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2022 ◽  
Vol 10 (1) ◽  
pp. 117-133
Author(s):  
Nicolás José Fernández-Martínez

Location detection in social-media microtexts is an important natural language processing task for emergency-based contexts where locative references are identified in text data. Spatial information obtained from texts is essential to understand where an incident happened, where people are in need of help and/or which areas have been affected. This information contributes to raising emergency situation awareness, which is then passed on to emergency responders and competent authorities to act as quickly as possible. Annotated text data are necessary for building and evaluating location-detection systems. The problem is that available corpora of tweets for location-detection tasks are either lacking or, at best, annotated with coarse-grained location types (e.g. cities, towns, countries, some buildings, etc.). To bridge this gap, we present our semi-automatically annotated corpus, the Fine-Grained LOCation Tweet Corpus (FGLOCTweet Corpus), an English tweet-based corpus for fine-grained location-detection tasks, including fine-grained locative references (i.e. geopolitical entities, natural landforms, points of interest and traffic ways) together with their surrounding locative markers (i.e. direction, distance, movement or time). It includes annotated tweet data for training and evaluation purposes, which can be used to advance research in location detection, as well as in the study of the linguistic representation of place or of the microtext genre of social media.


2021 ◽  
Author(s):  
Matthew Filteau ◽  
Brandn Green ◽  
Frances Kim ◽  
Ki_Ai McBride

Abstract Most states in the US have implemented Good Samaritan Laws (GSLs) that provide legal protections for anyone calling law enforcement and first responders trained to administer naloxone and reverse overdoses. Despite these laws, some bystanders are reluctant to call the authorities, prompting requests to increase naloxone access and administration training among lay persons. This study examines the perceptions of emergency first responders in a frontier and remote (FAR) state to understand their job responsibilities and perceptions of layperson naloxone administration training. This study includes 22 interviews with law enforcement, EMS and/or fire personnel, members of community organizations responsible for responding to opioid overdoses. The study finds widespread support for layperson naloxone training and administration throughout Montana due to rural first responders’ inability to meet the needs of residents and an overall lack of resources to address substance use. This study adds to the literature because of it focuses on first responders in a frontier and remote area (FAR) that would benefit from layperson naloxone education and administration training due to its geographic expansiveness and the area’s overall lack of resources. A harm reduction approach that trains laypeople to administer naloxone might be FAR residents’ best chance for survival after an opioid overdose.


2021 ◽  
Author(s):  
Keighobad Jafarzadegan ◽  
David Muñoz ◽  
Hamed Moftakhari ◽  
Joseph Gutenson ◽  
Guarav Savant ◽  
...  

Abstract. Deltas, estuaries, and wetlands are prone to frequent coastal flooding throughout the world. In addition, a large number of people in the United States have settled in these low-lying regions. Therefore, the ecological merit of wetlands for maintaining sustainable ecosystems highlights the importance of flood risk and hazard management in these regions. Typically, hydrodynamic models are used for coastal flood hazard mapping. The huge computational resources required for hydrodynamic modeling and the long-running time of these models (order of hours or days) are two major drawbacks that limit the application of these models for prompt decision-making by emergency responders. In the last decade, DEM-based classifiers based on Height Above Nearest Drainage (HAND) have been widely used for rapid flood hazard assessment demonstrating satisfactory performance for inland floods. The main limitation is the high sensitivity of HAND to the topography which degrades the accuracy of these methods in flat coastal regions. In addition, these methods are mostly used for a given return period and generate static hazard maps for past flood events. To cope with these two limitations, here we modify HAND and propose a composite hydrogeomorphic index for rapid flood hazard assessment in coastal areas. We also propose the development of hydrogeomorphic threshold operative curves for real-time flood hazard mapping. We select the Savannah river delta as a testbed, calibrate the proposed hydrogeomorphic index on Hurricane Matthew and validate the performance of the developed operative curves for Hurricane Irma. Validation results demonstrate that the operative curves can rapidly generate flood hazard maps with satisfactory accuracy. This indicates the high efficiency of our proposed methodology for fast and accurate estimation of hazard areas for an upcoming coastal flood event which can be beneficial for emergency responders and flood risk managers.


2021 ◽  
Author(s):  
Trung Chau

<div>Improvised Explosive Devices (IEDs) have been developed over the years across many nations around the world. IEDs used by terrorist actions and in warfare cause devastating death, injuries and damage. To protect the public, many emergency responders have to risk their lives by performing extremely hazardous tasks such as interacting with suspected IEDs. To prevent the emergency response teams from being negatively impacted by IEDs, many different kinds of response robots have been deployed in many locations worldwide – allowing first responders a safe way to interact with these menaces from a distance. This thesis contributes to the understanding of using robot arms with a Leader–Follower (LF) approach to help humans with performing dexterous operations like those which are inevitably required for manipulating IEDs remotely. The LF approach allows operators to remotely manipulate a robot arm without putting operators’ lives in danger. By physically controlling one arm from a safe distance, operators can successfully copy its movements to a second arm. As a result, we argue, this approach can be helpful for minimizing operator risk when interacting with suspicious devices while at the same time facilitating more intuitive remote control.</div>


2021 ◽  
Author(s):  
Trung Chau

<div>Improvised Explosive Devices (IEDs) have been developed over the years across many nations around the world. IEDs used by terrorist actions and in warfare cause devastating death, injuries and damage. To protect the public, many emergency responders have to risk their lives by performing extremely hazardous tasks such as interacting with suspected IEDs. To prevent the emergency response teams from being negatively impacted by IEDs, many different kinds of response robots have been deployed in many locations worldwide – allowing first responders a safe way to interact with these menaces from a distance. This thesis contributes to the understanding of using robot arms with a Leader–Follower (LF) approach to help humans with performing dexterous operations like those which are inevitably required for manipulating IEDs remotely. The LF approach allows operators to remotely manipulate a robot arm without putting operators’ lives in danger. By physically controlling one arm from a safe distance, operators can successfully copy its movements to a second arm. As a result, we argue, this approach can be helpful for minimizing operator risk when interacting with suspicious devices while at the same time facilitating more intuitive remote control.</div>


Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7905
Author(s):  
Martina Serafini ◽  
Federica Mariani ◽  
Isacco Gualandi ◽  
Francesco Decataldo ◽  
Luca Possanzini ◽  
...  

The next future strategies for improved occupational safety and health management could largely benefit from wearable and Internet of Things technologies, enabling the real-time monitoring of health-related and environmental information to the wearer, to emergency responders, and to inspectors. The aim of this study is the development of a wearable gas sensor for the detection of NH3 at room temperature based on the organic semiconductor poly(3,4-ethylenedioxythiophene) (PEDOT), electrochemically deposited iridium oxide particles, and a hydrogel film. The hydrogel composition was finely optimised to obtain self-healing properties, as well as the desired porosity, adhesion to the substrate, and stability in humidity variations. Its chemical structure and morphology were characterised by infrared spectroscopy and scanning electron microscopy, respectively, and were found to play a key role in the transduction process and in the achievement of a reversible and selective response. The sensing properties rely on a potentiometric-like mechanism that significantly differs from most of the state-of-the-art NH3 gas sensors and provides superior robustness to the final device. Thanks to the reliability of the analytical response, the simple two-terminal configuration and the low power consumption, the PEDOT:PSS/IrOx Ps/hydrogel sensor was realised on a flexible plastic foil and successfully tested in a wearable configuration with wireless connectivity to a smartphone. The wearable sensor showed stability to mechanical deformations and good analytical performances, with a sensitivity of 60 ± 8 μA decade−1 in a wide concentration range (17–7899 ppm), which includes the safety limits set by law for NH3 exposure.


Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 432
Author(s):  
Linda Elmhadhbi ◽  
Mohamed-Hedi Karray ◽  
Bernard Archimède ◽  
J. Neil Otte ◽  
Barry Smith

Managing complex disaster situations is a challenging task because of the large number of actors involved and the critical nature of the events themselves. In particular, the different terminologies and technical vocabularies that are being exchanged among Emergency Responders (ERs) may lead to misunderstandings. Maintaining a shared semantics for exchanged data is a major challenge. To help to overcome these issues, we elaborate a modular suite of ontologies called POLARISCO that formalizes the complex knowledge of the ERs. Such a shared vocabulary resolves inconsistent terminologies and promotes semantic interoperability among ERs. In this work, we discuss developing POLARISCO as an extension of Basic Formal Ontology (BFO) and the Common Core Ontologies (CCO). We conclude by presenting a real use-case to check the efficiency and applicability of the proposed ontology.


2021 ◽  
Vol 21 (10) ◽  
pp. 2993-3014
Author(s):  
Katy Burrows ◽  
David Milledge ◽  
Richard J. Walters ◽  
Dino Bellugi

Abstract. Information on the spatial distribution of triggered landslides following an earthquake is invaluable to emergency responders. Manual mapping using optical satellite imagery, which is currently the most common method of generating this landslide information, is extremely time consuming and can be disrupted by cloud cover. Empirical models of landslide probability and landslide detection with satellite radar data are two alternative methods of generating information on triggered landslides that overcome these limitations. Here we assess the potential of a combined approach, in which we generate an empirical model of the landslides using data available immediately following the earthquake using the random forest technique and then progressively add landslide indicators derived from Sentinel-1 and ALOS-2 satellite radar data to this model in the order they were acquired following the earthquake. We use three large case study earthquakes and test two model types: first, a model that is trained on a small part of the study area and used to predict the remainder of the landslides and, second, a preliminary global model that is trained on the landslide data from two earthquakes and used to predict the third. We assess model performance using receiver operating characteristic analysis and r2, and we find that the addition of the radar data can considerably improve model performance and robustness within 2 weeks of the earthquake. In particular, we observed a large improvement in model performance when the first ALOS-2 image was added and recommend that these data or similar data from other L-band radar satellites be routinely incorporated in future empirical models.


2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 97-97
Author(s):  
Olivia S. Allen ◽  
Mark Liu ◽  
Kevin Munjal ◽  
Rex Lomboy ◽  
Aarti Sonia Bhardwaj ◽  
...  

97 Background: The COVID-19 pandemic caused sudden changes in healthcare delivery, and new policies were rapidly implemented to ensure safety for patients and staff. However, COVID-19 testing requirements presented a barrier for many patients. Outdoor testing in New York City became less feasible during colder months, and oncology patients have additional concerns, such as limited mobility and immunosuppression. To address these barriers, we created an in-home COVID-19 testing program through a partnership between Community Paramedicine and Oncology: SWABBER (SWABS by Emergency Responders). We evaluated patient use of and satisfaction with SWABBER. Methods: SWABBER began in September 2020 as an interdisciplinary initiative to offer in-home, asymptomatic COVID-19 PCR testing for patients on active treatment in an effort to provide more coordinated care and improve patient experience. Tests were performed prior to the first day of each treatment cycle at no cost to patients. Randomly selected patients completed a brief survey about their experiences with the program, with questions on a seven-point Likert scale. Sociodemographic data was collected from the EMR, and we used a chi-square test to identify differences in patient use of SWABBER by race. Results: From September 8, 2020–April 1, 2021, we saw 7,204 patients for infusion, of whom 993 (14%) participated in SWABBER. The cohort of all patients receiving treatment was 45% White, 19% Black, 6% Asian, 29% Other, and 1% Unknown race. The SWABBER cohort was 36% White, 21% Black, 12% Asian, 29% Other, and 1% Unknown race. There was a significant difference in patient race between these two groups (P < 0.00001), with more Black and Asian patients in SWABBER compared to all patients receiving treatment. A total of 406 (41%) SWABBER patients completed the patient experience survey. The mean scores for overall experience and likelihood of recommending the program were 6.9, with standard deviations of 0.56 and 0.44, respectively. Conclusions: SWABBER enabled us to deliver care directly to patients’ homes, mitigating COVID-19 exposure while promoting accessible care and providing an increased benefit for minority patients. Through SWABBER, we achieved near-perfect patient experience ratings, reduced the burden of testing, created a safer environment for patients and staff, and kept cancer care on track. Future work will evaluate ways to maintain elevated patient experience and continue striving for inclusive care beyond the pandemic.[Table: see text]


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