This paper describes the implementation of a data logger for the real-time in-situ monitoring of hydrothermal systems. A compact mechanical structure ensures the security and reliability of data logger when used under...This paper describes the implementation of a data logger for the real-time in-situ monitoring of hydrothermal systems. A compact mechanical structure ensures the security and reliability of data logger when used under deep sea. The data logger is a battery powered instrument, which can connect chemical sensors （pH electrode, H2S electrode, H2 electrode） and temperature sensors. In order to achieve major energy savings, dynamic power management is implemented in hardware design and software design. The working current of the data logger in idle mode and active mode is 15 μA and 1.44 mA respectively, which greatly extends the working time of battery. The data logger has been successftdly tested in the first Sino-American Cooperative Deep Submergence Project from August 13 to September 3, 2005.展开更多
In the evolving situation of highly infectious coronavirus,the number of confirmed cases in India has largely increased,which has resulted in a shortage of health care resources.Thus,the Ministry of Health and Family ...In the evolving situation of highly infectious coronavirus,the number of confirmed cases in India has largely increased,which has resulted in a shortage of health care resources.Thus,the Ministry of Health and Family Welfare-Government of India issued guidelines for the‘Home isolation of COVID-19 positive patients’methodology for asymptomatic patients or with mild symptoms.During home isolation,the patients are required to monitor and record the pulse rate,body temperature,and oxygen saturation three times a day.This paper proposes a system that can request data from the required sensor to measure the pulse rate,body temperature,or oxygen saturation.The requested data is sensed by the respective sensor placed near the patients’body and sent to the CAN data logger over the CAN bus.The CAN data logger live streams the sensor values and stores the same to an excel sheet along with details like the patient’s name,patient’s age,and date.The physicians can then access this information.展开更多
Generating carbon credits in rural and wetland lagoon environments is important for the economic and social survival of the same.There are many methodologies to study and certificate the Carbon Sink such as the ISO 14...Generating carbon credits in rural and wetland lagoon environments is important for the economic and social survival of the same.There are many methodologies to study and certificate the Carbon Sink such as the ISO 14064,VCS VERRA,UNI-BNEUTRAL,GOLD STANDARD and others.Many methods done before 2018 are obsolete since research has developed greatly in recent years.The methods are all different,but they share a continuous and real monitoring of the environment to ensure a true CCS(Carbon Capture and Storage)action.In the case of absence of monitoring,the method uses a system of provision of carbon credits called“buffer”.This system allows maintaining a credit-generating activity even in the presence of important anomalies due to adverse weather events.This research shows the complex analytic web of the different sensors in a continuous environmental monitoring system via GSM(Global System for Mobile)Communication and IoT(Internet of Things).By 2011,a monitoring network was installed in the wetland environments of Northern Italy Venetian Lagoon(UNESCO heritage)and used to understand and validate,the CCS action.Thingspeak cloud platform is used to collect data and is used to send alert to the user if the biological sink is reversed to emission.The obtained large dataset was used to prepare a AI(Artificial Intelligence)model“CCS wetland forecast”by Google COLAB.This model can fit the trend to avoid the direct and spot chemical field analysis and demonstrate the real efficacy of the model chosen.This network is now implemented by the Italian national method UNI PdR 99:2021 BNeutral generation of carbon credits.展开更多
Rural communities in third world countries are concerned over land use changes resulting from resource exploitation. This is the case for the Bumbuna watershed in Sierra Leone following impoundment of the Bumbuna rese...Rural communities in third world countries are concerned over land use changes resulting from resource exploitation. This is the case for the Bumbuna watershed in Sierra Leone following impoundment of the Bumbuna reservoir in 2009. Farmers have increased activities along the riparian zones in protest against inundation of their farmlands. The dam operators warn this practice would threaten sustainable power supply;the farmers contend the reservoir is increasing and taking over their farms. However, it is difficult to resolve this issue without a means of quantifying the change and developing early warning systems for land cover in the watershed. This research presents a case for the use of remotely sensed Landsat data for quantification of land cover change and the development of predictive models to inform preparedness for imminent problems that may arise from land use practices. In situ water loggers, in combination with manual readings, recorded water levels in 30-minute intervals since 2009. These datasets combined with spectral values of Landsat 7 and Landsat 8 for the development of regression algorithms for predictive purposes. Digital photographs and satellite imagery illustrated the changes in land cover over time (a 33% water rise and 44% NDVI change from 2009 to 2015). These visual and spectral pictures confirm the usefulness of remotely sensed data for early warning systems in the watershed. Results of the regression analysis show Band 1 (Blue) and Band 4 (NIR) as statistically significant predictors for water level in the reservoir. The tests accounted for 84% (R2) of the data with p-values less than α at the 0.05 confidence level. However, future trials of the model will consider reducing the 4.6 error margin to minimize deviations from the observed data.展开更多
基金supported by the International Cooperative Key Project(Grant No.2004DFA04900)Ministry of Sciences and Technology of PRC,and the National Natural Science Foundation of China (Grant Nos.40637037 and 50675198)
文摘This paper describes the implementation of a data logger for the real-time in-situ monitoring of hydrothermal systems. A compact mechanical structure ensures the security and reliability of data logger when used under deep sea. The data logger is a battery powered instrument, which can connect chemical sensors （pH electrode, H2S electrode, H2 electrode） and temperature sensors. In order to achieve major energy savings, dynamic power management is implemented in hardware design and software design. The working current of the data logger in idle mode and active mode is 15 μA and 1.44 mA respectively, which greatly extends the working time of battery. The data logger has been successftdly tested in the first Sino-American Cooperative Deep Submergence Project from August 13 to September 3, 2005.
文摘In the evolving situation of highly infectious coronavirus,the number of confirmed cases in India has largely increased,which has resulted in a shortage of health care resources.Thus,the Ministry of Health and Family Welfare-Government of India issued guidelines for the‘Home isolation of COVID-19 positive patients’methodology for asymptomatic patients or with mild symptoms.During home isolation,the patients are required to monitor and record the pulse rate,body temperature,and oxygen saturation three times a day.This paper proposes a system that can request data from the required sensor to measure the pulse rate,body temperature,or oxygen saturation.The requested data is sensed by the respective sensor placed near the patients’body and sent to the CAN data logger over the CAN bus.The CAN data logger live streams the sensor values and stores the same to an excel sheet along with details like the patient’s name,patient’s age,and date.The physicians can then access this information.
文摘Generating carbon credits in rural and wetland lagoon environments is important for the economic and social survival of the same.There are many methodologies to study and certificate the Carbon Sink such as the ISO 14064,VCS VERRA,UNI-BNEUTRAL,GOLD STANDARD and others.Many methods done before 2018 are obsolete since research has developed greatly in recent years.The methods are all different,but they share a continuous and real monitoring of the environment to ensure a true CCS(Carbon Capture and Storage)action.In the case of absence of monitoring,the method uses a system of provision of carbon credits called“buffer”.This system allows maintaining a credit-generating activity even in the presence of important anomalies due to adverse weather events.This research shows the complex analytic web of the different sensors in a continuous environmental monitoring system via GSM(Global System for Mobile)Communication and IoT(Internet of Things).By 2011,a monitoring network was installed in the wetland environments of Northern Italy Venetian Lagoon(UNESCO heritage)and used to understand and validate,the CCS action.Thingspeak cloud platform is used to collect data and is used to send alert to the user if the biological sink is reversed to emission.The obtained large dataset was used to prepare a AI(Artificial Intelligence)model“CCS wetland forecast”by Google COLAB.This model can fit the trend to avoid the direct and spot chemical field analysis and demonstrate the real efficacy of the model chosen.This network is now implemented by the Italian national method UNI PdR 99:2021 BNeutral generation of carbon credits.
文摘Rural communities in third world countries are concerned over land use changes resulting from resource exploitation. This is the case for the Bumbuna watershed in Sierra Leone following impoundment of the Bumbuna reservoir in 2009. Farmers have increased activities along the riparian zones in protest against inundation of their farmlands. The dam operators warn this practice would threaten sustainable power supply;the farmers contend the reservoir is increasing and taking over their farms. However, it is difficult to resolve this issue without a means of quantifying the change and developing early warning systems for land cover in the watershed. This research presents a case for the use of remotely sensed Landsat data for quantification of land cover change and the development of predictive models to inform preparedness for imminent problems that may arise from land use practices. In situ water loggers, in combination with manual readings, recorded water levels in 30-minute intervals since 2009. These datasets combined with spectral values of Landsat 7 and Landsat 8 for the development of regression algorithms for predictive purposes. Digital photographs and satellite imagery illustrated the changes in land cover over time (a 33% water rise and 44% NDVI change from 2009 to 2015). These visual and spectral pictures confirm the usefulness of remotely sensed data for early warning systems in the watershed. Results of the regression analysis show Band 1 (Blue) and Band 4 (NIR) as statistically significant predictors for water level in the reservoir. The tests accounted for 84% (R2) of the data with p-values less than α at the 0.05 confidence level. However, future trials of the model will consider reducing the 4.6 error margin to minimize deviations from the observed data.
