The aim of this present study is to examine the efficacy of attribution retraining group therapy （ARGT） and to compare the responses of outpatients with major depression disorder （MDD）, generalized anxiety disord...The aim of this present study is to examine the efficacy of attribution retraining group therapy （ARGT） and to compare the responses of outpatients with major depression disorder （MDD）, generalized anxiety disorder （GAD） and obsessive-compulsive disorder （OCD）. We carried out a prospective uncontrolled intervention study with a 8-weeks of ARGT on sixty three outpatients with MDD, GAD or OCD. Hamilton rating scale for depression, Hamilton rating scale for anxiety, Yale-Brown obsessive-compulsive scale, attribution style questionnaire, self-esteem scale, index of well-being, and social disability screening schedule were administered before and after treatment. Significant improvement in symptoms and psychological and social functions from pre- to posttreatment occurred for all participants. The changes favored MDD patients. Our study suggested that ARGT may improve the symptoms and psychological-social functions of MDD, GAD, and OCD patients. MDD patients showed the best response.展开更多
Because of computational complexity,the deep neural network(DNN)in embedded devices is usually trained on high-performance computers or graphic processing units(GPUs),and only the inference phase is implemented in emb...Because of computational complexity,the deep neural network(DNN)in embedded devices is usually trained on high-performance computers or graphic processing units(GPUs),and only the inference phase is implemented in embedded devices.Data processed by embedded devices,such as smartphones and wearables,are usually personalized,so the DNN model trained on public data sets may have poor accuracy when inferring the personalized data.As a result,retraining DNN with personalized data collected locally in embedded devices is necessary.Nevertheless,retraining needs labeled data sets,while the data collected locally are unlabeled,then how to retrain DNN with unlabeled data is a problem to be solved.This paper proves the necessity of retraining DNN model with personalized data collected in embedded devices after trained with public data sets.It also proposes a label generation method by which a fake label is generated for each unlabeled training case according to users’feedback,thus retraining can be performed with unlabeled data collected in embedded devices.The experimental results show that our fake label generation method has both good training effects and wide applicability.The advanced neural networks can be trained with unlabeled data from embedded devices and the individualized accuracy of the DNN model can be gradually improved along with personal using.展开更多
Background/purpose:To evaluate the effects of attribution retraining on the perceived career barriers of undergraduate nursing students and to foster positive attributional styles.Methods:Ninety-four undergraduate nur...Background/purpose:To evaluate the effects of attribution retraining on the perceived career barriers of undergraduate nursing students and to foster positive attributional styles.Methods:Ninety-four undergraduate nursing students were recruited and randomly divided into two groups:the attribution retraining group and the control group.All students were assessed by the perceived career barriers inventory before and after the eight-week study.Results:Attribution retraining significantly influenced the students'perceived career barriers.The mean scores of vocational knowledge,professional knowledge,and social ability of the experimental group were significantly reduced compared to the control group(p<0.05).Conclusion:Attribution retraining provides opportunities for improving the undergraduate nursing students'vocational knowledge,professional knowledge,and social ability.Attribution retraining should be encouraged in undergraduate nursing programs in order to reduce the nursing shortage in China's Mainland.展开更多
Teacher-retraining course design is considered to be a challenge not only to the course participants but to the course designers as well, especially, when the participants enrolled turn out to have dramatically differ...Teacher-retraining course design is considered to be a challenge not only to the course participants but to the course designers as well, especially, when the participants enrolled turn out to have dramatically different professional background and conditions. This article supports the idea that changes to the course design should be made straightaway in response to the trainees＇ specific needs. The context for rural school teacher retraining at Novosibirsk State Technical University in Russia illustrates reasons for making immediate changes necessary as the course progressed, and reaction to them. The article discusses a model for a teacher retraining course in which EFL improvement is the core element.展开更多
Attribution Retraining is a hot research topic in the field of psychology and pedagogy,and has been paid more and more attention in the field of nursing education in recent years.This study comprehensively retrieved a...Attribution Retraining is a hot research topic in the field of psychology and pedagogy,and has been paid more and more attention in the field of nursing education in recent years.This study comprehensively retrieved attribution retraining related literature from Chinese and English databases and used literature analysis method to summarize the theoretical basis,assessment tools and application of attribution retraining in nursing education in China.The aim of this review is to promote the wider application of attribution retraining in the field of nursing education and provide reference for cultivating more excellent nursing talents.展开更多
Recently, there has been a growing interest in gait retraining to alter the gait parameters of different populations.In these gait retraining, peak plantar pressure (PPP) was considered as an important parameter of th...Recently, there has been a growing interest in gait retraining to alter the gait parameters of different populations.In these gait retraining, peak plantar pressure (PPP) was considered as an important parameter of the footbiomechanics. It has been found that high PPP correlates to the common foot deformities including pes planus/cavus. However, previous studies utilized excessive electronics in gait retraining, which is challenging toimplement daily especially when device cleaning, flexibility and portability are considered. Therefore, this studyinvestigated feasibility of a novel unpowered gait retraining for reducing high PPP. Twelve potential participantsidentified for investigation through a baseline PPP evaluation with Novel Pedar-x system. Participants received asingle session for the gait retraining with pebbles in the form of rigid spherical inserts (RSI) placed in locations ofhigh PPP inside the deformable insole. This provides tactile cues alerting the participants to alter their gait toreduce excess PPP. The PPP values were tracked in weekly follow-up sessions for 6 weeks. The results demonstrated that participants responded to RSI altering their gait to reduce PPP and maximum force by 14% and 10.5%after six weeks respectively. This study is valuable for physicians in reducing PPP when non-electronics arerequired.展开更多
If oil sands are to be eliminated from the energy market to protect the global environment,human health and longterm economic welfare,a significant number of workers will be displaced in the transition to renewable en...If oil sands are to be eliminated from the energy market to protect the global environment,human health and longterm economic welfare,a significant number of workers will be displaced in the transition to renewable energy technologies.This study outlines a cost-effective and convenient path for oil and gas workers in Alberta to be retrained in the burgeoning solar photovoltaic(PV)industry.Many oil and gas workers would be able to transfer fields with no additional training required.This study examines retraining options for the remainder of workers using the most closely matching skill equivalent PV job to minimize retraining time.The costs for retraining all oil sands workers are quantified and aggregated.The results show the total costs for retaining all oil sands workers in Alberta for the PV industry ranges between CAD$91.5 m and CAD$276.2 m.Thus,only 2-6%of federal,provincial,and territorial oil and gas subsidies for a single year would need to be reallocated to provide oil and gas workers with a new career of approximately equivalent pay.The results of this study clearly show that a rapid transition to sustainable energy production is feasible as costs of retraining oil and gas workers are far from prohibitive.展开更多
Development in medical intervention has significantly decreased the mortality rates for children with complex congenital heart disease(CHD)but among these survivors with complex heart disease there occurs a uniq...Development in medical intervention has significantly decreased the mortality rates for children with complex congenital heart disease(CHD)but among these survivors with complex heart disease there occurs a unique pattern of neuro-developmental and neuropsychology impairment characterized social interaction impairment,impulsive Behavior,and impaired executive functions.Presence of behavioral problem is found significantly high in pediatric population with chronic illness than children with absence of chronic illness.The sample of 200 children with congenital heart defect was selected between age 4-8 years using multistage stratified sampling.The childhood psychopathology measurement schedule(CPMS)by Dr.Savitha Malhotra was used for assessing Behavioral problems present in children with CHD.Pre-Post experimental design was used to investigate the study and the results were statistically analyzed using paired T test.The result revealed that the effectiveness of intervention program to retrain Behavior showed high significance.With increased survival rates,the aim of the intervention and research based on clinical practices gets a shift from short term medical assessment to long term assessment and intervention of morbidity.展开更多
The rapid development of technology has led to an ever-increasing demand for electrical energy.In the context of Timor-Leste,which still relies on fossil energy sources with high operational costs and significant envi...The rapid development of technology has led to an ever-increasing demand for electrical energy.In the context of Timor-Leste,which still relies on fossil energy sources with high operational costs and significant environmental impacts,electricity load forecasting is a strategic measure to support the energy transition towards the Net Zero Emission(NZE)target by 2050.This study aims to utilize historical electricity load data for the period 2013–2024,as well as data on external factors affecting electricity consumption,to forecast electricity load in Timor-Leste in the next 10 years(2025–2035).The forecasting results are expected to support efforts in energy distribution efficiency,reduce operational costs,and inform decisions related to the sustainable energy transition.The method used in this study consists of two main approaches:the causality method,represented by the econometric Principal Component Analysis(PCA)model,which involves external factors in the data processing process,and the time series method,utilizing the LSTM,XGBoost,and hybrid(LSTM+XGBoost)models.In the time series method,data processing is combined with two approaches:the sliding window and the rolling recursive forecast.The performance of each model is evaluated using the Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE).The model with the lowest MAPE(<10%)is considered the best-performing model,indicating the highest accuracy.Additionally,a Monte Carlo simulation with 50,000 iterations was used to process the data and measure the prediction uncertainty,as well as test the calibration of the electricity load projection data.The results showed that the hybrid model(LSTM+XGBoost)with a rolling forecast recursive approach is the best-performing model in predicting electricity load in Timor-Leste.This model yields an RMSE of 75.76 MW,an MAE of 55.76 MW,and an MAPE of 5.27%,indicating a high level of accuracy.In addition,the model is also indicated as one that fits the characteristics of electricity load in Timor-Leste,as it produces the lowest percentage of forecasting error in predicting electricity load.The integration of the best model with Monte Carlo Simulation,which yields a p-value of 0.565,suggests that the results of electricity load projections for the period 2025–2035 are well-calibrated,reliable,accurate,and unbiased.展开更多
Conventional automated machine learning(AutoML)technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments,leading to accuracy reduction in forecasting short-t...Conventional automated machine learning(AutoML)technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments,leading to accuracy reduction in forecasting short-term building energy loads.Moreover,their predictions are not transparent because of their black box nature.Hence,the building field currently lacks an AutoML framework capable of data quality enhancement,environment self-adaptation,and model interpretation.To address this research gap,an improved AutoML-based end-to-end data-driven modeling framework is proposed.Bayesian optimization is applied by this framework to find an optimal data preprocessing process for quality improvement of raw data.It bridges the gap where conventional AutoML technologies cannot automatically handle missing data and outliers.A sliding window-based model retraining strategy is utilized to achieve environment self-adaptation,contributing to the accuracy enhancement of AutoML technologies.Moreover,a local interpretable model-agnostic explanations-based approach is developed to interpret predictions made by the improved framework.It overcomes the poor interpretability of conventional AutoML technologies.The performance of the improved framework in forecasting one-hour ahead cooling loads is evaluated using two-year operational data from a real building.It is discovered that the accuracy of the improved framework increases by 4.24%–8.79%compared with four conventional frameworks for buildings with not only high-quality but also low-quality operational data.Furthermore,it is demonstrated that the developed model interpretation approach can effectively explain the predictions of the improved framework.The improved framework offers a novel perspective on creating accurate and reliable AutoML frameworks tailored to building energy load prediction tasks and other similar tasks.展开更多
基金supported by national science and technology support projects (No.2009BA177B07)Natural Science Foundation of Jiangsu Province,China (No.PBBS1-102350)
文摘The aim of this present study is to examine the efficacy of attribution retraining group therapy （ARGT） and to compare the responses of outpatients with major depression disorder （MDD）, generalized anxiety disorder （GAD） and obsessive-compulsive disorder （OCD）. We carried out a prospective uncontrolled intervention study with a 8-weeks of ARGT on sixty three outpatients with MDD, GAD or OCD. Hamilton rating scale for depression, Hamilton rating scale for anxiety, Yale-Brown obsessive-compulsive scale, attribution style questionnaire, self-esteem scale, index of well-being, and social disability screening schedule were administered before and after treatment. Significant improvement in symptoms and psychological and social functions from pre- to posttreatment occurred for all participants. The changes favored MDD patients. Our study suggested that ARGT may improve the symptoms and psychological-social functions of MDD, GAD, and OCD patients. MDD patients showed the best response.
基金supported by the National Natural Science Foundation of China under Grants No.61534002,No.61761136015,No.61701095.
文摘Because of computational complexity,the deep neural network(DNN)in embedded devices is usually trained on high-performance computers or graphic processing units(GPUs),and only the inference phase is implemented in embedded devices.Data processed by embedded devices,such as smartphones and wearables,are usually personalized,so the DNN model trained on public data sets may have poor accuracy when inferring the personalized data.As a result,retraining DNN with personalized data collected locally in embedded devices is necessary.Nevertheless,retraining needs labeled data sets,while the data collected locally are unlabeled,then how to retrain DNN with unlabeled data is a problem to be solved.This paper proves the necessity of retraining DNN model with personalized data collected in embedded devices after trained with public data sets.It also proposes a label generation method by which a fake label is generated for each unlabeled training case according to users’feedback,thus retraining can be performed with unlabeled data collected in embedded devices.The experimental results show that our fake label generation method has both good training effects and wide applicability.The advanced neural networks can be trained with unlabeled data from embedded devices and the individualized accuracy of the DNN model can be gradually improved along with personal using.
文摘Background/purpose:To evaluate the effects of attribution retraining on the perceived career barriers of undergraduate nursing students and to foster positive attributional styles.Methods:Ninety-four undergraduate nursing students were recruited and randomly divided into two groups:the attribution retraining group and the control group.All students were assessed by the perceived career barriers inventory before and after the eight-week study.Results:Attribution retraining significantly influenced the students'perceived career barriers.The mean scores of vocational knowledge,professional knowledge,and social ability of the experimental group were significantly reduced compared to the control group(p<0.05).Conclusion:Attribution retraining provides opportunities for improving the undergraduate nursing students'vocational knowledge,professional knowledge,and social ability.Attribution retraining should be encouraged in undergraduate nursing programs in order to reduce the nursing shortage in China's Mainland.
文摘Teacher-retraining course design is considered to be a challenge not only to the course participants but to the course designers as well, especially, when the participants enrolled turn out to have dramatically different professional background and conditions. This article supports the idea that changes to the course design should be made straightaway in response to the trainees＇ specific needs. The context for rural school teacher retraining at Novosibirsk State Technical University in Russia illustrates reasons for making immediate changes necessary as the course progressed, and reaction to them. The article discusses a model for a teacher retraining course in which EFL improvement is the core element.
文摘Attribution Retraining is a hot research topic in the field of psychology and pedagogy,and has been paid more and more attention in the field of nursing education in recent years.This study comprehensively retrieved attribution retraining related literature from Chinese and English databases and used literature analysis method to summarize the theoretical basis,assessment tools and application of attribution retraining in nursing education in China.The aim of this review is to promote the wider application of attribution retraining in the field of nursing education and provide reference for cultivating more excellent nursing talents.
文摘Recently, there has been a growing interest in gait retraining to alter the gait parameters of different populations.In these gait retraining, peak plantar pressure (PPP) was considered as an important parameter of the footbiomechanics. It has been found that high PPP correlates to the common foot deformities including pes planus/cavus. However, previous studies utilized excessive electronics in gait retraining, which is challenging toimplement daily especially when device cleaning, flexibility and portability are considered. Therefore, this studyinvestigated feasibility of a novel unpowered gait retraining for reducing high PPP. Twelve potential participantsidentified for investigation through a baseline PPP evaluation with Novel Pedar-x system. Participants received asingle session for the gait retraining with pebbles in the form of rigid spherical inserts (RSI) placed in locations ofhigh PPP inside the deformable insole. This provides tactile cues alerting the participants to alter their gait toreduce excess PPP. The PPP values were tracked in weekly follow-up sessions for 6 weeks. The results demonstrated that participants responded to RSI altering their gait to reduce PPP and maximum force by 14% and 10.5%after six weeks respectively. This study is valuable for physicians in reducing PPP when non-electronics arerequired.
基金supported by the Thompson Endowment and the Natural Sciences and Engineering Research Council of Canada.
文摘If oil sands are to be eliminated from the energy market to protect the global environment,human health and longterm economic welfare,a significant number of workers will be displaced in the transition to renewable energy technologies.This study outlines a cost-effective and convenient path for oil and gas workers in Alberta to be retrained in the burgeoning solar photovoltaic(PV)industry.Many oil and gas workers would be able to transfer fields with no additional training required.This study examines retraining options for the remainder of workers using the most closely matching skill equivalent PV job to minimize retraining time.The costs for retraining all oil sands workers are quantified and aggregated.The results show the total costs for retaining all oil sands workers in Alberta for the PV industry ranges between CAD$91.5 m and CAD$276.2 m.Thus,only 2-6%of federal,provincial,and territorial oil and gas subsidies for a single year would need to be reallocated to provide oil and gas workers with a new career of approximately equivalent pay.The results of this study clearly show that a rapid transition to sustainable energy production is feasible as costs of retraining oil and gas workers are far from prohibitive.
文摘Development in medical intervention has significantly decreased the mortality rates for children with complex congenital heart disease(CHD)but among these survivors with complex heart disease there occurs a unique pattern of neuro-developmental and neuropsychology impairment characterized social interaction impairment,impulsive Behavior,and impaired executive functions.Presence of behavioral problem is found significantly high in pediatric population with chronic illness than children with absence of chronic illness.The sample of 200 children with congenital heart defect was selected between age 4-8 years using multistage stratified sampling.The childhood psychopathology measurement schedule(CPMS)by Dr.Savitha Malhotra was used for assessing Behavioral problems present in children with CHD.Pre-Post experimental design was used to investigate the study and the results were statistically analyzed using paired T test.The result revealed that the effectiveness of intervention program to retrain Behavior showed high significance.With increased survival rates,the aim of the intervention and research based on clinical practices gets a shift from short term medical assessment to long term assessment and intervention of morbidity.
文摘The rapid development of technology has led to an ever-increasing demand for electrical energy.In the context of Timor-Leste,which still relies on fossil energy sources with high operational costs and significant environmental impacts,electricity load forecasting is a strategic measure to support the energy transition towards the Net Zero Emission(NZE)target by 2050.This study aims to utilize historical electricity load data for the period 2013–2024,as well as data on external factors affecting electricity consumption,to forecast electricity load in Timor-Leste in the next 10 years(2025–2035).The forecasting results are expected to support efforts in energy distribution efficiency,reduce operational costs,and inform decisions related to the sustainable energy transition.The method used in this study consists of two main approaches:the causality method,represented by the econometric Principal Component Analysis(PCA)model,which involves external factors in the data processing process,and the time series method,utilizing the LSTM,XGBoost,and hybrid(LSTM+XGBoost)models.In the time series method,data processing is combined with two approaches:the sliding window and the rolling recursive forecast.The performance of each model is evaluated using the Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE).The model with the lowest MAPE(<10%)is considered the best-performing model,indicating the highest accuracy.Additionally,a Monte Carlo simulation with 50,000 iterations was used to process the data and measure the prediction uncertainty,as well as test the calibration of the electricity load projection data.The results showed that the hybrid model(LSTM+XGBoost)with a rolling forecast recursive approach is the best-performing model in predicting electricity load in Timor-Leste.This model yields an RMSE of 75.76 MW,an MAE of 55.76 MW,and an MAPE of 5.27%,indicating a high level of accuracy.In addition,the model is also indicated as one that fits the characteristics of electricity load in Timor-Leste,as it produces the lowest percentage of forecasting error in predicting electricity load.The integration of the best model with Monte Carlo Simulation,which yields a p-value of 0.565,suggests that the results of electricity load projections for the period 2025–2035 are well-calibrated,reliable,accurate,and unbiased.
基金funded by the National Natural Science Foundation of China(No.52161135202)Hangzhou Key Scientific Research Plan Project(No.2023SZD0028).
文摘Conventional automated machine learning(AutoML)technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments,leading to accuracy reduction in forecasting short-term building energy loads.Moreover,their predictions are not transparent because of their black box nature.Hence,the building field currently lacks an AutoML framework capable of data quality enhancement,environment self-adaptation,and model interpretation.To address this research gap,an improved AutoML-based end-to-end data-driven modeling framework is proposed.Bayesian optimization is applied by this framework to find an optimal data preprocessing process for quality improvement of raw data.It bridges the gap where conventional AutoML technologies cannot automatically handle missing data and outliers.A sliding window-based model retraining strategy is utilized to achieve environment self-adaptation,contributing to the accuracy enhancement of AutoML technologies.Moreover,a local interpretable model-agnostic explanations-based approach is developed to interpret predictions made by the improved framework.It overcomes the poor interpretability of conventional AutoML technologies.The performance of the improved framework in forecasting one-hour ahead cooling loads is evaluated using two-year operational data from a real building.It is discovered that the accuracy of the improved framework increases by 4.24%–8.79%compared with four conventional frameworks for buildings with not only high-quality but also low-quality operational data.Furthermore,it is demonstrated that the developed model interpretation approach can effectively explain the predictions of the improved framework.The improved framework offers a novel perspective on creating accurate and reliable AutoML frameworks tailored to building energy load prediction tasks and other similar tasks.
