Fast Emergency Response Research Articles

Drones as an Example of the Use of Smart Technologies in Cooperation with the State Security Forces in the Context of the Smart Cities Concept in the Czech Republic

Using unmanned aerial vehicles (drones) in security has become common in the Czech Republic. This paper focuses on using drones as an example of the application of smart technologies in conjunction with the Internet of Things (IoT) and state security forces, especially in cooperation with the police, fire and medical emergency services in the context of the new Smart Cities concept. Drones are one of the most suitable examples of use along with IoT within the security sector, which play a key role in the daily protection of citizens' safety, public order security and crisis management. It is related to developing and implementing 5G networks, which allow real-time transmission of data obtained by drones directly to information systems, and is also gaining importance. This technology enables fast and efficient communication between the different actors involved in an intervention and creates a platform for fast and effective emergency response. Among other things, paying attention to cybersecurity, such as securing data transmissions, protecting against cyber threats, complying with security standards and legislation, and introducing special procedures and methodologies applicable in crisis, is essential.

ANALISIS KINERJA DAN BUDAYA MUTU DI IGD RS PKU MUHAMMADIYAH YOGYAKARTA II

Concern to quality service in nowadays becoming global issue that triggered by consciousness to give best service for patient or by request from eksternal side of hospital. Goverment push hospital to give good quality of service by giving evaluation of hospital standarization. PKU Muhammadiyah Hospital Jogjakarta unit II which new one of health service unit right now striving to improve quality of service. Result of the beginning observation findout evaluation of activity are often neglected, particularly on unit emergency. The objective of this observation is to find out performance and quality culture in management unit emergency of PKU Muhammadiyah Hospital Jogjakarta unit II, This observation using qualitative method with case study as observation plan that using interview as observation instrument involving board of management and officer of unit emergency of PKU Muhammadiyah Hospital Jogjakarta unit II. The Self Assesment accreditation in 2008 and first edition of accreditation standard in 2012 result show that quality service standard that using by unit emergency of PKU Muhammadiyah Jogjakarta unit II for develop their quality of service its guided line for survey of hospital accreditation in 2008 instructed by Indonesia health ministry that poured into activity guidance of unit emergency, but because lack availability of unit emergency officer make unit emergency can’t support implementation of unit emergency activity guidance. Working culture in unit emergency of PKU Muhammadiyah Hospital Jogjakarta unit II its only doing fast emergency response but not yet support quality service development because not yet develop service evaluation process. Service process evaluation hard to do because of limitation number of human resource. The result of unit emergency performance show by AKPPGD, DOA and result of accreditation in 2008 already achieve the standard, but not yet fulfill quality standard evaluation of health service management base on first edition of accreditation in 2012.

Soil Moisture Sensor-Based Landslide Monitoring: A Laboratory-Based Approach for Guwahati City

Various techniques for landslide mapping, monitoring and modelling are being employed in a variety of studies to keep people safe from landslides. Guwahati, a city in Assam (India) is surrounded by hills, with varied slope angles, become prone to landslide during monsoon season. Relative increase in the moisture content of soil is a major parameter for determining the occurrence of landslides that are induced by rainfall. An experimental model with varying slope angles is demonstrated to witness some proportionality behaviour of soil moisture value for the collected soil sample from landslide prone areas. The soil moisture sensor value increases with increase in slope angle. The toe position of moisture value also shows a significant display of data during landslide. This early warning module can be incorporated with the help of Blynk Application to send messages to the residents of landslide prone areas. This study would be a cost effective alternative for landslide early warning hazard monitoring and fast emergency response process and the model may be considered as a miniature version of real-life slope conditions for the hills of Guwahati city, Assam, India.

Global flood extent segmentation in optical satellite images

Floods are among the most destructive extreme events that exist, being the main cause of people affected by natural disasters. In the near future, estimated flood intensity and frequency are projected to increase. In this context, automatic and accurate satellite-derived flood maps are key for fast emergency response and damage assessment. However, current approaches for operational flood mapping present limitations due to cloud coverage on acquired satellite images, the accuracy of flood detection, and the generalization of methods across different geographies. In this work, a machine learning framework for operational flood mapping from optical satellite images addressing these problems is presented. It is based on a clouds-aware segmentation model trained in an extended version of the WorldFloods dataset. The model produces accurate and fast water segmentation masks even in areas covered by semitransparent clouds, increasing the coverage for emergency response scenarios. The proposed approach can be applied to both Sentinel-2 and Landsat 8/9 data, which enables a much higher revisit of the damaged region, also key for operational purposes. Detection accuracy and generalization of proposed model is carefully evaluated in a novel global dataset composed of manually labeled flood maps. We provide evidence of better performance than current operational methods based on thresholding spectral indices. Moreover, we demonstrate the applicability of our pipeline to map recent large flood events that occurred in Pakistan, between June and September 2022, and in Australia, between February and April 2022. Finally, the high-resolution (10-30m) flood extent maps are intersected with other high-resolution layers of cropland, building delineations, and population density. Using this workflow, we estimated that approximately 10 million people were affected and 700k buildings and 25,000 km^2 of cropland were flooded in 2022 Pakistan floods.

Improved immune algorithm for sudden cardiac death first aid drones site selection

AimsOut-of-hospital cardiac arrest (OHCA) requires a fast emergency response, while traditional emergency takes too long to meet the demand. Combining a drone with a defibrillator can provide rapid resuscitation of OHCA patients. The aims are to improve survival in OHCA and to minimize the total system cost. MethodsWe developed an integer planning model for sudden cardiac death (SCD) first aid drone siting based on a set covering model with the stability of the siting system as the main constraint, considering the rescue time and total system cost. Using 300 points to simulate potential cardiac arrest locations in the main municipal district of Tianjin, China, the SCD first aid drone siting points are solved using an improved immune algorithm. ResultsBased on the actual parameters set by the SCD first aid drone, 25 siting points were solved in the main municipal district of Tianjin, China. These 25 sites were able to cover 300 simulated potential demand points. The average rescue time was 127.18 s and the maximum rescue time was 296.99 s. The total system cost was 136,824.46 Yuan. Comparing the pre- and post-algorithm solutions, the system stability was improved by 42.22%, and the maximum number of siting points corresponding to demand points was reduced by 29.41% and the minimum number was increased by 16.86%, which is closer to the average. ConclusionsWe propose the SCD emergency system and use the improved immune algorithm for example solving. Comparing the solution results using the pre- and post-improvement algorithms, the cost solved by the post-improvement algorithm is less and the system is more stable.

A Gain With No Pain: Exploring Intelligent Traffic Signal Control for Emergency Vehicles

For the emergency response, every second counts. Intersections are prone to congestion, which greatly hinders the fast response of emergency vehicles. Although emergency vehicles possess the privilege to run a red light, it can be unsafe, and a congested intersection will prevent the exercise of this privilege. When an emergency vehicle arrives, the greedy preemption scheme offers a green signal promptly until it leaves the intersection. This guarantees a fast emergency response in most cases. However, this scheme will lead to an adverse impact on vehicles of conflicting directions and may not work when there are other emergency vehicles traveling from conflicting directions simultaneously. Employing deep reinforcement learning techniques, recent studies have shown promising results for traffic signal control. In this work, we deliver an early attempt to control the traffic signal for emergency vehicles through deep reinforcement learning, which ensures an expeditious emergency response in various scenarios and alleviates the negative influence on the traffic efficiency of conflicting directions. We conduct realistic simulations using traffic data in a real-world network with multiple intersections on different testing parameters. The results verify the feasibility and effectiveness of our model and indicate that our method notably outperforms the other five baseline methods in terms of various performance metrics.

Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach

Fast road emergency response can minimize the losses caused by traffic accidents. However, emergency rescue on urban arterial roads is faced with the high probability of congestion caused by accidents, which makes the planning of rescue path complicated. This paper proposes a refined path planning method for emergency rescue vehicles on congested urban arterial roads during traffic accidents. Firstly, a rescue path planning environment for emergency vehicles on congested urban arterial roads based on the Markov decision process is established, which focuses on the architecture of arterial roads, taking the traffic efficiency and vehicle queue length into consideration of path planning; then, the prioritized experience replay deep Q-network (PERDQN) reinforcement learning algorithm is used for path planning under different traffic control schemes. The proposed method is tested on the section of East Youyi Road in Xi’an, Shaanxi Province, China. The results show that compared with the traditional shortest path method, the rescue route planned by PERDQN reduces the arrival time to the accident site by 67.1%, and the queue length at upstream of the accident point is shortened by 16.3%, which shows that the proposed method is capable to plan the rescue path for emergency vehicles in urban arterial roads with congestion, shorten the arrival time, and reduce the vehicle queue length caused by accidents.

Stroke Recognition for First Aid Providers: A Systematic Review and Meta-Analysis.

AimTo perform a systematic review of the literature on the effectiveness of existing stroke recognition scales used in a prehospital setting and suitable for use by first aid providers. The systematic review will be used to inform an update of international first aid guidelines.MethodsWe followed the Cochrane Handbook for Systematic Reviews of Interventions methodology and report results according to PRISMA guidelines. We searched Medline, Embase and CENTRAL on May 25, 2020 for studies of stroke recognition scales used by first aid providers, paramedics and nurses for adults with suspected acute stroke in a prehospital setting. Outcomes included change in time to treatment, initial recognition of stroke, survival and discharge with favorable neurologic status, and increased layperson recognition of the signs of stroke. Two investigators reviewed abstracts, extracted and assessed the data for risk of bias. The certainty of evidence was evaluated using GRADE methodology.ResultsWe included 24 observational studies with 10,446 patients evaluating 10 stroke scales (SS). All evidence was of moderate to very low certainty. Use of the Kurashiki Prehospital SS (KPSS), Ontario Prehospital SS (OPSS) and Face Arm Speech Time SS (FAST) was associated with an increased number of suspected stroke patients arriving to a hospital within three hours and, for OPSS, a higher rate of thrombolytic therapy. The KPSS was associated with a decreased time from symptom onset to hospital arrival. Use of FAST Emergency Response (FASTER) was associated with decreased time from door to tomography and from symptom onset to treatment. The Los Angeles Prehospital Stroke Scale (LAPSS) was associated with an increased number of correct initial diagnoses. Meta-analysis found the summary estimate sensitivity of four scales ranged from 0.78 to 0.86. The FAST and Cincinnati Prehospital Stroke Scale (CPSS) were found to have a summary estimated sensitivity of 0.86, 95% CI [0.69-0.94] and 0.81, 95% CI [0.70-0.89], respectively.ConclusionStroke recognition scales used in the prehospital first aid setting improves the recognition and diagnosis of stroke, thereby aiding the emergency services to triage stroke victims directly down an appropriate stroke care pathway. Of those prehospital scales evaluated by more than a single study, FAST and Melbourne Ambulance Stroke Screen (MASS) were found to be the most sensitive for stroke recognition, while the CPSS had higher specificity. When blood glucose cannot be measured, the simplicity of FAST and CPSS makes these particular stroke scales appropriate for non-medical first aid providers.

SsHealth: Toward Secure, Blockchain-Enabled Healthcare Systems

The future of healthcare systems is being shaped by incorporating emerged technological innovations to drive new models for patient care. By acquiring, integrating, analyzing, and exchanging medical data at different system levels, new practices can be introduced, offering a radical improvement to healthcare services. This article presents a novel smart and secure Healthcare system (ssHealth), which, leveraging advances in edge computing and blockchain technologies, permits epidemics discovering, remote monitoring, and fast emergency response. The proposed system also allows for secure medical data exchange among local healthcare entities, thus realizing the integration of multiple national and international entities and enabling the correlation of critical medical events for, for example, emerging epidemics management and control. In particular, we develop a blockchain-based architecture and enable a flexible configuration thereof, which optimize medical data sharing between different health entities and fulfil the diverse levels of Quality of Service (QoS) that ssHealth may require. Finally, we highlight the benefits of the proposed ssHealth system and possible directions for future research.

On-site water level measurement method based on wavelength division multiplexing for harsh environments in nuclear power plants

A simple water level measurement method based on wavelength division multiplexing (WDM) is proposed and demonstrated. The measurement principle is based on the change of Fresnel reflection occurring at the end facet of the optical fiber tip (OFT). To increase the spatial resolution of water level sensing, a broadband light source (BLS) and an arrayed waveguide grating (AWG) are employed. The OFTs are multiplexed with the dedicated wavelength channels of AWG. By measuring all of the reflection powers reflected at the OFTs with a proposed on-site reflectometer, the water level can be monitored continuously for a fast emergency response. Moreover, it can be implemented easily with the commercially available optical components and devices with the simple configuration.

An updated assessment of lightning-related fatality and injury risk in Canada: 2002–2017

Cloud-to-ground lightning is a common and dangerous natural atmospheric hazard in southern Canada. Previous research conducted by the author and colleagues, using data from 1994 to 2003, estimated that lightning directly or indirectly kills 9–10 people and injures 92–164 more each year in Canada. Repeating the analysis using data from the same government agency and media sources for the 2002–2017 period, the author found that lightning-related mortality decreased to 2–3 deaths per year, roughly 0.08 deaths per million population. An average of 180 lightning-related injuries each year (5.3 per million population) was estimated for the same period, slightly greater than the maximum documented in the 1994–2003 analysis. About half of the drop in mortality between periods may be attributed to the reduction in reported deaths associated with lightning-ignited municipal fires since 2000. The remainder may be due to a combination of greater availability and use of communication technology, faster emergency response and medical treatment, and increased public awareness of lightning hazards and safety. Further research is required to explain why lightning-related injury rates have remained stable; better understand the interaction of technological, behavioral and other factors; and to determine the efficacy of past and potential future safety interventions.

A new medical staff allocation via simulation optimisation for an emergency department in Hong Kong

Whether triage targets can be achieved has been an imperative assessment of service qualities for an emergency department in healthcare management. In this research, we focus on triage targets and try to fully meet the target of fast emergency response for critical patients subject to triage requirements for other category patients by optimising the medical staff allocation in the emergency department. Main challenges stem from multiple stochastic constraints and the time-consuming simulation. To solve the stochastically constrained discrete optimisation via simulation problem, we develop a discrete-event simulation model and propose a simulated-annealing-based algorithm called ConSA that adopts a special searching mechanism and an efficient simulation budget allocation rule to find a high-quality configuration of medical staff. A case study based on the data from a public hospital in Hong Kong is carried out. Numerical experiments demonstrate that our algorithm leads to a 38.28% improvement in the main performance compared to the current staff allocation and dominates other algorithms in terms of computational efficiency and output accuracy. It indicates that our method is a good decision tool for hospital managers.

The Impact of "Informer on Site" in Disaster Management

"Informer on site" is a smart phone application that provides a new channel for the community to raise complaint regarding a disaster in Malaysia. It is focused on disseminating information on disasters such as floods, landslides, typhoons, and haze within a short time in a reliable manner. This application could prove to be an effective and efficient tool to facilitate smooth communication and allows for faster emergency response. This paper discusses the the impact of "Informer on Site" in disaster management.

Safety System With Harmless First Fault: Complete and IT-M System

In electrical power systems, such as data centers and hospitals, where service continuity is essential, the electrical operation needs a skilled organization and a team available at all times (24/7) to have a fast emergency response. The hospital management considers doctors, surgeons, administrators, medical devices rightly at the forefront, but electrical and technological systems and the team of technicians wrongly as ancillaries. This paper emphasizes the importance of the business continuity management for service continuity and highlights how medical IT-systems have not to carry out power outage for fault due to overcurrents and overvoltages. When a fault occurs, the protection system could include a countdown time so that emergency response team must solve the problem before the system collapses.

Evaluation of Natural Disaster Emergency Response Procedure Based on Petri Net

How to ensure a smooth, fast and efficient emergency response procedure becomes a highly concerned issue. However, a procedure of emergency plan may be confusing and inefficient in reality due to delay caused by waiting for decision-making, responding to conflicts and limited resource during the process of dealing with emergency. In this paper, we propose a colored stochastic Petri net to evaluate the security and complexity of emergency response procedure and the reasonableness of resource flow, so as to effectively analyze the potential deficiencies of emergency response procedure. We firstly establish a colored stochastic Petri net, and then convert the colored stochastic Petri net to an isomorphic Markov chain. Studying the structural properties of the colored stochastic Petri net and steady-state nature of the Markov chainprovides a scientific basis for the perfection of emergency plan. Meanwhile, it also ensures an ordered and efficient implementation of emergency response procedure in an emergency.