Vertical-External-Cavity Surface-Emitting Lasers (VECSELs) have gained significant attention over the past two decades due to their versatility in a wide range of photonic applications. This review focuses on VECSEL configurations for dual-wavelength emission, highlighting their use in high-resolution spectroscopy, terahertz (THz) generation, and advanced optical communication. We explore recent developments in VECSEL designs, including systems utilizing birefringent crystals for polarization-based frequency separation and configurations with dual-VECSEL chips or dual-gain regions within a single cavity. These two-wavelength VECSELs enable diverse operation modes, including narrow-linewidth, pulsed, multimode, and frequency-converted emission, with high-brightness output, excellent beam quality, and tunable wavelengths. Additionally, the review discusses advancements in dual-frequency VECSELs, with applications in LIDAR systems for environmental monitoring, highly stable optical clocks, and fiber sensors. We examine improvements in cavity design, semiconductor structures, and power stabilization, which have enhanced frequency stability and spectral purity, making VECSELs suitable for precision metrology and sensing applications. Full article

We developed an automated system to address orchard crop damage caused by Formosan macaques, a problem where traditional deterrent methods have proven to be ineffective. The system integrates an Internet Protocol camera with a You Only Look Once version 5 (YOLOv5) object detection model, which was trained on an augmented 6000-image dataset featuring a simulated monkey puppet in an indoor setting to validate its real-time identification capability through simulation. Upon target detection, a high-power laser, controlled via the Message Queuing Telemetry Transport protocol, is actuated to perform dynamic and non-invasive repelling. A web-based Human–Machine Interface (HMI) is provided, allowing users to remotely monitor and adjust strategies. This system offers a low-cost, highly efficient, and scalable solution for smart agriculture, with potential for expansion to other scenarios requiring a high degree of security and defense, such as warehouses and construction sites. Full article