Systemic robustness: a mean-field particle system approach

This paper is concerned with the problem of budget control in a large particle system modeled by stochastic differential equations involving hitting times, which arises from considerations of systemic risk in a regional financial network. Motivated by Tang and Tsai (Ann. Probab., 46(2018), pp. 1597{1650), we focus on the number or proportion of surviving entities that never default to measure the systemic robustness. First we show that both the mean-field particle system and its limiting McKean-Vlasov equation are well-posed by virtue of the notion of minimal solutions. We then establish a connection between the proportion of surviving entities in the large particle system and the probability of default in the limiting McKean-Vlasov equation as the size of the interacting particle system N tends to infinity. Finally, we study the asymptotic efficiency of budget control in different economy regimes: the expected number of surviving entities is of constant order in a negative economy; it is of order of the square root of N in a neutral economy; and it is of order N in a positive economy where the budget's effect is negligible.Comment: 33 page

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Research Progress on the Causes and Influencing Factors of Air Pollution in China

At the present stage, the safety environment of China is facing a large number of new risks and challenges, all kinds of environmental pollution problems are widespread, and the atmospheric environmental problems are particularly prominent. According to the discussion and research of a large number of scholars at home and abroad, a series of factors such as the development of urbanization, economic growth and changes in industrial structure, human consumption, and a large number of large-scale enterprises with high energy consumption are the important reasons for the aggravation of air pollution in our country. Starting with the socio-economic factors closely related to human activities, this paper establishes Sustainability Evaluation using Indicators (SEI), and explores the current situation of the research on the causes of air pollution in China through literature research and summary methods. It not only provides a scientific basis for the reasonable formulation of policies and strategies, but also makes it more convenient for the government to carry out accurate governance on this basis, which is of great significance to the construction of a beautiful China

McKean–Vlasov equations involving hitting times: Blow-ups and global solvability

International audienc

Construction of the hierarchical porous biochar with an ultrahigh specific surface area for application in high-performance lithium-ion capacitor cathode

Abstract Biochar with a highly accessible specific surface area can display a higher performance when it is used as the cathode of lithium-ion capacitors. Facing the complex composition and diversity of biomass precursors, there is a lack of a universally applicable method to construct hierarchical porous biochar controllably. In this work, a multi-stage activation strategy combining the feature of different activation methods is proposed for this target. To confirm the porous characteristic in prepared samples, N2 adsorption–desorption and transmission electron microscope were used. As the optimal sample, BC-P3K4S had the highest specific surface area of 3583.3 m2 g−1. Evaluated as the electrode for a lithium-ion capacitor, BC-P3K4S displayed a capacity of 139.1 mAh g−1 at 0.1 A g−1. After coupling it with pre-lithiated hard carbon, the full device exhibited a high energy density of 129.3 W h kg−1 at 153 W kg−1. The work outlined herein offers some insights into the preparation of hierarchical porous biochar from complex biomass by multistep activation method. Graphical Abstrac

Screening of renal clear cell carcinoma prognostic marker genes based on TCGA and GTEx chip data and construction of transcription factor-related regulatory networks

This study aimed to identify prognostic marker genes for renal clear cell carcinoma (RCCC) and construct a regulatory network of transcription factors and prognostic marker genes. Three hundred eighty-six genes were significantly differentially expressed in RCCC, with functional enrichment analysis suggesting a relationship between these genes and kidney function and development. Cox and Lasso regression analyses revealed 10 prognostic marker genes (RNASET2, MSC, DPEP1, FGF1, ATP1A1, CLDN10, PLG, SLC44A1, PCSK1N, and LGI4) that accurately predicted RCCC patient prognosis. Upstream transcription factors of these genes were also identified, and in vitro experiments suggested that ATP1A1 may play a key role in RCCC patient prognosis. The findings of this study provide important insights into the molecular mechanisms of RCCC and may have implications for personalized treatment strategies

Heterostructure catalyst coupled wood-derived carbon and cobalt-iron alloy/oxide for reversible oxygen conversion

Abstract As promising energy-storage devices, zinc–air batteries (ZABs) exhibit slow reaction kinetics for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) occurring at their electrodes. High-performance bifunctional catalysts must thus be synthesized to accelerate the reversible conversion of oxygen and improve the rate and overall performance of ZABs. Herein, we reported the promising prospects of self-supported composite electrodes composed of wood-derived carbon (WDC) and bimetallic cobalt-iron alloys/oxides (CoFe-CoFe2O4@WDC) as efficient electrocatalysts for alkaline ORR/OER. WDC provided a favorable three-phase interface for heterogeneous reactions owing to its layered porous structure and genetic stability, thereby enabling mass diffusion and improving reaction kinetics. The CoFe2O4 spinel surface was reduced to bimetallic CoFe alloy to form abundant heterostructure interfaces that promote electron transfer. Under alkaline conditions, the optimized composite electrode exhibited a remarkable high half-wave potential of 0.85 V and an exceptionally low overpotential of 1.49 V. It also exhibited stable performance over an impressive 2340 cycles in a ZAB. Theoretical calculations also confirmed that the heterointerface addresses the issue of proton scarcity throughout the reaction and actively facilitates the creation of O–O bonds during the reversible transformation of oxygen. This study introduces a new concept for developing bifunctional and efficient electrocatalysts based on charcoal and encourages the sustainable and high-value use of forest biomass resources. Graphical Abstrac