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
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
在人体运动中,小腿三头肌-肌腱复合体(triceps surae muscle-tendon unit, MTU)是有效完成力的产生和传递以及能量储存和释放的关键,对运动效率有重要影响,但其损伤率也居高不下。本文对MTU的功能和跑步中MTU生物力学特性适应变化进行概...在人体运动中,小腿三头肌-肌腱复合体(triceps surae muscle-tendon unit, MTU)是有效完成力的产生和传递以及能量储存和释放的关键,对运动效率有重要影响,但其损伤率也居高不下。本文对MTU的功能和跑步中MTU生物力学特性适应变化进行概述,以深入理解MTU在运动过程中的功能,并探讨不同外部因素对MTU生物力学特性的影响,为提升跑步表现与预防损伤提供科学依据。跑步时前掌着地跑和穿着刚度更大的碳板跑鞋可以以较为经济的MTU形态学变化模式进行收缩或者回弹能量,即肌肉和其余弹性元件能用更经济的方式进行收缩或者回弹能量,以及肌肉收缩发力时的长度更接近于最佳肌束长度,降低肌肉收缩引起的能耗;采用前掌着地跑、裸足跑或穿着极简鞋跑步增加了MTU力学载荷;跑姿再训练后跑步时MTU能用更经济的方式进行收缩或者回弹能量,而其余单纯力量训练对其影响研究不够充分且效果较差。未来研究可进一步通过改善运动模式、装备和训练方法来优化MTU的生物力学特性,关注MTU的相互协调和平衡发展,从而提高运动表现,减少MTU损伤的发生。展开更多
为实现不同光学模态信息优势互补,以助力电力设备故障检测与定位任务,该文采用可见光图像增强红外图像的纹理信息。针对现有红外-可见光图像配准技术难以精确对齐电力设备局部精细化结构的问题,首次提出自适应监督重训配准算法(adaptive...为实现不同光学模态信息优势互补,以助力电力设备故障检测与定位任务,该文采用可见光图像增强红外图像的纹理信息。针对现有红外-可见光图像配准技术难以精确对齐电力设备局部精细化结构的问题,首次提出自适应监督重训配准算法(adaptive registration algorithm with supervision and retraining,ARSR),主要包括双阶各向异性高斯方向导数机制(dual order anisotropic Gaussian directional derivative,Dual-AGDD)以及双视图匹配参数重训框架(double-view matching parameter retraining,DVMPR)。首先,提出Dual-AGDD完成特征点筛选与定向。1阶AGDD进行自适应电力设备局部细化角点检测,2阶AGDD构建高斯特征三角形确定特征点主方向,采用局部强度不变性方法构建特征描述子。接着,提出DVMPR框架对图像透视尺度与视野旋转进行制约校正。最后,基于3σ原则改进支持向量回归,对误匹配点进行剔除,完成异源数据配准。试验结果显示,对不同旋转和尺度差异、不同环境的电力设备异源图像进行配准时,该文算法的平均定位误差为2.65,平均配准精确率为98.57%,具有较强的图像旋转、尺度不变性和环境鲁棒性,显著优于现有CAO-C2F、SuperPoint-SuperGlue等配准算法,可提高电力设备精细化结构异源图像配准精度。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.
文摘在人体运动中,小腿三头肌-肌腱复合体(triceps surae muscle-tendon unit, MTU)是有效完成力的产生和传递以及能量储存和释放的关键,对运动效率有重要影响,但其损伤率也居高不下。本文对MTU的功能和跑步中MTU生物力学特性适应变化进行概述,以深入理解MTU在运动过程中的功能,并探讨不同外部因素对MTU生物力学特性的影响,为提升跑步表现与预防损伤提供科学依据。跑步时前掌着地跑和穿着刚度更大的碳板跑鞋可以以较为经济的MTU形态学变化模式进行收缩或者回弹能量,即肌肉和其余弹性元件能用更经济的方式进行收缩或者回弹能量,以及肌肉收缩发力时的长度更接近于最佳肌束长度,降低肌肉收缩引起的能耗;采用前掌着地跑、裸足跑或穿着极简鞋跑步增加了MTU力学载荷;跑姿再训练后跑步时MTU能用更经济的方式进行收缩或者回弹能量,而其余单纯力量训练对其影响研究不够充分且效果较差。未来研究可进一步通过改善运动模式、装备和训练方法来优化MTU的生物力学特性,关注MTU的相互协调和平衡发展,从而提高运动表现,减少MTU损伤的发生。
文摘为实现不同光学模态信息优势互补,以助力电力设备故障检测与定位任务,该文采用可见光图像增强红外图像的纹理信息。针对现有红外-可见光图像配准技术难以精确对齐电力设备局部精细化结构的问题,首次提出自适应监督重训配准算法(adaptive registration algorithm with supervision and retraining,ARSR),主要包括双阶各向异性高斯方向导数机制(dual order anisotropic Gaussian directional derivative,Dual-AGDD)以及双视图匹配参数重训框架(double-view matching parameter retraining,DVMPR)。首先,提出Dual-AGDD完成特征点筛选与定向。1阶AGDD进行自适应电力设备局部细化角点检测,2阶AGDD构建高斯特征三角形确定特征点主方向,采用局部强度不变性方法构建特征描述子。接着,提出DVMPR框架对图像透视尺度与视野旋转进行制约校正。最后,基于3σ原则改进支持向量回归,对误匹配点进行剔除,完成异源数据配准。试验结果显示,对不同旋转和尺度差异、不同环境的电力设备异源图像进行配准时,该文算法的平均定位误差为2.65,平均配准精确率为98.57%,具有较强的图像旋转、尺度不变性和环境鲁棒性,显著优于现有CAO-C2F、SuperPoint-SuperGlue等配准算法,可提高电力设备精细化结构异源图像配准精度。
