In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operati...In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operations.However,several challenges have emerged within the high-speed railway dispatching and command system,including the heavy workload faced by dispatchers,the difficulty of quantifying subjective expertise,and the need for effective training of professionals.Amid the growing application of artificial intelligence technologies in railway systems,this study leverages Large Language Model(LLM)technology.LLMs bring enhanced intelligence,predictive capabilities,robust memory,and adaptability to diverse real-world scenarios.This study proposes a human-computer interactive intelligent scheduling auxiliary training system built on LLM technology.The system offers capabilities including natural dialogue,knowledge reasoning,and human feedback learning.With broad applicability,the system is suitable for vocational education,guided inquiry,knowledge-based Q&A,and other training scenarios.Validation results demonstrate its effectiveness in auxiliary training,providing substantial support for educators,students,and dispatching personnel in colleges and professional settings.展开更多
In order to ensure the power supply in the society, it is necessary to arrange the power regulation system to detect, control and manage the power, and finally meet the social demand for electricity. With the developm...In order to ensure the power supply in the society, it is necessary to arrange the power regulation system to detect, control and manage the power, and finally meet the social demand for electricity. With the development of internet, power supply enterprises actively apply advanced technology. Power regulation and operation system is a complex technology, which has an important impact on the normal and reliable operation of the whole power system. Its main function is to realize the dispatching control of each link and step of the power grid according to the different power load faced by the power system, so as to ensure the safe and reliable operation of the power grid. It can be said that the safety management of power regulation and operation system is an important measure to ensure the safe and reliable operation of power grid system.展开更多
Air traffic control is an essential obligation in the aviation industry to have safe and efficient air transportation.Year by year,the workload and on-job-stress of the air traffic controllers are rapidly increasing d...Air traffic control is an essential obligation in the aviation industry to have safe and efficient air transportation.Year by year,the workload and on-job-stress of the air traffic controllers are rapidly increasing due to the rapid growth of air traveling.Controllers are usually dealing with multiple aircrafts at a time and must make quick and accurate decisions to ensure the safety of aircrafts.Heavy workload and high responsibilities create air traffic control a stressful job that sometimes could be error-prone and time-consuming,since controlling and decision-making are solely dependent on human intelligence.To provide effective solutions for the mentioned on the job challenges of the controllers,this study proposed an intelligent virtual assistant system(IVAS)to assist the controllers thereby to reduce the controllers’workload.Consisting of four main parts,which are voice recognition,display conversation on screen,task execution,and text to speech,the proposed system is developed with the aid of artificial intelligence(AI)techniques to make speedy decisions and be free of human interventions.IVAS is a computer-based system that can be activated by the voice of the air traffic controller and then appropriately assist to control the flight.IVAS identifies the words spoken by the controller and then a virtual assistant navigates to collect the data requested from the controllers,which allows additional or free time to the controllers to contemplate more on the work or could assist to another aircraft.The Google speech application programming interface(API)converts audio to text to recognize keywords.AI agent is trained using the Hidden marko model(HMM)algorithm such that it could learn the characteristics of the distinct voices of the controllers.At this stage,the proposed IVAS can be used to provide training for novice air traffic controllers effectively.The system is to be developed as a real-time system which could be used at the air traffic controlling base for actual traffic controlling purposes and the system is to be further upgraded to perform the task by recognizing keywords directly from the pilot voice command.展开更多
Stealth security has always been considered as an important guarantee for the vitality and combat effectiveness of submarines.In accordance with the stealth requirements of submarines performing stealth voyage tasks,t...Stealth security has always been considered as an important guarantee for the vitality and combat effectiveness of submarines.In accordance with the stealth requirements of submarines performing stealth voyage tasks,this paper proposes a stealth assistant decision system.Firstly,the submarine stealth posture is acquired.A fuzzy neural network inference engine based on improved simplified particle swarm optimization is designed.The auxiliary decision-making scheme for state control and maneuver avoidance of submarine and its equipment is automatically generated.Secondly,the simulation and deduction of the assistant decision-making scheme are realized by the calculation modules of sound source level,propagation loss,and stealth situation.The assistant decision-making scheme and simulation result provide decision support for the commander.Thirdly,the simulation experiment platform of the submarine stealth assistant decision system is constructed.The submarine stealth assistant decision system described in this paper can quickly and efficiently produce assistant decision-making schemes,including submarine and equipment control and maneuver avoidance.The scheme is in line with the combat experience and the results of the pre-model simulation experiments,whereas the simulation deduction evaluates the rationality and effectiveness of the selected scheme.The submarine stealth assistant decision system can adapt to a complex battlefield environment in addition to rapidly and accurately providing assistance in decision-making.展开更多
The traceability management system for pigs based on personal digital assistant （PDA） was constructed by software engineering method to provide the traceability management for pork safety. This traceability system i...The traceability management system for pigs based on personal digital assistant （PDA） was constructed by software engineering method to provide the traceability management for pork safety. This traceability system included information managements for pig breeding and pig quaran- tine inspection supervision. It also realized the record and supervision of pig information, feed usage, veterinary drug usage and quarantine inspec- tion. We mainly introduced the designs of systemic structure and functional structure of the traceability system and key techniques of system imple- mentation.展开更多
The framework of the assistant decision support system of cross-regional rural labor flow is established,the system combines the cross-regional rural labor flow with DSS,which provides the leaders with the maximum ass...The framework of the assistant decision support system of cross-regional rural labor flow is established,the system combines the cross-regional rural labor flow with DSS,which provides the leaders with the maximum assistant decision-making function in the regulation and guidance of rural labors as well as in relevant programs.The assistant decision support system functions are discussed,the function modules of this system are introduced from four aspects,including the analysis of labor flow,the prediction of labor flow,the regulation of cross-regional flow and the configuration of decision support system;based on the data base obtained from dynamic tracking of the migrant workers and combining other data sources,the data warehouse model is established,for example,in the analysis of the labor migration times,a star multi-dimensional data model is designed from the time dimension,place dimension,the type of work dimension,accompaniers dimension and so on;the trans-regional flow of rural labor force is analyzed and predicted by using OLAP from the labor's migration times,migration places and other various perspectives.The operation principles of the assistant decision support system of trans-regional labor flow are introduced,it is pointed out that the system serves the policy-makers of the regulation of labor flow and other relevant enterprises,the system will play an important role in the tracking monitoring and cross-regional regulation of the rural labor flow.展开更多
In Ambient Assistant Living(AAL) systems, it is a fundamental problem to ensure prompt delivery of detected events, such as irregular heart rate or fall of elderly, to a central processing device(e.g. gateway node). M...In Ambient Assistant Living(AAL) systems, it is a fundamental problem to ensure prompt delivery of detected events, such as irregular heart rate or fall of elderly, to a central processing device(e.g. gateway node). Most of recently proposed MAC protocols for low-power embedded sensing systems(e.g. wireless sensor networks) are designed with energy efficiency as the first goal, so they are not suitable for AAL systems. Although some multi-channel MAC protocols have been proposed to address the problem, most of those protocols ignore the cost of channel switching, which can have reverse effect on network performance, especially latency of data delivery. In this paper, we propose a Delay-Sensitive Multi-channel MAC protocol(DS-MMAC) for AAL systems, which can provide high packet delivery ratio and bound low latency for data delivered to the gateway node. The novelty of the protocol is that an efficient distributed time slot scheduling and channel assignment algorithm is combined with the process of route establishment, which takes the channel switching cost into account and reduces endto-end delay to meet the required delay bound of each data flow. The performance of the proposed protocol is evaluated through extensive simulations. Results show that DS-MMAC can bound low latency for delivering detected events in AAL system to the gateway, while providing high delivery reliability and low energy consumption.展开更多
This paper attempts to approach the interface of a robot from the perspective of virtual assistants.Virtual assistants can also be characterized as the mind of a robot,since they manage communication and action with t...This paper attempts to approach the interface of a robot from the perspective of virtual assistants.Virtual assistants can also be characterized as the mind of a robot,since they manage communication and action with the rest of the world they exist in.Therefore,virtual assistants can also be described as the brain of a robot and they include a Natural Language Processing(NLP)module for conducting communication in their human-robot interface.This work is focused on inquiring and enhancing the capabilities of this module.The problem is that nothing much is revealed about the nature of the human-robot interface of commercial virtual assistants.Therefore,any new attempt of developing such a capability has to start from scratch.Accordingly,to include corresponding capabilities to a developing NLP system of a virtual assistant,a method of systemic semantic modelling is proposed and applied.For this purpose,the paper briefly reviews the evolution of virtual assistants from the first assistant,in the form of a game,to the latest assistant that has significantly elevated their standards.Then there is a reference to the evolution of their services and their continued offerings,as well as future expectations.The paper presents their structure and the technologies used,according to the data provided by the development companies to the public,while an attempt is made to classify virtual assistants,based on their characteristics and capabilities.Consequently,a robotic NLP interface is being developed,based on the communicative power of a proposed systemic conceptual model that may enhance the NLP capabilities of virtual assistants,being tested through a small natural language dictionary in Greek.展开更多
Background:This study evaluates the ability of mobile AI voice assistants(AI-VAs)to provide accurate medical advice for early knee osteoarthritis(KOA)and compares their performance with conventional web searches and h...Background:This study evaluates the ability of mobile AI voice assistants(AI-VAs)to provide accurate medical advice for early knee osteoarthritis(KOA)and compares their performance with conventional web searches and human clinicians.Methods:From September to October 2024,two AI-VAs(Apple’s Siri and Huawei’s Xiaoyi)were tested on 15 KOA-related questions in Chinese and English.The assessment focused on the accuracy of voice recognition,response capabilities,and medical advice.Siri was further tested in four international regions(USA,UK,Germany,Hong Kong)using localized languages.Results:In Chinese-language tests,Siri and Xiaoyi showed comparable voice recognition(recognition accuracy:95.6%vs.93.3%)and response ability(speech response:88.9%vs.85.7%).However,Siri provided significantly more accurate medical advice(medical advice:95.6%vs.53.3%;Z=2.762,P<0.001).External validation via Global Quality Score further confirmed Siri’s superiority(mean Global Quality Score=4.0 vs.Xiaoai=0.9).Siri outperformed Xiaoyi in English-language tests(53.3%vs.0%).While Siri’s medical advice accuracy(95.6%)surpassed non-specialist clinicians(Z=2.685,P=0.007),it primarily reflects filtered search results(Baidu/Google)rather than clinical synthesis.Claims of equivalence to junior surgeons(98.2%)must be interpreted cautiously,as AI-VAs lack diagnostic reasoning capabilities.This distinction is critical to avoid overstating their role in clinical decision-making.Conclusion:Current AI-VAs offer limited value in providing precise medical advice for KOA,primarily serving as intermediaries for web search results.Their performance varies across languages,regions,and search engines.展开更多
This study analyzed how impressions are formed online depending on the type of social media and the implications that may come from the over-disclosure of information. Using a Qualtrics survey, 97 participants viewed ...This study analyzed how impressions are formed online depending on the type of social media and the implications that may come from the over-disclosure of information. Using a Qualtrics survey, 97 participants viewed the profile of a female teaching assistant on Twitter and Facebook. While there was little difference between the two social media, the use of self-disclosure on Twitter seemed slightly more inappropriate for sharing personal information.展开更多
With the shift in the definition of disease from non-alcoholic fatty liver disease(NAFLD)to metabolism-associated fatty liver disease(MAFLD),as well as the rapid evolution of pathological classification and therapeuti...With the shift in the definition of disease from non-alcoholic fatty liver disease(NAFLD)to metabolism-associated fatty liver disease(MAFLD),as well as the rapid evolution of pathological classification and therapeutic targets,traditional clinical teaching models face challenges such as outdated guideline updates,disjointed translation of scientific research,and limited skill training.This study proposes a dynamic training model integrating“guidelines,clinical practice,and scientific research.”Through stratified case-based teaching(e.g.,FibroScan simulator and metabolic sand table),dynamic guideline analysis(comparing old and new evidence),and the integration of scientific thinking(visualization of CAND1 protein mechanism),a teaching system that integrates theory and practice is constructed.Innovatively developed smart assistant tools(AI decision support system,VR liver biopsy simulator)and a multi-dimensional evaluation system(deviation analysis of diagnosis and treatment pathways,milestone assessment)are used while emphasizing metabolic medicine integration(continuous glucose monitoring and digital therapy)and ethical privacy protection(federated learning framework).This model aims to cultivate students’evidence-based decision-making skills and scientific research transformation thinking through dynamic knowledge base construction and interdisciplinary collaboration,providing sustainable teaching solutions to cope with the rapid iteration of NAFLD diagnosis and treatment.展开更多
Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,...Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.展开更多
For computer science majors in higher education institutions,programming courses are one of the most important professional foundation courses.Proficiency in independent programming skills is of great help to the stud...For computer science majors in higher education institutions,programming courses are one of the most important professional foundation courses.Proficiency in independent programming skills is of great help to the study of subsequent courses and the personal development of students.In the teaching process of programming courses,online judgement systems are often used to improve students’programming level.Traditional online judgement systems lack guidance for students,and it is often difficult for inexperienced students to find and correct errors in their codes by themselves.We propose an online judgement system that integrates a large model of error correction to help students find errors and improve their programming skills.展开更多
Background:Work engagement(WE)is critical to quality primary healthcare delivery.However,limited research has explored its levels and determinants among healthcare professionals in low-and middle-income countries.This...Background:Work engagement(WE)is critical to quality primary healthcare delivery.However,limited research has explored its levels and determinants among healthcare professionals in low-and middle-income countries.This study assessed the levels and correlates of work engagement among physician assistants(PAs)in Ghana.Methods:A cross-sectional study was conducted among 439 PAs from October to December 2024.Participants were recruited via emails,social media platforms,and posters featuring study links and scannable questionnaire codes.WE was measured using the validated Utrecht Work Engagement Scale questionnaire.Results:Overall,WE levels were average,with similar trends across the three subdomains.In the bootstrapped multivariate linear regression model,anxiety was negatively associated with WE(β=-0.49,95%confidence interval[CI]:-0.77 to-0.21).Conversely,working in an urban area(β=0.36,95%CI:0.05 to 0.67),holding the rank of PA/Senior PA(β=0.27,95%CI:0.03 to 0.52),reporting good self-rated health(β=0.54,95%CI:0.19 to 0.88),and working at health centers(β=0.86,95%CI:0.22 to 1.50)were positively associated with WElevels.Conclusion:WE levels are average in the study sample,highlighting the need for strategic interventions to improve and sustain the healthcare workforce's motivation and performance.Addressing workplace stressors,enhancing professional development opportunities,and fostering supportive work environments could improve engagement among PAs and healthcare professionals in general.Strengthening WE is essential for ensuring resilient quality primary healthcare systems and achieving the goals of universal health coverage.展开更多
BACKGROUND Colorectal cancer(CRC)can be prevented by screening and early detection.Colonoscopy is used for screening,and adenoma detection rate(ADR)is used as a key quality indicator of sufficient colonoscopy.However,...BACKGROUND Colorectal cancer(CRC)can be prevented by screening and early detection.Colonoscopy is used for screening,and adenoma detection rate(ADR)is used as a key quality indicator of sufficient colonoscopy.However,ADR can vary significantly among endoscopists,leading to missed polyps or cancer.Artificial intelligence(AI)has shown promise in improving ADR by assisting in real-time polyp identification or diagnosis.While multiple randomized controlled trials(RCTs)and metanalyses highlight the benefits of AI in increasing detection rates and reducing missed polyps,concerns remain about its real-world applicability,impact on procedure time,and cost-effectiveness.AIM To explore the current status of AI assistance colonoscopy in adenoma detection and improving quality of colonoscopy.METHODS This systematic review followed PRISMA guidelines,both PubMed and Web of Science databases were used for articles search.Metanalyses and systematic reviews that assessed AI's role during colonoscopy.English article only published between January 2000 and January 2025 were included.Articles related to nonadenoma indications were excluded.Data extraction was independently performed by two researchers for accuracy and consistency.RESULTS 22 articles met the inclusion criteria,with significant heterogeneity(I2=28%-91%)observed in multiple studies.The number of studies per metanalysis ranged from 5 to 33,with higher heterogeneity in analyses involving more than 18 RCTs.AI demonstrated improvement in ADR,with an approximate 20%increase across multiple studies.However,its effectiveness in detecting flat or serrated adenomas remains unproven.Endoscopists with low ADR benefit more from AI-colonoscopies,while expert endoscopists outperformed AI in ADR,adenoma miss rate,and the identification of advanced lesions.No significant change in withdrawal time was observed when comparing AI-assisted colonoscopy to conventional endoscopy.CONCLUSION While AI-assisted colonoscopy has been shown to improve procedural quality,particularly for junior endoscopists and those with lower ADR,its performance decreases when compared to expert endoscopists in real-time clinical practice.This is especially evident in non-randomized studies,where AI demonstrates limited real-world benefits despite its benefit in controlled settings.Furthermore,no meta-analyses have specifically examined AI's impact on the learning experience of fellows and residents.Some experts caution that reliance on AI may prevent trainees from developing essential observational skills,potentially leading to less thorough examinations.Further research is needed to determine the actual benefits of AI-colonoscopy,particularly its role in cancer prevention.As technology advances,improved outcomes are expected,especially in detecting small,flat,and lesions at difficult anatomical locations.展开更多
Objective:To evaluate mobile applications available for patients undergoing assisted reproduction and assess the extent of their clinical validation.Methods:A systematic search was conducted in the Apple App Store and...Objective:To evaluate mobile applications available for patients undergoing assisted reproduction and assess the extent of their clinical validation.Methods:A systematic search was conducted in the Apple App Store and Google Play between September 1,2023 and September 30,2023 to identify mobile applications related to assisted reproduction.Apps were evaluated using the mobile app rating scale(MARS).In parallel,a literature search of PubMed,Scopus,Embase,and Web of Science was performed to identify clinical studies related to mobile applications in assisted reproduction.Clinical validation status and MARS scores were recorded,and findings were synthesized to highlight the gap between commercially available apps and research-based evidence.Results:From 1143 apps screened,11 met the inclusion criteria.Mean MARS score across apps was 3.63,with Leeaf scoring the highest(4.60).However,only one application(Embie)was supported by published research.The literature research identified 13 relevant studies,mostly randomized controlled trials,cohort studies,or usability studies.While research-based apps demonstrated clinical utility(e.g.,MediEmo,PreLiFe,Patient Journey App),most were unavailable on app stores.This revealed a disconnect between research-backed applications and those accessible to patients.Conclusions:Although several mobile apps for assisted reproduction demonstrate high usability and quality,few are clinically validated.The lack of integration between research and practice highlights the need for stronger collaboration between researchers,developers,and policymakers to ensure that patients access safe and effective tools.展开更多
BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)is contraindicated for patients with cavernous transformation of the portal vein(CTPV)due to high surgery-related mortality risk.However,surgically assiste...BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)is contraindicated for patients with cavernous transformation of the portal vein(CTPV)due to high surgery-related mortality risk.However,surgically assisted TIPS(SATIPS)can significantly reduce the risk.AIM To evaluate the clinical efficacy of SATIPS,this study was conducted.METHODS One hundred and seven patients with CTPV and esophagogastric variceal bleeding were recruited from January 2023 to December 2024.The patients were recruited from three different hospitals.Overall,54 patients received SATIPS treatment(SATIPS group),while 53 patients did not receive SATIPS and underwent prophylactic endoscopic sclerosing ligation(control group).Subsequently,survival rates,incidence rates of gastrointestinal bleeding,incidence of hepatic encephalopathy rate,and the incidence of liver failure after treatment in both groups at 3 and 6 months were observed.RESULTS The survival rates for the SATIPS and control groups were 94.4%and 92.5%at 3 months(P value=0.72)and 94.4%and 73.6%at 6 months(P value=0.0051)respectively.The incidence of liver failure was 3.7%and 9.4%at 3 months(P value=0.26)and 3.7%and18.9%at 6 months(P value=0.016);the incidence of gastrointestinal bleeding was 5.6%and 37.7%at 3 months(P value<0.001)and 9.3%and 47.2%(P value<0.001)at 6 months;and the incidence of hepatic encephalopathy was 3.7%and 17.0%at 3 months(P value=0.026)and 7.4%and 26.4%at 6 months(P value=0.026)respectively.CONCLUSION For patients with CTPV,there were no optimal treatment.Regarding long-term efficacy,SATIPS can significantly reduce the rate of rebleeding,hepatic encephalopathy and liver failure,and is associated with better survival.展开更多
Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportatio...Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.展开更多
基金the Talent Fund of Beijing Jiaotong University(Grant No.2024XKRC055).
文摘In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operations.However,several challenges have emerged within the high-speed railway dispatching and command system,including the heavy workload faced by dispatchers,the difficulty of quantifying subjective expertise,and the need for effective training of professionals.Amid the growing application of artificial intelligence technologies in railway systems,this study leverages Large Language Model(LLM)technology.LLMs bring enhanced intelligence,predictive capabilities,robust memory,and adaptability to diverse real-world scenarios.This study proposes a human-computer interactive intelligent scheduling auxiliary training system built on LLM technology.The system offers capabilities including natural dialogue,knowledge reasoning,and human feedback learning.With broad applicability,the system is suitable for vocational education,guided inquiry,knowledge-based Q&A,and other training scenarios.Validation results demonstrate its effectiveness in auxiliary training,providing substantial support for educators,students,and dispatching personnel in colleges and professional settings.
文摘In order to ensure the power supply in the society, it is necessary to arrange the power regulation system to detect, control and manage the power, and finally meet the social demand for electricity. With the development of internet, power supply enterprises actively apply advanced technology. Power regulation and operation system is a complex technology, which has an important impact on the normal and reliable operation of the whole power system. Its main function is to realize the dispatching control of each link and step of the power grid according to the different power load faced by the power system, so as to ensure the safe and reliable operation of the power grid. It can be said that the safety management of power regulation and operation system is an important measure to ensure the safe and reliable operation of power grid system.
文摘Air traffic control is an essential obligation in the aviation industry to have safe and efficient air transportation.Year by year,the workload and on-job-stress of the air traffic controllers are rapidly increasing due to the rapid growth of air traveling.Controllers are usually dealing with multiple aircrafts at a time and must make quick and accurate decisions to ensure the safety of aircrafts.Heavy workload and high responsibilities create air traffic control a stressful job that sometimes could be error-prone and time-consuming,since controlling and decision-making are solely dependent on human intelligence.To provide effective solutions for the mentioned on the job challenges of the controllers,this study proposed an intelligent virtual assistant system(IVAS)to assist the controllers thereby to reduce the controllers’workload.Consisting of four main parts,which are voice recognition,display conversation on screen,task execution,and text to speech,the proposed system is developed with the aid of artificial intelligence(AI)techniques to make speedy decisions and be free of human interventions.IVAS is a computer-based system that can be activated by the voice of the air traffic controller and then appropriately assist to control the flight.IVAS identifies the words spoken by the controller and then a virtual assistant navigates to collect the data requested from the controllers,which allows additional or free time to the controllers to contemplate more on the work or could assist to another aircraft.The Google speech application programming interface(API)converts audio to text to recognize keywords.AI agent is trained using the Hidden marko model(HMM)algorithm such that it could learn the characteristics of the distinct voices of the controllers.At this stage,the proposed IVAS can be used to provide training for novice air traffic controllers effectively.The system is to be developed as a real-time system which could be used at the air traffic controlling base for actual traffic controlling purposes and the system is to be further upgraded to perform the task by recognizing keywords directly from the pilot voice command.
基金Funding National Natural Science Foundation of China(51709061,51779057).
文摘Stealth security has always been considered as an important guarantee for the vitality and combat effectiveness of submarines.In accordance with the stealth requirements of submarines performing stealth voyage tasks,this paper proposes a stealth assistant decision system.Firstly,the submarine stealth posture is acquired.A fuzzy neural network inference engine based on improved simplified particle swarm optimization is designed.The auxiliary decision-making scheme for state control and maneuver avoidance of submarine and its equipment is automatically generated.Secondly,the simulation and deduction of the assistant decision-making scheme are realized by the calculation modules of sound source level,propagation loss,and stealth situation.The assistant decision-making scheme and simulation result provide decision support for the commander.Thirdly,the simulation experiment platform of the submarine stealth assistant decision system is constructed.The submarine stealth assistant decision system described in this paper can quickly and efficiently produce assistant decision-making schemes,including submarine and equipment control and maneuver avoidance.The scheme is in line with the combat experience and the results of the pre-model simulation experiments,whereas the simulation deduction evaluates the rationality and effectiveness of the selected scheme.The submarine stealth assistant decision system can adapt to a complex battlefield environment in addition to rapidly and accurately providing assistance in decision-making.
基金supported by the Agricultural Science and Technology Innovation Project of Yunnan Province(2008LA020)sub-topic of National Key Technology R&D Program(2006BAD14B04)
文摘The traceability management system for pigs based on personal digital assistant （PDA） was constructed by software engineering method to provide the traceability management for pork safety. This traceability system included information managements for pig breeding and pig quaran- tine inspection supervision. It also realized the record and supervision of pig information, feed usage, veterinary drug usage and quarantine inspec- tion. We mainly introduced the designs of systemic structure and functional structure of the traceability system and key techniques of system imple- mentation.
基金Supported by the National Science & Technology Pillar Program(2006BAJ07B07)
文摘The framework of the assistant decision support system of cross-regional rural labor flow is established,the system combines the cross-regional rural labor flow with DSS,which provides the leaders with the maximum assistant decision-making function in the regulation and guidance of rural labors as well as in relevant programs.The assistant decision support system functions are discussed,the function modules of this system are introduced from four aspects,including the analysis of labor flow,the prediction of labor flow,the regulation of cross-regional flow and the configuration of decision support system;based on the data base obtained from dynamic tracking of the migrant workers and combining other data sources,the data warehouse model is established,for example,in the analysis of the labor migration times,a star multi-dimensional data model is designed from the time dimension,place dimension,the type of work dimension,accompaniers dimension and so on;the trans-regional flow of rural labor force is analyzed and predicted by using OLAP from the labor's migration times,migration places and other various perspectives.The operation principles of the assistant decision support system of trans-regional labor flow are introduced,it is pointed out that the system serves the policy-makers of the regulation of labor flow and other relevant enterprises,the system will play an important role in the tracking monitoring and cross-regional regulation of the rural labor flow.
基金supported by the International S&T Cooperation Program of China (ISTCP) under Grant No. 2013DFA10690the National Science Foundation of China (NSFC) under Grant No. 61100180
文摘In Ambient Assistant Living(AAL) systems, it is a fundamental problem to ensure prompt delivery of detected events, such as irregular heart rate or fall of elderly, to a central processing device(e.g. gateway node). Most of recently proposed MAC protocols for low-power embedded sensing systems(e.g. wireless sensor networks) are designed with energy efficiency as the first goal, so they are not suitable for AAL systems. Although some multi-channel MAC protocols have been proposed to address the problem, most of those protocols ignore the cost of channel switching, which can have reverse effect on network performance, especially latency of data delivery. In this paper, we propose a Delay-Sensitive Multi-channel MAC protocol(DS-MMAC) for AAL systems, which can provide high packet delivery ratio and bound low latency for data delivered to the gateway node. The novelty of the protocol is that an efficient distributed time slot scheduling and channel assignment algorithm is combined with the process of route establishment, which takes the channel switching cost into account and reduces endto-end delay to meet the required delay bound of each data flow. The performance of the proposed protocol is evaluated through extensive simulations. Results show that DS-MMAC can bound low latency for delivering detected events in AAL system to the gateway, while providing high delivery reliability and low energy consumption.
文摘This paper attempts to approach the interface of a robot from the perspective of virtual assistants.Virtual assistants can also be characterized as the mind of a robot,since they manage communication and action with the rest of the world they exist in.Therefore,virtual assistants can also be described as the brain of a robot and they include a Natural Language Processing(NLP)module for conducting communication in their human-robot interface.This work is focused on inquiring and enhancing the capabilities of this module.The problem is that nothing much is revealed about the nature of the human-robot interface of commercial virtual assistants.Therefore,any new attempt of developing such a capability has to start from scratch.Accordingly,to include corresponding capabilities to a developing NLP system of a virtual assistant,a method of systemic semantic modelling is proposed and applied.For this purpose,the paper briefly reviews the evolution of virtual assistants from the first assistant,in the form of a game,to the latest assistant that has significantly elevated their standards.Then there is a reference to the evolution of their services and their continued offerings,as well as future expectations.The paper presents their structure and the technologies used,according to the data provided by the development companies to the public,while an attempt is made to classify virtual assistants,based on their characteristics and capabilities.Consequently,a robotic NLP interface is being developed,based on the communicative power of a proposed systemic conceptual model that may enhance the NLP capabilities of virtual assistants,being tested through a small natural language dictionary in Greek.
文摘Background:This study evaluates the ability of mobile AI voice assistants(AI-VAs)to provide accurate medical advice for early knee osteoarthritis(KOA)and compares their performance with conventional web searches and human clinicians.Methods:From September to October 2024,two AI-VAs(Apple’s Siri and Huawei’s Xiaoyi)were tested on 15 KOA-related questions in Chinese and English.The assessment focused on the accuracy of voice recognition,response capabilities,and medical advice.Siri was further tested in four international regions(USA,UK,Germany,Hong Kong)using localized languages.Results:In Chinese-language tests,Siri and Xiaoyi showed comparable voice recognition(recognition accuracy:95.6%vs.93.3%)and response ability(speech response:88.9%vs.85.7%).However,Siri provided significantly more accurate medical advice(medical advice:95.6%vs.53.3%;Z=2.762,P<0.001).External validation via Global Quality Score further confirmed Siri’s superiority(mean Global Quality Score=4.0 vs.Xiaoai=0.9).Siri outperformed Xiaoyi in English-language tests(53.3%vs.0%).While Siri’s medical advice accuracy(95.6%)surpassed non-specialist clinicians(Z=2.685,P=0.007),it primarily reflects filtered search results(Baidu/Google)rather than clinical synthesis.Claims of equivalence to junior surgeons(98.2%)must be interpreted cautiously,as AI-VAs lack diagnostic reasoning capabilities.This distinction is critical to avoid overstating their role in clinical decision-making.Conclusion:Current AI-VAs offer limited value in providing precise medical advice for KOA,primarily serving as intermediaries for web search results.Their performance varies across languages,regions,and search engines.
文摘This study analyzed how impressions are formed online depending on the type of social media and the implications that may come from the over-disclosure of information. Using a Qualtrics survey, 97 participants viewed the profile of a female teaching assistant on Twitter and Facebook. While there was little difference between the two social media, the use of self-disclosure on Twitter seemed slightly more inappropriate for sharing personal information.
文摘With the shift in the definition of disease from non-alcoholic fatty liver disease(NAFLD)to metabolism-associated fatty liver disease(MAFLD),as well as the rapid evolution of pathological classification and therapeutic targets,traditional clinical teaching models face challenges such as outdated guideline updates,disjointed translation of scientific research,and limited skill training.This study proposes a dynamic training model integrating“guidelines,clinical practice,and scientific research.”Through stratified case-based teaching(e.g.,FibroScan simulator and metabolic sand table),dynamic guideline analysis(comparing old and new evidence),and the integration of scientific thinking(visualization of CAND1 protein mechanism),a teaching system that integrates theory and practice is constructed.Innovatively developed smart assistant tools(AI decision support system,VR liver biopsy simulator)and a multi-dimensional evaluation system(deviation analysis of diagnosis and treatment pathways,milestone assessment)are used while emphasizing metabolic medicine integration(continuous glucose monitoring and digital therapy)and ethical privacy protection(federated learning framework).This model aims to cultivate students’evidence-based decision-making skills and scientific research transformation thinking through dynamic knowledge base construction and interdisciplinary collaboration,providing sustainable teaching solutions to cope with the rapid iteration of NAFLD diagnosis and treatment.
基金FEDER/Ministry of Science and Innovation-State Research Agency/Project PID2020-112667RB-I00 funded by MCIN/AEI/10.13039/501100011033the Basque Government,IT1726-22+2 种基金by the predoctoral contracts PRE_2022_2_0022 and EP_2023_1_0015 of the Basque Governmentpartially supported by the Italian MIUR,PRIN 2020 Project“COMMON-WEARS”,N.2020HCWWLP,CUP:H23C22000230005co-funding from Next Generation EU,in the context of the National Recovery and Resilience Plan,through the Italian MUR,PRIN 2022 Project”COCOWEARS”(A framework for COntinuum COmputing WEARable Systems),N.2022T2XNJE,CUP:H53D23003640006.
文摘Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.
基金supported by Research and Construction of Experimental Teaching Aid Platform for Programming under the Teaching Reform Research Project of Shandong University。
文摘For computer science majors in higher education institutions,programming courses are one of the most important professional foundation courses.Proficiency in independent programming skills is of great help to the study of subsequent courses and the personal development of students.In the teaching process of programming courses,online judgement systems are often used to improve students’programming level.Traditional online judgement systems lack guidance for students,and it is often difficult for inexperienced students to find and correct errors in their codes by themselves.We propose an online judgement system that integrates a large model of error correction to help students find errors and improve their programming skills.
文摘Background:Work engagement(WE)is critical to quality primary healthcare delivery.However,limited research has explored its levels and determinants among healthcare professionals in low-and middle-income countries.This study assessed the levels and correlates of work engagement among physician assistants(PAs)in Ghana.Methods:A cross-sectional study was conducted among 439 PAs from October to December 2024.Participants were recruited via emails,social media platforms,and posters featuring study links and scannable questionnaire codes.WE was measured using the validated Utrecht Work Engagement Scale questionnaire.Results:Overall,WE levels were average,with similar trends across the three subdomains.In the bootstrapped multivariate linear regression model,anxiety was negatively associated with WE(β=-0.49,95%confidence interval[CI]:-0.77 to-0.21).Conversely,working in an urban area(β=0.36,95%CI:0.05 to 0.67),holding the rank of PA/Senior PA(β=0.27,95%CI:0.03 to 0.52),reporting good self-rated health(β=0.54,95%CI:0.19 to 0.88),and working at health centers(β=0.86,95%CI:0.22 to 1.50)were positively associated with WElevels.Conclusion:WE levels are average in the study sample,highlighting the need for strategic interventions to improve and sustain the healthcare workforce's motivation and performance.Addressing workplace stressors,enhancing professional development opportunities,and fostering supportive work environments could improve engagement among PAs and healthcare professionals in general.Strengthening WE is essential for ensuring resilient quality primary healthcare systems and achieving the goals of universal health coverage.
文摘BACKGROUND Colorectal cancer(CRC)can be prevented by screening and early detection.Colonoscopy is used for screening,and adenoma detection rate(ADR)is used as a key quality indicator of sufficient colonoscopy.However,ADR can vary significantly among endoscopists,leading to missed polyps or cancer.Artificial intelligence(AI)has shown promise in improving ADR by assisting in real-time polyp identification or diagnosis.While multiple randomized controlled trials(RCTs)and metanalyses highlight the benefits of AI in increasing detection rates and reducing missed polyps,concerns remain about its real-world applicability,impact on procedure time,and cost-effectiveness.AIM To explore the current status of AI assistance colonoscopy in adenoma detection and improving quality of colonoscopy.METHODS This systematic review followed PRISMA guidelines,both PubMed and Web of Science databases were used for articles search.Metanalyses and systematic reviews that assessed AI's role during colonoscopy.English article only published between January 2000 and January 2025 were included.Articles related to nonadenoma indications were excluded.Data extraction was independently performed by two researchers for accuracy and consistency.RESULTS 22 articles met the inclusion criteria,with significant heterogeneity(I2=28%-91%)observed in multiple studies.The number of studies per metanalysis ranged from 5 to 33,with higher heterogeneity in analyses involving more than 18 RCTs.AI demonstrated improvement in ADR,with an approximate 20%increase across multiple studies.However,its effectiveness in detecting flat or serrated adenomas remains unproven.Endoscopists with low ADR benefit more from AI-colonoscopies,while expert endoscopists outperformed AI in ADR,adenoma miss rate,and the identification of advanced lesions.No significant change in withdrawal time was observed when comparing AI-assisted colonoscopy to conventional endoscopy.CONCLUSION While AI-assisted colonoscopy has been shown to improve procedural quality,particularly for junior endoscopists and those with lower ADR,its performance decreases when compared to expert endoscopists in real-time clinical practice.This is especially evident in non-randomized studies,where AI demonstrates limited real-world benefits despite its benefit in controlled settings.Furthermore,no meta-analyses have specifically examined AI's impact on the learning experience of fellows and residents.Some experts caution that reliance on AI may prevent trainees from developing essential observational skills,potentially leading to less thorough examinations.Further research is needed to determine the actual benefits of AI-colonoscopy,particularly its role in cancer prevention.As technology advances,improved outcomes are expected,especially in detecting small,flat,and lesions at difficult anatomical locations.
基金funded by Vietnam National University Ho Chi Minh City(VNU-HCM)under grant number NCM2020-28-01.
文摘Objective:To evaluate mobile applications available for patients undergoing assisted reproduction and assess the extent of their clinical validation.Methods:A systematic search was conducted in the Apple App Store and Google Play between September 1,2023 and September 30,2023 to identify mobile applications related to assisted reproduction.Apps were evaluated using the mobile app rating scale(MARS).In parallel,a literature search of PubMed,Scopus,Embase,and Web of Science was performed to identify clinical studies related to mobile applications in assisted reproduction.Clinical validation status and MARS scores were recorded,and findings were synthesized to highlight the gap between commercially available apps and research-based evidence.Results:From 1143 apps screened,11 met the inclusion criteria.Mean MARS score across apps was 3.63,with Leeaf scoring the highest(4.60).However,only one application(Embie)was supported by published research.The literature research identified 13 relevant studies,mostly randomized controlled trials,cohort studies,or usability studies.While research-based apps demonstrated clinical utility(e.g.,MediEmo,PreLiFe,Patient Journey App),most were unavailable on app stores.This revealed a disconnect between research-backed applications and those accessible to patients.Conclusions:Although several mobile apps for assisted reproduction demonstrate high usability and quality,few are clinically validated.The lack of integration between research and practice highlights the need for stronger collaboration between researchers,developers,and policymakers to ensure that patients access safe and effective tools.
文摘BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)is contraindicated for patients with cavernous transformation of the portal vein(CTPV)due to high surgery-related mortality risk.However,surgically assisted TIPS(SATIPS)can significantly reduce the risk.AIM To evaluate the clinical efficacy of SATIPS,this study was conducted.METHODS One hundred and seven patients with CTPV and esophagogastric variceal bleeding were recruited from January 2023 to December 2024.The patients were recruited from three different hospitals.Overall,54 patients received SATIPS treatment(SATIPS group),while 53 patients did not receive SATIPS and underwent prophylactic endoscopic sclerosing ligation(control group).Subsequently,survival rates,incidence rates of gastrointestinal bleeding,incidence of hepatic encephalopathy rate,and the incidence of liver failure after treatment in both groups at 3 and 6 months were observed.RESULTS The survival rates for the SATIPS and control groups were 94.4%and 92.5%at 3 months(P value=0.72)and 94.4%and 73.6%at 6 months(P value=0.0051)respectively.The incidence of liver failure was 3.7%and 9.4%at 3 months(P value=0.26)and 3.7%and18.9%at 6 months(P value=0.016);the incidence of gastrointestinal bleeding was 5.6%and 37.7%at 3 months(P value<0.001)and 9.3%and 47.2%(P value<0.001)at 6 months;and the incidence of hepatic encephalopathy was 3.7%and 17.0%at 3 months(P value=0.026)and 7.4%and 26.4%at 6 months(P value=0.026)respectively.CONCLUSION For patients with CTPV,there were no optimal treatment.Regarding long-term efficacy,SATIPS can significantly reduce the rate of rebleeding,hepatic encephalopathy and liver failure,and is associated with better survival.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia through research group No.(RG-NBU-2022-1234).
文摘Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.
