A diverse array of microbes in and on the human body constitute the microbiota.These micro-residents continuously interact with the human host through the language of metabolites to dictate the host’s physiology in h...A diverse array of microbes in and on the human body constitute the microbiota.These micro-residents continuously interact with the human host through the language of metabolites to dictate the host’s physiology in health and illnesses.Any biotic and abiotic component ensuring a balanced host-microbiota interaction are potential microbiome therapeutic agents to overcome human diseases.Plant metabolites are continually being used to treat various illnesses.These metabolites target the host’s metabolic machinery and host-gut microbiota interactions to overcome human diseases.Despite the paramount therapeutic significance of the factors affecting host-microbiota interactions,a comprehensive overview of the modulatory role of plant-derived metabolites in host-microbiota interactions is lacking.The current review puts an effort into comprehending the role of medicinal plants in gut microbiota modulation to mitigate various human illnesses.It would develop a holistic understanding of hostmicrobiota interactions and the role of effectors in health and diseases.展开更多
With the increasing presence of robots in our daily life,there is a strong need and demand for the strategies to acquire a high quality interaction between robots and users by enabling robots to understand users’mood...With the increasing presence of robots in our daily life,there is a strong need and demand for the strategies to acquire a high quality interaction between robots and users by enabling robots to understand users’mood,intention,and other aspects.During human-human interaction,personality traits have an important influence on human behavior,decision,mood,and many others.Therefore,we propose an efficient computational framework to endow the robot with the capability of understanding the user’s personality traits based on the user’s nonverbal communication cues represented by three visual features including the head motion,gaze,and body motion energy,and three vocal features including voice pitch,voice energy,and mel-frequency cepstral coefficient(MFCC).We used the Pepper robot in this study as a communication robot to interact with each participant by asking questions,and meanwhile,the robot extracts the nonverbal features from each participant’s habitual behavior using its on-board sensors.On the other hand,each participant’s personality traits are evaluated with a questionnaire.We then train the ridge regression and linear support vector machine(SVM)classifiers using the nonverbal features and personality trait labels from a questionnaire and evaluate the performance of the classifiers.We have verified the validity of the proposed models that showed promising binary classification performance on recognizing each of the Big Five personality traits of the participants based on individual differences in nonverbal communication cues.展开更多
This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior ...This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.展开更多
目的系统评价我国研究生与导师互动(导学互动)影响因素的质性研究,为强化护理研究生导学互动提供参考。方法检索PubMed、Web of Science、JSTOR、ProQuest和中国知网、万方数据、维普数据库有关我国导学互动影响因素质性研究的文献,检...目的系统评价我国研究生与导师互动(导学互动)影响因素的质性研究,为强化护理研究生导学互动提供参考。方法检索PubMed、Web of Science、JSTOR、ProQuest和中国知网、万方数据、维普数据库有关我国导学互动影响因素质性研究的文献,检索时限均为建库至2023年8月,采用澳大利亚JBI循证卫生保健中心(2016)质性研究质量评价标准进行文献质量评价,采用Meta整合方法对结果进行整合分析。结果共纳入14项研究,提炼36个研究结果,归纳成10个新的类别,综合成3个整合结果:导师个体因素,包括导师动机、能力及人格特质;研究生个体因素,包括研究生动机、能力、人格特质和社会关系;外界环境因素,包括科研团队因素、学校制度因素以及社会因素。结论我国研究生教育导师与学生互动受到导师、学生及外界环境的影响,需从多方面着手干预,以形成和谐导学关系,保障护理研究生教育质量。展开更多
Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent...Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.展开更多
基金financial support under Maharshi Dayanand University Rohtak for a Post-Seed Research Grant(DRD/23/75)sanctioned to Dr.NS Chauhan.
文摘A diverse array of microbes in and on the human body constitute the microbiota.These micro-residents continuously interact with the human host through the language of metabolites to dictate the host’s physiology in health and illnesses.Any biotic and abiotic component ensuring a balanced host-microbiota interaction are potential microbiome therapeutic agents to overcome human diseases.Plant metabolites are continually being used to treat various illnesses.These metabolites target the host’s metabolic machinery and host-gut microbiota interactions to overcome human diseases.Despite the paramount therapeutic significance of the factors affecting host-microbiota interactions,a comprehensive overview of the modulatory role of plant-derived metabolites in host-microbiota interactions is lacking.The current review puts an effort into comprehending the role of medicinal plants in gut microbiota modulation to mitigate various human illnesses.It would develop a holistic understanding of hostmicrobiota interactions and the role of effectors in health and diseases.
基金supported by the EU-Japan coordinated R&D project on“Culture Aware Robots and Environmental Sensor Systems for Elderly Support,”commissioned by the Ministry of Internal Affairs and Communications of Japan and EC Horizon 2020 Research and Innovation Programme(737858)financial supports from the Air Force Office of Scientific Research(AFOSR-AOARD/FA2386-19-1-4015)。
文摘With the increasing presence of robots in our daily life,there is a strong need and demand for the strategies to acquire a high quality interaction between robots and users by enabling robots to understand users’mood,intention,and other aspects.During human-human interaction,personality traits have an important influence on human behavior,decision,mood,and many others.Therefore,we propose an efficient computational framework to endow the robot with the capability of understanding the user’s personality traits based on the user’s nonverbal communication cues represented by three visual features including the head motion,gaze,and body motion energy,and three vocal features including voice pitch,voice energy,and mel-frequency cepstral coefficient(MFCC).We used the Pepper robot in this study as a communication robot to interact with each participant by asking questions,and meanwhile,the robot extracts the nonverbal features from each participant’s habitual behavior using its on-board sensors.On the other hand,each participant’s personality traits are evaluated with a questionnaire.We then train the ridge regression and linear support vector machine(SVM)classifiers using the nonverbal features and personality trait labels from a questionnaire and evaluate the performance of the classifiers.We have verified the validity of the proposed models that showed promising binary classification performance on recognizing each of the Big Five personality traits of the participants based on individual differences in nonverbal communication cues.
文摘This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.
文摘目的系统评价我国研究生与导师互动(导学互动)影响因素的质性研究,为强化护理研究生导学互动提供参考。方法检索PubMed、Web of Science、JSTOR、ProQuest和中国知网、万方数据、维普数据库有关我国导学互动影响因素质性研究的文献,检索时限均为建库至2023年8月,采用澳大利亚JBI循证卫生保健中心(2016)质性研究质量评价标准进行文献质量评价,采用Meta整合方法对结果进行整合分析。结果共纳入14项研究,提炼36个研究结果,归纳成10个新的类别,综合成3个整合结果:导师个体因素,包括导师动机、能力及人格特质;研究生个体因素,包括研究生动机、能力、人格特质和社会关系;外界环境因素,包括科研团队因素、学校制度因素以及社会因素。结论我国研究生教育导师与学生互动受到导师、学生及外界环境的影响,需从多方面着手干预,以形成和谐导学关系,保障护理研究生教育质量。
文摘Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.
