Conventional automated machine learning(AutoML)technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments,leading to accuracy reduction in forecasting short-t...Conventional automated machine learning(AutoML)technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments,leading to accuracy reduction in forecasting short-term building energy loads.Moreover,their predictions are not transparent because of their black box nature.Hence,the building field currently lacks an AutoML framework capable of data quality enhancement,environment self-adaptation,and model interpretation.To address this research gap,an improved AutoML-based end-to-end data-driven modeling framework is proposed.Bayesian optimization is applied by this framework to find an optimal data preprocessing process for quality improvement of raw data.It bridges the gap where conventional AutoML technologies cannot automatically handle missing data and outliers.A sliding window-based model retraining strategy is utilized to achieve environment self-adaptation,contributing to the accuracy enhancement of AutoML technologies.Moreover,a local interpretable model-agnostic explanations-based approach is developed to interpret predictions made by the improved framework.It overcomes the poor interpretability of conventional AutoML technologies.The performance of the improved framework in forecasting one-hour ahead cooling loads is evaluated using two-year operational data from a real building.It is discovered that the accuracy of the improved framework increases by 4.24%–8.79%compared with four conventional frameworks for buildings with not only high-quality but also low-quality operational data.Furthermore,it is demonstrated that the developed model interpretation approach can effectively explain the predictions of the improved framework.The improved framework offers a novel perspective on creating accurate and reliable AutoML frameworks tailored to building energy load prediction tasks and other similar tasks.展开更多
Activation of inflammatory responses regulates the transmission of pain pathways through an integrated network in the peripheral and central nervous systems.The immunopotentiator thymosin alpha-1(Tal)has recently been...Activation of inflammatory responses regulates the transmission of pain pathways through an integrated network in the peripheral and central nervous systems.The immunopotentiator thymosin alpha-1(Tal)has recently been reported to have anti-inflammatory and neuroprotective functions in rodents.However,how Tα1 affects inflammatory pain remains unclear.In the present study,intraperitoneal injection of Tal attenuated complete Freund's adjuvant(CFA)-induced pain hypersensitivity,and decreased the up-regulation of pro-inflammatory cytokines(TNF-α,IL-1β,and IL-6)in inflamed skin and the spinal cord.We found that CFA-induced peripheral inflammation evoked strong microglial activation,but the effect was reversed by Tα1.Notably,Tα1 reversed the CFA-induced up-regulation of vesicular glutamate transporter(VGLUT)and down-regulated the vesicular γ-aminobutyric acid transporter(VGAT)in the spinal cord.Taken together,these results suggest that Tα1 plays a therapeutic role in inflammatory pain and in the modulation of microgliainduced pro-inflammatory cytokine production in addition to mediation of VGLUT and VGAT expression in the spinal cord.展开更多
基金funded by the National Natural Science Foundation of China(No.52161135202)Hangzhou Key Scientific Research Plan Project(No.2023SZD0028).
文摘Conventional automated machine learning(AutoML)technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments,leading to accuracy reduction in forecasting short-term building energy loads.Moreover,their predictions are not transparent because of their black box nature.Hence,the building field currently lacks an AutoML framework capable of data quality enhancement,environment self-adaptation,and model interpretation.To address this research gap,an improved AutoML-based end-to-end data-driven modeling framework is proposed.Bayesian optimization is applied by this framework to find an optimal data preprocessing process for quality improvement of raw data.It bridges the gap where conventional AutoML technologies cannot automatically handle missing data and outliers.A sliding window-based model retraining strategy is utilized to achieve environment self-adaptation,contributing to the accuracy enhancement of AutoML technologies.Moreover,a local interpretable model-agnostic explanations-based approach is developed to interpret predictions made by the improved framework.It overcomes the poor interpretability of conventional AutoML technologies.The performance of the improved framework in forecasting one-hour ahead cooling loads is evaluated using two-year operational data from a real building.It is discovered that the accuracy of the improved framework increases by 4.24%–8.79%compared with four conventional frameworks for buildings with not only high-quality but also low-quality operational data.Furthermore,it is demonstrated that the developed model interpretation approach can effectively explain the predictions of the improved framework.The improved framework offers a novel perspective on creating accurate and reliable AutoML frameworks tailored to building energy load prediction tasks and other similar tasks.
基金supported by the Foundation for Distinguished Young Talents in Higher Education of Guangdong Province, China (2016KQNCX019 and 2016KQNCX027)the National Natural Science Foundation of China (31571041)+1 种基金the Guangdong Provincial Department of Education Innovating Strong National Engineering Major Project (2014GKXM031)Guangdong Provincial Universities and Colleges Pearl River Scholar Funded Scheme (2016)
文摘Activation of inflammatory responses regulates the transmission of pain pathways through an integrated network in the peripheral and central nervous systems.The immunopotentiator thymosin alpha-1(Tal)has recently been reported to have anti-inflammatory and neuroprotective functions in rodents.However,how Tα1 affects inflammatory pain remains unclear.In the present study,intraperitoneal injection of Tal attenuated complete Freund's adjuvant(CFA)-induced pain hypersensitivity,and decreased the up-regulation of pro-inflammatory cytokines(TNF-α,IL-1β,and IL-6)in inflamed skin and the spinal cord.We found that CFA-induced peripheral inflammation evoked strong microglial activation,but the effect was reversed by Tα1.Notably,Tα1 reversed the CFA-induced up-regulation of vesicular glutamate transporter(VGLUT)and down-regulated the vesicular γ-aminobutyric acid transporter(VGAT)in the spinal cord.Taken together,these results suggest that Tα1 plays a therapeutic role in inflammatory pain and in the modulation of microgliainduced pro-inflammatory cytokine production in addition to mediation of VGLUT and VGAT expression in the spinal cord.
