Dicksonsharma8656

Data-driven intelligent diagnosis model plays a key role in the monitoring and maintenance of mechanical equipment. However, due to practical limitations, the fault data is difficult to obtain, which makes model training unsatisfactory and results in poor testing performance. Based on the characteristics of 1-D mechanical vibration signal, this paper proposes Supervised Data Augmentation (SDA) as a regularization method to provide more effective training samples, which includes Cut-Flip and Mix-Normal. Cut-Flip is used directly on the raw sample without parameter selection. Mix-Normal mixes the data and labels of a random sample with a random normal sample at a certain ratio. The proposed SDA is verified on two bearing datasets with some popular intelligent diagnosis networks. Besides, we also design a Batch Normalization CNN (BNCNN) to learn the small dataset. Results show that SDA can significantly improve the classification accuracy of BNCNN by 10%-30% under 1-8 samples of each class. The proposed method also shows a competitive performance with existing advanced methods. Finally, we further discuss each data augmentation method through a series of ablation experiments and summarize the advantages and disadvantages of the proposed SDA.Automatic train stop control (ATSC) is a key function of the automatic train operation (ATO) system. An accurate braking process model can help to improve the control strategy. In this paper, the braking process for stop control of high-speed trains is formulated as a single-point time delay model, based on the principle of practical braking processes. Furthermore, a Picard iteration based identification method is first applied to the time delay system, and a train braking process identification method is proposed. The method is straightforward, and the parameters can be identified based on the principle of ordinary differential equations. The effectiveness of the braking process model and the identification method is illustrated by real-life experimental data.The government and the market are the two main means for resource allocation, and both play important roles in economic development and environmental protection. Based on the theoretical mechanism analysis, this study empirically investigated the relationship between government intervention, market development, and China's provincial pollution emission efficiency by using the static panel OLS, system generalized method of moments (SYS-GMM), and panel threshold effect model during the period 2000-2017. The results show that the impact of government intervention on China's provincial pollution emission efficiency shows a non-linear U-shaped curve relationship, and there is a positive correlation between market development and provincial pollution emission efficiency in China. Government intervention and market development are complementary, rather than a substitute for each other, in promoting China's provincial pollution emission efficiency. Epacadostat When government intervention is set as the threshold variable, the impact of government intervention on China's provincial pollution emission efficiency shows the feature of "promotes first, then inhibits." However, when market development is set as the threshold variable, government intervention is only conducive to the improvement of China's provincial pollution emission efficiency at a moderate marketization level. Lastly, some policy implications related to the government and the market in enhancing China's provincial pollution emission efficiency are presented.

Ambient air pollution is recognized as one of the leading causes of global burden of disease. Involvement of air pollution in respiratory and cardiovascular diseases was first recognized, and then cumulative data has indicated that the intestinal tract could be also damaged.

To review and discuss the current epidemiological and animal data on the effects of air pollution on intestinal homeostasis.

An extensive literature search was conducted using Google Scholar and Pubmed to gather relevant human and animal studies that have reported the effects of any air pollutant on the intestine.

Exposure to several gaseous and particulate matter components of air pollution have been associated either positively or negatively with the onset of various intestinal diseases including appendicitis, gastroenteric disorders, irritable bowel syndrome, inflammatory bowel diseases, and peptic ulcers. Several atmospheric pollutants have been associated with modifications of gut microbiota in humans. Animal studies have showed that inhalation of atmospheric particulate matter can lead to modifications of gut microbiota, impairments of oxidative and inflammatory intestinal balances, and disruption of gut epithelial permeability.

Overall, the literature appears to indicate that the gut is an underestimated target of adverse health effects induced by air pollution. It is therefore important to develop additional studies that aim to better understand the link between air pollutants and gastro-intestinal diseases.

Overall, the literature appears to indicate that the gut is an underestimated target of adverse health effects induced by air pollution. It is therefore important to develop additional studies that aim to better understand the link between air pollutants and gastro-intestinal diseases.

To investigate potential effects of short- and medium-term exposure to low levels of ozone (O3) on glucose-homeostasis in non-diabetic older adults.

166 non-diabetic, older participants in Beijing were deemed eligible to partake in this longitudinal population-based study. Observations were recorded on three separate occasions from November 2016 up until January 2018. Concentrations of outdoor O3 were monitored throughout the study period. Biomarkers indicative of glucose-homeostasis, including fasting blood glucose, insulin, HbAlc, glycated albumin percentage (glycated albumin/albumin), HOMA-IR and HOMA-B were measured at 3 sessions. A linear mixed effects model with random effects was adopted to quantify the effect of O3 across a comprehensive set of glucose-homeostasis markers.

Short-term O3 exposure positively associated with increased fasting blood glucose, insulin, HOMA-IR and HOMA-B. The effect on glucose occurred at 3-, 5-, 6- and 7-days, although the largest effect manifested on 6-days (5.6%, 95% CI 1.