Spatiotemporal variations and risk characteristics of potential non-point source pollution driven by LUCC in the Loess Plateau Region, China

With increasing human activities, regional substrate conditions have undergone significant changes. These changes have resulted in temporal and spatial variations of non-point source pollution sources, which has a significant impact on the quality of the regional soil, surface water, and groundwater environments. This study focused on the human-disturbed Loess Plateau region and used an enhanced potential non-point-source pollution index (PNPI) model to explore the dynamic changes of regional potential non-point-source pollution (PNP) and the associated risk due to land use and land cover change (LUCC) over the past 31 years. The Loess Plateau region is mainly composed of cultivated land, grassland and forest, which together account for 93.5% of the watershed area. From 1990 to 2020, extensive soil and water conservation measures were implemented throughout the Loess Plateau region, resulting in a significant reduction in the non-point source pollution risk. Using the quantile classification method, the study area’s PNP risk values were categorized into five distinct levels. The results revealed a polarization phenomenon of PNP risk in the region, with an increase in non-point source pollution risk in the human-influenced areas and a rapid expansion of the very high-risk area. However, the non-point source pollution risk in the upstream water source area of the watershed reduced over the study period. In recent years, the rapid urbanization of the Loess Plateau region has been the primary reason for the rapid expansion of the very high PNP risk area throughout the watershed. This study highlights the significant impact of LUCC on the dynamic changes in PNP risk within the Loess Plateau region, providing crucial insights into future conservation and urban planning policies aimed at enhancing the ecological health and environmental quality of the region

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Decay model of energy storage battery life under multiple influencing factors of grid dispatching

Energy storage batteries work under constantly changing operating conditions such as temperature, depth of discharge, and discharge rate, which will lead to serious energy loss and low utilization rate of the battery, resulting in a sharp attenuation of life, and the battery often fails before the end of its service life. Battery replacement leads to increasing energy storage costs, and in order to ensure the efficient, safe and reliable operation of batteries under complex working conditions of the power grid, effective management of batteries is required. The battery model is the theoretical basis of the management algorithm, and life prediction is the key technology to ensure battery safety. In view of the above practical application requirements, this paper studies the dynamic modeling of energy storage battery life based on multi-parameter information, and the results show that the proposed life model accurately reflects the battery life under multi-parameter information

Decay model of energy storage battery life under multiple influencing factors of grid dispatching

Energy storage batteries work under constantly changing operating conditions such as temperature, depth of discharge, and discharge rate, which will lead to serious energy loss and low utilization rate of the battery, resulting in a sharp attenuation of life, and the battery often fails before the end of its service life. Battery replacement leads to increasing energy storage costs, and in order to ensure the efficient, safe and reliable operation of batteries under complex working conditions of the power grid, effective management of batteries is required. The battery model is the theoretical basis of the management algorithm, and life prediction is the key technology to ensure battery safety. In view of the above practical application requirements, this paper studies the dynamic modeling of energy storage battery life based on multi-parameter information, and the results show that the proposed life model accurately reflects the battery life under multi-parameter information

Serum metabolism characteristics of patients with myocardial injury after noncardiac surgery explored by the untargeted metabolomics approach

Abstract Background Myocardial injury after noncardiac surgery (MINS) is one of the most common complications associated with postoperative adverse cardiovascular outcomes and mortality. However, MINS often fails to be timely diagnosed due to the absence of clinical symptoms and limited diagnostic methods. The metabolomic analysis might be an efficient way to discover new biomarkers of MINS. Characterizing the metabolomic features of MINS patients may provide new insight into the diagnosis of MINS. Methods In this study, serum samples from 20 matched patients with or without MINS (n = 10 per group) were subjected to untargeted metabolomics analysis to investigate comprehensive metabolic information. Differential metabolites were identified, and the enriched metabolic pathway was determined based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results A comprehensive analysis revealed 124 distinct metabolites, predominantly encompassing lipids, amino acids and other compounds. The observed modifications in metabolic pathways in patients with or without MINS showed significant clustering in cholesterol metabolism, aldosterone synthesis and secretion, primary bile acid biosynthesis, as well as cysteine and methionine metabolism. Four specific metabolites (taurocholic acid, L-pyroglutamic acid, taurochenodeoxycholic acid, and pyridoxamine) exhibited promising potential as biomarkers for prognosticating MINS. Conclusions This study contributes valuable insights into the metabolomic features of MINS and the discovery of potential biomarkers which may help the early diagnosis of MINS. The identified metabolites and altered pathways offer valuable insights into the molecular underpinnings of MINS, paving the way for improved diagnostic approaches and potential intervention strategies

Image Segmentation of Thermal Waving Inspection based on Particle Swarm Optimization Fuzzy Clustering Algorithm

The Fuzzy C-Mean clustering (FCM) algorithm is an effective image segmentation algorithm which combines the clustering of non-supervised and the idea of the blurry aggregate, it is widely applied to image segmentation, but it has many problems, such as great amount of calculation, being sensitive to initial data values and noise in images, and being vulnerable to fall into the shortcoming of local optimization. To conquer the problems of FCM, the algorithm of fuzzy clustering based on Particle Swarm Optimization (PSO) was proposed, this article first uses the PSO algorithm of a powerful global search capability to optimize FCM centers, and then uses this center to partition the images, the speed of the image segmentation was boosted and the segmentation accuracy was improved. The results of the experiments show that the PSO-FCM algorithm can effectively avoid the disadvantage of FCM, boost the speed and get a better image segmentation result