Early casualty estimates and medical help management after the M7.3 Kermanshah earthquake of November 12, 2017 in Iran

2021 ◽  
Vol 16 (1) ◽  
pp. 49-57
Author(s):  
Amir Mansour Farahbod, PhD ◽  
Max Wyss, PhD
Keyword(s):  
Real Time ◽  
Small Area ◽  
First Responders ◽  
Large Earthquake ◽  
Health Facilities ◽  
News Reports ◽  
Time Estimates ◽  
Medical Response ◽  
Final Count

Medical responses to fatal earthquakes have to be rapid to save lives. Here we report the QLARM alert that was issued less than an hour after the magnitude 7.3 Kermanshah, Iran, earthquake of 2017 and the following medical response. The near-real-time estimates of fatalities were 520, on average, and it took official and news reports about 2 days to settle on a minimum of 630 fatalities as a final count. The response of various Iranian agencies was rapid and effective, facilitated by the relatively small area of the disaster (radius of about 50 km). Although this disaster was not large enough to require international first responders to rush to the scene, it is clear that in very large earthquake disasters, a fast, accurately informed response saves lives. For international teams to be of optimal use, the locations and functionality levels of health facilities should be known. This information could be included in the earthquake alerts, but the necessary worldwide data on hospitals are currently not available.  

scholarly journals Real-time automatic uncertainty estimation of coseismic single rectangular fault model using GNSS data

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Keitaro Ohno ◽  
Yusaku Ohta ◽  
Satoshi Kawamoto ◽  
Satoshi Abe ◽  
Ryota Hino ◽  
...  
Keyword(s):  
Real Time ◽  
Disaster Response ◽  
Order Approximation ◽  
Nonlinear Problems ◽  
Large Earthquake ◽  
Fault Model ◽  
Deformation Monitoring ◽  
Model Parameters ◽  
The Sea Of Japan ◽  
Plate Interface

AbstractRapid estimation of the coseismic fault model for medium-to-large-sized earthquakes is key for disaster response. To estimate the coseismic fault model for large earthquakes, the Geospatial Information Authority of Japan and Tohoku University have jointly developed a real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD can estimate the single rectangular fault model and slip distribution along the assumed plate interface. The single rectangular fault model is useful as a first-order approximation of a medium-to-large earthquake. However, in its estimation, it is difficult to obtain accurate results for model parameters due to the strong effect of initial values. To solve this problem, this study proposes a new method to estimate the coseismic fault model and model uncertainties in real time based on the Bayesian inversion approach using the Markov Chain Monte Carlo (MCMC) method. The MCMC approach is computationally expensive and hyperparameters should be defined in advance via trial and error. The sampling efficiency was improved using a parallel tempering method, and an automatic definition method for hyperparameters was developed for real-time use. The calculation time was within 30 s for 1 × 106 samples using a typical single LINUX server, which can implement real-time analysis, similar to REGARD. The reliability of the developed method was evaluated using data from recent earthquakes (2016 Kumamoto and 2019 Yamagata-Oki earthquakes). Simulations of the earthquakes in the Sea of Japan were also conducted exhaustively. The results showed an advantage over the maximum likelihood approach with a priori information, which has initial value dependence in nonlinear problems. In terms of application to data with a small signal-to-noise ratio, the results suggest the possibility of using several conjugate fault models. There is a tradeoff between the fault area and slip amount, especially for offshore earthquakes, which means that quantification of the uncertainty enables us to evaluate the reliability of the fault model estimation results in real time.

scholarly journals Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-Based Solutions in Rehabilitation

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1804
Author(s):  
Dimitar Stanev ◽  
Konstantinos Filip ◽  
Dimitrios Bitzas ◽  
Sokratis Zouras ◽  
Georgios Giarmatzis ◽  
...  
Keyword(s):  
Real Time ◽  
Small Error ◽  
Grand Challenge ◽  
Muscle Forces ◽  
Time Estimates ◽  
Musculoskeletal Models ◽  
Open Source Framework ◽  
Measurement Units ◽  
Technical Details ◽  
Significant Milestone

This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking. Emphasis was given to reduce computational latency while maintaining accuracy as compared to the offline counterpart. Notably, we highlight the influence of adequate filtering and differentiation under noisy conditions and its importance for consequent dynamic calculations. Real-time estimates of the joint moments, muscle forces, and reaction loads closely resemble OpenSim’s offline analyses. Model-based estimation of ground reaction wrenches demonstrates that even a small error can negatively affect other estimated quantities. An application of the developed system is demonstrated in the context of rehabilitation and gait retraining. We expect that such a system will find numerous applications in laboratory settings and outdoor conditions with the advent of predicting or sensing environment interactions. Therefore, we hope that this open-source framework will be a significant milestone for solving this grand challenge.

Progress Towards Arrays of Microcalorimeter X-Ray Detectors

2001 ◽  
Vol 7 (S2) ◽  
pp. 1050-1051 ◽  
Author(s):  
S.W. Nam ◽  
D.A. Wollman ◽  
Dale E. Newbury ◽  
G.C. Hilton ◽  
K.D. Irwin ◽  
...  
Keyword(s):  
Real Time ◽  
Energy Resolution ◽  
Count Rate ◽  
Small Area ◽  
High Performance ◽  
X Ray ◽  
Pixel Array ◽  
Primary Advantage ◽  
Low X ◽  
Single Pixel

The high performance of single-pixel microcalorimeter EDS (μ,cal EDS) has been shown to be very useful for a variety of microanalysis cases. The primary advantage of jxcal EDS over conventional EDS is the factor of 25 improvement in energy resolution (∽3 eV in real-time). This level of energy resolution is particularly important for applications such as nanoscale contaminant analysis where it is necessary to resolve peak overlaps at low x-ray energies. Because μcal EDS offers practical solutions to many microanalysis problems, several companies are proceeding with commercialization of single-pixel μal EDS technology. Two drawbacks limiting the application of uxal EDS are its low count rate (∽500 s−1) and small area (∽0.04 mm for a bare single pixel, ∽5 mm2 with a polycapillary optic). We are developing a 32x32 pixel array with a total area of 40 mm2 and with a total count rate between 105 s−1 and 106 s−1.

scholarly journals Operational Research to Assess the Real-Time Impact of COVID-19 on TB and HIV Services: The Experience and Response from Health Facilities in Harare, Zimbabwe

2021 ◽  
Vol 6 (2) ◽  
pp. 94
Author(s):  
Pruthu Thekkur ◽  
Kudakwashe C. Takarinda ◽  
Collins Timire ◽  
Charles Sandy ◽  
Tsitsi Apollo ◽  
...  
Keyword(s):  
Hiv Testing ◽  
Treatment Outcomes ◽  
Real Time ◽  
Treatment Success ◽  
Health Facilities ◽  
Case Detection ◽  
Monthly Data ◽  
Hiv Services ◽  
Tb Treatment ◽  
Set Up

When COVID-19 was declared a pandemic, there was concern that TB and HIV services in Zimbabwe would be severely affected. We set up real-time monthly surveillance of TB and HIV activities in 10 health facilities in Harare to capture trends in TB case detection, TB treatment outcomes and HIV testing and use these data to facilitate corrective action. Aggregate data were collected monthly during the COVID-19 period (March 2020–February 2021) using EpiCollect5 and compared with monthly data extracted for the pre-COVID-19 period (March 2019–February 2020). Monthly reports were sent to program directors. During the COVID-19 period, there was a decrease in persons with presumptive pulmonary TB (40.6%), in patients registered for TB treatment (33.7%) and in individuals tested for HIV (62.8%). The HIV testing decline improved in the second 6 months of the COVID-19 period. However, TB case finding deteriorated further, associated with expiry of diagnostic reagents. During the COVID-19 period, TB treatment success decreased from 80.9 to 69.3%, and referral of HIV-positive persons to antiretroviral therapy decreased from 95.7 to 91.7%. Declining trends in TB and HIV case detection and TB treatment outcomes were not fully redressed despite real-time monthly surveillance. More support is needed to transform this useful information into action.

Estimation of Truck Traffic Volume from Single Loop Detectors with Lane-to-Lane Speed Correlation

2003 ◽  
Vol 1856 (1) ◽  
pp. 106-117 ◽  
Author(s):  
Jaimyoung Kwon ◽  
Pravin Varaiya ◽  
Alexander Skabardonis
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
The United States ◽  
Truck Traffic ◽  
Single Loop ◽  
Loop Detectors ◽  
Time Estimates ◽  
Surveillance Technology ◽  
Traffic Volumes ◽  
Urban Freeway

An algorithm for real-time estimation of truck traffic in multilane freeways was proposed. The algorithm used data from single loop detectors—the most widely installed surveillance technology for urban freeways in the United States. The algorithm worked for those freeway locations that have a truck-free lane and exhibit high lane-to-lane speed correlation. These conditions are met by most urban freeway locations. The algorithm produced real-time estimates of the truck traffic volumes at the location. It also can be used to produce alternative estimates of the mean effective vehicle length, which can improve speed estimates from single loop detector data. The algorithm was tested with real freeway data and produced estimates of truck traffic volumes with only 5.7% error. It also captured the daily patterns of truck traffic and mean effective vehicle length. Applied to loop data on Interstate 710 near Long Beach, California, during the dockworkers’ lockout October 1 to 9, 2002, the algorithm found a 32% reduction in five-axle truck volume.

Adapting Collision Warnings to Real-Time Estimates of Driver Distraction

2008 ◽  
pp. 501-518 ◽  
Author(s):  
Matthew Smith ◽  
Gerald Witt ◽  
Debbie Bakowski ◽  
Dave Leblanc ◽  
John Lee
Keyword(s):  
Real Time ◽  
Driver Distraction ◽  
Time Estimates

scholarly journals Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-based Solutions in Rehabilitation

2021 ◽  
Author(s):  
Dimitar Stanev ◽  
Konstantinos Filip ◽  
Dimitrios Bitzas ◽  
Sokratis Zouras ◽  
Georgios Giarmatzis ◽  
...  
Keyword(s):  
Real Time ◽  
Small Error ◽  
Grand Challenge ◽  
Muscle Forces ◽  
Time Estimates ◽  
Musculoskeletal Models ◽  
Open Source Framework ◽  
Measurement Units ◽  
Technical Details ◽  
Significant Milestone

This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking. Emphasis was given to reduce computational latency while maintaining accuracy as compared to the offline counterpart. Notably, we highlight the influence of adequate filtering and differentiation under noisy conditions and its importance for consequent dynamic calculations. Real-time estimates of the joint moments, muscle forces, and reaction loads closely resemble OpenSim's offline analyses. Model-based estimation of ground reaction wrenches demonstrates that even a small error can negatively affect other estimated quantities. An application of the developed system is demonstrated in the context of rehabilitation and gait retraining. We expect that such a system will find numerous applications in laboratory settings and outdoor conditions with the advent of predicting or sensing environment interactions. Therefore, we hope that this open-source framework will be a significant milestone for solving this grand challenge.

scholarly journals Dokter Tunggu, A Hospital Application for Patient of Healthcare and Social Security Agency

2021 ◽  
Vol 8 (8) ◽  
pp. 450-458
Author(s):  
Muhammad Riyadi ◽  
Rhian Indradewa ◽  
Tantri Yanuar Rahmat Syah
Keyword(s):  
Social Security ◽  
Real Time ◽  
Health Facilities ◽  
Referral Hospital ◽  
Capital City ◽  
Business Planning ◽  
Level I ◽  
A Company ◽  
Social Security Agency

PT. Zaps Technology is a company engaged in technology and information by producing application products with the name Dokter Tunggu (Doku). The application was created to eliminate queues that often occur in Healthcare and Social Security Agency patient services at level I Hospitals and Health Facilities. Place of company at Bekasi Jawa Barat, The location is said to be chosen because Bekasi is one of the supporting areas for the capital city and has a variety of complete business facilities. This company's strategy is to create innovations in Healthcare and Social Security Agency patient services where the application made has various features that are able to eliminate queues. The application has an online referral menu on the application so that Hospitals, Level I Facilities and Healthcare and Social Security Agency patients are easier to take advantage of BPJS services. The waiting doctor application will display real time conditions at the referral hospital so that BPJS users can monitor the condition of BPJS services at the destination Hospital. Keywords: Dokter Tunggu, Hospital, Online Sevice, Business Planning.

scholarly journals Learning In Spike Trains: Estimating Within-Session Changes In Firing Rate Using Weighted Interpolation

2016 ◽  
Author(s):  
Greg Jensen ◽  
Fabian Muñoz ◽  
Vincent P. Ferrera
Keyword(s):  
Real Time ◽  
Electrophysiological Study ◽  
Monte Carlo Algorithm ◽  
Small Samples ◽  
Weighted Interpolation ◽  
Time Dynamics ◽  
Firing Rates ◽  
The Past ◽  
Time Estimates ◽  
Quantitative Procedure

AbstractThe electrophysiological study of learning is hampered by modern procedures for estimating firing rates: Such procedures usually require large datasets, and also require that included trials be functionally identical. Unless a method can track the real-time dynamics of how firing rates evolve, learning can only be examined in the past tense. We propose a quantitative procedure, called ARRIS, that can uncover trial-by-trial firing dynamics. ARRIS provides reliable estimates of firing rates based on small samples using the reversible-jump Markov chain Monte Carlo algorithm. Using weighted interpolation, ARRIS can also provide estimates that evolve over time. As a result, both real-time estimates of changing activity, and of task-dependent tuning, can be obtained during the initial stages of learning.

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