Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginn...Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginners to grasp the intricate composition rules of formulas.To address this gap,we introduce Formula-S,a situated visualization method for TCM formula learning in augmented reality(AR)and evaluate its performance.This study aims to evaluate the effectiveness of Formula-S in enhancing TCM formula learning for beginners by comparing it with traditional text-based formula learning and web-based visualization.Methods Formula-S is an interactive AR tool designed for TCM formula learning,featuring three modes(3D,Web,and Table).The dataset included TCM formulas and herb properties extracted from authoritative references,including textbook and the SymMap database.In Formula-S,the hierarchical visualization of the formulas as herbal medicine compositions,is linked to the multidimensional herb attribute visualization and embedded in the real world,where real herb samples are presented.To evaluate its effectiveness,a controlled study(n=30)was conducted.Participants who had no formal TCM knowledge were tasked with herbal medicine identification,formula composition,and recognition.In the study,participants interacted with the AR tool through HoloLens 2.Data were collected on both task performance(accuracy and response time)and user experience,with a focus on task efficiency,accuracy,and user preference across the different learning modes.Results The situated visualization method of Formula-S had comparable accuracy to other methods but shorter response time for herbal formula learning tasks.Regarding user experience,our new approach demonstrated the highest system usability and lowest task load,effectively reducing cognitive load and allowing users to complete tasks with greater ease and efficiency.Participants reported that Formula-S enhanced their learning experience through its intuitive interface and immersive AR environment,suggesting this approach offers usability advantages for TCM education.Conclusions The situated visualization method in Formula-S offers more efficient and accurate searching capabilities compared to traditional and web-based methods.Additionally,it provides superior contextual understanding of TCM formulas,making it a promising new solution for TCM learning.展开更多
The rapid advancement of information technology has promoted the development of informatization in universities. The freshmen welcome information system of universities is the first important system to showcase the di...The rapid advancement of information technology has promoted the development of informatization in universities. The freshmen welcome information system of universities is the first important system to showcase the digital level of the university to new students. With the expansion of the enrollment scale of universities, improving the efficiency of welcoming work has become an urgent problem to be solved. This article analyzes the characteristics and existing problems of the welcoming work, combined with the main technical methods of information system construction, and based on the comprehensive situation of information systems in our university, proposes the construction goals and ideas of the welcoming information system, summarizes the construction process and operation results of the welcome system, and explores possible directions for future optimization.展开更多
As a result of breakthroughs in computational approaches mixed with a boom in multi-omics data,the development of numerous digital medicines and bioinformatics tools have aided in speeding up the healthcare industry p...As a result of breakthroughs in computational approaches mixed with a boom in multi-omics data,the development of numerous digital medicines and bioinformatics tools have aided in speeding up the healthcare industry process.The traditional healthcare development method has been further rationalized with the introduction of artificial intelligence(AI),deep learning(DL),and machine learning(ML).Wide-ranging biological and clinical data in the form of big data,which is stored in various databases worldwide,serve as the raw material for AI-based methods and aid in the precise identification of patterns and models.These patterns and models can be used to identify novel therapeutically active molecules with significantly less time,financial investment,and workforce.This review article provides insights into understanding the principles of AI technologies such as next-generation sequencing(NGS),natural language processing(NLP),radiological images,patients-electronic medical records(EMR),and drug discovery as well as how they should be used in ethical,economic,and social ramifications of AI.This review also highlights various applications of AI in the healthcare industry,along with the analyses of different AI technologies.Additionally,it will offer helpful suggestions to assist decision-makers in creating an AI plan that would support their shift to a digital healthcare system.展开更多
Polymer informatics faces challenges owing to data scarcity arising from complex chemistries,experimental limitations,and process-ing-dependent properties.This review presents the recent advances in data-efficient mac...Polymer informatics faces challenges owing to data scarcity arising from complex chemistries,experimental limitations,and process-ing-dependent properties.This review presents the recent advances in data-efficient machine learning for polymers.First,data preparation tech-niques such as data augmentation and rational representation help expand the dataset size and develop useful features for learning.Second,modeling approaches,including classical algorithms and physics-informed methods,enhance the model robustness and reliability under limited data conditions.Third,learning strategies,such as transferlearning and active learning,aim to improve generalization and guide efficient data ac-quisition.This review concludes by outlining future opportunities in machine learning for small-data scenarios in polymers.This review is expect-ed to serve as a useful tool for newcomers and offer deeper insights for experienced researchers in the field.展开更多
Volunteer engagement in emergency management,focusing on mitigating adverse consequences,has attracted scholarly and practitioner attention.Digital volunteering helps overcome the limitations of traditional on-site vo...Volunteer engagement in emergency management,focusing on mitigating adverse consequences,has attracted scholarly and practitioner attention.Digital volunteering helps overcome the limitations of traditional on-site volunteering through extensive volunteering opportunities in emergency management.This study utilizes a case text analysis and interviews to investigate and categorize digital volunteer services in emergency scenarios.Based on two key dimensions—direct recipients of volunteer services and the nature of the services rendered—the study presents four types of digital volunteer services:bridging,supportive,complementary,and collaborative.Moreover,it delineates eight role archetypes digital volunteers assume in emergency response situations along with their primary service contributions.Compared to conventional on-site volunteer services,digital volunteer services offer unique advantages while facing specific challenges.Finally,this study offers recommendations in four dimensions for the robust development of digital volunteer services,contributing to more effective and sustainable emergency management practices.展开更多
As a key national project,a newly built plateau railway features a large proportion of tunnels and high construction difficulty.To reduce the voids in the secondary lining of tunnels and address issues such as ineffec...As a key national project,a newly built plateau railway features a large proportion of tunnels and high construction difficulty.To reduce the voids in the secondary lining of tunnels and address issues such as ineffective vibration of the vault,vault voiding,and the inability to monitor the casting status during tunnel lining construction with ordinary lining trolleys,a new smart lining trolley with large clearance that integrates functions such as vibration,automatic casting,and pressure monitoring has been developed.This was achieved by combining the functional design of the new smart lining trolley,comparing traditional construction techniques,and introducing information-based and intelligent design concepts.Through simulation calculations using finite element software modeling,it is verified that the structural stiffness,strength,and other performance parameters of the smart lining trolley meet the technical design requirements.展开更多
We are now in the era of big data for biodiversity science.More and larger datasets on species geographic distributions,community composition,and functional traits are now becoming more increasingly than ever before.C...We are now in the era of big data for biodiversity science.More and larger datasets on species geographic distributions,community composition,and functional traits are now becoming more increasingly than ever before.Correctly applying taxon names is a prerequisite for robust biodiversity studies of all taxonomic groups.展开更多
Targeted cancer therapy has emerged as a promising alternative to conventional chemotherapy,which is often plagued by poor selectivity,off-target effects,and drug resistance.Among the various targeting agents in devel...Targeted cancer therapy has emerged as a promising alternative to conventional chemotherapy,which is often plagued by poor selectivity,off-target effects,and drug resistance.Among the various targeting agents in development,peptides stand out for their unique advantages,including minimal immunogenicity,high tissue penetration,and ease of modification.Their small size,specificity,and flexibility allow them to target cancer cells while minimizing damage to healthy tissue selectively.Peptide-based therapies have shown great potential in enhancing the efficacy of drug delivery,improving tumor imaging,and reducing adverse effects.With cancer responsible for millions of deaths worldwide,the development of peptide-based therapeutics offers new hope in addressing the limitations of current treatments.As detailed studies on different aspects of targeting peptides are crucial for optimizing drug development,this review provides a comprehensive overview of the literature on tumor-targeting peptides,including their structure,sources,modes of action,and their application in cancer therapy—both as standalone agents and in fusion drugs.Additionally,various computational tools for peptide-based tumor-targeting drug design and validation are explored.The promising results from these studies highlight peptides as ideal candidates for targeted cancer therapies,offering valuable insights for researchers and accelerating the discovery of novel anti-tumor peptide base drug candidates.展开更多
In the health field,longitudinal studies involve the recording of clinical observations of the same sample of pa-tients over successive periods,referred to as waves.This type of database serves as a valuable source of...In the health field,longitudinal studies involve the recording of clinical observations of the same sample of pa-tients over successive periods,referred to as waves.This type of database serves as a valuable source of infor-mation and insights,particularly when examining the temporal aspect,allowing the extraction of relevant and non-obvious knowledge.The triadic concept analysis theory has been proposed to describe the ternary re-lationships between objects,attributes,and conditions.In this study,we present a methodology for exploring longitudinal health databases using both the triadic theory and triadic rules,which are similar to association rules but incorporate temporal relations.Through four case studies,we demonstrate the potential of applying triadic analysis to longitudinal databases to identify risk patterns,enhance decision-making processes,and deepen our understanding of temporal dynamics.These findings suggest a promising approach for describing longitudinal databases and obtaining insights to improve clinical decision-support systems for disease treatment.展开更多
In the information age,the movement of livelihood factors between urban and rural areas,particularly involving farmers in rural tourism destinations,has stimulated the spatial revitalization and functional renewal of ...In the information age,the movement of livelihood factors between urban and rural areas,particularly involving farmers in rural tourism destinations,has stimulated the spatial revitalization and functional renewal of these destinations,thereby facilitating the advancement of rural revitalization.The examination of livelihood strategy selection among farmers in rural tourist destinations during the information age is a critical scientific inquiry for the sustainable development of rural tourism.This research holds substantial significance for enhancing the livelihood capabilities of farmers and contributing to the revitalization of rural industries.This study utilizes Xijiang Miao Village as a case study to conduct an in-depth analysis of farmers’livelihood strategy selection through the application of the entropy evaluation method and the binary logistic regression model.The findings indicate that the robust development of rural tourism has led to significant alterations in the original composition of livelihood capital in tourist destinations.Currently,four distinct types of livelihoods have been identified:agricultural-based,migrantwork-oriented,tourism-specialized,and tourism-supplemented.Due to the uneven distribution of livelihood capital,the predominant livelihood modes for farmers in rural tourism destinations remain the agricultural-based and tourism-supplemented types.Human capital and economic capital are the primary factors influencing the strategic choices made by farmers.In this context,a livelihood selection strategy for farmers in rural tourist destinations,specifically in Xijiang Miao Village,is proposed,taking into account the background of rural informatization.展开更多
Background Medical informatics accumulated vast amounts of data for clinical diagnosis and treatment.However,limited access to follow-up data and the difficulty in integrating data across diverse platforms continue to...Background Medical informatics accumulated vast amounts of data for clinical diagnosis and treatment.However,limited access to follow-up data and the difficulty in integrating data across diverse platforms continue to pose significant barriers to clinical research progress.In response,our research team has embarked on the development of a specialized clinical research database for cardiology,thereby establishing a comprehensive digital platform that facilitates both clinical decision-making and research endeavors.Methods The database incorporated actual clinical data from patients who received treatment at the Cardiovascular Medicine Department of Chinese PLA General Hospital from 2012 to 2021.It included comprehensive data on patients'basic information,medical history,non-invasive imaging studies,laboratory test results,as well as peri-procedural information related to interventional surgeries,extracted from the Hospital Information System.Additionally,an innovative artificial intelligence(AI)-powered interactive follow-up system had been developed,ensuring that nearly all myocardial infarction patients received at least one post-discharge follow-up,thereby achieving comprehensive data management throughout the entire care continuum for highrisk patients.Results This database integrates extensive cross-sectional and longitudinal patient data,with a focus on higher-risk acute coronary syndrome patients.It achieves the integration of structured and unstructured clinical data,while innovatively incorporating AI and automatic speech recognition technologies to enhance data integration and workflow efficiency.It creates a comprehensive patient view,thereby improving diagnostic and follow-up quality,and provides high-quality data to support clinical research.Despite limitations in unstructured data standardization and biological sample integrity,the database's development is accompanied by ongoing optimization efforts.Conclusion The cardiovascular specialty clinical database is a comprehensive digital archive integrating clinical treatment and research,which facilitates the digital and intelligent transformation of clinical diagnosis and treatment processes.It supports clinical decision-making and offers data support and potential research directions for the specialized management of cardiovascular diseases.展开更多
This study examines the current state of informatization education among county high school students.While students demonstrate a strong demand for informatization education,they face significant challenges,including ...This study examines the current state of informatization education among county high school students.While students demonstrate a strong demand for informatization education,they face significant challenges,including inadequate hardware,limited access to online learning resources,and insufficient teacher proficiency in informatization education.Through a questionnaire survey,the research reveals an urgent need for expanded information technology courses and specialized training programs.In response,this paper proposes strategies such as increasing investment in IT education,optimizing teaching methodologies,and providing additional learning opportunities to enhance student engagement and comprehensively improve the quality of IT education and learning outcomes in county high schools.展开更多
BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)adm...BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)admission in Medical Information Mart for Intensive Care(MIMIC-IV),a prediction system for the ED triage stage would be helpful.Previous methods such as the quick Sequential Organ Failure Assessment(qSOFA)are more suitable for screening than for prediction in the ED,and we aimed to fi nd a light-weight,convenient prediction method through machine learning.METHODS:We accessed the MIMIC-IV for sepsis patient data in the EDs.Our dataset comprised demographic information,vital signs,and synthetic features.Extreme Gradient Boosting(XGBoost)was used to predict the risk of developing sepsis within 24 h after ED admission.Additionally,SHapley Additive exPlanations(SHAP)was employed to provide a comprehensive interpretation of the model's results.Ten percent of the patients were randomly selected as the testing set,while the remaining patients were used for training with 10-fold cross-validation.RESULTS:For 10-fold cross-validation on 14,957 samples,we reached an accuracy of 84.1%±0.3%and an area under the receiver operating characteristic(ROC)curve of 0.92±0.02.The model achieved similar performance on the testing set of 1,662 patients.SHAP values showed that the fi ve most important features were acuity,arrival transportation,age,shock index,and respiratory rate.CONCLUSION:Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage.This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.展开更多
Due to the recent developments in communications technology,cognitive computations have been used in smart healthcare techniques that can combine massive medical data,artificial intelligence,federated learning,bio-ins...Due to the recent developments in communications technology,cognitive computations have been used in smart healthcare techniques that can combine massive medical data,artificial intelligence,federated learning,bio-inspired computation,and the Internet of Medical Things.It has helped in knowledge sharing and scaling ability between patients,doctors,and clinics for effective treatment of patients.Speech-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising results.Since the subject’s speech can be remotely recorded and submitted for further examination,it offers a quick,economical,dependable,and noninvasive prospective alternative detection approach.However,the two main requirements of this are higher accuracy and lower computational complexity and,in many cases,these two requirements do not correlate with each other.This problem has been taken up in this paper to develop a low computational complexity-based neural network with higher accuracy.A cascaded perceptual functional link artificial neural network(PFLANN)is used to capture the nonlinearity in the data for better classification performance with low computational complexity.The proposed model is being tested for multiple respiratory diseases,and the analysis of various performance matrices demonstrates the superior performance of the proposed model both in terms of accuracy and complexity.展开更多
This paper introduces a cutting-edge framework for personalized chronic pain management,leveraging the power of artificial intelligence(AI)and personality insights.It explores the intricate relationship between person...This paper introduces a cutting-edge framework for personalized chronic pain management,leveraging the power of artificial intelligence(AI)and personality insights.It explores the intricate relationship between personality traits and pain perception,expression,and management,identifying key correlations that influence an individual’s experience of pain.By integrating personality psychology with AI-driven personality assessment,this framework offers a novel approach to tailoring chronic pain management strategies for each patient’s unique personality profile.It highlights the relevance of well-established personality theories such as the Big Five and the Myers-Briggs Type Indicator(MBTI)in shaping personalized pain management plans.Additionally,the paper introduces multimodal AI-driven personality assessment,emphasizing the ethical considerations and data collection processes necessary for its implementation.Through illustrative case studies,the paper exemplifies how this framework can lead to more effective and patient-centered pain relief,ultimately enhancing overall well-being.In conclusion,the paper positions the need of an“AI-Powered Holistic Pain Management Initiative”which has the potential to transform chronic pain management by providing personalized,data-driven solutions and create a multifaceted research impact influencing clinical practice,patient outcomes,healthcare policy,and the broader scientific community’s understanding of personalized medicine and AI-driven interventions.展开更多
Tax risk behavior causes serious loss of fiscal revenue,damages the country’s public infrastructure,and disturbs the market economic order of fair competition.In recent years,tax risk detection,driven by information ...Tax risk behavior causes serious loss of fiscal revenue,damages the country’s public infrastructure,and disturbs the market economic order of fair competition.In recent years,tax risk detection,driven by information technology such as data mining and artificial intelligence,has received extensive attention.To promote the high-quality development of tax risk detection methods,this paper provides the first comprehensive overview and summary of existing tax risk detection methods worldwide.More specifi-cally,it first discusses the causes and negative impacts of tax risk behaviors,along with the development of tax risk detection.It then focuses on data-mining-based tax risk detection methods utilized around the world.Based on the different principles employed by the algorithms,existing risk detection methods can be divided into two categories:relationship-based and non-relationship-based.A total of 14 risk detection methods are identified,and each method is thoroughly explored and analyzed.Finally,four major technical bottlenecks of current data-driven tax risk detection methods are analyzed and discussed,including the difficulty of integrating and using fiscal and tax fragmented knowledge,unexplainable risk detection results,the high cost of risk detection algorithms,and the reliance of existing algorithms on labeled information.After investigating these issues,it is concluded that knowledge-guided and datadriven big data knowledge engineering will be the development trend in the field of tax risk in the future;that is,the gradual transition of tax risk detection from informatization to intelligence is the future development direction.展开更多
Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM...Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.展开更多
Peanut allergy is majorly related to severe food induced allergic reactions.Several food including cow's milk,hen's eggs,soy,wheat,peanuts,tree nuts(walnuts,hazelnuts,almonds,cashews,pecans and pistachios),fis...Peanut allergy is majorly related to severe food induced allergic reactions.Several food including cow's milk,hen's eggs,soy,wheat,peanuts,tree nuts(walnuts,hazelnuts,almonds,cashews,pecans and pistachios),fish and shellfish are responsible for more than 90%of food allergies.Here,we provide promising insights using a large-scale data-driven analysis,comparing the mechanistic feature and biological relevance of different ingredients presents in peanuts,tree nuts(walnuts,almonds,cashews,pecans and pistachios)and soybean.Additionally,we have analysed the chemical compositions of peanuts in different processed form raw,boiled and dry-roasted.Using the data-driven approach we are able to generate new hypotheses to explain why nuclear receptors like the peroxisome proliferator-activated receptors(PPARs)and its isoform and their interaction with dietary lipids may have significant effect on allergic response.The results obtained from this study will direct future experimeantal and clinical studies to understand the role of dietary lipids and PPARisoforms to exert pro-inflammatory or anti-inflammatory functions on cells of the innate immunity and influence antigen presentation to the cells of the adaptive immunity.展开更多
Since the establishment of the Collaboratory for the Study of Earthquake Predictability,China(CSEP-CN)center,no comprehensive study has been conducted on the parameter models of the Pattern Informatics(PI)method withi...Since the establishment of the Collaboratory for the Study of Earthquake Predictability,China(CSEP-CN)center,no comprehensive study has been conducted on the parameter models of the Pattern Informatics(PI)method within the China Seismic Experimental Site(CSES)region.Additionally,the boundary issues of the study area have been a subject of ongoing debate.Tian et al.(2024)indicates that variations in seismic activity within the region impact the predictive efficacy of the PI method.展开更多
Predictive modeling of photocatalytic NO removal is highly desirable for efficient air pollution abatement.However,great challenges remain in precisely predicting photocatalytic performance and understanding interacti...Predictive modeling of photocatalytic NO removal is highly desirable for efficient air pollution abatement.However,great challenges remain in precisely predicting photocatalytic performance and understanding interactions of diverse features in the catalytic systems.Herein,a dataset of g-C_(3) N_(4)-based catalysts with 255 data points was collected from peer-reviewed publications and machine learning(ML)model was proposed to predict the NO removal rate.The result shows that the Gradient Boosting Decision Tree(GBDT)demonstrated the greatest prediction accuracy with R 2 of 0.999 and 0.907 on the training and test data,respectively.The SHAP value and feature importance analysis revealed that the empirical categories for NO removal rate,in the order of importance,were catalyst characteristics>reaction process>preparation conditions.Moreover,the partial dependence plots broke the ML black box to further quantify the marginal contributions of the input features(e.g.,doping ratio,flow rate,and pore volume)to the model output outcomes.This ML approach presents a pure data-driven,interpretable framework,which provides new insights into the influence of catalyst characteristics,reaction process,and preparation conditions on NO removal.展开更多
文摘Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginners to grasp the intricate composition rules of formulas.To address this gap,we introduce Formula-S,a situated visualization method for TCM formula learning in augmented reality(AR)and evaluate its performance.This study aims to evaluate the effectiveness of Formula-S in enhancing TCM formula learning for beginners by comparing it with traditional text-based formula learning and web-based visualization.Methods Formula-S is an interactive AR tool designed for TCM formula learning,featuring three modes(3D,Web,and Table).The dataset included TCM formulas and herb properties extracted from authoritative references,including textbook and the SymMap database.In Formula-S,the hierarchical visualization of the formulas as herbal medicine compositions,is linked to the multidimensional herb attribute visualization and embedded in the real world,where real herb samples are presented.To evaluate its effectiveness,a controlled study(n=30)was conducted.Participants who had no formal TCM knowledge were tasked with herbal medicine identification,formula composition,and recognition.In the study,participants interacted with the AR tool through HoloLens 2.Data were collected on both task performance(accuracy and response time)and user experience,with a focus on task efficiency,accuracy,and user preference across the different learning modes.Results The situated visualization method of Formula-S had comparable accuracy to other methods but shorter response time for herbal formula learning tasks.Regarding user experience,our new approach demonstrated the highest system usability and lowest task load,effectively reducing cognitive load and allowing users to complete tasks with greater ease and efficiency.Participants reported that Formula-S enhanced their learning experience through its intuitive interface and immersive AR environment,suggesting this approach offers usability advantages for TCM education.Conclusions The situated visualization method in Formula-S offers more efficient and accurate searching capabilities compared to traditional and web-based methods.Additionally,it provides superior contextual understanding of TCM formulas,making it a promising new solution for TCM learning.
文摘The rapid advancement of information technology has promoted the development of informatization in universities. The freshmen welcome information system of universities is the first important system to showcase the digital level of the university to new students. With the expansion of the enrollment scale of universities, improving the efficiency of welcoming work has become an urgent problem to be solved. This article analyzes the characteristics and existing problems of the welcoming work, combined with the main technical methods of information system construction, and based on the comprehensive situation of information systems in our university, proposes the construction goals and ideas of the welcoming information system, summarizes the construction process and operation results of the welcome system, and explores possible directions for future optimization.
文摘As a result of breakthroughs in computational approaches mixed with a boom in multi-omics data,the development of numerous digital medicines and bioinformatics tools have aided in speeding up the healthcare industry process.The traditional healthcare development method has been further rationalized with the introduction of artificial intelligence(AI),deep learning(DL),and machine learning(ML).Wide-ranging biological and clinical data in the form of big data,which is stored in various databases worldwide,serve as the raw material for AI-based methods and aid in the precise identification of patterns and models.These patterns and models can be used to identify novel therapeutically active molecules with significantly less time,financial investment,and workforce.This review article provides insights into understanding the principles of AI technologies such as next-generation sequencing(NGS),natural language processing(NLP),radiological images,patients-electronic medical records(EMR),and drug discovery as well as how they should be used in ethical,economic,and social ramifications of AI.This review also highlights various applications of AI in the healthcare industry,along with the analyses of different AI technologies.Additionally,it will offer helpful suggestions to assist decision-makers in creating an AI plan that would support their shift to a digital healthcare system.
基金supported by the National Natural Science Foundation of China(No.22473006)the Central Government Guiding Local Science and Technology Development Fund(No.2025ZY01029).
文摘Polymer informatics faces challenges owing to data scarcity arising from complex chemistries,experimental limitations,and process-ing-dependent properties.This review presents the recent advances in data-efficient machine learning for polymers.First,data preparation tech-niques such as data augmentation and rational representation help expand the dataset size and develop useful features for learning.Second,modeling approaches,including classical algorithms and physics-informed methods,enhance the model robustness and reliability under limited data conditions.Third,learning strategies,such as transferlearning and active learning,aim to improve generalization and guide efficient data ac-quisition.This review concludes by outlining future opportunities in machine learning for small-data scenarios in polymers.This review is expect-ed to serve as a useful tool for newcomers and offer deeper insights for experienced researchers in the field.
基金funded by the Major Program of the National Fund of Philosophy and Social Science of China(Grant No.:21&ZD163).
文摘Volunteer engagement in emergency management,focusing on mitigating adverse consequences,has attracted scholarly and practitioner attention.Digital volunteering helps overcome the limitations of traditional on-site volunteering through extensive volunteering opportunities in emergency management.This study utilizes a case text analysis and interviews to investigate and categorize digital volunteer services in emergency scenarios.Based on two key dimensions—direct recipients of volunteer services and the nature of the services rendered—the study presents four types of digital volunteer services:bridging,supportive,complementary,and collaborative.Moreover,it delineates eight role archetypes digital volunteers assume in emergency response situations along with their primary service contributions.Compared to conventional on-site volunteer services,digital volunteer services offer unique advantages while facing specific challenges.Finally,this study offers recommendations in four dimensions for the robust development of digital volunteer services,contributing to more effective and sustainable emergency management practices.
文摘As a key national project,a newly built plateau railway features a large proportion of tunnels and high construction difficulty.To reduce the voids in the secondary lining of tunnels and address issues such as ineffective vibration of the vault,vault voiding,and the inability to monitor the casting status during tunnel lining construction with ordinary lining trolleys,a new smart lining trolley with large clearance that integrates functions such as vibration,automatic casting,and pressure monitoring has been developed.This was achieved by combining the functional design of the new smart lining trolley,comparing traditional construction techniques,and introducing information-based and intelligent design concepts.Through simulation calculations using finite element software modeling,it is verified that the structural stiffness,strength,and other performance parameters of the smart lining trolley meet the technical design requirements.
基金supported by the Innovation Program of Shanghai Municipal Education Commission(No.2023ZKZD36).
文摘We are now in the era of big data for biodiversity science.More and larger datasets on species geographic distributions,community composition,and functional traits are now becoming more increasingly than ever before.Correctly applying taxon names is a prerequisite for robust biodiversity studies of all taxonomic groups.
文摘Targeted cancer therapy has emerged as a promising alternative to conventional chemotherapy,which is often plagued by poor selectivity,off-target effects,and drug resistance.Among the various targeting agents in development,peptides stand out for their unique advantages,including minimal immunogenicity,high tissue penetration,and ease of modification.Their small size,specificity,and flexibility allow them to target cancer cells while minimizing damage to healthy tissue selectively.Peptide-based therapies have shown great potential in enhancing the efficacy of drug delivery,improving tumor imaging,and reducing adverse effects.With cancer responsible for millions of deaths worldwide,the development of peptide-based therapeutics offers new hope in addressing the limitations of current treatments.As detailed studies on different aspects of targeting peptides are crucial for optimizing drug development,this review provides a comprehensive overview of the literature on tumor-targeting peptides,including their structure,sources,modes of action,and their application in cancer therapy—both as standalone agents and in fusion drugs.Additionally,various computational tools for peptide-based tumor-targeting drug design and validation are explored.The promising results from these studies highlight peptides as ideal candidates for targeted cancer therapies,offering valuable insights for researchers and accelerating the discovery of novel anti-tumor peptide base drug candidates.
文摘In the health field,longitudinal studies involve the recording of clinical observations of the same sample of pa-tients over successive periods,referred to as waves.This type of database serves as a valuable source of infor-mation and insights,particularly when examining the temporal aspect,allowing the extraction of relevant and non-obvious knowledge.The triadic concept analysis theory has been proposed to describe the ternary re-lationships between objects,attributes,and conditions.In this study,we present a methodology for exploring longitudinal health databases using both the triadic theory and triadic rules,which are similar to association rules but incorporate temporal relations.Through four case studies,we demonstrate the potential of applying triadic analysis to longitudinal databases to identify risk patterns,enhance decision-making processes,and deepen our understanding of temporal dynamics.These findings suggest a promising approach for describing longitudinal databases and obtaining insights to improve clinical decision-support systems for disease treatment.
基金Sponsored by Science and Technology Project of Qiandongnan Prefecture“The Integration Effect of Green‘Culture-Tourism’Industry in Qiandongnan Prefecture and Its Enhancement Mechanism”(202305)Science and Technology Project of Qiandongnan Prefecture“Construction and Empirical Study of an Integration Model for Tourism and Culture Industry in Qiandongnan from the Perspective of Quality Development”(2022084).
文摘In the information age,the movement of livelihood factors between urban and rural areas,particularly involving farmers in rural tourism destinations,has stimulated the spatial revitalization and functional renewal of these destinations,thereby facilitating the advancement of rural revitalization.The examination of livelihood strategy selection among farmers in rural tourist destinations during the information age is a critical scientific inquiry for the sustainable development of rural tourism.This research holds substantial significance for enhancing the livelihood capabilities of farmers and contributing to the revitalization of rural industries.This study utilizes Xijiang Miao Village as a case study to conduct an in-depth analysis of farmers’livelihood strategy selection through the application of the entropy evaluation method and the binary logistic regression model.The findings indicate that the robust development of rural tourism has led to significant alterations in the original composition of livelihood capital in tourist destinations.Currently,four distinct types of livelihoods have been identified:agricultural-based,migrantwork-oriented,tourism-specialized,and tourism-supplemented.Due to the uneven distribution of livelihood capital,the predominant livelihood modes for farmers in rural tourism destinations remain the agricultural-based and tourism-supplemented types.Human capital and economic capital are the primary factors influencing the strategic choices made by farmers.In this context,a livelihood selection strategy for farmers in rural tourist destinations,specifically in Xijiang Miao Village,is proposed,taking into account the background of rural informatization.
基金Noncommunicable Chronic Diseases-National Science and Technology Major Project(2023ZD0503906)。
文摘Background Medical informatics accumulated vast amounts of data for clinical diagnosis and treatment.However,limited access to follow-up data and the difficulty in integrating data across diverse platforms continue to pose significant barriers to clinical research progress.In response,our research team has embarked on the development of a specialized clinical research database for cardiology,thereby establishing a comprehensive digital platform that facilitates both clinical decision-making and research endeavors.Methods The database incorporated actual clinical data from patients who received treatment at the Cardiovascular Medicine Department of Chinese PLA General Hospital from 2012 to 2021.It included comprehensive data on patients'basic information,medical history,non-invasive imaging studies,laboratory test results,as well as peri-procedural information related to interventional surgeries,extracted from the Hospital Information System.Additionally,an innovative artificial intelligence(AI)-powered interactive follow-up system had been developed,ensuring that nearly all myocardial infarction patients received at least one post-discharge follow-up,thereby achieving comprehensive data management throughout the entire care continuum for highrisk patients.Results This database integrates extensive cross-sectional and longitudinal patient data,with a focus on higher-risk acute coronary syndrome patients.It achieves the integration of structured and unstructured clinical data,while innovatively incorporating AI and automatic speech recognition technologies to enhance data integration and workflow efficiency.It creates a comprehensive patient view,thereby improving diagnostic and follow-up quality,and provides high-quality data to support clinical research.Despite limitations in unstructured data standardization and biological sample integrity,the database's development is accompanied by ongoing optimization efforts.Conclusion The cardiovascular specialty clinical database is a comprehensive digital archive integrating clinical treatment and research,which facilitates the digital and intelligent transformation of clinical diagnosis and treatment processes.It supports clinical decision-making and offers data support and potential research directions for the specialized management of cardiovascular diseases.
文摘This study examines the current state of informatization education among county high school students.While students demonstrate a strong demand for informatization education,they face significant challenges,including inadequate hardware,limited access to online learning resources,and insufficient teacher proficiency in informatization education.Through a questionnaire survey,the research reveals an urgent need for expanded information technology courses and specialized training programs.In response,this paper proposes strategies such as increasing investment in IT education,optimizing teaching methodologies,and providing additional learning opportunities to enhance student engagement and comprehensively improve the quality of IT education and learning outcomes in county high schools.
基金supported by the National Key Research and Development Program of China(2021YFC2500803)the CAMS Innovation Fund for Medical Sciences(2021-I2M-1-056).
文摘BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)admission in Medical Information Mart for Intensive Care(MIMIC-IV),a prediction system for the ED triage stage would be helpful.Previous methods such as the quick Sequential Organ Failure Assessment(qSOFA)are more suitable for screening than for prediction in the ED,and we aimed to fi nd a light-weight,convenient prediction method through machine learning.METHODS:We accessed the MIMIC-IV for sepsis patient data in the EDs.Our dataset comprised demographic information,vital signs,and synthetic features.Extreme Gradient Boosting(XGBoost)was used to predict the risk of developing sepsis within 24 h after ED admission.Additionally,SHapley Additive exPlanations(SHAP)was employed to provide a comprehensive interpretation of the model's results.Ten percent of the patients were randomly selected as the testing set,while the remaining patients were used for training with 10-fold cross-validation.RESULTS:For 10-fold cross-validation on 14,957 samples,we reached an accuracy of 84.1%±0.3%and an area under the receiver operating characteristic(ROC)curve of 0.92±0.02.The model achieved similar performance on the testing set of 1,662 patients.SHAP values showed that the fi ve most important features were acuity,arrival transportation,age,shock index,and respiratory rate.CONCLUSION:Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage.This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.
文摘Due to the recent developments in communications technology,cognitive computations have been used in smart healthcare techniques that can combine massive medical data,artificial intelligence,federated learning,bio-inspired computation,and the Internet of Medical Things.It has helped in knowledge sharing and scaling ability between patients,doctors,and clinics for effective treatment of patients.Speech-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising results.Since the subject’s speech can be remotely recorded and submitted for further examination,it offers a quick,economical,dependable,and noninvasive prospective alternative detection approach.However,the two main requirements of this are higher accuracy and lower computational complexity and,in many cases,these two requirements do not correlate with each other.This problem has been taken up in this paper to develop a low computational complexity-based neural network with higher accuracy.A cascaded perceptual functional link artificial neural network(PFLANN)is used to capture the nonlinearity in the data for better classification performance with low computational complexity.The proposed model is being tested for multiple respiratory diseases,and the analysis of various performance matrices demonstrates the superior performance of the proposed model both in terms of accuracy and complexity.
文摘This paper introduces a cutting-edge framework for personalized chronic pain management,leveraging the power of artificial intelligence(AI)and personality insights.It explores the intricate relationship between personality traits and pain perception,expression,and management,identifying key correlations that influence an individual’s experience of pain.By integrating personality psychology with AI-driven personality assessment,this framework offers a novel approach to tailoring chronic pain management strategies for each patient’s unique personality profile.It highlights the relevance of well-established personality theories such as the Big Five and the Myers-Briggs Type Indicator(MBTI)in shaping personalized pain management plans.Additionally,the paper introduces multimodal AI-driven personality assessment,emphasizing the ethical considerations and data collection processes necessary for its implementation.Through illustrative case studies,the paper exemplifies how this framework can lead to more effective and patient-centered pain relief,ultimately enhancing overall well-being.In conclusion,the paper positions the need of an“AI-Powered Holistic Pain Management Initiative”which has the potential to transform chronic pain management by providing personalized,data-driven solutions and create a multifaceted research impact influencing clinical practice,patient outcomes,healthcare policy,and the broader scientific community’s understanding of personalized medicine and AI-driven interventions.
基金supported by the Key Research and Development Project in Shaanxi Province (2023GXLH-024)the National Natural Science Foundation of China (62250009,62002282,62037001,and 62192781).
文摘Tax risk behavior causes serious loss of fiscal revenue,damages the country’s public infrastructure,and disturbs the market economic order of fair competition.In recent years,tax risk detection,driven by information technology such as data mining and artificial intelligence,has received extensive attention.To promote the high-quality development of tax risk detection methods,this paper provides the first comprehensive overview and summary of existing tax risk detection methods worldwide.More specifi-cally,it first discusses the causes and negative impacts of tax risk behaviors,along with the development of tax risk detection.It then focuses on data-mining-based tax risk detection methods utilized around the world.Based on the different principles employed by the algorithms,existing risk detection methods can be divided into two categories:relationship-based and non-relationship-based.A total of 14 risk detection methods are identified,and each method is thoroughly explored and analyzed.Finally,four major technical bottlenecks of current data-driven tax risk detection methods are analyzed and discussed,including the difficulty of integrating and using fiscal and tax fragmented knowledge,unexplainable risk detection results,the high cost of risk detection algorithms,and the reliance of existing algorithms on labeled information.After investigating these issues,it is concluded that knowledge-guided and datadriven big data knowledge engineering will be the development trend in the field of tax risk in the future;that is,the gradual transition of tax risk detection from informatization to intelligence is the future development direction.
基金authors are thankful to the Deanship of Scientific Research at Najran University for funding this work,under the Research Groups Funding Program Grant Code(NU/RG/SERC/12/27).
文摘Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.
文摘Peanut allergy is majorly related to severe food induced allergic reactions.Several food including cow's milk,hen's eggs,soy,wheat,peanuts,tree nuts(walnuts,hazelnuts,almonds,cashews,pecans and pistachios),fish and shellfish are responsible for more than 90%of food allergies.Here,we provide promising insights using a large-scale data-driven analysis,comparing the mechanistic feature and biological relevance of different ingredients presents in peanuts,tree nuts(walnuts,almonds,cashews,pecans and pistachios)and soybean.Additionally,we have analysed the chemical compositions of peanuts in different processed form raw,boiled and dry-roasted.Using the data-driven approach we are able to generate new hypotheses to explain why nuclear receptors like the peroxisome proliferator-activated receptors(PPARs)and its isoform and their interaction with dietary lipids may have significant effect on allergic response.The results obtained from this study will direct future experimeantal and clinical studies to understand the role of dietary lipids and PPARisoforms to exert pro-inflammatory or anti-inflammatory functions on cells of the innate immunity and influence antigen presentation to the cells of the adaptive immunity.
基金supported by the Joint Funds of the National Natural Science Foundation of China(Grant No.U2039207).
文摘Since the establishment of the Collaboratory for the Study of Earthquake Predictability,China(CSEP-CN)center,no comprehensive study has been conducted on the parameter models of the Pattern Informatics(PI)method within the China Seismic Experimental Site(CSES)region.Additionally,the boundary issues of the study area have been a subject of ongoing debate.Tian et al.(2024)indicates that variations in seismic activity within the region impact the predictive efficacy of the PI method.
基金supported by the National Natural Science Foundation of China(Nos.22172019,22225606,22176029)Excellent Youth Foundation of Sichuan Scientific Committee Grant in China(No.2021JDJQ0006).
文摘Predictive modeling of photocatalytic NO removal is highly desirable for efficient air pollution abatement.However,great challenges remain in precisely predicting photocatalytic performance and understanding interactions of diverse features in the catalytic systems.Herein,a dataset of g-C_(3) N_(4)-based catalysts with 255 data points was collected from peer-reviewed publications and machine learning(ML)model was proposed to predict the NO removal rate.The result shows that the Gradient Boosting Decision Tree(GBDT)demonstrated the greatest prediction accuracy with R 2 of 0.999 and 0.907 on the training and test data,respectively.The SHAP value and feature importance analysis revealed that the empirical categories for NO removal rate,in the order of importance,were catalyst characteristics>reaction process>preparation conditions.Moreover,the partial dependence plots broke the ML black box to further quantify the marginal contributions of the input features(e.g.,doping ratio,flow rate,and pore volume)to the model output outcomes.This ML approach presents a pure data-driven,interpretable framework,which provides new insights into the influence of catalyst characteristics,reaction process,and preparation conditions on NO removal.
