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\"\"Divorce in China\" explores institutional constraints and gendered outcomes of divorce in China\"-- Provided by publisher.

In this paper, we study the heat engine where a charged AdS black hole surrounded by dark energy is the working substance and the mechanical work is done via the PdV term in the first law of black hole thermodynamics in the extended phase space. We first investigate the effects of a kind of dark energy (quintessence field in this paper) on the efficiency of the RN-AdS black holes as the heat engine defined as a rectangular closed path in the P – V plane. We get the exact efficiency formula and find that the quintessence field can improve the heat engine efficiency, which will increase as the field density ρ q grows. At some fixed parameters, we find that a larger volume difference between the smaller black holes( V 1 ) and the bigger black holes( V 2 ) will lead to a lower efficiency, while the bigger pressure difference P 1 - P 4 will make the efficiency higher, but it is always smaller than 1 and will never be beyond the Carnot efficiency, which is the maximum value of the efficiency constrained by thermodynamics laws; this is consistent to the heat engine in traditional thermodynamics. After making some special choices for the thermodynamical quantities, we find that the increase of the electric charge Q and the normalization factor a can also promote the heat engine efficiency, which would infinitely approach the Carnot limit when Q or a goes to infinity.

Football matches have a high degree of attention and the analysis technology used for video contents has important practical significance and good application prospects. However, due to the diversity of conditions, i.e., football venues, clothing colors, etc., there is no universal tracker that can perfectly adapt to all scenarios. Due to its excellent feature extraction capabilities, deep learning technology has been widely used in the field of computer vision in recent years. The main objective of this article is the extraction of player’s trajectory in a football game, i.e., the path tracking of the player’s goal. To achieve this objective, deep learning technology is used for automatic extraction of the characteristic features of the player’s target in the context of target detection and tracking. The target detection method employed in this study is based on deep learning by forming new multi-scale features and modifying the generation rules of anchor points of the captured videos, making it more suitable for small target detection tasks in football match scenes. The generation rules are based on a complex decision support system for target tracking. This decision support system uses the method of constructing a similarity matrix to transform the multi-target tracking problem into a data association problem that can be solved by the Hungarian algorithm. The proposed approach is compared against state-of-the-art techniques in terms of area under the curve (AUC) value, track set scene distribution, number of frames, and other parameters. Based on the experimental results, the proposed approach outperforms these existing techniques with much better results.

With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inevitably influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, methodology and performance. This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era. Through this article, we devote to: (1) introduce new insights and ideas; (2) highlight recent techniques and benchmarks; (3) look ahead into future trends. Specifically, we will emphasize the dramatic differences brought by deep learning and remaining grand challenges. We expect that this review paper would serve as a reference book for researchers in this field. Related resources are also collected in our Github repository (https://github.com/Jyouhou/SceneTextPapers).

Recently, a great many deep convolutional neural network (CNN)-based methods have been proposed for hyperspectral image (HSI) classification. Although the proposed CNN-based methods have the advantages of spatial feature extraction, they are difficult to handle the sequential data with and CNNs are not good at modeling the long-range dependencies. However, the spectra of HSI are a kind of sequential data, and HSI usually contains hundreds of bands. Therefore, it is difficult for CNNs to handle HSI processing well. On the other hand, the Transformer model, which is based on an attention mechanism, has proved its advantages in processing sequential data. To address the issue of capturing relationships of sequential spectra in HSI in a long distance, in this study, Transformer is investigated for HSI classification. Specifically, in this study, a new classification framework titled spatial-spectral Transformer (SST) is proposed for HSI classification. In the proposed SST, a well-designed CNN is used to extract the spatial features, and a modified Transformer (a Transformer with dense connection, i.e., DenseTransformer) is proposed to capture sequential spectra relationships, and multilayer perceptron is used to finish the final classification task. Furthermore, dynamic feature augmentation, which aims to alleviate the overfitting problem and therefore generalize the model well, is proposed and added to the SST (SST-FA). In addition, to address the issue of limited training samples in HSI classification, transfer learning is combined with SST, and another classification framework titled transferring-SST (T-SST) is proposed. At last, to mitigate the overfitting problem and improve the classification accuracy, label smoothing is introduced for the T-SST-based classification framework (T-SST-L). The proposed SST, SST-FA, T-SST, and T-SST-L are tested on three widely used hyperspectral datasets. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, which shows that the concept of Transformer opens a new window for HSI classification.

As competitive HMG-CoA reductase (HMGCR) inhibitors, statins not only reduce cholesterol and improve cardiovascular risk, but also exhibit pleiotropic effects that are independent of their lipid-lowering effects. Among them, the anti-cancer properties of statins have attracted much attention and indicated the potential of statins as repurposed drugs for the treatment of cancer. A large number of clinical and epidemiological studies have described the anticancer properties of statins, but the evidence for anticancer effectiveness of statins is inconsistent. It may be that certain molecular subtypes of cancer are more vulnerable to statin therapy than others. Whether statins have clinical anticancer effects is still an active area of research. Statins appear to enhance the efficacy and address the shortcomings associated with conventional cancer treatments, suggesting that statins should be considered in the context of combined therapies for cancer. Here, we present a comprehensive review of the potential of statins in anti-cancer treatments. We discuss the current understanding of the mechanisms underlying the anti-cancer properties of statins and their effects on different malignancies. We also provide recommendations for the design of future well-designed clinical trials of the anti-cancer efficacy of statins.

The protective role of sodium glucose cotransporter 2 (SGLT2) inhibitors in renal outcomes has been revealed by large cardiovascular outcome trials among patients with type 2 diabetes. However, the effect of SGLT2 inhibitors on lupus nephritis (LN) and its underlying mechanisms remain unknown. We applied empagliflozin treatment to lupus-prone MRL/ mice to explore the renal protective potential of SGLT2 inhibitors. An SGLT2 knockout monoclonal podocyte cell line was generated using the CRISPR/Cas9 system to examine the cellular and molecular mechanisms. In MRL/ mice treated with empagliflozin, the levels of mouse anti-dsDNA IgG-specific antibodies, serum creatinine and proteinuria were markedly decreased. For renal pathology assessment, both the glomerular and tubulointerstitial damages were lessened by administration of empagliflozin. The levels of SGLT2 expression were increased and colocalised with decreased synaptopodin in the renal biopsy samples from patients with LN and MRL/ mice with nephritis. The SGLT2 inhibitor empagliflozin could alleviated podocyte injury by attenuating inflammation and enhanced autophagy by reducing mTORC1 activity. Nine patients with LN treated with SGLT2 inhibitors with more than 2 months of follow-up showed that the use of SGLT2 inhibitors was associated with a significant decrease in proteinuria from 29.6% to 96.3%. Moreover, the estimated glomerular filtration rate (eGFR) was relatively stable during the treatment with SGLT2 inhibitors. This study confirmed the renoprotective effect of SGLT2 inhibitors in lupus mice, providing more evidence for non-immunosuppressive therapies to improve renal function in classic autoimmune kidney diseases such as LN.

The Illustrated Commentary on the Wuzhen Pian Based on the Zhouyi (周易悟真篇圖注 Zhouyi Wuzhen Pian Tuzhu), authored by the Ming dynasty Confucian scholar Cheng Yiming 程易明, is an illustrated alchemical text that integrates the elixir methodology of Wuzhen pian 悟真篇 (the Awakening to Reality) with the images and numbers (xiangshu 象數) system of The Book of Changes (Zhouyi 周易). Centered on Daoist alchemical theory and elucidated through “Yijing diagrams” (yitu 易圖, diagrams based on the Yijing), it stands as a masterpiece within the tradition of alchemical Yijing studies (dandao yixue 丹道易學). Building on a review of the scholarly history of The Wuzhen Pian, this article focuses on the alchemical Yijing diagrams (dandao yitu 丹道易圖) in the Illustrated Commentary, exploring their terminological definitions, theoretical origins, and diagrammatic systems. By analyzing the structure of cosmology and internal alchemy practice theory (neidan gongfulun 內丹工夫論) as presented in these diagrams, this article demonstrates that the Illustrated Commentary not only inherits the theoretical legacy of early Yijing diagram scholars such as Chen Tuan (陳摶) and Yu Yan (俞琰), but also displays a unique systematic and intuitive approach to illustrating neidan practices through xiangshu diagrams (象數圖解). Notably, diagrams such as “Mundane Continuation vs. Alchemical Inversion” (shunfan nixian 順凡逆仙), the “Three-Five-One Mathematical Model” (sanwuyi shuli moxing 三五一數理模型), and the “Fire Phases” (huohou 火候) reveal attempts to construct an alchemical theoretical system centered on Yijing diagrams. The article further posits that the Illustrated Commentary bridges the gap between images–numbers Yijing studies (xiangshu yixue 象數易學) and alchemical visual hermeneutics, offering a fresh perspective centered on internal alchemy for the study of “Yijing Diagram Studies” (yitu xue 易圖學).

Backscattering suppression in silicon-on-insulator (SOI) is one of the central issues to reduce energy loss and signal distortion, enabling for capability improvement of modern information processing systems. Valley physics provides an intriguing way for robust information transfer and unidirectional coupling in topological nanophotonics. Here we realize topological transport in a SOI valley photonic crystal slab. Localized Berry curvature near zone corners guarantees the existence of valley-dependent edge states below light cone, maintaining in-plane robustness and light confinement simultaneously. Topologically robust transport at telecommunication is observed along two sharp-bend interfaces in subwavelength scale, showing flat-top high transmission of ~10% bandwidth. Topological photonic routing is achieved in a bearded-stack interface, due to unidirectional excitation of valley-chirality-locked edge state from the phase vortex of a nanoscale microdisk. These findings show the prototype of robustly integrated devices, and open a new door towards the observation of non-trivial states even in non-Hermitian systems. Backscattering is one of the major factors that limit the performance of integrated nanophotonics. Here, He et al. realize topologically protected, robust and unidirectional coupling as well as optical transport on a silicon-on-insulator platform by exploiting the valley degree of freedom.

Reactive oxygen species (ROS), such as superoxides (O 2  •−) and hydroxyl groups (OH·), are short-lived molecules containing unpaired electrons. Intracellular ROS are believed to be mainly produced by the mitochondria and NADPH oxidase (NOX) and can be associated with various physiological processes, such as proliferation, cell signaling, and oxygen homeostasis. In recent years, many studies have indicated that ROS play crucial roles in regulating ultraviolet (UV)-induced photodamage of the skin, including exogenous aging, which accounts for 80% of aging. However, to the best of our knowledge, the detailed signaling pathways, especially those related to the mechanisms underlying apoptosis in which ROS are involved have not been reviewed previously. In this review, we elaborate on the biological characteristics of ROS and its role in regulating UV-induced photodamage of the skin.