随着观测技术的发展,生态学研究尺度不断扩大。生态系统观测从小规模合作、短时间个人观测向大规模、长时间、跨学科、多因子联合观测转变。传感器技术的革新带来了生态观测在时空尺度的扩展与精确度上的提升,致使生态学观测数据的容量...随着观测技术的发展,生态学研究尺度不断扩大。生态系统观测从小规模合作、短时间个人观测向大规模、长时间、跨学科、多因子联合观测转变。传感器技术的革新带来了生态观测在时空尺度的扩展与精确度上的提升,致使生态学观测数据的容量、产生速度与数据种类飞速增长。对生态系统数据获取、存储与管理的传统方法无疑不再能满足现代生态学研究的要求。因此,我们建议以大数据时代的数据存储、管理与处理技术为基础,整合生态物联观测网络(Internet of Ecology)、公民科学观测网络以及基于标准化数据管理的研究者网络互联,建立整合生态系统观测平台来应对这一困境。为生态学研究者打造一站式生态观测服务,是大数据时代下生态系统观测的大势所趋。展开更多
A non-intrusive design for monitoring everyday activities of an elderly person is presented. The proposed system is intended to be used in the bedroom, allowing the elders to stay at home in a safe environment. The re...A non-intrusive design for monitoring everyday activities of an elderly person is presented. The proposed system is intended to be used in the bedroom, allowing the elders to stay at home in a safe environment. The required hardware design is simple in structure and cost effective. The sensor design is implemented by using capacitive sensors and an Arduino microcontroller unit. And a real time graphical user interface is implemented to monitor the elderly person. The performance analysis shows that the sensor design is able to differentiate between a human body and a house pet.展开更多
We are evaluating dryland cotton production in Martin County, Texas, measuring cotton lint yield per unit of rainfall. Our goal is to collect rainfall data per 250 - 400 ha. Upon selection of a rainfall gauge, we real...We are evaluating dryland cotton production in Martin County, Texas, measuring cotton lint yield per unit of rainfall. Our goal is to collect rainfall data per 250 - 400 ha. Upon selection of a rainfall gauge, we realized that the cost of using, for example, a tipping bucket-type rain gauge would be too expensive and thus searched for an alternative method. We selected an all-in-one commercially available weather station;hereafter, referred to as a Personal Weather Station (PWS) that is both wireless and solar powered. Our objective was to evaluate average measurements of rainfall obtained with the PWS and to compare these to measurements obtained with an automatic weather station (AWS). For this purpose, we installed four PWS deployed within 20 m of the Plant Stress and Water Conservation Meteorological Tower that was used as our AWS, located at USDA-ARS Cropping Systems Research Laboratory, Lubbock, TX. In addition, we measured and compared hourly average values of short-wave irradiance (R<sub>g</sub>), air temperature (T<sub>air</sub>) and relative humidity (RH), and wind speed (WS), and calculated values of dewpoint temperature (T<sub>dew</sub>). This comparison was done over a 242-day period (1 October 2022-31 May 2023) and results indicated that there was no statistical difference in measurements of rainfall between the PWS and AWS. Hourly average values of R<sub>g</sub> measured with the PWS and AWS agreed on clear days, but PWS measurements were higher on cloudy days. There was no statistical difference between PWS and AWS hourly average measurements of T<sub>air</sub>, RH, and calculated T<sub>dew</sub>. Hourly average measurements of R<sub>g</sub> and WS were more variable. We concluded that the PWS we selected will provide adequate values of rainfall and other weather variables to meet our goal of evaluating dryland cotton lint yield per unit rainfall.展开更多
Rapid estimates of impact areas following large earthquakes constitute the cornerstone of emergency response scenarios.However,collecting information through traditional practices usually requires a large amount of ma...Rapid estimates of impact areas following large earthquakes constitute the cornerstone of emergency response scenarios.However,collecting information through traditional practices usually requires a large amount of manpower and material resources,slowing the response time.Social media has emerged as a source of real-time‘citizen-sensor data’for disasters and can thus contribute to the rapid acquisition of disaster information.This paper proposes an approach to quickly estimate the impact area following a large earthquake via social media.Specifically,a spatial logistic growth model(SLGM)is proposed to describe the spatial growth of citizen-sensor data influenced by the earthquake impact strength after an earthquake;a framework is then developed to estimate the earthquake impact area by combining social media data and other auxiliary data based on the SLGM.The reliability of our approach is demonstrated in two earthquake cases by comparing the detected areas with official intensity maps,and the time sensitivity of the social media data in the SLGM is discussed.The results illustrate that our approach can effectively estimate the earthquake impact area.We verify the external validity of our model across other earthquake events and provide further insights into extracting more valuable earthquake information using social media.展开更多
文摘随着观测技术的发展,生态学研究尺度不断扩大。生态系统观测从小规模合作、短时间个人观测向大规模、长时间、跨学科、多因子联合观测转变。传感器技术的革新带来了生态观测在时空尺度的扩展与精确度上的提升,致使生态学观测数据的容量、产生速度与数据种类飞速增长。对生态系统数据获取、存储与管理的传统方法无疑不再能满足现代生态学研究的要求。因此,我们建议以大数据时代的数据存储、管理与处理技术为基础,整合生态物联观测网络(Internet of Ecology)、公民科学观测网络以及基于标准化数据管理的研究者网络互联,建立整合生态系统观测平台来应对这一困境。为生态学研究者打造一站式生态观测服务,是大数据时代下生态系统观测的大势所趋。
文摘A non-intrusive design for monitoring everyday activities of an elderly person is presented. The proposed system is intended to be used in the bedroom, allowing the elders to stay at home in a safe environment. The required hardware design is simple in structure and cost effective. The sensor design is implemented by using capacitive sensors and an Arduino microcontroller unit. And a real time graphical user interface is implemented to monitor the elderly person. The performance analysis shows that the sensor design is able to differentiate between a human body and a house pet.
文摘We are evaluating dryland cotton production in Martin County, Texas, measuring cotton lint yield per unit of rainfall. Our goal is to collect rainfall data per 250 - 400 ha. Upon selection of a rainfall gauge, we realized that the cost of using, for example, a tipping bucket-type rain gauge would be too expensive and thus searched for an alternative method. We selected an all-in-one commercially available weather station;hereafter, referred to as a Personal Weather Station (PWS) that is both wireless and solar powered. Our objective was to evaluate average measurements of rainfall obtained with the PWS and to compare these to measurements obtained with an automatic weather station (AWS). For this purpose, we installed four PWS deployed within 20 m of the Plant Stress and Water Conservation Meteorological Tower that was used as our AWS, located at USDA-ARS Cropping Systems Research Laboratory, Lubbock, TX. In addition, we measured and compared hourly average values of short-wave irradiance (R<sub>g</sub>), air temperature (T<sub>air</sub>) and relative humidity (RH), and wind speed (WS), and calculated values of dewpoint temperature (T<sub>dew</sub>). This comparison was done over a 242-day period (1 October 2022-31 May 2023) and results indicated that there was no statistical difference in measurements of rainfall between the PWS and AWS. Hourly average values of R<sub>g</sub> measured with the PWS and AWS agreed on clear days, but PWS measurements were higher on cloudy days. There was no statistical difference between PWS and AWS hourly average measurements of T<sub>air</sub>, RH, and calculated T<sub>dew</sub>. Hourly average measurements of R<sub>g</sub> and WS were more variable. We concluded that the PWS we selected will provide adequate values of rainfall and other weather variables to meet our goal of evaluating dryland cotton lint yield per unit rainfall.
基金supported by National Natural Science Foundation of China[grant number 41271399].
文摘Rapid estimates of impact areas following large earthquakes constitute the cornerstone of emergency response scenarios.However,collecting information through traditional practices usually requires a large amount of manpower and material resources,slowing the response time.Social media has emerged as a source of real-time‘citizen-sensor data’for disasters and can thus contribute to the rapid acquisition of disaster information.This paper proposes an approach to quickly estimate the impact area following a large earthquake via social media.Specifically,a spatial logistic growth model(SLGM)is proposed to describe the spatial growth of citizen-sensor data influenced by the earthquake impact strength after an earthquake;a framework is then developed to estimate the earthquake impact area by combining social media data and other auxiliary data based on the SLGM.The reliability of our approach is demonstrated in two earthquake cases by comparing the detected areas with official intensity maps,and the time sensitivity of the social media data in the SLGM is discussed.The results illustrate that our approach can effectively estimate the earthquake impact area.We verify the external validity of our model across other earthquake events and provide further insights into extracting more valuable earthquake information using social media.
