In order to investigate the feasibility of serum creatine kinase （CK） and blood urea nitrogen （BUN） in monitoring pre-competition training of badminton athletes, the pre-competition training load of 20 badminton a...In order to investigate the feasibility of serum creatine kinase （CK） and blood urea nitrogen （BUN） in monitoring pre-competition training of badminton athletes, the pre-competition training load of 20 badminton athletes was studied, and serum CK and BUN were determined before, immediate and next morning after training. The results showed that after intensive training for one week, serum CK levels were significantly increased by 57.53 mmol/L （P〈0.05）. After regulation of the training intensity, average serum CK levels were increased by 21.79 mmol/L （P〈0.05）. BUN contents were increased by 0.83 mmol/L on average with the difference being not significant （P〉0.05）. After intermittent training, there was significant difference in the average increased levels of serum CK in athletes （P〈0.05）. There was significant difference before and after regulation of training （P〈0.05）. The increased levels of BUN were 0.78 mmol/L without significant difference （P〉0.05）. It was concluded that serum CK was one of the biochemical indicators monitoring the training load sensitivity of badminton athletes, but BUN was of little value in monitoring the training load. Both serum CK and BUN recovered slowly after one-week intensive training and intermittent training, suggesting the metabolic mechanism of human body in training needs further study.展开更多
How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classif...How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.展开更多
In maritime industry, personnel’s training is considered by shipping companies as a top priority matter on the list of factors affecting competitiveness in operating vessels. This paper presents the importance and th...In maritime industry, personnel’s training is considered by shipping companies as a top priority matter on the list of factors affecting competitiveness in operating vessels. This paper presents the importance and the effects of training Electro-Technical Experts in the context of latest developments, particularly the advent of the “Electric Ship” and the “Communicative Ship” analyzing the feedback received from several relevant two-days seminars for “Ship Electrical and Electronic Systems for Electro-Technical Officers”, in North East European countries. The pre-test and post-test self assessment method that has been used for more efficient interaction between trainers and trainees is analyzed using t-statistics. The attendees have had diverse basic backgrounds, yet company experts Fleet Engineers on merchant or war ships. The training’s effectiveness and gain is discussed in this paper and further proposals for the Electrical and Electronic training are presented through the valuable feedback for improvement.展开更多
Background: The Maternal and Child Survival Program of United States Agency for International Development conducted a study in 2017 to assess the outcome of an initiative to strengthen Expanded Programme on Immunizati...Background: The Maternal and Child Survival Program of United States Agency for International Development conducted a study in 2017 to assess the outcome of an initiative to strengthen Expanded Programme on Immunization (EPI) pre-service training. The pre-service training initiative was undertaken by the Ministry of Health (MOH) with support from partners in 2012-2016. The overall objective of the study was to assess the adoption and effectiveness of the initiative in the competency (knowledge, skills and attitude) of graduate nurses. Methods: The study included a conveniently selected sample of 14 pre-service training institutions, 23 field practicum sites, and 29 health facilities in western Kenya, and used quantitative and qualitative methods of data collection. Results: All pre-service training institutions were found to have adapted the WHO EPI prototype curriculum. Overall, tutors followed training method in the classroom as suggested in the curriculum, except evaluation of students’ learning lacked tests or quizzes. Students had opportunities for hands-on practical experience in the field practicum sites. Graduate nurses were found to have acquired the skills for vaccinating children. However, some pre-service training institutions lacked functional skills labs for practical learning of students. In addition, students did not receive up-to-date information on EPI program, and lacked knowledge and skills on monitoring and documentation of EPI coverage during preservice training. Conclusions: It appears that the EPI pre-service training strengthening initiatives facilitated competency-based EPI training of nurses in Kenya. However, preservice training institutions still have scope for improvement in the skills lab, hand-washing practice, providing up-to-date information, and training students on coverage monitoring and documentation.展开更多
Objective: To explore the effect of systematic pre-job training for isolation ward nurses during the Corona Virus Disease 2019 (COVID-19) pandemic. Methods: Establish a pre-job training program for the isolation ...Objective: To explore the effect of systematic pre-job training for isolation ward nurses during the Corona Virus Disease 2019 (COVID-19) pandemic. Methods: Establish a pre-job training program for the isolation ward for COVID-19, standardize the content of theoretical and skill training, formulate training SOPs, and conduct training for the nurses using online teaching assessment, video teaching, on-site scenario simulation operation drills, as well as real-time protection guidance and supervision. 60 nurses from non-infectious departments temporarily selected by the hospital were trained;the theoretical knowledge scores, quarantine techniques, and nursing quality of nurses before and after the training were compared, and the effect of the intervention was evaluated. Results: The scores of the COVID-19 protection theory test were 81.17 ± 8.46 after the nurses were trained for 3 days. The pass rates of hand hygiene compliance tests and protective clothing putting-on and taking-off practices were 96.67% and 100%, respectively. There was no significant difference between the scores of the COVID-19 protection theory test for the nurses that were trained for 3 days and the scores for the nurses originally at the quarantine zone (81.59 ± 7.59, P > 0.05). The pass rate of hand hygiene compliance and the pass rate of protective clothing putting-on and taking-off practices were significantly improved compared with those before training (81.67% and 56.67% respectively, P < 0.001). The scores of the COVID-19 protection theory test at 30 days of training were 95.67 ± 5.89, which were significantly higher than those at 3 days of training (P < 0.001). The qualified rate of disinfection and quarantine in the first month for the trained nurses entering the isolation ward was 89.47%;compared with that for the nurses originally in the isolation wards (94.7%), there was no significant difference (P > 0.05). The comprehensive nursing ability scores of bedside nurses in the first month of training were 80.14 ± 5.63, which were lower than those of nurses originally in the isolation wards (86.88 ± 4.53, P Conclusion: Systematic pre-job training for nurses in isolation wards can help improve nurses’ knowledge of the COVID-19, self-protection awareness, and protection skills, and can quickly train nurses who are competent for work in isolation wards. It is an important guarantee of “zero infection” for medical staff, and it can quickly and effectively help medical institutions respond to the COVID-19 pandemic in an emergency.展开更多
针对畜禽疫病文本语料匮乏、文本内包含大量疫病名称及短语等未登录词问题,提出了一种结合词典匹配的BERT-BiLSTM-CRF畜禽疫病文本分词模型。以羊疫病为研究对象,构建了常见疫病文本数据集,将其与通用语料PKU结合,利用BERT(Bidirectiona...针对畜禽疫病文本语料匮乏、文本内包含大量疫病名称及短语等未登录词问题,提出了一种结合词典匹配的BERT-BiLSTM-CRF畜禽疫病文本分词模型。以羊疫病为研究对象,构建了常见疫病文本数据集,将其与通用语料PKU结合,利用BERT(Bidirectional encoder representation from transformers)预训练语言模型进行文本向量化表示;通过双向长短时记忆网络(Bidirectional long short-term memory network,BiLSTM)获取上下文语义特征;由条件随机场(Conditional random field,CRF)输出全局最优标签序列。基于此,在CRF层后加入畜禽疫病领域词典进行分词匹配修正,减少在分词过程中出现的疫病名称及短语等造成的歧义切分,进一步提高了分词准确率。实验结果表明,结合词典匹配的BERT-BiLSTM-CRF模型在羊常见疫病文本数据集上的F1值为96.38%,与jieba分词器、BiLSTM-Softmax模型、BiLSTM-CRF模型、未结合词典匹配的本文模型相比,分别提升11.01、10.62、8.3、0.72个百分点,验证了方法的有效性。与单一语料相比,通用语料PKU和羊常见疫病文本数据集结合的混合语料,能够同时对畜禽疫病专业术语及疫病文本中常用词进行准确切分,在通用语料及疫病文本数据集上F1值都达到95%以上,具有较好的模型泛化能力。该方法可用于畜禽疫病文本分词。展开更多
文摘In order to investigate the feasibility of serum creatine kinase （CK） and blood urea nitrogen （BUN） in monitoring pre-competition training of badminton athletes, the pre-competition training load of 20 badminton athletes was studied, and serum CK and BUN were determined before, immediate and next morning after training. The results showed that after intensive training for one week, serum CK levels were significantly increased by 57.53 mmol/L （P〈0.05）. After regulation of the training intensity, average serum CK levels were increased by 21.79 mmol/L （P〈0.05）. BUN contents were increased by 0.83 mmol/L on average with the difference being not significant （P〉0.05）. After intermittent training, there was significant difference in the average increased levels of serum CK in athletes （P〈0.05）. There was significant difference before and after regulation of training （P〈0.05）. The increased levels of BUN were 0.78 mmol/L without significant difference （P〉0.05）. It was concluded that serum CK was one of the biochemical indicators monitoring the training load sensitivity of badminton athletes, but BUN was of little value in monitoring the training load. Both serum CK and BUN recovered slowly after one-week intensive training and intermittent training, suggesting the metabolic mechanism of human body in training needs further study.
基金supported by the National Natural Science Foundation of China(U1435220)
文摘How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.
文摘In maritime industry, personnel’s training is considered by shipping companies as a top priority matter on the list of factors affecting competitiveness in operating vessels. This paper presents the importance and the effects of training Electro-Technical Experts in the context of latest developments, particularly the advent of the “Electric Ship” and the “Communicative Ship” analyzing the feedback received from several relevant two-days seminars for “Ship Electrical and Electronic Systems for Electro-Technical Officers”, in North East European countries. The pre-test and post-test self assessment method that has been used for more efficient interaction between trainers and trainees is analyzed using t-statistics. The attendees have had diverse basic backgrounds, yet company experts Fleet Engineers on merchant or war ships. The training’s effectiveness and gain is discussed in this paper and further proposals for the Electrical and Electronic training are presented through the valuable feedback for improvement.
文摘Background: The Maternal and Child Survival Program of United States Agency for International Development conducted a study in 2017 to assess the outcome of an initiative to strengthen Expanded Programme on Immunization (EPI) pre-service training. The pre-service training initiative was undertaken by the Ministry of Health (MOH) with support from partners in 2012-2016. The overall objective of the study was to assess the adoption and effectiveness of the initiative in the competency (knowledge, skills and attitude) of graduate nurses. Methods: The study included a conveniently selected sample of 14 pre-service training institutions, 23 field practicum sites, and 29 health facilities in western Kenya, and used quantitative and qualitative methods of data collection. Results: All pre-service training institutions were found to have adapted the WHO EPI prototype curriculum. Overall, tutors followed training method in the classroom as suggested in the curriculum, except evaluation of students’ learning lacked tests or quizzes. Students had opportunities for hands-on practical experience in the field practicum sites. Graduate nurses were found to have acquired the skills for vaccinating children. However, some pre-service training institutions lacked functional skills labs for practical learning of students. In addition, students did not receive up-to-date information on EPI program, and lacked knowledge and skills on monitoring and documentation of EPI coverage during preservice training. Conclusions: It appears that the EPI pre-service training strengthening initiatives facilitated competency-based EPI training of nurses in Kenya. However, preservice training institutions still have scope for improvement in the skills lab, hand-washing practice, providing up-to-date information, and training students on coverage monitoring and documentation.
文摘Objective: To explore the effect of systematic pre-job training for isolation ward nurses during the Corona Virus Disease 2019 (COVID-19) pandemic. Methods: Establish a pre-job training program for the isolation ward for COVID-19, standardize the content of theoretical and skill training, formulate training SOPs, and conduct training for the nurses using online teaching assessment, video teaching, on-site scenario simulation operation drills, as well as real-time protection guidance and supervision. 60 nurses from non-infectious departments temporarily selected by the hospital were trained;the theoretical knowledge scores, quarantine techniques, and nursing quality of nurses before and after the training were compared, and the effect of the intervention was evaluated. Results: The scores of the COVID-19 protection theory test were 81.17 ± 8.46 after the nurses were trained for 3 days. The pass rates of hand hygiene compliance tests and protective clothing putting-on and taking-off practices were 96.67% and 100%, respectively. There was no significant difference between the scores of the COVID-19 protection theory test for the nurses that were trained for 3 days and the scores for the nurses originally at the quarantine zone (81.59 ± 7.59, P > 0.05). The pass rate of hand hygiene compliance and the pass rate of protective clothing putting-on and taking-off practices were significantly improved compared with those before training (81.67% and 56.67% respectively, P < 0.001). The scores of the COVID-19 protection theory test at 30 days of training were 95.67 ± 5.89, which were significantly higher than those at 3 days of training (P < 0.001). The qualified rate of disinfection and quarantine in the first month for the trained nurses entering the isolation ward was 89.47%;compared with that for the nurses originally in the isolation wards (94.7%), there was no significant difference (P > 0.05). The comprehensive nursing ability scores of bedside nurses in the first month of training were 80.14 ± 5.63, which were lower than those of nurses originally in the isolation wards (86.88 ± 4.53, P Conclusion: Systematic pre-job training for nurses in isolation wards can help improve nurses’ knowledge of the COVID-19, self-protection awareness, and protection skills, and can quickly train nurses who are competent for work in isolation wards. It is an important guarantee of “zero infection” for medical staff, and it can quickly and effectively help medical institutions respond to the COVID-19 pandemic in an emergency.
文摘针对畜禽疫病文本语料匮乏、文本内包含大量疫病名称及短语等未登录词问题,提出了一种结合词典匹配的BERT-BiLSTM-CRF畜禽疫病文本分词模型。以羊疫病为研究对象,构建了常见疫病文本数据集,将其与通用语料PKU结合,利用BERT(Bidirectional encoder representation from transformers)预训练语言模型进行文本向量化表示;通过双向长短时记忆网络(Bidirectional long short-term memory network,BiLSTM)获取上下文语义特征;由条件随机场(Conditional random field,CRF)输出全局最优标签序列。基于此,在CRF层后加入畜禽疫病领域词典进行分词匹配修正,减少在分词过程中出现的疫病名称及短语等造成的歧义切分,进一步提高了分词准确率。实验结果表明,结合词典匹配的BERT-BiLSTM-CRF模型在羊常见疫病文本数据集上的F1值为96.38%,与jieba分词器、BiLSTM-Softmax模型、BiLSTM-CRF模型、未结合词典匹配的本文模型相比,分别提升11.01、10.62、8.3、0.72个百分点,验证了方法的有效性。与单一语料相比,通用语料PKU和羊常见疫病文本数据集结合的混合语料,能够同时对畜禽疫病专业术语及疫病文本中常用词进行准确切分,在通用语料及疫病文本数据集上F1值都达到95%以上,具有较好的模型泛化能力。该方法可用于畜禽疫病文本分词。
