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The heat flux width (λq) is a key parameter determining the heat load at divertor targets. In recent years, drifts have been found to play a remarkable role in the edge plasma transport, while its influence on λq has not been well understood. Investigations of the influence of drifts on λq, systematic simulations using the SOLPS-ITER code are performed in this work. The statistics of the simulation results show that the drift under favorable/unfavorable B T tends to increase the λq in the outer/inner side and decrease the λq in the other side, which is consistent with the experiment observations. At the upstream and the target, the mechanisms of the influence of the drifts on λq are different. The upstream λq (λq,u) is directly affected by the drift-induced convective heat flux, while λq at the target (λq,t) is indirectly influenced through heat conduction (in the high-recycling regime) and the sheath (in the detached regime) due to the change of plasma parameters there. Furthermore, the synergetic effect of geometry and drift under favorable B T leads to an anomalously large λq,t in the inner side at high density. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) industry is expanding rapidly in the context of addressing the challenge of climate change and energy crisis. As a technology-intensive product, large number of new synthetic chemicals are applied in LIBs production. Organic fluorine additives are added in multiple components of LIBs, many of these additives are novel per-and polyfluoroalkyl substances (with fully fluorinated methyl or methylene carbon atom), here, in this work we referred to this kind of compounds as LIB-PFAS. LIB-PFAS may cause high exposure to personnel in contact with LIBs materials during production and disposal processes, thus posing potential health risks to these occupational workers. The general population can be exposed to LIB-PFAS when they are released into the surrounding air, soil, and water. In order to assess potential health risks associated with LIB-PFAS, this review systematically summarizes the chemical structures, analytical methods, possible emission routes and human exposure pathways, their bioaccumulation, distribution, and elimination behaviors in humans, and existing toxicity data of LIB-PFAS. In particular, the screening and identification methods for 57 LIB-PFAS are discussed, as well as the investigation of emission processes and exposure risks during the manufacturing and disposal of LIB. Although the available scientific data is still limited, this review provide an insightful view and novel perspective for further research on the occupational and environmental health impacts of LIB-PFAS. [ABSTRACT FROM AUTHOR]

Credit evaluation has always been an important part of the financial field. The existing credit evaluation methods have difficulty in solving the problems of redundant data features and imbalanced samples. In response to the above issues, an ensemble model combining an advanced feature selection algorithm and an optimized loss function is proposed, which can be applied in the field of credit evaluation and improve the risk management ability of financial institutions. Firstly, the Boruta algorithm is embedded for feature selection, which can effectively reduce the data dimension and noise and improve the model's capacity for generalization by automatically identifying and screening out features that are highly correlated with target variables. Then, the GHM loss function is incorporated into the XGBoost model to tackle the issue of skewed sample distribution, which is common in classification, and further improve the classification and prediction performance of the model. The comparative experiments on four large datasets demonstrate that the proposed method is superior to the existing mainstream methods and can effectively extract features and handle the problem of imbalanced samples. [ABSTRACT FROM AUTHOR]

As the power grid undergoes transformation and the Internet's influence grows, the electricity market is evolving towards informatization. The expanding scale of the power grid and the increasing complexity of operating conditions have generated a substantial amount of data in the power market. The traditional power marketing model is no longer suitable for the modern power market's development trend. To tackle this challenge, this study employs random forest and RBF models for processing electricity market data. Additionally, it explores the synergy of graph convolutional network and spectral clustering algorithms to enhance the accuracy and efficiency of data mining, enabling a comprehensive analysis of data features. The experimental results successfully extracted various electricity consumption features. This approach contributes to the informatization efforts of power grid enterprises, enhances power data perception capabilities, and offers reliable support for decision makers. [ABSTRACT FROM AUTHOR]

Hybrid gold-based perovskite derivatives typically exhibit low optical bandgaps and high optical absorption coefficients, rendering them promising for photovoltaic applications. In this study, we successfully synthesized six new hybrid gold-based perovskite derivatives, namely [(C6H8N2)(AuI4)(AuI2)](3AMPY), [(C6H14N2)(AuI4)(AuI2)](3AMP), [(C8H12N)(AuI4)](2PEAI), [(C4H14N2O)(AuI4)2](OBA), [(C6H18N2O2)3(AuI4)4(I3)2](DDA), and [(C10H26N2O3)(AuI4)(I3)](TOTA), through a straightforward and efficient hydrothermal method, achieving millimeter-sized single crystals. The structural analysis of the single crystals revealed variations in crystal structures arising from differences in constituent units and their spatial positioning relationships. First-principles calculations ascertained their high optical absorption coefficients in the visible light spectrum and indirect bandgap properties. Theoretical models indicated that the spectroscopic limited maximum efficiency (SLME) values of 3AMPY, 2PEAI, DDA, and TOTA approached approximately 30% in films of 0.5 μm thickness, signifying their potential candidacy as solar cell absorbers. [ABSTRACT FROM AUTHOR]