Virtual reality(VR)technology revitalises rehabilitation training by creating rich,interactive virtual rehabilitation scenes and tasks that deeply engage patients.Robotics with immersive VR environments have the poten...Virtual reality(VR)technology revitalises rehabilitation training by creating rich,interactive virtual rehabilitation scenes and tasks that deeply engage patients.Robotics with immersive VR environments have the potential to significantly enhance the sense of immersion for patients during training.This paper proposes a rehabilitation robot system.The system integrates a VR environment,the exoskeleton entity,and research on rehabilitation assessment metrics derived from surface electromyographic signal(sEMG).Employing more realistic and engaging virtual stimuli,this method guides patients to actively participate,thereby enhancing the effectiveness of neural connection reconstruction—an essential aspect of rehabilitation.Furthermore,this study introduces a muscle activation model that merges linear and non-linear states of muscle,avoiding the impact of non-linear shape factors on model accuracy present in traditional models.A muscle strength assessment model based on optimised generalised regression(WOAGRNN)is also proposed,with a root mean square error of 0.017,347 and a mean absolute percentage error of 1.2461%,serving as critical assessment indicators for the effectiveness of rehabilitation.Finally,the system is preliminarily applied in human movement experiments,validating the practicality and potential effectiveness of VRcentred rehabilitation strategies in medical recovery.展开更多
Next point-of-interest(POI)recommendation has been applied by many internet companies to enhance the user travel experience.Recent research advocates deep-learning methods to model long-term check-in sequences and min...Next point-of-interest(POI)recommendation has been applied by many internet companies to enhance the user travel experience.Recent research advocates deep-learning methods to model long-term check-in sequences and mine mobility patterns of people to improve recommendation performance.Existing approaches model general user preferences based on historical check-ins and can be termed as preference pattern models.The preference pattern is different from the intention pattern,in that it does not emphasize the user mobility pattern of revisiting POIs,which is a common behavior and kind of intention for users.An effective module is needed to predict when and where users will repeat visits.In this paper,we propose a Spatio-Temporal Intention Learning Self-Attention Network(STILSAN)for next POI recommendation.STILSAN employs a preference-intention module to capture the user’s long-term preference and recognizes the user’s intention to revisit some specific POIs at a specific time.Meanwhile,we design a spatial encoder module as a pretrained model for learning POI spatial feature by simulating the spatial clustering phenomenon and the spatial proximity of the POIs.Experiments are conducted on two real-world check-in datasets.The experimental results demonstrate that all the proposed modules can effectively improve recommendation accuracy and STILSAN yields outstanding improvements over the state-of-the-art models.展开更多
White Hypsizygus marmoreus is a popular edible mushroom.Its mycelium is easy to be contaminated by Penicillium,which leads to a decrease in its quality and yield.Penicillium could compete for limited space and nutrien...White Hypsizygus marmoreus is a popular edible mushroom.Its mycelium is easy to be contaminated by Penicillium,which leads to a decrease in its quality and yield.Penicillium could compete for limited space and nutrients through rapid growth and produce a variety of harmful gases,such as benzene,aldehydes,phenols,etc.,to inhibit the growth of H.marmoreus mycelium.A series of changes occurred in H.marmoreus proteome after contamination when detected by the label-free tandem mass spectrometry(MS/MS)technique.Some proteins with up-regulated expression worked together to participate in some processes,such as the non-toxic transformation of harmful gases,glutathione metabolism,histone modification,nucleotide excision repair,clearing misfolded proteins,and synthesizing glutamine,which were mainly used in response to biological stress.The proteins with down-regulated expression are mainly related to the processes of ribosome function,protein processing,spliceosome,carbon metabolism,glycolysis,and gluconeogenesis.The reduction in the function of these proteins affected the production of the cell components,which might be an adjustment to adapt to growth retardation.This study further enhanced the understanding of the biological stress response and the growth restriction adaptation mechanisms in edible fungi.It also provided a theoretical basis for protein function exploration and edible mushroom food safety research.展开更多
基金National Key Research and Development Program of China,Grant/Award Number:2022YFB4700701National Outstanding Youth Science Fund Project of National Natural Science Foundation of China,Grant/Award Number:52025054。
文摘Virtual reality(VR)technology revitalises rehabilitation training by creating rich,interactive virtual rehabilitation scenes and tasks that deeply engage patients.Robotics with immersive VR environments have the potential to significantly enhance the sense of immersion for patients during training.This paper proposes a rehabilitation robot system.The system integrates a VR environment,the exoskeleton entity,and research on rehabilitation assessment metrics derived from surface electromyographic signal(sEMG).Employing more realistic and engaging virtual stimuli,this method guides patients to actively participate,thereby enhancing the effectiveness of neural connection reconstruction—an essential aspect of rehabilitation.Furthermore,this study introduces a muscle activation model that merges linear and non-linear states of muscle,avoiding the impact of non-linear shape factors on model accuracy present in traditional models.A muscle strength assessment model based on optimised generalised regression(WOAGRNN)is also proposed,with a root mean square error of 0.017,347 and a mean absolute percentage error of 1.2461%,serving as critical assessment indicators for the effectiveness of rehabilitation.Finally,the system is preliminarily applied in human movement experiments,validating the practicality and potential effectiveness of VRcentred rehabilitation strategies in medical recovery.
基金supported by Chongqing Technology Innovation and Application Development Project[grant number cstc2021jscx-dxwtBX0023]funding from Chongqing Changan Automobile Co.,Ltd.,Dongfeng Motor Corporation,and Dongfeng Changxing Tech Co.,Ltd.
文摘Next point-of-interest(POI)recommendation has been applied by many internet companies to enhance the user travel experience.Recent research advocates deep-learning methods to model long-term check-in sequences and mine mobility patterns of people to improve recommendation performance.Existing approaches model general user preferences based on historical check-ins and can be termed as preference pattern models.The preference pattern is different from the intention pattern,in that it does not emphasize the user mobility pattern of revisiting POIs,which is a common behavior and kind of intention for users.An effective module is needed to predict when and where users will repeat visits.In this paper,we propose a Spatio-Temporal Intention Learning Self-Attention Network(STILSAN)for next POI recommendation.STILSAN employs a preference-intention module to capture the user’s long-term preference and recognizes the user’s intention to revisit some specific POIs at a specific time.Meanwhile,we design a spatial encoder module as a pretrained model for learning POI spatial feature by simulating the spatial clustering phenomenon and the spatial proximity of the POIs.Experiments are conducted on two real-world check-in datasets.The experimental results demonstrate that all the proposed modules can effectively improve recommendation accuracy and STILSAN yields outstanding improvements over the state-of-the-art models.
基金funded by the Shandong Provincial Natural Science Foundation,China(ZR2020QC005)the National Natural Science Foundation of China(32272789)+3 种基金the National Natural Science Foundation of China(32000041)the Shandong Edible Fungus Agricultural Technology System(SDAIT-07-02)the Shandong Provincial Key Research and Development Plan(2021ZDSYS28)the Qingdao Agricultural University Scientific Research Foundation(6631120076)。
文摘White Hypsizygus marmoreus is a popular edible mushroom.Its mycelium is easy to be contaminated by Penicillium,which leads to a decrease in its quality and yield.Penicillium could compete for limited space and nutrients through rapid growth and produce a variety of harmful gases,such as benzene,aldehydes,phenols,etc.,to inhibit the growth of H.marmoreus mycelium.A series of changes occurred in H.marmoreus proteome after contamination when detected by the label-free tandem mass spectrometry(MS/MS)technique.Some proteins with up-regulated expression worked together to participate in some processes,such as the non-toxic transformation of harmful gases,glutathione metabolism,histone modification,nucleotide excision repair,clearing misfolded proteins,and synthesizing glutamine,which were mainly used in response to biological stress.The proteins with down-regulated expression are mainly related to the processes of ribosome function,protein processing,spliceosome,carbon metabolism,glycolysis,and gluconeogenesis.The reduction in the function of these proteins affected the production of the cell components,which might be an adjustment to adapt to growth retardation.This study further enhanced the understanding of the biological stress response and the growth restriction adaptation mechanisms in edible fungi.It also provided a theoretical basis for protein function exploration and edible mushroom food safety research.
