Natural disasters are not negligible factors that have significant impacts on a country’s development. Madagascar cannot escape cyclones, floods and drought due to its geographical situation. The objective in this wo...Natural disasters are not negligible factors that have significant impacts on a country’s development. Madagascar cannot escape cyclones, floods and drought due to its geographical situation. The objective in this work is to assess the risks and vulnerability to these hazards in order to strengthen the resilience of the Malagasy population. Our approach is based on multi-criteria spatial analysis using the Analytical Hierarchy Process (AHP). The results form decision spatial information that can be used at the strategic level of natural risk and disaster management. This work focuses on the degree of vulnerability and it was found in this study that the Androy and Atsimo-Atsinanana regions are the most vulnerable to major hazards in Madagascar not only because of their exposure to risk but also because of their very low socio-economic status.展开更多
在胡同里修建感应道路、把巨大的天安门广场变成人民公园、把鸟巢用一座小型景山覆盖掉,在CBD上空竖起一座巨大的悬浮综合体,马岩松领衔的MAD事务所把对2050年北京的疯狂想象带到了威尼斯DIOCESI博物馆。在那里,一个名为"MAD IN CH...在胡同里修建感应道路、把巨大的天安门广场变成人民公园、把鸟巢用一座小型景山覆盖掉,在CBD上空竖起一座巨大的悬浮综合体,马岩松领衔的MAD事务所把对2050年北京的疯狂想象带到了威尼斯DIOCESI博物馆。在那里,一个名为"MAD IN CHINA……一个关于未来的实践"的个展与威尼斯建筑双年展同期举行,双年展策展人Richard Burdett看过后也由衷地评论道,"MAD以独立实践的方式出现在威尼斯是中国开始多元对话的标志,‘北京2050’将国际评论界对中国的关注点引向了未来,而不再仅仅是过去。"……31岁的马岩松在继获得第一个国际大型建筑设计权(梦露大厦)后又一次放肆地笑了。展开更多
Cu-Cr alloys are widely applied in electronic,aerospace and nuclear industries,due to their high strength and high conductivity.However,their terrible softening resistance limits wider applications.This paper presents...Cu-Cr alloys are widely applied in electronic,aerospace and nuclear industries,due to their high strength and high conductivity.However,their terrible softening resistance limits wider applications.This paper presents a novel strategy of integrating mechanism features into interpretable machine learning(ML)to develop softening-resistant Cu-Cr alloys and to understand their mechanisms.First,the mechanism features were specially designed to describe mechanisms potentially vital to softening resistance,and they were obtained through first-principles calculations.Those mechanism features that described interfacial segregation and solute diffusion exhibited significant Gini importance during feature selection.Only integrated with them,did ML models achieve great performance,accurate predictions,and successful development of Cu-0.4Cr-0.10La/Ce(wt.%)alloys with excellent softening resistance.Then,the contributions of these mechanism features to the predictions were interpreted by a game theoretic approach,but unexpectedly,they were not fully consistent with interpretations that we expected from mechanism features.Finally,investigation targeted at these inconsistencies gave novel insights into softening resistance mechanisms.The Cu-Cr-La/Ce alloys’excellent softening resistance was not induced by a prevailing mechanism of La/Ce atoms segregating at phase interfaces,nor by an expected mechanism of La/Ce atoms improving the Cr atom jump energy barriers.Instead,it was caused by a unique mechanism in which La/Ce atoms competed with Cr atoms for vacancies and therefore depleted the available vacancies for the Cr atom jump.This paper demonstrates a new paradigm of developing softening-resistant Cu-Cr alloys and understanding their mechanisms via mechanism-informed interpretable ML.展开更多
The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population,especially in relation to falls.While falls can lead to significant cognitive impairments,timely i...The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population,especially in relation to falls.While falls can lead to significant cognitive impairments,timely intervention can mitigate their adverse effects.In this context,the need for non-invasive,efficient monitoring systems becomes paramount.Although wearable sensors have gained traction for monitoring health activities,they may cause discomfort during prolonged use,especially for the elderly.To address this issue,we present an intelligent,non-invasive Software-Defined Radio Frequency(SDRF)sensing system,tailored red for monitoring elderly people’s falls during routine activities.Harnessing the power of deep learning and machine learning,our system processes the Wireless Channel State Information(WCSI)generated during regular and fall activities.By employing sophisticated signal processing techniques,the system captures unique patterns that distinguish falls from normal activities.In addition,we use statistical features to streamline data processing,thereby optimizing the computational efficiency of the system.Our experiments,conducted for a typical home environment while using treadmill,demonstrate the robustness of the system.The results show high classification accuracies of 92.5%,95.1%,and 99.8%for three Artificial Intelligence(AI)algorithms.Notably,the SDRF-based approach offers flexibility,cost-effectiveness,and adaptability through software modifications,circumventing the need for hardware overhaul.This research attempts to bridge the gap in RF-based sensing for elderly fall monitoring,providing a solution that combines the benefits of non-invasiveness with the precision of deep learning and machine learning.展开更多
文摘Natural disasters are not negligible factors that have significant impacts on a country’s development. Madagascar cannot escape cyclones, floods and drought due to its geographical situation. The objective in this work is to assess the risks and vulnerability to these hazards in order to strengthen the resilience of the Malagasy population. Our approach is based on multi-criteria spatial analysis using the Analytical Hierarchy Process (AHP). The results form decision spatial information that can be used at the strategic level of natural risk and disaster management. This work focuses on the degree of vulnerability and it was found in this study that the Androy and Atsimo-Atsinanana regions are the most vulnerable to major hazards in Madagascar not only because of their exposure to risk but also because of their very low socio-economic status.
文摘在胡同里修建感应道路、把巨大的天安门广场变成人民公园、把鸟巢用一座小型景山覆盖掉,在CBD上空竖起一座巨大的悬浮综合体,马岩松领衔的MAD事务所把对2050年北京的疯狂想象带到了威尼斯DIOCESI博物馆。在那里,一个名为"MAD IN CHINA……一个关于未来的实践"的个展与威尼斯建筑双年展同期举行,双年展策展人Richard Burdett看过后也由衷地评论道,"MAD以独立实践的方式出现在威尼斯是中国开始多元对话的标志,‘北京2050’将国际评论界对中国的关注点引向了未来,而不再仅仅是过去。"……31岁的马岩松在继获得第一个国际大型建筑设计权(梦露大厦)后又一次放肆地笑了。
基金supported by the National Key Research and Development Program of China(Grant No.2021YFB3803100)the National Natural Science Foundation of China(Grant No.U2202255)+3 种基金the Science and Technology Innovation Program of Hunan Province(Grant No.2021GK2016)the Major Science and Technology Pro-gram of Wuhu,China(Grant No.2021zd02)the Hunan Natural Science Fund for Distinguished Young Scholars(No.2024JJ2076)the Project of Innovation-Driven Plan and the Project of State Key Laboratory of Powder Metallurgy,Central South University,Changsha,China.
文摘Cu-Cr alloys are widely applied in electronic,aerospace and nuclear industries,due to their high strength and high conductivity.However,their terrible softening resistance limits wider applications.This paper presents a novel strategy of integrating mechanism features into interpretable machine learning(ML)to develop softening-resistant Cu-Cr alloys and to understand their mechanisms.First,the mechanism features were specially designed to describe mechanisms potentially vital to softening resistance,and they were obtained through first-principles calculations.Those mechanism features that described interfacial segregation and solute diffusion exhibited significant Gini importance during feature selection.Only integrated with them,did ML models achieve great performance,accurate predictions,and successful development of Cu-0.4Cr-0.10La/Ce(wt.%)alloys with excellent softening resistance.Then,the contributions of these mechanism features to the predictions were interpreted by a game theoretic approach,but unexpectedly,they were not fully consistent with interpretations that we expected from mechanism features.Finally,investigation targeted at these inconsistencies gave novel insights into softening resistance mechanisms.The Cu-Cr-La/Ce alloys’excellent softening resistance was not induced by a prevailing mechanism of La/Ce atoms segregating at phase interfaces,nor by an expected mechanism of La/Ce atoms improving the Cr atom jump energy barriers.Instead,it was caused by a unique mechanism in which La/Ce atoms competed with Cr atoms for vacancies and therefore depleted the available vacancies for the Cr atom jump.This paper demonstrates a new paradigm of developing softening-resistant Cu-Cr alloys and understanding their mechanisms via mechanism-informed interpretable ML.
基金supported in part by the Institute of Advanced Technology,University of Science and Technology of China (USTC) under Grant PF02023001Ythe Zayed Health Center at United Arab Emirates University (UAEU) under Grant G00003476COMSATS University Islamabad,Attock Campus。
文摘The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population,especially in relation to falls.While falls can lead to significant cognitive impairments,timely intervention can mitigate their adverse effects.In this context,the need for non-invasive,efficient monitoring systems becomes paramount.Although wearable sensors have gained traction for monitoring health activities,they may cause discomfort during prolonged use,especially for the elderly.To address this issue,we present an intelligent,non-invasive Software-Defined Radio Frequency(SDRF)sensing system,tailored red for monitoring elderly people’s falls during routine activities.Harnessing the power of deep learning and machine learning,our system processes the Wireless Channel State Information(WCSI)generated during regular and fall activities.By employing sophisticated signal processing techniques,the system captures unique patterns that distinguish falls from normal activities.In addition,we use statistical features to streamline data processing,thereby optimizing the computational efficiency of the system.Our experiments,conducted for a typical home environment while using treadmill,demonstrate the robustness of the system.The results show high classification accuracies of 92.5%,95.1%,and 99.8%for three Artificial Intelligence(AI)algorithms.Notably,the SDRF-based approach offers flexibility,cost-effectiveness,and adaptability through software modifications,circumventing the need for hardware overhaul.This research attempts to bridge the gap in RF-based sensing for elderly fall monitoring,providing a solution that combines the benefits of non-invasiveness with the precision of deep learning and machine learning.
