Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine...Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine preparation used to treat RA.ZF may cause liver injury.In this study,we aimed to develop a prediction model for abnormal liver function caused by ZF.Methods This retrospective study collected data from multiple centers from January 2018 to April 2023.Abnormal liver function was set as the target variable according to the alanine transaminase(ALT)level.Features were screened through univariate analysis and sequential forward selection for modeling.Ten machine learning and deep learning models were compared to find the model that most effectively predicted liver function from the available data.Results This study included 1,913 eligible patients.The LightGBM model exhibited the best performance(accuracy=0.96)out of the 10 learning models.The predictive metrics of the LightGBM model were as follows:precision=0.99,recall rate=0.97,F1_score=0.98,area under the curve(AUC)=0.98,sensitivity=0.97 and specificity=0.85 for predicting ALT<40 U/L;precision=0.60,recall rate=0.83,F1_score=0.70,AUC=0.98,sensitivity=0.83 and specificity=0.97 for predicting 40≤ALT<80 U/L;and precision=0.83,recall rate=0.63,F1_score=0.71,AUC=0.97,sensitivity=0.63 and specificity=1.00 for predicting ALT≥80 U/L.ZF-induced abnormal liver function was found to be associated with high total cholesterol and triglyceride levels,the combination of TNF-αinhibitors,JAK inhibitors,methotrexate+nonsteroidal anti-inflammatory drugs,leflunomide,smoking,older age,and females in middle-age(45-65 years old).Conclusion This study developed a model for predicting ZF-induced abnormal liver function,which may help improve the safety of integrated administration of ZF and Western medicine.展开更多
Accurate detection of fashion design attributes is essential for trend analyses and recommendation systems.Among these attributes,the neckline style plays a key role in shaping garment aesthetics.However,the presence ...Accurate detection of fashion design attributes is essential for trend analyses and recommendation systems.Among these attributes,the neckline style plays a key role in shaping garment aesthetics.However,the presence of complex backgrounds and varied body postures in real-world fashion images presents challenges for reliable neckline detection.To address this problem,this research builds a comprehensive fashion neckline database from online shop images and proposes an efficient fashion neckline detection model based on the YOLOv8 architecture(FN-YOLO).First,the proposed model incorporates a BiFormer attention mechanism into the backbone,enhancing its feature extraction capability.Second,a lightweight multi-level asymmetry detector head(LADH)is designed to replace the original head,effectively reducing the computational complexity and accelerating the detection speed.Last,the original loss function is replaced with Wise-IoU,which improves the localization accuracy of the detection box.The experimental results demonstrate that FN-YOLO achieves a mean average precision(mAP)of 81.7%,showing an absolute improvement of 3.9%over the original YOLOv8 model,and a detection speed of 215.6 frame/s,confirming its suitability for real-time applications in fashion neckline detection.展开更多
This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globall...This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.展开更多
Objective and Impact Statement:High-intensity focused ultrasound(HIFU)therapy is a promising noninvasive method that induces coagulative necrosis in diseased tissues through thermal and cavitation effects,while avoidi...Objective and Impact Statement:High-intensity focused ultrasound(HIFU)therapy is a promising noninvasive method that induces coagulative necrosis in diseased tissues through thermal and cavitation effects,while avoiding surrounding damage to surrounding normal tissues.Introduction:Accurate and real-time acquisition of the focal region temperature field during HIFU treatment marked enhances therapeutic efficacy,holding paramount scientific and practical value in clinical cancer therapy.Methods:In this paper,we initially designed and assembled an integrated HIFU system incorporating diagnostic,therapeutic,and temperature measurement functionalities to collect ultrasound echo signals and temperature variations during HIFU therapy.Furthermore,we introduced a novel multimodal teacher-student model approach,which utilizes the shared self-expressive coefficients and the deep canonical correlation analysis layer to aggregate each modality data,then through knowledge distillation strategies,transfers the knowledge from the teacher model to the student model.Results:By investigating the relationship between the phantoms,in vitro,and in vivo ultrasound echo signals and temperatures,we successfully achieved real-time reconstruction of the HIFU focal 2D temperature field region with a maximum temperature error of less than 2.5℃.Conclusion:Our method effectively monitored the distribution of the HIFU temperature field in real time,providing scientifically precise predictive schemes for HIFU therapy,laying a theoretical foundation for subsequent personalized treatment dose planning,and providing efficient guidance for noninvasive,nonionizing cancer treatment.展开更多
This paper described a distance learning system, which allows Internet users to conduct a lesson in real time from any kinds attached computers. Participants can jointly view and edit relevant multimedia informatio...This paper described a distance learning system, which allows Internet users to conduct a lesson in real time from any kinds attached computers. Participants can jointly view and edit relevant multimedia information distributed through Internet. Teachers and students can also simultaneously communicate by voice and text to discuss the problems. Teacher can broadcast streaming PowerPoint presentation in real time to network users. In addition to sliders, presenters can broadcast video and audio simultaneously to deliver a live multimedia show online, and store their presentations for on demand playback. Teachers distributed in different places can also use cooperative editing tool to edit and encode existing digital content. We discussed some important design principles of the system. Then, the system configuration and the results of evaluation are also presented. The system has proved to be applicable to the distance learning based on CSCW (Computer Support Cooperative Work) in Internet.展开更多
Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research comm...Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.展开更多
This paper presents a learning-based control framework for fast(<1.5 s)and accurate manipulation of a flexible object,i.e.,whip targeting.The framework consists of a motion planner learned or optimized by an algori...This paper presents a learning-based control framework for fast(<1.5 s)and accurate manipulation of a flexible object,i.e.,whip targeting.The framework consists of a motion planner learned or optimized by an algorithm,Online Impedance Adaptation Control(OIAC),a sim2real mechanism,and a visual feedback component.The experimental results show that a soft actor-critic algorithm outperforms three Deep Reinforcement Learning(DRL),a nonlinear optimization,and a genetic algorithm in learning generalization of motion planning.It can greatly reduce average learning trials(to<20 of others)and maximize average rewards(to>3 times of others).Besides,motion tracking errors are greatly reduced to 13.29 and 22.36 of constant impedance control by the OIAC of the proposed framework.In addition,the trajectory similarity between simulated and physical whips is 89.09.The presented framework provides a new method integrating data-driven and physics-based algorithms for controlling fast and accurate arm manipulation of a flexible object.展开更多
The time resolution of the existing traffic flow prediction model is too big to be applied to adaptive signal timing optimization.Based on the view of the platoon dispersion model,the relationship between vehicle arri...The time resolution of the existing traffic flow prediction model is too big to be applied to adaptive signal timing optimization.Based on the view of the platoon dispersion model,the relationship between vehicle arrival at the downstream intersection and vehicle departure from the upstream intersection was analyzed.Then,a high-resolution traffic flow prediction model based on deep learning was developed.The departure flow rate from the upstream and the arrival flow rate at the downstream intersection was taking as the input and output in the proposed model,respectively.Finally,the parameters of the proposed model were trained by the field data,and the proposed model was implemented to forecast the arrival flow rate of the downstream intersection.Results show that the proposed model can better capture the fluctuant traffic flow and reduced MAE,MRE,and RMSE by 9.53%,39.92%,and 3.56%,respectively,compared with traditional models and algorithms,such as Robertson's model and artificial neural network.Therefore,the proposed model can be applied for realtime adaptive signal timing optimization.展开更多
Nowadays,English movies have been considered as one of the most effective ways to assist students to keep interest in improving their English abilities and enlarge their English vocabularies,as well as their cross cul...Nowadays,English movies have been considered as one of the most effective ways to assist students to keep interest in improving their English abilities and enlarge their English vocabularies,as well as their cross cultural communicative ability.Based on the functions of English movies,which make them meet the needs of college students’English acquisition,this paper analyzes the advantages of English movies in English learning.展开更多
基金supported by the Budgeted Fund of Shanghai University of Traditional Chinese Medicine(Natural Science)(No.2021LK037)the Open Project of Qinghai Province Key Laboratory of Tibetan Medicine Pharmacology and Safety Evaluation(No.2021-ZY-03).
文摘Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine preparation used to treat RA.ZF may cause liver injury.In this study,we aimed to develop a prediction model for abnormal liver function caused by ZF.Methods This retrospective study collected data from multiple centers from January 2018 to April 2023.Abnormal liver function was set as the target variable according to the alanine transaminase(ALT)level.Features were screened through univariate analysis and sequential forward selection for modeling.Ten machine learning and deep learning models were compared to find the model that most effectively predicted liver function from the available data.Results This study included 1,913 eligible patients.The LightGBM model exhibited the best performance(accuracy=0.96)out of the 10 learning models.The predictive metrics of the LightGBM model were as follows:precision=0.99,recall rate=0.97,F1_score=0.98,area under the curve(AUC)=0.98,sensitivity=0.97 and specificity=0.85 for predicting ALT<40 U/L;precision=0.60,recall rate=0.83,F1_score=0.70,AUC=0.98,sensitivity=0.83 and specificity=0.97 for predicting 40≤ALT<80 U/L;and precision=0.83,recall rate=0.63,F1_score=0.71,AUC=0.97,sensitivity=0.63 and specificity=1.00 for predicting ALT≥80 U/L.ZF-induced abnormal liver function was found to be associated with high total cholesterol and triglyceride levels,the combination of TNF-αinhibitors,JAK inhibitors,methotrexate+nonsteroidal anti-inflammatory drugs,leflunomide,smoking,older age,and females in middle-age(45-65 years old).Conclusion This study developed a model for predicting ZF-induced abnormal liver function,which may help improve the safety of integrated administration of ZF and Western medicine.
基金Fundamental Research Funds for the Central Universities,China(Nos.2232020G-08 and 2232020E-03)Shanghai University Knowledge Service Platform,China(No.13S107024)。
文摘Accurate detection of fashion design attributes is essential for trend analyses and recommendation systems.Among these attributes,the neckline style plays a key role in shaping garment aesthetics.However,the presence of complex backgrounds and varied body postures in real-world fashion images presents challenges for reliable neckline detection.To address this problem,this research builds a comprehensive fashion neckline database from online shop images and proposes an efficient fashion neckline detection model based on the YOLOv8 architecture(FN-YOLO).First,the proposed model incorporates a BiFormer attention mechanism into the backbone,enhancing its feature extraction capability.Second,a lightweight multi-level asymmetry detector head(LADH)is designed to replace the original head,effectively reducing the computational complexity and accelerating the detection speed.Last,the original loss function is replaced with Wise-IoU,which improves the localization accuracy of the detection box.The experimental results demonstrate that FN-YOLO achieves a mean average precision(mAP)of 81.7%,showing an absolute improvement of 3.9%over the original YOLOv8 model,and a detection speed of 215.6 frame/s,confirming its suitability for real-time applications in fashion neckline detection.
文摘This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.
基金supported in part by the Natural Science Foundation of China(No.U22A20259)the Shenzhen Basic Science Research(No.JCYJ20200109110006136)the Interdisciplinary Research Program of Hust 2023JCYJ043.
文摘Objective and Impact Statement:High-intensity focused ultrasound(HIFU)therapy is a promising noninvasive method that induces coagulative necrosis in diseased tissues through thermal and cavitation effects,while avoiding surrounding damage to surrounding normal tissues.Introduction:Accurate and real-time acquisition of the focal region temperature field during HIFU treatment marked enhances therapeutic efficacy,holding paramount scientific and practical value in clinical cancer therapy.Methods:In this paper,we initially designed and assembled an integrated HIFU system incorporating diagnostic,therapeutic,and temperature measurement functionalities to collect ultrasound echo signals and temperature variations during HIFU therapy.Furthermore,we introduced a novel multimodal teacher-student model approach,which utilizes the shared self-expressive coefficients and the deep canonical correlation analysis layer to aggregate each modality data,then through knowledge distillation strategies,transfers the knowledge from the teacher model to the student model.Results:By investigating the relationship between the phantoms,in vitro,and in vivo ultrasound echo signals and temperatures,we successfully achieved real-time reconstruction of the HIFU focal 2D temperature field region with a maximum temperature error of less than 2.5℃.Conclusion:Our method effectively monitored the distribution of the HIFU temperature field in real time,providing scientifically precise predictive schemes for HIFU therapy,laying a theoretical foundation for subsequent personalized treatment dose planning,and providing efficient guidance for noninvasive,nonionizing cancer treatment.
基金Supported by Innovation Fund of China(0 0 C2 6 2 2 42 10 6 41)
文摘This paper described a distance learning system, which allows Internet users to conduct a lesson in real time from any kinds attached computers. Participants can jointly view and edit relevant multimedia information distributed through Internet. Teachers and students can also simultaneously communicate by voice and text to discuss the problems. Teacher can broadcast streaming PowerPoint presentation in real time to network users. In addition to sliders, presenters can broadcast video and audio simultaneously to deliver a live multimedia show online, and store their presentations for on demand playback. Teachers distributed in different places can also use cooperative editing tool to edit and encode existing digital content. We discussed some important design principles of the system. Then, the system configuration and the results of evaluation are also presented. The system has proved to be applicable to the distance learning based on CSCW (Computer Support Cooperative Work) in Internet.
基金supported by the Auckland Medical Research Foundation,No.1117017(to CPU)
文摘Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.
基金supported in part by the Brødrene Hartmanns(No.A36775)Thomas B.Thriges(No.7648-2106)+1 种基金Fabrikant Mads Clausens(No.2023-0210)EnergiFyn funds.
文摘This paper presents a learning-based control framework for fast(<1.5 s)and accurate manipulation of a flexible object,i.e.,whip targeting.The framework consists of a motion planner learned or optimized by an algorithm,Online Impedance Adaptation Control(OIAC),a sim2real mechanism,and a visual feedback component.The experimental results show that a soft actor-critic algorithm outperforms three Deep Reinforcement Learning(DRL),a nonlinear optimization,and a genetic algorithm in learning generalization of motion planning.It can greatly reduce average learning trials(to<20 of others)and maximize average rewards(to>3 times of others).Besides,motion tracking errors are greatly reduced to 13.29 and 22.36 of constant impedance control by the OIAC of the proposed framework.In addition,the trajectory similarity between simulated and physical whips is 89.09.The presented framework provides a new method integrating data-driven and physics-based algorithms for controlling fast and accurate arm manipulation of a flexible object.
文摘The time resolution of the existing traffic flow prediction model is too big to be applied to adaptive signal timing optimization.Based on the view of the platoon dispersion model,the relationship between vehicle arrival at the downstream intersection and vehicle departure from the upstream intersection was analyzed.Then,a high-resolution traffic flow prediction model based on deep learning was developed.The departure flow rate from the upstream and the arrival flow rate at the downstream intersection was taking as the input and output in the proposed model,respectively.Finally,the parameters of the proposed model were trained by the field data,and the proposed model was implemented to forecast the arrival flow rate of the downstream intersection.Results show that the proposed model can better capture the fluctuant traffic flow and reduced MAE,MRE,and RMSE by 9.53%,39.92%,and 3.56%,respectively,compared with traditional models and algorithms,such as Robertson's model and artificial neural network.Therefore,the proposed model can be applied for realtime adaptive signal timing optimization.
文摘Nowadays,English movies have been considered as one of the most effective ways to assist students to keep interest in improving their English abilities and enlarge their English vocabularies,as well as their cross cultural communicative ability.Based on the functions of English movies,which make them meet the needs of college students’English acquisition,this paper analyzes the advantages of English movies in English learning.
