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With the increasing importance of the stock market, it is of great practical significance to accurately describe the systemic risk of the stock market and conduct more accurate early warning research on it. However, the existing research on the systemic risk of the stock market lacks multi-dimensional factors, and there is still room for improvement in the forecasting model. Therefore, to further measure the systemic risk profile of the Chinese stock market, establish a risk early warning system suitable for the Chinese stock market, and improve the risk management awareness of investors and regulators. This paper proposes a combination model of EEMD-LSTM, which can describe the complex nonlinear interaction. Firstly, 35 stock market systemic risk indicators are selected from the perspectives of macroeconomic operation, market cross-contagion and the stock market itself to build a comprehensive indicator system that conforms to the reality of China. Furthermore, based on TEI@I complex system methodology, an EEMD-LSTM model is proposed. The EEMD method is adopted to decompose the composite index sequence into intrinsic mode function components (IMF) of different scales and one trend term. Then the LSTM algorithm is used to predicted and model the decomposed sub-sequences. Finally, the forecast result of the composite index is obtained through integration. The empirical results show that the stock market systemic risk index constructed in this paper can effectively identify important risk events within the sample period. In addition, compared with the benchmark model, the EEMD-LSTM model constructed in this paper shows a stronger early warning ability for systemic financial risks in the stock market.

Existing lane-changing models generally neglect the detailed modeling of lane-changing actions and model lane-changing only as an instantaneous event. In this study, an intertunnel weaving section was taken as the background, the lane-changing duration and distance in the lane-changing process were taken as the main research objects. The detailed modeling of a lane-changing action was emphasized. Aerial videos of intertunnel weaving sections were collected, and accurate vehicle trajectory data were extracted. Basic data analysis shows that the lane-changing duration has a lognormal distribution and the lane-changing distance has a normal distribution. To analyze the difference of the lane-changing behavior characteristics in different lane-changing environments, based on the lead spacing and lag spacing in the target lane, a hierarchical clustering algorithm was applied to classify the lane-changing environment into six different types. Then, a deep neural network regression model was applied to model the lane-changing process for each environment type. The results show that the horizontal distribution, vertical distribution and statistical characteristics of the lane changing points under different lane-changing environments are significantly different. The prediction accuracy of the lane-changing distance after classification is improved by at least 61%, and the prediction accuracy of the lane-changing duration after classification is improved by at least 57%. It is also found that lane-changing behavior characteristics with large or small lag spacing are easier to predict, while in the other cases, the randomness of the lane-changing behavior characteristics is more obvious. The research results can be incorporated into lane-changing decision assistance systems and micro traffic simulation models to make the assistance system safer and more effective, and the simulation outputs should be more realistic and accurate.

To enhance the engineering properties of cement solidified silty soft soil, nano-oxides such as nano-SiO 2 (NS), nano-Al 2 O 3 (NA), nano-MgO(NM) and nano-Fe 3 O 4 (NF) were selected for modification. The unconfined compressive strength, one-dimensional consolidation and water stability tests were carried out, and the microstructure was analyzed by X-ray diffraction, scanning electron microscopy and mercury intrusion tests. The test results show that nano-oxides can significantly improve the compressive strength of cement solidified silty soft soil. After curing for 28 days, The unconfined compressive strength of samples modified with 1.5% NS, NA, NF, and NM increased by 187.44%, 171.67%, 122.55%, and 189.37%, respectively, compared to that of pure cement soil. With the increase of nano-oxide content, the void ratio and compression coefficient of the sample gradually decrease, and the strength residual coefficient gradually increases. Microscopic test analysis shows that the addition of nano-oxides causes the difference in the type and quantity of hydration products in cement solidified soil, promotes the formation of an effective whole of soil particles wrapped by gel products, transforms the pores from inter-particle pores into intra-particle pores, and improves the integrity of microstructure. Furthermore, the microscopic mechanism of nano-oxide modified cement solidified silty soft soil is established, which can provide theoretical guidance for related research and engineering.

Electrocatalysis holds immense promise for producing high‐value chemicals and fuels through the carbon dioxide reduction reaction (CO2RR), advancing global sustainability and carbon neutrality. However, conventional electrocatalysts based on transition metals are often limited by significant overpotentials. Since the discovery of the first hexagonal MAB (h‐MAB) phase, Ti2InB2, and its 2D derivative in 2019, 2D hexagonal transition metal borides (h‐MBenes) have emerged as promising candidates for various electrochemical applications. This study presents the first theoretical investigation into the CO2RR catalytic properties of pristine h‐MBenes (h‐MB) and their ─O (h‐MBO) and ─OH (h‐MBOH) terminated counterparts, focusing on metals such as Sc, Ti, V, Zr, Nb, Hf, and Ta. These results reveal while h‐MB and h‐MBO exhibit poor catalytic performance due to overly strong or weak interactions with CO2, h‐MBOH shows great promise. Notably, ScBOH, TiBOH, and ZrBOH display exceptionally low limiting potentials (UL) of −0.46, −0.53, and −0.64 V, respectively. These findings uncover the unique role of ─OH in tuning the electronic properties of h‐MBenes, thereby optimizing intermediate adsorption, which prevents excessive binding and enhances catalytic efficiency. This research offers valuable insights into the potential of h‐MBenes as highly efficient CO2RR catalysts, underscoring their versatility and significant prospects for electrochemical applications.

Oxidative stress has long been known as a pathogenic factor of ulcerative colitis (UC) and colitis-associated colorectal cancer (CAC), but the effects of secondary carbonyl lesions receive less emphasis. In inflammatory conditions, reactive oxygen species (ROS), such as superoxide anion free radical ( O 2 ∙ - ), hydrogen peroxide (H2O2), and hydroxyl radical ( H O ∙ ), are produced at high levels and accumulated to cause oxidative stress (OS). In oxidative status, accumulated ROS can cause protein dysfunction and DNA damage, leading to gene mutations and cell death. Accumulated ROS could also act as chemical messengers to activate signaling pathways, such as NF-κB and p38 MAPK, to affect cell proliferation, differentiation, and apoptosis. More importantly, electrophilic carbonyl compounds produced by lipid peroxidation may function as secondary pathogenic factors, causing further protein and membrane lesions. This may in turn exaggerate oxidative stress, forming a vicious cycle. Electrophilic carbonyls could also cause DNA mutations and breaks, driving malignant progression of UC. The secondary lesions caused by carbonyl compounds may be exceptionally important in the case of host carbonyl defensive system deficit, such as aldo-keto reductase 1B10 deficiency. This review article updates the current understanding of oxidative stress and carbonyl lesions in the development and progression of UC and CAC.

Stock prediction utilizing machine learning and deep learning models has attracted increasing attention in recent years. While recent research has made substantial progress in stock forecasting, many existing models perform inconsistently across markets and are sensitive to random initialization settings, which raises concerns about their robustness, generalizability, and practical applications. To address these issues, we propose ATTention-integrated Convolutional Neural Network-based Conditional Generative Adversarial Network (ATTCNN-CGAN), which leverages real historical samples as inputs to the generator, enabling more deterministic forecasting. Moreover, conventional generative adversarial networks (GANs) often employ recurrent generators that rely on sequential hidden state propagation, which can lead to longer gradient paths and may complicate adversarial optimization. Our approach utilizes convolutional neural networks (CNNs) for both the generator and the discriminator, which avoids the recurrent state propagation and extracts local temporal patterns in parallel, thereby enhancing training stability. Experiments on nine US large-cap stocks, conducted across multiple input lengths and random seeds, demonstrate that ATTCNN-CGAN achieves improved mean predictive performance compared to strong baselines under the tested configurations and exhibits more consistent performance across stocks. Furthermore, ablation studies suggest that the multi-head attention feature extraction, enhanced by the innovative dual-average aggregation strategy, boosts optimized feature representations and the forecasting performance. The findings highlight the potential of multi-head attention enhanced with diverse aggregation strategies for time-series forecasting.

Transient receptor potential vanilloid 4 (TRPV4) is a calcium-permeable cation channel that has been associated with several types of cancer. However, its biological significance, as well as its related mechanism in endometrial cancer (EC) still remains elusive. In this study, we examined the function of calcium in EC, with a specific focus on TRPV4 and its downstream pathway. We reported here on the findings that a high level of serum ionized calcium was significantly correlated with advanced EC progression, and among all the calcium channels, TRPV4 played an essential role, with high levels of TRPV4 expression associated with cancer progression both in vitro and in vivo. Proteomic and bioinformatics analysis revealed that TRPV4 was involved in cytoskeleton regulation and Rho protein pathway, which regulated EC cell migration. Mechanistic investigation demonstrated that TRPV4 and calcium influx acted on the cytoskeleton via the RhoA/ROCK1 pathway, ending with LIMK/cofilin activation, which had an impact on F-actin and paxillin (PXN) levels. Overall, our findings indicated that ionized serum calcium level was significantly associated with poor outcomes and calcium channel TRPV4 should be targeted to improve therapeutic and preventive strategies in EC.

Steroid receptor-associated and regulated protein (SRARP) suppresses tumor progression and modulates steroid receptor signaling by interacting with estrogen receptors and androgen receptors in breast cancer. In endometrial cancer (EC), progesterone receptor (PR) signaling is crucial for responsiveness to progestin therapy. The aim of this study was to investigate the role of SRARP in tumor progression and PR signaling in EC. Ribonucleic acid sequencing data from the Cancer Genome Atlas, Clinical Proteomic Tumor Analysis Consortium, and Gene Expression Omnibus were used to analyze the clinical significance of SRARP and its correlation with PR expression in EC. The correlation between SRARP and PR expression was validated in EC samples obtained from Peking University People's Hospital. SRARP function was investigated by lentivirus-mediated overexpression in Ishikawa and HEC-50B cells. Cell Counting Kit-8 assays, cell cycle analyses, wound healing assays, and Transwell assays were used to evaluate cell proliferation, migration, and invasion. Western blotting and quantitative real-time polymerase chain reaction were used to evaluate gene expression. The effects of SRARP on the regulation of PR signaling were determined by co-immunoprecipitation, PR response element (PRE) luciferase reporter assay, and PR downstream gene detection. Higher SRARP expression was significantly associated with better overall survival and disease-free survival and less aggressive EC types. SRARP overexpression suppressed growth, migration, and invasion in EC cells, increased E-cadherin expression, and decreased N-cadherin and Wnt family member 7A (WNT7A) expression. SRARP expression was positively correlated with PR expression in EC tissues. In SRARP-overexpressing cells, PR isoform B (PRB) was upregulated and SRARP bound to PRB. Significant increases in PRE-based luciferase activity and expression levels of PR target genes were observed in response to medroxyprogesterone acetate. This study illustrates that SRARP exerts a tumor-suppressive effect by inhibiting the epithelial-mesenchymal transition via Wnt signaling in EC. In addition, SRARP positively modulates PR expression and interacts with PR to regulate PR downstream target genes.

This paper presents a three-dimensional, acoustic streaming-driven circular micromixer with sharp-edge structures and the coupling mechanism between acoustic streaming and background flow in biological systems. A piezoelectric transducer induces vibrations in the sharp-edge structures, generating a localized, intense acoustic field that produces a nonlinear acoustic streaming vortex at the tip. The disk-shaped mixing chamber design enhances acoustic field perturbation. This study incorporates the actual background flow field into the model to elucidate the strong interaction between acoustic streaming and steady-state flow. In the sharp-edge structural region, structural curvature induces local variations in acoustic amplitude, generating a non-zero mean Reynolds stress that significantly perturbs the background laminar flow, reduces flow stability, and substantially enhances mixing. The effects of displacement amplitude, Reynolds number, sharp-edge angle, and excitation frequency on the mixing efficiency are systematically investigated. Furthermore, the mixing performances of two different fluids, water and blood, are compared to elucidate the influence of fluid properties on mixing behavior. This mechanism provides theoretical support for microscale active mixing and offers novel insights for microfluidic device design.

It is still a great challenge to determine the size of critical nuclei, which is crucial for a comprehensive understanding of crystallization and for testing the controversial crystallization theories. Here, we propose a method to determine the size of critical secondary nuclei on growth faces of poly(butylene succinate) single crystals in solution, basing on the probability of statistically selecting crystallizable units in random copolymers. In a dilute solution and for a given crystallization temperature, we reveal that the size of critical secondary nuclei was independent of supersaturation, contrary to the well-accepted prediction of existing theories which expect that the size of the critical nucleus increases with decreasing supersaturation. Accounting correctly for the dilution-caused change in the steady-state concentration of clusters of various sizes, we remedy inconsistencies of existing theoretical approaches in deriving the correct size of critical secondary nuclei in solution being independent of supersaturation. Determining the size of critical nuclei remains challenging. Here the authors propose a method to determine the size of critical secondary nuclei on growth faces of poly(butylene succinate) single crystals in solution, using the probability of statistically selecting crystallizable units in random copolymers.