scholarly journals Emergency Infrastructure and Locational Extraction: Problematizing Computer Assisted Dispatch Systems as Public Good

2021 ◽  
Vol 19 (2) ◽  
pp. 187-198
Author(s):  
James N. Gilmore ◽  
McKinley DuRant
Keyword(s):  
South Carolina ◽  
Public Good ◽  
Emergency Response ◽  
Large Scale ◽  
Third Party ◽  
Computer Assisted ◽  
Emergency Situations ◽  
News Reports ◽  
Life Saving ◽  
Dispatch System

This article analyzes the increasing articulation between third-party software and emergency infrastructures through a focus on the computer-assisted dispatch system RapidDeploy, which purports to help 9-1-1 responders more accurately and efficiently respond to emergency situations. We build from research that focuses on overlaps between surveillance and emergency response to demonstrate how large-scale data mining practices—in particular, location extraction—are repurposed as beneficial, if not life-saving, measures. In focusing on the capacity to extract and analyze location in the name of public good, we discursively analyze how RapidDeploy’s official company blog constructs the benevolence of data collection. We then demonstrate how these viewpoints are reproduced in the news reports in one city—Charleston, South Carolina—where RapidDeploy has formally partnered with emergency response services. This analysis is used to demonstrate how words like “data” and “cloud” continue to be used as vague buzzwords for companies situated at the intersections of surveillance and civic function, and to argue for greater attention to how the trend towards platformization continues to blur the relationships between surveillance and emergency response systems. This case study examines the public face of this company and, as such, analyzes the language used to gain public assent for the software and its function.

Model for integrated assessment of rescue operations efficiency at group use of robotics for elimination of consequences of large-scale technogenic emergencies

2020 ◽  
pp. 90-101
Author(s):  
Андрей Владимирович Байков ◽  
Константин Валерьевич Александров
Keyword(s):  
Decision Making ◽  
Emergency Response ◽  
Integrated Assessment ◽  
Large Scale ◽  
Methodological Approach ◽  
Scheduling Theory ◽  
Rescue Operation ◽  
Scientific Methods ◽  
Emergency Rescue ◽  
Emergency Situations

Применение одного робототехнического средства (РТС) в зоне чрезвычайной ситуации (ЧС) малоэффективно из-за множества выполняемых задач, что обусловливает необходимость привлечения группы РТС для уменьшения времени ликвидации ЧС. В статье представлена математическая модель выбора группы РТС для ликвидации ЧС. Разработанная модель комплексной оценки эффективности проведения аварийно-спасательных работ при групповом применении РТС в техногенной ЧС является научно-методическим аппаратом по обоснованию состава группировки системы РТС МЧС России, необходимых для выполнения задач в ЧС. The use of one robotic in the emergency zone is not very effective due to the multitude of tasks performed in the emergency zone. This determines the need to use the group of robotics to reduce the time for emergency elimination. The relevance of the work is determined by the scattered scientific and methodological system for substantiating the composition of the robotics grouping of the Ministry of Emergency Situations of Russia, as well as by errors in decision making on the use of robotics in emergencies. The paper presents a mathematical model for choosing a group of robotic equipment for emergency response. The model for integrated assessment of the rescue operation effectiveness with application of group of robotics is based on the “scheduling theory”. The paper proposes scientific methods for the development of quantitatively substantiated recommendations for decision-making, where mathematical models of ordering various purposeful actions in time are constructed and analyzed taking into account the objective function and different restrictions. The developed model for integrated assessment of the effectiveness of emergency rescue operations at group application of robotics during technogenic emergency is a scientific and methodological approach for substantiating the structure of robotics grouping of EMERCOM of Russia required to solve tasks in an emergency.

IMPLEMENTATIONS EMERGENCY RESPONSE SYSTEM IN CASE OF ACCIDENTS AND EMERGENCY SITUATIONS (ERA-GLONASS) FOR CARS AND TRUCKS IN UZBEKISTAN

2020 ◽  
Author(s):  
Akmal Rustamov
Keyword(s):  
Emergency Response ◽  
Call Center ◽  
Emergency Services ◽  
Main Idea ◽  
Emergency Situation ◽  
High Mountain ◽  
Additional Information ◽  
Emergency Situations ◽  
Emergency Response System ◽  
Response System

The paper addresses the problem of increasing transportation safety due to usage of new possibilities provided by modern technologies. The proposed approach extends such systems as ERA-GLONASS and eCall via service network composition enabling not only transmitting additional information but also information fusion for defining required emergency means as well as planning for a whole emergency response operation. The main idea of the approach is to model the cyber physical human system components by sets of services representing them. The services are provided with the capability of self- contextualization to autonomously adapt their behaviors to the context of the car-driver system. The approach is illustrated via an accident emergency situation response scenario. “ERA-GLONASS” is the Russian state emergency response system for accidents, aimed at improving road safety and reducing the death rate from accidents by reducing the time for warning emergency services. In fact, this is a partially copied European e Call system with some differences in the data being transmitted and partly backward compatible with the European parent. The principle of the system is quite simple and logical: in the event of an accident, the module built into the car in fully automatic mode and without human intervention determines the severity of the accident, determines the vehicle’s location via GLONASS or GPS, establishes connection with the system infrastructure and in accordance with the protocol, transfers the necessary data on the accident (a certain distress signal). Having received the distress signal, the employee of the call center of the system operator should call the on-board device and find out what happened. If no one answers, send the received data to Sistema-112 and send it to the exact coordinates of the team of rescuers and doctors, and the last one to arrive at the place is given 20 minutes. And all this, I repeat, without the participation of a person: even if people caught in an accident will not be able to independently call emergency services, the data on the accident will still be transferred. In this work intended to add some information about applying system project in Uzbek Roads especially mountain regions like “Kamchik” pass. The Kamchik Pass is a high mountain pass at an elevation of 2.306 m above the sea level, located in the Qurama Mountains in eastern Uzbekistan and its length is about 88km.The road to reach the pass is asphalted, but there are rough sections where the asphalt has disappeared. It’s called A373. The old road over the pass was by passed by a tunnel built in 1999. On the horizon, the snow-capped peaks of the Fan Mountains come into view. The pass is located in the Fergana Valley between the Tashkent and Namangan Regions.

News production theory and practice

Pragmatics ◽  
2008 ◽  
Vol 18 (1) ◽  
pp. 59-85 ◽  
Author(s):  
Tom Van Hout ◽  
Geert Jacobs
Keyword(s):  
Process Analysis ◽  
Theory And Practice ◽  
Computer Assisted ◽  
Press Releases ◽  
News Production ◽  
News Reports ◽  
Business News ◽  
Writing Practices ◽  
The Individual ◽  
And Control

This paper considers notions of agency, interaction and power in business news journalism. In the first part, we present a bird’s eye view of news access theory as it is reflected in selected sociological and anthropological literature on the ethnography of news production. Next, we show how these theoretical notions can be applied to the study of press releases and particularly to the linguistic pragmatic analysis of the specific social and textual practices that surround their transformation into news reports. Drawing on selected fieldwork data collected at the business desk of a major Flemish quality newspaper, we present an innovative methodology combining newsroom ethnography and computer-assisted writing process analysis which documents how a reporter discovers a story, introduces it into the newsroom, writes and reflects on it. In doing so, we put the individual journalist’s writing practices center stage, zoom in on the specific ways in which he interacts with sources and conceptualize power in terms of his dependence on press releases. Following Beeman & Peterson (2001), we argue in favor of a view of journalism as ‘interpretive practice’ and of news production as a process of entextualization involving multiple actors who struggle over authority, ownership and control.

Cardiac Arrhythmia Detection using Dual Tree Wavelet Transform and Convolutional Neural Network

2021 ◽  
Author(s):  
K Reddy Madhavi ◽  
Padmavathi kora ◽  
L Venkateswara Reddy ◽  
J Avanija ◽  
KLS Soujanya ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Cardiac Cells ◽  
P Wave ◽  
Computer Assisted ◽  
Ecg Signals ◽  
Coronary Diseases ◽  
Life Saving ◽  
Life Threatening ◽  
Assisted Diagnosis

Abstract The non-stationary ECG signals are used as a key tools in screening coronary diseases. ECG recording is collected from millions of cardiac cells’ and depolarization and re-polarization conducted in a synchronized manner as: The P-wave occurs first, followed by the QRScomplex and the T-wave, which will repeat in each beat. The signal is altered in a cardiac beat period for different heart conditions. This change can be observed in order to diagnose the patient’s heart status. There are life-threatening (critical) and non-life - threatening (noncritical) arrhythmia (abnormal Heart). Critical arrhythmia gives little time for surgery, whereas non-critical needs additional life-saving care. Simple naked eye diagnosis can mislead the detection. At that point, Computer Assisted Diagnosis (CAD) is therefore required. In this paper Dual Tree Wavelet Transform (DTWT) used as a feature extraction technique along with Convolution Neural Network (CNN) to detect abnormal Heart. The findings of this research and associated studies are without any cumbersome artificial environments. The CAD method proposed has high generalizability; it can help doctors efficiently identify diseases and decrease misdiagnosis.

Crowdsourcing Incident Information for Emergency Response using Open Data Sources in Smart Cities

2018 ◽  
Vol 2672 (1) ◽  
pp. 198-208 ◽  
Author(s):  
Fan Zuo ◽  
Abdullah Kurkcu ◽  
Kaan Ozbay ◽  
Jingqin Gao
Keyword(s):  
Social Media ◽  
Prior Knowledge ◽  
Emergency Response ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Smart Cities ◽  
Hurricane Sandy ◽  
Support Vector ◽  
Twitter Data ◽  
Emergency Events

Emergency events affect human security and safety as well as the integrity of the local infrastructure. Emergency response officials are required to make decisions using limited information and time. During emergency events, people post updates to social media networks, such as tweets, containing information about their status, help requests, incident reports, and other useful information. In this research project, the Latent Dirichlet Allocation (LDA) model is used to automatically classify incident-related tweets and incident types using Twitter data. Unlike the previous social media information models proposed in the related literature, the LDA is an unsupervised learning model which can be utilized directly without prior knowledge and preparation for data in order to save time during emergencies. Twitter data including messages and geolocation information during two recent events in New York City, the Chelsea explosion and Hurricane Sandy, are used as two case studies to test the accuracy of the LDA model for extracting incident-related tweets and labeling them by incident type. Results showed that the model could extract emergency events and classify them for both small and large-scale events, and the model’s hyper-parameters can be shared in a similar language environment to save model training time. Furthermore, the list of keywords generated by the model can be used as prior knowledge for emergency event classification and training of supervised classification models such as support vector machine and recurrent neural network.

scholarly journals Fluids of the Future

2021 ◽  
Vol 7 ◽  
Author(s):  
Thomas H. Edwards ◽  
Guillaume L. Hoareau
Keyword(s):  
Acute Care ◽  
Fluid Resuscitation ◽  
Large Scale ◽  
Large Temperature ◽  
Ongoing Research ◽  
Military Research ◽  
Animal Data ◽  
Life Saving ◽  
The Future ◽  
Significant Burden

Fluids are a vital tool in the armament of acute care clinicians in both civilian and military resuscitation. We now better understand complications from inappropriate resuscitation with currently available fluids; however, fluid resuscitation undeniably remains a life-saving intervention. Military research has driven the most significant advances in the field of fluid resuscitation and is currently leading the search for the fluids of the future. The veterinary community, much like our civilian human counterparts, should expect the fluid of the future to be the fruit of military research. The fluids of the future not only are expected to improve patient outcomes but also be field expedient. Those fluids should be compatible with military environments or natural disaster environments. For decades, military personnel and disaster responders have faced the peculiar demands of austere environments, prolonged field care, and delayed evacuation. Large scale natural disasters present field limitations often similar to those encountered in the battlefield. The fluids of the future should, therefore, have a long shelf-life, a small footprint, and be resistant to large temperature swings, for instance. Traumatic brain injury and hemorrhagic shock are the leading causes of preventable death for military casualties and a significant burden in civilian populations. The military and civilian health systems are focusing efforts on field-expedient fluids that will be specifically relevant for the management of those conditions. Fluids are expected to be compatible with blood products, increase oxygen-carrying capabilities, promote hemostasis, and be easy to administer in the prehospital setting, to match the broad spectrum of current acute care challenges, such as sepsis and severe systemic inflammation. This article will review historical military and civilian contributions to current resuscitation strategies, describe the expectations for the fluids of the future, and describe select ongoing research efforts with a review of current animal data.

scholarly journals Unobtrusive monitoring: Statistical dissemination latency estimation in Bitcoin’s peer-to-peer network

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243475
Author(s):  
David Mödinger ◽  
Jan-Hendrik Lorenz ◽  
Rens W. van der Heijden ◽  
Franz J. Hauck
Keyword(s):  
Bayesian Model ◽  
Large Scale ◽  
Risk Estimation ◽  
Peer To Peer ◽  
Third Party ◽  
Network Resources ◽  
Connection Number ◽  
Peer Network ◽  
Time Required ◽  
Peer To Peer Network

The cryptocurrency system Bitcoin uses a peer-to-peer network to distribute new transactions to all participants. For risk estimation and usability aspects of Bitcoin applications, it is necessary to know the time required to disseminate a transaction within the network. Unfortunately, this time is not immediately obvious and hard to acquire. Measuring the dissemination latency requires many connections into the Bitcoin network, wasting network resources. Some third parties operate that way and publish large scale measurements. Relying on these measurements introduces a dependency and requires additional trust. This work describes how to unobtrusively acquire reliable estimates of the dissemination latencies for transactions without involving a third party. The dissemination latency is modelled with a lognormal distribution, and we estimate their parameters using a Bayesian model that can be updated dynamically. Our approach provides reliable estimates even when using only eight connections, the minimum connection number used by the default Bitcoin client. We provide an implementation of our approach as well as datasets for modelling and evaluation. Our approach, while slightly underestimating the latency distribution, is largely congruent with observed dissemination latencies.

scholarly journals Citizen workshops in public libraries to disseminate and discuss primary care research results: a scaling-up study

2020 ◽  
Author(s):  
José Massougbodji ◽  
Hervé Tchala Vignon Zomahoun ◽  
Evehouenou Lionel Adisso ◽  
Jasmine Sawadogo ◽  
Valérie Borde ◽  
...  
Keyword(s):  
Knowledge Translation ◽  
Financial Incentives ◽  
General Public ◽  
Large Scale ◽  
Public Libraries ◽  
Scale Up ◽  
Scaling Up ◽  
Computer Assisted ◽  
Knowledge Gain ◽  
Research Results

Abstract Background Little is known about engaging patients and stakeholders in the process of scaling up effective knowledge translation interventions targeting the general public. Using an integrated knowledge translation approach, we aimed to scale up and evaluate an effective pilot program of disseminating research results in public libraries. Methods We conducted a scaling-up study targeting the general public. Based on our successful pilot project, we co-developed and implemented a larger-scale program of free citizen workshops in public libraries, this time in close research partnership with stakeholders and patient representatives. Citizen workshops, each facilitated by one participating physician and one science communicator, consisted of a 45-min computer-assisted presentation and a 45-min open exchange. Additional scale-up costs included offering financial incentives to stakeholders involved and the purchase of audio-visual equipment. The intervention outcome was knowledge gained. Scale-up outcomes were satisfaction, appropriateness, coverage, time and costs. An evaluation questionnaire was used to collect data of interest. Both quantitative and qualitative analyses were performed. Results The workshop theme chosen by patient and stakeholder representatives was the high prevalence of medication overuse among people over 65 years of age. From April to May 2019, 26 workshops were given in 25 public libraries reaching 362 people. Eighteen participating physicians and six science communicators facilitated the workshops. Participants reported significant knowledge gain (mean difference 2.1, 95% CI 2.0–2.2, P < .001). Median score for overall public satisfaction was 9/10 (IQR 8–10). A high level of appropriateness of the workshops was globally rated by the public participants Coverage was 92.6% of the total number of public libraries targeted. Costs were $6,051.84 CAD for workshop design and $22,935.41 CAD for scaling them up. Conclusion This project successfully established a large-scale and successful KT bridge between researchers, clinicians, and citizens via public libraries. This study provides a model for a dissemination practice that benefits the general public by both engaging them in the dissemination process and by targeting them directly.

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