Furniture and equipment modeled as rocking rigid bodies within buildings are vulnerable to overturning during earthquakes,and their responses are strongly influenced by floor-level seismic amplification.However,predic...Furniture and equipment modeled as rocking rigid bodies within buildings are vulnerable to overturning during earthquakes,and their responses are strongly influenced by floor-level seismic amplification.However,predicting their seismic behavior typically involves analyses of building structural responses and rocking-body dynamics,making conventional approaches computationally expensive.Existing methods also struggle to adequately capture the nonlinear interactions among seismic motion characteristics,structural amplification effects,and the geometric properties of rocking bodies.To address this issue,this study proposes a data-driven method for predicting the seismic response of in-building rocking rigid bodies using a deep neural network(DNN).Floor seismic responses were obtained from city-scale nonlinear time-history analyses and used to calculate corresponding rocking-body responses.A multidimensional database was then established,covering various ground-motion intensity measures,building heights,and rigid-body geometries.Based on this database,a DNN model was developed for rapid overturning prediction.Results show that the proposed model achieves high computational efficiency and an accuracy of 94.37%on the test set,outperforming conventional machine learning methods.Dimensionality reduction further decreases input features and training time while preserving strong predictive performance.The proposed approach provides an efficient and intelligent framework for seismic assessment and risk analysis of rocking components in buildings.展开更多
Dietary lipids are essential for brain health.However,high-fat diets(HFD)have produced conflicting results in studies on aging brain health,likely due to variations in fatty acid composition.While aging-related glial ...Dietary lipids are essential for brain health.However,high-fat diets(HFD)have produced conflicting results in studies on aging brain health,likely due to variations in fatty acid composition.While aging-related glial lipid accumulation is exacerbated by saturated fatty acids(SFAs)-rich HFD,it remains unclear whether replacing SFAs with n-3 polyunsaturated fatty acids(PUFAs)can mitigate this effect and the associated neuroinflammation.This study investigates the effects of SFAs-rich and n-3 PUFAs-rich HFDs in 18-monthold C57BL/6J mice over a 12-week period.Mice fed the SFAs-rich HFD showed increased body weight,elevated glucose and lipid levels,and lipid accumulation in glial cells.Behavioral tests,including novel object recognition and the Barnes maze,revealed significant cognitive impairments in these mice.In contrast,the n-3PUFAs-rich HFD increased brain docosahexaenoic acid levels,activated the ATP-binding cassette transporter Al/apolipoprotein E pathway,reduced lipid accumulation in glial cells,and ultimately reversed cognitive decline.Consistent with these findings,the n-3 PUFAs-rich diet also attenuated inflammatory markers such as tumor necrosis factorαand interleukin 1β,and decreased oxidative stress markers including malondialdehyde and oxidized glutathioneeduced glutathione.These findings provide novel insights into the role of fatty acid composition in HFD affecting aged brain health,offering strong evidence for the neuroprotective benefits of n-3 PUFAs-enriched diets.展开更多
Not long ago,China released its latest top 10 list of technological innovations in the energy sector.This heavyweight“national list”represents a brandnew coordinate system for examining regional development and cont...Not long ago,China released its latest top 10 list of technological innovations in the energy sector.This heavyweight“national list”represents a brandnew coordinate system for examining regional development and contributions of cities.展开更多
基金National Natural Science Foundation of China under Grant No.52308478the Scientific Research Fund of Institute of Engineering Mechanics,China Earthquake Administration under Grant No.2024D03+1 种基金the Cultivation project Funds for Beijing University of Civil Engineering and Architecture(X25023)BUCEA-BIG Joint Research Center for Hospital Construction。
摘要Furniture and equipment modeled as rocking rigid bodies within buildings are vulnerable to overturning during earthquakes,and their responses are strongly influenced by floor-level seismic amplification.However,predicting their seismic behavior typically involves analyses of building structural responses and rocking-body dynamics,making conventional approaches computationally expensive.Existing methods also struggle to adequately capture the nonlinear interactions among seismic motion characteristics,structural amplification effects,and the geometric properties of rocking bodies.To address this issue,this study proposes a data-driven method for predicting the seismic response of in-building rocking rigid bodies using a deep neural network(DNN).Floor seismic responses were obtained from city-scale nonlinear time-history analyses and used to calculate corresponding rocking-body responses.A multidimensional database was then established,covering various ground-motion intensity measures,building heights,and rigid-body geometries.Based on this database,a DNN model was developed for rapid overturning prediction.Results show that the proposed model achieves high computational efficiency and an accuracy of 94.37%on the test set,outperforming conventional machine learning methods.Dimensionality reduction further decreases input features and training time while preserving strong predictive performance.The proposed approach provides an efficient and intelligent framework for seismic assessment and risk analysis of rocking components in buildings.
基金support from Shenzhen Fundamental Research Program(JYCJ20220530161401004)Shenzhen Science and Technology Program(JCYJ20220818102810022)+1 种基金The Key Program for Clinical Research at Peking University Shenzhen Hospital(LCYJZD2022006)The Major Science and Technology Projects in the Xinjiang Uygur Autonomous Region(2022A02004)。
摘要Dietary lipids are essential for brain health.However,high-fat diets(HFD)have produced conflicting results in studies on aging brain health,likely due to variations in fatty acid composition.While aging-related glial lipid accumulation is exacerbated by saturated fatty acids(SFAs)-rich HFD,it remains unclear whether replacing SFAs with n-3 polyunsaturated fatty acids(PUFAs)can mitigate this effect and the associated neuroinflammation.This study investigates the effects of SFAs-rich and n-3 PUFAs-rich HFDs in 18-monthold C57BL/6J mice over a 12-week period.Mice fed the SFAs-rich HFD showed increased body weight,elevated glucose and lipid levels,and lipid accumulation in glial cells.Behavioral tests,including novel object recognition and the Barnes maze,revealed significant cognitive impairments in these mice.In contrast,the n-3PUFAs-rich HFD increased brain docosahexaenoic acid levels,activated the ATP-binding cassette transporter Al/apolipoprotein E pathway,reduced lipid accumulation in glial cells,and ultimately reversed cognitive decline.Consistent with these findings,the n-3 PUFAs-rich diet also attenuated inflammatory markers such as tumor necrosis factorαand interleukin 1β,and decreased oxidative stress markers including malondialdehyde and oxidized glutathioneeduced glutathione.These findings provide novel insights into the role of fatty acid composition in HFD affecting aged brain health,offering strong evidence for the neuroprotective benefits of n-3 PUFAs-enriched diets.
摘要Not long ago,China released its latest top 10 list of technological innovations in the energy sector.This heavyweight“national list”represents a brandnew coordinate system for examining regional development and contributions of cities.