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The large-scale adoption of electric vehicles has created an urgent need for the orderly management of charging loads in residential communities. While existing research on community-based orderly charging architectures and control strategies primarily focuses on connected charging piles (CPs) equipped with remote power control functions. However, in practical scenarios, most residential communities still rely on unconnected charging piles (UCPs) that lack remote communication capabilities, making it difficult to practically deploy many intelligent orderly architectures and control strategies that rely on communication with charging piles. Therefore, this paper proposes a non-intrusive orderly charging architecture tailored for UCPs. This architecture does not require modifying the hardware of UCPs; instead, it introduces pile-end management units (PMUs) to interact with users for orderly charging, thereby facilitating easier deployment and promotion. Based on this architecture, an optimized control strategy using the GD-SA (greedy-simulated annealing) algorithm for orderly charging is constructed, which considers the dual constraints of transformer capacity and charging demand. Case studies on a typical community in Tianjin, China, demonstrate that with the proposed order charging architecture and strategy, when users fully accept the orderly charging approach, the peak load can be reduced by over 17% compared to uncontrolled charging scenarios. Additionally, the effectiveness of the method has been validated through sensitivity analysis of user acceptance, stress scenario testing, and statistical analysis with a 95% confidence interval. Finally, this paper summarizes the practical value potential of supporting UCPs in achieving orderly charging, while also pointing out the limitations of the current research and identifying directions for further in-depth exploration.

Baseflow is a crucial component of river flow in alpine inland basins, playing an essential role in watershed ecological health and water resource management. In high-latitude cold regions, seasonal freeze-thaw processes make baseflow formation mechanisms particularly complex. However, the dominant factors affecting baseflow and their relative contributions remain unclear, limiting the accuracy of flow estimation and effective water resource management. This study employed baseflow separation techniques and statistical methods, including the Mann-Kendall test, to investigate temporal trends and abrupt changes in baseflow and the baseflow index (BFI) at multiple time scales (annual, seasonal, and monthly) from 2005 to 2012. Additionally, the timing of snowmelt and its impact on baseflow were examined. Key findings include the following: (1) Baseflow and BFI showed distinct temporal variability with non-significant upward trends across all time scales. Annual BFI ranged from 0.48 to 0.61, contributing approximately 50% of total runoff. (2) At the seasonal scale, baseflow remained relatively stable in spring, increased in autumn, and showed non-significant decreases in summer and winter. Monthly baseflow exhibited an increasing trend. (3) The snowmelt period occurred between April and May, with baseflow during this period strongly correlated with climatic factors in the following order: winter precipitation > positive accumulated temperature > winter air temperature > negative accumulated temperature. The strongest positive correlation was observed between baseflow and winter precipitation (R = 0.724), while negative correlations were found with accumulated temperatures and winter air temperature. These findings offer valuable insights for predicting water resource availability and managing flood and ice-jam risks in cold regions.

Background Postpartum dairy cows experiencing excessive lipolysis are prone to severe immunosuppression. Despite the extensive understanding of the gut microbial regulation of host immunity and metabolism, its role during excessive lipolysis in cows is largely unknown. Herein, we investigated the potential links between the gut microbiome and postpartum immunosuppression in periparturient dairy cows with excessive lipolysis using single immune cell transcriptome, 16S amplicon sequencing, metagenomics, and targeted metabolomics. Results The use of single-cell RNA sequencing identified 26 clusters that were annotated to 10 different immune cell types. Enrichment of functions of these clusters revealed a downregulation of functions in immune cells isolated from a cow with excessive lipolysis compared to a cow with low/normal lipolysis. The results of metagenomic sequencing and targeted metabolome analysis together revealed that secondary bile acid (SBA) biosynthesis was significantly activated in the cows with excessive lipolysis. Moreover, the relative abundance of gut Bacteroides sp. OF04 − 15BH , Paraprevotella clara , Paraprevotella xylaniphila , and Treponema sp. JC4 was mainly associated with SBA synthesis. The use of an integrated analysis showed that the reduction of plasma glycolithocholic acid and taurolithocholic acid could contribute to the immunosuppression of monocytes (CD14 + MON) during excessive lipolysis by decreasing the expression of GPBAR1 . Conclusions Our results suggest that alterations in the gut microbiota and their functions related to SBA synthesis suppressed the functions of monocytes during excessive lipolysis in transition dairy cows. Therefore, we concluded that altered microbial SBA synthesis during excessive lipolysis could lead to postpartum immunosuppression in transition cows. 8-C9RL3NXjP8b3LTSGg7i4 Video Abstract

The emergence of antibiotic-resistant beta-hemolytic Streptococcus agalactiae strains poses increasing threat to human beings globally. As an attempt to create a novel lysin with improved activity against S. agalactiae, a chimeric lysin, ClyV, was constructed by fusing the enzymatically active domain (EAD) from PlyGBS lysin (GBS180) and the cell wall binding domain (CBD) from PlyV12 lysin (V12CBD). Plate lysis assay combined with lytic kinetic analysis demonstrated that ClyV has improved activity than its parental enzymatic domain GBS180 against multiple streptococci. Biochemical characterization showed that ClyV is active from pH 7 to 10, with the optimum pH of 9, and is stable under NaCl concentration of < 500 mM. In a S. agalactiae infection model, a single intraperitoneally administration of 0.1 mg/mouse of ClyV protected 100% mice, while it was observed that ~ 29% survive in group that received a single dose of 0.1 mg/mouse of GBS180. Moreover, a high dose of 0.8 mg/mouse ClyV did not show any adverse effects to the health or survival rate of the mice. Considering the robust bactericidal activity and good safety profile of ClyV, it represents a potential candidate for the treatment of S. agalactiae infections.

In western China coal mines, the mining-induced caving zone is regarded as a main pathway for water and sand inrush mixture hazards. The paper experimentally studied the flow behavior and the mechanism of water and sand mixture through mining-induced caving zones. Transport experiments are performed by using a laboratory-scale model, and the caving zone is modelled by using different sizes of glass beads. Four different sand sizes are used for the sand layer. The test results reveal that the mass flow rate of sand and water mixture increases with the increase of the initial water head. And an equation is proposed for the mass flow rate of sand and water mixtures that correctly reproduces the data for all the conditions. In addition, the sudden decreases in water head loss is monitored at the commencement of the water and sand flow, which would result in a large number of sand particles that rapidly start up and make the kinetic energy transfer from water to sand.

Icings, a significant hydrogeological phenomenon in permafrost regions, form when groundwater flows to the surface or through river crevices and freezes under low temperatures. These formations pose serious threats to infrastructure, including roads, railways, and bridges, while also serving as vital freshwater resources. Despite their importance, the mechanisms governing icing formation and the quantitative relationships between groundwater-controlling factors—such as freeze–thaw processes and precipitation—and icing distribution remain poorly understood. This knowledge gap hinders disaster prevention efforts and the sustainable utilization of water resources in cold regions. This study investigates the development patterns and influencing factors of icings in Eruu, a high-latitude permafrost region, using Landsat 4–5 TM, Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI imagery with a 30 m resolution (2005–2024) and meteorological and geothermal data. By combining NDSI and MDII, the differentiation accuracy of water bodies was improved, and the K-Means clustering algorithm was applied to extract the icing region. The results revealed that the annual icing surface area ranged from 208,800 to 459,000 m2, with a minimum in 2009 and a maximum in 2011. The average annual increase was approximately 4304.5 m2 (p = 0.0255). Icings began freezing in October, radiating outward from the center, and melted by late May or early June. The Pearson correlation analysis showed (1) a strong negative correlation between snowfall and icing area (r = −0.544); (2) a positive correlation between freezing duration and icing area (r = 0.471); and (3) over the study period, annual average temperature and total precipitation exhibited no obvious change trend, with weak positive correlations between icing area and total precipitation (r = 0.290) and annual average temperature (r = 0.248). The observations of icing areas will be further applied to disaster prevention efforts. Additionally, the source of icings is clean and can be extracted for drinking purposes. Therefore, these findings enhance the understanding of icing mechanisms, support the prediction of icing development, and inform disaster prevention and resource management in permafrost regions.

Fault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning‐based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation‐based augmentation) may lead to distortion of multi‐view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real‐world datasets validate the effectiveness of the proposed method. This study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted.

Insulator defect detection is crucial for the stable operation of power systems. It has become a mainstream research direction to realise insulator defect detection based on the combination of line images captured by UAVs and deep learning techniques. However, the existing high‐quality insulator defect detection models still face problems such as relying on massive‐labelled data and huge model parameters. Especially on resource‐constrained devices, it becomes a challenge to strike a balance between model lightweighting and performance. Although the knowledge distillation technique provides a solution for model lightweighting, the loss of information in the distillation process leads to the performance degradation of small models, which in turn creates a paradox between lightweighting and performance. Hence, an insulator defect detection method based on federated knowledge distillation is proposed. The method not only realises the lightweighting of the model, but also effectively improves the model performance by collaboratively training the model through the federated learning approach. Moreover, the asynchronous aggregation approach and model freshness mechanism designed in the method further enhance the training efficiency and collaborative effect. The experimental results show that the detection accuracy and efficiency of this paper's method on public datasets are significantly better than the benchmark algorithm. A new framework for insulator defect detection methods in resource‐constrained environments is proposed. With the help of the clever design of knowledge distillation and federated learning, that is, model freshness, this paper achieves the lightweighting of the traditional deep learning‐based insulator defect detection model and ensures the guarantee of the model performance after the lightweighting.

Soil temperature is an important index of climate change, and the analysis of soil temperature change is of great significance for understanding climate change and ecohydrological processes. This study was based on the measured meteorological data of a meteorological station, combined with the soil temperature data of 0–10, 10–40, 40–100 and 100–200 cm from the Global Land Data Assimilation System (GLDAS-NOAH). The Mann–Kendall test, wavelet analysis, linear tendency estimation and other methods were used to analyze the variability, periodicity and trend of soil temperature in Lhasa from 2006 to 2022. The results showed that the soil temperature of different soil layers had abrupt changes in annual and seasonal time series, and all showed a warming phenomenon after abrupt changes. In terms of periodicity, the average annual soil temperature of different soil layers has similar periodic changes, and the periodic oscillation is strong around 10a, which is the main cycle of soil temperature change. The soil temperature in Lhasa showed a significant rising trend in the interannual and seasonal time series, and the average annual rising trend of soil temperature was greater than that of air temperature. The correlation between soil temperature and mean air temperature (MAT), maximum air temperature (Tmax), minimum air temperature (Tmin) and snow depth (SD) was investigated by Pearson correlation analysis. Soil temperature in spring, autumn and winter had a strong correlation with MAT, Tmax and Tmin, showing a significant positive correlation. The negative correlation between soil temperature and SD in 0–40 cm soil in spring and winter was more severe. The research results show that Lhasa has experienced a rise in air temperature and soil temperature in the past 17 years, and reveal the specific changes in soil temperature in Lhasa against the background of climate change. These findings have reference significance for understanding the impact of climate change on the natural environment.

Understanding biological mechanisms is fundamental for improving animal production and health to meet the growing demand for high-quality protein. As an emerging biotechnology, single-cell transcriptomics has been gradually applied in diverse aspects of animal research, offering an effective method to study the gene expression of high-throughput single cells of different tissues/organs in animals. In an unprecedented manner, researchers have identified cell types/subtypes and their marker genes, inferred cellular fate trajectories, and revealed cell‒cell interactions in animals using single-cell transcriptomics. In this paper, we introduce the development of single-cell technology and review the processes, advancements, and applications of single-cell transcriptomics in animal research. We summarize recent efforts using single-cell transcriptomics to obtain a more profound understanding of animal nutrition and health, reproductive performance, genetics, and disease models in different livestock species. Moreover, the practical experience accumulated based on a large number of cases is highlighted to provide a reference for determining key factors (e.g., sample size, cell clustering, and cell type annotation) in single-cell transcriptomics analysis. We also discuss the limitations and outlook of single-cell transcriptomics in the current stage. This paper describes the comprehensive progress of single-cell transcriptomics in animal research, offering novel insights and sustainable advancements in agricultural productivity and animal health.