BACKGROUND Colorectal cancer(CRC)is the third most diagnosed malignancy worldwide and a frequent comorbidity among these patients is type 2 diabetes mellitus(T2DM).The coexistence of these conditions poses significant...BACKGROUND Colorectal cancer(CRC)is the third most diagnosed malignancy worldwide and a frequent comorbidity among these patients is type 2 diabetes mellitus(T2DM).The coexistence of these conditions poses significant challenges to glycemic management,particularly during chemotherapy.AIM To assess the effects of individualized exercise training(IET)on glycemic control and nutritional status in patients with T2DM undergoing chemotherapy for CRC.METHODS In this retrospective study,clinical data from 245 patients with T2DM and on chemotherapy for CRC between November 2023 and December 2024 were analyzed.Patients were stratified into two groups according to their treatment regimens:The standard care(SC)group(n=111),which received conventional chemotherapy and diabetes management,and an IET group(n=134),which received additional personalized exercise interventions alongside SC.Parameters assessed included fasting plasma glucose,glycosylated hemoglobin,glycemic variability indices,nutritional biomarkers,markers of intestinal permeability,and adverse events.RESULTS Patients in the IET group demonstrated significant improvements in glycemic control,nutritional biomarkers,and glycemic variability(all P<0.05),compared with the SC group.The markers of intestinal permeability also improved significantly in the IET group(P<0.05).Meanwhile,no statistically significant difference in the incidence of adverse events was found between the two groups(P>0.05).These findings suggest that individualized exercise interventions can enhance metabolic,nutritional,and gastrointestinal outcomes without increasing treatment-related risks.CONCLUSION Personalized exercise training may offer clinically meaningful benefits in glycemic regulation and nutritional status for patients with T2DM and on chemotherapy for CRC.展开更多
Objective: to analyze the mid-term effect of individualized training of dual tutoring system for newly recruited undergraduate nurses, and provides reference for later training. Methods: 60 newly recruited undergradua...Objective: to analyze the mid-term effect of individualized training of dual tutoring system for newly recruited undergraduate nurses, and provides reference for later training. Methods: 60 newly recruited undergraduate nurses were selected for research, and divided into groups according to time. The control group (January 2017 to December 2018) received routine training, and the observation group (January 2019 to December 2021) received personalized training under the dual tutorial system. Compare the training effect. Results: the scores of training theoretical knowledge (92.4 ± 3.8) and professional skills (91.5 ± 4.5) in the observation group were higher than those in the control group (85.8 ± 4.2) and (87.1 ± 4.0) (t9.382, 7.003, P0.000, 0.000). The scores of cognitive value, social support and communication skills, frustration coping and self reflection in the observation group (37.8 ± 3.5), (23.7 ± 2.6), (24.8 ± 2.3), (24.0 ± 2.5), (12.6 ± 1.8) were higher than those in the control group (33.4 ± 3.2), (21.5 ± 3.1), (22.0 ± 2.7), (21.7 ± 3.1) (10.8 ± 1.4) points (t8.035, 6.978, 7.324, 7.163, 7.323, P0.000, 0.004, 0.000, 0.003, 0.000). The total satisfaction rate of the observation group was 96.7% higher than that of the control group (80.0%) (X24.754, P0.029). The self-efficacy score of the observation group (27.2 ± 3.4) was higher than that of the control group (24.6 ± 4.0) (t4.713, P0.009). Conclusion: the individualized training of dual tutor system in newly recruited undergraduate nurses is beneficial to the improvement of the overall level of nurses, their comprehensive ability and their self-efficacy.展开更多
China is rapidly becoming an aging society, leading to a significant demand for chronic disease management and personalized healthcare. The development of rehabilitation and assistive robotics in China has gatheredsign...China is rapidly becoming an aging society, leading to a significant demand for chronic disease management and personalized healthcare. The development of rehabilitation and assistive robotics in China has gatheredsignificant attention not only in research fields but also in industries. Such robots aim to either guide patientsin completing therapeutic training or assist people with impaired functions in performing their daily activities.In the past decades, we have witnessed the advancement in rehabilitation and assistive robotics, with diversemechanical designs, functionalities, and purposes. However, the construction of dedicated regulations and policiesis relatively lagged compared with the flourishing development in research fields. Moreover, these kinds of robotsare working or collaborating closely with human beings, bringing unprecedented considerations on ethical issues.This paper aims to provide an overview of major dilemmas in the development of rehabilitation and assistiverobotics in China and propose several potential solutions.展开更多
Artificial intelligence(AI)is profoundly reshaping the practical logic and theoretical boundaries of bel canto education.This study takes“technical path-aesthetic challenge-paradigm reconstruction”as the analytical ...Artificial intelligence(AI)is profoundly reshaping the practical logic and theoretical boundaries of bel canto education.This study takes“technical path-aesthetic challenge-paradigm reconstruction”as the analytical framework to systematically explore the innovative mechanism and practical path of AI-empowered vocal education.At the technical level,an acoustic parameter modeling system is constructed based on deep learning.Through MEL spectrum analysis,forresonance tracking and breath dynamics modeling,the traditional singing assessment that relies on auditory experience is transformed into quantifiable and interpretable acoustic indicators(such as harmonic energy ratio,vowel resonance stability,etc.),achieving precise diagnosis and real-time intervention of vocal defects.Experimental data shows that this system reduces the learners’pitch error rate by 42%and increases their breath control efficiency by 35%.In the dimension of personalized training,an adaptive teaching engine based on reinforcement learning was developed to dynamically generate a three-dimensional matching scheme of“voice features-track difficulty-training intensity”,significantly reducing the time for beginners to master core vocal techniques(by an average of 28%).However,the conflict between AI quantitative indicators and the traditional aesthetic standards of vocal music has become prominent:the algorithm’s preference for standardized formuster distribution may suppress the singer’s unique timbre personality and artistic expression tension.To this end,a“dual-track evaluation model”is proposed:taking acoustic parameters as the technical benchmark and emotional expression and artistic appeal as the aesthetic benchmark,and achieving a dynamic balance between the two through expert annotation and group consensus algorithms.The research further reconstructs the teaching paradigm,advocating that AI tools be positioned as“aesthetic collaborators”rather than substitutes,and builds a new type of teacher-student relationship of“human-machine co-teaching-co-evaluation-co-creation”.This research provides a solution that is both technically feasible and aesthetically reasonable for the digital transformation of bel canto education,revealing the underlying logic of the deep integration of art and technology in the AI era.展开更多
In recent years,implementations enabling Distributed Analytics(DA)have gained considerable attention due to their ability to perform complex analysis tasks on decentralised data by bringing the analysis to the data.Th...In recent years,implementations enabling Distributed Analytics(DA)have gained considerable attention due to their ability to perform complex analysis tasks on decentralised data by bringing the analysis to the data.These concepts propose privacy-enhancing alternatives to data centralisation approaches,which have restricted applicability in case of sensitive data due to ethical,legal or social aspects.Nevertheless,the immanent problem of DA-enabling architectures is the black-box-alike behaviour of the highly distributed components originating from the lack of semantically enriched descriptions,particularly the absence of basic metadata for data sets or analysis tasks.To approach the mentioned problems,we propose a metadata schema for DA infrastructures,which provides a vocabulary to enrich the involved entities with descriptive semantics.We initially perform a requirement analysis with domain experts to reveal necessary metadata items,which represents the foundation of our schema.Afterwards,we transform the obtained domain expert knowledge into user stories and derive the most significant semantic content.In the final step,we enable machine-readability via RDF(S)and SHACL serialisations.We deploy our schema in a proof-of-concept monitoring dashboard to validate its contribution to the transparency of DA architectures.Additionally,we evaluate the schema’s compliance with the FAIR principles.The evaluation shows that the schema succeeds in increasing transparency while being compliant with most of the FAIR principles.Because a common metadata model is critical for enhancing the compatibility between multiple DA infrastructures,our work lowers data access and analysis barriers.It represents an initial and infrastructure-independent foundation for the FAIRification of DA and the underlying scientific data management.展开更多
The FAIR Guidelines attempts to make digital data Findable, Accessible, Interoperable, and Reusable(FAIR). To prepare FAIR data, a new data science discipline known as data stewardship is emerging and, as the FAIR Gui...The FAIR Guidelines attempts to make digital data Findable, Accessible, Interoperable, and Reusable(FAIR). To prepare FAIR data, a new data science discipline known as data stewardship is emerging and, as the FAIR Guidelines gain more acceptance, an increase in the demand for data stewards is expected. Consequently, there is a need to develop curricula to foster professional skills in data stewardship through effective knowledge communication. There have been a number of initiatives aimed at bridging the gap in FAIR data management training through both formal and informal programmes. This article describes the experience of developing a digital initiative for FAIR data management training under the Digital Innovations and Skills Hub(DISH) project. The FAIR Data Management course offers 6 short on-demand certificate modules over 12 weeks. The modules are divided into two sets: FAIR data and data science. The core subjects cover elementary topics in data science, regulatory frameworks, FAIR data management, intermediate to advanced topics in FAIR Data Point installation, and FAIR data in the management of healthcare and semantic data. Each week, participants are required to devote 7–8 hours of self-study to the modules, based on the resources provided. Once they have satisfied all requirements, students are certified as FAIR data scientists and qualified to serve as both FAIR data stewards and analysts. It is expected that in-depth and focused curricula development with diverse participants will build a core of FAIR data scientists for Data Competence Centres and encourage the rapid adoption of the FAIR Guidelines for research and development.展开更多
文摘BACKGROUND Colorectal cancer(CRC)is the third most diagnosed malignancy worldwide and a frequent comorbidity among these patients is type 2 diabetes mellitus(T2DM).The coexistence of these conditions poses significant challenges to glycemic management,particularly during chemotherapy.AIM To assess the effects of individualized exercise training(IET)on glycemic control and nutritional status in patients with T2DM undergoing chemotherapy for CRC.METHODS In this retrospective study,clinical data from 245 patients with T2DM and on chemotherapy for CRC between November 2023 and December 2024 were analyzed.Patients were stratified into two groups according to their treatment regimens:The standard care(SC)group(n=111),which received conventional chemotherapy and diabetes management,and an IET group(n=134),which received additional personalized exercise interventions alongside SC.Parameters assessed included fasting plasma glucose,glycosylated hemoglobin,glycemic variability indices,nutritional biomarkers,markers of intestinal permeability,and adverse events.RESULTS Patients in the IET group demonstrated significant improvements in glycemic control,nutritional biomarkers,and glycemic variability(all P<0.05),compared with the SC group.The markers of intestinal permeability also improved significantly in the IET group(P<0.05).Meanwhile,no statistically significant difference in the incidence of adverse events was found between the two groups(P>0.05).These findings suggest that individualized exercise interventions can enhance metabolic,nutritional,and gastrointestinal outcomes without increasing treatment-related risks.CONCLUSION Personalized exercise training may offer clinically meaningful benefits in glycemic regulation and nutritional status for patients with T2DM and on chemotherapy for CRC.
文摘Objective: to analyze the mid-term effect of individualized training of dual tutoring system for newly recruited undergraduate nurses, and provides reference for later training. Methods: 60 newly recruited undergraduate nurses were selected for research, and divided into groups according to time. The control group (January 2017 to December 2018) received routine training, and the observation group (January 2019 to December 2021) received personalized training under the dual tutorial system. Compare the training effect. Results: the scores of training theoretical knowledge (92.4 ± 3.8) and professional skills (91.5 ± 4.5) in the observation group were higher than those in the control group (85.8 ± 4.2) and (87.1 ± 4.0) (t9.382, 7.003, P0.000, 0.000). The scores of cognitive value, social support and communication skills, frustration coping and self reflection in the observation group (37.8 ± 3.5), (23.7 ± 2.6), (24.8 ± 2.3), (24.0 ± 2.5), (12.6 ± 1.8) were higher than those in the control group (33.4 ± 3.2), (21.5 ± 3.1), (22.0 ± 2.7), (21.7 ± 3.1) (10.8 ± 1.4) points (t8.035, 6.978, 7.324, 7.163, 7.323, P0.000, 0.004, 0.000, 0.003, 0.000). The total satisfaction rate of the observation group was 96.7% higher than that of the control group (80.0%) (X24.754, P0.029). The self-efficacy score of the observation group (27.2 ± 3.4) was higher than that of the control group (24.6 ± 4.0) (t4.713, P0.009). Conclusion: the individualized training of dual tutor system in newly recruited undergraduate nurses is beneficial to the improvement of the overall level of nurses, their comprehensive ability and their self-efficacy.
基金the Zhejiang Provincial Philosophy and Social Science Foundation(No.22NDQN293YB)the Fund of the Science and Technology Commission of Shanghai Municipality(No.20DZ2220400)。
文摘China is rapidly becoming an aging society, leading to a significant demand for chronic disease management and personalized healthcare. The development of rehabilitation and assistive robotics in China has gatheredsignificant attention not only in research fields but also in industries. Such robots aim to either guide patientsin completing therapeutic training or assist people with impaired functions in performing their daily activities.In the past decades, we have witnessed the advancement in rehabilitation and assistive robotics, with diversemechanical designs, functionalities, and purposes. However, the construction of dedicated regulations and policiesis relatively lagged compared with the flourishing development in research fields. Moreover, these kinds of robotsare working or collaborating closely with human beings, bringing unprecedented considerations on ethical issues.This paper aims to provide an overview of major dilemmas in the development of rehabilitation and assistiverobotics in China and propose several potential solutions.
文摘Artificial intelligence(AI)is profoundly reshaping the practical logic and theoretical boundaries of bel canto education.This study takes“technical path-aesthetic challenge-paradigm reconstruction”as the analytical framework to systematically explore the innovative mechanism and practical path of AI-empowered vocal education.At the technical level,an acoustic parameter modeling system is constructed based on deep learning.Through MEL spectrum analysis,forresonance tracking and breath dynamics modeling,the traditional singing assessment that relies on auditory experience is transformed into quantifiable and interpretable acoustic indicators(such as harmonic energy ratio,vowel resonance stability,etc.),achieving precise diagnosis and real-time intervention of vocal defects.Experimental data shows that this system reduces the learners’pitch error rate by 42%and increases their breath control efficiency by 35%.In the dimension of personalized training,an adaptive teaching engine based on reinforcement learning was developed to dynamically generate a three-dimensional matching scheme of“voice features-track difficulty-training intensity”,significantly reducing the time for beginners to master core vocal techniques(by an average of 28%).However,the conflict between AI quantitative indicators and the traditional aesthetic standards of vocal music has become prominent:the algorithm’s preference for standardized formuster distribution may suppress the singer’s unique timbre personality and artistic expression tension.To this end,a“dual-track evaluation model”is proposed:taking acoustic parameters as the technical benchmark and emotional expression and artistic appeal as the aesthetic benchmark,and achieving a dynamic balance between the two through expert annotation and group consensus algorithms.The research further reconstructs the teaching paradigm,advocating that AI tools be positioned as“aesthetic collaborators”rather than substitutes,and builds a new type of teacher-student relationship of“human-machine co-teaching-co-evaluation-co-creation”.This research provides a solution that is both technically feasible and aesthetically reasonable for the digital transformation of bel canto education,revealing the underlying logic of the deep integration of art and technology in the AI era.
基金this work was supported by the German Ministry for Research and Education(BMBF)as part of the SMITH consortium(SW,LN,YUY,SD and OB,grant no.01ZZ1803K)
文摘In recent years,implementations enabling Distributed Analytics(DA)have gained considerable attention due to their ability to perform complex analysis tasks on decentralised data by bringing the analysis to the data.These concepts propose privacy-enhancing alternatives to data centralisation approaches,which have restricted applicability in case of sensitive data due to ethical,legal or social aspects.Nevertheless,the immanent problem of DA-enabling architectures is the black-box-alike behaviour of the highly distributed components originating from the lack of semantically enriched descriptions,particularly the absence of basic metadata for data sets or analysis tasks.To approach the mentioned problems,we propose a metadata schema for DA infrastructures,which provides a vocabulary to enrich the involved entities with descriptive semantics.We initially perform a requirement analysis with domain experts to reveal necessary metadata items,which represents the foundation of our schema.Afterwards,we transform the obtained domain expert knowledge into user stories and derive the most significant semantic content.In the final step,we enable machine-readability via RDF(S)and SHACL serialisations.We deploy our schema in a proof-of-concept monitoring dashboard to validate its contribution to the transparency of DA architectures.Additionally,we evaluate the schema’s compliance with the FAIR principles.The evaluation shows that the schema succeeds in increasing transparency while being compliant with most of the FAIR principles.Because a common metadata model is critical for enhancing the compatibility between multiple DA infrastructures,our work lowers data access and analysis barriers.It represents an initial and infrastructure-independent foundation for the FAIRification of DA and the underlying scientific data management.
基金VODAN-Africathe Philips Foundation+2 种基金the Dutch Development Bank FMOCORDAIDthe GO FAIR Foundation for supporting this research
文摘The FAIR Guidelines attempts to make digital data Findable, Accessible, Interoperable, and Reusable(FAIR). To prepare FAIR data, a new data science discipline known as data stewardship is emerging and, as the FAIR Guidelines gain more acceptance, an increase in the demand for data stewards is expected. Consequently, there is a need to develop curricula to foster professional skills in data stewardship through effective knowledge communication. There have been a number of initiatives aimed at bridging the gap in FAIR data management training through both formal and informal programmes. This article describes the experience of developing a digital initiative for FAIR data management training under the Digital Innovations and Skills Hub(DISH) project. The FAIR Data Management course offers 6 short on-demand certificate modules over 12 weeks. The modules are divided into two sets: FAIR data and data science. The core subjects cover elementary topics in data science, regulatory frameworks, FAIR data management, intermediate to advanced topics in FAIR Data Point installation, and FAIR data in the management of healthcare and semantic data. Each week, participants are required to devote 7–8 hours of self-study to the modules, based on the resources provided. Once they have satisfied all requirements, students are certified as FAIR data scientists and qualified to serve as both FAIR data stewards and analysts. It is expected that in-depth and focused curricula development with diverse participants will build a core of FAIR data scientists for Data Competence Centres and encourage the rapid adoption of the FAIR Guidelines for research and development.
