Applied Immobilized algae bacteria (ABI) to remove ammonia of freshwater aquaculture wastewater. Temperature (T),PH,light intensity (I),dissolved oxygen (DO) and filling rate five factors plays important role in the p...Applied Immobilized algae bacteria (ABI) to remove ammonia of freshwater aquaculture wastewater. Temperature (T),PH,light intensity (I),dissolved oxygen (DO) and filling rate five factors plays important role in the process of ammonia nitrogen removal ,related data between ammonia removal and five factors was received through multi-factor orthogonal test,and established relations model between the five factor and nitrogen removal. The results show that five-factors had significant effect on AR,and the best combinations for removing AR was temperature 30 ℃,pH=7.0,light intensity 6 000 lux,dissolved oxygen 5.0 mg/L and the fill rate 10%. According to the experimental data,equation model was proposed and coefficient of determination R2 =0.864 8,P<0.05. Samples T-test was done between the model predictions and the actual measured values.Test results showed that the significant difference of overall mean value sig. (2-tailed) was 0.978 (P>0.05),it Shows that had no significant difference between model predictions and the actual measured value,and model had a high degree of fitting.展开更多
Objective To explore a corresponding model that would bridge the gap between the practice of clinical teaching of nursing students in clinical settings in China and the clinical instructors' standard teaching of n...Objective To explore a corresponding model that would bridge the gap between the practice of clinical teaching of nursing students in clinical settings in China and the clinical instructors' standard teaching of nursing students in clinical settings in the Philippines. Methods A phenomenological-qualitative,non-experimental type of research was utilized to recruit 27 staff nurses/clinical teachers in China. Data was collected by using audio-tapes and taking notes in the interviews. Results A content analysis was conducted and the sorted into four themes:(1) The things that they were able to teach their students;(2) The things that they were not able to teach their students;(3) The difficulties encountered in doing Clinical Teaching for their students;(4) Suggestions to improve Clinical Teaching. Conclusion According to the findings,the "CP's Proposed Nursing Clinical Teaching Model"should be introduced into the clinical set-up for staff nurses/clinical teachers handling nursing students in China.展开更多
Historic Background Chinese medicine （CM） has over 2,000 years of history in China and was the only health care system providing care for Chinese prior to the introduction of Western medicine （WM） into China. WM ...Historic Background Chinese medicine （CM） has over 2,000 years of history in China and was the only health care system providing care for Chinese prior to the introduction of Western medicine （WM） into China. WM was slowly introduced into China by missionaries from the middle 17th century to the beginning of the 19th century. By 1840, WM began to take root in China. Western hospitals, clinics, medical schools and nursing schools spread rapidly throughout the country. At that time, with the might and power portrayed by Western countries,展开更多
Background:The global impact of the highly contagious COVID-19 virus has created unprecedented challenges,significantly impacting public health and economies worldwide.This research article conducts a time series anal...Background:The global impact of the highly contagious COVID-19 virus has created unprecedented challenges,significantly impacting public health and economies worldwide.This research article conducts a time series analysis of COVID-19 data across various countries,including India,Brazil,Russia,and the United States,with a particular emphasis on total confirmed cases.Methods:The proposed approach combines auto-regressive integrated moving average(ARIMA)'s ability to capture linear trends and seasonality with long short-term memory(LSTM)networks,which are designed to learn complex nonlinear dependencies in the data.This hybrid approach surpasses both individual models and existing ARIMA-artificial neural network(ANN)hybrids,which often struggle with highly nonlinear time series like COVID-19 data.By integrating ARIMA and LSTM,the model aims to achieve superior forecasting accuracy compared to baseline models,including ARIMA,Gated Recurrent Unit(GRU),LSTM,and Prophet.Results:The hybrid ARIMA-LSTM model outperformed the benchmark models,achieving a mean absolute percentage error(MAPE)score of 2.4%.Among the benchmark models,GRU performed the best with a MAPE score of 2.9%,followed by LSTM with a score of 3.6%.Conclusions:The proposed ARIMA-LSTM hybrid model outperforms ARIMA,GRU,LSTM,Prophet,and the ARIMA-ANN hybrid model when evaluating using metrics like MAPE,symmetric mean absolute percentage error,and median absolute percentage error across all countries analyzed.These findings have the potential to significantly improve preparedness and response efforts by public health authorities,allowing for more efficient resource allocation and targeted interventions.展开更多
基金Supported by the National Natural Science Foundation of China(No.30972260)~~
文摘Applied Immobilized algae bacteria (ABI) to remove ammonia of freshwater aquaculture wastewater. Temperature (T),PH,light intensity (I),dissolved oxygen (DO) and filling rate five factors plays important role in the process of ammonia nitrogen removal ,related data between ammonia removal and five factors was received through multi-factor orthogonal test,and established relations model between the five factor and nitrogen removal. The results show that five-factors had significant effect on AR,and the best combinations for removing AR was temperature 30 ℃,pH=7.0,light intensity 6 000 lux,dissolved oxygen 5.0 mg/L and the fill rate 10%. According to the experimental data,equation model was proposed and coefficient of determination R2 =0.864 8,P<0.05. Samples T-test was done between the model predictions and the actual measured values.Test results showed that the significant difference of overall mean value sig. (2-tailed) was 0.978 (P>0.05),it Shows that had no significant difference between model predictions and the actual measured value,and model had a high degree of fitting.
文摘Objective To explore a corresponding model that would bridge the gap between the practice of clinical teaching of nursing students in clinical settings in China and the clinical instructors' standard teaching of nursing students in clinical settings in the Philippines. Methods A phenomenological-qualitative,non-experimental type of research was utilized to recruit 27 staff nurses/clinical teachers in China. Data was collected by using audio-tapes and taking notes in the interviews. Results A content analysis was conducted and the sorted into four themes:(1) The things that they were able to teach their students;(2) The things that they were not able to teach their students;(3) The difficulties encountered in doing Clinical Teaching for their students;(4) Suggestions to improve Clinical Teaching. Conclusion According to the findings,the "CP's Proposed Nursing Clinical Teaching Model"should be introduced into the clinical set-up for staff nurses/clinical teachers handling nursing students in China.
文摘Historic Background Chinese medicine （CM） has over 2,000 years of history in China and was the only health care system providing care for Chinese prior to the introduction of Western medicine （WM） into China. WM was slowly introduced into China by missionaries from the middle 17th century to the beginning of the 19th century. By 1840, WM began to take root in China. Western hospitals, clinics, medical schools and nursing schools spread rapidly throughout the country. At that time, with the might and power portrayed by Western countries,
文摘Background:The global impact of the highly contagious COVID-19 virus has created unprecedented challenges,significantly impacting public health and economies worldwide.This research article conducts a time series analysis of COVID-19 data across various countries,including India,Brazil,Russia,and the United States,with a particular emphasis on total confirmed cases.Methods:The proposed approach combines auto-regressive integrated moving average(ARIMA)'s ability to capture linear trends and seasonality with long short-term memory(LSTM)networks,which are designed to learn complex nonlinear dependencies in the data.This hybrid approach surpasses both individual models and existing ARIMA-artificial neural network(ANN)hybrids,which often struggle with highly nonlinear time series like COVID-19 data.By integrating ARIMA and LSTM,the model aims to achieve superior forecasting accuracy compared to baseline models,including ARIMA,Gated Recurrent Unit(GRU),LSTM,and Prophet.Results:The hybrid ARIMA-LSTM model outperformed the benchmark models,achieving a mean absolute percentage error(MAPE)score of 2.4%.Among the benchmark models,GRU performed the best with a MAPE score of 2.9%,followed by LSTM with a score of 3.6%.Conclusions:The proposed ARIMA-LSTM hybrid model outperforms ARIMA,GRU,LSTM,Prophet,and the ARIMA-ANN hybrid model when evaluating using metrics like MAPE,symmetric mean absolute percentage error,and median absolute percentage error across all countries analyzed.These findings have the potential to significantly improve preparedness and response efforts by public health authorities,allowing for more efficient resource allocation and targeted interventions.
