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Since 2014, the electric vehicle industry in China has flourished and has been accompanied by rapid growth in the power battery industry led by lithium-ion battery (LIB) development. Due to a variety of factors, LIBs have been widely used, but user abuse and battery quality issues have led to explosion accidents that have caused loss of life and property. Current strategies to address battery safety concerns mainly involve enhancing the intrinsic safety of batteries and strengthening safety controls with approaches such as early warning systems to alert users before thermal runaway and ensure user safety. In this paper, we discuss the current research status and trends in two areas, intrinsic battery safety risk control and early warning methods, with the goal of promoting the development of safe LIB solutions in new energy applications.

The safety and efficiency of lithium‐ion batteries (LIBs) suggest a promising future for this technology, particularly in the automobile industry. However, thermal runaway—wherein a LIB undergoes an uncontrollable increase in temperature that may result in smoke, fire, or explosion—represents an important and widely studied failure type. Since the electrodes of LIBs are manufactured from porous composite materials, their heterogeneity can significantly influence the effective material characteristics and microscale behaviors of LIBs during operation. Microstructure geometric and electrochemical–thermal models are typically used to investigate these impacts. Herein, a microstructure geometric model is constructed of LIB's electrodes. A virtual multiphysics model is used to simulate the overcharging thermal runaway condition. The model's accuracy is validated through real‐world experiments. The model shows greater accuracy compared to the result from a conventional homogeneous geometric model and better reflects the heterogeneous internal phenomenon. The model is applied to a variable analysis in order to investigate how the varying heterogeneity of the cathode's porosity impacts the cell during overcharging thermal runaway behavior. Our results indicate that decreasing porosity heterogeneity at the cathode may delay thermal runaway, owing to the heterogeneous impact on particle diffusion behaviors and the side reaction rate. A method for calculating the heterogeneous behavior of lithium‐ion battery thermal runaway is proposed. With the reconstructed heterogeneous microstructure geometric model of lithium‐ion battery electrode, a multiphysics model is applied to simulate the overcharging thermal runaway and investigate the impact of heterogeneity on the thermal runaway. It provides a new idea for improving the thermal safety of lithium‐ion battery.

Abstract FtsY is the receptor of the signal recognition particle that mediates the targeting of integral membrane proteins in bacteria. It was shown that in Escherichia coli, the N-terminal region of FtsY contributes to its interaction with the membrane, but it is not inserted into the membrane. However, this study presents evidence that in Streptomyces coelicolor, FtsY has a hydrophobic region at its N-terminus, which forms a membrane insertion structure and contributes significantly to the binding between FtsY and membrane. Through membrane protein extraction followed by immunoblotting, we demonstrated that deletion of the N-terminal residues 11–39 from the S. coelicolor FtsY (ScFtsY) drastically reduced its membrane-binding capability and that the N-terminus of ScFtsY alone was capable of targeting the soluble EGFP protein onto the membrane with high efficiency. Furthermore, in a labeling experiment with the membrane-impermeable probe Mal-PEG, the ScFtsY N-terminal region was protected by the membrane and was not labeled. This observation indicates that this region was inserted into the membrane.

Precise semantic representation is important for allowing machines to truly comprehend the meaning of natural language text, especially biomedical literature. Although the semantic relations among words in a single sentence may be accurately represented with existing approaches, relations between two sentences cannot yet be accurately modeled, which leads to a lack of contextual information and difficulty in performing interpretable semantic inference. Additionally, it is challenging to merge semantic representations curated by different experts. These critical challenges are insufficiently addressed by existing methods. In this paper, we present a framework for structured semantic representation (FSSR) to address these issues. FSSR uses a double-layer structure Construct that combines Paradigm and Instance to represent the semantics of a word or a sentence. It uses six types of rules to represent the semantic relations between sentence Constructs and uses a Computational Model to represent an action. FSSR is a graph-based representation of semantics, in which a node represents a Construct or a Paradigm. Two nodes are connected by an edge (a rule). In addition, FSSR enables interpretable inference and active acquisition of new information, as illustrated in a case study. This case study models the semantics of a cancer prognostic analysis article and reproduces its text results and charts. We provide a website that visualizes the inference process (http://cragraph.synergylab.cn). •A graph-based framework for structured semantic representation is proposed.•FSSR uses Constructs and six types rules to model semantics and semantic relations.•FSSR connects Constructs by rules to build a directed graph.•FSSR enables interpretable semantic inference and active acquisition of new information.•A case study on cancer prognostic analysis demonstrates FSSR's effectiveness and proof-of-concept.

Predicted interactions are a valuable complement to experimentally reported interactions in molecular mechanism studies, particularly for higher organisms, for which reported experimental interactions represent only a small fraction of their total interactomes. With careful engineering consideration of the lessons from previous efforts, the Predicted Arabidopsis Interactome Resource (PAIR; http://www.cls.zju. edu.cn/pair/) presents 149,900 potential molecular interactions, which are expected to cover ~24% of the entire interactome with ~40% precision. This study demonstrates that, although PAIR still has limited coverage, it is rich enough to capture many significant functional linkages within and between higher-order biological systems, such as pathways and biological processes. These inferred interactions can nicely power several network topology-based systems biology analyses, such as gene set linkage analysis, protein function prediction, and identification of regulatory genes demonstrating insignificant expression changes. The drastically expanded molecular network in PAIR has considerably improved the capability of these analyses to integrate existing knowledge and suggest novel insights into the function and coordination of genes and gene networks.

Object detection is a typical multi-task learning application, which optimizes classification and regression simultaneously. However, classification loss always dominates the multi-task loss in anchor-based methods, hampering the consistent and balanced optimization of the tasks. In this paper, we find that shifting the bounding boxes can change the division of positive and negative samples in classification, meaning classification depends on regression. Moreover, we summarize three important conclusions about fine-tuning loss weights, considering different datasets, optimizers and regression loss functions. Based on the above conclusions, we propose Adaptive Loss Weight Adjustment(ALWA) to solve the imbalance in optimizing anchor-based methods according to statistical characteristics of losses. By incorporating ALWA into previous state-of-the-art detectors, we achieve a significant performance gain on PASCAL VOC and MS COCO, even with L1, SmoothL1 and CIoU loss. The code is available at https://github.com/ywx-hub/ALWA.

We consider in this paper numerical approximation and simulation of a two-species Keller-Segel model. The model enjoys an energy dissipation law, mass conservation and bound or positivity preserving for the population density of two species. We construct a class of very efficient numerical schemes based on the generalized scalar auxiliary variable with relaxation which preserve unconditionally the essential properties of the model at the discrete level. We conduct a sequence of numerical tests to validate the properties of these schemes, and to study the blow-up phenomena of the model in a three-dimensional domain in parabolic-elliptic form and parabolic-parabolic form.

Intervertebral discs (IVDs) are complex structures that consist of three parts, namely, nucleus pulposus, annulus fibrosus and cartilage endplates. With aging, IVDs gradually degenerate as a consequence of many factors, such as microenvironment changes and cell death. Human clinical trial and animal model studies have documented that cell death, particularly apoptosis and autophagy, significantly contribute to IVD degeneration. The mechanisms underlying this phenomenon include the activation of apoptotic pathways and the regulation of autophagy in response to nutrient deprivation and multiple stresses. In this review, we briefly summarize recent progress in understanding the function and regulation of apoptosis and autophagy signaling pathways. In particular, we focus on studies that reveal the functional mechanisms of these pathways in IVD degeneration.

Extracellular vesicles (EVs) are vesicles with a lipid bilayer membrane on the outside, which are widely found in various body fluids and contain biological macromolecules such as DNA, RNA, lipids and proteins on the inside. EVs were once thought to be vesicles for the removal of waste materials, but are now known to be involved in a variety of pathophysiological processes in many diseases. This study examines the advantage of EVs and the challenges associated with their application. A more rational use of the advantageous properties of EVs such as composition specificity, specific targeting, circulatory stability, active penetration of biological barriers, high efficient drug delivery vehicles and anticancer vaccines, oxidative phosphorylation activity and enzymatic activity, and the resolution of shortcomings such as isolation and purification methods, storage conditions and pharmacokinetics and biodistribution patterns during drug delivery will facilitate the clinical application of EVs.

Developing interfacial solar steam generation-based water desalination and purification systems is considered a viable solution to freshwater shortages and energy crises. The design and fabrication of thermal materials with broad solar absorption is critical for efficient utilization of full solar spectrum. Herein, a wood-based hydrogel evaporator coated with C 3 N 4 photocatalyst was explored for simultaneous solar evaporation and photocatalytic degradation. C 3 N 4 photocatalyst can absorb ultraviolet photons to generate electron–hole pairs for photocatalytic degradation. At the same time, PDA@ZIF-8 can capture near-infrared visible photons to produce heat, which further improves the photocatalytic efficiency. The optimized evaporator achieves a TC photocatalytic degradation of 96.70%. It also acquires a high solar evaporation rate of 2.64 kg m −2  h −1 with an energy conversion efficiency of 89.23% at 1.0 kW m −2 solar irradiation. This multifunctional wood-based hydrogel evaporator provides a feasible freshwater purification and waste treatment solution.