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Background Due to the ageing population and evolving lifestyles occurring in China, middle-aged and elderly populations have become high-risk groups for cardiovascular disease (CVD). The aim of this study was to analyse the incidence characteristics of CVD in these populations and develop a prediction model by using data from the China Health and Retirement Longitudinal Study (CHARLS). Methods We used follow-up data from the CHARLS to analyse CVD incidence in the Chinese middle-aged and elderly population over a time span of 9 years. Five machine learning (ML) algorithms were employed for risk prediction. Data preprocessing included missing value imputation via random forest. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (Lasso CV) method with cross-validation prior to model training. The application of the synthetic minority over-sampling technique (SMOTE) to address class imbalance. Model performance was evaluated via analyses including the area under the ROC curve (AUC), precision, recall, F1 score, and SHAP plots for interpretability. Results In accordance with the exclusion criteria, 12,580, 12,061, 11,545, and 11,619 participants were enrolled in four follow-up rounds. The cumulative incidence (CI) of CVD at 2, 4, 7, and 9 years was 2.846%, 8.971%, 17.869% and 20.518%,, respectively. Significant differences in CVD incidence were observed across gender, age, ethnicity, and region, with higher rates observed in females and in the northeast region. Ultimately, 8,080 participants and 24 features were analysed for CVD risk prediction. Five ML models were built based on these features. Although the LGB model achieves an AUC of 0.818, indicating strong overall performance, its F1 score and recall rate are relatively low, at 0.509 and 43.1%, respectively. Shapley additive explanations (SHAP) analyses revealed the importance of key features, such as night sleep duration, TG levels, and waist circumference, in predicting outcomes, and highlighted the nonlinear relationships between these features and CVD risk. Conclusions Gender, age, ethnicity, and region are significant factors influencing CVD incidence. Although the LGB model demonstrates good overall performance, its low F1 score and recall rate reveal limitations in identifying high-risk cardiovascular disease patients.

Respiratory disease, such as lung cancer, is a major risk factor that seriously endangers human health. In the development of new drugs, conventional preclinical and clinical testing stages rely on cell culture and animal experiment. However, the two methods may fall short of fully representing human biology, thereby presenting an opportunity to advance technological innovation. In this review, we focus on the structure and the composition of supporting cells of organ-on-a-chip (OOAC), whose most notable feature is its biomimetic representation of the human body. Its core advantage lies in its biomimetic human structure, which enables broad application scenarios in the field of pulmonary diseases including lung cancer, pneumonia, pulmonary edema, and pulmonary embolism. Finally, this review summarizes the current challenges and prospects of OOAC, highlighting its vast potential for advancement.

In order to improve detection accuracy while minimizing computational overhead, a modified algorithm is proposed based on the YOLO11n baseline. The innovation incorporates a lightweight ADown module into the P4 and P5 layers of the backbone network, strategically reducing computational complexity. Simultaneously, a multi-scale attention mechanism with parallel structure is integrated into the detection head to enhance feature representation, while a micro-detection head is appended to specifically improve the detection of tiny objects. Based on the classic metrics, including parameter count, mAP@50, mAP@50-95, recall, and FPS, the ablation experiments are performed to validate the improvement of the improved algorithm on the CCTSDB2021 dataset. Furthermore, comparative experiments against traditional YOLO variants are conducted on both CCTSDB2021 and TT100K-2021 datasets. Experimental results demonstrate significant improvements across all evaluated metrics for the improved algorithm, highlighting its exceptional capability to balance high accuracy with minimal computational complexity.

Due to the ageing population and evolving lifestyles occurring in China, middle-aged and elderly populations have become high-risk groups for cardiovascular disease (CVD). The aim of this study was to analyse the incidence characteristics of CVD in these populations and develop a prediction model by using data from the China Health and Retirement Longitudinal Study (CHARLS). We used follow-up data from the CHARLS to analyse CVD incidence in the Chinese middle-aged and elderly population over a time span of 9 years. Five machine learning (ML) algorithms were employed for risk prediction. Data preprocessing included missing value imputation via random forest. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (Lasso CV) method with cross-validation prior to model training. The application of the synthetic minority over-sampling technique (SMOTE) to address class imbalance. Model performance was evaluated via analyses including the area under the ROC curve (AUC), precision, recall, F1 score, and SHAP plots for interpretability. In accordance with the exclusion criteria, 12,580, 12,061, 11,545, and 11,619 participants were enrolled in four follow-up rounds. The cumulative incidence (CI) of CVD at 2, 4, 7, and 9 years was 2.846%, 8.971%, 17.869% and 20.518%,, respectively. Significant differences in CVD incidence were observed across gender, age, ethnicity, and region, with higher rates observed in females and in the northeast region. Ultimately, 8,080 participants and 24 features were analysed for CVD risk prediction. Five ML models were built based on these features. Although the LGB model achieves an AUC of 0.818, indicating strong overall performance, its F1 score and recall rate are relatively low, at 0.509 and 43.1%, respectively. Shapley additive explanations (SHAP) analyses revealed the importance of key features, such as night sleep duration, TG levels, and waist circumference, in predicting outcomes, and highlighted the nonlinear relationships between these features and CVD risk. Gender, age, ethnicity, and region are significant factors influencing CVD incidence. Although the LGB model demonstrates good overall performance, its low F1 score and recall rate reveal limitations in identifying high-risk cardiovascular disease patients.

Metallic additive manufacturing process parameters, such as inclination angle and minimum radius, impose constraints on the printable lattice cell configurations in complex components. As a result, their mechanical properties are usually lower than their design values. Meanwhile, due to unavoidable process constraints (e.g., additional support structure), engineering structures filled with various lattice cells usually fail to be printed or cannot achieve the designed mechanical performances. Optimizing the cell configuration and printing process are effective ways to solve these problems, but this is becoming more and more difficult and costly with the increasing demand for properties. Therefore, it is very important to redesign the existing printable lattice structures to improve their mechanical properties. In this paper, inspired by the macro- and meso-structures of bamboo, a bionic lattice structure was partitioned, and the cell rod had a radius gradient, similar to the macro-scale bamboo joint and meso-scale bamboo tube, respectively. Experimental and simulated results showed that this design can significantly enhance the mechanical properties without adding mass and changing the printable cell configuration. Finally, the compression and shear properties of the Bambusa-lattice structure were analyzed. Compared with the original scheme, the bamboo lattice structure design can improve the strength by 1.51 times (β=1.5). This proposed strategy offers an effective pathway to manipulate the mechanical properties of lattice structures simultaneously, which is useful for practical applications.

Coal is a non-renewable fossil energy source on which humanity relies heavily, and producing one ton of raw coal requires the discharge of 2–7 tons of mine water from the ground. The huge drainage task increases the cost of coal mining in coal mines significantly, so saving the drainage cost while guaranteeing the safe production of coal mines is a problem that needs to be solved urgently. Most of the fuzzy controllers used in the traditional dynamic planning methods applied to mine drainage are two-dimensional fuzzy controllers with limited control effect, so the traditional two-dimensional fuzzy controllers are improved by introducing the rate of change of gushing water to form a three-dimensional fuzzy controller with three-dimensional control of instantaneous section—water level—rate of change of gushing water, and at the same time, the optimized dynamic planning method is designed by combining the Avoiding Peak Filling Valley strategy and the optimal dy-namic planning method is used in conjunction with the un-optimized dynamic planning method. The optimized dynamic planning method is applied to the same coal mine water silo gushing water experiments; experimental comparison found that the pumping station system before the optimi-zation of the electricity consumed is 52,586 yuan/day, while after the optimization of the electricity consumed is reduced to 41,692 yuan/day, the cost per day consumed compared with the previous reduction of 20.69%, a year can be saved 3,969,730 yuan. Therefore, the mine water bin drainage method based on fuzzy control and Avoiding Peak Filling Valley strategy proposed in this paper can be used as an improvement method of the existing mine drainage method, which can further ex-pand the economic benefits of coal mines and realize safe production while realizing cost savings.

Every year, a large amount of solid waste such as fly ash and slag is generated worldwide. If these solid wastes are used in concrete mixes to make concrete, it can effectively save resources and protect the environment. The compressive strength of concrete is an essential indicator for testing its quality, and its prediction is affected by many factors. It is difficult to predict its strength accurately. Therefore, based on the current popular machine learning supervised learning algorithms: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVR), three models established a nonlinear mapping between multi-factor features and target feature concrete compressive strength. Using the three completed training models, we validated the test set with 206 example sets, and the Root Mean Square Error (RMSE), fitting coefficient (R 2 ), and Mean Absolute Error (MAE) were used as evaluation metrics. The validation results showed that the values of RMSE, R 2 , and MAE for the RF model were 0.1, 0.9, and 0.21, respectively; the values of XGBoost model were 0.05, 0.95, and 0.15, respectively. The values of SVR were 0.15, 0.86, and 0.3, respectively. As a result, Extreme Gradient Boosting (XGBoost) has better generalization ability and prediction accuracy than the other two algorithms.

Aiming at the reasonable width of the narrow coal pillar of a fully mechanized caving face and the safety support of roadway, taking the coal pillar in the section between 110503 and 110505 face of the Yushuling Coal mine as the research background, a model of the hard basic roof fracture structure of fully mechanized caving face is established through theoretical analysis, and the roadway with narrow coal pillar is analyzed mechanically. Combined with the geological conditions of the working face, it is concluded that the low‐stress area is less than 3.29 m. When the internal stress field of the low‐stress environment is considered in the roadway layout, the influence of mining and the essential roof hardness should be considered. The reasonable size of the narrow coal pillar is 3 ~ 6 m, thinking that the load borne by the coal pillar is less than the ultimate strength of the coal pillar. The limit equilibrium theory calculates that the reasonable width of a coal pillar is at least 4 m. The stress and displacement of coal pillars with different widths of 3, 4, 5 and 6 m are analyzed by numerical simulation, and the 4 m narrow coal pillars are simulated and verified. Field industrial tests show that coal pillar and roadway surrounding rock deformation are small under asymmetric surrounding rock control. The research results have been successfully applied to engineering practice and can provide a reference for the research method of narrow coal pillar width under a hard basic roof. Layout of 110503 and 110505 working places

Human pluripotent stem-cell-derived islets (hPSC-islets) are a promising cell resource for diabetes treatment 1 , 2 . However, this therapeutic strategy has not been systematically assessed in large animal models physiologically similar to humans, such as non-human primates 3 . In this study, we generated islets from human chemically induced pluripotent stem cells (hCiPSC-islets) and show that a one-dose intraportal infusion of hCiPSC-islets into diabetic non-human primates effectively restored endogenous insulin secretion and improved glycemic control. Fasting and average pre-prandial blood glucose levels significantly decreased in all recipients, accompanied by meal or glucose-responsive C-peptide release and overall increase in body weight. Notably, in the four long-term follow-up macaques, average hemoglobin A1c dropped by over 2% compared with peak values, whereas the average exogenous insulin requirement reduced by 49% 15 weeks after transplantation. Collectively, our findings show the feasibility of hPSC-islets for diabetic treatment in a preclinical context, marking a substantial step forward in clinical translation of hPSC-islets. Improved glycemic control after transplantation of human pluripotent stem-cell-derived islets for diabetes treatment in non-human primates.

The microscopic topography of tooth surface affects the nonlinear dynamic characteristics of the gear system. However, few studies have fully taken into account the effects of microscopic topography on time-varying meshing stiffness (TVMS) and backlash in gear dynamics. In this context, this study derives TVMS and time-varying backlash with fractal characteristics based on fractal theory and introduced them into a 6-DOF nonlinear dynamic model. With various nonlinear dynamics analysis tools, the dynamic characteristics of the gear system under different fractal parameters are investigated. The results indicate that the increase in the fractal dimension or the decrease in the characteristic scale coefficient leads to a smoother tooth surface, larger TVMS, and smaller amplitude of backlash. The effect of fractal dimension is more sensitive than characteristic scale coefficient. Furthermore, in the low-speed region, the increase in fractal dimension has a positive effect on the dynamic response of the system and can reduce the amplitude of dynamic transmission error. In the high-speed region, the opposite is true. It is worth pointing out that the influence of fractal dimension on gear dynamic characteristics is nonlinear. Considering the machining cost and dynamic response of gear, the fractal dimension of 1.5 is the best choice. The influence of characteristic scale coefficient on system dynamics is similar to that of fractal dimension, but the intensity is much weaker.