Search Results (14,658)

Group A rotavirus (RVA) is a leading causative agent of diarrhea in both young animals and humans. In China, multiple genotypes are commonly found within the bovine population. In this study, we investigated 1917 fecal samples from calves with diarrhea between 2022 and 2025, with 695 testing positive for RVA, yielding an overall detection rate of 36.25%. The highest positivity rate was observed in Hohhot (38.98%), and annual detection rates ranged from 26.75% in 2022 to 42.22% in 2025. A bovine rotavirus (BRV) strain, designated 0205HG, was successfully isolated from a fecal sample of a newborn calf. Its presence was confirmed through cytopathic effects (CPEs), the indirect immunofluorescence assay (IFA), electron microscopy (EM), and high-throughput sequencing. Genomic characterization identified the strain as having the G6-P[¹]-I2-R2-C2-M2-A3-N2-T6-E2-H3 genotype constellation. The structural proteins VP2 and VP7, along with nonstructural genes NSP1–NSP4, shared high sequence identity with Chinese bovine strains, whereas VP1, VP4, and NSP5 clustered more closely with human rotaviruses, and VP3 was related to feline strains. These findings highlight the genetic diversity and interspecies reassortment of BRVs in China, underlining the importance of continued surveillance and evolutionary analysis. Full article

Eucalyptus has become a major plantation crop in southern China, with a carbon sequestration capacity significantly higher than that of other species. However, its long-term carbon sequestration capacity and regional-scale potential remain highly uncertain due to commonly applied short-rotation management practices. The InTEC (Integrated Terrestrial Ecosystem Carbon) model is a process-based biogeochemical model that simulates carbon dynamics in terrestrial ecosystems by integrating physiological processes, environmental drivers, and management practices. In this study, the InTEC model was enhanced with an optimized eucalyptus module (InTEC_euc) and a data assimilation module (InTEC_DA), and driven by multiple remote sensing products (Net Primary Productivity (NPP) and carbon density) to simulate the carbon budgets of eucalyptus plantations from 2003 to 2023. The results indicated notable improvements in the performance of the InTEC_euc model when driven by different datasets: carbon density simulation showed improvements in R² (0.07–0.56), reductions in MAE (5.99–28.51 Mg C ha⁻¹), reductions in RMSE (8.1–31.85 Mg C ha⁻¹), and improvements in rRMSE (12.37–51.82%), excluding NPP_Lin. The carbon density-driven InTEC_euc model outperformed the NPP-driven model, with improvements in R² (0.28), MAE (−8.15 Mg C ha⁻¹), RMSE (−9.43 Mg C ha⁻¹), and rRMSE (−15.34%). When the InTEC_DA model was employed, R² values for carbon density improved by 0–0.03 (excluding ACD_Yan), with MAE reductions between 0.17 and 7.22 Mg C ha⁻¹, RMSE reductions between 0.33 and 12.94 Mg C ha⁻¹ and rRMSE improvements ranging from 0.51 to 20.22%. The carbon density-driven InTEC_DA model enabled the production of high-resolution and accurate carbon budget estimates for eucalyptus plantations from 2003 to 2023, with average NPP, Net Ecosystem Productivity (NEP), and Net Biome Productivity (NBP) values of 17.80, 10.09, and 9.32 Mg C ha⁻¹ yr⁻¹, respectively, offering scientific insights and technical support for the management of eucalyptus plantations in alignment with carbon neutrality targets. Full article

Backfilling materials are commonly employed materials in mines for filling mining waste, and the strength of the consolidated backfill formed by the binding material directly influences the stability of the surrounding rock and production safety in mines. The traditional approach to obtaining the strength of the backfill demands a considerable amount of manpower and time. The rapid and precise acquisition and optimization of backfill strength parameters hold utmost significance for mining safety. In this research, the authors carried out a backfill strength experiment with five experimental parameters, namely concentration, cement–sand ratio, waste rock–tailing ratio, curing time, and curing temperature, using an orthogonal design. They collected 174 sets of backfill strength parameters and employed six population optimization algorithms, including the Artificial Ecosystem-based Optimization (AEO) algorithm, Aquila Optimization (AO) algorithm, Germinal Center Optimization (GCO), Sand Cat Swarm Optimization (SCSO), Sparrow Search Algorithm (SSA), and Walrus Optimization Algorithm (WaOA), in combination with the CatBoost algorithm to conduct a prediction study of backfill strength. The study also utilized the Shapley Additive explanatory (SHAP) method to analyze the influence of different parameters on the prediction of backfill strength. The results demonstrate that when the population size was 60, the AEO-CatBoost algorithm model exhibited a favorable fitting effect (R² = 0.947, VAF = 93.614), and the prediction error was minimal (RMSE = 0.606, MAE = 0.465), enabling the accurate and rapid prediction of the strength parameters of the backfill under different ratios and curing conditions. Additionally, an increase in curing temperature and curing time enhanced the strength of the backfill, and the influence of the waste rock–tailing ratio on the strength of the backfill was negative at a curing temperature of 50 °C, which is attributed to the change in the pore structure at the microscopic level leading to macroscopic mechanical alterations. When the curing conditions are adequate and the parameter ratios are reasonable, the smaller the porosity rate in the backfill, the greater the backfill strength will be. This study offers a reliable and accurate method for the rapid acquisition of backfill strength and provides new technical support for the development of filling mining technology. Full article

Compression pants, as functional sportswear providing external pressure, are widely used to enhance athletic performance and accelerate recovery. However, systematic investigations into their effectiveness during anaerobic exercise and the impact of different pressure levels on performance and post-exercise recovery remain limited. This randomized crossover controlled trial recruited 20 healthy male university students to compare the effects of four garment conditions: non-compressive pants (NCP), moderate-pressure compression pants (MCP), high-pressure compression pants (HCP), and ultra-high-pressure compression pants (UHCP). Anaerobic performance was assessed through vertical jump, agility tests, and the Wingate anaerobic test, with indicators including time at peak power (TPP), peak power (PP), average power (AP), minimum power (MP), power drop (PD), and total energy produced (TEP). Post-exercise blood lactate concentrations and heart rate responses were also monitored. The results showed that both HCP and UHCP significantly improved vertical jump height (p < 0.01), while MCP outperformed all other conditions in agility performance (p < 0.05). In the Wingate test, MCP achieved a shorter TPP compared to NCP (p < 0.05), with significantly higher AP, lower PD, and greater TEP than all other groups (p < 0.05), whereas HCP showed an advantage only in PP over NCP (p < 0.05). Post-exercise, all compression pant groups recorded significantly higher peak blood lactate (Lamax) levels than NCP (p < 0.05), with MCP showing the fastest lactate clearance rate. Heart rate analysis revealed that HCP and UHCP induced higher maximum heart rates (HR_max) (p < 0.05), while MCP exhibited superior heart rate recovery at 3, 5, and 10 min post-exercise (p< 0.05). These findings suggest that compression pants with different pressure levels yield distinct effects on anaerobic performance and physiological recovery. Moderate-pressure compression pants demonstrated the most balanced and beneficial outcomes across multiple performance and recovery metrics, providing practical implications for the individualized design and application of compression garments in athletic training and rehabilitation. Full article

Small water bodies are widely spread and play crucial roles in supporting regional agricultural and aquaculture activities. PlanetScope imagery has a high resolution (3 m) with daily global coverage and has obviously enhanced small water body mapping. Recent studies have demonstrated the effectiveness of deep learning for mapping small water bodies using PlanetScope; however, a persistent challenge remains in the scarcity of high-quality, manually annotated water masks used for model training, which limits the generalization capability of data-driven deep learning models. In this study, we propose a transfer learning framework that leverages Sentinel-2 data to improve PlanetScope-based small water body mapping, capitalizing on the spectral interoperability between PlanetScope and Sentinel-2 bands and the abundance of open-source Sentinel-2 water masks. Eight state-of-the-art segmentation models have been explored. Additionally, this paper presents the first assessment of the VMamba model for small water body mapping, building on its demonstrated success in segmentation tasks. The models were pre-trained using Sentinel-2-derived water masks and subsequently fine-tuned with a limited set (1292 image patches, 256 × 256 pixels in each patch) of manually annotated PlanetScope labels. Experiments were conducted using 5648 image patches and two areas of 9636 km² and 2745 km², respectively. Among the evaluated methods, VMamba achieved higher accuracy compared with both CNN- and Transformer-based models. This study highlights the efficacy of combining global Sentinel-2 datasets for pre-training with localized fine-tuning, which not only enhances mapping accuracy but also reduces reliance on labor-intensive manual annotation in regional small water body mapping. Full article

Background: We aimed to explore the mechanism by which extracellular-5′-nucleotidase (NT5E) regulates macrophage polarization via regenerating islet-derived protein 3 beta (Reg3β) and other plasma proteins that mediate immune-cell effects on myocarditis. Methods: The involvement of NT5E in Reg3β-induced macrophage polarization was first analyzed using RNA sequencing, Western blotting, and quantitative polymerase chain reaction. Mendelian randomization was employed to identify NT5E and various plasma proteins as potential therapeutic targets for myocarditis. Mediation analysis, enrichment analysis, protein–protein interaction network analysis, drug prediction, molecular docking, and single-cell RNA sequencing were integrated to further evaluate the biological functions and pharmacological potential of the identified targets. Finally, phenome-wide association studies were conducted to assess the safety of targeting these proteins. Results: NT5E expression was elevated in Reg3β-stimulated M2 macrophages. The expression of Arg-1, a marker of M2 macrophages, decreased upon NT5E knockdown, suggesting that NT5E is involved in the Reg3β-mediated polarization of macrophages to the M2 phenotype. Mendelian randomization analysis identified NT5E and 80 other plasma proteins as being causally associated with myocarditis. Mediation analysis revealed 12 immune-cell types were mediators of the effects of plasma protein on myocarditis progression. Drug prediction identified candidates such as ICN 1229 and chrysin, which showed strong binding affinities in molecular docking analyses. These findings may contribute to the development of effective treatments for myocarditis. Conclusions: NT5E plays a dual role in Reg3β-induced macrophage polarization and in interacting with plasma proteins that influence the onset and progression of myocarditis through immune-cell pathways. Full article

Under the background of “dual-carbon” strategy and global energy transition, the metallurgical industry, which accounts for 15–20% of industrial energy consumption, urgently needs to reduce the energy consumption and emission of DC power supply of electric furnaces. Aiming at the existing 400–800 V/≥3000 A industrial-frequency transformer-rectifier system with low efficiency, large volume, heat dissipation difficulties and other bottlenecks, this thesis proposes and realizes a high-frequency integrated DC power supply scheme for high-power electric furnaces: high-frequency transformer core and rectifier circuit are deeply integrated, which breaks through and reduces the volume of the system by more than 40%, and significantly reduces the iron consumption; multiple cores and three windings in parallel are used for the system. The topology of multiple cores and three windings in parallel enables several independent secondary stages to share the large current of 3000 A level uniformly, eliminating the local overheating and current imbalance; the combination of high-frequency rectification and phase-shift control strategy enhances the input power factor to more than 0.95 and cuts down the grid-side harmonics remarkably. The authors have completed the design of 100 kW prototype, magneto-electric joint simulation, thermal structure coupling analysis, control algorithm development and field comparison test, and the results show that the program compared with the traditional industrial-frequency system efficiency increased by 12–15%, the system temperature rise reduced by 20 K, electrode voltage increased by 10–15%, the input power of furnace increased by 12%, and the harmonic index meets the requirements of the traditional industrial-frequency system. The results show that the efficiency of this scheme is 12–15% higher than the traditional IF system, the temperature rise in the system is 20 K lower, the voltage at the electrode end is 10–15% higher, the input power of the furnace is increased by 12%, and the harmonic indexes meet the requirements of GB/T 14549, which verifies the value of the scheme for realizing high efficiency, miniaturization, and reliable DC power supply in metallurgy. Full article

Urban wetlands play a crucial role in maintaining ecological balance, carbon sequestration, and water purification. Sediments are key carriers for wetlands to store elements such as carbon, nitrogen, and phosphorus in the aquatic environment. This study analyzed different sediment layers of seven wetlands in Yangzhou, aiming to explore the relationship between physicochemical factors and microbial communities in wetland sediments, as well as to predict the functions of microbial communities. Functional prediction of microbial communities was conducted based on amplicon sequencing analysis, and the neutral community model was used to determine the formation and evolution process of microbial communities. The results showed that in three wetlands, namely Zhuyu Bay (ZYW), Luyang Lake (LYH), and Runyang Wetland (RYSD), the contents of carbon components (total carbon, total soluble carbon, microbial biomass carbon) in the 0–20 cm sediment layer were higher, while the carbon component contents in Baoying Lake (BYH) showed the opposite trend. Among them, the contents of total nitrogen, alkali-hydrolyzable nitrogen, total phosphorus, available phosphorus, total potassium, and available potassium in the 0–20 cm sediment layer of Runyang Wetland (RYSD) were significantly the highest. This indicates that in Runyang Wetland (RYSD), the 0–20 cm layer has more abundant carbon components and mineral nutrients compared to the 20–40 cm layer. Among the seven wetlands, it was found that the content of total potassium was all greater than 10 g/kg, which was much higher than the contents of total phosphorus and total nitrogen. Analysis of microbial communities revealed that the dominant archaeal phyla were Thaumarchaeota and Euryarchaeota, and the dominant bacterial phyla were Proteobacteria and Acidobacteria. The distribution of functional genes was mainly concentrated in Zhuyu Bay (ZYW) and Luyang Lake (LYH). Zhuyu Bay Wetland (ZYW) had potential advantages in light utilization function, and Luyang Lake (LYH) had potential advantages in carbon and nitrogen cycle functions. The assembly process of the archaeal community was mainly affected by stochastic processes, while the bacterial community was mainly affected by deterministic processes. However, water content, total phosphorus, and available potassium all had strong correlations with both archaeal and bacterial communities. The research results preliminarily reveal the connections between the physicochemical properties of sediments, microbial communities, and their potential functions in Yangzhou urban wetlands, providing an important scientific basis for the protection and management of wetland ecosystems. Full article

To address challenges in automated charging operations of bulk explosive trucks in open-pit mines—specifically difficulties in borehole identification, positioning inaccuracies, and low operational efficiency—this study proposes an intelligent collaborative charging system integrating three modular components: (1) an explosive transport vehicle (with onboard terminal, explosive compartment, and mobility system enabling optimal routing and quantitative dispensing), (2) a charging robot (equipped with borehole detection, loading mechanisms, and mobility system for optimized search path planning and precision positioning), and (3) interconnection systems (coupling devices and interfaces facilitating auxiliary explosive transfer). This approach resolves three critical limitations of conventional systems: (i) mechanical arm-based borehole detection difficulties, (ii) blast hole positioning inaccuracies, and (iii) complex transport routing. The experimental results demonstrate that the intelligent cooperative charging method for open-pit mines achieves an 18% improvement in operational efficiency through intelligent collaboration among its modular components, while simultaneously realizing automated and intelligent charging operations. This advancement has significant implications for promoting intelligent development in open-pit mining operations. Full article

Anthropogenic noise pollution is a significant global environmental issue that adversely affects the behavior, physiology, and auditory functions of aquatic species. However, studies on the effects of underwater noise on early developmental stages of fish remain scarce, particularly regarding the differential impacts of daytime versus nighttime noise exposure. In this study, zebrafish (Danio rerio) embryos were exposed to control group (no additional noise), daytime noise (100–1000 Hz, 130 dB, from 08:00 to 20:00) or nighttime noise (100–1000 Hz, 130 dB, from 20:00 to 08:00) for 5 days, and their embryonic development and oxidative stress levels were analyzed. Compared to the control group, the results indicated that exposure to both daytime and nighttime noise led to delays in embryo hatching time and a significant decrease in larval heart rate. Notably, exposure to nighttime noise significantly increased the larval deformity rate. Noise exposure, particularly at night, elevated the activities of catalase (CAT) and glutathione peroxidase (GPX), as well as the concentration of malondialdehyde (MDA), accompanied by upregulation of antioxidant-related gene expression levels. Nighttime noise exposure significantly increased the abnormality rate of otolith development in larvae and markedly downregulated the expression levels of otop1 related to otolith development regulation, while daytime noise exposure only induced a slight increase in the otolith abnormality rate. After noise exposure, the number of lateral neuromasts in larvae decreased slightly, yet genes (slc17a8 and capgb) related to hair cell development were significantly upregulated. Overall, this study demonstrates that both daytime and nighttime noise can induce oxidative stress and impair embryonic development of zebrafish, with nighttime noise causing more severe damage. Full article

In the digital era, secure data sharing has become a core requirement for enabling cross-domain collaboration, cloud computing, and Internet of Things (IoT) applications, as well as a critical measure for safeguarding privacy and defending against malicious attacks. In light of the risks of data leakage and misuse in open environments, achieving efficient, controllable, and privacy-preserving data sharing has emerged as a key research focus. This paper first provides a systematic review of the prevailing secure data sharing technologies, including proxy re-encryption, searchable encryption, key agreement and distribution, and attribute-based encryption, summarizing their design principles and application features. Subsequently, game-theoretic modeling based on incentive theory is introduced to construct a strategic interaction framework between data owners and data users, aiming to analyze and optimize benefit allocation mechanisms. Furthermore, the paper explores the integration of game theory with secure sharing mechanisms to enhance the sustainability and stability of the data sharing ecosystem. Finally, it outlines the critical challenges currently faced in secure data sharing and discusses future research directions, offering theoretical insights and technical references for building a more comprehensive data sharing framework. Full article

In the renewable energy conversion system, water electrolysis technology is widely regarded as the core means to achieve clean hydrogen production. However, the anodic oxygen evolution reaction (OER) has become a key bottleneck limiting the overall water splitting efficiency due to its slow kinetic process and high overpotential. This study proposes a novel Co₃O₄-TiO₂/CNTs p-n heterojunction catalyst, which was synthesized by hydrothermal method and significantly improved OER activity by combining heterojunction interface regulation and light field enhancement mechanism. Under illumination conditions, the catalyst achieved an overpotential of 390 mV at a current density of 10 mA cm⁻², which is superior to the performance of the dark state (410 mV) and single component Co₃O₄-TiO₂ catalysts. The material characterization results indicate that the p-n heterojunction structure effectively promotes the separation and migration of photogenerated carriers and enhances the visible light absorption capability. This work expands the design ideas of energy catalytic materials by constructing a collaborative electric light dual field regulation system, providing a new strategy for developing efficient and low-energy water splitting electrocatalysts, which is expected to play an important role in the future clean energy production and storage field. Full article

This paper proposes a low-complexity signal detection method for orthogonal time frequency space (OTFS) communication systems, based on a separable convolutional neural network (SeCNN), termed SeCNN-OTFS. A novel SeparableBlock architecture is introduced, which integrates residual connections and a channel attention mechanism to enhance feature discrimination and training stability under high Doppler conditions. By decomposing standard convolutions into depthwise and pointwise operations, the model achieves a substantial reduction in computational complexity. To validate its effectiveness, simulations are conducted under a standard OTFS configuration with 64-QAM modulation, comparing the proposed SeCNN-OTFS with conventional CNN-based models and classical linear estimators, such as least squares (LS) and minimum mean square error (MMSE). The results show that SeCNN-OTFS consistently outperforms LS and MMSE, and when the signal-to-noise ratio (SNR) exceeds 12.5 dB, its bit error rate (BER) performance becomes nearly identical to that of 2D-CNN. Notably, SeCNN-OTFS requires only 19% of the parameters compared to 2D-CNN, making it highly suitable for resource-constrained environments such as satellite and IoT communication systems. For scenarios where higher accuracy is required and computational resources are sufficient, the CNN-OTFS model—with conventional convolutional layers replacing the separable convolutional layers—can be adopted as a more precise alternative. Full article

Virtual AI-driven streamers have been gradually used in live commerce, and they may affect consumer impulsive purchase intention. Drawing on the stimulus–organism–response (S-O-R) model, this research examined consumer impulsive purchase intention in virtual AI streaming. Based on survey data from 411 predominantly young and educated virtual AI streaming users recruited through snowball sampling, we found that perceived responsiveness, perceived likeability, perceived expertise, and perceived anthropomorphism of virtual AI streamers are associated with trust and flow experience, both of which predict consumers’ impulsive purchase intentions. The fsQCA identified two paths that lead to impulsive purchase intention. The results imply that live streaming platforms need to engender consumers’ trust and flow experience in order to increase their impulsive purchase intention. Full article

Water deficiency and low water use efficiency severely constrain wheat yield in dryland regions. This study aimed to identify suitable tillage methods and straw management to improve dry matter production, grain yield, and water use efficiency of wheat in the dryland winter wheat–summer bean (hereafter referred to as wheat-soybean) double-cropping system. A long-term located field experiment (onset in October 2009) with two tillage methods—plowing (PT) and rotary tillage (RT)—and two straw management—no straw mulching (NS) and straw mulching (SM)—was conducted at a typical dryland in China. The wheat yield and yield component, dry matter accumulation and translocation characteristics, and water use efficiency were investigated from 2014 to 2018. Straw management significantly affected wheat yield and yield components, while tillage methods had no significant effect. Furthermore, the interaction of tillage methods and straw management significantly affected yield and yield components except for the spike number. RTSM significantly increased the spike number, grains per spike, 1000-grain weight, harvest index, and grain yield by 12.5%, 8.4%, 6.0%, 3.4%, and 13.4%, respectively, compared to PTNS. Likewise, RTSM significantly increased the aforementioned indicators by 14.8%, 10.1%, 7.5%, 3.6%, and 20.5%, compared to RTNS. Mechanistic analysis revealed that, compared to NS, SM not only significantly enhanced pre-anthesis and post-anthesis dry matter accumulation, and pre-anthesis dry matter tanslocation to grain, but also significantly improved pre-sowing water storage, water consumption during wheat growth, water use efficiency, and water-saving for produced per kg grain yield, with the greatest improvements obtained under RT than PT. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) analysis confirmed RTSM’s yield superiority was mainly ascribed to straw-induced improvements in dry matter and water productivity. In a word, rotary tillage with straw mulching could be recommended as a suitable practice for high-yield wheat production in a dryland wheat-soybean double-cropping system. Full article