As the economy and technology keep growing,the mode of shared bikes gains popularity under these circumstances.At the current period,university students become fond of using shared bikes,which changes body health and ...As the economy and technology keep growing,the mode of shared bikes gains popularity under these circumstances.At the current period,university students become fond of using shared bikes,which changes body health and life style of university students.When this mode combines the characteristics and functions of shared bikes,it has a great impact on the awareness and motivation of work-out of university students.Supported by Internet technology,shared bikes meet the need of people that they can use them at any time,which is new and innovative to the university students.This article provides university students with shared bike service in campus by analyzing the influence of“Internet+shared bike”on health and sports of university students.It will promote the effective application of shared bikes.展开更多
S Background Music therapy is a promising complementary intervention for addressing various mental health conditions.Despite evidence of the beneficial effects of music,the acoustic features that make music effective ...S Background Music therapy is a promising complementary intervention for addressing various mental health conditions.Despite evidence of the beneficial effects of music,the acoustic features that make music effective in therapeutic contexts remain elusive.Aims This study aimed to identify and validate distinctive acoustic features of healing music.Methods We constructed a healing music dataset(HMD)based on nominations from related professionals and extracted 370 acoustic features.Healing-distinctive acoustic features were identified as those that were(1)independent from genre within the HMD,(2)significantly different from music pieces in a classical music dataset(CMD)and(3)similar to pieces in a five-element music dataset(FEMD).We validated the identified features by comparing jazz pieces in the HMD with a jazz music dataset(JMD).We also examined the emotional properties of the features in a Chinese affective music system(CAMS).Results The HMD comprised 165 pieces.Among all the acoustic features,74.59%shared commonalities across genres,and 26.22%significantly differed between the HMD classical pieces and the CMD.The equivalence test showed that the HMD and FEMD did not differ significantly in 9.46%of the features.The potential healing-distinctive acoustic features were identified as the standard deviation of the roughness,mean and period entropy of the third coefficient of the mel-frequency cepstral coefficients.In a three-dimensional space defined by these features,HMD's jazz pieces could be distinguished from those of the JMD.These three features could significantly predict both subjective valence and arousal ratings in the CAMS.Conclusions The distinctive acoustic features of healing music that have been identified and validated in this study have implications for the development of artificial intelligence models for identifying therapeutic music,particularly in contexts where access to professional expertise may be limited.This study contributes to the growing body of research exploring the potential of digital technologies for healthcare interventions.展开更多
文摘As the economy and technology keep growing,the mode of shared bikes gains popularity under these circumstances.At the current period,university students become fond of using shared bikes,which changes body health and life style of university students.When this mode combines the characteristics and functions of shared bikes,it has a great impact on the awareness and motivation of work-out of university students.Supported by Internet technology,shared bikes meet the need of people that they can use them at any time,which is new and innovative to the university students.This article provides university students with shared bike service in campus by analyzing the influence of“Internet+shared bike”on health and sports of university students.It will promote the effective application of shared bikes.
基金supported by the National Natural Science Foundation of China(62101324)the Shanghai Sailing Program(20YF1442000)+2 种基金the Academic Leader of the Health Discipline of Shanghai Municipal Health Commission(2022XD025)the Qihang Program of Shanghai Mental Health Center(2020-QH-01)the Hospital Program of Shanghai Mental Health Center(2020-YJ01)。
文摘S Background Music therapy is a promising complementary intervention for addressing various mental health conditions.Despite evidence of the beneficial effects of music,the acoustic features that make music effective in therapeutic contexts remain elusive.Aims This study aimed to identify and validate distinctive acoustic features of healing music.Methods We constructed a healing music dataset(HMD)based on nominations from related professionals and extracted 370 acoustic features.Healing-distinctive acoustic features were identified as those that were(1)independent from genre within the HMD,(2)significantly different from music pieces in a classical music dataset(CMD)and(3)similar to pieces in a five-element music dataset(FEMD).We validated the identified features by comparing jazz pieces in the HMD with a jazz music dataset(JMD).We also examined the emotional properties of the features in a Chinese affective music system(CAMS).Results The HMD comprised 165 pieces.Among all the acoustic features,74.59%shared commonalities across genres,and 26.22%significantly differed between the HMD classical pieces and the CMD.The equivalence test showed that the HMD and FEMD did not differ significantly in 9.46%of the features.The potential healing-distinctive acoustic features were identified as the standard deviation of the roughness,mean and period entropy of the third coefficient of the mel-frequency cepstral coefficients.In a three-dimensional space defined by these features,HMD's jazz pieces could be distinguished from those of the JMD.These three features could significantly predict both subjective valence and arousal ratings in the CAMS.Conclusions The distinctive acoustic features of healing music that have been identified and validated in this study have implications for the development of artificial intelligence models for identifying therapeutic music,particularly in contexts where access to professional expertise may be limited.This study contributes to the growing body of research exploring the potential of digital technologies for healthcare interventions.
