Search Results (472)

This paper proposes a GNSS-compatible method for characterizing the motion of UAV-based navigation enhancement platforms, designed to provide reliable navigation and positioning services in emergency scenarios where GNSS signals are unavailable or severely degraded. The method maps UAV trajectories into standard GNSS navigation messages by establishing a correspondence between ephemeris parameters and platform positions through coordinate transformation and Taylor series expansion. To address modeling inaccuracies, the approach incorporates truncation error analysis and motion-assumption compensation via parameter optimization. This design enables UAV-mounted pseudolite systems to broadcast GNSS-compatible signals without modifying existing receivers, significantly enhancing rapid deployment capabilities in complex or degraded environments. Simulation results confirm precise positional representation in static scenarios and robust error control under dynamic motion through higher-order modeling and optimized broadcast strategies. UAV flight tests demonstrated a theoretical maximum error of 0.4262 m and an actual maximum error of 3.1878 m under real-world disturbances, which is within operational limits. Additional experiments confirmed successful message parsing with standard GNSS receivers. The proposed method offers a lightweight, interoperable solution for integrating UAV platforms into GNSS-enhanced positioning systems, supporting timely and accurate navigation services in emergency and disaster relief operations. Full article

Background/Objectives: On 6 June 2023, two varicella cases were reported at a highly vaccinated elementary school in Gyeonggi Province, Republic of Korea. We investigated the outbreak to describe its transmission dynamics; quantify attack rates in school, household, and private-academy settings; and assess the impact of coordinated control measures. Methods: A case-series study included 89 teachers and students who had contact with suspected patients. Using case definitions, laboratory tests, questionnaires, and environmental assessments, we evaluated exposures and factors facilitating spread. Results: Varicella developed in 23 of 89 contacts (25.8%); laboratory confirmation was obtained in 2 (8.7% of cases). The mean incubation period was 13 days. Epidemic-curve and network analyses indicated that the outbreak began with a single index case and extended through household contacts and private educational facilities, ultimately involving multiple schools. Conclusions: Breakthrough transmission can occur even when single-dose coverage exceeds 95%, particularly as vaccine-induced immunity may wane over time. Poorly regulated extracurricular facilities, such as private academies, act as bridging hubs that amplify spread across grades and even between schools. For timely detection and control, these venues should be incorporated into routine varicella surveillance, and rapid, coordinated infection-control measures are required across all educational settings. Full article

This paper describes the spatiotemporal distribution and risk factors of African Swine Fever (ASF) in Uganda for the period of 2010 through 2023. The study utilized a comprehensive dataset from monthly reports (2010–2023) from District Veterinary Officers (DVOs), the Ministry of Agriculture, Animal Industry and Fisheries (MAAIF), and the Food and Agriculture Organization, Uganda. Using GPS coordinates, ASF cases were mapped using QGIS to show ASF distribution and spread in Uganda. Moran’s I analysis was used to delineate clusters of ASF. A total of 1521 ASF cases were recorded. The data show that cases of ASF were disseminated throughout the country, with more cases of ASF documented in the central region and border districts (hotspots for ASF), and few cases were reported in Acholi, Karamoja, and Lango, Ankole, West Nile, and Kigezi sub-regions. The time series analysis revealed incidences of ASF disease occurring year-round; notable peak cases were observed in some districts, and districts with ≥30,000 pigs reported higher cases of ASF. The Moran’s I (≥1) analysis showed that ASF is either aggregated (p = 0.01), especially in central districts bordering Tanzania and lake shores, or sporadic in occurrence. The disease was present in 66% of the districts, with ASF occurring throughout the year. More cases were aggregated in central and border districts and districts with large pig populations (≥30,000). Sporadic cases were reported in districts bordering the DRC, Sudan, Kenya, the lake shores, Karamoja, Acholi, and Lango sub-regions. Full article

This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic Soil Moisture Monitoring Network. All products were standardized to a 0.25° × 0.25° grid in the WGS-84 coordinate system through reprojection and resampling for consistent comparison. Daily averaged station observations were matched to product pixels using a 10 km radius buffer, with the mean station value as the reference for each time series after rigorous quality control. Results reveal distinct performance rankings, with SMAP-based products, particularly the SMAP_IB descending orbit variant, achieving the lowest unbiased root mean square deviation (ubRMSD) and highest correlation with in situ data. Blended products like ESA CCI and NOAA SMOPS, alongside reanalysis datasets such as ERA5 and MERRA2, outperformed SMOS and China’s FY3 products. The SoMo.ml product showed the broadest spatial coverage and strong temporal consistency, while FY3-based products showed limitations in spatial reliability and seasonal dynamics capture. These findings provide critical insights for selecting appropriate soil moisture datasets to enhance sustainable agricultural practices, optimize water resource allocation, monitor ecosystem resilience, and support climate adaptation strategies, therefore advancing sustainable development across diverse geographical regions in China. Full article

In the context of the “double carbon” goals and energy transformation, the integration of energy and transportation has emerged as a crucial trend in their coordinated development. Wind power prediction serves as the cornerstone technology for ensuring efficient operations within this integrated framework. This paper introduces a wind power prediction methodology based on an NGBoost–GRU fusion model and devises an innovative dynamic charging optimization strategy for electric vehicles (EVs) through deep collaboration. By integrating the dynamic feature extraction capabilities of GRU for time series data with the strengths of NGBoost in modeling nonlinear relationships and quantifying uncertainties, the proposed approach achieves enhanced performance. Specifically, the dual GRU fusion strategy effectively mitigates error accumulation and leverages spatial clustering to boost data homogeneity. These advancements collectively lead to a significant improvement in the prediction accuracy and reliability of wind power generation. Experiments on the dataset of a wind farm in Gansu Province demonstrate that the model achieves excellent performance, with an RMSE of 36.09 kW and an MAE of 29.96 kW at the 12 h prediction horizon. Based on this predictive capability, a “wind-power-charging collaborative optimization framework” is developed. This framework not only significantly enhances the local consumption rate of wind power but also effectively cuts users’ charging costs by approximately 18.7%, achieving a peak-shaving effect on grid load. As a result, it substantially improves the economic efficiency and stability of system operation. Overall, this study offers novel insights and robust support for optimizing the operation of integrated energy systems. Full article

Accurate monitoring of rice-planting areas underpins food security and evidence-based farm management. Recent work has advanced along three complementary lines—multi-source data fusion (to mitigate cloud and spectral confusion), temporal feature extraction (to exploit phenology), and deep-network architecture optimization. However, even the best fusion- and time-series-based approaches still struggle to preserve fine spatial details in sub-meter scenes. Targeting this gap, we propose an HRNet-CA-enhanced DeepLabV3+ that retains the original model’s strengths while resolving its two key weaknesses: (i) detail loss caused by repeated down-sampling and feature-pyramid compression and (ii) boundary blurring due to insufficient multi-scale information fusion. The Xception backbone is replaced with a High-Resolution Network (HRNet) to maintain full-resolution feature streams through multi-resolution parallel convolutions and cross-scale interactions. A coordinate attention (CA) block is embedded in the decoder to strengthen spatially explicit context and sharpen class boundaries. The rice dataset consisted of 23,295 images (11,295 rice + 12,000 non-rice) via preprocessing and manual labeling and benchmarked the proposed model against classical segmentation networks. Our approach boosts boundary segmentation accuracy to 92.28% MIOU and raises texture-level discrimination to 95.93% F1, without extra inference latency. Although this study focuses on architecture optimization, the HRNet-CA backbone is readily compatible with future multi-source fusion and time-series modules, offering a unified path toward operational paddy mapping in fragmented sub-meter landscapes. Full article

The rapid advancement of digital information technology in rural China has positioned smart agro-rural development as a key driver of agricultural modernization. This study focuses on the theme of digital rural construction (DRC) and high-quality agricultural development (HAD), combining the two into smart agriculture and rural development. Utilizing panel data from 31 Chinese provinces from 2011 to 2022, a comprehensive evaluation index system is constructed to assess development levels. The entropy weight method and kernel density estimation are employed to evaluate indicator performance and capture dynamic distribution patterns. A coupling coordination model is used to analyze the spatio-temporal evolution of the interaction between the two systems, while a hybrid SD-LSTM (System Dynamics–Long Short-Term Memory) model forecasts coordination trends over the next six years. Results reveal a steady upward trend in both systems, with coordination levels improving from “moderate imbalance” to “moderate coordination.” A distinct spatial pattern emerges, characterized by “high in the east, low in the west” and a mismatch between high coupling and low coordination. Forecasts suggest a continued progression toward “good coordination.” The findings offer policy implications for enhancing digital village initiatives, accelerating rural technological diffusion, and strengthening regional collaboration—providing valuable insights into advancing China’s smart rural transformation and agricultural modernization. Full article

The objective of this study was to improve measurement accuracy in the evaluation of autonomous agricultural tractor performance by addressing external disturbances, such as sensor installation errors, vibrations, and heading-induced bias that occur during the measurement of the conventional rear three-point hitch (Rear 3-Point) system. To mitigate these disturbances, the measurement point was relocated to the cab, where external interference is comparatively minimal. However, in compliance with the ISO 12188 standard, the Rear 3-Point system must be used as the reference measurement point. Therefore, its coordinates were indirectly estimated using an extended Kalman filter (EKF) and artificial intelligence (AI)-based techniques. A hybrid model was developed in which a transformer-based AI model was trained using the Rear 3-Point coordinates predicted by EKF as the ground truth. While traditional time-series models, such as LSTM and GRU, show limitations in predicting nonlinear data, the application of an attention mechanism was found to enhance prediction performance by effectively learning temporal dependencies and vibration patterns. The experimental results show that the EKF-based estimation achieved a precision of RMSE 1.6 mm, a maximum error of 12.6 mm, and a maximum standard deviation of 3.9 mm compared to actual measurements. From the perspective of experimental design, the proposed hybrid model was able to predict the trajectory of the autonomous agricultural tractor with significantly reduced external disturbances when compared to the actual measured Rear 3-Point coordinates, while also complying with the ISO 12188 standard. These findings suggest that the proposed approach provides an effective and integrated solution for developing high-precision autonomous agricultural systems. Full article

Current data-driven methods for leak localization (LL) in water distribution networks (WDNs) rely on two unrealistic assumptions: they frame LL as a node-classification task, requiring leak examples for every node—which rarely exists in practice—and they validate models using random data splits, ignoring the temporal structure inherent in hydraulic time-series data. To address these limitations, we propose a temporal, regression-based alternative that directly predicts the leak coordinates, training exclusively on past observations and evaluating performance strictly on future data. By comparing five machine-learning techniques—k-nearest neighbors, linear regression, decision trees, support vector machines, and multilayer perceptrons—in both classification and regression modes, and using both random and temporal splits, we show that conventional evaluation methods can misleadingly inflate model accuracy by up to four-fold. Our results highlight the importance and suitability of a temporally consistent, regression-based approach for realistic and reliable leak localization in WDNs. Full article

Gait analysis provides crucial insights into neuromuscular coordination and postural control, especially in ageing populations and rehabilitation contexts. This study investigates the complexity of muscle activation and ground reaction force patterns during gait by applying detrended fluctuation analysis (DFA) to electromyography (EMG) and force-sensitive resistor (FSR) signals. Data from a two-arm randomised clinical trial (RCT) supplemented with an observational control group were used in this study. Participants performed a single-task walking protocol, with EMG recorded from the tibialis anterior and lateral gastrocnemius muscles of both legs and FSR sensors placed under the feet. Gait cycles were segmented using heel-strike detection from the FSR signal, enabling analysis of individual strides. For each gait cycle, DFA was applied to quantify the long-range temporal correlations in the EMG and FSR time series. Results revealed consistent $\alpha$-scaling exponents across cycles, with EMG signals exhibiting moderate persistence ($\alpha \approx 0.85$–$0.92$) and FSR signals showing higher persistence ($\alpha \approx 1.5$), which is indicative of stable and repeatable gait patterns. These findings support the utility of DFA as a nonlinear signal processing tool for characterising gait dynamics, offering potential markers for gait stability, motor control, and intervention effects in populations practising movement-based therapies such as Tai Chi. Future work will extend this analysis to dual-task conditions and comparative group studies. Full article

The analysis of rockfall events provides critical insights for deciphering planetary geological processes and reconstructing environmental evolutionary timelines. Conventional visual interpretation methods that rely on orbiter imagery can be inefficient due to their massive datasets and subtle morphological signatures. While deep learning technologies, particularly object detection models, demonstrate transformative potential, they require specific adaptation to planetary imaging constraints, including low contrast, grayscale inputs, and small-target detection. Our coordinated optimization strategy integrates dynamic cropping optimization with architectural innovations: Kolmogorov–Arnold Network based C3 module (KANC3) replaces RepC3 through Legendre polynomial decomposition to strengthen feature representation, while our dynamic cropping strategy significantly improves small-target detection in low-contrast grayscale imagery by mitigating background and target imbalance. Experimental validation on the optimized RMaM-2020 dataset demonstrates that Real-Time Detection Transformer with a ResNet-18 backbone and Kolmogorov–Arnold Network based C3 module (RT-DETR-R18-KANC3) achieves 0.982 precision, 0.955 recall, and 0.964 mAP50 under low-contrast conditions, representing a 1% improvement over the baseline model and exceeding YOLO-series models by >40% in relative performance metrics. Full article

The stability of gearbox bearings is crucial to the operational efficiency and safety of industrial equipment, as their faults can lead to downtime, economic losses, and safety risks. Traditional models face difficulties in handling complex industrial time-series data due to insufficient feature extraction capabilities and poor training stability. Although transformers show advantages in fault diagnosis, their ability to model local dependencies is limited. To improve feature extraction from time-series data and enhance model robustness, this paper proposes an innovative method based on the ViT. Time-series data were converted into two-dimensional images using polar coordinate transformation and Gramian matrices to enhance classification stability. A lightweight front-end encoder and depthwise feature extractor, combined with multi-scale depthwise separable convolution modules, were designed to enhance fine-grained features, while two-dimensional rotary position encoding preserved temporal information and captured temporal dependencies. The constructed RoPE-DWTrans model implemented a unified feature extraction process, significantly improving cross-dataset adaptability and model performance. Experimental results demonstrated that the RoPE-DWTrans model achieved excellent classification performance on the combined MCC5 and HUST gearbox datasets. In the fault category diagnosis task, classification accuracy reached 0.953, with precision at 0.959, recall at 0.973, and an F1 score of 0.961; in the fault category and severity diagnosis task, classification accuracy reached 0.923, with precision at 0.932, recall at 0.928, and an F1 score of 0.928. Compared with existing methods, the proposed model showed significant advantages in robustness and generalization ability, validating its effectiveness and application potential in industrial fault diagnosis. Full article

The escalation of international geopolitical conflicts has triggered shocks in the global energy supply and demand pattern. The importance of increasing the resilience of energy systems to risk has become increasingly prominent. At the same time, energy demand has shown substantial growth, driven by the continuous expansion of economic scales. Improving utilization efficiency to enhance energy resilience while achieving coordinated development between economic growth and environmental protection has become a critical priority. This study takes pilot zones for green finance reform and innovations as a quasi-natural experiment and selects panel data from 30 provinces in China from 2013 to 2022 as the research sample. The empirical analysis constructs a staggered difference-in-differences (DID) model to investigate the impact of pilot zones for green finance reform and innovations on energy resilience, while exploring their heterogeneity and mechanism of action. The research shows that: ① The policy of pilot zones for green finance reform and innovations has significantly enhanced China’s energy resilience capacity. This conclusion still holds after a series of robustness tests. ② Mechanism analysis shows that the pilot zones for green finance reform and innovation policy enhance energy resilience by elevating green innovation capacity and optimizing industrial structure. ③ Heterogeneity analysis reveals that policy effects exhibit significant regional disparities. The enhancement effect of pilot zones for green finance reform and innovation policy on energy resilience is more pronounced in the eastern region compared to the central and western regions. This research provides empirical evidence and theoretical support for local governments to refine green finance policy systems and explore novel pathways for optimizing energy resilience. Full article

This paper focuses on the provincial integrated energy system in northern China, which is characterized by the large-scale integration of renewable energy, thorough coupling of electricity and heat, and interactive operation of sources, loads, and storages. When conducting time-series production simulation with the daily rolling optimization dispatching method, the embedded daily optimal dispatching model fails to effectively charge and discharge electric and thermal energy storages across days to accommodate the curtailed electricity from renewable energy. Thus, a new embedded daily optimal dispatching model is proposed. The new model adopts a strategy of converting the stored energy of electric and thermal energy storages at the end of the dispatching day into equivalent coal consumption, respectively, and deducting it from the objective function of the optimal dispatching model. Through theoretical analysis, the reasonable range of the conversion coefficient is determined, enabling the model to use electric and thermal energy storages to store the curtailed electricity during surplus power generation in a dispatching day and accommodate it in subsequent days. A case study based on a provincial electricity–heat integrated energy system in northern China shows that the curtailment of renewable energy with the suggested strategy is much less than that with the traditional strategy, verifying the effectiveness of the proposed model. Full article

Agricultural modernization is the key path and core strategy for the transformation from traditional agriculture to modern agriculture, and it constitutes the cornerstone of China’s modernization system. Gansu Province is a typical ecologically fragile area, and a multi-ethnic province in the northwest of China. In recent years, through the application of efficient water-saving technologies, the industrialization of characteristic agriculture and institutional innovation, it has initially achieved the coordinated increase in production and ecological benefits. However, the lagging infrastructure and low efficiency of factor allocation still restrict its systematic transformation process. Based on the panel data of 14 prefectures and cities in Gansu Province from 2013 to 2022, this paper constructs an evaluation index system for agricultural modernization, and reveals the spatio-temporal evolution characteristics of agricultural modernization in Gansu Province. Further, this paper combines the Theil index and the obstacle degree model to analyze the regional differences and development bottlenecks of agricultural modernization in Gansu Province. The research finds that the overall level of agricultural modernization in Gansu Province has improved but is still in a stage of continuous development. Spatially, the western and central regions have a higher level of development, while the southern region is relatively lower. The time series analysis results show that the overall regional differences in agricultural modernization in Gansu Province have narrowed. From 2013 to 2018, the differences within the regions were dominant; after 2018, they were jointly affected by both within-region and between-region differences. The results of the obstacle factor analysis show that the modernization of agricultural industrial operation is the main obstacle factor, followed by the green modernization of agriculture. Based on these findings, this paper proposes suggestions such as strengthening regional coordination, enhancing production and operation capabilities, and promoting ecological construction. It is expected that, through the continuous development of the level of agricultural modernization, the coordinated development of agricultural modernization in Gansu Province can be promoted, and further, the well-being of the people can be enhanced, and the rural revitalization strategy can be advanced in depth. Full article