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Considering economic, environmental, and social issues, the sustainability of the supply chain has drawn considerable attention due to societal and environmental changes within the supply chain network. The strategic study of the entire supply chain process and maximizing an organization’s competitive advantage depend heavily on supplier selection based on sustainable indicators. Selecting sustainable suppliers for the supply chain is challenging since it is a multi-criteria decision-making (MCDM) problem with significant uncertainty in the decision-making process. This study uses the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique and single-valued neutrosophic sets (SVNS) to deal with the challenge of choosing a sustainable supplier with insufficient information. This method reduces the influence of personal experience and preference on the final evaluation results and the problem of excessive individual regret caused by factor correlation and improves the consistency of evaluation results. Finally, the method’s success and adaptability are demonstrated by sensitivity analysis and additional comparison analysis, and the benefits and drawbacks of the suggested framework are examined. Compared to other approaches, it can assist decision-makers in communicating fuzzy and uncertain information, offering a perspective and approach for MCDM in the face of such situations, and helping them select suppliers of high caliber and who practice sustainable business practices.

The thermal management and reduction of energy consumption in cooling systems have become major trends with the continued growth of high heat dissipation data centers and the challenging energy situation. However, the existing studies have been limited to studying the influences of individual factors on energy saving and thermal management and have not been systematically summarized. Thus, this paper reviews the key factors in achieving thermal management and reducing energy consumption in each cooling system, the corresponding research, and optimization methods. To achieve these goals, in this paper, literature surveys on data center cooling systems are investigated. For data center air cooling, thermal management is mainly related to the uniform distribution of hot and cold air. Adjusting the porosity of perforated tiles can reduce energy consumption. For liquid cooling and free cooling systems, climate conditions, cooling system structural design, coolant type, and flow rate are key factors in achieving thermal management and reducing energy consumption. This paper provides the power usage effectiveness (PUE) values of the cooling systems in some cases. A summary of the key factors can provide directions for research on thermal management and energy reduction, and a summary of previous research can provide a basis for future optimization.

The design of functional materials with desired properties is essential in driving technological advances in areas such as energy storage, catalysis and carbon capture 1 , 2 – 3 . Generative models accelerate materials design by directly generating new materials given desired property constraints, but current methods have a low success rate in proposing stable crystals or can satisfy only a limited set of property constraints 4 , 5 , 6 , 7 , 8 , 9 , 10 – 11 . Here we present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. Compared with previous generative models 4 , 12 , structures produced by MatterGen are more than twice as likely to be new and stable, and more than ten times closer to the local energy minimum. After fine-tuning, MatterGen successfully generates stable, new materials with desired chemistry, symmetry and mechanical, electronic and magnetic properties. As a proof of concept, we synthesize one of the generated structures and measure its property value to be within 20% of our target. We believe that the quality of generated materials and the breadth of abilities of MatterGen represent an important advancement towards creating a foundational generative model for materials design. MatterGen is a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints.

While many structural damage detection methods have been developed in recent decades, few data‐driven methods in unsupervised learning mode have been developed to solve the practical difficulties in data acquisition for civil infrastructures in different scenarios. To address such a challenge, this article proposes a number of improved unsupervised novelty detection methods and conducts extensive comparative studies on a laboratory scale steel bridge to examine their performances of damage detection. The key concept behind unsupervised novelty detection in this article is that only normal data from undamaged/baseline structural scenarios are required to train statistical models with these methods. Then, these trained models are used to identify abnormal testing data from damaged scenarios. To detect structural damage in the form of loosening bolts in the steel bridge, four machine‐learning methods (i.e., K‐nearest neighbors method, Gaussian mixture models, one‐class support vector machines, density peaks‐based fast clustering method) and one deep learning method using a deep auto‐encoder are selected. Meanwhile, some modifications and improvements are made to enable these methods to detect structural damage in unsupervised novelty detection mode. In their comparative studies, the advantages and disadvantages of these methods are analyzed based on their results of structural damage detection. Recently, deep learning‐based damage detection is a very hot topic. This article conducted extensive comparative studies using state‐of‐the‐art methods of deep learning‐based damage detection methods to figure out the pros and cons of each method.

Deep venous thrombosis (DVT) is a part of venous thromboembolism (VTE) that clinically manifests as swelling and pain in the lower limbs. The most serious clinical complication of DVT is pulmonary embolism (PE), which has a high mortality rate. To date, its underlying mechanisms are not fully understood, and patients usually present with clinical symptoms only after the formation of the thrombus. Thus, it is essential to understand the underlying mechanisms of deep vein thrombosis for an early diagnosis and treatment of DVT. In recent years, many studies have concluded that Neutrophil Extracellular Traps (NETs) are closely associated with DVT. These are released by neutrophils and, in addition to trapping pathogens, can mediate the formation of deep vein thrombi, thereby blocking blood vessels and leading to the development of disease. Therefore, this paper describes the occurrence and development of NETs and discusses the mechanism of action of NETs on deep vein thrombosis. It aims to provide a direction for improved diagnosis and treatment of deep vein thrombosis in the near future.

All-solid-state Li metal batteries (ASSLMBs) promise superior safety and energy density compared to conventional Li-ion batteries. However, their widespread adoption is hindered by detrimental interfacial reactions between solid-state electrolytes (SSEs) and the Li negative electrode, compromising long-term cycling stability. The challenges in directly observing these interfaces impede a comprehensive understanding of reaction mechanisms, necessitating first-principle simulations for designing novel interlayer materials. To overcome these limitations, we develop a database-supported high-throughput screening (DSHTS) framework for identifying stable interlayer materials compatible with both Li and SSEs. Using Li 3 InCl 6 as a model SSE, we identify Li 3 OCl as a potential interlayer material. Experimental validation demonstrates significantly improved electrochemical performance in both symmetric- and full-cell configurations. A Li|Li 3 OCl|Li 3 InCl 6 |LiCoO 2 cell exhibits an initial discharge capacity of 154.4 mAh/g (1.09 mA/cm 2 , 2.5–4.2 V vs . Li/Li + , 303 K) with 76.36% capacity retention after 1000 cycles. Notably, a cell with a conventional In-Li 6 PS 5 Cl interlayer delivers only 132.4 mAh/g and fails after 760 cycles. An additional interlayer-containing battery with Li(Ni 0.8 Co 0.1 Mn 0.1 )O 2 as the positive electrode achieves an initial discharge capacity of 151.3 mAh/g (3.84 mA/cm 2 , 2.5–4.2 V vs . Li/Li + , 303 K), maintaining stable operation over 1650 cycles. The results demonstrate the promise of the DSHTS framework for identifying interlayer materials. All-solid-state lithium metal batteries face interfacial instability challenges. Here, authors develop a computational screening framework to identify Li 3 OCl as a stable interlayer between Li 3 InCl 6 electrolyte and the Li metal electrode, improving the capacity retention of all-solid-state batteries.

K‐ion batteries (KIBs) have attracted tremendous attention and seen significant development because of their low price, high operating voltage, and properties similar to those of Li‐ion batteries. In the field of development of full batteries, exploring high‐performing and low‐cost anode materials for K‐ion storage is a crucial challenge. Owing to their excellent cost effectiveness, abundant precursors, and environmental benignancy, hard carbons (HCs) are considered promising anode materials for KIBs. As a result, researchers have devoted much effort to quantify the properties and to understand the underlying mechanisms of HC‐based anodes. In this review, we mainly introduce the electrochemical reaction mechanism of HCs in KIBs, and summarize approaches to further improve the electrochemical performance in HC‐based materials for K‐ion storage. In addition, we also highlight some advanced in situ characterization methods for understanding the evolutionary process underlying the potassiation–depotassiation process, which is essential for the directional electrochemical performance optimization of KIBs. Finally, we raise some challenges in developing smart‐structured HC anode materials for KIBs, and propose rational design principles and perspectives serving as the guidance for the targeted optimization of HC‐based KIBs. Hard carbons are considered as a promising anode material for K‐ion batteries. This review presents the electrochemical reaction mechanism of hard carbons in K‐ion batteries, and summarize rational approaches to further improve the electrochemical performance in hard carbon‐based materials for K‐ion storage. In addition, some advanced in situ characterization methods for understanding the storage mechanism are also highlighted.

The obstacle avoidance problem of unmanned aerial vehicle (UAV) mainly refers to the design of a method that can safely reach the target point from the starting point in an unknown flight environment. In this paper, we mainly propose an obstacle avoidance method composed of three modules: environment perception, algorithm obstacle avoidance and motion control. Our method realizes the function of reasonable and safe obstacle avoidance of UAV in low-altitude complex environments. Firstly, we use the light detection and ranging (LiDAR) sensor to perceive obstacles around the environment. Next, the sensor data is processed by the vector field histogram (VFH) algorithm to output the desired speed of drone flight. Finally, the expected speed value is sent to the quadrotor flight control and realizes autonomous obstacle avoidance flight of the drone. We verify the effectiveness and feasibility of the proposed method in 3D simulation environment.

Acetylation contributes to the bioactivity of numerous medicinally important natural products. However, little is known about the acetylation on sugar moieties. Here we report a saponin acetyltransferase from Astragalus membranaceus . AmAT7-3 is discovered through a stepwise gene mining approach and characterized as the xylose C3′/C4′- O -acetyltransferse of astragaloside IV ( 1 ). To elucidate its catalytic mechanism, complex crystal structures of AmAT7-3/ 1 and AmAT7-3 A310G / 1 are obtained, which reveal a large active pocket decided by a specific sequence AADAG. Combining with QM/MM computation, the regiospecificity of AmAT7-3 is determined by sugar positioning modulated by surrounding amino acids including #A310 and #L290. Furthermore, a small mutant library is built using semi-rational design, where variants A310G and A310W are found to catalyze specific C3′- O and C4′- O acetylation, respectively. AmAT7-3 and its variants are also employed to acetylate other bioactive saponins. This work expands the understanding of saponin acetyltransferases, and provide efficient catalytic tools for saponin acetylation. Currently little is known about the acetylation on sugar moieties. Here the authors report a saponin acetyltransferase from Astragalus membranaceus , AmAT7-3, and utilise crystal structures and QM/MM computation to elucidate the catalytic mechanism: they generate mutants for specific site acetylation.

Venous thromboembolism is a very common and costly health problem. Deep-vein thrombosis (DVT) can cause permanent damage to the venous system and lead to swelling, ulceration, gangrene, and other symptoms in the affected limb. In addition, more than half of the embolus of pulmonary embolism comes from venous thrombosis, which is the most serious cause of death, second only to ischemic heart disease and stroke patients. It can be seen that deep-vein thrombosis has become a serious disease affecting human health. In recent years, with the deepening of research, inflammatory response is considered to be an important pathway to trigger venous thromboembolism, in which the transcription factor NF-κB is the central medium of inflammation, and the NF-κB signaling pathway can regulate the pro-inflammatory and coagulation response. Thus, to explore the mechanism and make use of it may provide new solutions for the prevention and treatment of thrombosis.