Sterile inflammation of peritoneal membrane caused by peritoneal dialysis: focus on the communication between immune cells and peritoneal stroma.

Peritoneal dialysis is a widely used method for treating kidney failure. However, over time, the peritoneal structure and function can deteriorate, leading to the failure of this therapy. This deterioration is primarily caused by infectious and sterile inflammation. Sterile inflammation, which is inflammation without infection, is particularly concerning as it can be subtle and often goes unnoticed. The onset of sterile inflammation involves various pathological processes. Peritoneal cells detect signals that promote inflammation and release substances that attract immune cells from the bloodstream. These immune cells contribute to the initiation and escalation of the inflammatory response. The existing literature extensively covers the involvement of different cell types in the sterile inflammation, including mesothelial cells, fibroblasts, endothelial cells, and adipocytes, as well as immune cells such as macrophages, lymphocytes, and mast cells. These cells work together to promote the occurrence and progression of sterile inflammation, although the exact mechanisms are not fully understood. This review aims to provide a comprehensive overview of the signals from both stromal cells and components of immune system, as well as the reciprocal interactions between cellular components, during the initiation of sterile inflammation. By understanding the cellular and molecular mechanisms underlying sterile inflammation, we may potentially develop therapeutic interventions to counteract peritoneal membrane damage and restore normal function.

Governance and competitiveness evaluation of China's financial asset management corporations.

With the development of non-performing assets market and the requirement of maintaining national financial security and stability, asset management corporations (AMC) have set up in recent years, facing the dilemma of governance reform and competitiveness improvement. This paper puts forward the Governance-Performance-Competitiveness theory of asset management corporations, and constructs the comprehensive competitiveness evaluation index system of based on internal and external governance mechanisms, and studies the factors and degrees of internal and external governance affecting competitiveness by combining principal component analysis method and grey correlation analysis method. The empirical results show that the asset scale and profitability of asset management corporations directly determine their competitiveness level to a large extent, and the correlation between external governance mechanism and competitiveness is stronger than that of internal governance mechanism. Clustering and grouping are conducted based on the size of competitiveness, and China's local asset management corporation system presents a diamond structure. China's local financial asset management corporations should strengthen the top-level design of internal governance, attach importance to external governance, expand and strengthen the asset scale, and carry out full-chains business while implementing a differentiated development model to achieve sustainable development when adhering to the principal business of non-performing assets.

The spatial spillover effect of ICT development level on regional CO2 emissions.

Information and communication technology plays an essential role in affecting CO2 emissions and promoting green and sustainable development. This paper empirically examines the effect of the level of urban ICT development on CO2 emissions using panel data for Chinese cities from 2010 to 2020. On this basis, a spatial Durbin model is used to analyze the spatial spillover effects of ICT development level affecting regional CO2 emissions, and its direct, indirect and overall effects are further analyzed. The conclusions of the study are as follows. (1) The CO2 emissions of Chinese cities are correlated by spatial distance and show high-high or low-low clustering characteristics. (2) ICT development can reduce CO2 emissions in the region and increase CO2 emissions in neighboring regions. This conclusion still holds after several robustness tests. And when the level of ICT development increases by 1 standard deviation, CO2 emissions are reduced by 64%. (3) The mechanism of action shows that ICT can influence regional CO2 emissions through its technological innovation effect and industrial upgrading effect. (4) The impact of ICT development on regional carbon emissions is characterized by significant heterogeneity depending on the city's geographical location and whether or not environmental information is publicly available.

The impact of digital transformation on the total factor productivity of heavily polluting enterprises.

In the digital economy, the relationship between digital transformation and a company's total factor productivity has profound implications for high-quality business development. Heavy polluters are given more environmental responsibility because of their high pollution and emission characteristics. This paper analyses the theoretical framework for the impact of digital transformation on the total factor productivity of heavily polluting firms. Using a sample of Shanghai and Shenzhen A-share heavy polluters from 2010 to 2020, we explore how the digital transformation of heavy polluters affects the total factor productivity of firms. The study found that the digital transformation of heavily polluting companies can effectively improve total factor productivity, internally by increasing their level of green technology innovation and externally by increasing their willingness and capacity for corporate social responsibility. At the same time, digital transformation can improve total factor productivity by reducing cost stickiness, revealing the "black box" in which digital transformation affects the total factor productivity of an enterprise. It was further found that the digital transformation of companies with high levels of environmental investment, large enterprises, those in non-manufacturing industries, and heavy polluters of a state-owned nature had a more significant impact on total factor productivity. The findings of the study provide empirical evidence for the digital transformation of heavily polluting companies to improve productivity and the green transformation of the economy for companies under the low carbon goal.

Novel Low-Power Construction of Chaotic S-Box in Multilayer Perceptron.

Multilayer perceptron is composed of massive distributed neural processors interconnected. The nonlinear dynamic components in these processors expand the input data into a linear combination of synapses. However, the nonlinear mapping ability of original multilayer perceptron is limited when processing high complexity information. The introduction of more powerful nonlinear components (e.g., S-box) to multilayer perceptron can not only reinforce its information processing ability, but also enhance the overall security. Therefore, we combine the methods of cryptography and information theory to design a low-power chaotic S-box (LPC S-box) with entropy coding in the hidden layer to make the multilayer perceptron process information more efficiently and safely. In the performance test, our S-box architecture has good properties, which can effectively resist main known attacks (e.g., Berlekamp Massey-attack and Ronjom-Helleseth attack). This interdisciplinary work can attract more attention from academia and industry to the security of multilayer perceptron.

Evaluation of digital economy development level based on multi-attribute decision theory.

The maturity and commercialization of emerging digital technologies represented by artificial intelligence, cloud computing, block chain and virtual reality are giving birth to a new and higher economic form, that is, digital economy. Digital economy is different from the traditional industrial economy. It is clean, efficient, green and recyclable. It represents and promotes the future direction of global economic development, especially in the context of the sudden COVID-19 pandemic as a continuing disaster. Therefore, it is essential to establish the comprehensive evaluation model of digital economy development scientifically and reasonably. In this paper, first on the basis of literature analysis, the relevant indicators of digital economy development are collected manually and then screened by the grey dynamic clustering and rough set reduction theory. The evaluation index system of digital economy development is constructed from four dimensions: digital innovation impetus support, digital infrastructure construction support, national economic environment and digital policy guarantee, digital integration and application. Next the subjective weight and objective weight are calculated by the group FAHP method, entropy method and improved CRITIC method, and the combined weight is integrated with the thought of maximum variance. The grey correlation analysis and improved VIKOR model are combined to systematically evaluate the digital economy development level of 31 provinces and cities in China from 2013 to 2019. The results of empirical analysis show that the overall development of China's digital economy shows a trend of superposition and rise, and the development of digital economy in the four major economic zones is unbalanced. Finally, we put forward targeted opinions on the construction of China's provincial digital economy.

Waste image classification based on transfer learning and convolutional neural network.

The rapid economic and social development has led to a rapid increase in the output of domestic waste. How to realize waste classification through intelligent methods has become a key factor for human beings to achieve sustainable development. Traditional waste classification technology has low efficiency and low accuracy. To improve the efficiency and accuracy of waste classification processing, this paper proposes a DenseNet169 waste image classification model based on transfer learning. Because of the disadvantages of the existing public waste dataset, such as uneven distribution of data, single background, obvious features, and small sample size of the waste image, the waste image dataset NWNU-TRASH is constructed. The dataset has the advantages of balanced distribution, high diversity, and rich background, which is more in line with real needs. 70% of the dataset is used as the training set and 30% as the test set. Based on the deep learning network DenseNet169 pre-trained model, we can form a DenseNet169 model suitable for this experimental dataset. The experimental results show that the accuracy of classification is over 82% in the DenseNet169 model after the transfer learning, which is better than other image classification algorithms.