Multistage multi-cluster hydraulic fracturing has enabled the economic exploitation of shale reservoirs,but the interpretation of hydraulic fracture parameters is challenging.The pressure signals after pump shutdown a...Multistage multi-cluster hydraulic fracturing has enabled the economic exploitation of shale reservoirs,but the interpretation of hydraulic fracture parameters is challenging.The pressure signals after pump shutdown are influenced by hydraulic fractures,which can reflect the geometric features of hydraulic fracture.The shutdown pressure can be used to interpret the hydraulic fracture parameters in a real-time and cost-effective manner.In this paper,a mathematical model for shutdown pressure evolution is developed considering the effects of wellbore friction,perforation friction and fluid loss in fractures.An efficient numerical simulation method is established by using the method of characteristics.Based on this method,the impacts of fracture half-length,fracture height,opened cluster and perforation number,and filtration coefficient on the evolution of shutdown pressure are analyzed.The results indicate that a larger fracture half-length may hasten the decay of shutdown pressure,while a larger fracture height can slow down the decay of shutdown pressure.A smaller number of opened clusters and perforations can significantly increase the perforation friction and decrease the overall level of shutdown pressure.A larger filtration coefficient may accelerate the fluid filtration in the fracture and hasten the drop of the shutdown pressure.The simulation method of shutdown pressure,as well as the analysis results,has important implications for the interpretation of hydraulic fracture parameters.展开更多
The tool for analyzing and evaluating system characteristics based on the AADL model can achieve real-time,reliability,security,and schedulability analysis and evaluation for software-intensive systems.It provides a c...The tool for analyzing and evaluating system characteristics based on the AADL model can achieve real-time,reliability,security,and schedulability analysis and evaluation for software-intensive systems.It provides a complete solution for quality analysis of real-time,reliability,safety,and schedulability in the design and demonstration stages of software-intensive systems.By using the system′s multi-characteristic(real-time capability,reliability,safety,schedulability)analysis and evaluation tool based on AADL models,it can meet the software non-functional requirements stipulated by the existing model development standards and specifications.This effectively enhances the efficiency of demonstrating the compliance of the system′s non-functional quality attributes in the design work of our unit′s software-intensive system.It can also improve the performance of our unit′s software-intensive system in engineering inspections and requirement reviews conducted by various organizations.The improvement in the quality level of software-intensive systems can enhance the market competitiveness of our unit′s electronic products.展开更多
Dear Sir,Iam Dr.Kavitha S,from the Department of Electronics and Communication Engineering,Nandha Engineering College,Erode,Tamil Nadu,India.I write to present the detection of glaucoma using extreme learning machine(...Dear Sir,Iam Dr.Kavitha S,from the Department of Electronics and Communication Engineering,Nandha Engineering College,Erode,Tamil Nadu,India.I write to present the detection of glaucoma using extreme learning machine(ELM)and fractal feature analysis.Glaucoma is the second most frequent cause of permanent blindness in展开更多
As multimedia data sharing increases,data security in mobile devices and its mechanism can be seen as critical.Biometrics combines the physiological and behavioral qualities of an individual to validate their characte...As multimedia data sharing increases,data security in mobile devices and its mechanism can be seen as critical.Biometrics combines the physiological and behavioral qualities of an individual to validate their character in real-time.Humans incorporate physiological attributes like a fingerprint,face,iris,palm print,finger knuckle print,Deoxyribonucleic Acid(DNA),and behavioral qualities like walk,voice,mark,or keystroke.The main goal of this paper is to design a robust framework for automatic face recognition.Scale Invariant Feature Transform(SIFT)and Speeded-up Robust Features(SURF)are employed for face recognition.Also,we propose a modified Gabor Wavelet Transform for SIFT/SURF(GWT-SIFT/GWT-SURF)to increase the recognition accuracy of human faces.The proposed scheme is composed of three steps.First,the entropy of the image is removed using Discrete Wavelet Transform(DWT).Second,the computational complexity of the SIFT/SURF is reduced.Third,the accuracy is increased for authentication by the proposed GWT-SIFT/GWT-SURF algorithm.A comparative analysis of the proposed scheme is done on real-time Olivetti Research Laboratory(ORL)and Poznan University of Technology(PUT)databases.When compared to the traditional SIFT/SURF methods,we verify that the GWT-SIFT achieves the better accuracy of 99.32%and the better approach is the GWT-SURF as the run time of the GWT-SURF for 100 images is 3.4 seconds when compared to the GWT-SIFT which has a run time of 4.9 seconds for 100 images.展开更多
In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest...In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.展开更多
Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-...Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-stained live cell cultures. Because these images do not have adequate textural variations. Manual cell segmentation requires massive labor and is a time consuming process. This paper describes an automated cell segmentation method for localizing the cells of Chinese hamster ovary cell culture. Several kinds of high-dimensional feature descriptors, K-means clustering method and Chan-Vese model-based level set are used to extract the cellular regions. The region extracted are used to classify phases in cell cycle. The segmentation results were experimentally assessed. As a result, the proposed method proved to be significant for cell isolation. In the evaluation experiments, we constructed a database of Chinese Hamster Ovary Cell’s microscopic images which includes various photographing environments under the guidance of a biologist.展开更多
The unique topography and historical and cultural background have determined the diversity and uniqueness of kiln architecture in the Tongchuan area.In addition to the double-slope residential architecture,traditional...The unique topography and historical and cultural background have determined the diversity and uniqueness of kiln architecture in the Tongchuan area.In addition to the double-slope residential architecture,traditional kiln dwellings with regional characteristics such as Leaning on the cliff cave dwelling,ground Pit cave dwelling and Freestanding cave dwellings have also been formed.This paper takes the inheritance and protection of traditional kiln as the starting point,and through field research and literature analysis,we have systematically collected images,measured data,and drawn up horizontal and vertical profiles and three-dimensional structure drawings of the traditional kiln dwellings in Tongchuan,and concluded the three types of forms and structural characteristics and artistic form characteristics of the traditional kiln dwellings in Tongchuan.The aim is to provide a basis and reference for the protection and inheritance of tangible and intangible cultural heritage in Shaanxi,as well as for subsequent research in this field.展开更多
Brain tumors are potentially fatal presence of cancer cells over a human brain,and they need to be segmented for accurate and reliable planning of diag-nosis.Segmentation process must be carried out in different regio...Brain tumors are potentially fatal presence of cancer cells over a human brain,and they need to be segmented for accurate and reliable planning of diag-nosis.Segmentation process must be carried out in different regions based on which the stages of cancer can be accurately derived.Glioma patients exhibit a different level of challenge in terms of cancer or tumors detection as the Magnetic Resonance Imaging(MRI)images possess varying sizes,shapes,positions,and modalities.The scanner used for sensing the location of tumors cells will be sub-jected to additional protocols and measures for accuracy,in turn,increasing the time and affecting the performance of the entire model.In this view,Convolutional Neural Networks deliver suitable models for efficient segmentation and thus delivered promising results.The previous strategies and models failed to adhere to diversity of sizes and shapes,proving to be a well-established solution for detecting tumors of bigger size.Tumors tend to be smaller in size and shape during their premature stages and they can easily evade the algorithms of Convolutional Neural Network(CNN).This proposal intends to furnish a detailed model for sensing early stages of cancer and hence perform segmentation irrespective of the current size and shape of tumors.The size of networks and layers will lead to a significant weightage when multiple kernel sizes are involved,especially in multi-resolution environments.On the other hand,the proposed model is designed with a novel approach including a dilated convolution and level-based learning strat-egy.When the convolution process is dilated,the process of feature extraction deals with multiscale objective and level-based learning eliminates the shortcoming of previous models,thereby enhancing the quality of smaller tumors cells and shapes.The level-based learning approach also encapsulates the feature recon-struction processes which highlights the sensing of small-scale tumors growth.Inclusively,segmenting the images is performed with better accuracy and hence detection becomes better when compared to that of hierarchical approaches.展开更多
We propose a self-supervising learning framework for finding the dominant eigenfunction-eigenvalue pairs of linear and self-adjoint operators.We represent target eigenfunctions with coordinate-based neural networks an...We propose a self-supervising learning framework for finding the dominant eigenfunction-eigenvalue pairs of linear and self-adjoint operators.We represent target eigenfunctions with coordinate-based neural networks and employ the Fourier positional encodings to enable the approximation of high-frequency modes.We formulate a self-supervised training objective for spectral learning and propose a novel regularization mechanism to ensure that the network finds the exact eigenfunctions instead of a space spanned by the eigenfunctions.Furthermore,we investigate the effect of weight normalization as a mechanism to alleviate the risk of recovering linear dependent modes,allowing us to accurately recover a large number of eigenpairs.The effectiveness of our methods is demonstrated across a collection of representative benchmarks including both local and non-local diffusion operators,as well as high-dimensional time-series data from a video sequence.Our results indicate that the present algorithm can outperform competing approaches in terms of both approximation accuracy and computational cost.展开更多
Objective:To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes.Methods:In this retrospective cohort study,we s...Objective:To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes.Methods:In this retrospective cohort study,we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions.Classification of all-cause,30-day readmission outcomes were modeled using logistic regression,artificial neural network,and Easy Ensemble.F1 statistic,sensitivity,and positive predictive value were used to evaluate the model performance.Results:We identified 14 most influential data features(4 numeric features and 10 categorical features)and evaluated 3 machine learning models with numerous sampling methods(oversampling,undersampling,and hybrid techniques).The deep learning model offered no improvement over traditional models(logistic regression and Easy Ensemble)for predicting readmission,whereas the other two algorithms led to much smaller differences between the training and testing datasets.Conclusions:Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes.But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.展开更多
Brain-Computer Interface(BCI)technology is a way for humans to explore the mysteries of the brain and has applications in many areas of real life.People use this technology to capture brain waves and analyze the elect...Brain-Computer Interface(BCI)technology is a way for humans to explore the mysteries of the brain and has applications in many areas of real life.People use this technology to capture brain waves and analyze the electroencephalograph(EEG)signal for feature extraction.Take the medical field as an example,epilepsy disease is threatening human health every moment.We propose a convolutional neural network SECNN-LSTM framework based on the attention mechanism can automatically perform feature extraction and analysis on the collected EEG signals of patients to complete the prediction of epilepsy diseases,overcoming the problem that the disease requires long time EEG monitoring and analysis by manual,which is a large workload and relatively subjective,and improving the prediction accuracy of epilepsy diseases by adding the attention mechanism module.Through experimental tests,the algorithm of SECNN-LSTM can effectively predict the EEG signal of epilepsy disease,and the correct recognition rate is improved.The experiment has some reference value for the subsequent research of EEG signals in other fields in deep learning.展开更多
<正>Objective To investigate the clinical features and outcomes of high-risk acute promyelocytic leukemia(APL)patients.Methods A retrospective analysis was conducted to compare the clinical characteristics and p...<正>Objective To investigate the clinical features and outcomes of high-risk acute promyelocytic leukemia(APL)patients.Methods A retrospective analysis was conducted to compare the clinical characteristics and prognosis of 118 high-risk APL patients(WBC≥10×10~9/L)and 234 low and intermedia-risk patients(WBC<10×10~9/L)from January 2003 to April 2015。展开更多
Objective To investigate the characteristics and outcome of glomerulonephritis in patients with both antineutrophil cytoplasmic antibody and anti-glomerular basement membrane antibody.Methods The sera of 23 antiGBM gl...Objective To investigate the characteristics and outcome of glomerulonephritis in patients with both antineutrophil cytoplasmic antibody and anti-glomerular basement membrane antibody.Methods The sera of 23 antiGBM glomerulonephritis patients were collected and were tested for ANCA respectively.Characteristics and outcome of patients with coexisting anti-GBM antibody展开更多
Objective To summarize the clinical of different racial patients with celiac disease(CD)and analyze the disease prevalence,diagnosis and treatment in Chinese population.Methods All the patients were diagnosed as CD an...Objective To summarize the clinical of different racial patients with celiac disease(CD)and analyze the disease prevalence,diagnosis and treatment in Chinese population.Methods All the patients were diagnosed as CD and enrolled in Beijing United Family Hospital between January 2005 and July 2015.Clinical data including nationality,age,symptoms,endoscopic and patho-展开更多
Due to the combined influences such as ore-forming temperature,fluid and metal sources,sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc(Pb-Zn)depo...Due to the combined influences such as ore-forming temperature,fluid and metal sources,sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc(Pb-Zn)deposits.Therefore,trace elements in sphalerite have long been utilized to distinguish Pb-Zn deposit types.However,previous discriminant diagrams usually contain two or three dimensions,which are limited to revealing the complicated interrelations between trace elements of sphalerite and the types of Pb-Zn deposits.In this study,we aim to prove that the sphalerite trace elements can be used to classify the Pb-Zn deposit types and extract key factors from sphalerite trace elements that can dis-criminate Pb-Zn deposit types using machine learning algorithms.A dataset of nearly 3600 sphalerite spot analyses from 95 Pb-Zn deposits worldwide determined by LA-ICP-MS was compiled from peer-reviewed publications,containing 12 elements(Mn,Fe,Co,Cu,Ga,Ge,Ag,Cd,In,Sn,Sb,and Pb)from 5 types,including Sedimentary Exhalative(SEDEX),Mississippi Valley Type(MVT),Volcanic Massive Sulfide(VMS),skarn,and epithermal deposits.Random Forests(RF)is applied to the data processing and the results show that trace elements of sphalerite can successfully discriminate different types of Pb-Zn deposits except for VMS deposits,most of which are falsely distinguished as skarn and epithermal types.To further discriminate VMS deposits,future studies could focus on enlarging the capacity of VMS deposits in datasets and applying other geological factors along with sphalerite trace elements when con-structing the classification model.RF’s feature importance and permutation feature importance were adopted to evaluate the element significance for classification.Besides,a visualized tool,t-distributed stochastic neighbor embedding(t-SNE),was used to verify the results of both classification and evalua-tion.The results presented here show that Mn,Co,and Ge display significant impacts on classification of Pb-Zn deposits and In,Ga,Sn,Cd,and Fe also have relatively important effects compared to the rest ele-ments,confirming that Pb-Zn deposits discrimination is mainly controlled by multi-elements in spha-lerite.Our study hence shows that machine learning algorithm can provide new insights into conventional geochemical analyses,inspiring future research on constructing classification models of mineral deposits using mineral geochemistry data.展开更多
Micro-robots have the characteristics of small size,light weight and flexible movement.To design a micro three-legged crawling robot with multiple motion directions,a novel driving scheme based on the inverse piezoele...Micro-robots have the characteristics of small size,light weight and flexible movement.To design a micro three-legged crawling robot with multiple motion directions,a novel driving scheme based on the inverse piezoelectric effect of piezoelectric ceramics was proposed.The three legs of the robot were equipped with piezoelectric bimorphs as drivers,respectively.The motion principles were analyzed and the overall force analysis was carried out with the theoretical mechanics method.The natural frequency,mode shape and amplitude were analyzed with simulation software COMSOL Multiphysics,the optimal size was determined through parametric analysis,and then the micro three-legged crawling robot was manufactured.The effects of different driving voltages,different driving frequencies,different motion bases and different loads on the motion speed of the robot were tested.It is shown that the maximum speed of single-leg driving was 35.41 cm/s,the switching ability between different motion directions was measured,and the movements in six different directions were achieved.It is demonstrated the feasibility of multi-directional motion of the structure.The research may provide a reference for the design and development of miniature piezoelectric three-legged crawling robots.展开更多
We pose and study a scheduling problem for an electric load to develop an Internet of Things(IoT)control system for power appliances,which takes advantage of real-time dynamic energy pricing.Using historical pricing d...We pose and study a scheduling problem for an electric load to develop an Internet of Things(IoT)control system for power appliances,which takes advantage of real-time dynamic energy pricing.Using historical pricing data from a large U.S.power supplier,we study and compare several dynamic scheduling policies,which can be implemented in a smart home to activate a major appliance(dishwasher,washing machine,clothes dryer)at an optimal time of the day,to minimize electricity costs.We formulate our scheduling task as a supervised machine learning classification problem which activates the load during one of two preferred time bins.The features used in the machine learning problem are hourly market,spot and day-ahead prices along with delayed label of the prior day.We find that boosting tree-based algorithms outperform any other classification approach with measurable reduction of energy costs over certain types of naive and static policies.We observe that the delayed label has most predictive power across features,followed,on average,by spot,hourly market,and day-ahead energy prices.We further discuss implementation issues using a micro controller system coupled with cloud-based serverless computing and dynamic data storage.Our test system includes an interactive voice interface via an intelligent personal assistant.展开更多
OBJECTIVE:To summarize the potential characteristics of convalescent patients with coronavirus disease 2019(COVID-19)in China based on emerging clinical tongue data and guide the treatment and recovery of COVID-19 pat...OBJECTIVE:To summarize the potential characteristics of convalescent patients with coronavirus disease 2019(COVID-19)in China based on emerging clinical tongue data and guide the treatment and recovery of COVID-19 patients from the perspective of Traditional Chinese Medicine tongue diagnosis.METHODS:In this study,we developed and validated radiomics-based and lab-based methods as a novel approach to provide individualized pretreatment evaluation by analyzing different features to mine the orderliness behind tongue data of convalescent patients.In addition,this study analyzed the tongue features of convalescent patients from clinical tongue qualitative values,including thick and thin,fur,peeling,fat and lean,tooth marks and cracked,and greasy and putrid fur.RESULTS:We included 2164 tongue images in total(34%from day 0,35.4%from day 14 and 30.6%from day 28)from convalescent patients.The significance results are shown as follows.Firstly,as the recovery time prolongs,the L average values of tongue and coat decrease from 60.21 to 57.18 and from 60.06 to 57.03 respectively.Secondly,the decrease of abnormality rate of tongue coat,included greasy tongue fur,putrid fur,teeth-mark,thick-thin fur,are of significant statistical difference(P<0.05).Thirdly,the average value of gray-level cooccurrence matrices increases from 0.173 to 0.194,the average value of entropy increases from 0.606 to 0.665,the average value of inverse difference normalized decrease from 0.981 to 0.979,and the average value of dissimilarity decrease from 0.1576 to 0.1828.The details of other radiomics features are describe in results section.CONCLUSIONS:Our experiment shows that patients in different recovery periods have a relationship with quantitative values of tongue images,including L color space of the tongue and coat radiomics features analysis.This relationship can help clinical doctors master the recovery and health of patients as soon as possible and improve their understanding of the potential mechanisms underlying the dynamic changes and mechanisms underlying COVID-19.展开更多
基金The work is supported by the Sub-Project of“Research on Key Technologies and Equipment of Reservoir Stimulation”of China National Petroleum Corporation Post–14th Five-Year Plan Forward-Looking Major Science and Technology Project“Research on New Technology of Monitoring and Diagnosis of Horizontal Well Hydraulic Fracture Network Distribution Pattern”(2021DJ4502).
文摘Multistage multi-cluster hydraulic fracturing has enabled the economic exploitation of shale reservoirs,but the interpretation of hydraulic fracture parameters is challenging.The pressure signals after pump shutdown are influenced by hydraulic fractures,which can reflect the geometric features of hydraulic fracture.The shutdown pressure can be used to interpret the hydraulic fracture parameters in a real-time and cost-effective manner.In this paper,a mathematical model for shutdown pressure evolution is developed considering the effects of wellbore friction,perforation friction and fluid loss in fractures.An efficient numerical simulation method is established by using the method of characteristics.Based on this method,the impacts of fracture half-length,fracture height,opened cluster and perforation number,and filtration coefficient on the evolution of shutdown pressure are analyzed.The results indicate that a larger fracture half-length may hasten the decay of shutdown pressure,while a larger fracture height can slow down the decay of shutdown pressure.A smaller number of opened clusters and perforations can significantly increase the perforation friction and decrease the overall level of shutdown pressure.A larger filtration coefficient may accelerate the fluid filtration in the fracture and hasten the drop of the shutdown pressure.The simulation method of shutdown pressure,as well as the analysis results,has important implications for the interpretation of hydraulic fracture parameters.
文摘The tool for analyzing and evaluating system characteristics based on the AADL model can achieve real-time,reliability,security,and schedulability analysis and evaluation for software-intensive systems.It provides a complete solution for quality analysis of real-time,reliability,safety,and schedulability in the design and demonstration stages of software-intensive systems.By using the system′s multi-characteristic(real-time capability,reliability,safety,schedulability)analysis and evaluation tool based on AADL models,it can meet the software non-functional requirements stipulated by the existing model development standards and specifications.This effectively enhances the efficiency of demonstrating the compliance of the system′s non-functional quality attributes in the design work of our unit′s software-intensive system.It can also improve the performance of our unit′s software-intensive system in engineering inspections and requirement reviews conducted by various organizations.The improvement in the quality level of software-intensive systems can enhance the market competitiveness of our unit′s electronic products.
文摘Dear Sir,Iam Dr.Kavitha S,from the Department of Electronics and Communication Engineering,Nandha Engineering College,Erode,Tamil Nadu,India.I write to present the detection of glaucoma using extreme learning machine(ELM)and fractal feature analysis.Glaucoma is the second most frequent cause of permanent blindness in
文摘As multimedia data sharing increases,data security in mobile devices and its mechanism can be seen as critical.Biometrics combines the physiological and behavioral qualities of an individual to validate their character in real-time.Humans incorporate physiological attributes like a fingerprint,face,iris,palm print,finger knuckle print,Deoxyribonucleic Acid(DNA),and behavioral qualities like walk,voice,mark,or keystroke.The main goal of this paper is to design a robust framework for automatic face recognition.Scale Invariant Feature Transform(SIFT)and Speeded-up Robust Features(SURF)are employed for face recognition.Also,we propose a modified Gabor Wavelet Transform for SIFT/SURF(GWT-SIFT/GWT-SURF)to increase the recognition accuracy of human faces.The proposed scheme is composed of three steps.First,the entropy of the image is removed using Discrete Wavelet Transform(DWT).Second,the computational complexity of the SIFT/SURF is reduced.Third,the accuracy is increased for authentication by the proposed GWT-SIFT/GWT-SURF algorithm.A comparative analysis of the proposed scheme is done on real-time Olivetti Research Laboratory(ORL)and Poznan University of Technology(PUT)databases.When compared to the traditional SIFT/SURF methods,we verify that the GWT-SIFT achieves the better accuracy of 99.32%and the better approach is the GWT-SURF as the run time of the GWT-SURF for 100 images is 3.4 seconds when compared to the GWT-SIFT which has a run time of 4.9 seconds for 100 images.
基金The studies mentioned in this paper were supported in part by Grants R01 CA160205 and R01 CA197150 from the National Cancer Institute,National Institutes of Health,USAGrant HR15-016 from Oklahoma Center for the Advancement of Science and Technology,USA.
文摘In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.
文摘Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-stained live cell cultures. Because these images do not have adequate textural variations. Manual cell segmentation requires massive labor and is a time consuming process. This paper describes an automated cell segmentation method for localizing the cells of Chinese hamster ovary cell culture. Several kinds of high-dimensional feature descriptors, K-means clustering method and Chan-Vese model-based level set are used to extract the cellular regions. The region extracted are used to classify phases in cell cycle. The segmentation results were experimentally assessed. As a result, the proposed method proved to be significant for cell isolation. In the evaluation experiments, we constructed a database of Chinese Hamster Ovary Cell’s microscopic images which includes various photographing environments under the guidance of a biologist.
基金National Social Science Foundation of the Arts Key Project“Research on the Architecture Art and Folk Culture of Chinese Traditional Houses on the Land‘Silk Road’(Number:18AH008)”One of the Periodic Achievements of the Project Entrusted by the Ministry of Culture and Tourism:“Yellow River Culture and Chinese Civilization:Rescue Research on Shaanxi Traditional Residential Buildings and Residential Folk Culture”(Project Approval No.21HH02).
文摘The unique topography and historical and cultural background have determined the diversity and uniqueness of kiln architecture in the Tongchuan area.In addition to the double-slope residential architecture,traditional kiln dwellings with regional characteristics such as Leaning on the cliff cave dwelling,ground Pit cave dwelling and Freestanding cave dwellings have also been formed.This paper takes the inheritance and protection of traditional kiln as the starting point,and through field research and literature analysis,we have systematically collected images,measured data,and drawn up horizontal and vertical profiles and three-dimensional structure drawings of the traditional kiln dwellings in Tongchuan,and concluded the three types of forms and structural characteristics and artistic form characteristics of the traditional kiln dwellings in Tongchuan.The aim is to provide a basis and reference for the protection and inheritance of tangible and intangible cultural heritage in Shaanxi,as well as for subsequent research in this field.
文摘Brain tumors are potentially fatal presence of cancer cells over a human brain,and they need to be segmented for accurate and reliable planning of diag-nosis.Segmentation process must be carried out in different regions based on which the stages of cancer can be accurately derived.Glioma patients exhibit a different level of challenge in terms of cancer or tumors detection as the Magnetic Resonance Imaging(MRI)images possess varying sizes,shapes,positions,and modalities.The scanner used for sensing the location of tumors cells will be sub-jected to additional protocols and measures for accuracy,in turn,increasing the time and affecting the performance of the entire model.In this view,Convolutional Neural Networks deliver suitable models for efficient segmentation and thus delivered promising results.The previous strategies and models failed to adhere to diversity of sizes and shapes,proving to be a well-established solution for detecting tumors of bigger size.Tumors tend to be smaller in size and shape during their premature stages and they can easily evade the algorithms of Convolutional Neural Network(CNN).This proposal intends to furnish a detailed model for sensing early stages of cancer and hence perform segmentation irrespective of the current size and shape of tumors.The size of networks and layers will lead to a significant weightage when multiple kernel sizes are involved,especially in multi-resolution environments.On the other hand,the proposed model is designed with a novel approach including a dilated convolution and level-based learning strat-egy.When the convolution process is dilated,the process of feature extraction deals with multiscale objective and level-based learning eliminates the shortcoming of previous models,thereby enhancing the quality of smaller tumors cells and shapes.The level-based learning approach also encapsulates the feature recon-struction processes which highlights the sensing of small-scale tumors growth.Inclusively,segmenting the images is performed with better accuracy and hence detection becomes better when compared to that of hierarchical approaches.
基金Project supported by the U.S.Department of Energy under the Advanced Scientific Computing Research Program(No.DE-SC0019116)the U.S.Air Force Office of Scientific Research(No.AFOSR FA9550-20-1-0060)。
文摘We propose a self-supervising learning framework for finding the dominant eigenfunction-eigenvalue pairs of linear and self-adjoint operators.We represent target eigenfunctions with coordinate-based neural networks and employ the Fourier positional encodings to enable the approximation of high-frequency modes.We formulate a self-supervised training objective for spectral learning and propose a novel regularization mechanism to ensure that the network finds the exact eigenfunctions instead of a space spanned by the eigenfunctions.Furthermore,we investigate the effect of weight normalization as a mechanism to alleviate the risk of recovering linear dependent modes,allowing us to accurately recover a large number of eigenpairs.The effectiveness of our methods is demonstrated across a collection of representative benchmarks including both local and non-local diffusion operators,as well as high-dimensional time-series data from a video sequence.Our results indicate that the present algorithm can outperform competing approaches in terms of both approximation accuracy and computational cost.
基金supported in part by the Key Research and Development Program for Guangdong Province(No.2019B010136001)in part by Hainan Major Science and Technology Projects(No.ZDKJ2019010)+3 种基金in part by the National Key Research and Development Program of China(No.2016YFB0800803 and No.2018YFB1004005)in part by National Natural Science Foundation of China(No.81960565,No.81260139,No.81060073,No.81560275,No.61562021,No.30560161 and No.61872110)in part by Hainan Special Projects of Social Development(No.ZDYF2018103 and No.2015SF 39)in part by Hainan Association for Academic Excellence Youth Science and Technology Innovation Program(No.201515)
文摘Objective:To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes.Methods:In this retrospective cohort study,we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions.Classification of all-cause,30-day readmission outcomes were modeled using logistic regression,artificial neural network,and Easy Ensemble.F1 statistic,sensitivity,and positive predictive value were used to evaluate the model performance.Results:We identified 14 most influential data features(4 numeric features and 10 categorical features)and evaluated 3 machine learning models with numerous sampling methods(oversampling,undersampling,and hybrid techniques).The deep learning model offered no improvement over traditional models(logistic regression and Easy Ensemble)for predicting readmission,whereas the other two algorithms led to much smaller differences between the training and testing datasets.Conclusions:Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes.But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.
基金supported by the National Natural Science Foundation of China(Grant No.42075007)the Open Grants of the State Key Laboratory of Severe Weather(No.2021LASW-B19).
文摘Brain-Computer Interface(BCI)technology is a way for humans to explore the mysteries of the brain and has applications in many areas of real life.People use this technology to capture brain waves and analyze the electroencephalograph(EEG)signal for feature extraction.Take the medical field as an example,epilepsy disease is threatening human health every moment.We propose a convolutional neural network SECNN-LSTM framework based on the attention mechanism can automatically perform feature extraction and analysis on the collected EEG signals of patients to complete the prediction of epilepsy diseases,overcoming the problem that the disease requires long time EEG monitoring and analysis by manual,which is a large workload and relatively subjective,and improving the prediction accuracy of epilepsy diseases by adding the attention mechanism module.Through experimental tests,the algorithm of SECNN-LSTM can effectively predict the EEG signal of epilepsy disease,and the correct recognition rate is improved.The experiment has some reference value for the subsequent research of EEG signals in other fields in deep learning.
文摘<正>Objective To investigate the clinical features and outcomes of high-risk acute promyelocytic leukemia(APL)patients.Methods A retrospective analysis was conducted to compare the clinical characteristics and prognosis of 118 high-risk APL patients(WBC≥10×10~9/L)and 234 low and intermedia-risk patients(WBC<10×10~9/L)from January 2003 to April 2015。
文摘Objective To investigate the characteristics and outcome of glomerulonephritis in patients with both antineutrophil cytoplasmic antibody and anti-glomerular basement membrane antibody.Methods The sera of 23 antiGBM glomerulonephritis patients were collected and were tested for ANCA respectively.Characteristics and outcome of patients with coexisting anti-GBM antibody
文摘Objective To summarize the clinical of different racial patients with celiac disease(CD)and analyze the disease prevalence,diagnosis and treatment in Chinese population.Methods All the patients were diagnosed as CD and enrolled in Beijing United Family Hospital between January 2005 and July 2015.Clinical data including nationality,age,symptoms,endoscopic and patho-
基金We would like to acknowledge the financial support of the Ministry of Science and Technology of China(Grant No.2021YFC2900300)the National Natural Science Foundation of China(Grant Nos.41772074 and 42172103).
文摘Due to the combined influences such as ore-forming temperature,fluid and metal sources,sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc(Pb-Zn)deposits.Therefore,trace elements in sphalerite have long been utilized to distinguish Pb-Zn deposit types.However,previous discriminant diagrams usually contain two or three dimensions,which are limited to revealing the complicated interrelations between trace elements of sphalerite and the types of Pb-Zn deposits.In this study,we aim to prove that the sphalerite trace elements can be used to classify the Pb-Zn deposit types and extract key factors from sphalerite trace elements that can dis-criminate Pb-Zn deposit types using machine learning algorithms.A dataset of nearly 3600 sphalerite spot analyses from 95 Pb-Zn deposits worldwide determined by LA-ICP-MS was compiled from peer-reviewed publications,containing 12 elements(Mn,Fe,Co,Cu,Ga,Ge,Ag,Cd,In,Sn,Sb,and Pb)from 5 types,including Sedimentary Exhalative(SEDEX),Mississippi Valley Type(MVT),Volcanic Massive Sulfide(VMS),skarn,and epithermal deposits.Random Forests(RF)is applied to the data processing and the results show that trace elements of sphalerite can successfully discriminate different types of Pb-Zn deposits except for VMS deposits,most of which are falsely distinguished as skarn and epithermal types.To further discriminate VMS deposits,future studies could focus on enlarging the capacity of VMS deposits in datasets and applying other geological factors along with sphalerite trace elements when con-structing the classification model.RF’s feature importance and permutation feature importance were adopted to evaluate the element significance for classification.Besides,a visualized tool,t-distributed stochastic neighbor embedding(t-SNE),was used to verify the results of both classification and evalua-tion.The results presented here show that Mn,Co,and Ge display significant impacts on classification of Pb-Zn deposits and In,Ga,Sn,Cd,and Fe also have relatively important effects compared to the rest ele-ments,confirming that Pb-Zn deposits discrimination is mainly controlled by multi-elements in spha-lerite.Our study hence shows that machine learning algorithm can provide new insights into conventional geochemical analyses,inspiring future research on constructing classification models of mineral deposits using mineral geochemistry data.
基金supported by the National Natural Science Foundation of China (grant no.51505133)by Key Research Project in Colleges and Universities of Henan Province (23A460010)by Opening Project of Henan Engineering Laboratory of Photoelectric Sensor and Intelligent Measurement and Control,Henan Polytechnic University (grant no.HELPSIMC-2020-006).
文摘Micro-robots have the characteristics of small size,light weight and flexible movement.To design a micro three-legged crawling robot with multiple motion directions,a novel driving scheme based on the inverse piezoelectric effect of piezoelectric ceramics was proposed.The three legs of the robot were equipped with piezoelectric bimorphs as drivers,respectively.The motion principles were analyzed and the overall force analysis was carried out with the theoretical mechanics method.The natural frequency,mode shape and amplitude were analyzed with simulation software COMSOL Multiphysics,the optimal size was determined through parametric analysis,and then the micro three-legged crawling robot was manufactured.The effects of different driving voltages,different driving frequencies,different motion bases and different loads on the motion speed of the robot were tested.It is shown that the maximum speed of single-leg driving was 35.41 cm/s,the switching ability between different motion directions was measured,and the movements in six different directions were achieved.It is demonstrated the feasibility of multi-directional motion of the structure.The research may provide a reference for the design and development of miniature piezoelectric three-legged crawling robots.
文摘We pose and study a scheduling problem for an electric load to develop an Internet of Things(IoT)control system for power appliances,which takes advantage of real-time dynamic energy pricing.Using historical pricing data from a large U.S.power supplier,we study and compare several dynamic scheduling policies,which can be implemented in a smart home to activate a major appliance(dishwasher,washing machine,clothes dryer)at an optimal time of the day,to minimize electricity costs.We formulate our scheduling task as a supervised machine learning classification problem which activates the load during one of two preferred time bins.The features used in the machine learning problem are hourly market,spot and day-ahead prices along with delayed label of the prior day.We find that boosting tree-based algorithms outperform any other classification approach with measurable reduction of energy costs over certain types of naive and static policies.We observe that the delayed label has most predictive power across features,followed,on average,by spot,hourly market,and day-ahead energy prices.We further discuss implementation issues using a micro controller system coupled with cloud-based serverless computing and dynamic data storage.Our test system includes an interactive voice interface via an intelligent personal assistant.
基金Supported by National key research and development plan-Clinical Evaluation of TCM Intervention in COVID-19 Recovery(No.2020YFC0845000)Clinical study on the prevention and treatment of COVID-19 with integrated Chinese and Western Medicine(No.2020YFC0841600)National Administration of Traditional Chinese Medicine-TCM Emergency Response Project for COVID-19(No.2020ZYLCYJ04)。
文摘OBJECTIVE:To summarize the potential characteristics of convalescent patients with coronavirus disease 2019(COVID-19)in China based on emerging clinical tongue data and guide the treatment and recovery of COVID-19 patients from the perspective of Traditional Chinese Medicine tongue diagnosis.METHODS:In this study,we developed and validated radiomics-based and lab-based methods as a novel approach to provide individualized pretreatment evaluation by analyzing different features to mine the orderliness behind tongue data of convalescent patients.In addition,this study analyzed the tongue features of convalescent patients from clinical tongue qualitative values,including thick and thin,fur,peeling,fat and lean,tooth marks and cracked,and greasy and putrid fur.RESULTS:We included 2164 tongue images in total(34%from day 0,35.4%from day 14 and 30.6%from day 28)from convalescent patients.The significance results are shown as follows.Firstly,as the recovery time prolongs,the L average values of tongue and coat decrease from 60.21 to 57.18 and from 60.06 to 57.03 respectively.Secondly,the decrease of abnormality rate of tongue coat,included greasy tongue fur,putrid fur,teeth-mark,thick-thin fur,are of significant statistical difference(P<0.05).Thirdly,the average value of gray-level cooccurrence matrices increases from 0.173 to 0.194,the average value of entropy increases from 0.606 to 0.665,the average value of inverse difference normalized decrease from 0.981 to 0.979,and the average value of dissimilarity decrease from 0.1576 to 0.1828.The details of other radiomics features are describe in results section.CONCLUSIONS:Our experiment shows that patients in different recovery periods have a relationship with quantitative values of tongue images,including L color space of the tongue and coat radiomics features analysis.This relationship can help clinical doctors master the recovery and health of patients as soon as possible and improve their understanding of the potential mechanisms underlying the dynamic changes and mechanisms underlying COVID-19.
