Real-Time Covid-19 Risk Level Indicator for the Library Users

2021 ◽  
pp. 224-231
Author(s):  
Sadiq Arsalan ◽  
Khan Maaz Ahmed
Keyword(s):  
Real Time ◽  
Risk Level ◽  
Level Indicator ◽  
Library Users

scholarly journals A Comprehensive Real-Time Indoor Air-Quality Level Indicator

2016 ◽  
Vol 8 (9) ◽  
pp. 881 ◽  
Author(s):  
Jungho Kang ◽  
Kwang-Il Hwang
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Real Time ◽  
Indoor Air ◽  
Quality Level ◽  
Level Indicator

MARINE-EO project: Monitoring and forecast of eutrophication around fish farms, a Maliakos Gulf case (Eastern Mediterranean).

2021 ◽  
Author(s):  
Amandine Declerck ◽  
Matthias Delpey ◽  
Thibaut Voirand ◽  
Ioanna Varkitzi
Keyword(s):  
Water Quality ◽  
High Resolution ◽  
Real Time ◽  
Chlorophyll A ◽  
Satellite Observation ◽  
Risk Level ◽  
Fish Farms ◽  
Ocean Modelling ◽  
Monitoring And Forecasting

<p>Keywords: eutrophication; high resolution ocean modeling ; Chla satellite data ; biogeochemistry</p><p>Maliakos Gulf corresponds to mesotrophic waters that can reach eutrophic conditions and are occasionally subject to Harmful Algal Blooms (HAB) (Varkitzi et al. 2018). At the same time, it is an important fish farming and aquaculture production area. A large issue is thus related to the monitoring and forecasting of the risk of occurrence of algae blooms in the Gulf. For this purpose, the present study couples predictions from a high-resolution numerical ocean model with satellite observation to improve the monitoring and anticipation of threats for the local fish farms induced by occasional eutrophication.</p><p>This solution is developed in the frame of the MARINE-EO project (https://marine-eo.eu/). It combines satellite observation with high-resolution ocean modelling to provide detailed information as a support to fish farms management and operations. It is implemented in an operational platform, which provides continuous information in real time as well as short term predictions. The deployed solution uses CMEMS physical products as an input data and offers to refine this solution in order to provide a local information on site using a downscaling strategy. High resolution satellite products and ocean modelling allow to include the impact of local coastal processes on currents and water quality parameters to provide a proper monitoring and forecasting solution at the scale of a specific fish farm.</p><p>To model specific eutrophication processes, a NPZD (Nutrients-Phytoplankton-Zooplankton-Detritus) biogeochemical model is used. Included in the MOHID Water modelling system, the water quality module (Mateus, 2006) considering 18 properties: nutrients and organic matter (nitrogen, phosphorus and silica biogeochemical cycles), oxygen and organisms (phytoplankton and zooplankton) was deployed in the western Aegean Sea. The simulated chlorophyll a concentrations are used to compute a risk level for the eutrophication occurrence. To complete this indicator, another risk level was based on the eutrophication variation following Primpas et al. (2010) formulation. In addition to model forecasts, ocean color observations from the Sentinel-2 MSI and Landsat-8 OLI sensors are used to provide high resolution chlorophyll a concentrations maps in case of bloom events. The processing chain uses the sixth version of the Quasi-Analytical Algorithm initially developed by Lee et al. (2002) and an empirical relation based on a database built using the HydroLight software to compute chlorophyll a concentration.</p><p>Two past eutrophication events monitored in situ (Varkitzi et al. 2018) were studied to assess the accuracy of the developed tool. Although few in situ data were available on environmental input (as rivers flow and nutrient concentrations), it was possible using statistics to reproduce qualitatively these blooms. Finally, an operational demonstration was conducted during 2 months of the 2020 autumn season, to showcase real time monitoring and predictive perspectives.</p>

scholarly journals A Distributed Multi-Tier Emergency Alerting System Exploiting Sensors-Based Event Detection to Support Smart City Applications

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 170 ◽  
Author(s):  
Daniel G. Costa ◽  
Francisco Vasques ◽  
Paulo Portugal ◽  
Ana Aguiar
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Smart City ◽  
Smart Cities ◽  
Sensor Nodes ◽  
Risk Level ◽  
Critical Events ◽  
Emergency Situations ◽  
Alerting System ◽  
Emergency Systems

The development of efficient sensing technologies and the maturation of the Internet of Things (IoT) paradigm and related protocols have considerably fostered the expansion of sensor-based monitoring applications. A great number of those applications has been developed to monitor a set of information for better perception of the environment, with some of them being dedicated to identifying emergency situations. Current IoT-based emergency systems have limitations when considering the broader scope of smart cities, exploiting one or just a few monitoring variables or even allocating high computational burden to regular sensor nodes. In this context, we propose a distributed multi-tier emergency alerting system built around a number of sensor-based event detection units, providing real-time georeferenced information about the occurrence of critical events, while taking as input a configurable number of different scalar sensors and GPS data. The proposed system could then be used to detect and to deliver emergency alarms, which are computed based on the detected events, the previously known risk level of the affected areas and temporal information. Doing so, modularized and flexible perceptions of critical events are provided, according to the particularities of each considered smart city scenario. Besides implementing the proposed system in open-source electronic platforms, we also created a real-time visualization application to dynamically display emergency alarms on a map, demonstrating a feasible and useful application of the system as a supporting service. Therefore, this innovative approach and its corresponding physical implementation can bring valuable results for smart cities, potentially supporting the development of adaptive IoT-based emergency-aware applications.

Real-Time Dispatching Operation Risk Assessment Based on Fault Tree Theory

2013 ◽  
Vol 732-733 ◽  
pp. 909-914
Author(s):  
En Wang ◽  
Wei Wei ◽  
Bing Dong Wang ◽  
Zhe Liu
Keyword(s):  
Real Time ◽  
Fault Tree ◽  
Test System ◽  
Control Measures ◽  
Risk Level ◽  
Time Data ◽  
Risk Indices ◽  
Risk Sources ◽  
Line Overload ◽  
Operation Risk

Reasonable dispatching strategies are important to guarantee secure and reliable operation of power system. Now the operators use real-time data and their experience to dispatch the system, but the huge data system brings much inconvenience to operators. In this paper, the risk theory is introduced into dispatching operation quantitative assessment, and a real-time dispatching operation risk analysis method is proposed. Fault tree is used to simulate dispatching operation process and comprehensively analyze the system risk in both under-successful and failure state of the operation. Risk indices are given to quantitatively reflect the risk level of each operation step based on local information, such as voltage out-of-limit risk, line overload risk, load curtailment risk, so that the operators can clearly recognize the risk and risk sources in each operation step and take corresponding pre-control measures. Finally, the effectiveness and practicability of proposed method is validated by an IEEE RTS test system.

scholarly journals An Ambulatory Blood Pressure Monitor Mobile Health System for Early Warning for Stroke Risk: Longitudinal Observational Study (Preprint)

2019 ◽  
Author(s):  
Guangyu Wang ◽  
Silu Zhou ◽  
Shahbaz Rezaei ◽  
Xin Liu ◽  
Anpeng Huang
Keyword(s):  
Blood Pressure ◽  
Observational Study ◽  
Real Time ◽  
Early Warning ◽  
Stroke Risk ◽  
Risk Level ◽  
Blood Pressure Monitor ◽  
Pressure Monitor ◽  
Blood Pressure Data ◽  
Longitudinal Observational Studies

BACKGROUND Stroke, as a leading cause of death around the globe, has become a heavy burden on our society. Studies show that stroke can be predicted and prevented if a person’s blood pressure (BP) status is appropriately monitored via an ambulatory blood pressure monitor (ABPM) system. However, currently there exists no efficient and user-friendly ABPM system to provide early warning for stroke risk in real-time. Moreover, most existing ABPM devices measure BP during the deflation of the cuff, which fails to reflect blood pressure accurately. OBJECTIVE In this study, we sought to develop a new ABPM mobile health (mHealth) system that was capable of monitoring blood pressure during inflation and could detect early stroke-risk signals in real-time. METHODS We designed an ABPM mHealth system that is based on mobile network infrastructure and mobile apps. The proposed system contains two major parts: a new ABPM device in which an inflation-type BP measurement algorithm is embedded, and an abnormal blood pressure data analysis algorithm for stroke-risk prediction services at our health data service center. For evaluation, the ABPM device was first tested using simulated signals and compared with the gold standard of a mercury sphygmomanometer. Then, the performance of our proposed mHealth system was evaluated in an observational study. RESULTS The results are presented in two main parts: the device test and the longitudinal observational studies of the presented system. The average measurement error of the new ABPM device with the inflation-type algorithm was less than 0.55 mmHg compared to a reference device using simulated signals. Moreover, the results of correlation coefficients and agreement analyses show that there is a strong linear correlation between our device and the standard mercury sphygmomanometer. In the case of the system observational study, we collected a data set with 88 features, including real-time data, user information, and user records. Our abnormal blood pressure data analysis algorithm achieved the best performance, with an area under the curve of 0.904 for the low risk level, 0.756 for the caution risk level, and 0.912 for the high-risk level. Our system enables a patient to be aware of their risk in real-time, which improves medication adherence with risk self-management. CONCLUSIONS To our knowledge, this device is the first ABPM device that measures blood pressure during the inflation process and has obtained a government medical license. Device tests and longitudinal observational studies were conducted in Peking University hospitals, and they showed the device’s high accuracy for BP measurements, its efficiency in detecting early signs of stroke, and its efficiency at providing an early warning for stroke risk.

scholarly journals An Ambulatory Blood Pressure Monitor Mobile Health System for Early Warning for Stroke Risk: Longitudinal Observational Study

10.2196/14926 ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. e14926 ◽  
Author(s):  
Guangyu Wang ◽  
Silu Zhou ◽  
Shahbaz Rezaei ◽  
Xin Liu ◽  
Anpeng Huang
Keyword(s):  
Blood Pressure ◽  
Observational Study ◽  
Real Time ◽  
Early Warning ◽  
Stroke Risk ◽  
Risk Level ◽  
Blood Pressure Monitor ◽  
Pressure Monitor ◽  
Blood Pressure Data ◽  
Longitudinal Observational Studies

Background Stroke, as a leading cause of death around the globe, has become a heavy burden on our society. Studies show that stroke can be predicted and prevented if a person’s blood pressure (BP) status is appropriately monitored via an ambulatory blood pressure monitor (ABPM) system. However, currently there exists no efficient and user-friendly ABPM system to provide early warning for stroke risk in real-time. Moreover, most existing ABPM devices measure BP during the deflation of the cuff, which fails to reflect blood pressure accurately. Objective In this study, we sought to develop a new ABPM mobile health (mHealth) system that was capable of monitoring blood pressure during inflation and could detect early stroke-risk signals in real-time. Methods We designed an ABPM mHealth system that is based on mobile network infrastructure and mobile apps. The proposed system contains two major parts: a new ABPM device in which an inflation-type BP measurement algorithm is embedded, and an abnormal blood pressure data analysis algorithm for stroke-risk prediction services at our health data service center. For evaluation, the ABPM device was first tested using simulated signals and compared with the gold standard of a mercury sphygmomanometer. Then, the performance of our proposed mHealth system was evaluated in an observational study. Results The results are presented in two main parts: the device test and the longitudinal observational studies of the presented system. The average measurement error of the new ABPM device with the inflation-type algorithm was less than 0.55 mmHg compared to a reference device using simulated signals. Moreover, the results of correlation coefficients and agreement analyses show that there is a strong linear correlation between our device and the standard mercury sphygmomanometer. In the case of the system observational study, we collected a data set with 88 features, including real-time data, user information, and user records. Our abnormal blood pressure data analysis algorithm achieved the best performance, with an area under the curve of 0.904 for the low risk level, 0.756 for the caution risk level, and 0.912 for the high-risk level. Our system enables a patient to be aware of their risk in real-time, which improves medication adherence with risk self-management. Conclusions To our knowledge, this device is the first ABPM device that measures blood pressure during the inflation process and has obtained a government medical license. Device tests and longitudinal observational studies were conducted in Peking University hospitals, and they showed the device’s high accuracy for BP measurements, its efficiency in detecting early signs of stroke, and its efficiency at providing an early warning for stroke risk.

Suggest Recommendation for Library Users Using Graphs

2019 ◽  
Vol 9 (1) ◽  
pp. 41-51
Author(s):  
Gheorghe-Cătălin Crișan
Keyword(s):  
Real Time ◽  
Optimization Problems ◽  
Present Problem ◽  
Entire System ◽  
Library Users ◽  
Different Sources ◽  
Similarity Scores

AbstractThe aim of this paper is to prove the usefulness of graphs in solving an ever-present problem for library users: finding books they like and they are looking for. Graphs are known as an important tool in solving conditioned optimization problems. We propose a graph-based system of recommendation which can be easy used in a library for assisting and helping users in finding in real time the books they like. The main advantage of the proposed graph-based approach lies in the ease with which new data or even new entities from different sources are added to the graph without disturbing the entire system. The system uses the similarity scores in order to find the similarity between objects and to get the best recommendation for a user’s request. In the end, we will compare the results from used formulas..

scholarly journals Manifestation of emergent properties in risk assessment of oil and gas companies

2019 ◽  
Vol 65 ◽  
pp. 08001
Author(s):  
Inesa Khvostina ◽  
Nataliia Havadzyn ◽  
Nataliia Yurchenko
Keyword(s):  
Risk Assessment ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Assessment Process ◽  
Impact Factors ◽  
Risk Level ◽  
Emergent Properties ◽  
Gas Industry ◽  
Level Indicator ◽  
Original Algorithm

The article presents a study on risks in oil and gas industry and reveals their causes investigating enterprises activity as a result of emergent properties of systems. The original algorithm of risk assessment process based on emergent properties study is offered. A taxonomy approach and factor analysis are used for purposes of risk evaluation. The risk assessment consists of risks taxonomy, database structure development, identification of risks through impact factors evaluation; economic system emergent properties risks prediction, an integral risk level indicator calculation using taxonomy approach, correlation analysis of integral indicators of risk assessment, preventive measures for minimizing of negative impacts and reducing risks.

scholarly journals Increasing safety level of airport ground operations by implementing an autonomous system of supervision and security

2018 ◽  
Vol 2018 (9) ◽  
pp. 8-14
Author(s):  
Anita Linka ◽  
Agnieszka Wróblewska
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Autonomous System ◽  
Risk Level ◽  
Current Position ◽  
Safety Level ◽  
Port Area ◽  
Airport Ground Operations ◽  
Basic Parameters

This article describes the Ground Safety (GS) system and shows the possibilities of its use. System is dedicated to the supervision of the airport's ground space. GS enables comprehensive control and traffic management on the entire port area. In addition to real-time supervision, it is possible, among another, to archive and restore data preserving its integrity, two-way communication, and also bulk messages broadcasting. The equipment of the airport guests in the transmitter allows constant access to its basic parameters and current position. The article describes the possibilities of reducing the risk level of occurrence dangerous and undesirable incidents. It also shows how to increase the overall security of the unit.

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