In the wave of digital and intelligent applications,artificial intelligence(AI)is transforming the development trajectories of industries across the globe.Traditional Chinese medicine(TCM),as a cultural treasure of th...In the wave of digital and intelligent applications,artificial intelligence(AI)is transforming the development trajectories of industries across the globe.Traditional Chinese medicine(TCM),as a cultural treasure of the Chinese nation,carries thousands of years of wisdom and practical experience.However,in the context of the rapid advancements in modern medicine and technology,TCM faces dual challenges:preserving its heritage while innovating.DeepSeek,a major achievement in the field of AI,offers a new opportunity for the development of TCM with its powerful technological capabilities.Exploring the integration of DeepSeek with TCM not only helps modernize the practice but also promises unique contributions to global health.展开更多
The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,s...The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.展开更多
Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high c...Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high computational overhead.This study proposes a lightweight integrated framework for grasp detection and imitation learning,named GD-IL;it comprises a grasp detection algorithm based on manipulability and Gaussian mixture model(manipulability-GMM),and a grasp trajectory generation algorithm based on a two-stage robot imitation learning algorithm(TS-RIL).In the manipulability-GMM algorithm,we apply GMM clustering and ellipse regression to the object point cloud,propose two judgment criteria to generate multiple candidate grasp bounding boxes for the robot,and use manipulability as a metric for selecting the optimal grasp bounding box.The stages of the TS-RIL algorithm are grasp trajectory learning and robot pose optimization.In the first stage,the robot grasp trajectory is characterized using a second-order dynamic movement primitive model and Gaussian mixture regression(GMM).By adjusting the function form of the forcing term,the robot closely approximates the target-grasping trajectory.In the second stage,a robot pose optimization model is built based on the derived pose error formula and manipulability metric.This model allows the robot to adjust its configuration in real time while grasping,thereby effectively avoiding singularities.Finally,an algorithm verification platform is developed based on a Robot Operating System and a series of comparative experiments are conducted in real-world scenarios.The experimental results demonstrate that GD-IL significantly improves the effectiveness and robustness of grasp detection and trajectory imitation learning,outperforming existing state-of-the-art methods in execution efficiency,manipulability,and success rate.展开更多
Helicobacter pylori-associated gastritis(HPAG)is a common condition of the gastrointestinal tract.However,extensive and long-term antibiotic use has resulted in numerous adverse effects,including increased resistance,...Helicobacter pylori-associated gastritis(HPAG)is a common condition of the gastrointestinal tract.However,extensive and long-term antibiotic use has resulted in numerous adverse effects,including increased resistance,gastrointestinal dysfunction,and increased recurrence rates.When these concerns develop,traditional Chinese medicine(TCM)may have advantages.TCM is based on the concept of completeness and aims to eliminate pathogens and strengthen the body.It has the potential to prevent this condition while also boosting the rate of Helicobacter pylori eradication.This review elaborates on the mechanism of TCM treatment for HPAG based on cellular signalling pathways,which reflects the flexibility of TCM in treating diseases and the advantages of multi-level,multipathway,and multi-target treatments for HPAG.展开更多
OBJECTIVE:To research the subtyping and treatment of depression by leveraging studying on extensive Traditional Chinese Medicine(TCM)experiences through artificial intelligence(AI).METHODS:We retrieved depression-rela...OBJECTIVE:To research the subtyping and treatment of depression by leveraging studying on extensive Traditional Chinese Medicine(TCM)experiences through artificial intelligence(AI).METHODS:We retrieved depression-related literature published from inception to April 2023 from databases.From these sources,we extracted symptoms,signs,and prescriptions associated with depression.By utilizing the tree number system in the medical subject headings(MeSH),we established a hierarchical relationship matrix for symptoms/signs,as well as depression sample fingerprints.Using an unsupervised clustering algorithm,we constructed a machine learning model for classifying depression patients.Furthermore,we conducted an analysis of medication rules for each depression cluster.RESULTS:We created a My Structured Query Language(MySQL)database containing datasets of depression-symptoms/signs and depression-herbs,through mining 3522 published clinical literatures on TCM diagnosis and treatment for depression.We established hierarchical relationships among symptoms/signs of depression patients.Our unsupervised clustering analysis revealed that depression patients could be classified into 9 subtypes,with each subtype corresponding to a specific treatment prescription.Notably,one of the depression subtypes was consistently treated by Qi-tonifying formulas and herbs.This finding was further supported by data from Qi-deficiency patients,as there was a high similarity in the top symptoms/signs shared between this subtype and Qi-deficiency diagnosed by TCM.CONCLUSIONS:This study identified the subtypes and TCM treatment of depression by using machine learning and text mining.展开更多
One in every eight men in the US is diagnosed with prostate cancer,making it the most common cancer in men.Gleason grading is one of the most essential diagnostic and prognostic factors for planning the treatment of p...One in every eight men in the US is diagnosed with prostate cancer,making it the most common cancer in men.Gleason grading is one of the most essential diagnostic and prognostic factors for planning the treatment of prostate cancer patients.Traditionally,urological pathologists perform the grading by scoring the morphological pattern,known as the Gleason pattern,in histopathology images.However,thismanual grading is highly subjective,suffers intra-and inter-pathologist variability and lacks reproducibility.An automated grading system could be more efficient,with no subjectivity and higher accuracy and reproducibility.Automated methods presented previously failed to achieve sufficient accuracy,lacked reproducibility and depended on high-resolution images such as 40×.This paper proposes an automated Gleason grading method,ProGENET,to accurately predict the grade using low-resolution images such as 10×.This method first divides the patient’s histopathology whole slide image(WSI)into patches.Then,it detects artifacts and tissue-less regions and predicts the patch-wise grade using an ensemble network of CNN and transformer models.The proposed method adapted the International Society of Urological Pathology(ISUP)grading system and achieved 90.8%accuracy in classifying the patches into healthy and Gleason grades 1 through 5 using 10×WSI,outperforming the state-of-the-art accuracy by 27%.Finally,the patient’s grade was determined by combining the patch-wise results.The method was also demonstrated for 4−class grading and binary classification of prostate cancer,achieving 93.0%and 99.6%accuracy,respectively.The reproducibility was over 90%.Since the proposedmethod determined the grades with higher accuracy and reproducibility using low-resolution images,it is more reliable and effective than existing methods and can potentially improve subsequent therapy decisions.展开更多
AIM:To develop a traditional Chinese medicine(TCM)knowledge graph(KG)for diabetic retinopathy(DR)diagnosis and treatment by integrating literature and medical records,thereby enhancing TCM knowledge accessibility and ...AIM:To develop a traditional Chinese medicine(TCM)knowledge graph(KG)for diabetic retinopathy(DR)diagnosis and treatment by integrating literature and medical records,thereby enhancing TCM knowledge accessibility and providing innovative approaches for TCM inheritance and DR management.METHODS:First,a KG framework was established with a schema-layer design.Second,high-quality literature and electronic medical records served as data sources.Named entity recognition was performed using the ALBERT-BiLSTMCRF model,and semantic relationships were curated by domain experts.Third,knowledge fusion was mainly achieved through an alias library.Subsequently,the data layer was mapped to the schema layer to refine the KG,and knowledge was stored in Neo4j.Finally,exploratory work on intelligent question answering was conducted based on the constructed KG.RESULTS:In Neo4j,a KG for TCM diagnosis and treatment was constructed,incorporating 6 types of labels,5 types of relationships,5 types of attributes,822 nodes,and 1,318 relationship instances.This systematic KG supports logical reasoning and intelligent question answering.The question answering model achieved a precision of 95%,a recall of 95%,and a weighted F1-score of 95%.CONCLUSION:This study proposes a semi-automatic knowledge-mapping scheme to balance integration efficiency and accuracy.Clinical data-driven entity and relationship construction enables digital dialectical reasoning.Exploratory applications show the KG’s potential in intelligent question answering,providing new insights for TCM health management.展开更多
Objective To determine the correlation between traditional Chinese medicine(TCM)inspec-tion of spirit classification and the severity grade of depression based on facial features,offer-ing insights for intelligent int...Objective To determine the correlation between traditional Chinese medicine(TCM)inspec-tion of spirit classification and the severity grade of depression based on facial features,offer-ing insights for intelligent intergrated TCM and western medicine diagnosis of depression.Methods Using the Audio-Visual Emotion Challenge and Workshop(AVEC 2014)public dataset on depression,which conclude 150 interview videos,the samples were classified ac-cording to the TCM inspection of spirit classification:Deshen(得神,presence of spirit),Shaoshen(少神,insufficiency of spirit),and Shenluan(神乱,confusion of spirit).Meanwhile,based on Beck Depression Inventory-II(BDI-II)score for the severity grade of depression,the samples were divided into minimal(0-13,Q1),mild(14-19,Q2),moderate(20-28,Q3),and severe(29-63,Q4).Sixty-eight landmarks were extracted with a ResNet-50 network,and the feature extracion mode was stadardized.Random forest and support vectior machine(SVM)classifiers were used to predict TCM inspection of spirit classification and the severity grade of depression,respectively.A Chi-square test and Apriori association rule mining were then applied to quantify and explore the relationships.Results The analysis revealed a statistically significant and moderately strong association be-tween TCM spirit classification and the severity grade of depression,as confirmed by a Chi-square test(χ^(2)=14.04,P=0.029)with a Cramer’s V effect size of 0.243.Further exploration us-ing association rule mining identified the most compelling rule:“moderate depression(Q3)→Shenluan”.This rule demonstrated a support level of 5%,indicating this specific co-occur-rence was present in 5%of the cohort.Crucially,it achieved a high Confidence of 86%,mean-ing that among patients diagnosed with Q3,86%exhibited the Shenluan pattern according to TCM assessment.The substantial Lift of 2.37 signifies that the observed likelihood of Shenlu-an manifesting in Q3 patients is 2.37 times higher than would be expected by chance if these states were independent-compelling evidence of a highly non-random association.Conse-quently,Shenluan emerges as a distinct and core TCM diagnostic manifestation strongly linked to Q3,forming a clinically significant phenotype within this patient subgroup.展开更多
Ribonucleic acid(RNA)hybridization is widely used in popular RNA simulation software in bioinformatics.However limited by the exponential computational complexity of combin atorial problems,it is challenging to decide...Ribonucleic acid(RNA)hybridization is widely used in popular RNA simulation software in bioinformatics.However limited by the exponential computational complexity of combin atorial problems,it is challenging to decide,within an acceptable time,whether a specific RNA hybridization is effective.We hereby introduce a machine learning based technique to address this problem.Sample machine learning(ML)models tested in the training phase include algorithms based on the boosted tree(BT)random forest(RF),decision tree(DT)and logistic regression(LR),and the corresponding models are obtained.Given the RNA molecular coding training and testing sets,the trained machine learning models are applied to predict the classification of RNA hybridization results.The experiment results show that the op timal predictive accuracies are 96.2%,96.6%,96.0%and 69.8%for the RF,BT,DT and LR-based approaches,respectively,un der the strong constraint condition,compared with traditiona representative methods.Furthermore,the average computation efficiency of the RF,BT,DT and LR-based approaches are208679,269756,184333 and 187458 times higher than that o existing approach,respectively.Given an RNA design,the BT based approach demonstrates high computational efficiency and better predictive accuracy in determining the biological effective ness of molecular hybridization.展开更多
As one of the most important applications of digitalization,intelligence,and service,the digital twin(DT)breaks through the constraints of time,space,cost,and security on physical entities,expands and optimizes the re...As one of the most important applications of digitalization,intelligence,and service,the digital twin(DT)breaks through the constraints of time,space,cost,and security on physical entities,expands and optimizes the relevant functions of physical entities,and enhances their application value.This phenomenon has been widely studied in academia and industry.In this study,the concept and definition of DT,as utilized by scholars and researchers in various fields of industry,are summarized.The internal association between DT and related technologies is explained.The four stages of DT development history are identified.The fundamentals of the technology,evaluation indexes,and model frameworks are reviewed.Subsequently,a conceptual ternary model of DT based on time,space,and logic is proposed.The technology and application status of typical DT systems are described.Finally,the current technical challenges of DT technology are analyzed,and directions for future development are discussed.展开更多
A real-time pricing system of electricity is a system that charges different electricity prices for different hours of the day and for different days, and is effective for reducing the peak and flattening the load cur...A real-time pricing system of electricity is a system that charges different electricity prices for different hours of the day and for different days, and is effective for reducing the peak and flattening the load curve. In this paper, using a Markov decision process (MDP), we propose a modeling method and an optimal control method for real-time pricing systems. First, the outline of real-time pricing systems is explained. Next, a model of a set of customers is derived as a multi-agent MDP. Furthermore, the optimal control problem is formulated, and is reduced to a quadratic programming problem. Finally, a numerical simulation is presented.展开更多
The harmonic index of a graph?G? is defined as where d(u) denotes the degree of a vertex u in G . In this work, we give another expression for the Harmonic index. Using this expression, we give the minimum value of th...The harmonic index of a graph?G? is defined as where d(u) denotes the degree of a vertex u in G . In this work, we give another expression for the Harmonic index. Using this expression, we give the minimum value of the harmonic index for any triangle-free graphs with order n and minimum degree δ ≥ k for k≤ n/2? and show the corresponding extremal graph is the complete graph.展开更多
Mapping rice cultivation is indispensable for monitoring food supply conditions in Bangladesh because of the economical importance of the crop for supporting ever increasing population in the country. In this paper, w...Mapping rice cultivation is indispensable for monitoring food supply conditions in Bangladesh because of the economical importance of the crop for supporting ever increasing population in the country. In this paper, we extract the rice paddy field using the MODIS satellite data for five districts of Pabna, Manikganj, Sherpur, Sylhet, and Gazipur, each of which is characterized with its own aspects in terms of rice cultivation. Land classification is implemented using the vegetation index information derived from the red (band 1) and near-infrared (band 2) bands of MODIS 8-day composite time series data for the two time periods of 2001-2003 and 2011-2013. Results of unsupervised classification indicate that the paddy area coverage increased about 4% and 1% in Gazipur and Sylhet, respectively. In Pabna, Manikganj, and Sherpur, on the other hand, paddy area decreased by 10%, 2% and 5%, respectively, whereas notable increase of 12%, 2% and 7% was found in homestead area coverage, which is becoming more and more important for better management of small-scale agroforestry. At the same time, in Sherpur and Sylhet, forest area increased by 1% and 2% over the same time period. As a validation of these results, the changes detected in Gazipur are compared with those previously derived from the analysis of Landsat data with higher spatial resolution of 30 m as compared with that of MODIS (250 m). Also, the seasonal rice cropping pattern is studied in these five districts for discriminating cultivated rice types. These changes suggest that as a whole, efforts are being made to increase the food production, though the influence of population pressure and economic growth is apparent in these regions.展开更多
Online social media networks are gaining attention worldwide,with an increasing number of people relying on them to connect,communicate and share their daily pertinent event-related information.Event detection is now ...Online social media networks are gaining attention worldwide,with an increasing number of people relying on them to connect,communicate and share their daily pertinent event-related information.Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People.In this paper,a novel Event Detection model based on Scoring and Word Embedding(ED-SWE)is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets.The proposed ED-SWE model can distill high-quality tweets,reduce the negative impact of the advent of spam,and identify latent events in the data streams automatically.Moreover,a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data.In order to further improve the performance of the Expectation-Maximization(EM)iteration algorithm,a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely.Finally,a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event.Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.展开更多
Objective： The progression of human cancer is characterized by the accumulation of genetic instability. An increasing number of experimental genetic molecular techniques have been used to detect chromosome aberration...Objective： The progression of human cancer is characterized by the accumulation of genetic instability. An increasing number of experimental genetic molecular techniques have been used to detect chromosome aberrations. Previous studies on chromosome abnormalities often focused on identifying the frequent loci of chromosome alterations, but rarely addressed the issue of interrelationship of chromosomal abnormalities. In the last few years, several mathematical models have been employed to construct models of carcinogenesis, in an attempt to identify the time order and cause-and-effect relationship of chromosome aberrations. The principles and applications of these models are reviewed and compared in this paper. Mathematical modeling of carcinogenesis can contribute to our understanding of the molecular genetics of tumor development, and identification of cancer related genes, thus leading to improved clinical practice of cancer.展开更多
With the widespread use of Internet,the amount of data in the field of traditional Chinese medicine(TCM)is growing exponentially.Consequently,there is much attention on the collection of useful knowledge as well as it...With the widespread use of Internet,the amount of data in the field of traditional Chinese medicine(TCM)is growing exponentially.Consequently,there is much attention on the collection of useful knowledge as well as its effective organization and expression.Knowledge graphs have thus emerged,and knowledge reasoning based on this tool has become one of the hot spots of research.This paper first presents a brief introduction to the development of knowledge graphs and knowledge reasoning,and explores the significance of knowledge reasoning.Secondly,the mainstream knowledge reasoning methods,including knowledge reasoning based on traditional rules,knowledge reasoning based on distributed feature representation,and knowledge reasoning based on neural networks are introduced.Then,using stroke as an example,the knowledge reasoning methods are expounded,the principles and characteristics of commonly used knowledge reasoning methods are summarized,and the research and applications of knowledge reasoning techniques in TCM in recent years are sorted out.Finally,we summarize the problems faced in the development of knowledge reasoning in TCM,and put forward the importance of constructing a knowledge reasoning model suitable for the field of TCM.展开更多
Frequent itemset mining is an essential problem in data mining and plays a key role in many data mining applications.However,users’personal privacy will be leaked in the mining process.In recent years,application of ...Frequent itemset mining is an essential problem in data mining and plays a key role in many data mining applications.However,users’personal privacy will be leaked in the mining process.In recent years,application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method.Local differential privacy means that users first perturb the original data and then send these data to the aggregator,preventing the aggregator from revealing the user’s private information.We propose a novel framework that implements frequent itemset mining under local differential privacy and is applicable to user’s multi-attribute.The main technique has bitmap encoding for converting the user’s original data into a binary string.It also includes how to choose the best perturbation algorithm for varying user attributes,and uses the frequent pattern tree(FP-tree)algorithm to mine frequent itemsets.Finally,we incorporate the threshold random response(TRR)algorithm in the framework and compare it with the existing algorithms,and demonstrate that the TRR algorithm has higher accuracy for mining frequent itemsets.展开更多
Though most of tourists tend to visit multiple sightseeing spots during their sightseeing tours,it is difficult for them to efficiently acquire information necessary for their sightseeing tour planning.Additionally,in...Though most of tourists tend to visit multiple sightseeing spots during their sightseeing tours,it is difficult for them to efficiently acquire information necessary for their sightseeing tour planning.Additionally,in rural area,many people hope to use public transportation which has not developed as much as in urban areas.The present study aims to design and develop a support system of sightseeing tour planning in Japanese rural areas,adopting the information related to real timetables of public transportation on both the sea and the ground,and genetic algorism(GA).The system was developed integrating moving route recommendation system,web-geographic information systems(Web-GIS),and augmented reality(AR)application.Furthermore,Kagawa Prefecture in the western part was selected as the operation target area.The operation of the system was conducted for two months,targeting those inside and outside the operation target area,and web questionnaire surveys were conducted.From the evaluation results based on the web questionnaire surveys,the usefulness of all the original functions as well as of the entire system was analyzed.Additionally,though some users could not easily use the system,it is expected that they will get used to utilizing it by their continuous use.展开更多
In the early stage of software development,a software requirements specification(SRS)is essential,and whether the requirements are clear and explicit is the key.However,due to various reasons,there may be a large numb...In the early stage of software development,a software requirements specification(SRS)is essential,and whether the requirements are clear and explicit is the key.However,due to various reasons,there may be a large number of misunderstandings.To generate high-quality software requirements specifications,numerous researchers have developed a variety of ways to improve the quality of SRS.In this paper,we propose a questions extraction method based on SRS elements decomposition,which evaluates the quality of SRS in the form of numerical indicators.The proposed method not only evaluates the quality of SRSs but also helps in the detection of defects,especially the description problem and omission defects in SRSs.To verify the effectiveness of the proposed method,we conducted a controlled experiment to compare the ability of checklist-based review(CBR)and the proposed method in the SRS review.The CBR is a classicmethod of reviewing SRS defects.After a lot of practice and improvement for a long time,CBR has excellent review ability in improving the quality of software requirements specifications.The experimental results with 40 graduate studentsmajoring in software engineering confirmed the effectiveness and advantages of the proposed method.However,the shortcomings and deficiencies of the proposed method are also observed through the experiment.Furthermore,the proposed method has been tried out by engineers with practical work experience in software development industry and received good feedback.展开更多
文摘In the wave of digital and intelligent applications,artificial intelligence(AI)is transforming the development trajectories of industries across the globe.Traditional Chinese medicine(TCM),as a cultural treasure of the Chinese nation,carries thousands of years of wisdom and practical experience.However,in the context of the rapid advancements in modern medicine and technology,TCM faces dual challenges:preserving its heritage while innovating.DeepSeek,a major achievement in the field of AI,offers a new opportunity for the development of TCM with its powerful technological capabilities.Exploring the integration of DeepSeek with TCM not only helps modernize the practice but also promises unique contributions to global health.
基金supported in part by the National Natural Science Foundation of China under Grant 62371181in part by the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029+1 种基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2021R1A2B5B02087169)supported under the framework of international cooperation program managed by the National Research Foundation of Korea(2022K2A9A1A01098051)。
文摘The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.
基金Supported by National Natural Science Foundation of China(Grant No.52475280)Shaanxi Provincial Natural Science Basic Research Program(Grant No.2025SYSSYSZD-105).
文摘Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high computational overhead.This study proposes a lightweight integrated framework for grasp detection and imitation learning,named GD-IL;it comprises a grasp detection algorithm based on manipulability and Gaussian mixture model(manipulability-GMM),and a grasp trajectory generation algorithm based on a two-stage robot imitation learning algorithm(TS-RIL).In the manipulability-GMM algorithm,we apply GMM clustering and ellipse regression to the object point cloud,propose two judgment criteria to generate multiple candidate grasp bounding boxes for the robot,and use manipulability as a metric for selecting the optimal grasp bounding box.The stages of the TS-RIL algorithm are grasp trajectory learning and robot pose optimization.In the first stage,the robot grasp trajectory is characterized using a second-order dynamic movement primitive model and Gaussian mixture regression(GMM).By adjusting the function form of the forcing term,the robot closely approximates the target-grasping trajectory.In the second stage,a robot pose optimization model is built based on the derived pose error formula and manipulability metric.This model allows the robot to adjust its configuration in real time while grasping,thereby effectively avoiding singularities.Finally,an algorithm verification platform is developed based on a Robot Operating System and a series of comparative experiments are conducted in real-world scenarios.The experimental results demonstrate that GD-IL significantly improves the effectiveness and robustness of grasp detection and trajectory imitation learning,outperforming existing state-of-the-art methods in execution efficiency,manipulability,and success rate.
基金Supported by National Natural Science Foundation of China,No.82374323and Hunan Graduate Research Innovation Project,No.2023CX15.
文摘Helicobacter pylori-associated gastritis(HPAG)is a common condition of the gastrointestinal tract.However,extensive and long-term antibiotic use has resulted in numerous adverse effects,including increased resistance,gastrointestinal dysfunction,and increased recurrence rates.When these concerns develop,traditional Chinese medicine(TCM)may have advantages.TCM is based on the concept of completeness and aims to eliminate pathogens and strengthen the body.It has the potential to prevent this condition while also boosting the rate of Helicobacter pylori eradication.This review elaborates on the mechanism of TCM treatment for HPAG based on cellular signalling pathways,which reflects the flexibility of TCM in treating diseases and the advantages of multi-level,multipathway,and multi-target treatments for HPAG.
基金Supported by the National Key Research and Development Plan:Regulatory Pathways and Mechanisms of Conception and Governor Vessels Surface Stimulation in the Treatment of “Uterus and Brain” Disorders (No. 2022YFC3500405)Taishan Scholar Youth Project of Shandong Province (No. tsqn202306188)+3 种基金the National Natural Science Foundation of China:Epigenetic Regulation of Vascular Neural Unit Function by Vascular Endothelial Histone Deacetylase:a New Antidepressant Application and Mechanism of Huangqi Guizhi Wuwu Decoction (No. 82274128)the National Natural Science Foundation of China:Study on the Anti-depression Mechanism of Electroacupuncture based on the Regulation of Biological Clock Gene in Prefrontal Cortex (No. 81973948)Joint Fund of Shandong Provincial Natural Science Foundation:High-Throughput Screening and Key Target Validation of Traditional Chinese Medicine Blood-Activating and Stasis-Resolving Components using a Vascular Microenvironment Simulation Chip (No. ZR2021LZY020)Student Research Training Program of Shandong University of Traditional Chinese Medicine:the Therapeutic Mechanism of Huangqi Guizhi Wuwu Decoction on Chronic Unpredictable Mild Stress Model Mice Based on the Endothelial Nitric Oxide Synthase-Nitric oxide Pathway Research of Vascular Endothelial Eells (No. 202210441008)
文摘OBJECTIVE:To research the subtyping and treatment of depression by leveraging studying on extensive Traditional Chinese Medicine(TCM)experiences through artificial intelligence(AI).METHODS:We retrieved depression-related literature published from inception to April 2023 from databases.From these sources,we extracted symptoms,signs,and prescriptions associated with depression.By utilizing the tree number system in the medical subject headings(MeSH),we established a hierarchical relationship matrix for symptoms/signs,as well as depression sample fingerprints.Using an unsupervised clustering algorithm,we constructed a machine learning model for classifying depression patients.Furthermore,we conducted an analysis of medication rules for each depression cluster.RESULTS:We created a My Structured Query Language(MySQL)database containing datasets of depression-symptoms/signs and depression-herbs,through mining 3522 published clinical literatures on TCM diagnosis and treatment for depression.We established hierarchical relationships among symptoms/signs of depression patients.Our unsupervised clustering analysis revealed that depression patients could be classified into 9 subtypes,with each subtype corresponding to a specific treatment prescription.Notably,one of the depression subtypes was consistently treated by Qi-tonifying formulas and herbs.This finding was further supported by data from Qi-deficiency patients,as there was a high similarity in the top symptoms/signs shared between this subtype and Qi-deficiency diagnosed by TCM.CONCLUSIONS:This study identified the subtypes and TCM treatment of depression by using machine learning and text mining.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘One in every eight men in the US is diagnosed with prostate cancer,making it the most common cancer in men.Gleason grading is one of the most essential diagnostic and prognostic factors for planning the treatment of prostate cancer patients.Traditionally,urological pathologists perform the grading by scoring the morphological pattern,known as the Gleason pattern,in histopathology images.However,thismanual grading is highly subjective,suffers intra-and inter-pathologist variability and lacks reproducibility.An automated grading system could be more efficient,with no subjectivity and higher accuracy and reproducibility.Automated methods presented previously failed to achieve sufficient accuracy,lacked reproducibility and depended on high-resolution images such as 40×.This paper proposes an automated Gleason grading method,ProGENET,to accurately predict the grade using low-resolution images such as 10×.This method first divides the patient’s histopathology whole slide image(WSI)into patches.Then,it detects artifacts and tissue-less regions and predicts the patch-wise grade using an ensemble network of CNN and transformer models.The proposed method adapted the International Society of Urological Pathology(ISUP)grading system and achieved 90.8%accuracy in classifying the patches into healthy and Gleason grades 1 through 5 using 10×WSI,outperforming the state-of-the-art accuracy by 27%.Finally,the patient’s grade was determined by combining the patch-wise results.The method was also demonstrated for 4−class grading and binary classification of prostate cancer,achieving 93.0%and 99.6%accuracy,respectively.The reproducibility was over 90%.Since the proposedmethod determined the grades with higher accuracy and reproducibility using low-resolution images,it is more reliable and effective than existing methods and can potentially improve subsequent therapy decisions.
基金Supported by Hunan Province Traditional Chinese Medicine Research Project(No.B2023043)Hunan Provincial Department of Education Scientific Research Project(No.22B0386)+1 种基金Research Project of Hunan Provincial Health Commission(No.20256982)Hunan University of Traditional Chinese Medicine Campus Level Research Fund Project(No.2022XJZKC004).
文摘AIM:To develop a traditional Chinese medicine(TCM)knowledge graph(KG)for diabetic retinopathy(DR)diagnosis and treatment by integrating literature and medical records,thereby enhancing TCM knowledge accessibility and providing innovative approaches for TCM inheritance and DR management.METHODS:First,a KG framework was established with a schema-layer design.Second,high-quality literature and electronic medical records served as data sources.Named entity recognition was performed using the ALBERT-BiLSTMCRF model,and semantic relationships were curated by domain experts.Third,knowledge fusion was mainly achieved through an alias library.Subsequently,the data layer was mapped to the schema layer to refine the KG,and knowledge was stored in Neo4j.Finally,exploratory work on intelligent question answering was conducted based on the constructed KG.RESULTS:In Neo4j,a KG for TCM diagnosis and treatment was constructed,incorporating 6 types of labels,5 types of relationships,5 types of attributes,822 nodes,and 1,318 relationship instances.This systematic KG supports logical reasoning and intelligent question answering.The question answering model achieved a precision of 95%,a recall of 95%,and a weighted F1-score of 95%.CONCLUSION:This study proposes a semi-automatic knowledge-mapping scheme to balance integration efficiency and accuracy.Clinical data-driven entity and relationship construction enables digital dialectical reasoning.Exploratory applications show the KG’s potential in intelligent question answering,providing new insights for TCM health management.
基金Research and Development Plan of Key Areas of Hunan Science and Technology Department (2022SK2044)Clinical Research Center for Depressive Disorder in Hunan Province (2021SK4022)。
文摘Objective To determine the correlation between traditional Chinese medicine(TCM)inspec-tion of spirit classification and the severity grade of depression based on facial features,offer-ing insights for intelligent intergrated TCM and western medicine diagnosis of depression.Methods Using the Audio-Visual Emotion Challenge and Workshop(AVEC 2014)public dataset on depression,which conclude 150 interview videos,the samples were classified ac-cording to the TCM inspection of spirit classification:Deshen(得神,presence of spirit),Shaoshen(少神,insufficiency of spirit),and Shenluan(神乱,confusion of spirit).Meanwhile,based on Beck Depression Inventory-II(BDI-II)score for the severity grade of depression,the samples were divided into minimal(0-13,Q1),mild(14-19,Q2),moderate(20-28,Q3),and severe(29-63,Q4).Sixty-eight landmarks were extracted with a ResNet-50 network,and the feature extracion mode was stadardized.Random forest and support vectior machine(SVM)classifiers were used to predict TCM inspection of spirit classification and the severity grade of depression,respectively.A Chi-square test and Apriori association rule mining were then applied to quantify and explore the relationships.Results The analysis revealed a statistically significant and moderately strong association be-tween TCM spirit classification and the severity grade of depression,as confirmed by a Chi-square test(χ^(2)=14.04,P=0.029)with a Cramer’s V effect size of 0.243.Further exploration us-ing association rule mining identified the most compelling rule:“moderate depression(Q3)→Shenluan”.This rule demonstrated a support level of 5%,indicating this specific co-occur-rence was present in 5%of the cohort.Crucially,it achieved a high Confidence of 86%,mean-ing that among patients diagnosed with Q3,86%exhibited the Shenluan pattern according to TCM assessment.The substantial Lift of 2.37 signifies that the observed likelihood of Shenlu-an manifesting in Q3 patients is 2.37 times higher than would be expected by chance if these states were independent-compelling evidence of a highly non-random association.Conse-quently,Shenluan emerges as a distinct and core TCM diagnostic manifestation strongly linked to Q3,forming a clinically significant phenotype within this patient subgroup.
基金supported by the National Natural Science Foundation of China(U1204608,61472370,61672469,61822701)
文摘Ribonucleic acid(RNA)hybridization is widely used in popular RNA simulation software in bioinformatics.However limited by the exponential computational complexity of combin atorial problems,it is challenging to decide,within an acceptable time,whether a specific RNA hybridization is effective.We hereby introduce a machine learning based technique to address this problem.Sample machine learning(ML)models tested in the training phase include algorithms based on the boosted tree(BT)random forest(RF),decision tree(DT)and logistic regression(LR),and the corresponding models are obtained.Given the RNA molecular coding training and testing sets,the trained machine learning models are applied to predict the classification of RNA hybridization results.The experiment results show that the op timal predictive accuracies are 96.2%,96.6%,96.0%and 69.8%for the RF,BT,DT and LR-based approaches,respectively,un der the strong constraint condition,compared with traditiona representative methods.Furthermore,the average computation efficiency of the RF,BT,DT and LR-based approaches are208679,269756,184333 and 187458 times higher than that o existing approach,respectively.Given an RNA design,the BT based approach demonstrates high computational efficiency and better predictive accuracy in determining the biological effective ness of molecular hybridization.
基金the National Natural Science Foundation of China,Nos.62072388 and U21A20515Fuxiaquan Self-Created Area Collaborative Special Project,No.3ZCQXT202001+3 种基金Fujian Science and Technology Plan Industrial Guiding Project,No.2H0047Fujian Natural Science Foundation Project,No.2J016012019 Fujian Science and Technology Plan Innovation Fund Project,No.2C0021and Fujian Sunshine Charity Foundation.
文摘As one of the most important applications of digitalization,intelligence,and service,the digital twin(DT)breaks through the constraints of time,space,cost,and security on physical entities,expands and optimizes the relevant functions of physical entities,and enhances their application value.This phenomenon has been widely studied in academia and industry.In this study,the concept and definition of DT,as utilized by scholars and researchers in various fields of industry,are summarized.The internal association between DT and related technologies is explained.The four stages of DT development history are identified.The fundamentals of the technology,evaluation indexes,and model frameworks are reviewed.Subsequently,a conceptual ternary model of DT based on time,space,and logic is proposed.The technology and application status of typical DT systems are described.Finally,the current technical challenges of DT technology are analyzed,and directions for future development are discussed.
文摘A real-time pricing system of electricity is a system that charges different electricity prices for different hours of the day and for different days, and is effective for reducing the peak and flattening the load curve. In this paper, using a Markov decision process (MDP), we propose a modeling method and an optimal control method for real-time pricing systems. First, the outline of real-time pricing systems is explained. Next, a model of a set of customers is derived as a multi-agent MDP. Furthermore, the optimal control problem is formulated, and is reduced to a quadratic programming problem. Finally, a numerical simulation is presented.
文摘The harmonic index of a graph?G? is defined as where d(u) denotes the degree of a vertex u in G . In this work, we give another expression for the Harmonic index. Using this expression, we give the minimum value of the harmonic index for any triangle-free graphs with order n and minimum degree δ ≥ k for k≤ n/2? and show the corresponding extremal graph is the complete graph.
文摘Mapping rice cultivation is indispensable for monitoring food supply conditions in Bangladesh because of the economical importance of the crop for supporting ever increasing population in the country. In this paper, we extract the rice paddy field using the MODIS satellite data for five districts of Pabna, Manikganj, Sherpur, Sylhet, and Gazipur, each of which is characterized with its own aspects in terms of rice cultivation. Land classification is implemented using the vegetation index information derived from the red (band 1) and near-infrared (band 2) bands of MODIS 8-day composite time series data for the two time periods of 2001-2003 and 2011-2013. Results of unsupervised classification indicate that the paddy area coverage increased about 4% and 1% in Gazipur and Sylhet, respectively. In Pabna, Manikganj, and Sherpur, on the other hand, paddy area decreased by 10%, 2% and 5%, respectively, whereas notable increase of 12%, 2% and 7% was found in homestead area coverage, which is becoming more and more important for better management of small-scale agroforestry. At the same time, in Sherpur and Sylhet, forest area increased by 1% and 2% over the same time period. As a validation of these results, the changes detected in Gazipur are compared with those previously derived from the analysis of Landsat data with higher spatial resolution of 30 m as compared with that of MODIS (250 m). Also, the seasonal rice cropping pattern is studied in these five districts for discriminating cultivated rice types. These changes suggest that as a whole, efforts are being made to increase the food production, though the influence of population pressure and economic growth is apparent in these regions.
基金The work reported in this paper has been supported by UK-Jiangsu 20-20 World Class University Initiative programme.
文摘Online social media networks are gaining attention worldwide,with an increasing number of people relying on them to connect,communicate and share their daily pertinent event-related information.Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People.In this paper,a novel Event Detection model based on Scoring and Word Embedding(ED-SWE)is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets.The proposed ED-SWE model can distill high-quality tweets,reduce the negative impact of the advent of spam,and identify latent events in the data streams automatically.Moreover,a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data.In order to further improve the performance of the Expectation-Maximization(EM)iteration algorithm,a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely.Finally,a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event.Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.
基金supported by a grant from the Education Department of Zhejiang Province (No.Y200803235)
文摘Objective： The progression of human cancer is characterized by the accumulation of genetic instability. An increasing number of experimental genetic molecular techniques have been used to detect chromosome aberrations. Previous studies on chromosome abnormalities often focused on identifying the frequent loci of chromosome alterations, but rarely addressed the issue of interrelationship of chromosomal abnormalities. In the last few years, several mathematical models have been employed to construct models of carcinogenesis, in an attempt to identify the time order and cause-and-effect relationship of chromosome aberrations. The principles and applications of these models are reviewed and compared in this paper. Mathematical modeling of carcinogenesis can contribute to our understanding of the molecular genetics of tumor development, and identification of cancer related genes, thus leading to improved clinical practice of cancer.
基金The National Key R&D Program of China(2018AAA0102100)Hunan Provincial Department of Education Outstanding Youth Project(22B0385)+2 种基金Open Fund of the Domestic First-class Discipline Construction Project of Chinese Medicine of Hunan University of Chinese Medicine(2018ZYX17)Electronic Science and Technology Discipline Open Fund Project of School of Information Science and Engineering,Hunan University of Chinese Medicine(2018-2)Hunan University of Chinese Medicine Graduate Innovation Project(2022CX122)。
文摘With the widespread use of Internet,the amount of data in the field of traditional Chinese medicine(TCM)is growing exponentially.Consequently,there is much attention on the collection of useful knowledge as well as its effective organization and expression.Knowledge graphs have thus emerged,and knowledge reasoning based on this tool has become one of the hot spots of research.This paper first presents a brief introduction to the development of knowledge graphs and knowledge reasoning,and explores the significance of knowledge reasoning.Secondly,the mainstream knowledge reasoning methods,including knowledge reasoning based on traditional rules,knowledge reasoning based on distributed feature representation,and knowledge reasoning based on neural networks are introduced.Then,using stroke as an example,the knowledge reasoning methods are expounded,the principles and characteristics of commonly used knowledge reasoning methods are summarized,and the research and applications of knowledge reasoning techniques in TCM in recent years are sorted out.Finally,we summarize the problems faced in the development of knowledge reasoning in TCM,and put forward the importance of constructing a knowledge reasoning model suitable for the field of TCM.
基金This paper is supported by the Inner Mongolia Natural Science Foundation(Grant Number:2018MS06026,Sponsored Authors:Liu,H.and Ma,X.,Sponsors’Websites:http://kjt.nmg.gov.cn/)the Science and Technology Program of Inner Mongolia Autonomous Region(Grant Number:2019GG116,Sponsored Authors:Liu,H.and Ma,X.,Sponsors’Websites:http://kjt.nmg.gov.cn/).
文摘Frequent itemset mining is an essential problem in data mining and plays a key role in many data mining applications.However,users’personal privacy will be leaked in the mining process.In recent years,application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method.Local differential privacy means that users first perturb the original data and then send these data to the aggregator,preventing the aggregator from revealing the user’s private information.We propose a novel framework that implements frequent itemset mining under local differential privacy and is applicable to user’s multi-attribute.The main technique has bitmap encoding for converting the user’s original data into a binary string.It also includes how to choose the best perturbation algorithm for varying user attributes,and uses the frequent pattern tree(FP-tree)algorithm to mine frequent itemsets.Finally,we incorporate the threshold random response(TRR)algorithm in the framework and compare it with the existing algorithms,and demonstrate that the TRR algorithm has higher accuracy for mining frequent itemsets.
文摘Though most of tourists tend to visit multiple sightseeing spots during their sightseeing tours,it is difficult for them to efficiently acquire information necessary for their sightseeing tour planning.Additionally,in rural area,many people hope to use public transportation which has not developed as much as in urban areas.The present study aims to design and develop a support system of sightseeing tour planning in Japanese rural areas,adopting the information related to real timetables of public transportation on both the sea and the ground,and genetic algorism(GA).The system was developed integrating moving route recommendation system,web-geographic information systems(Web-GIS),and augmented reality(AR)application.Furthermore,Kagawa Prefecture in the western part was selected as the operation target area.The operation of the system was conducted for two months,targeting those inside and outside the operation target area,and web questionnaire surveys were conducted.From the evaluation results based on the web questionnaire surveys,the usefulness of all the original functions as well as of the entire system was analyzed.Additionally,though some users could not easily use the system,it is expected that they will get used to utilizing it by their continuous use.
基金This work was partially supported by the Natural Science Foundation of Jiangsu Province under Grant No.BK20201462partially supported by the Scientific Research Support Project of Jiangsu Normal University under Grant No.21XSRX001.
文摘In the early stage of software development,a software requirements specification(SRS)is essential,and whether the requirements are clear and explicit is the key.However,due to various reasons,there may be a large number of misunderstandings.To generate high-quality software requirements specifications,numerous researchers have developed a variety of ways to improve the quality of SRS.In this paper,we propose a questions extraction method based on SRS elements decomposition,which evaluates the quality of SRS in the form of numerical indicators.The proposed method not only evaluates the quality of SRSs but also helps in the detection of defects,especially the description problem and omission defects in SRSs.To verify the effectiveness of the proposed method,we conducted a controlled experiment to compare the ability of checklist-based review(CBR)and the proposed method in the SRS review.The CBR is a classicmethod of reviewing SRS defects.After a lot of practice and improvement for a long time,CBR has excellent review ability in improving the quality of software requirements specifications.The experimental results with 40 graduate studentsmajoring in software engineering confirmed the effectiveness and advantages of the proposed method.However,the shortcomings and deficiencies of the proposed method are also observed through the experiment.Furthermore,the proposed method has been tried out by engineers with practical work experience in software development industry and received good feedback.
