Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were col...Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.展开更多
Based on the research on the project course theory of "integration of theory and practice" in higher vocational education and the analysis of practical teaching in colleges and universities at home and abroa...Based on the research on the project course theory of "integration of theory and practice" in higher vocational education and the analysis of practical teaching in colleges and universities at home and abroad, combined with literature research, case analysis, system theory and other research methods, the project-based teaching goal, model, content and means of "integration of doing, learning and teaching" in higher vocational education is explored, and the project-based teaching model of "Landscape Planning and Design" is discussed combined with the application of information-based teaching methods. So as to provide references for carrying out the project-based teaching in similar courses in higher vocational colleges and really achieve docking the actual post requirements with the course to provide the basis for achieving the purpose of cultivating skilled talents in higher vocational education.展开更多
To investigate the effect of increasing dietary zinc supplementation on body weight and learning ability in rats.Zinc supplemental diet contained 200, 400, 600, 800 or 1 600 mg/kg zinc,respectively.Y-labyrinth test wa...To investigate the effect of increasing dietary zinc supplementation on body weight and learning ability in rats.Zinc supplemental diet contained 200, 400, 600, 800 or 1 600 mg/kg zinc,respectively.Y-labyrinth test was applied to exam the learning and memory function of rats.Significantly greater weight gain was observed in rats fed with 400 mg/kg zinc diet than in rats fed with 200 mg/kg zinc diet(P<0.05). During the early experiment, lower weight increments were notably observed in rats with 600, 800 or 1 600 mg/kg zinc supplementation than that in control group, respectively. But the influence on weight relief became weaker in pace with time on the whole. Learning and memory function for rats were strikingly improved at level of 200 mg/kg zinc diet compared with the control level(P<0.05), and were damaged in varying degrees at higher(except 1 600 mg/kg) zinc supplementation levels in feeds, among which,800 mg/kg zinc dosage had produced obviously lesion for learning ability in rats compared with normal, 200 or 1 600 mg/kg zinc levels(P<0.05, respectively).[Conclusion]These results suggest that different levels of zinc supplementation have some incompletely parallel effects on the growth, memory and capacity to learn in rats.展开更多
Background:Nonalcoholic fatty liver disease(NAFLD)is a public health challenge and significant cause of morbidity and mortality worldwide.Early identification is crucial for disease intervention.We recently proposed a...Background:Nonalcoholic fatty liver disease(NAFLD)is a public health challenge and significant cause of morbidity and mortality worldwide.Early identification is crucial for disease intervention.We recently proposed a nomogram-based NAFLD prediction model from a large population cohort.We aimed to explore machine learning tools in predicting NAFLD.Methods:A retrospective cross-sectional study was performed on 15315 Chinese subjects(10373 training and 4942 testing sets).Selected clinical and biochemical factors were evaluated by different types of machine learning algorithms to develop and validate seven predictive models.Nine evaluation indicators including area under the receiver operating characteristic curve(AUROC),area under the precision-recall curve(AUPRC),accuracy,positive predictive value,sensitivity,F1 score,Matthews correlation coefficient(MCC),specificity and negative prognostic value were applied to compare the performance among the models.The selected clinical and biochemical factors were ranked according to the importance in prediction ability.Results:Totally 4018/10373(38.74%)and 1860/4942(37.64%)subjects had ultrasound-proven NAFLD in the training and testing sets,respectively.Seven machine learning based models were developed and demonstrated good performance in predicting NAFLD.Among these models,the XGBoost model revealed the highest AUROC(0.873),AUPRC(0.810),accuracy(0.795),positive predictive value(0.806),F1 score(0.695),MCC(0.557),specificity(0.909),demonstrating the best prediction ability among the built models.Body mass index was the most valuable indicator to predict NAFLD according to the feature ranking scores.Conclusions:The XGBoost model has the best overall prediction ability for diagnosing NAFLD.The novel machine learning tools provide considerable beneficial potential in NAFLD screening.展开更多
The aim of this paper is to describe and to reflect on the experience of the authors in setting up a new model of learning environment in management education in a University in Brazil, which was initially called Mana...The aim of this paper is to describe and to reflect on the experience of the authors in setting up a new model of learning environment in management education in a University in Brazil, which was initially called Management Practice Laboratory （MPL）. The MPL environment was conceived as a physical and conceptual space where students could learn and practice the principles and techniques of working in organizations in its three levels operational, tactical, and strategic. The foundations of the project come from social constructivist perspective on learning, from experiential learning literature and from researches that call for a new epistemological ground in management learning. In this paper, the authors will stress some challenges and frustrations with the project since these could be helpful to those interested in similar initiatives. Due to limited space, only two challenges will be stressed： （1） the construction of legitimacy for the project; and （2） the persistent dissonance between theory and practice. The authors conclude that there is room for innovation in the way management is taught and learned in universities since one shows courage to overcome the challenges and frustrations one will certainly deal with展开更多
This paper studies the division of labor and economic development under global value chains in North South trade by mainly investigating the changes of production hours and cost per unit along with more and more outpu...This paper studies the division of labor and economic development under global value chains in North South trade by mainly investigating the changes of production hours and cost per unit along with more and more output and increasing trade value in several industries in the U.S., because the U. S. is at the leading position in the division of labor by global value chains. The empirical evidence reveals that more international outsourcing, there will be more detailed division of labor, and the industry unit production time and production cost will show more declining trend year by year. This is consistent with that the global value chains and the outsourcing play more and more important roles in the international division of labor and economic growth in both developed and developing countries, and helps explain the integration of workforce across countries in the global value chains.展开更多
"Learning by Doing"是由美国卡内基·梅隆大学率先提出的一种旨在强化工程学科的学生全面实践能力和工程素养的教学模式。其目的就是让学生在"做"的过程中,深刻掌握相关的技术和技能,获得远超过课堂教学的教..."Learning by Doing"是由美国卡内基·梅隆大学率先提出的一种旨在强化工程学科的学生全面实践能力和工程素养的教学模式。其目的就是让学生在"做"的过程中,深刻掌握相关的技术和技能,获得远超过课堂教学的教学效果。本文首先介绍了"LearningbyDoing"的概念及作用,然后详细讨论了在"WindowsCE嵌入式系统"课程中实施"LearningbyDoing"的具体做法以及经验得失。展开更多
"Learning by Doing"是一种旨在强化工程学科的学生全面实践能力和工程素养的教学模式。其目的就是让学生在"做"的过程中,深刻掌握相关的技术和技能,获得远超过课堂教学的教学效果。本文阐述了在"嵌入式系统..."Learning by Doing"是一种旨在强化工程学科的学生全面实践能力和工程素养的教学模式。其目的就是让学生在"做"的过程中,深刻掌握相关的技术和技能,获得远超过课堂教学的教学效果。本文阐述了在"嵌入式系统程序设计实习"课程中实施"Learning by Doing"的具体方法以及一些经验得失。展开更多
"嵌入式移动平台应用开发"课程是电子信息科学与技术专业的专业课,以培养学生的嵌入式软件开发能力为目的。将Learning by doing教学模式应用到嵌入式移动平台应用开发课程中,通过改革授课方式、教学内容组织以及考核方式,使..."嵌入式移动平台应用开发"课程是电子信息科学与技术专业的专业课,以培养学生的嵌入式软件开发能力为目的。将Learning by doing教学模式应用到嵌入式移动平台应用开发课程中,通过改革授课方式、教学内容组织以及考核方式,使学生在做中理解所学的知识,融会贯通,实操能力和编程动手能力得到提高。通过实践,取得了良好的教学效果,培养了学生的创新精神和解决实际问题的能力。展开更多
Energy and security remain the main two challenges in Wireless Sensor Networks(WSNs).Therefore,protecting these WSN networks from Denial of Service(DoS)and Distributed DoS(DDoS)is one of the WSN networks security task...Energy and security remain the main two challenges in Wireless Sensor Networks(WSNs).Therefore,protecting these WSN networks from Denial of Service(DoS)and Distributed DoS(DDoS)is one of the WSN networks security tasks.Traditional packet deep scan systems that rely on open field inspection in transport layer security packets and the open field encryption trend are making machine learning-based systems the only viable choice for these types of attacks.This paper contributes to the evaluation of the use machine learning algorithms in WSN nodes traffic and their effect on WSN network life time.We examined the performance metrics of different machine learning classification categories such asK-Nearest Neighbour(KNN),Logistic Regression(LR),Support Vector Machine(SVM),Gboost,Decision Tree(DT),Na飗e Bayes,Long Short Term Memory(LSTM),and Multi-Layer Perceptron(MLP)on aWSN-dataset in different sizes.The test results proved that the statistical and logical classification categories performed the best on numeric statistical datasets,and the Gboost algorithm showed the best performance compared to different algorithms on average of all performance metrics.The performance metrics used in these validations were accuracy,F1-score,False Positive Ratio(FPR),False Negative Ratio(FNR),and the training execution time.Moreover,the test results showed the Gboost algorithm got 99.6%,98.8%,0.4%0.13%in accuracy,F1-score,FPR,and FNR,respectively.At training execution time,it obtained 1.41 s for the average of all training time execution datasets.In addition,this paper demonstrated that for the numeric statistical data type,the best results are in the size of the dataset ranging from3000 to 6000 records and the percentage between categories is not less than 50%for each category with the other categories.Furthermore,this paper investigated the effect of Gboost on the WSN lifetime,which resulted in a 32%reduction compared to other Gboost-free scenarios.展开更多
BACKGROUND The quality of a radiotherapy plan often depends on the knowledge and expertise of the plan designers.AIM To predict the uninvolved liver dose in stereotactic body radiotherapy(SBRT)for liver cancer using a...BACKGROUND The quality of a radiotherapy plan often depends on the knowledge and expertise of the plan designers.AIM To predict the uninvolved liver dose in stereotactic body radiotherapy(SBRT)for liver cancer using a neural network-based method.METHODS A total of 114 SBRT plans for liver cancer were used to test the neural network method.Sub-organs of the uninvolved liver were automatically generated.Correlations between the volume of each sub-organ,uninvolved liver dose,and neural network prediction model were established using MATLAB.Of the cases,70%were selected as the training set,15%as the validation set,and 15%as the test set.The regression R-value and mean square error(MSE)were used to evaluate the model.RESULTS The volume of the uninvolved liver was related to the volume of the corresponding sub-organs.For all sets of Rvalues of the prediction model,except for D_(n0)which was 0.7513,all R-values of D_(n10)-D_(n100)and D_(nmean)were>0.8.The MSE of the prediction model was also low.CONCLUSION We developed a neural network-based method to predict the uninvolved liver dose in SBRT for liver cancer.It is simple and easy to use and warrants further promotion and application.展开更多
文摘Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.
文摘Based on the research on the project course theory of "integration of theory and practice" in higher vocational education and the analysis of practical teaching in colleges and universities at home and abroad, combined with literature research, case analysis, system theory and other research methods, the project-based teaching goal, model, content and means of "integration of doing, learning and teaching" in higher vocational education is explored, and the project-based teaching model of "Landscape Planning and Design" is discussed combined with the application of information-based teaching methods. So as to provide references for carrying out the project-based teaching in similar courses in higher vocational colleges and really achieve docking the actual post requirements with the course to provide the basis for achieving the purpose of cultivating skilled talents in higher vocational education.
文摘To investigate the effect of increasing dietary zinc supplementation on body weight and learning ability in rats.Zinc supplemental diet contained 200, 400, 600, 800 or 1 600 mg/kg zinc,respectively.Y-labyrinth test was applied to exam the learning and memory function of rats.Significantly greater weight gain was observed in rats fed with 400 mg/kg zinc diet than in rats fed with 200 mg/kg zinc diet(P<0.05). During the early experiment, lower weight increments were notably observed in rats with 600, 800 or 1 600 mg/kg zinc supplementation than that in control group, respectively. But the influence on weight relief became weaker in pace with time on the whole. Learning and memory function for rats were strikingly improved at level of 200 mg/kg zinc diet compared with the control level(P<0.05), and were damaged in varying degrees at higher(except 1 600 mg/kg) zinc supplementation levels in feeds, among which,800 mg/kg zinc dosage had produced obviously lesion for learning ability in rats compared with normal, 200 or 1 600 mg/kg zinc levels(P<0.05, respectively).[Conclusion]These results suggest that different levels of zinc supplementation have some incompletely parallel effects on the growth, memory and capacity to learn in rats.
基金supported by grants from the National Natural Science Foundation of China(81970543 and 81570591)Zhejiang Provincial Medical&Hygienic Science and Technology Project of China(2018KY385)Zhejiang Provincial Natural Science Foundation of China(LY20H160023)。
文摘Background:Nonalcoholic fatty liver disease(NAFLD)is a public health challenge and significant cause of morbidity and mortality worldwide.Early identification is crucial for disease intervention.We recently proposed a nomogram-based NAFLD prediction model from a large population cohort.We aimed to explore machine learning tools in predicting NAFLD.Methods:A retrospective cross-sectional study was performed on 15315 Chinese subjects(10373 training and 4942 testing sets).Selected clinical and biochemical factors were evaluated by different types of machine learning algorithms to develop and validate seven predictive models.Nine evaluation indicators including area under the receiver operating characteristic curve(AUROC),area under the precision-recall curve(AUPRC),accuracy,positive predictive value,sensitivity,F1 score,Matthews correlation coefficient(MCC),specificity and negative prognostic value were applied to compare the performance among the models.The selected clinical and biochemical factors were ranked according to the importance in prediction ability.Results:Totally 4018/10373(38.74%)and 1860/4942(37.64%)subjects had ultrasound-proven NAFLD in the training and testing sets,respectively.Seven machine learning based models were developed and demonstrated good performance in predicting NAFLD.Among these models,the XGBoost model revealed the highest AUROC(0.873),AUPRC(0.810),accuracy(0.795),positive predictive value(0.806),F1 score(0.695),MCC(0.557),specificity(0.909),demonstrating the best prediction ability among the built models.Body mass index was the most valuable indicator to predict NAFLD according to the feature ranking scores.Conclusions:The XGBoost model has the best overall prediction ability for diagnosing NAFLD.The novel machine learning tools provide considerable beneficial potential in NAFLD screening.
文摘The aim of this paper is to describe and to reflect on the experience of the authors in setting up a new model of learning environment in management education in a University in Brazil, which was initially called Management Practice Laboratory （MPL）. The MPL environment was conceived as a physical and conceptual space where students could learn and practice the principles and techniques of working in organizations in its three levels operational, tactical, and strategic. The foundations of the project come from social constructivist perspective on learning, from experiential learning literature and from researches that call for a new epistemological ground in management learning. In this paper, the authors will stress some challenges and frustrations with the project since these could be helpful to those interested in similar initiatives. Due to limited space, only two challenges will be stressed： （1） the construction of legitimacy for the project; and （2） the persistent dissonance between theory and practice. The authors conclude that there is room for innovation in the way management is taught and learned in universities since one shows courage to overcome the challenges and frustrations one will certainly deal with
文摘This paper studies the division of labor and economic development under global value chains in North South trade by mainly investigating the changes of production hours and cost per unit along with more and more output and increasing trade value in several industries in the U.S., because the U. S. is at the leading position in the division of labor by global value chains. The empirical evidence reveals that more international outsourcing, there will be more detailed division of labor, and the industry unit production time and production cost will show more declining trend year by year. This is consistent with that the global value chains and the outsourcing play more and more important roles in the international division of labor and economic growth in both developed and developing countries, and helps explain the integration of workforce across countries in the global value chains.
文摘"Learning by Doing"是由美国卡内基·梅隆大学率先提出的一种旨在强化工程学科的学生全面实践能力和工程素养的教学模式。其目的就是让学生在"做"的过程中,深刻掌握相关的技术和技能,获得远超过课堂教学的教学效果。本文首先介绍了"LearningbyDoing"的概念及作用,然后详细讨论了在"WindowsCE嵌入式系统"课程中实施"LearningbyDoing"的具体做法以及经验得失。
文摘"Learning by Doing"是一种旨在强化工程学科的学生全面实践能力和工程素养的教学模式。其目的就是让学生在"做"的过程中,深刻掌握相关的技术和技能,获得远超过课堂教学的教学效果。本文阐述了在"嵌入式系统程序设计实习"课程中实施"Learning by Doing"的具体方法以及一些经验得失。
文摘"嵌入式移动平台应用开发"课程是电子信息科学与技术专业的专业课,以培养学生的嵌入式软件开发能力为目的。将Learning by doing教学模式应用到嵌入式移动平台应用开发课程中,通过改革授课方式、教学内容组织以及考核方式,使学生在做中理解所学的知识,融会贯通,实操能力和编程动手能力得到提高。通过实践,取得了良好的教学效果,培养了学生的创新精神和解决实际问题的能力。
文摘Energy and security remain the main two challenges in Wireless Sensor Networks(WSNs).Therefore,protecting these WSN networks from Denial of Service(DoS)and Distributed DoS(DDoS)is one of the WSN networks security tasks.Traditional packet deep scan systems that rely on open field inspection in transport layer security packets and the open field encryption trend are making machine learning-based systems the only viable choice for these types of attacks.This paper contributes to the evaluation of the use machine learning algorithms in WSN nodes traffic and their effect on WSN network life time.We examined the performance metrics of different machine learning classification categories such asK-Nearest Neighbour(KNN),Logistic Regression(LR),Support Vector Machine(SVM),Gboost,Decision Tree(DT),Na飗e Bayes,Long Short Term Memory(LSTM),and Multi-Layer Perceptron(MLP)on aWSN-dataset in different sizes.The test results proved that the statistical and logical classification categories performed the best on numeric statistical datasets,and the Gboost algorithm showed the best performance compared to different algorithms on average of all performance metrics.The performance metrics used in these validations were accuracy,F1-score,False Positive Ratio(FPR),False Negative Ratio(FNR),and the training execution time.Moreover,the test results showed the Gboost algorithm got 99.6%,98.8%,0.4%0.13%in accuracy,F1-score,FPR,and FNR,respectively.At training execution time,it obtained 1.41 s for the average of all training time execution datasets.In addition,this paper demonstrated that for the numeric statistical data type,the best results are in the size of the dataset ranging from3000 to 6000 records and the percentage between categories is not less than 50%for each category with the other categories.Furthermore,this paper investigated the effect of Gboost on the WSN lifetime,which resulted in a 32%reduction compared to other Gboost-free scenarios.
基金Supported by the Open Fund for Scientific Research of Jiangxi Cancer Hospital,No.2021J15the Gulin People's Hospital-The Affiliated Hospital of Southwest Medical University Science and Technology Strategic Cooperation Project,No.2022GLXNYDFY05the Sichuan Provincial Medical Research Project Plan,No.S21004.
文摘BACKGROUND The quality of a radiotherapy plan often depends on the knowledge and expertise of the plan designers.AIM To predict the uninvolved liver dose in stereotactic body radiotherapy(SBRT)for liver cancer using a neural network-based method.METHODS A total of 114 SBRT plans for liver cancer were used to test the neural network method.Sub-organs of the uninvolved liver were automatically generated.Correlations between the volume of each sub-organ,uninvolved liver dose,and neural network prediction model were established using MATLAB.Of the cases,70%were selected as the training set,15%as the validation set,and 15%as the test set.The regression R-value and mean square error(MSE)were used to evaluate the model.RESULTS The volume of the uninvolved liver was related to the volume of the corresponding sub-organs.For all sets of Rvalues of the prediction model,except for D_(n0)which was 0.7513,all R-values of D_(n10)-D_(n100)and D_(nmean)were>0.8.The MSE of the prediction model was also low.CONCLUSION We developed a neural network-based method to predict the uninvolved liver dose in SBRT for liver cancer.It is simple and easy to use and warrants further promotion and application.
