Analysis of the Preventive Medicine Undergraduate Curriculum in China: The West China School of Public Heath Experience: A Case Study

In China, the preventive medicine undergraduate professional training program is the major approach to training public health practitioners. The history of undergraduate education in public health/preventive medicine in China is reviewed utilizing the West China School of Public Health at Sichuan University as an example for analyzing this undergraduate major and its curriculum. Needed reforms in the Chinese undergraduate preventive medicine programs are presented, including review of the traditional preventive medicine course content, revision of its curriculum structure, the need to increase practical experience and to develop variety in teaching and assessment techniques, and systematic planning for curriculum reform. Current efforts at reform of the preventive medicine undergraduate program at Sichuan University’s West China School of Public Health are presented

Propagation Analysis of 2.4 GHz Wireless Sensor Network Signal in a Plantation

Wireless sensor network is a popular technology on information acquisition and processing, which has been widely used in plantation ecological monitoring domain. The plantation environments, including antenna height-gain, depolarization, terrain, humidity and many factors have great influences on the propagation of 2.4GHz wireless sensor network radio frequency signal. In this paper, a complete research for propagation law of 2.4GHz wireless sensor network signal in plantation environment is presented, with using regression of support vector machines based on experimental data. A single variable prediction model is established on field strength of wireless sensor network signal in plantation environment, thus compares it with the original experience prediction model and measured data. The establishment of aforesaid model provides an important theoretical support for determining the max effective communication range of wireless sensor node and the nodes' rational distribution. It will certainly promote the application of wireless sensor network in plantation ecological monitoring field

A Wildlife Monitoring System Based on Wireless Image Sensor Networks

Survival and development of wildlife sustains the balance and stability of the entire ecosystem. Wildlife monitoring can provide lots of information such as wildlife species, quantity, habits, quality of life and habitat conditions, to help researchers grasp the status and dynamics of wildlife resources, and to provide basis for the effective protection, sustainable use, and scientific management of wildlife resources. Wildlife monitoring is the foundation of wildlife protection and management. Wireless Sensor Networks (WSN) technology has become the most popular technology in the field of information. With advance of the CMOS image sensor technology, wireless sensor networks combined with image sensors, namely Wireless Image Sensor Networks (WISN) technology, has emerged as an alternative in monitoring applications. Monitoring wildlife is one of its most promising applications. In this paper, system architecture of the wildlife monitoring system based on the wireless image sensor networks was presented to overcome the shortcomings of the traditional monitoring methods. Specifically, some key issues including design of wireless image sensor nodes and software process design have been studied and presented. A self-powered rotatable wireless infrared image sensor node based on ARM and an aggregation node designed for large amounts of data were developed. In addition, their corresponding software was designed. The proposed system is able to monitor wildlife accurately, automatically, and remotely in all-weather condition, which lays foundations for applications of wireless image sensor networks in wildlife monitoring

Multiobjective Collaborative Optimization of Argon Bottom Blowing in a Ladle Furnace Using Response Surface Methodology

In order to consider both the refining efficiency of the ladle furnace (LF) and the quality of molten steel, the water model experiment is carried out. In this study, the single factor analysis, central composite design principle, response surface methodology, visual analysis of response surface, and multiobjective optimization are used to obtain the optimal arrangement scheme of argon blowing of LF, design the experimental scheme, establish the prediction models of mixing time (MT) and slag eye area (SEA), analyze the comprehensive effects of different factors on MT and SEA, and obtain the optimal process parameters, respectively. The results show that when the identical porous plug radial position is 0.6R and the separation angle is 135°, the mixing behavior is the best. Moreover, the optimized parameter combination is obtained based on the response surface model to simultaneously meet the requirements of short MT and small SEA in the LF refining process. Meanwhile, compared with the predicted values, the errors of MT and SEA for different conditions from the experimental values are 1.3% and 2.1%, 1.3% and 4.2%, 2.5% and 3.4%, respectively, which is beneficial to realizing the modeling of argon bottom blowing in the LF refining process and reducing the interference of human factors

Effect of vitamin D supplementation on risk of breast cancer: a systematic review and meta-analysis of randomized controlled trials

Supplemental Materia

One step methane production based on catalytic pressurized calcium looping gasification with in-situ CO2 capture and self-sustained heat supply

Catalytic steam hydrogasification of coal is a direct method for methane production. Calcium looping concept is usually used in coal gasification process for in-situ carbon dioxide removal and heat supply. In this paper, a new process combining catalytic steam hydrogasification and calcium looping was proposed and investigated using a self designed instantaneously feeding reactor under high-temperature and pressurized conditions. The effects of operation conditions (including hydrogen concentration with a range of 0-50 vol%, gasification pressure with a range of 0.1-3.5 MPa, gasification temperature with a range of 700-800 degrees C, and gasification-calcination cycle number up to six) on the performance of the new process have been studied. The results show that: (i) increasing H-2 concentration is beneficial to methane products; (ii) high temperature and low pressure are not conducive to methane production and carbon dioxide capture as well as the self-sustained heat supply in gasifier; (iii) the methane content and carbon conversion can be maintained at 30-40 vol% and 75-80% for the durability tests. According to the performance of gas products, 750 degrees C 3.5 MPa and Ca/C = 0.5 are suggested for the new process. In addition, the gasification reactivity can be affected by the Ca-K-Char interaction as indicated by the XRD, FTIR and SEM-EDX analysis

Analysis of the Preventive Medicine Undergraduate Curriculum in China: The West China School of Public Heath Experience: A Case Study

In China, the preventive medicine undergraduate professional training program is the major approach to training public health practitioners. The history of undergraduate education in public health/preventive medicine in China is reviewed utilizing the West China School of Public Health at Sichuan University as an example for analyzing this undergraduate major and its curriculum. Needed reforms in the Chinese undergraduate preventive medicine programs are presented, including review of the traditional preventive medicine course content, revision of its curriculum structure, the need to increase practical experience and to develop variety in teaching and assessment techniques, and systematic planning for curriculum reform. Current efforts at reform of the preventive medicine undergraduate program at Sichuan University’s West China School of Public Health are presented

Short-term daily reference evapotranspiration forecasting using temperature-based deep learning models in different climate zones in China

The reference evapotranspiration (ETo) pertains to the evapotranspiration of cold-season grasses with an approximate height of 0.12 m or full-covered alfalfa with a height of 0.50 m. Accurate short-term ETo forecasts are indispensable for informed irrigation decisions by relevant departments and individuals. Four deep learning (DL) models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (Bi-LSTM), and Bidirectional GRU (Bi-GRU), as well as two calibrated empirical models (Hargreaves-Samani (HS) and reduced-set Penman–Monteith (RPM)), were used to evaluate the performance of the ETo forecast with a lead time of 1–7 d using temperature forecasts in different climates. The results reveal that the DL models and calibrated HS and RPM models exhibited comparable trends in the ETo forecasts for lead times of 1–7 d. Nonetheless, the DL models consistently outperformed the HS and RPM models across the diverse climatic regions in China. The DL models displayed an average root mean square error (RMSE) and mean absolute error (MAE) of less than 0.887 and 0.633 mm/d, respectively. Moreover, the mean correlation coefficient (R) and accuracy (ACC) exceeded 0.807% and 89.701%, respectively. Among the DL models, the LSTM model demonstrated slightly superior performance in short-term daily ETo forecasts in diverse climates. The LSTM model exhibited RMSE and MAE ranges of 0.563–0.875 mm/d and 0.418–0.626 mm/d, respectively, along with R and ACC ranges of 0.81–0.90 and 89.94–98.11%, respectively. Furthermore, even with an increase in lead time, the DL models continued to exhibit strong predictive capabilities, consistently surpassing the performance of the HS and RPM models. Overall, the trained DL models presented an exceptional ability to forecast the short-term daily ETo in various climatic regions of China. These models require only a few input variables and readily available data, making them highly advantageous for practical applications in ETo forecasting. Such models hold promise for significantly enhancing regional agricultural water-resource planning and management

Effect of NaCl concentration on microbiological properties in NaCl assistant anaerobic fermentation : hydrolase activity and microbial community distribution

Previous studies have demonstrated that sludge hydrolysis and short-chain fatty acids (SCFAs) production were improved through NaCl assistant anaerobic fermentation. However, the effect of NaCl concentrations on hydrolase activity and microbial community structure was rarely reported. In this study, it was found that α-glucosidase activity and some carbohydrate-degrading bacteria were inhibited in NaCl tests, owing to their vulnerability to high NaCl concentration. Correspondingly, the microbial community richness and diversity were reduced compared with the control test, while the evenness was not affected by NaCl concentration. By contrast, the protease activity was increased in the presence of NaCl and reached the highest activity at the NaCl concentration of 20 g/L. The protein-degrading and SCFAs-producing bacteria (e.g., Clostridium algidicarnis and Proteiniclasticum) were enriched in the presence of NaCl, which were salt-tolerant.Published versio