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At present, there is still a lack of standardized and institutionalized management of laboratory animal carcasses and related wastes in China, and effective supervision of their harmless disposal process, especially an effective whole-process biosafety supervision system. Using computer networks, global satellite positioning systems, wireless communications, geographic information systems and other multidisciplinary cutting-edge technologies, we preliminarily established an information management system for harmless disposal of laboratory animal carcasses and related wastes, which could be closely combined with biosafety supervision to realize dynamic tracking and monitoring in the whole process. The established system can strengthen the standardization and institutionalization management of experimental animal carcasses and related wastes, promote the safety control of medical waste, prevent the spread of diseases, protect the environment and ensure human health.

According to the World Health Organization (WHO), pneumonia kills about 2 million children under the age of 5 every year. Traditional machine learning methods can be used to diagnose chest X-rays of pneumonia in children, but there is a privacy and security issue in centralizing the data for training. Federated learning prevents data privacy leakage by sharing only the model and not the data, and it has a wide range of application in the medical field. We use federated learning method for classification, which effectively protects data security. And for the data heterogeneity phenomenon existing in the actual scenario, which will seriously affect the classification effect, we propose a method based on two-end control variables. Specifically, based on the classical federated learning FedAvg algorithm, we modify the loss function on the client side by adding a regular term or a penalty term, and add momentum after the average aggregation on the server side. The federated learning approach prevents the data privacy leakage problem compared to the traditional machine learning approach. In order to solve the problem of low classification accuracy due to data heterogeneity, our proposed method based on two-end control variables achieves an average improvement of 2% and an accuracy of 98% on average, and 99% individually, compared to the previous federated learning algorithms and the latest diffusion model-based method. The classification results and methodology of this study can be utilized by clinicians worldwide to improve the overall detection of pediatric pneumonia. [ABSTRACT FROM AUTHOR]

Valley shrink deformation is a common physical and mechanical phenomenon during the reservoir operation of high arch dams. This phenomenon is particularly significant in the initial stages of impoundment and can severely impact the operational safety of the arch dam. This study focuses on the arch dam under construction in the upstream of the Lancang River. By considering the secondary structural planes F115 and F111 at the dam site, a numerical model of valley shrink deformation was established. The study investigates the characteristics of valley shrink deformation during the initial impoundment phase of the arch dam. The results show that during the impoundment process, the valley slopes on both sides of the reservoir shrink towards the center of the valley. The deformation on the right bank is greater than that on the left bank due to the influence of the F115 fault. When the elevation is lower than the exposure position of F115, the deformation on the right bank increases with elevation; when it is higher than this position, the deformation decreases. On the left bank, due to the influence of F111 and the seepage field, the deformation value increases first and then decreases with the increase in elevation. After the water level stabilizes, the deformation rate decreases and approaches zero. This research results provide a reference for addressing valley shrink deformation issues during the initial impoundment of the arch dam. [ABSTRACT FROM AUTHOR]

In the process of promoting the commercialization of proton exchange membrane fuel cells, the long-term durability of the fuel cell has become a key consideration. While existing durability tests are critical for assessing cell performance, they are often time-consuming and do not quickly reflect the impact of actual operating conditions on the cell. In this study, improved testing protocols were utilized to solve this problem, which is designed to shorten the testing cycle and evaluate the degradation of the cell performance under real operating conditions more efficiently. Accelerated durability analysis for evaluating the MEA lifetime and performance decay process was carried out through two testing protocols—open circuit voltage (OCV)-based accelerated durability testing (ADT) and relative humidity (RH) cycling-based ADT. OCV-based ADT revealed that degradation owes to a combined mechanical and chemical process. RH cycling-based ADT shows that degradation comes from a mainly mechanical process. In situ fluoride release rate technology was employed to elucidate the degradation of the proton exchange membrane during the ADT. It was found that the proton exchange membrane suffered more serious damage under OCV-based ADT. The loss of F− after the durability test was up to 3.50 × 10−4 mol/L, which was 4.3 times that of the RH cycling-based ADT. In addition, the RH cycling-based ADT had a significant effect on the catalyst layer, and the electrochemically active surface area decreased by 48.6% at the end of the ADT. Moreover, it was observed that the agglomeration of the catalysts was more obvious than that of OCV-based ADT by transmission electron microscopy. It is worth noting that both testing protocols have no obvious influence on the gas diffusion layer, and the contact angle of gas diffusion layers does not change significantly. These findings contribute to understanding the degradation behavior of proton exchange membrane fuel cells under different working conditions, and also provide a scientific basis for developing more effective testing protocols. [ABSTRACT FROM AUTHOR]