The testing of large structures is limited by high costs and long cycles, making scaling methods an attractive solution. However, the scaling process of elastic rings introduces complexities in multi-parameter geometr...The testing of large structures is limited by high costs and long cycles, making scaling methods an attractive solution. However, the scaling process of elastic rings introduces complexities in multi-parameter geometric distortions, leading to a diminution in the predictive accuracy of the distorted similitude. To address this challenge, this study formulates a novel set of scaling laws, tailored to account for the intricate geometric distortions associated with elastic rings. The proposed scaling laws are formulated based on the intrinsic deformation characteristics of elastic rings, rather than the traditional systemic governing equations. Numerical and experimental cases are conducted to assess the efficacy and precision of the proposed scaling laws, and the obtained results are compared with those achieved by traditional methods. The outcomes demonstrate that the scaling laws put forth by this study significantly enhance the predictive capabilities for deformations of elastic rings.展开更多
Significant diurnal temperature variations in mountainous rack railways cause stiffness mismatches between the rack structure and simply supported bridges,leading to critical failures like bolt loosening and rack frac...Significant diurnal temperature variations in mountainous rack railways cause stiffness mismatches between the rack structure and simply supported bridges,leading to critical failures like bolt loosening and rack fractures.This study develops a dynamic model of the vehicle-rack-bridge system based on train-track-bridge interaction theory,integrating gear-rack meshing and wheel-rail contact mechanisms.The model analyzes the dynamic response of bridges with varying spans under combined thermal and dynamic loading.Numerical simulations,conducted using finite element analysis,reveal peak vibration accelerations of 1.3 m/s^(2)for the rack,3.0 m/s^(2)for the rail,1.2 m/s^(2)for the sleeper,and 0.1 m/s^(2)for the bridge,with maximum stresses of 3 MPa in the rack,8 MPa in the rail,and 25 MPa in connecting bolts.The results show significant span-dependent amplification of stress and strain in the rack system under thermo-mechanical loading,exceeding material strength limits at 60-meter spans.An innovative elastic connection method is proposed to mitigate stress concentrations effectively,en-hancing system durability.This study introduces a novel approach to modeling complex thermo-mechanical interactions in rack railway systems,validated through extensive simulations,and provides a practical solution for improving structural resilience,offering theoretical guidance for optimizing rack-bridge system design to ensure operational safety in extreme environmental conditions.展开更多
Intervention strategies to control non-point source nitrogen(N)and phosphorus(P)pollution in agriculture are expensive and there is a trade-off between engineering cost and treatment effectiveness.Implementing strateg...Intervention strategies to control non-point source nitrogen(N)and phosphorus(P)pollution in agriculture are expensive and there is a trade-off between engineering cost and treatment effectiveness.Implementing strategies often result in unsatisfactory outcomes and massive engineering costs when managing diffusive pollution in agricultural catchments.To address this issue,this paper proposes a robust,handy,catchment N&P decision support system(CNPDSS),an Android-based smartphone system integrated with a web-based geographic information system(GIS).The CNPDSS aims to provide artificial intelligence-driven decisions that minimize N&P loadings and engineering costs for mitigating pollution in agricultural catchments.It consists of four components:a general user interface(GUI),GIS,N&P pollution modeling(NPPM),and a DSS.The CNPDSS simplifies the GUI and integrates GIS modules to create a user-friendly interface,enabling non-professional users to operate the system easily through intuitive actions.The NPPM uses straightforward empirical models to predict N&P loadings,enhancing efficiency by avoiding excessive parameters.Taking into account the N&P movement pathway in the catchment,the DSS incorporates three control measures:source reduction in farmland(before migration stage),process retention by ecological ditch(midway transport stage),and down-end purification by constructed wetland(waterbody discharge stage),to formulate a comprehensive ternary controlling strategy.To optimize the cost-effectiveness of any proposed N&P control strategies for sub-catchments,a differential evolution algorithm(DEA)is employed in CNPDSS to carry out a dual-objective decision-making optimization computation.In this study,the CNPDSS is applied to a case study in an agricultural catchment in Central China to develop the most cost-effective ternary N&P control strategies that ensure the catchment water quality within Criterion Ⅲ of the Chinese Surface Water Quality Standard GB3838-2002 is met(total N concentration≤1.0 mg L^(-1)and total P concentration≤0.2 mg L^(-1)).Our results demonstrate that the CNPDSS is feasible and also possesses an adaptive design and flexible architecture to enable its generalization and extension to support strong hands-on applications in other catchments.展开更多
Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of w...Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of wildfire pre-vention,monitoring,disaster response,and post-fire recovery.This review examines the potential utility of Digital Twin in wildfire management and aims to inspire further exploration and experimentation by researchers and practitioners in the fields of environment,forestry,fire ecology,and firefighting services.By creating virtual replicas of wildfire in the physical world,a Digital Twin platform facilitates data integration from multiple sources,such as remote sensing,weather forecast-ing,and ground-based sensors,providing a holistic view of emergency response and decision-making.Furthermore,Digital Twin can support simulation-based training and scenario testing for prescribed fire planning and firefighting to improve preparedness and response to evacuation and rescue.Successful applications of Digital Twin in wildfire management require horizontal collaboration among researchers,practitioners,and stakeholders,as well as enhanced resource sharing and data exchange.This review seeks a deeper understanding of future wildland fire management from a technological perspective and inspiration of future research and implementation.Further research should focus on refining and validating Digital Twin models and the integration into existing fire management operations,and then demonstrating them in real wildland fires.展开更多
In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical...In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical span graph must be updated to meet more stringent engineering requirements.Given this,this study introduces the support vector machine(SVM),along with multiple ensemble(bagging,adaptive boosting,and stacking)and optimization(Harris hawks optimization(HHO),cuckoo search(CS))techniques,to overcome the limitations of the traditional methods.The analysis indicates that the hybrid model combining SVM,bagging,and CS strategies has a good prediction performance,and its test accuracy reaches 0.86.Furthermore,the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases.Compared with previous empirical or semi-empirical methods,the new model overcomes the interference of subjective factors and possesses higher interpretability.Since relying solely on one technology cannot ensure prediction credibility,this study further introduces genetic programming(GP)and kriging interpolation techniques.The explicit expressions derived through GP can offer the stability probability value,and the kriging technique can provide interpolated definitions for two new subclasses.Finally,a prediction platform is developed based on the above three approaches,which can rapidly provide engineering feedback.展开更多
Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now en...Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now encompasses large-scale electronic medical records(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI’s potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction.This survey provides an insightful overview of TCM AI research,summarizing related research tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice.To critically assess the current state of the field,this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of comprehensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelligent,personalized decision support systems.展开更多
A ground girder is laid on the preprocessed subgrade by gravity compaction and integrally uniformly supported by subgrade in maglev transit.The settlement of the maglev subgrade inevitably affects the vibration state ...A ground girder is laid on the preprocessed subgrade by gravity compaction and integrally uniformly supported by subgrade in maglev transit.The settlement of the maglev subgrade inevitably affects the vibration state of the medium and low speed maglev coupled system by the additional deformation of the maglev track.This study investigated the dynamic properties of the coupled vibration system affected by the subgrade settlement.First,a theoretical coupled vibration model of a maglev train-track-ground girder system with uneven subgrade settlement was proposed and verified.Then,the effect mechanism of the coupled system caused by the uneven subgrade settlement was explored.Finally,settlement types and subgrade support voiding were examined.The analysis showed that the uneven subgrade settlement considerably increased the dynamic responses of the levitation control system and maglev vehicle while having a minor influence on those of the track-ground girder.The influence of a single ground girder settling was the strongest,and adjacent sides’settling of two ground girders was the weakest for the vibration of a maglev train.An extremely large uneven settlement exceeding 6 mm led to active levitation control system instability.The subgrade support voiding enlarged the vehicle-induced vibration of the track ground girder.展开更多
In order to support the physical research on the EAST tokamak,a new positive ion source with designed beam energy of 120 keV was proposed to be developed.Accelerator structure is one of the key components of the ion s...In order to support the physical research on the EAST tokamak,a new positive ion source with designed beam energy of 120 keV was proposed to be developed.Accelerator structure is one of the key components of the ion source.Through the finite element analysis method,the electrostatic analyses of insulators and grid plates were carried out,the material and structure parameters of insulators were determined.The maximum electric field around each insulator is about 4 kV/mm,and the maximum electric field between grids is about 14 kV/mm,which can meet the 120 keV withstand voltage holding.The insulation system for the positive ion source accelerator with 120 keV is designed,and the connection and basic parameters of insulators and support flanges are analyzed and determined.展开更多
Artificial intelligence(AI)is rapidly transforming radiology and computed tomography(CT)imaging by enabling automated image analysis,improved diagnostic accuracy,and clinical decision-support.We performed a systematic...Artificial intelligence(AI)is rapidly transforming radiology and computed tomography(CT)imaging by enabling automated image analysis,improved diagnostic accuracy,and clinical decision-support.We performed a systematic review of peerreviewed studies published between January 1,2010 and March 31,2025 to quantify reported gains in diagnostic performance and workflow efficiency,to evaluate clinical decision-support benefits and risks,and to identify integration priorities.We searched PubMed,IEEE Xplore,Scopus,ScienceDirect,and Google Scholar and screened 128 records;26 studies met the inclusion criteria.Extracted data included study design,AI architecture,sample size,and quantitative performance metrics;study quality was assessed using Newcastle-Ottawa Scales(NOS),Cochrane RoB 2,or AMSTAR 2 as appropriate.Across included studies,AI applications in CT showed consistent improvements in sensitivity,specificity,and time-to-diagnosis in specific tasks(notably lung-nodule detection and intracranial hemorrhage triage),with reported detection-rate increases up to~20%and reduced turnaround times in several real-world implementations.Barriers include dataset bias,limited external validation,interpretability(“black-box”)concerns,workflow integration challenges,and evolving regulatory issues.Economic analyses suggest potentially favorable return on investment(ROI)in high-volume settings but are sensitive to licensing and infrastructure costs.To realize AI's benefits in CT imaging,rigorous multi-center validation,transparent reporting,humancentered workflow design,and post-deployment surveillance are essential.展开更多
The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A...The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A survey was performed with 200 participants living in Michigan,in urban,suburban and rural areas.Questions covered in the survey included how often and how bad traffic congestion occurred,how familiar travelers were with ITS technologies(adaptive traffic signals,real time monitoring of the traffic)and how much support travelers would provide for ITS initiatives.Results reveal that there is a high degree of traffic congestion awareness,there is low public awareness of ITS technologies.While respondents who were aware of ITS solutions had positive views about deploying them,especially in urban areas,they were less supportive of ITS solutions than they were among those who did not know much about these.Factors including area of residence,commute time and age were perceived to influence ITS along with more positive attitudes to ITS amongst urban dwellers and younger respondents.Analysis of key barriers to ITS implementation reflected high initial costs,challenges with technical integration and users’concerns surrounding privacy.展开更多
Thedevelopment of artificial intelligence(AI)has sparked the invention of chatbots,which are intelligent conversational agents.These chatbots have the potential to completely transform how people interact while enhanc...Thedevelopment of artificial intelligence(AI)has sparked the invention of chatbots,which are intelligent conversational agents.These chatbots have the potential to completely transform how people interact while enhancing user experience.This study explores the building along with its execution of a chatbot for customer service support at a university reception using recurrent neural networks(RNNs).To increase user requests,the accuracy of the information,and overall satisfaction with the service,it evaluates machine learningmodels including RNN,XLNet,and Bidirectional Encoder Representations from Transformers(BERT).In this research project,data were gathered from university offices and students,documenting an array of daily questions that frequently arise at themain reception desk of the university.The recurrent neural network algorithm was trained using the gathered dataset,and it performed admirably.The model attained a low loss value of 0.0167 and an accuracy of 1.0000.The results presented demonstrate the efficiency with which the RNN model performed by precisely identifying and responding to the questions that were recorded.The thesis studies the pros and cons of RNN and measures how it performs when compared to the advanced XLNet and BERT algorithms.These systems’efficiency can be gauged utilizing assessment metrics like accuracy,precision,and consistency of responses.展开更多
Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusio...Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature space.Then,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior features.Finally,a support vector machine(SVM)classifier is used to classify intrusion behaviors.The proposed model is verified by experiments on the NSL-KDD dataset.The results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion classification.The proposed method can provide new insight into network intrusion detection.展开更多
BACKGROUND We have innovatively amalgamated membrane blood purification and centrifugal blood cell separation technologies to address the limitations of current artificial liver support(ALS)models,and develop a versat...BACKGROUND We have innovatively amalgamated membrane blood purification and centrifugal blood cell separation technologies to address the limitations of current artificial liver support(ALS)models,and develop a versatile plasma purification system(VPPS)through centrifugal plasma separation.AIM To investigate the influence of VPPS on long-term rehospitalization and mortality rates among patients with acute-on-chronic liver failure(ACLF).METHODS This real-world,prospective study recruited inpatients diagnosed with ACLF from the Second Xiangya Hospital of Central South University between October 2021 and March 2024.Patients were categorized into the VPPS and non-VPPS groups based on the distinct ALS models administered to them.Self-administered questionnaires,clinical records,and self-reported data served as the primary methods for data collection.The laboratory results were evaluated at six distinct time points.All patients were subjected to follow-up assessments for>12 months.Kaplan-Meier survival analyses and Cox proportional hazards models were used to evaluate the risks of hospitalization and mortality during the follow-up period.RESULTS A cohort of 502 patients diagnosed with ACLF was recruited,with 260 assigned to the VPPS group.On comparing baseline characteristics,the VPPS group exhibited a significantly shorter length of stay,higher incidence of spontaneous peritonitis and pulmonary aspergillosis compared to the non-VPPS group(P<0.05).Agehazard ratio(HR=1.142,95%CI:1.01-1.23,P=0.018),peritonitis(HR=2.825,95%CI:1.07-6.382,P=0.026),albumin(HR=0.67,95%CI:0.46-0.942,P=0.023),total bilirubin(HR=1.26,95%CI:1.01-3.25,P=0.021),international normalized ratio(HR=1.97,95%CI:1.21-2.908,P=0.014),and VPPS/non-VPPS(HR=3.24,95%CI:2.152-4.76,P<0.001)were identified as significant independent predictors of mortality in both univariate and multivariate analyses throughout the follow-up period.Kaplan-Meier survival analyses demonstrated significantly higher rehospitalization and mortality rates in the non-VPPS group compared to the VPPS group during follow-up of≥2 years(log-rank test,P<0.001).CONCLUSION These findings suggest that VPPS is safe and has a positive influence on prognostic outcomes in patients with ACLF.展开更多
Language education has predominantly focused on classroom instruction and pedagogical strategies,yet the sociological aspects of learning-especially the family’s role-are often overlooked in English as a Foreign Lang...Language education has predominantly focused on classroom instruction and pedagogical strategies,yet the sociological aspects of learning-especially the family’s role-are often overlooked in English as a Foreign Language(EFL)contexts.This study explores how parental attitudes,support systems,and disciplinary approaches influence Turkish university students’oral fluency in English.While educators and curricula are frequently held accountable for low communicative competence,this paper posits that sociocultural factors originating from the home environment play a crucial role.Utilizing a qualitative-dominant mixed-methods design with 210 undergraduate EFL learners and 24 lecturers from four Turkish universities,data were gathered through questionnaires,interviews,and parental focus groups.Findings indicate that students with supportive and engaged parents exhibit greater self-efficacy,confidence,and linguistic agency in communication tasks.In contrast,those from indifferent or authoritarian families experience anxiety,low motivation,and stagnation in oral skills.Grounded in Bourdieu’s social capital theory,Bronfenbrenner’s ecological systems theory,and Coleman’s social context of learning,this study conceptualizes the family as an“invisible classroom”where linguistic habits,discipline,and cultural aspirations are cultivated.The paper concludes by proposing a Family-Engaged EFL Fluency Model(FE-EFM)that integrates learner development within a framework of sociological co-responsibility involving parents,educators,and institutions.展开更多
Considering the characteristics of deep thick top coal roadway,in which the high ground stress,coal seam with low strength,and a large range of surrounding rock fragmentation,the pressure relief anchor box beam suppor...Considering the characteristics of deep thick top coal roadway,in which the high ground stress,coal seam with low strength,and a large range of surrounding rock fragmentation,the pressure relief anchor box beam support system with high strength is developed.The high-strength bearing characteristics and coupling yielding support mechanism of this support system are studied by the mechanical tests of composite members and the combined support system.The test results show that under the coupling effect of support members,the peak stress of the box-shaped support beam in the anchor box beam is reduced by 21.9%,and the average deformation is increased by 135.0%.The ultimate bending bearing capacity of the box-shaped support beam is 3.5 times that of traditional channel beam.The effective compressive stress zone applied by the high prestressed cable is expanded by 26.4%.On this basis,the field support comparison test by the anchor channel beam,the anchor I-shaped beam and the anchor box beam are carried out.Compared with those of the previous two,the surrounding rock convergence of the latter is decreased by 41.2%and 22.2%,respectively.The field test verifies the effectiveness of the anchor box beam support system.展开更多
Decision support systems(DSS)based on physically based numerical models are standard tools used by water services and utilities.However,few DSS based on holistic approaches combining distributed hydrological,hydraulic...Decision support systems(DSS)based on physically based numerical models are standard tools used by water services and utilities.However,few DSS based on holistic approaches combining distributed hydrological,hydraulic,and hydrogeological models are operationally exploited.This holistic approach was adopted for the development of the AquaVar DSS,used for water resource management in the French Mediterranean Var watershed.The year 2019 marked the initial use of the DSS in its operational environment.Over the next 5 years,multiple hydrological events allowed to test the performance of the DSS.The results show that the tool is capable of simulating peak flows associated with two extreme rainfall events(storms Alex and Aline).For a moderate flood,the real-time functionality was able to simulate forecast discharges 26 h before the flood peak,with a maximum local error of 30%.Finally,simulations for the drought period 2022-2023 highlighted the essential need for DSS to evolve in line with changing climatic conditions,which give rise to unprecedented hydrological processes.The lessons learned from these first 5 years of AquaVar use under operational conditions are synthesized,addressing various topics such as DSS modularity,evolution,data positioning,technology,and governance.展开更多
BACKGROUND Artificial intelligence(AI)is gaining widespread traction in surgical disciplines,particularly in gastrointestinal(GI)surgery,where it offers opportunities to enhance decision-making,improve accuracy,and op...BACKGROUND Artificial intelligence(AI)is gaining widespread traction in surgical disciplines,particularly in gastrointestinal(GI)surgery,where it offers opportunities to enhance decision-making,improve accuracy,and optimize patient outcomes across the entire surgical continuum.AIM To comprehensively evaluate current AI applications in GI surgery,highlighting its role in preoperative planning,intraoperative guidance,postoperative monitoring,endoscopic diagnosis,and surgical education.METHODS This systematic review was conducted in accordance with PRISMA guidelines.We searched the Web of Science Core Collection through March 31,2025 using the terms“artificial intelligence”AND“gastrointestinal surgery”.Inclusion criteria:Original,English-language,full-text articles indexed under the“Surgery”category reporting quantitative AI performance metrics in GI surgery.Exclusion criteria:Reviews,editorials,letters,conference abstracts,non-English publications,ESCI/SSCI/Index Chemicus-only papers,studies without full text,and articles outside the surgical domain.Full texts of potentially eligible studies were assessed,yielding 45 studies from an initial 955 records for qualitative and quantitative synthesis.RESULTS The included studies demonstrated that AI has superior performance compared to traditional clinical tools in areas such as risk prediction,lesion detection,nerve identification,and complication forecasting.Notably,convolutional neural networks,random forests,support vector machines,and reinforcement learning models were commonly used.AI-enhanced systems improved diagnostic accuracy,procedural safety,documentation quality,and educational feedback.However,there are several limitations,such as lack of external validation,dataset standardization,and explainability.CONCLUSION AI is transforming GI surgery from preoperative risk assessment to postoperative care and training.While many tools now match or exceed expert-level performance,successful clinical adoption requires transparent,validated models that seamlessly integrate into surgical workflows.With continued multidisciplinary collaboration,AI is positioned to become a trusted companion in surgical practice.展开更多
Objectives:Recently,the global esports industry has experienced remarkable growth,leading to an expansion in the scale and influence of professional player communities.However,despite this outward growth,systems to pr...Objectives:Recently,the global esports industry has experienced remarkable growth,leading to an expansion in the scale and influence of professional player communities.However,despite this outward growth,systems to protect players’mental health remain inadequate.Comprehensive analysis of structural risk factors,including performance pressure,public evaluation,and career instability,remains insufficient.This study,aimed to explore stressors encountered by esports athletes,coping strategies,and the role of social support systems in safeguarding mental health.Using the transactional model of stress and coping,the job demands–resources model,and social support theory,the study adopts an integrated perspective to examine challenges faced by athletes in the competitive esports environment.Methods:A qualitative case study was conducted involving in-depth interviews and nonparticipant observations with 11 esports athletes who competed at national or international levels,as well as two team managers.Thematic analysis identified recurring patterns in the data,and credibility was ensured through triangulation and cross-review among researchers.Results:Esports athletes experience multiple interacting stressors,including performance demands,emotional strain duringmatches,and continuous evaluation on socialmedia.In response,they employed coping strategies—problem-focused,emotion-focused,and avoidance-based,which provided temporary relief but often led to burnout and self-regulation failure owing to absence of support systems.Social support networks had ambivalent effects:while offering comfort,they also intensified pressure through negative feedback and high expectations from fans and online communities.Conclusion:The findings show that mental health issues among esports athletes are not only related to individual factors but are closely linked to performance-driven structures,competitive environments,and social relationships.This study integrates the transactional model of stress and coping,the JobDemands–Resourcesmodel,and social support theoryto provide comprehensive analysis.It also offers practical recommendations,including psychological counseling,emotional labor programs,and improved communication with families and fan communities.展开更多
Abnormal driving behavior includes driving distraction,fatigue,road anger,phone use,and an exceptionally happy mood.Detecting abnormal driving behavior in advance can avoid traffic accidents and reduce the risk of tra...Abnormal driving behavior includes driving distraction,fatigue,road anger,phone use,and an exceptionally happy mood.Detecting abnormal driving behavior in advance can avoid traffic accidents and reduce the risk of traffic conflicts.Traditional methods of detecting abnormal driving behavior include using wearable devices to monitor blood pressure,pulse,heart rate,blood oxygen,and other vital signs,and using eye trackers to monitor eye activity(such as eye closure,blinking frequency,etc.)to estimate whether the driver is excited,anxious,or distracted.Traditional monitoring methods can detect abnormal driving behavior to a certain extent,but they will affect the driver’s normal driving state,thereby introducing additional driving risks.This research uses the combined method of support vector machine and dlib algorithm to extract 68 facial feature points from the human face,and uses an SVM model as a strong classifier to classify different abnormal driving statuses.The combined method reaches high accuracy in detecting road anger and fatigue status and can be used in an intelligent vehicle cabin to improve the driving safety level.展开更多
BACKGROUND The treatment of patients with liver cancer after surgery with the artificial liver support system combined with traditional Chinese medicine(TCM)for strengthening the body and removing blood stasis is a ne...BACKGROUND The treatment of patients with liver cancer after surgery with the artificial liver support system combined with traditional Chinese medicine(TCM)for strengthening the body and removing blood stasis is a new idea.AIM To analyze the post-surgical effect of the artificial liver support system with TCM in patients with liver cancer.METHODS Ninety-eight patients with liver cancer who underwent surgical treatment at the Fifth People’s Hospital of Huai’an from January 2023-2024 were selected and divided into two groups(49 patients each)via random lottery method.Both groups underwent surgery.The control group received artificial liver support,and the observation group was additionally treated with TCM for strengthening the body and removing blood stasis.Gastrointestinal recovery,liver function,tumor marker levels,immune function,and safety were compared between both groups.RESULTS There were significant differences in the levels of indicators related to gastrointestinal recovery between the groups(P<0.05).After treatment,the levels of alanine aminotransferase,aspartate aminotransferase,total bilirubin,and gamma-glutamyl transpeptidase in the observation group were lower,whereas the albumin level was higher(P<0.05).After treatment,tumor marker levels in the observation group were relatively lower(P<0.05).After treatment,compared to the control group,the CD4+level in the observation group was higher and the CD8+level was lower(P<0.05).There was no significant difference in the incidence of adverse reactions between both groups(P>0.05).CONCLUSION Combining the artificial liver support system with TCM significantly improves liver and gastrointestinal functions,enhances immune responses,and reduces tumor marker levels with high safety,suggesting that it could be a promising approach for optimizing postoperative care and improving patient outcomes,potentially reducing complications and enhancing quality of life.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.52405095,12272089,and 92360305)the Guangdong Basic and Applied Basic Research Foundation of China(No.2023A1515110557)+4 种基金the Natural Science Foundation of Liaoning Province of China(No.2023-BSBA-102)the Open Fund of National Key Laboratory of Particle Transport and Separation Technology of China(No.WZKF-2024-6)the Open Project of Guangxi Key Laboratory of Automobile Components and Vehicle Technology of China(Nos.2024GKLACVTKF07 and 2024GKLACVTKF06)the Basic Research Projects of Liaoning Provincial Department of Education of China(No.JYTQN2023162)the Fundamental Research Funds for the Central Universities of China(No.N2403022)。
文摘The testing of large structures is limited by high costs and long cycles, making scaling methods an attractive solution. However, the scaling process of elastic rings introduces complexities in multi-parameter geometric distortions, leading to a diminution in the predictive accuracy of the distorted similitude. To address this challenge, this study formulates a novel set of scaling laws, tailored to account for the intricate geometric distortions associated with elastic rings. The proposed scaling laws are formulated based on the intrinsic deformation characteristics of elastic rings, rather than the traditional systemic governing equations. Numerical and experimental cases are conducted to assess the efficacy and precision of the proposed scaling laws, and the obtained results are compared with those achieved by traditional methods. The outcomes demonstrate that the scaling laws put forth by this study significantly enhance the predictive capabilities for deformations of elastic rings.
基金Supported by the Sichuan Science and Technology Program(Grant Nos.2021YFD0211,2023ZDZX0011).
文摘Significant diurnal temperature variations in mountainous rack railways cause stiffness mismatches between the rack structure and simply supported bridges,leading to critical failures like bolt loosening and rack fractures.This study develops a dynamic model of the vehicle-rack-bridge system based on train-track-bridge interaction theory,integrating gear-rack meshing and wheel-rail contact mechanisms.The model analyzes the dynamic response of bridges with varying spans under combined thermal and dynamic loading.Numerical simulations,conducted using finite element analysis,reveal peak vibration accelerations of 1.3 m/s^(2)for the rack,3.0 m/s^(2)for the rail,1.2 m/s^(2)for the sleeper,and 0.1 m/s^(2)for the bridge,with maximum stresses of 3 MPa in the rack,8 MPa in the rail,and 25 MPa in connecting bolts.The results show significant span-dependent amplification of stress and strain in the rack system under thermo-mechanical loading,exceeding material strength limits at 60-meter spans.An innovative elastic connection method is proposed to mitigate stress concentrations effectively,en-hancing system durability.This study introduces a novel approach to modeling complex thermo-mechanical interactions in rack railway systems,validated through extensive simulations,and provides a practical solution for improving structural resilience,offering theoretical guidance for optimizing rack-bridge system design to ensure operational safety in extreme environmental conditions.
基金financially supported by the National Key Research and Development Program of China(2024YFD1700104 and 2022YFE0209200-03)the National Natural Science Foundation of China(42161144002 and 41977156)+3 种基金the Guangxi Natural Science Foundation,China(2022GXNSFBA035625)the Guangxi Technology Base and Talent Subject,China(Guike AD22035927)the Shandong Key Research and Development Project,China(2022TZXD0045)the State Key Laboratory of Earth System Numerical Modeling and Application,Institute of Atmospheric Physics,Chinese Academy of Sciences。
文摘Intervention strategies to control non-point source nitrogen(N)and phosphorus(P)pollution in agriculture are expensive and there is a trade-off between engineering cost and treatment effectiveness.Implementing strategies often result in unsatisfactory outcomes and massive engineering costs when managing diffusive pollution in agricultural catchments.To address this issue,this paper proposes a robust,handy,catchment N&P decision support system(CNPDSS),an Android-based smartphone system integrated with a web-based geographic information system(GIS).The CNPDSS aims to provide artificial intelligence-driven decisions that minimize N&P loadings and engineering costs for mitigating pollution in agricultural catchments.It consists of four components:a general user interface(GUI),GIS,N&P pollution modeling(NPPM),and a DSS.The CNPDSS simplifies the GUI and integrates GIS modules to create a user-friendly interface,enabling non-professional users to operate the system easily through intuitive actions.The NPPM uses straightforward empirical models to predict N&P loadings,enhancing efficiency by avoiding excessive parameters.Taking into account the N&P movement pathway in the catchment,the DSS incorporates three control measures:source reduction in farmland(before migration stage),process retention by ecological ditch(midway transport stage),and down-end purification by constructed wetland(waterbody discharge stage),to formulate a comprehensive ternary controlling strategy.To optimize the cost-effectiveness of any proposed N&P control strategies for sub-catchments,a differential evolution algorithm(DEA)is employed in CNPDSS to carry out a dual-objective decision-making optimization computation.In this study,the CNPDSS is applied to a case study in an agricultural catchment in Central China to develop the most cost-effective ternary N&P control strategies that ensure the catchment water quality within Criterion Ⅲ of the Chinese Surface Water Quality Standard GB3838-2002 is met(total N concentration≤1.0 mg L^(-1)and total P concentration≤0.2 mg L^(-1)).Our results demonstrate that the CNPDSS is feasible and also possesses an adaptive design and flexible architecture to enable its generalization and extension to support strong hands-on applications in other catchments.
基金funded by the National Natural Science Foundation of China(NSFC No.52322610)Hong Kong Research Grants Council Theme-based Research Scheme(T22-505/19-N).
文摘Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of wildfire pre-vention,monitoring,disaster response,and post-fire recovery.This review examines the potential utility of Digital Twin in wildfire management and aims to inspire further exploration and experimentation by researchers and practitioners in the fields of environment,forestry,fire ecology,and firefighting services.By creating virtual replicas of wildfire in the physical world,a Digital Twin platform facilitates data integration from multiple sources,such as remote sensing,weather forecast-ing,and ground-based sensors,providing a holistic view of emergency response and decision-making.Furthermore,Digital Twin can support simulation-based training and scenario testing for prescribed fire planning and firefighting to improve preparedness and response to evacuation and rescue.Successful applications of Digital Twin in wildfire management require horizontal collaboration among researchers,practitioners,and stakeholders,as well as enhanced resource sharing and data exchange.This review seeks a deeper understanding of future wildland fire management from a technological perspective and inspiration of future research and implementation.Further research should focus on refining and validating Digital Twin models and the integration into existing fire management operations,and then demonstrating them in real wildland fires.
基金supported by the National Natural Science Foundation of China(Grant No.42177164)the Distinguished Youth Science Foundation of Hunan Province of China(Grant No.2022JJ10073)the Outstanding Youth Project of Hunan Provincial Department of Education,China(Grant No.23B0008).
文摘In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical span graph must be updated to meet more stringent engineering requirements.Given this,this study introduces the support vector machine(SVM),along with multiple ensemble(bagging,adaptive boosting,and stacking)and optimization(Harris hawks optimization(HHO),cuckoo search(CS))techniques,to overcome the limitations of the traditional methods.The analysis indicates that the hybrid model combining SVM,bagging,and CS strategies has a good prediction performance,and its test accuracy reaches 0.86.Furthermore,the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases.Compared with previous empirical or semi-empirical methods,the new model overcomes the interference of subjective factors and possesses higher interpretability.Since relying solely on one technology cannot ensure prediction credibility,this study further introduces genetic programming(GP)and kriging interpolation techniques.The explicit expressions derived through GP can offer the stability probability value,and the kriging technique can provide interpolated definitions for two new subclasses.Finally,a prediction platform is developed based on the above three approaches,which can rapidly provide engineering feedback.
基金supported by the National Key Research and Development Program (No.2023YFC3502604)the National Natural Science Foundation of China (Nos.U23B2062, 82274352,82174533, 82374302, 82204941)+3 种基金the Noncommunicable Chronic Diseases-National Science and Technology Major Project (No.2023ZD0505700)the Beijing-Tianjin-Hebei Basic Research Cooperation Project (No.22JCZXJC00070)the State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture (No.SKL2024Z0102)Key R&D project of Ningxia Autonomous Region (No.2022BEG02036).
文摘Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now encompasses large-scale electronic medical records(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI’s potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction.This survey provides an insightful overview of TCM AI research,summarizing related research tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice.To critically assess the current state of the field,this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of comprehensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelligent,personalized decision support systems.
基金National Natural Science Foundation of China under Grant Nos.52478467and 52108417Guangdong Basic and Applied Basic Research Foundation under Grant No.2024A1515012569the Natural Science Basic Research Program of Shaanxi under Grant No.2021JQ-101。
文摘A ground girder is laid on the preprocessed subgrade by gravity compaction and integrally uniformly supported by subgrade in maglev transit.The settlement of the maglev subgrade inevitably affects the vibration state of the medium and low speed maglev coupled system by the additional deformation of the maglev track.This study investigated the dynamic properties of the coupled vibration system affected by the subgrade settlement.First,a theoretical coupled vibration model of a maglev train-track-ground girder system with uneven subgrade settlement was proposed and verified.Then,the effect mechanism of the coupled system caused by the uneven subgrade settlement was explored.Finally,settlement types and subgrade support voiding were examined.The analysis showed that the uneven subgrade settlement considerably increased the dynamic responses of the levitation control system and maglev vehicle while having a minor influence on those of the track-ground girder.The influence of a single ground girder settling was the strongest,and adjacent sides’settling of two ground girders was the weakest for the vibration of a maglev train.An extremely large uneven settlement exceeding 6 mm led to active levitation control system instability.The subgrade support voiding enlarged the vehicle-induced vibration of the track ground girder.
基金supported by National Natural Science Foundation of China(No.11975261)。
文摘In order to support the physical research on the EAST tokamak,a new positive ion source with designed beam energy of 120 keV was proposed to be developed.Accelerator structure is one of the key components of the ion source.Through the finite element analysis method,the electrostatic analyses of insulators and grid plates were carried out,the material and structure parameters of insulators were determined.The maximum electric field around each insulator is about 4 kV/mm,and the maximum electric field between grids is about 14 kV/mm,which can meet the 120 keV withstand voltage holding.The insulation system for the positive ion source accelerator with 120 keV is designed,and the connection and basic parameters of insulators and support flanges are analyzed and determined.
文摘Artificial intelligence(AI)is rapidly transforming radiology and computed tomography(CT)imaging by enabling automated image analysis,improved diagnostic accuracy,and clinical decision-support.We performed a systematic review of peerreviewed studies published between January 1,2010 and March 31,2025 to quantify reported gains in diagnostic performance and workflow efficiency,to evaluate clinical decision-support benefits and risks,and to identify integration priorities.We searched PubMed,IEEE Xplore,Scopus,ScienceDirect,and Google Scholar and screened 128 records;26 studies met the inclusion criteria.Extracted data included study design,AI architecture,sample size,and quantitative performance metrics;study quality was assessed using Newcastle-Ottawa Scales(NOS),Cochrane RoB 2,or AMSTAR 2 as appropriate.Across included studies,AI applications in CT showed consistent improvements in sensitivity,specificity,and time-to-diagnosis in specific tasks(notably lung-nodule detection and intracranial hemorrhage triage),with reported detection-rate increases up to~20%and reduced turnaround times in several real-world implementations.Barriers include dataset bias,limited external validation,interpretability(“black-box”)concerns,workflow integration challenges,and evolving regulatory issues.Economic analyses suggest potentially favorable return on investment(ROI)in high-volume settings but are sensitive to licensing and infrastructure costs.To realize AI's benefits in CT imaging,rigorous multi-center validation,transparent reporting,humancentered workflow design,and post-deployment surveillance are essential.
文摘The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A survey was performed with 200 participants living in Michigan,in urban,suburban and rural areas.Questions covered in the survey included how often and how bad traffic congestion occurred,how familiar travelers were with ITS technologies(adaptive traffic signals,real time monitoring of the traffic)and how much support travelers would provide for ITS initiatives.Results reveal that there is a high degree of traffic congestion awareness,there is low public awareness of ITS technologies.While respondents who were aware of ITS solutions had positive views about deploying them,especially in urban areas,they were less supportive of ITS solutions than they were among those who did not know much about these.Factors including area of residence,commute time and age were perceived to influence ITS along with more positive attitudes to ITS amongst urban dwellers and younger respondents.Analysis of key barriers to ITS implementation reflected high initial costs,challenges with technical integration and users’concerns surrounding privacy.
文摘Thedevelopment of artificial intelligence(AI)has sparked the invention of chatbots,which are intelligent conversational agents.These chatbots have the potential to completely transform how people interact while enhancing user experience.This study explores the building along with its execution of a chatbot for customer service support at a university reception using recurrent neural networks(RNNs).To increase user requests,the accuracy of the information,and overall satisfaction with the service,it evaluates machine learningmodels including RNN,XLNet,and Bidirectional Encoder Representations from Transformers(BERT).In this research project,data were gathered from university offices and students,documenting an array of daily questions that frequently arise at themain reception desk of the university.The recurrent neural network algorithm was trained using the gathered dataset,and it performed admirably.The model attained a low loss value of 0.0167 and an accuracy of 1.0000.The results presented demonstrate the efficiency with which the RNN model performed by precisely identifying and responding to the questions that were recorded.The thesis studies the pros and cons of RNN and measures how it performs when compared to the advanced XLNet and BERT algorithms.These systems’efficiency can be gauged utilizing assessment metrics like accuracy,precision,and consistency of responses.
基金supported in part by the National Natural Science Foundation of China(No.62001333)the Scientific Research Project of Education Department of Hubei Province(No.D20221702).
文摘Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature space.Then,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior features.Finally,a support vector machine(SVM)classifier is used to classify intrusion behaviors.The proposed model is verified by experiments on the NSL-KDD dataset.The results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion classification.The proposed method can provide new insight into network intrusion detection.
基金Supported by Natural Science Foundation of Hunan Province,China,No.2022JJ30842 and No.2024JJ6560Clinical Medical Research Center for Viral Hepatitis of Hunan Province,No.2023SK4009Beijing iGandan Foundation,No.RGGJJ-2021-017 and No.iGandanF-1082022-RGG023.
文摘BACKGROUND We have innovatively amalgamated membrane blood purification and centrifugal blood cell separation technologies to address the limitations of current artificial liver support(ALS)models,and develop a versatile plasma purification system(VPPS)through centrifugal plasma separation.AIM To investigate the influence of VPPS on long-term rehospitalization and mortality rates among patients with acute-on-chronic liver failure(ACLF).METHODS This real-world,prospective study recruited inpatients diagnosed with ACLF from the Second Xiangya Hospital of Central South University between October 2021 and March 2024.Patients were categorized into the VPPS and non-VPPS groups based on the distinct ALS models administered to them.Self-administered questionnaires,clinical records,and self-reported data served as the primary methods for data collection.The laboratory results were evaluated at six distinct time points.All patients were subjected to follow-up assessments for>12 months.Kaplan-Meier survival analyses and Cox proportional hazards models were used to evaluate the risks of hospitalization and mortality during the follow-up period.RESULTS A cohort of 502 patients diagnosed with ACLF was recruited,with 260 assigned to the VPPS group.On comparing baseline characteristics,the VPPS group exhibited a significantly shorter length of stay,higher incidence of spontaneous peritonitis and pulmonary aspergillosis compared to the non-VPPS group(P<0.05).Agehazard ratio(HR=1.142,95%CI:1.01-1.23,P=0.018),peritonitis(HR=2.825,95%CI:1.07-6.382,P=0.026),albumin(HR=0.67,95%CI:0.46-0.942,P=0.023),total bilirubin(HR=1.26,95%CI:1.01-3.25,P=0.021),international normalized ratio(HR=1.97,95%CI:1.21-2.908,P=0.014),and VPPS/non-VPPS(HR=3.24,95%CI:2.152-4.76,P<0.001)were identified as significant independent predictors of mortality in both univariate and multivariate analyses throughout the follow-up period.Kaplan-Meier survival analyses demonstrated significantly higher rehospitalization and mortality rates in the non-VPPS group compared to the VPPS group during follow-up of≥2 years(log-rank test,P<0.001).CONCLUSION These findings suggest that VPPS is safe and has a positive influence on prognostic outcomes in patients with ACLF.
文摘Language education has predominantly focused on classroom instruction and pedagogical strategies,yet the sociological aspects of learning-especially the family’s role-are often overlooked in English as a Foreign Language(EFL)contexts.This study explores how parental attitudes,support systems,and disciplinary approaches influence Turkish university students’oral fluency in English.While educators and curricula are frequently held accountable for low communicative competence,this paper posits that sociocultural factors originating from the home environment play a crucial role.Utilizing a qualitative-dominant mixed-methods design with 210 undergraduate EFL learners and 24 lecturers from four Turkish universities,data were gathered through questionnaires,interviews,and parental focus groups.Findings indicate that students with supportive and engaged parents exhibit greater self-efficacy,confidence,and linguistic agency in communication tasks.In contrast,those from indifferent or authoritarian families experience anxiety,low motivation,and stagnation in oral skills.Grounded in Bourdieu’s social capital theory,Bronfenbrenner’s ecological systems theory,and Coleman’s social context of learning,this study conceptualizes the family as an“invisible classroom”where linguistic habits,discipline,and cultural aspirations are cultivated.The paper concludes by proposing a Family-Engaged EFL Fluency Model(FE-EFM)that integrates learner development within a framework of sociological co-responsibility involving parents,educators,and institutions.
基金Project(2023YFC2907600)supported by the National Key Research and Development Program of ChinaProjects(42277174,42477166)supported by the National Natural Science Foundation of China+1 种基金Project(2024JCCXSB01)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(KFJJ24-01M)supported by the State Key Laboratory of Explosion Science and Safety Protection,Beijing Institute of Technology,China。
文摘Considering the characteristics of deep thick top coal roadway,in which the high ground stress,coal seam with low strength,and a large range of surrounding rock fragmentation,the pressure relief anchor box beam support system with high strength is developed.The high-strength bearing characteristics and coupling yielding support mechanism of this support system are studied by the mechanical tests of composite members and the combined support system.The test results show that under the coupling effect of support members,the peak stress of the box-shaped support beam in the anchor box beam is reduced by 21.9%,and the average deformation is increased by 135.0%.The ultimate bending bearing capacity of the box-shaped support beam is 3.5 times that of traditional channel beam.The effective compressive stress zone applied by the high prestressed cable is expanded by 26.4%.On this basis,the field support comparison test by the anchor channel beam,the anchor I-shaped beam and the anchor box beam are carried out.Compared with those of the previous two,the surrounding rock convergence of the latter is decreased by 41.2%and 22.2%,respectively.The field test verifies the effectiveness of the anchor box beam support system.
文摘Decision support systems(DSS)based on physically based numerical models are standard tools used by water services and utilities.However,few DSS based on holistic approaches combining distributed hydrological,hydraulic,and hydrogeological models are operationally exploited.This holistic approach was adopted for the development of the AquaVar DSS,used for water resource management in the French Mediterranean Var watershed.The year 2019 marked the initial use of the DSS in its operational environment.Over the next 5 years,multiple hydrological events allowed to test the performance of the DSS.The results show that the tool is capable of simulating peak flows associated with two extreme rainfall events(storms Alex and Aline).For a moderate flood,the real-time functionality was able to simulate forecast discharges 26 h before the flood peak,with a maximum local error of 30%.Finally,simulations for the drought period 2022-2023 highlighted the essential need for DSS to evolve in line with changing climatic conditions,which give rise to unprecedented hydrological processes.The lessons learned from these first 5 years of AquaVar use under operational conditions are synthesized,addressing various topics such as DSS modularity,evolution,data positioning,technology,and governance.
文摘BACKGROUND Artificial intelligence(AI)is gaining widespread traction in surgical disciplines,particularly in gastrointestinal(GI)surgery,where it offers opportunities to enhance decision-making,improve accuracy,and optimize patient outcomes across the entire surgical continuum.AIM To comprehensively evaluate current AI applications in GI surgery,highlighting its role in preoperative planning,intraoperative guidance,postoperative monitoring,endoscopic diagnosis,and surgical education.METHODS This systematic review was conducted in accordance with PRISMA guidelines.We searched the Web of Science Core Collection through March 31,2025 using the terms“artificial intelligence”AND“gastrointestinal surgery”.Inclusion criteria:Original,English-language,full-text articles indexed under the“Surgery”category reporting quantitative AI performance metrics in GI surgery.Exclusion criteria:Reviews,editorials,letters,conference abstracts,non-English publications,ESCI/SSCI/Index Chemicus-only papers,studies without full text,and articles outside the surgical domain.Full texts of potentially eligible studies were assessed,yielding 45 studies from an initial 955 records for qualitative and quantitative synthesis.RESULTS The included studies demonstrated that AI has superior performance compared to traditional clinical tools in areas such as risk prediction,lesion detection,nerve identification,and complication forecasting.Notably,convolutional neural networks,random forests,support vector machines,and reinforcement learning models were commonly used.AI-enhanced systems improved diagnostic accuracy,procedural safety,documentation quality,and educational feedback.However,there are several limitations,such as lack of external validation,dataset standardization,and explainability.CONCLUSION AI is transforming GI surgery from preoperative risk assessment to postoperative care and training.While many tools now match or exceed expert-level performance,successful clinical adoption requires transparent,validated models that seamlessly integrate into surgical workflows.With continued multidisciplinary collaboration,AI is positioned to become a trusted companion in surgical practice.
文摘Objectives:Recently,the global esports industry has experienced remarkable growth,leading to an expansion in the scale and influence of professional player communities.However,despite this outward growth,systems to protect players’mental health remain inadequate.Comprehensive analysis of structural risk factors,including performance pressure,public evaluation,and career instability,remains insufficient.This study,aimed to explore stressors encountered by esports athletes,coping strategies,and the role of social support systems in safeguarding mental health.Using the transactional model of stress and coping,the job demands–resources model,and social support theory,the study adopts an integrated perspective to examine challenges faced by athletes in the competitive esports environment.Methods:A qualitative case study was conducted involving in-depth interviews and nonparticipant observations with 11 esports athletes who competed at national or international levels,as well as two team managers.Thematic analysis identified recurring patterns in the data,and credibility was ensured through triangulation and cross-review among researchers.Results:Esports athletes experience multiple interacting stressors,including performance demands,emotional strain duringmatches,and continuous evaluation on socialmedia.In response,they employed coping strategies—problem-focused,emotion-focused,and avoidance-based,which provided temporary relief but often led to burnout and self-regulation failure owing to absence of support systems.Social support networks had ambivalent effects:while offering comfort,they also intensified pressure through negative feedback and high expectations from fans and online communities.Conclusion:The findings show that mental health issues among esports athletes are not only related to individual factors but are closely linked to performance-driven structures,competitive environments,and social relationships.This study integrates the transactional model of stress and coping,the JobDemands–Resourcesmodel,and social support theoryto provide comprehensive analysis.It also offers practical recommendations,including psychological counseling,emotional labor programs,and improved communication with families and fan communities.
文摘Abnormal driving behavior includes driving distraction,fatigue,road anger,phone use,and an exceptionally happy mood.Detecting abnormal driving behavior in advance can avoid traffic accidents and reduce the risk of traffic conflicts.Traditional methods of detecting abnormal driving behavior include using wearable devices to monitor blood pressure,pulse,heart rate,blood oxygen,and other vital signs,and using eye trackers to monitor eye activity(such as eye closure,blinking frequency,etc.)to estimate whether the driver is excited,anxious,or distracted.Traditional monitoring methods can detect abnormal driving behavior to a certain extent,but they will affect the driver’s normal driving state,thereby introducing additional driving risks.This research uses the combined method of support vector machine and dlib algorithm to extract 68 facial feature points from the human face,and uses an SVM model as a strong classifier to classify different abnormal driving statuses.The combined method reaches high accuracy in detecting road anger and fatigue status and can be used in an intelligent vehicle cabin to improve the driving safety level.
文摘BACKGROUND The treatment of patients with liver cancer after surgery with the artificial liver support system combined with traditional Chinese medicine(TCM)for strengthening the body and removing blood stasis is a new idea.AIM To analyze the post-surgical effect of the artificial liver support system with TCM in patients with liver cancer.METHODS Ninety-eight patients with liver cancer who underwent surgical treatment at the Fifth People’s Hospital of Huai’an from January 2023-2024 were selected and divided into two groups(49 patients each)via random lottery method.Both groups underwent surgery.The control group received artificial liver support,and the observation group was additionally treated with TCM for strengthening the body and removing blood stasis.Gastrointestinal recovery,liver function,tumor marker levels,immune function,and safety were compared between both groups.RESULTS There were significant differences in the levels of indicators related to gastrointestinal recovery between the groups(P<0.05).After treatment,the levels of alanine aminotransferase,aspartate aminotransferase,total bilirubin,and gamma-glutamyl transpeptidase in the observation group were lower,whereas the albumin level was higher(P<0.05).After treatment,tumor marker levels in the observation group were relatively lower(P<0.05).After treatment,compared to the control group,the CD4+level in the observation group was higher and the CD8+level was lower(P<0.05).There was no significant difference in the incidence of adverse reactions between both groups(P>0.05).CONCLUSION Combining the artificial liver support system with TCM significantly improves liver and gastrointestinal functions,enhances immune responses,and reduces tumor marker levels with high safety,suggesting that it could be a promising approach for optimizing postoperative care and improving patient outcomes,potentially reducing complications and enhancing quality of life.
