Search Results (744)

This paper addresses the need for compact, low-cost soil moisture sensors operating at low microwave frequencies that can provide accurate, texture-aware (i.e., sensitive to different soil particle-size distributions such as sand and loam) characterization of soil permittivity and moisture content. Conventional techniques and many existing microwave resonator sensors are constrained by limited penetration depth, relatively large or complex structures, and calibration procedures that do not robustly account for different soil textures and moisture ranges. A dual-port microstrip square split ring resonator (SRR) sensor on Rogers RO3010 (Rmit University, Melbourne, Australia) is designed for operation at 1.3 GHz and analyzed using full-wave 3D electromagnetic simulations. The structure employs a T-shaped feedline and a shunt quarter-wavelength matching section to achieve strong field confinement in the sensing region and effective impedance matching. Soil is modeled as sandy and loamy superstrates over practical agricultural moisture ranges, with their complex permittivities drawn from reference datasets. Empirical calibration models are then developed, including polynomial curve fitting between resonance frequency shift and real permittivity, machine-learning-based calibration using resonance frequency and transmission loss features, and multiple linear regression linking moisture content to both real and imaginary permittivity components. The sensor exhibits a resonance frequency shift of about 115 MHz over 0–30% moisture for sand and 0–40% for loam, with a maximum sensitivity of 3.4%. Calibration models achieve mean absolute error below 1.22%, root mean square error under 1.58%, and coefficients of determination R² > 0.98 for both soil textures. These results demonstrate that a compact 1.3 GHz square SRR sensor with data-driven calibration, i.e., empirical models learned from simulated and measured S-parameters, enables sensitive, reproducible, and texture-aware soil moisture estimation suitable for agricultural and environmental monitoring. Full article

Free-space optical (FSO) communication provides high-capacity wireless transmission but suffers from reduced optical coupling efficiency when compact Cassegrain telescopes are employed because the secondary mirror blocks the central portion of the incident beam. This paper proposes a vortex beam-assisted FSO communication system that combines aperture-matched optical coupling with machine learning-based signal recovery. Unlike a conventional Gaussian beam, the annular intensity distribution of a Laguerre–Gaussian vortex beam is matched to the unobstructed annular aperture of a centrally obscured Cassegrain telescope, thereby reducing obstruction-induced optical loss. The coupling characteristics are analyzed using an annular aperture overlap model and experimentally validated in a 100 m free-space optical link employing Cassegrain transmitter and receiver front-ends. The mean measured telescope-output power is increased by more than 30% over the Gaussian reference. To overcome the system-induced signal aliasing, Transformer and long short-term memory (LSTM) equalizers are optimized and applied. Both models substantially outperform optimized threshold detection, while the LSTM achieves the lowest observed error rate with markedly fewer multiply–accumulate operations than the Transformer. These results show that aperture-matched optical coupling and computationally efficient sequence equalization, such as LSTM is a crucial component of compact, high-performance telescope-assisted FSO systems. Full article

Insulator defect detection remains challenging due to low recognition accuracy for aging, breakage, flashover, and similar faults, limited capability in identifying small targets, and inadequate cross-scale feature fusion under complex backgrounds. To address these issues, this study develops an enhanced detection model, YOLO11-MG, which integrates multi-scale edge information selection and enhancement with global information fusion. In the backbone, the original C3K2 module is replaced by C3K2-MSEIS, which mitigates detail loss during downsampling and strengthens feature representation for small objects and blurred boundaries through a multi-scale edge information selection and enhancement strategy. In the neck, a Gather-and-Distribute (GD) mechanism is introduced; by combining Low-GD and High-GD designs, it enables lossless transmission and effective interaction of cross-scale features. Additionally, the C2PSA module is incorporated to realize adaptive feature weight allocation. Experiments demonstrate that YOLO11-MG achieves precision, recall, mAP@0.5, and mAP@50–95 of 82.3%, 96%, 88.1%, and 70.3%, representing improvements of 5.7%, 4%, 3.1%, and 5.6% over the baseline YOLO11s, respectively. Compared with contemporary YOLO variants such as YOLOv8s, YOLOv10m, and YOLOv12m, the proposed model attains a better accuracy–speed trade-off: it improves mAP@0.5 by 6.3% and accelerates inference by 23.4% relative to YOLOv8s, while keeping 27.67 M parameters—on par with lightweight models but with notably stronger global feature interaction and edge preservation. Overall, the method achieves high detection accuracy, computational efficiency, and real-time inference capability, making it well suited for UAV-based inspection and offering reliable technical support for intelligent insulator fault diagnosis in power systems. Full article

With the rapid penetration of electric vehicles (EVs) and renewable energy generation in distribution networks, the coordinated scheduling of flexible EV loads and uncertain renewable resources has become a critical research focus in modern power systems. This study investigates the collaborative optimal dispatch of thermal units, EVs, and renewable power generation. Different from conventional closed-loop game-based bi-level optimization, this paper constructs a transmission–distribution integrated scheduling framework and proposes a sequential hierarchical progressive optimization strategy for EV charging and discharging dispatch to fully tap the cross-level coordination potential of power grids. The upper transmission layer optimizes the joint operation of thermal units, wind power, and photovoltaic units to minimize the overall power supply cost, where the inequality power balance constraint is reasonably adopted to reserve power regulation margin for renewable fluctuation and meet practical engineering operation requirements. To effectively address the severe uncertainty of renewable power output without relying on accurate probability distribution information, information gap decision theory (IGDT) is employed to realize robust scheduling with risk-averse and opportunity-seeking decision adaptability. In the lower distribution layer, a theoretically grounded nodal electricity price (NEP) model integrating node loss sensitivity (NLS) and node load rate (NLR) is applied to substitute iterative power flow calculation, which realizes the spatial optimal allocation of EV charging and discharging nodes while significantly improving computational efficiency. The proposed framework comprehensively minimizes network power loss and user charging cost. Finally, extensive simulations based on the IEEE 33-node distribution system verify the effectiveness, computational superiority, and robustness of the proposed sequential hierarchical coordinated scheduling strategy. Full article

Light fidelity (LiFi) is progressively evolving as a highly promising communication technology because of its unique benefits, available spectrum, low implementation costs, and adaptive beamforming capabilities. Despite their advantages, existing LiFi networks remain constrained by limited data rates, coverage area, and information security in practical environments. Therefore, a high-speed, high-capacity, and secure quantum key distribution (QKD)-assisted integrated multi-wavelengths (450/532/620 nm) LiFi system using mode division multiplexing (MDM) is proposed. The results demonstrate that the proposed system achieves maximum transmission distances of 20.5–22 m and 19–22 m using different Laguerre–Gaussian (LG) and Hermite–Gaussian (HG) mode indices {[0,0], [0,10], [0,20], [0,30]}, at an aggregate data rate of 40 Gbps. Furthermore, the minimum acceptable transmitter angles of 30–90° for irradiance angles of 20–80° are required to maintain the target bit error rate (BER) of 10⁻⁹. The minimum photodetector detection areas required at transmission distances of 20–30 m are 1–2 cm² at the minimum BER limit. Moreover, the proposed system exhibits optimum performance, achieving an optical loss of −39.47 dB, −49.03 dBm received power, and 45.39 dB signal-to-noise ratio for 1–10 photons/pulse. Compared with existing studies, the proposed system demonstrates enhanced overall performance across various communication metrics. Full article

Infrared-selective stealth films, which concurrently offer high visible transmittance, suppressed infrared emission, and selective thermal dissipation, have emerged as compelling candidates for infrared protection and thermal-target stealth. However, traditional multilayer architectures are predominantly designed through empirical trial-and-error protocols, which inherently hinder the synergistic optimisation of multiband spectral performance and yield suboptimal parameter-tuning efficiency. To circumvent this bottleneck, we introduce a reinforcement learning (RL)-driven multi-objective optimisation framework that automates the design of composite thin-film configurations. The optimised multilayer film structure consists of TiO₂/ITO/Ag/ZnO/SiO₂, with layer thicknesses of 180, 656, 10, 33.75 and 50 nm, respectively. Spectral characterisation reveals a weighted average visible transmittance of 79.77% over the 0.38–0.78 μm range, alongside blackbody-weighted average emissivities of 33.93%, 72.93%, and 19.94% in the 3–5, 5–8, and 8–14 μm bands, respectively. Consequently, the spectral profile exhibits high visible transparency, deep suppression of emissivity within the atmospheric windows (3–5 and 8–14 μm), and markedly elevated emissivity in the non-atmospheric band (5–8 μm). Analysis of the electromagnetic field distribution and power-loss density along the thickness direction reveals that the energy transmission and dissipation behaviours across distinct bands are synergistically governed by multilayer interference, interfacial multiple reflections, and lossy interlayer coupling mechanisms. Furthermore, angle-resolved infrared-emissivity analysis calibrated against the normal-incidence FDTD spectrum confirms that the structure retains robust polarisation adaptability and pronounced spectral selectivity at incidence angles up to 80°. The above results demonstrate the effectiveness of the reinforcement learning-driven optimisation framework for the automated co-design of multiband spectral responses. Moreover, the uncovered multilayer interference and loss-coupling mechanisms furnish a solid physical foundation for further performance refinement and rational design of transparent stealth coatings. Full article

Real-time multi-sensor human activity recognition (HAR) requires accurate models and a system architecture capable of distributing computation, exchanging compact outputs, and maintaining temporal consistency across asynchronous streams. This paper presents a distributed HAR framework in which five wearable sensors are associated with local embedded nodes that perform acquisition, windowing, preprocessing, and convolutional neural network–long short-term memory (CNN–LSTM) inference. Each node transmits a timestamped six-class softmax vector, and a central node applies approximate synchronization and learned probability-level fusion. The framework was evaluated with ten participants whose data were not used for model development. It achieved 95.868% accuracy and a 95.642% macro-F1-score. During continuous operation, the system sustained 47.949 predictions/s, with a mean post-window end-to-end latency of 33.963 ms and a mean synchronization span of 13.788 ms. Relative to complete-window transmission, the numerical payload decreased by 97.69%, and central-node energy per prediction decreased by 52.2% compared with centralized real-time processing. Under 30% independent probability-message loss, accuracy remained at 94.31%. Full article

Akabane virus (AKAV) is a Culicoides-borne arbovirus that causes congenital malformations and reproductive losses in ruminants, resulting in substantial economic losses in livestock production. Because vector activity and virus transmission are strongly influenced by environmental conditions, defining climatic factors associated with AKAV distribution is critical for understanding its epidemiology. However, such relationships have not been systematically evaluated in East Asia. In this study, we applied the Köppen–Geiger climate classification to characterize regional climatic zones in South Korea and Japan and examined their associations with AKAV case counts. AKAV cases were predominantly observed in temperate climate zones (Cfa and Cwa). Temperature-related variables showed consistent positive associations with AKAV case counts, with a 1 °C increase in annual mean temperature associated with approximately 1.38–1.56-fold increases in reported cases. In contrast, precipitation variables exhibited weak or negative associations. These findings indicate that temperature is an important climatic factor associated with AKAV case counts, suggesting that climate-dependent modulation of vector dynamics may contribute to observed patterns of AKAV occurrence. This study provides a climate-based framework for understanding the spatial distribution of AKAV and supports the development of targeted surveillance and control strategies under changing environmental conditions. Full article

Securing legal titles to land for transmission lines shapes the pace and cost of the energy transition and sharpens the conflict between security of supply and the protection of the landscape and of ownership rights. The article evaluates the legal and financial instruments compensating for the siting and operation of transmission facilities: whether the Polish framework is complete, how far impact zones enter the planning provisions of a large city, and where Poland stands among EU member states. Doctrinal analysis and a comparative review of twelve EU jurisdictions are combined with a case study of the 254 local plans in force in Kraków and with model calculations of electromagnetic fields. Power networks appear in 48% of Kraków’s plans, yet restricted-use zones in only 24%; EU models range from full compensation with a 25% statutory supplement (Sweden) to a gratuitous easement (Romania). The Polish model, based on a valuer’s appraisal, occupies an intermediate position and includes no component rewarding the compulsory character of the encumbrance; recommendations for future legislation aim to accelerate the acquisition of titles. Compensation design is therefore an institutional condition of the energy transition: it governs how fast corridors can be secured and how the costs of decarbonisation are distributed, and uncompensated losses mark where the social acceptance of grid expansion is most at risk. Full article

Visible fracture detection in underground roadways is challenging because fracture targets are often elongated, weakly contrasted, irregularly distributed, and easily confused with complex rock-wall textures. In addition, uneven illumination, dust interference, and blurred boundaries further reduce the reliability of conventional crack detection and segmentation methods. To improve fracture instance segmentation under such conditions, this study proposes YOLOv13n-seg-crack, an improved lightweight instance segmentation model based on a self-constructed YOLOv13n-seg baseline. The proposed model introduces three main improvements. First, a C2f-CA module is embedded into the backbone to enhance spatial-position perception and directional feature representation for elongated fractures. Second, a shallow high-resolution branch and auxiliary feature paths, denoted as B2 + H2 + P2, are constructed to strengthen the transmission of fine edge and texture information for small and discontinuous fracture targets. Third, an Edge-aware SIoU (EA-SIoU) loss is designed by adding edge-consistency and aspect-ratio constraints, thereby improving bounding-box localization for narrow and irregular fracture regions. Experiments were conducted on the public Crack Segmentation Dataset and an expanded self-built underground roadway dataset collected at the Woniushan Experimental Base. On the public dataset, YOLOv13n-seg-crack achieved detection Precision, Recall, mAP50, and mAP50:95 of 84.56%, 65.49%, 71.51%, and 52.72%, respectively, and mask Precision, Recall, mAP50, and mAP50:95 of 74.94%, 60.38%, 59.52%, and 21.99%, respectively. Compared with YOLOv13n-seg, the detection mAP50 and mask mAP50 increased by 1.91 and 3.19 percentage points, respectively, while the model maintained an inference speed of 168.73 FPS. Repeated-seed experiments, ablation studies, and degraded-image tests further demonstrate the stability and robustness of the proposed improvements. On the self-built underground roadway dataset containing 100 images and 118 annotated fracture instances, YOLOv13n-seg-crack improved detection mAP50 from 68.72% to 73.36% and mask mAP50 from 30.76% to 33.74%. These results indicate that the proposed method provides an effective and lightweight solution for visible fracture detection and instance segmentation in complex underground roadway scenes. Full article

The role of the Zn/Mg ratio in regulating microstructure, precipitation evolution and coarsening resistance in Al–Zn–Mg-based alloys was investigated by microhardness testing, scanning electron microscopy (SEM), and transmission electron microscopy (TEM) during isothermal aging at 150 °C. Three alloy compositions were designed with Zn/Mg ratios of 1.6, 2.4, and 3.9, whereas the combined Zn and Mg level was fixed at 6.0 wt%. All alloys exhibited a typical age-hardening response, whereas the maximum peak hardness was obtained at an intermediate Zn/Mg level rather than at the two extremes. The optimal composition (Zn/Mg = 2.4) reached 137 HV, which is attributable to the formation of the finest precipitates (~3 nm) and the highest number density. Moreover, this alloy exhibited the smallest hardness loss (ΔH = 19 HV) after prolonged aging (192 h). TEM analysis indicated that this alloy exhibited the lowest coarsening rate constant, Kr = 0.43 at 192 h. Furthermore, the variation in Zn/Mg ratio affected grain boundary precipitation, leading to a minimized PFZ width at Zn/Mg = 2.4 while maintaining a similar discontinuous distribution of grain boundary precipitates among the alloys. Overall, tailoring the Zn/Mg balance offers an effective strategy to achieve refined precipitates, improved coarsening resistance, and enhanced mechanical performance with superior thermal stability. Full article

The effect of internal pressure on the layered microstructural evolution of N36 zirconium alloy cladding tubes was systematically studied under simulated loss-of-coolant accident (LOCA) biaxial creep conditions at 900 °C. The tested specimens were characterized by electron channeling contrast imaging, energy-dispersive X-ray spectroscopy, electron backscatter diffraction, and transmission electron microscopy. The results show that all specimens formed a typical layered cross-sectional structure consisting of an oxide film, an oxygen-rich α-Zr (α(O)) layer, and a prior-β transformed layer. The thickness of the α(O) layer and the oxygen diffusion depth changed markedly with internal pressure. The thickness of the α(O) layer was approximately 21 μm for the 0.8 MPa specimen and 11 μm for the 1.9 MPa specimen, respectively. The lower-pressure specimen exhibited a wider oxygen-affected region, whereas the higher-pressure specimen showed a steeper oxygen gradient. In the prior-β transformed layer, lath-like α structures formed under both conditions, but their spatial arrangement and orientation distribution were different. Under lower internal pressure, the laths were more regularly arranged and showed a more complete colony structure. Under higher internal pressure, the laths were more interwoven, and the orientation distribution became more scattered. Meanwhile, the high-pressure specimen retained a higher local orientation gradient and a higher degree of lattice distortion. These results indicate that the above microstructural differences mainly arise from the effect of internal pressure on the high-temperature exposure history. A higher internal pressure causes earlier instability of the specimen, thereby shortening the effective time for oxygen diffusion and microstructural evolution, rather than directly changing the oxidation or phase transformation process. Full article

Power transformers are an integral part of electrical power system infrastructure and play a vital role in the efficient and reliable transmission of bulk electrical power to distribution networks. The careful use of these assets enables the optimization of transmission voltages in power networks, a crucial step for reducing electrical energy losses, enhancing grid reliability, and ensuring uninterrupted electricity supply to end-users in interconnected power networks. Operational reliability of power transformers is a critical aspect in ensuring a continuous power supply. The health index (HI) is a crucial diagnostic tool to determine their real condition. Historically, HI assessments were based on scoring and weighting. Lately, however, there has been a significant change in the attitude towards the use of artificial intelligence (AI) and machine learning (ML) to predict the health of high-voltage power transformers. Although developments are taking place, the existing studies on ML-based HI prediction models for power transformers largely rely on an incomplete dataset containing improper parameters. Moreover, dependency on traditional ML models is a significant limitation when it comes to achieving a higher degree of predictive accuracy. This article presents a sophisticated method for determining the overall health condition of power transformers. A total of twenty of the most appropriate and highly relevant input parameters were selected to effectively evaluate the transformer condition. The dataset for these parameters was collected from real-time testing in accordance with international industry standards (i.e., IEC, IEEE, and ASTM), conducted at 220 kV and 500 kV grid stations in the Multan and Lahore regions, operated by the National Grid Company (NGC) in Pakistan. This comprehensive dataset was fed to five state-of-the-art ML models. The Categorical Boosting Regression (CatBoost Regressor) model demonstrated superior performance, achieving the highest accuracy (R² Score) of 97.2% and the lowest mean absolute error (MAE) of 1.73. The best-performing model was then employed to predict the health index of the power transformers at the 500 kV grid station, Rahim Yar Khan, and the 500 kV grid station, Multan, as a practical case study. To demonstrate the economic importance of the proposed framework, an economic analysis was conducted via an iterative, parameter-skipping imputation strategy for maintenance cost optimization of the electrical power grid. The results verify that the omission of four diagnostic tests (i.e., Dissipation Factor, Capacitance, Insulation Resistance, and Transformer Turn Ratio) can reduce the economic burden by 46.99%, yielding a cost saving of 259,000 PKR per transformer unit. The implementation of this data-driven framework in the national grid can significantly reduce maintenance costs and facilitate an operational shift from traditional preventive maintenance to advanced predictive maintenance. Full article

Sheep- and goatpox are highly contagious, transboundary viral diseases caused by capripoxviruses (CaPV), severely impacting small ruminant production and resulting in significant economic losses. Our study aimed to analyze the spatiotemporal SGP distribution using the available genome sequence data and to evaluate the recombination occurrence. For this, the WAHIS, WOAH and FAO databases were utilized for the epidemiological analysis of SGP outbreaks. The cross-correlation was calculated to assess the impact of massive animal movements associated with the Islamic holiday of Eid al-Adha on the SGP epizootic situation, and phylodynamic and phylogeographic analysis, as well as a recombination analysis were performed. A total of 1629 SGP outbreaks were reported to WAHIS during 2010–2024, with the majority occurring in Mongolia, the Balkan countries, Russia and the Middle East. Over 60% of all SGP outbreaks were associated with croplands or grasslands, with the highest proportion corresponding to animal densities of 10–50 head/km². Statistically significant positive cross-correlation (p < 0.05) was identified between the month of the Eid al-Adha celebration and the number of SGP outbreaks in Russia, Mongolia, Bulgaria and Tajikistan, while in Greece and China no significant correlation was found. The inferred goat pox virus (GTPV) transmission pathways from China to Vietnam and from India to Bangladesh; for the sheeppox virus SPPV, the routes between Kazakhstan and Russia, Kazakhstan and India, as well as between Russia and China, had the strongest Bayes factor support. Intra-specific recombination events were not detected for the SSPV and GTPV datasets. However, inter-specific CaPV recombination analysis identified a single recombination event in GTPV. Therefore, the use of molecular epidemiological tools, along with the time-calibrated phylodynamic and phylogeographic analyses, has significant applications in the local and international surveillance of the occurrence of SGP outbreaks and for identification of potential recombination events. Full article

Once regarded as a benign, clinically overlooked organism, Theileria orientalis has undergone a paradigm shift in its perceived status and is now recognised as a tick-borne haemoparasite of clinical and economic importance. Severe outbreaks characterised by haemolytic anaemia, reproductive losses, and mortality, primarily associated with the Ikeda and Chitose genotypes, have been increasingly reported in Australasia, Asia, and North America. In Europe and parts of Africa, however, the epidemiological status of T. orientalis remains poorly defined, despite increasing molecular evidence of circulation and reports of predominantly subclinical infections. The parasite is transmitted transstadially in ixodid ticks, with Haemaphysalis longicornis as the principal confirmed biological vector, although vector competence remains unverified for most suspected tick species. This narrative review was based on a comprehensive PubMed search from database inception up to the date of initial manuscript submission, supplemented by reference-list screening and targeted searches. This review synthesises current knowledge on taxonomy, life cycle, transmission, genetic diversity based on the major piroplasm surface protein gene, global epidemiology, pathogenesis, clinical manifestations, co-infections, production losses, diagnostics, and control strategies. Key gaps include limited understanding of the mechanisms driving erythrocyte destruction, insufficient experimental confirmation of vector competence, absence of licensed vaccines, and incomplete epidemiological data in several regions. Future research priorities are discussed in the context of climate change and the expanding geographic distribution of tick vectors. Full article