Background:The aging global population necessitates innovative strategies to enhance older adults’health and quality of life.Physical activity(PA)is crucial for healthy aging,yet many older adults struggle to exercis...Background:The aging global population necessitates innovative strategies to enhance older adults’health and quality of life.Physical activity(PA)is crucial for healthy aging,yet many older adults struggle to exercise regularly.Artificial intelligence(AI)-powered social robots offer an interactive,engaging,and personalized solution to promote PA among this demographic.This systematic review investigated the role of AI-powered social robots in encouraging PA in older adults.Methods:We conducted a systematic literature search in databases including PubMed,IEEE Xplore,Scopus,Cochrane Library,and Web of Science,focusing on studies published until February 2024.We included peer-reviewed articles reporting empiricalfindings on designing,implementing,and evaluating AI-enabled social robots to promote PA among older adults.Studies were conducted in nursing homes,rehabilita-tion centers,community centers,and home environments.Results:A total of 19 studies were included in the review.Analysis reveals that AI-powered social robots effectively motivate older adults to engage in PAs,leading to increased exercise adherence,higher engagement levels,and extended training durations.Social robots have demon-strated effectiveness across various environments,including nursing homes,rehabilitation centers,community centers,home environments,and elder care facilities.In structured environments like nursing homes and rehabilitation centers,robots help maintain regular exercise routines,improving adherence and recovery outcomes.In community and elder care centers,robots promote PA and social engagement by facilitating group exercises and enhancing participation.In home environments,robots provide personalized support for daily activities,offering reminders and engagement,which fosters long-term activity engagement.User acceptance and satisfaction are high,with participantsfinding the robots engaging and enjoyable.Additionally,several studies indicate potential health benefits,such as improved medication adherence,better sleep patterns,and enhanced overall well-being.Nevertheless,additional research is imperative to address unresolved issues concerning the technolog-ical maintenance costs,design constraints,and adaptability of AI-powered social robots to specific user demographics.Conclusion:AI-powered social robots play a promising role in promoting PA among older adults,enhancing their health,well-being,and inde-pendence.This review provides insights for researchers,designers,and healthcare professionals developing AI-enabled social robotic systems for older adults.展开更多
Fritillaria cirrhosa D.Don(Liliaceae)is an endangered perennial bulbous plant and its dry bulb is a valuable med-icinal material with antitussive and expectorant effects.Nevertheless,lack of resources and expensive pr...Fritillaria cirrhosa D.Don(Liliaceae)is an endangered perennial bulbous plant and its dry bulb is a valuable med-icinal material with antitussive and expectorant effects.Nevertheless,lack of resources and expensive prices make it difficult to meet clinical needs.This study presents a regeneration system aimed at overcoming the challenge of inadequate supply in F.cirrhosa,focusing on:(1)callus induction,(2)bulblets and adventitious bud induction,and(3)artificial seed production.Callus development was achieved in 84.93%on Murashige and Skoog(MS)medium fortified with 1.0 mg·L^(-1) picloram.The optimal medium for callus differentiation into regenerated bulb-lets was MS medium supplemented with 1.0 mg·L^(-1)6-benzyladenine(6-BA)and 0.2 mg·L^(-1)α-naphthaleneacetic acid(NAA).Subsequently,bulblets and adventitious buds were induced from regenerated bulblet sections cul-tured on MS medium fortified with 0.3 mg·L^(-1)6-BA+1.0 mg·L^(-1)2,4-dichlorophenoxyacetic acid(2,4-D),2.0 mg·L^(-1)6-BA+0.5 mg·L^(-1),and the induction rates were 88.17%and 84.24%,respectively.The regenerated bulblets were transplanted into a substrate of humus soil,river sand,and pearlite(1:1:1)after low-temperature treatment.The germination rate was 42.80%after culture for 30 days.Regenerated bulblets were used for encap-sulations in liquid MS medium containing 3%sucrose(w/v)+0.5 mg·L^(-1) NAA+2.0 mg·L^(-1)6-BA+3%sodium alginate(w/v)with a 10 min exposure to 2%CaCl_(2).Under non-aseptic conditions,the germination rate reached 81.67%,while the rooting rate was 20.56%after 45 days.The capsule added 1.0 g·L^(-1) carbendazim and 1.0 g·L^(-1) activated carbon was the best component of artificial seeds.This study successfully established an efficient regen-eration system for the rapid propagation of F.cirrhosa,involving in vitro bulblet regeneration and artificial seed production.This method introduces a novel approach for efficient breeding and germplasm preservation,making it suitable for large-scale industrial resource production.展开更多
Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also i...Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.展开更多
1故障现象Siemens Artis Zee系列数字减影血管造影系统(digital subtraction angiography,DSA)在使用过程中,不能进行摄影采集,只能进行X线透视。同时,检查室内参考图像显示屏底侧区域的信息栏显示“Bypass”,控制室内实时图像显示屏右...1故障现象Siemens Artis Zee系列数字减影血管造影系统(digital subtraction angiography,DSA)在使用过程中,不能进行摄影采集,只能进行X线透视。同时,检查室内参考图像显示屏底侧区域的信息栏显示“Bypass”,控制室内实时图像显示屏右侧区域的各个曝光参数变灰,且不能被修改。展开更多
A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating condition...A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.展开更多
基金supported by the Ministry of Education of Humanities and Social Science project(Grant No.22YJC890024).
文摘Background:The aging global population necessitates innovative strategies to enhance older adults’health and quality of life.Physical activity(PA)is crucial for healthy aging,yet many older adults struggle to exercise regularly.Artificial intelligence(AI)-powered social robots offer an interactive,engaging,and personalized solution to promote PA among this demographic.This systematic review investigated the role of AI-powered social robots in encouraging PA in older adults.Methods:We conducted a systematic literature search in databases including PubMed,IEEE Xplore,Scopus,Cochrane Library,and Web of Science,focusing on studies published until February 2024.We included peer-reviewed articles reporting empiricalfindings on designing,implementing,and evaluating AI-enabled social robots to promote PA among older adults.Studies were conducted in nursing homes,rehabilita-tion centers,community centers,and home environments.Results:A total of 19 studies were included in the review.Analysis reveals that AI-powered social robots effectively motivate older adults to engage in PAs,leading to increased exercise adherence,higher engagement levels,and extended training durations.Social robots have demon-strated effectiveness across various environments,including nursing homes,rehabilitation centers,community centers,home environments,and elder care facilities.In structured environments like nursing homes and rehabilitation centers,robots help maintain regular exercise routines,improving adherence and recovery outcomes.In community and elder care centers,robots promote PA and social engagement by facilitating group exercises and enhancing participation.In home environments,robots provide personalized support for daily activities,offering reminders and engagement,which fosters long-term activity engagement.User acceptance and satisfaction are high,with participantsfinding the robots engaging and enjoyable.Additionally,several studies indicate potential health benefits,such as improved medication adherence,better sleep patterns,and enhanced overall well-being.Nevertheless,additional research is imperative to address unresolved issues concerning the technolog-ical maintenance costs,design constraints,and adaptability of AI-powered social robots to specific user demographics.Conclusion:AI-powered social robots play a promising role in promoting PA among older adults,enhancing their health,well-being,and inde-pendence.This review provides insights for researchers,designers,and healthcare professionals developing AI-enabled social robotic systems for older adults.
基金funded by the National Key Research and Development Program of China(2018YFC1706101)the Science and Technology Program of Sichuan Province,China(2021YFS0045).
文摘Fritillaria cirrhosa D.Don(Liliaceae)is an endangered perennial bulbous plant and its dry bulb is a valuable med-icinal material with antitussive and expectorant effects.Nevertheless,lack of resources and expensive prices make it difficult to meet clinical needs.This study presents a regeneration system aimed at overcoming the challenge of inadequate supply in F.cirrhosa,focusing on:(1)callus induction,(2)bulblets and adventitious bud induction,and(3)artificial seed production.Callus development was achieved in 84.93%on Murashige and Skoog(MS)medium fortified with 1.0 mg·L^(-1) picloram.The optimal medium for callus differentiation into regenerated bulb-lets was MS medium supplemented with 1.0 mg·L^(-1)6-benzyladenine(6-BA)and 0.2 mg·L^(-1)α-naphthaleneacetic acid(NAA).Subsequently,bulblets and adventitious buds were induced from regenerated bulblet sections cul-tured on MS medium fortified with 0.3 mg·L^(-1)6-BA+1.0 mg·L^(-1)2,4-dichlorophenoxyacetic acid(2,4-D),2.0 mg·L^(-1)6-BA+0.5 mg·L^(-1),and the induction rates were 88.17%and 84.24%,respectively.The regenerated bulblets were transplanted into a substrate of humus soil,river sand,and pearlite(1:1:1)after low-temperature treatment.The germination rate was 42.80%after culture for 30 days.Regenerated bulblets were used for encap-sulations in liquid MS medium containing 3%sucrose(w/v)+0.5 mg·L^(-1) NAA+2.0 mg·L^(-1)6-BA+3%sodium alginate(w/v)with a 10 min exposure to 2%CaCl_(2).Under non-aseptic conditions,the germination rate reached 81.67%,while the rooting rate was 20.56%after 45 days.The capsule added 1.0 g·L^(-1) carbendazim and 1.0 g·L^(-1) activated carbon was the best component of artificial seeds.This study successfully established an efficient regen-eration system for the rapid propagation of F.cirrhosa,involving in vitro bulblet regeneration and artificial seed production.This method introduces a novel approach for efficient breeding and germplasm preservation,making it suitable for large-scale industrial resource production.
文摘Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.
文摘1故障现象Siemens Artis Zee系列数字减影血管造影系统(digital subtraction angiography,DSA)在使用过程中,不能进行摄影采集,只能进行X线透视。同时,检查室内参考图像显示屏底侧区域的信息栏显示“Bypass”,控制室内实时图像显示屏右侧区域的各个曝光参数变灰,且不能被修改。
基金funding from the National Natural Science Foundation of China,China(12172104,52102226)the Shenzhen Science and Technology Innovation Commission,China(JCYJ20200109113439837)the Stable Supporting Fund of Shenzhen,China(GXWD2020123015542700320200728114835006)。
文摘A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.
