This is a comparative study examining the influence of a small-scale dementia unit and a traditional dementia unit on behaviors of the residents. The small-scale unit and the traditional unit were selected through two...This is a comparative study examining the influence of a small-scale dementia unit and a traditional dementia unit on behaviors of the residents. The small-scale unit and the traditional unit were selected through two phases in Vancouver, Canada. Seven residents from each facility completed the study. Physical environmental assessments were performed using two tools： PEAP （professional environmental assessment protocol） and TESS-NH （therapeutic environment screening survey for nursing homes）. For the assessment of residents＇ behaviors, three assessment tools were used： MOSES （multidimensional observation scale for elderly subjects）, MDS （minimum data set） and DCM （dementia care mapping）. The study found that the residents living in a small-scale environment were more engaged in activities and more likely to respond in understanding their fellow residents. Residents living in a traditional long-term care exhibited fewer signs of social interaction. The findings suggest that a small-scale homelike environment could positively influence people with dementia to be more engaged in social exchanges and activities, and consequently help in reducing their withdrawn behavior.展开更多
The properties of electrons in matter are of fundamental importance.They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior o...The properties of electrons in matter are of fundamental importance.They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets.Modeling and simulation of such diverse applications rely primarily on density functional theory(DFT),which has become the principal method for predicting the electronic structure of matter.While DFT calculations have proven to be very useful,their computational scaling limits them to small systems.We have developed a machine learning framework for predicting the electronic structure on any length scale.It shows up to three orders of magnitude speedup on systems where DFT is tractable and,more importantly,enables predictions on scales where DFT calculations are infeasible.Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances materials science to frontiers intractable with any current solutions.展开更多
文摘This is a comparative study examining the influence of a small-scale dementia unit and a traditional dementia unit on behaviors of the residents. The small-scale unit and the traditional unit were selected through two phases in Vancouver, Canada. Seven residents from each facility completed the study. Physical environmental assessments were performed using two tools： PEAP （professional environmental assessment protocol） and TESS-NH （therapeutic environment screening survey for nursing homes）. For the assessment of residents＇ behaviors, three assessment tools were used： MOSES （multidimensional observation scale for elderly subjects）, MDS （minimum data set） and DCM （dementia care mapping）. The study found that the residents living in a small-scale environment were more engaged in activities and more likely to respond in understanding their fellow residents. Residents living in a traditional long-term care exhibited fewer signs of social interaction. The findings suggest that a small-scale homelike environment could positively influence people with dementia to be more engaged in social exchanges and activities, and consequently help in reducing their withdrawn behavior.
基金This work was in part supported by the Center for Advanced Systems Understanding(CASUS)which is financed by Germany’s Federal Ministry of Education and Research(BMBF)and by the Saxon state government out of the State budget approved by the Saxon State Parliament.
文摘The properties of electrons in matter are of fundamental importance.They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets.Modeling and simulation of such diverse applications rely primarily on density functional theory(DFT),which has become the principal method for predicting the electronic structure of matter.While DFT calculations have proven to be very useful,their computational scaling limits them to small systems.We have developed a machine learning framework for predicting the electronic structure on any length scale.It shows up to three orders of magnitude speedup on systems where DFT is tractable and,more importantly,enables predictions on scales where DFT calculations are infeasible.Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances materials science to frontiers intractable with any current solutions.
