Search Results (5,102)

Dual-band weather radar networks enable complementary multi-radar observations, improving the accuracy, three-dimensional characterization, and early warning capability for severe convective weather. S-band radar provides strong penetration and long detection range but suffers from limited spatial resolution, whereas X-band radar offers high resolution with weaker penetration, posing challenges for dual-frequency data fusion. To address the resolution mismatch and fusion modeling issues between dual-band radars, this study proposes a super-resolution reconstruction method for S-band reflectivity based on dual-frequency radar observations. S-band and X-band radar data, together with key polarimetric parameters, are jointly incorporated into a deep neural network-based fusion model to enhance the spatial resolution of S-band reflectivity. Experimental results under typical severe weather conditions demonstrate that the proposed method achieves improved detail recovery and structural reconstruction, with the model achieving PSNR 30.84, SSIM 0.8755, and MAE 0.24178, which shows obvious advantages compared with other models and effectively enhances radar network data quality, and it outperforms single S-band super-resolution approaches in both objective metrics and subjective evaluations. Full article

Long-distance coal gangue slurry transportation pipelines are critical components of underground coal mine green backfilling systems, yet blockage failures severely threaten their safe and efficient operation. Existing distributed acoustic sensing (DAS)-based monitoring methods for such pipelines suffer from three key limitations: insufficient fixed-point quantitative accuracy, lack of verified blockage-specific characteristic indicators, and limited quantitative severity assessment capability. To address these gaps, this paper proposes a novel feature-level fusion monitoring method integrating DAS, fiber Bragg grating (FBG), and piezoelectric accelerometers for accurate blockage identification and quantitative evaluation in coal gangue slurry pipelines. A slurry pipeline circulation test platform with gradient blockage simulation (0% to 76.42%) and a synchronous multi-sensor monitoring system were developed. Through multi-domain signal analysis, three blockage-correlated characteristic frequencies were identified and cross-validated by synchronous multi-sensor data: 1.5 Hz (system background vibration), 26 Hz (blockage-induced fluid–structure resonance, verified by the Euler–Bernoulli beam theory with a theoretical value of 25.7 Hz), and 174 Hz (transient flow impact). The DAS phase change rate exhibited a unimodal nonlinear response to blockage degree, with the peak occurring at 40.94% blockage. On this basis, a sine-fitting quantitative inversion model was developed, achieving a high goodness of fit (R² = 0.985), and leave-one-out cross-validation confirmed its excellent robustness with a mean relative prediction error of 3.77%. Finally, a collaborative monitoring framework was built to fully leverage the complementary advantages of each sensor, realizing full-process blockage monitoring covering global blockage localization, precise quantitative severity calibration, and high-frequency transient risk early warning. The proposed method provides a robust experimental and technical foundation for real-time early warning, precise localization, and quantitative diagnosis of long-distance slurry pipeline blockages and holds important engineering application value for the safe and efficient operation of underground coal mine green backfilling systems. Full article

Charge transport underpins essential biological processes, including cellular respiration, photosynthesis, and enzymatic catalysis. Advances in molecular electronics have enabled single-molecule measurements that unequivocally establish redox-active proteins as efficient electron conductors, with their metal cofactors serving as intrinsic redox relays. By contrast, ubiquitous non-redox proteins lacking such redox centers have long been considered poor conductors. However, recent research has challenged this view, demonstrating that efficient charge transport in non-redox proteins can be mediated through polypeptide backbones, aromatic side-chain arrays, and hydrogen bond networks. This review surveys progress in understanding the single-molecule conductance of non-redox proteins. Firstly, we elucidate the fundamental transport mechanisms, highlighting the interplay between coherent tunneling and thermally activated hopping. We then provide an overview of state-of-the-art experimental techniques for single-molecule characterization. Through analysis of diverse systems spanning short peptides to large enzymes, we illustrate how aromatic amino acid networks and dynamic conformational fluctuations govern conductance, enabling emerging applications in label-free biosensing and single-molecule protein/DNA sequencing. Finally, we discuss persistent challenges and outline future opportunities for integrating protein-based conductors into bioelectronic devices. This review aims to stimulate further research and pave the way for novel applications harnessing protein conductance. Full article

High-frequency water quality monitoring generates large volumes of sub-daily observations, but concise and scalable indicators for diagnosing short-term instability remain limited. Using four-hourly records from 336 national automatic monitoring stations in Southwest China (November 2022–September 2024), we constructed a nine-parameter water quality index (WQI) and developed a normalized Shannon entropy–coefficient of variation (h–CV) framework to characterize short-term instability in fixed three-day windows. A composite separation index combining the Kolmogorov–Smirnov distance of pollution-event counts and the effect size of entropy distributions, together with bootstrap resampling, identified CV ≈ 0.10 as an operational threshold for high-fluctuation windows. The joint h–CV distribution revealed four typical short-term dynamic patterns and showed good consistency across three-, five-, and seven-day windows. At the station scale, instability hotspots were concentrated in southern Yunnan–Guizhou–Guangxi, the southeastern margins of the Sichuan Basin, and several mid-lower mainstream reaches, whereas alpine headwaters and upstream segments remained relatively stable. Overall, the proposed framework provides an interpretable and generalizable tool for short-term water-quality diagnosis, with practical value for risk zoning, early warning, and monitoring network optimization. Full article

Wildfires at the wildland–urban interface (WUI) have increased in frequency and severity under global warming and intensified human activities. As a representative high-altitude mountainous region in southwestern China, Yunnan features complex topography, steep climatic gradients, and dispersed settlements interwoven with wildlands, making it a fire-prone area where wildfire management is particularly challenging. However, a fine-scale WUI dataset is currently lacking for this region. To address this gap, we refined WUI classification thresholds using a one-factor-at-a-time (OFAT) method and generated the first fine-resolution WUI map of Yunnan. Seasonal wildfire driving factors from 2004 to 2023 were quantified, and machine learning models were applied to produce seasonal susceptibility maps. SHapley Additive exPlanations (SHAP) were employed to interpret the dominant contributing factors. The resulting WUI covers 25,730.67 km², accounting for 6.5% of Yunnan’s land area. Random forest models effectively captured seasonal wildfire susceptibility patterns, with AUC values exceeding 0.83 across all seasons. High susceptibility zones (>0.5) comprised 30.09% of the WUI in spring, 25.74% in winter, 22.61% in autumn, and 13.74% in summer. SHAP analysis revealed that anthropogenic factors consistently drive wildfire occurrence, while climatic conditions in the preceding season influence vegetation status and subsequently affect wildfire likelihood in the current season. By integrating static “where” mapping with dynamic “when” susceptibility analysis, this study establishes a comprehensive “When–Where” framework that supports both long-term WUI planning and short-term seasonal early warning. The integration of fine scale WUI mapping with seasonal susceptibility modeling enhances wildfire risk management in complex high-altitude regions. These findings provide a scientific basis for location-specific, time-sensitive, and full-chain wildfire management in mountainous landscapes and contribute to cross-border ecological security governance in the Indo-China Peninsula. Full article

This study uses multidimensional indicators of macroeconomics, supply and demand, cost, and market microstructure to construct an intelligent framework integrated with optimized Exponentially Weighted Moving Average (EWMA) denoising for price forecasting and black early warning for egg futures in China from 2014 to 2023. Black early warning serves as a non-parametric early warning method that identifies abnormal price increases and falls based on historical fluctuation thresholds. As the first livestock future contract listed in China, accurate egg price forecasting is crucial for risk prevention and market control and regulation. First, LASSO regression was used to screen the core driving factors of egg futures prices. Nine key indicators were identified and input into the hybrid Temporal Convolutional Network–Gated Recurrent Unit (TCN-GRU) prediction model. To address the high-frequency noise in the original price series, two-dimensional optimization was performed on traditional EWMA denoising to achieve more adaptive noise filtering. By applying the black early warning method, the obtained future egg price fluctuations were more consistent with the actual situation. In addition, empirical analysis of multi-horizon forecasting and early warning for t + 1, t + 5, and t + 10 was carried out to further verify the model’s prediction accuracy. The results show that compared with the single TCN model, the single GRU model, and the TCN-GRU model without denoising, the TCN-GRU model integrated with optimized EWMA denoising achieves better prediction performance on the test set. In terms of the early warning matching rate, it reaches 83.33% for the t + 1 horizon, and the prediction accuracy for the t + 5 and t + 10 horizons decreases regularly but remains stable above 60%. In contrast, the highest early warning matching rate of the model without denoising is only 22.22% across all horizons, which has no practical early warning value. The early warning signals generated by the optimized EWMA denoising-based TCN-GRU model can effectively identify abnormal sharp rises and falls in egg futures prices, providing effective support for hedging and risk management for market participants. The study’s limitations are discussed, as well as future research directions. The findings provide a basis for decision making for agricultural producers and future investors and support the development of China’s agricultural product market. Full article

Oil analysis is one of the main means to obtain the working status of important friction pairs in ship and Marine engineering equipment at present. Analyzing the wear mechanism by analyzing the particle size, morphology, properties and other characteristics of metal abrasive particles in the oil is an important basis for achieving health monitoring and scientific maintenance of ship and Marine engineering equipment. Classifying the abrasive particles in the oil according to their particle size is an important step in sample pretreatment. This paper proposes a two-stage sorting microfluidic chip for wear debris based on magnetophoresis. By setting up external permanent magnets in a stepwise manner in the primary and secondary sorting areas, gradient magnetic fields of different magnitudes were formed. The effects of different sample flow rates, sheath fluid flow rates and sheath flow ratios on the pre-focusing before sorting and the sorting effect were studied. The primary sorting of ferromagnetic metal wear particles larger than 50 µm and the secondary sorting of those smaller than 50 µm have been achieved. The primary sorting can serve as an early warning for abnormal equipment wear, while the secondary sorting can provide data support for the scientific formulation of maintenance plans based on equipment requirements. This work provides a new idea and method for the rapid determination of lubricating oil contamination in engineering equipment. Full article

Botswana’s semi-arid climate makes it vulnerable to climate change, particularly drought, which threatens agricultural productivity. This study assesses drought impact on Botswana’s agricultural sector using Climate Hazards Center Infrared Precipitation with Station (CHIRPS) rainfall data and Climate Hazards Center Infrared Temperature with Station (CHIRTS) temperature data (25 km) to compute the Standardized Precipitation Index (SPI), Standardized Temperature Condition Index (STCI) and Standardized Precipitation Evapotranspiration Index (SPEI) at seasonal/annual time scales (1, 3, 6 and 12 months). The indices are used to assess their ability to predict crop yields using national data during Botswana’s rainy season, while employing univariate and multivariate statistical models. Statistical models also linked historical drought patterns to yield variability with the Percentage Area Affected (PAA) by drought, identifying key predictors. A majority of the crops (sunflower, maize, sorghum and pulses) showed variability which was best explained by SPEI 6 more particularly under the PAA multivariate models, with the highest and moderate explanatory power (R²) found in sunflower (0.48) and maize (0.43). However, variability in millet was best explained by SPI-3, although the R² was low (0.26). Other crops displayed positive coefficients within the models, which may be attributed to the varieties grown being drought tolerant. Nevertheless, the impacts from drought, which resulted in low yields, were shown by the negative coefficients across most crops. For a more holistic approach, the study also employed questionnaire data to capture first-hand local knowledge. The results showed drought to be among the indicators of climate change that were mostly perceived as well as its effects, in which yield decline, crop damage and crop pests and diseases were among the most perceived effects. Overall, this highlighted the sector’s vulnerability to the changes in climate. The study therefore underscores the need for integrated drought early warning systems, adaptive agricultural/water management and insights for policymakers to enhance drought resilience in Botswana, aligning with global sustainability goals. Full article

Continuous ward monitoring, remote patient monitoring, and self-monitoring can generate high-frequency physiological data streams, yet clinical benefit depends on whether signals lead to timely escalation without excessive non-actionable alerts and workflow burden. This scoping review, reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), mapped AI-driven and algorithm-supported decision support approaches using continuous, remote, or self-monitoring patient data for early deterioration detection or prediction and escalation support, with emphasis on nursing relevance, workflow integration, alert burden, and implementation outcomes. PubMed (MEDLINE), Ovid MEDLINE, Web of Science Core Collection, and Scopus were searched on 14 February 2026. The search identified 47 records; 12 duplicates were removed; 35 records were screened; 28 were excluded; and 7 full-text reports were included. The included evidence comprised two original studies, two protocol/design papers, and three reviews. Within these included sources, decision support was commonly described as linking monitoring inputs to interpretive outputs, such as tiered alerts or risk predictions, and then to escalation-related actions or response pathways. Because the evidence base was small and heterogeneous, the review should be interpreted as exploratory evidence mapping rather than as a basis for broad generalization. Within the included studies, key reporting gaps included inconsistent description of escalation endpoints, limited standardized reporting of alert burden and acknowledgment patterns, incomplete workflow descriptions in some remote monitoring evidence, and limited attention to maintenance risks such as dataset shift. Full article

Heat stress is a major constraint to global egg production, as rising temperatures increasingly challenge the physiological limits of commercial layer chickens. This review integrates current advances in stress physiology to demonstrate that heat stress is not merely a thermoregulatory problem but a multi-systemic disruption involving neuroendocrine overload, metabolic imbalance, oxidative damage, immune suppression, and gastrointestinal barrier breakdown. These interacting pathways collectively impair egg production, shell quality, feed efficiency, and hen welfare. The review also synthesizes emerging mitigation strategies across environmental control, nutritional interventions, genetic and breeding innovations, welfare-oriented housing systems, and precision monitoring technologies. Studies indicate that targeted cooling, antioxidant, and electrolyte supplementation, the selection of heat-tolerant strains, enriched environments, and sensor-based early-warning systems can significantly enhance egg-laying hen resilience. Remaining gaps include a limited understanding of multi-stressor interactions, microbiome-mediated thermal tolerance, and the large-scale implementation of precision tools. The review provides a forward-looking framework for improving heat resilience in modern layer systems. Full article

Timely labor market monitoring is essential for policy design and operational planning, yet annual reports can mask turning points and subgroup heterogeneity. This paper develops a reproducible monitoring and prediction framework using administrative statistics from the General Organization for Social Insurance (GOSI) in the Saudi Open Data Portal. We document descriptive patterns in formal participation and insurable wages, including age-group dispersion, stable correlation structure, and explicit handling of an anomalous wage release and limited missing wage entries. We then formulate from non-salary administrative descriptors. Under leakage control, Random Forest models achieve accuracy around 0.71 across releases. Most errors are concentrated between adjacent wage bands, which is consistent with threshold discretization of a continuous wage distribution. To support operational deployment, we add out-of-time validation across releases and probabilistic assessment, showing that predictive skill transfers across updates and that calibration improves the reliability of probability scores for monitoring thresholds. Overall, the results indicate that administrative releases contain persistent actionable signals for wage segmentation without salary-derived inputs, supporting forecasting-oriented surveillance and early-warning dashboards. Full article

Environmental contamination with fluorinated compounds has increased markedly due to their widespread use in industry, medicine, and agriculture. Fluoride ions and fluoroquinolone antibiotics may enter soils through fertilizers, wastewater, and manure application, where they can interact with plant-associated microbial communities. In the present study, we investigated the effects of inorganic fluoride (applied as sodium fluoride, NaF) and the fluoroquinolone antibiotic pefloxacin on the growth and microbiome composition of Eruca sativa L. Plants were cultivated under controlled conditions and exposed for four weeks to NaF or pefloxacin at equimolar concentrations of 10 and 20 µM/kg soil. Morphological parameters, including biomass accumulation, root length, leaf dimensions, and leaf area, were not significantly affected by either treatment. Nevertheless, increased variability of growth traits was observed, particularly in plants exposed to NaF. High-throughput sequencing of the 16S rRNA gene revealed pronounced, treatment-specific alterations in both rhizosphere and phyllosphere bacterial communities. The rhizosphere microbiome was relatively stable at higher taxonomic levels but exhibited selective enrichment of Actinomycetota, including the class Thermoleophilia, under NaF exposure. In contrast, the phyllosphere microbiome showed strong sensitivity to fluoride, with a marked increase in Betaproteobacteria, dominated by Burkholderiales. Changes induced by pefloxacin were weaker and more diffuse. Our results demonstrate that plant-associated microbiomes respond to fluorinated compounds at concentrations that do not induce visible plant stress. The phyllosphere microbiome, in particular, represents a sensitive indicator of fluoride exposure and may serve as an early-warning system for environmental contamination. Full article

Lithium-ion batteries (LIBs) may experience thermal runaway (TR) under thermal abuse conditions, posing significant safety risks to energy storage systems, electric vehicles, and portable electronics. To ensure the safety of LIB-powered applications, developing an effective TR early warning method is crucial. This study employs polyimide-coated femtosecond fiber Bragg grating (FBG) sensors to investigate TR characteristics in 18,650 LIBs (LiNi_1/3Mn_1/3Co_1/3O₂/graphite), including TR onset temperature determination and the evolution of temperature and radial strain at different states of charge (SOCs). Compared with existing studies, the polyimide-coated femtosecond FBGs employed here offer superior breakage resistance and high-temperature tolerance, enabling more precise temperature and strain measurements. For radial strain monitoring obtained during high-temperature-induced LIBs thermal runaway experiments, temperature compensation was achieved using polyimide-coated femtosecond FBG temperature sensors, yielding higher-accuracy strain evolution profiles. Experimental results demonstrate that the higher-SOC LIBs exhibit more severe TR eruptions, with 1.76× higher peak temperatures and 1.3× greater mass loss than low-SOC LIBs. The proposed scheme pioneers an new approach to effective active safety warning of LIBs thermal runaway. Full article

The reconstruction of nodal prices and generation patterns in electricity markets with network constraints constitutes a challenging inverse analysis problem due to congestion-induced non-uniqueness and limited observability. This study introduces node symmetry analysis as a novel early indicator of locational marginal price (LMP) growth in power systems with high renewable energy penetration. Symmetric nodes, defined as nodes with identical generation cost structures and comparable network topology, exhibit near-identical price signals under uncongested conditions. In this study, the term “price” refers to the LMP obtained from the DC-OPF market-clearing model under scenarios with high renewable energy penetration. Deviations from this symmetry, quantified through price differences between symmetric node pairs (ΔLMP), serve as sensitive indicators of emerging network stress and congestion, providing early warning of peak-price events. Using DC power flow sensitivities and congestion indicators, LMPs are reconstructed in a simplified five-node test system under three scenarios: baseline operation, severe transmission congestion, and high renewable generation variability. Results show strong correlations between symmetry violations and system-wide price increases. In congested scenarios, ΔLMP exceeding €2/MWh consistently precedes peak prices by 1–2 h, demonstrating the metric’s predictive capability. Integration of storage further highlights the operational value of symmetry-based analysis, showing reductions in curtailed renewable generation and peak prices. The proposed framework offers a computationally efficient and interpretable tool for congestion diagnosis, price trend forecasting, and inverse market analysis, with potential scalability to larger AC networks and stochastic scenarios. These findings provide actionable insights for system operators, market participants, and regulators seeking to enhance flexibility, reliability, and economic efficiency in high-renewable electricity markets. Full article

Effective water resource management requires understanding the causal mechanisms driving water quality dynamics, yet extracting actionable insights from complex multivariate monitoring data remains a persistent challenge. This study presents CEcBaN (CCM-ECCM-Bayesian Networks), a decision-support tool that integrates Convergent Cross Mapping (CCM) for detecting dynamical coupling, Extended CCM (ECCM) for identifying temporal lags and causal directionality, and Bayesian network (BN) modeling for probabilistic scenario-based inference. The tool was designed to enable managers and researchers without programming expertise to reconstruct causal networks from routine monitoring data, distinguish direct from indirect effects, and evaluate intervention scenarios. CEcBaN was validated using four synthetic datasets with known causal structures, achieving superior specificity (0.83) and edge count accuracy (25% error) compared to Transfer Entropy (0.47 specificity, 139% error), Granger causality (0.82, 39% error), and the PC algorithm (0.83, 46% error). Application to Lake Kinneret, Israel, demonstrated the tool’s utility across three water quality challenges: (1) nitrogen cycling, where the nitrification pathway was reconstructed and seasonal stratification was identified as a key modulator (accuracy 0.931); (2) thermal dynamics, where a transition from atmosphere-driven to internally regulated heat transfer during stratification was revealed (2.1-fold increase in coupling strength); and (3) cyanobacterial bloom prediction, where prior phytoplankton community composition provided a 4–6-week early warning window (accuracy 0.846). CEcBaN advances causal inference in water resource management by making these analytical methods accessible through an intuitive interface. Full article