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Abstract The role of dairy products in cardiovascular disease (CVD) prevention remains controversial. This study investigates the association between dairy consumption and CVD incidence using data from the China Kadoorie Biobank and the UK Biobank, complemented by an updated meta-analysis. Among Chinese participants, regular dairy consumption (primarily whole milk) is associated with a 9% increased risk of coronary heart disease (CHD) and a 6% reduced risk of stroke compared to non-consumers. Among British participants, total dairy consumption is linked to lower risks of CVD, CHD, and ischemic stroke, with cheese and semi-skimmed/skimmed milk contributing to reduced CVD risk. Meta-analysis reveals that total dairy consumption is associated with a 3.7% reduced risk of CVD and a 6% reduced risk of stroke. Notably, inverse associations with CVD incidence are observed for cheese and low-fat dairy products. Current evidence suggests that dairy consumption, particularly cheese, may have protective effects against CVD and stroke.

Abstract Background The role of plant-based dietary patterns in preventing cardiovascular disease (CVD) among individuals with prediabetes and diabetes remains unclear. We aimed to evaluate the associations of plant-based diet index (PDI), healthful PDI (hPDI), and unhealthful PDI (uPDI) with cardiovascular disease (CVD) risk and explore potential contributing factors among people with prediabetes and diabetes. Methods A total of 17,926 participants with prediabetes and 7798 with diabetes were enrolled from the UK Biobank between 2006 and 2010 and followed until the end of 2020. We calculated the PDI, hPDI, and uPDI based on 18 major food groups including plant-based foods and animal-based foods and applied Cox proportional hazard models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD risk related to PDI, hPDI, and uPDI. Decomposition analysis was performed to assess the role of dietary components, and mediation analysis was performed to assess the potential mediating role of serum biomarkers underlying these associations. Results A total of 2324 CVD events were documented among individuals with prediabetes, while 1461 events occurred among patients with diabetes. An inverse association was found between hPDI and CVD risk among individuals with prediabetes (HR T3 vs. T1 = 0.88, 95% CI = 0.79–0.98, P trend = 0.025) but not those with diabetes. A positive association was found between uPDI and CVD risk among individuals with prediabetes (HR T3 vs. T1 = 1.17, 95% CI = 1.05–1.30, P trend = 0.005) and those with diabetes (HR T3 vs. T1 = 1.14, 95% CI = 1.00–1.29, P trend = 0.043). High-sugar-sweetened beverages (SSB) intake accounted for 35% of the hPDI-CVD association and 15% of the uPDI-CVD association among individuals with prediabetes, whereas low intake of whole grain accounted for 36% of the association among patients with diabetes. Elevated cystatin C levels explained the largest proportion of the association between uPDI and CVD risk among individuals with prediabetes (15%, 95% CI = 7–30%) and diabetes (44%, 95% CI = 9–86%). Conclusions Adherence to an unhealthy plant-based diet is associated with a higher CVD risk in people with prediabetes or diabetes, which may be partially attributed to low consumption of whole grains, high intake of SSB, and high blood cystatin C levels.

Background and purpose: With the advancement of Industry 4.0 and the vigorous development of the Internet, search engines have become one of the important platforms for the public to obtain information. As the world's largest Chinese search engine, Baidu is widely popular with the Chinese public. Similar to Google Trends, the Baidu Index is one of the most important databases on the Internet and even in the entire data age. This study adopted the Baidu Index to investigate the public's attention to "Lifelong Education" and analyze its changing trend and geographical distribution, to grasp the public's attention to it in a more comprehensive way. Materials and methods: The Baidu Index was used to obtain the attention of the keyword “Lifelong Education" from July to November 2023. The characteristics of attention are analyzed from three aspects: trend research, demand graph, and user portrait. Finally, it focuses on the comparison between the keyword "Lifelong Education" and the related word "Lifelong Education Platform". Results: The term "Lifelong Education" showed two fluctuations during the period, both of which showed an initial rise and then a decline. For the demand graph, the public pays the most attention to "Lifelong Education Platform", "Lifelong Learning" and "Compulsory Education". The group that pays more attention to "Lifelong Education" is mainly distributed in East China, mostly female, 20~29 years old. Conclusion: Similar to Google Trends, the Baidu Index is a useful tool to study the trend of keyword searches, insight into the change in netizens' demand and monitoring the trend of media public opinion. The results show that the main reasons for the two obvious changes in search volume are that the National Open University of China opens freely lifelong education platform and provides free and comprehensive learning resources, and "Lifelong Education" appears as a translation in the National College English Test Band 4 (CET-4) in June 2023.

[Objective] With the ongoing urbanization in China, the frequency of heavy-duty vehicles traversing buried pipelines is steadily increasing. Therefore, it is of paramount importance to investigate the mechanical response patterns of buried pipelines subjected to heavyduty vehicle rolling in order to ensure the safety of oil and gas pipelines. [Methods] Field tests were conducted to investigate the rolling of heavy-duty vehicles on a buried X70 pipeline. Strain gauge technology was employed to gather pipeline response data and summarize the response patterns of soil pressure and axial stress. Additionally, an ABAQUS software-based finite element simulation model was established to simulate the soil-pipeline interaction using a nonlinear contact model. The vehicle load was simplified as the dead load of a moving plane source, and a DLOAD subprogram was developed to implement the loading. Subsequently, simulations were conducted based on the test conditions. [Results] The results demonstrated a negligible deviation between the simulated and tested values of the additional axial stress in the pipeline. Furthermore, the simulated distribution patterns of soil pressure and axial stress aligned with those observed in the tests, thus the response patterns of soil pressure and axial stress were verified. Based on the verified finite element model, the impact of six parameters, i.e.,vehicle load, vehicle speed, pipe diameter, pipe wall thickness, fill height, and pipeline internal pressure, on the axial stress of the pipeline was explored through simulation by employing a single-variable approach. [Conclusion] The topsoil pressure and axial stress at the section beneath the crawler belts on both sides are higher than those at the central section. Conversely, the bottom axial stress is lower than that at the central section. Augmenting the pipe diameter, pipe wall thickness, or fill height can diminish the additional axial stress induced by vehicle rolling on the pipeline. However, altering the vehicle speed and pipeline internal pressure does not yield significant effects on the axial stress.The obtained mechanical response patterns of the buried pipeline can be used as a reference for evaluating the safety margin of pipelines under heavy-duty vehicle rolling.

Abstract Addressing the limitations of existing landslide displacement prediction models in capturing the dynamic characteristics of data changes, this study introduces a novel dynamic displacement prediction model for landslides. The proposed method combines Variational Mode Decomposition (VMD) with Sparrow Search Optimization (SSO) and Long Short-Term Memory (LSTM) techniques to formulate a comprehensive VMD–SSO–LSTM model. Through the application of VMD, the method dissects cumulative displacement and rainfall data, thereby extracting distinct components such as trend, periodicity, and fluctuation components for displacement, as well as low-frequency and high-frequency components for rainfall. Furthermore, leveraging Gray Correlational Analysis, the interrelationships between the periodic component of displacement and the low-frequency component of rainfall, as well as the fluctuation component of displacement and the high-frequency component of rainfall, are established. Building upon this foundation, the SSO–LSTM model dynamically predicts the interrelated displacement components, synthesizing the predicted values of each component to generate real-time dynamic forecasts. Simulation results underscore the effectiveness of the proposed VMD–SSO–LSTM model, indicating root-mean-square error (RMSE) and mean absolute percentage error (MAPE) values of 1.2329 mm and 0.1624%, respectively, along with a goodness of fit (R2) of 0.9969. In comparison to both back propagation (BP) prediction model and LSTM prediction model, the VMD–SSO–LSTM model exhibits heightened predictive accuracy.