Rubidium salt can effectively relieve the symptoms of DSS-induced ulcerative colitis.

Inflammatory bowel disease (IBD) is a chronic condition that afflicts individuals repeatedly and cannot be cured at present, which has seriously affected the quality of life of patients. Minerals Containing Rubidium (MCR) from Guangxi Yuechengling, which Professor Zhao Lichun purified, were explored. Against this backdrop, the present study investigates the efficacy of rubidium salt in ulcerative colitis.

Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds.

Semi-arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, especially under the compound environmental stress. Our goal is to address this issue with a scientifically rigorous approach.

Weakened hydrological oscillation period increased the frequency of river algal blooms.

The evolution of riverine aquatic ecosystems typically exhibits notable characteristic with cumulative, enduring, and hysteresis. Exploring the non-linear response of riverine ecology to long-term hydrological fluctuations become a major challenge in contemporary interdisciplinary research. In response to the critical issue of frequent river algal blooms in the lower Han River, which is impacted by Asian largest inter-basin water diversion project.

IInception-CBAM-IBiGRU based fault diagnosis method for asynchronous motors.

Aiming at the problems of insufficient extraction of asynchronous motor fault features by traditional deep learning algorithms and poor diagnosis of asynchronous motor faults in robust noise environments, this paper proposes an end-to-end fault diagnosis method for asynchronous motors based on IInception-CBAM-IBiGRU. The method first uses a signal-to-grayscale image conversion method to convert one-dimensional vibration signals into two-dimensional images and initially extracts shallow features through two-dimensional convolution; then the Improved Inception (IInception) module is used as a residual block to learning features at different scales with a residual structure, and extracts its important feature information through the Convolutional Block Attention Module (CBAM) to extract important feature information and adjust the weight parameters; then the feature information is input to the Improved Bi-directional Gate Recurrent Unit (IBiGRU) to extract its timing features further; finally, the fault identification is achieved by the SoftMax function. The primary hyperparameters in the model are optimized by the Weighted Mean Of Vectors Algorithm (INFO).

Flow backward alleviated the river algal blooms.

Mechanistic understanding and prediction of river algal blooms remain challenging. It is generally believed that these blooms are formed by the slowdown of water dynamics in tributaries due to the support of the main stream. However, few studies have investigated the impact of flow backward caused by the difference in water dynamics between the main stream and tributaries.

Non-steady state fluctuations in water levels exacerbate long-term and seasonal degradation of water quality in river-connected lakes.

The hydrological regimes and environmental changes in large riverine lakes are known for their complexity and high level of uncertainty. Scientifically uncovering the response mechanisms of water environments under complex hydrological conditions has become a challenging research objective, in the interdisciplinary of environmental science and hydrology. This study delved into the unstable response process between water level and quality of Poyang Lake, the largest freshwater lake as well as one of the most intense hydrological variability water bodies in China.

Supervised Attention-Based Bidirectional Long Short-Term Memory Network for Nonlinear Dynamic Soft Sensor Application.

Soft sensors are mathematical methods that describe the dependence of primary variables on secondary variables. A nonlinear characteristic commonly appears in modern industrial process data with increasing complexity and dynamics, which has brought challenges to soft sensor modeling. To solve these issues, a novel supervised attention-based bidirectional long short-term memory (SA-BiLSTM) is first proposed in this paper to handle the nonlinear industrial process modeling with dynamic features.

Phosphorus - The main limiting factor in riverine ecosystems in China.

River receive substantial nutrient inputs, and serve as the main channel for nitrogen and phosphorus to enter the lake, their nutrient control is of great significance to the alleviation of lake eutrophication. While nutrient limitation affects the primary productivity of water ecosystems and the biodiversity of aquatic communities, identifying the limiting factors in riverine ecosystems across China remains elusive. Here, we explore which nutrients have a stronger effect on nutritional balance and aquatic ecosystems in China's rivers based on the total nitrogen (TN) and total phosphorus (TP) observations from 1412 sampling sites in 2018.

Understanding China's industrialization driven water pollution stress in 2002-2015-A multi-pollutant based net gray water footprint analysis.

China produces a large amount of industrial effluent with multiple pollutants contained, along with a flourishing economy. This study aims to examine the dynamics between China's industrialization and accompanying environmental pressure based on the gray water footprint (GWF) concept. A newly proposed net GWF (NetGWF) and the decoupling index (DI) are applied to evaluate China's industrial activities during 2002-2015 in different modes considering typical, all, and individual pollutants.

Urbanization and haze-governance performance: Evidence from China's 248 cities.

Using a panel data set of 248 Chinese cities at the prefecture level and above from 2004 to 2013, this study employs the data envelopment analysis (DEA) method based on a non-angular and non-radial directional distance function (DDF) combined with the overall technology, to measure the haze-governance performance. Furthermore, we construct a composite index based on the nighttime light (NTL) data to reflect the urbanization level, and use a spatial Durbin model (SDM) to investigate the effect and its mechanism of urbanization on the haze-governance performance. The results show a significant U-shaped curve relationship between urbanization and haze-governance performance for the samples of both the whole country and sub-regions.