Emergency Response Time Research Articles

Societal Impact of Test Automation: Reducing Human Error in Critical Systems

This article explores the profound societal impact of test automation across critical sectors such as healthcare, finance, transportation, and energy. It examines how automated testing processes significantly reduce human error, enhance system reliability, and improve service quality. The article presents compelling evidence from various studies and reports, demonstrating substantial improvements in medical diagnostics, financial fraud detection, transportation safety, and energy distribution efficiency. Beyond error reduction, the article discusses broader societal benefits, including enhanced accuracy in data processing, faster emergency response times, improved service quality, and strategic resource allocation. The article underscores the crucial role of test automation in addressing the challenges posed by increasingly complex technological systems and its far-reaching implications for public safety, economic stability, and overall quality of life.

Inequalities in emergency service accessibility: spatial analysis of urban infrastructure

PurposeThis study aims to investigate the relationship between emergency response times and crash severity in New York City, focusing on spatial disparities and their implications. It examines how these disparities impact disadvantaged neighborhoods, particularly regarding traffic safety and emergency service accessibility.Design/methodology/approachThe research uses comprehensive spatial analysis techniques, including hotspot mapping, network analysis for travel time modeling, local bivariate correlation analysis and service area calculations. It maps crash data alongside emergency facility locations, considering peak-hour traffic. The Area Deprivation Index (ADI) is integrated to evaluate socioeconomic factors influencing accessibility. This approach provides a nuanced understanding of how emergency response times correlate with crash severity at the census block level, accounting for socioeconomic disparities.FindingsThis study reveals significant disparities in emergency response times across New York City. In high-poverty, predominantly minority areas, response times are 2–3 min longer than average, correlating with a 15% increase in severe injury rates. Over 20% of neighborhoods show correlations between response times and crash severity, with positive linear (5.51%), negative linear (10.72%), concave (2.44%) and convex (2.80%) relationships. Blocks with positive linear relationships have a mean ADI rank of 3.918. During peak hours, 69.7% of Manhattan blocks show negative relationships, the highest among boroughs.Originality/valueThis research highlights spatial justice issues in urban emergency response systems, emphasizing the need for localized, data-driven planning and infrastructure adjustments. By integrating the ADI, the multifaceted approach reveals the complex dynamics of socioeconomic factors and emergency service accessibility that have not yet been investigated in diverse urban communities.

Determinants of Nyumba Kumi Community Policing Initiative on Maintenance of Law and Order in Migori County, Kenya

Purpose: The study sought to determine the key factors influencing the success or failure of the Nyumba Kumi initiative in maintaining law and order within Suna West Sub-County, Migori County, Kenya. More specifically, it set out to explore how community awareness, collaboration with law enforcers, technology in use, and information sharing influence the success of this community policing endeavor. The study further points out difficulties inhibiting the success of this initiative, such as frayed relations between the community and police force, mistrust, and issues related to personal vendettas, which have undermined the initiative's goal of reducing insecurity. Methodology: This study utilized a descriptive cross-sectional research type to investigate the extent to which selected associated factors affected the implementation of the Nyumba Kumi initiative in Suna West Sub-County. Local community members, Nyumba Kumi leaders, and local administration officials were interviewed using structured questionnaires. A total sample of 210 respondents was represented from all key stakeholder groups using a stratified random sampling technique. Statistical analyses were performed to summarize variables of interest with the help of descriptive statistics like means, and standard deviations (SD) using SPSS version 26. The study also used inferential statistics (Pearson product-moment correlation) to examine the relationships between variables. Findings were tabulated and graphically displayed for trends and comparisons between indicators. Findings: The study achieved a high response rate, highlighting strong positive relationships between the maintenance of law and order and several key factors. Significant associations were found with community awareness (r = 0.759, p < 0.001), collaboration with law enforcement (r = 0.793, p < 0.001), technology use (r = 0.450, p < 0.001), and information sharing (r = 0.673, p < 0.001), all of which contributed to enhanced security in Suna West sub-county, Migori County. These findings indicate that the Nyumba Kumi initiative thrives on increased community awareness, effective collaboration with security agencies, and the integration of technology. Despite challenges such as strained relationships between community members and law enforcement, the initiative has led to reduced crime rates, quicker emergency response times, and more peaceful conflict resolution in the area. Unique Contribution to Theory, Practice and Policy: The study reinforces key theories related to community policing. It supports Collective Efficacy Theory by demonstrating how communities contribute to law and order through informal support and social control. It also bolsters Social Cohesion Theory, emphasizing the significance of strong social bonds for effective community policing. Furthermore, it validates social cognitive theory by illustrating how observational learning and social interactions influence community behavior in security initiatives. From a practical standpoint, the study recommends enhancing community training, fostering collaboration with law enforcement, integrating technologies such as mobile applications and GIS, and improving information sharing. These strategies serve as a practical framework for strengthening grassroots security efforts and enhancing the effectiveness of community policing within the Nyumba Kumi initiatives. Additionally, the study offers valuable policy recommendations that advocate for supporting community training, encouraging collaboration with law enforcement, promoting technology use, and enhancing information sharing. Implementing these policies would significantly improve community policing outcomes by facilitating better cooperation between communities and law enforcement agencies.

Impact of Citizens Do-It-Yourself Activities on Digital Platform of Community Policing in Malawi

Digital platform has become catalyst for effective and efficient enforcement of rule of law in human society. This study examines impact of citizens do-it-yourself activities on digital platform of community policing in Malawi. Under exploratory sequential mixed methods design, Interviews followed purposive sampling targeting four focus group discussions of 10 participants each using interview guide instrument. House hold survey identified 432 respondents guided by Yamene (1969) formular, and were randomly distributed within 10 centers of Muloza, structured questionnaire was used to collect data. Transcribed interviews were coded, then grouped into sub themes, further merged into themes responding to research questions with help of NVivo application. House hold survey analysis implored descriptive statistics in SPSS version 20. Guided by pragmatics philosophical assumptions under lens of social-disorganization, democratic participation, and broken window theories, findings reveal citizens have participated in virtual interaction through ICTs in community policing, characterized by formal and non-formal wide participation improving police accountability and transparency in handling policing and, citizens are satisfied with level of engagement during virtual interaction. Multinominal regression shows gender has no impact on factors that influence Do-It-Yourself participation are (1) communication and sharing information between citizens and police p=.747, (2) to be aware of rights violations and safety needs, (3) people’s involvement in rights violations and suspicious activities p=.708, (4) citizen’s involvement in crime prevention and community policing efforts, (5) to maintain community dialogue and engagement p=.088, (6) looking for faster emergency response times, and (7) to provide evidence and share information with authorities p=.806. Effectiveness of digital participation of Do-It-Yourself is that it is at random and influenced by different causes surrounding personal interests. As such they could be engaged on daily, weekly, occasionally or monthly basis as long as citizens secure their environments. The discussion is that while other studies reveal that ICTs may reduce corruption, this current study reveals that flexibility and user friendly of ICTs allowing formal and non-formal wide participation that has improved police accountability and transparency in policing empowers citizens to participate claiming for their rights to secure their environments in community policing. Based on findings the study recommends community guardians including duty bearers like police officers to enlighten citizens to embrace ICTs for more security empowerment to take part in security issues of their times.

Retrospective Evaluation of Falls From Height Cases Admitted to the Pre-Hospital Emergency Healthcare System

Aim: The aim of this study is to analyze the demographic characteristics, timing aspects (time of occurrence, emergency response time, ambulance arrival time), and hospital arrival times in pre-hospital cases of falls in Ankara. Material and Methods: This cross-sectional observational epidemiological study examined data from patients who received pre-hospital emergency care due to fall-related trauma in Ankara between January 1, 2019, and December 31, 2023. Patients were identified through calls made to the central 112 Call Center and documented in the Emergency Health Automation System. Demographic details, injury epidemiology (ICD-10 codes, injury mechanism, incident timing), and emergency response times (command response time, time from ambulance dispatch to scene arrival, transport time) were collected. Results: This study includes 16,136 patients, of whom 6,346 are female and 9,790 are male, with an average age of 38.3 ± 24.3 years. The predominant ICD-10 diagnosis codes are W03, W10, W12, W13, W14, W17, Y30, and X80. The year 2019 saw the highest number of incidents (n=4,014), with a decrease observed in 2020 (n=2,790). The highest number of falls occurred in July, while the lowest was in February, with the fewest falls reported in winter and the most in summer throughout the five years. Most incidents occurred between 08:00 and 15:59 (n=8,366), followed by 16:00 to 23:59 (n=6,456); 1,314 incidents occurred between 00:00 and 07:59. Falls occurred most frequently during working hours (61.9%, n=9,983). The average command center response time was 216.4 ± 203.6 seconds, station response time was 39.6 ± 47.8 seconds, and transport time was 371.6 ± 249.9 seconds. Most patients were transported to Training and Research Hospital (n=7,827), followed by State Hospital (n=3,422), University Hospital (n=2,210), and Private Hospital (n=249). Secondary transfers were mostly related to Training and Research Hospital (n=1,387), primarily for specialist needs (n=1,165). There were 32 patients who received on-scene intervention and 231 patients who died. Significantly increased response times were observed from 2019 to 2023 (p

Enhancing Smart City Infrastructure: An Iot-Integrated System Approach for Real-Time Urban Management

Objectives: The primary objective of this study is to evaluate the effects of new information technologies on the workability, scalability, and reliability of urban installations. This study also aims to enhance smart city infrastructure by integrating various Internet of Things (IoT) systems, particularly for real-time smart city management. Methods : A complete experimental setup was established in a controlled urban environment encompassing numerous IoT technologies, such as environmental sensors, traffic management units, and public security systems, as well as data communication units. The Tensorflow framework, along with Python, was used to perform simulations and experiments. The system design was categorised into four key levels: data reception and transmission, computation, network and data analytics, and application. The proposed systems were subsequently tested in real-world environments, including zones frequently hosting urban events, to analyse key performance indicators. Findings : The results demonstrate significant improvements in urban management through IoT initiatives, including a 25% reduction in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) levels, leading to decreased respiratory hospital admissions and enhanced air quality and health benefits. Additionally, notable advancements were observed in noise control, energy efficiency, and emergency response. Specifically, the findings recorded a 10 dB decrease in nighttime noise, a 30% reduction in energy consumption for public lighting, and a 40% faster emergency response time. These outcomes highlight the broad impact of IoT on urban sustainability and safety. Novelty : This paper introduces a novel concept focused on the design perspective of IoT-connected systems to boost smart city infrastructure, with particular emphasis on the real-time management of urban environments. Keywords: Smart Cities, IoT Technologies, Urban Infrastructure, Real ­Time Urban Management, System Architecture

Optimal distribution of emergency resources to accidents for pre-rescue in chemical industrial parks

Medical and fire resources are scheduled to chemical industrial parks which have caused mass casualties due to accidents. However, most of models of emergency logistics do not account for pre-rescue before large-scale rescue which may increase the casualty rate because of the long emergency response time and distance. This paper presents a mathematical model proposed for optimal distribution of emergency resources for rescuing the trapped victims who are highly likely to be seriously injured or even killed before the arrival of ambulances and fire engines. Due to the different types and risks of accidents at various locations have different impacts on people, we consider the detailed characteristics of accident scenarios and maximum tolerance time for the trapped victims to determine the priority of rescue sites. In addition, this paper proposes two indices of pavement resistance coefficient and obstacle to reflect the environment after accidents, which are not involved in other models of emergency resources distribution. Based on these, a penalty cost objective function is established to maximize the rescue rate. The optimal delivery route which reflects the distribution process is planned for the robot that will save more casualties. Two simulations and a real accident case study were conducted by minimizing the objective function, and the numerical results showed the reliability and effectiveness of the presented model for making optimal decisions for emergency distribution to accidents in chemical industrial parks.

How do different navigation systems affect emergency response time? A prospective simulation study

ObjectiveThere has not been a theoretical test run in Germany that compares different navigation systems with an industry solution (MapTrip112). The aim of this study was to compare navigation systems...

Safety Helmet with Collision Detection System

A comprehensive smart helmet system integrating various sensors and communication modules has been developed for enhancing safety and functionality during motorcycle rides. The research addresses the critical issue of road safety for motorcyclists by introducing an accident detection system using accelerometers and gyroscopes, coupled with an emergency response system. The implementation utilizes an ESP32 microcontroller, MPU6050 sensor, GSM module, and GPS module. The findings showcase the effectiveness of the collision detection system and its potential to significantly improve emergency response times. The abstract underscores the importance of such technological interventions in reducing accident-related fatalities and injuries among motorcyclists. The discussion emphasizes the need for wider adoption of similar smart helmet systems and highlights the potential for future enhancements in both hardware and software components. The outcomes suggest a paradigm shift in motorcycle safety measures, urging policymakers and manufacturers to consider integrating these technologies into standard safety protocols.

Smart Accident Detection and Emergency Response System for Enhanced Road Safety

In the realm of road safety, prompt and accurate accident response is paramount to mitigating fatal injuries and saving lives. The existing approach, reliant on manual reporting to authorities or the injured party's ability to communicate with emergency services, introduces delays that can prove fatal. The presented system uses a combination of IoT modules to detect the accident and alert the EMS. It uses an accelerometer to detect the accident, a sim module equipped with a 2G sim to send an alert message consisting of the drivers details, car details, and crash location, acquired by the GPS module to the authorities and few selected individuals. The goal is to drastically reduce emergency response time, improving the likelihood of survival and quick recovery of the injured. This innovative approach not only addresses the current gaps in accident reporting but also contributes to an overall improvement in road safety infrastructure. This work uses a collection of sensors to identify an accident and point out the accident location. This data is sent as a text message to emergency services through which immediate help can be received.

Determining the possibility of passage through narrow alleys using ultrasonic sensors

The complex road topography of South Korea presents significant challenges to the timely arrival of emergency vehicles. Compounding the issue, obstacles such as legally or illicitly constructed structures, and improperly parked or stationary vehicles, frequently obstruct the path of emergency vehicles. To address these challenges, this study introduces a novel system aimed at enhancing emergency response times. The system employs ultrasonic sensors that can be integrated into personal devices to measure the width of the numerous narrow alleys prevalent in Korea's densely populated regions. Experiments demonstrate that within a 1-meter range in front of a narrow alley with widths varying between 270 cm and 450 cm where vehicle maneuvering is possible, it's possible to accurately gauge the width using two ultrasonic sensors, achieving a precision within a 5 cm margin of error. This level of accuracy enables the practical assessment of whether emergency vehicles can access the area in real-time by identifying the alley's narrowest point. The proposed system is a cost-effective method using easy-to-buy devices for augmenting emergency preparedness and enhancing emergency response times by ensuring that emergency vehicles can navigate through alleys, thereby fostering a safer living environment.

Novel Approach for Vehicle Accident Tracking using IoT

Abstract: In recent years, there has been significant interestin integrating Internet of Things (IoT) technology into vehicle safety systems to enhance emergency response capabilities and road safety. This paper introduces a comprehensive Vehicle Accident Tracking System (VATS) that utilizes IoT principles. The proposed system incorporates various sensors and communication devices installed within vehicles to monitor theirstatus and detect potential accidents in real-time. When anaccident is detected, the system automatically triggers an alert mechanism, notifying relevant stakeholders such as emergency services and fleet managers. Key components of the VATSinclude accelerometers, GPS modules, and onboard communication units, which work together to provide precise accident geospatial data and vehicle condition data. The systemutilizes cloud-based platforms for data storage and processing, ensuring seamless integration with existing infrastructure and scalability. By continuously monitoring and analyzing vehicle telemetry data, the VATS facilitates proactive accident prevention measures and post-accident analysis. Additionally, it offers benefits such as reduced emergency response times, improved accident documentation for insurance claims, and enhanced overall road safety. Privacy and security concerns areaddressed through encryption protocols and access control mechanisms to protect sensitive information. Overall, the Vehicle Accident Tracking System presented in this paper represents a significant advancement in leveraging IoT technology to mitigate the impact of accidents on roadways, thereby contributing to a safer and more efficient transportation ecosystem.

The Progress and Future of Artificial Intelligence in Nursing Care: A Review

Introduction The utilization of novel technologies in contemporary times not only reduces the cost associated with healthcare but also improves the efficiency of hospital resources and elevates the standard of medical assistance. One of the new technologies used in the field of health is Artificial Intelligence (AI). The purpose of this study is to investigate the application of AI in the field of nursing. Materials and Methods The present investigation was conducted in the year 2023 utilizing a review methodology and an innovative scientific inquiry. Comprehensive research was performed in reliable databases such as PubMed, Scopus, Google Scholar, Science Direct, and Springer, using the keywords AI, smart hospital, nursing, and health care to accomplish the intended objectives. From 2016 to 2023,120 articles were chosen as the initial selection. Studies that were not related to the subject matter were excluded from the analysis afterward. Results After searching and eliminating duplicate articles through objective screening, a total of 98 articles were reviewed, with 63 ultimately selected for the study. Within the realm of nursing care, research has been conducted in various areas, such as electronic health records (13 studies), health information collection and analysis (27 studies), healthcare cost analysis (16 studies), and the implementation of smart technology and hospitals (7 studies). The integration of AI technology has shown promise in enhancing nursing care by reducing diagnostic errors, improving emergency response times, improving patient care quality and psychological support, and enabling remote care for elderly patients through the use of smart technology. Conclusion AI is a significant technological advancement that can directly impact the operational effectiveness of healthcare organizations. This is achieved through the optimization of healthcare business processes and the enhancement of patient safety. However, limited research has been conducted regarding the affordability and economic aspects of AI implementation. Therefore, it is advisable for healthcare policymakers to establish the necessary infrastructure to leverage this technology, which will facilitate a more effective therapeutic and economic approach.

Contract Employment and Service Delivery in Tertiary Healthcare Institutions in Enugu State Nigeria

The study examinede Contract Employment and Service Delivery in Tertiary Healthcare Institutions in Enugu State. Specifically, the study sought to: determine the contribution of contract employment to healthcare delivery in tertiary healthcare institutions, examine the extent to which contract employment has assisted healthcare delivery in regard to emergency response in the tertiary healthcare institutions and ascertain the effect of contract employment on Equipment maintenance for special procedure in tertiary healthcare institutions in Enugu State Nigeria. The study adopted descriptive research design. The population of the study was 3694. Freund and Williams formular was adopted in determining the sample size of 3342. Tables, frequency distribution and Mean Scores were adopted in the analysis of data while z-test analysis was used in testing the hypotheses. The findings revealed that Contract employment has significantly contributed to healthcare delivery in tertiary healthcare institutions in Enugu State, Nigeria (where Z- value = 5.215 and the critical Zvalue= 1.96), that contract employment had assisted in healthcare delivery in regard to emergency response time in tertiary healthcare institutions in Enugu State, Nigeria ((whereZ- value = 4.096 and the critical Z-value= 1.96) and that Contract employment had significant positive effect on Equipment maintenance for special procedure in tertiary healthcare institutions in Enugu State Nigeria (where Z- value = 5.983 and the critical Zvalue= 1.96). The study concluded that judicious use of contract employment can enhance service delivery in these institutions by providing specialized expertise, promoting efficiency, and managing costs effectively. The study recommended.

Effect of Emergency Green Channel Optimization on Ischemic Stroke Patients: Impact on Emergency Response Time, Effectiveness, Anxiety, and Acute Stress.

The aim was to assess the effectiveness of an optimized emergency green channel in the treatment of acute ischemic stroke (AIS) patients and its effect on emergency response time, effectiveness, anxiety, and acute stress. A retrospective analysis was conducted on 349 AIS patients treated with intravenous thrombolysis from January 2019 to May 2022. The patients were divided into those who received optimized emergency green channel treatment (155) and those who did not (194). Propensity score matching (PSM) was used to balance the admission pathways, living conditions, insurance methods, and residential locations of the 2 patient groups. The key metrics comprised the times from onset to admission, admission to computed tomography (CT), CT to thrombolysis, admission to thrombolysis (door-to-needle time (DNT)), National Institute of Health Stroke Scale (NIHSS) scores at various intervals post thrombolysis, heart rate, blood pressure, and scores in Self-Rating Anxiety Scale (SAS) and the Stanford Acute Stress Reaction Questionnaire (SASRQ). Post PSM, 118 patients were analyzed (54 control and 64 observation). The observation group showed significantly lower time results than the control group, which included the following: the time from onset to admission (t = 31.428, P < .001), door-to-imaging time (t = 27.317, P < .001), imaging-to-needle time (t = 20.951, P < .001, and DNT (t = 25.954, P < .001). Significant differences were observed in 24 hour-post thrombolysis NIHSS scores, heart rate, blood pressure, SAS, and SASRQ scores (P < .05) but not in NIHSS scores at 7 and 30 days. The optimized emergency green channel process effectively reduced the treatment time for ischemic stroke patients, improved rescue efficiency, and positively influenced the psychological stress levels of patients post treatment.

The Effect of Emergency Level and Response Time on Patients' Length of Stay in the Emergency Room of Muhammadiyah Hospital Kediri

Background: Emergency services are medical actions needed by emergency patients. Overcrowding in the Emergency Room that happen with increasing number of patient which has an impact on length of stay and lead to patient dissatisfaction. Purpose: The aim of research was to analyze the effect of emergency level and response time on length of stay in the emergency room of Muhammadiyah Hospital Kediri. Method: The research design uses analytic observational with a cross sectional approach. The independent variables are level of emergency and response time. The dependent variable is Length of Stay. The population was all patients in the ER of Muhammadiyah Kediri Hospital at August - October 2022 as much as 142 patients. Samples taked used Simple random sampling technique as many as 105 respondents. Data collected used observation sheets and analyzed with multiple linear regression. Results: The results showed that partially and simultaneously of independent variables (Emergency Level and Response time) had a p-value less than 0.05 so that affected the length of stay. Conclusion: The results showed that emergencies level of patients caused experience a worsening of their condition or it will get worse if there are complications in patient's if they are not treated immediately, in addition to that, the officers respond quickly in services in the emergency room from the time patient arrives until treated will affect length of stay in the emergency room. This showed that’s there is an influenced of Emergency Level and Response Time on the Length of Patient's Stay.

Edge AI-Enabled Road Fixture Monitoring System

Effective monitoring of road fixtures is essential for urban safety and functionality. However, traditional inspections are time-consuming, costly, and error prone, while current automated solutions struggle with high initial setup costs, limited flexibility preventing wide adaptation, and reliance on centralized processing that can delay response times. This study introduces an edge AI-based remote road fixture monitoring system which automatically and continuously updates the information of the road digital twin (DT). The main component is a small-sized edge device consisting of a camera, GPS, and IMU sensors designed to be installed in typical cars. The device captures images, detects the fixture, and estimates their location by employing deep learning and feature matching. This information is transmitted to a dedicated cloud server and represented on a user-friendly user interface. Experiments were conducted to test the system’s performance. The results showed that the device could successfully detect the fixture and estimate their global coordinates. Outputs were marked and shown on the road DT, proving the integrated and smooth operation of the whole system. The proposed Edge AI device demonstrated that it could significantly reduce the data size by 80–84% compared to traditional methods. With a satisfactory object detection accuracy of 65%, the system effectively identifies traffic poles, stop signs, and streetlights, integrating these findings into a digital twin for real-time monitoring. The proposed system improves road monitoring by cutting down on maintenance and emergency response times, increasing the ease of data use, and offering a foundation for an overview of urban road fixtures’ current state. However, the system’s reliance on the quality of data collected under varying environmental conditions suggests potential improvements for consistent performance across diverse scenarios.

Emergency response actions modeling and time analysis: Considering priority of actions

When a major chemical accident occurs, emergency response is an important measure to reduce accident losses. Some actions in an emergency response may be more important than others in reducing accident losses, so they need to be carried out as a matter of priority. The impact of the number of emergency personnel on the relationship between emergency response actions and time of emergency response process is analyzed in this paper in the circumstance that the number of responders is not enough to carry out emergency actions with priority at the same time, and a Petri-net based approach is proposed to model and analyze the actions. An emergency response action that needs to consider its priority is divided into two stages, one is to determine the personnel for executing the action, the other is the execution of the action. This division facilitates the handling of dynamic changes in the relationship between actions and differences in start time of the actions. The approach is illustrated by taking the emergency response of on-site emergency personnel of a chemical plant to a fire as an example. The efficiency of the emergency process is analyzed, and for the action of rescuing the wounded, which has the highest priority in the example, the time and the probability when considering the priority of emergency response actions are compared with those when not considering the priority.

Intelligent railroad inspection and monitoring

Railways are essential to the global transportation infrastructure, providing eco-friendly and economical solutions for the movement of freight and passengers. Inspecting and maintaining extensive rail networks timely poses significant challenges. My group and collaborators have focused on automated railroad inspection technologies, emphasizing the use of deep learning and computer vision to overcome the limitations of traditional manual inspections. Our research introduces groundbreaking real-time inspection methods, leveraging a specialized dataset of railroad components for enhanced instance segmentation models, achieving unprecedented accuracy and inference speeds. The developed computer vision systems efficiently detect track components and their changes over time, and also quantify rail surface defects. Additionally, our work extends to improving railroad crossing safety, utilizing deep learning frameworks for the detection of unusual pedestrian behaviors and object identification, aimed at reducing crossing incidents and improving emergency response times. Our future research directions aim to further refine the cost-effectiveness and autonomy of railroad inspection systems. Through these innovations, we hope to aid in the inspection and maintenance of railroads, offering practical solutions for railroad and other civil engineering applications.

Analysis of factors affecting the return of spontaneous circulation in elderly patients with out-of-hospital cardiac arrest

To investigate the factors that influence the return of spontaneous circulation (ROSC) in elderly patients suffering from out-of-hospital cardiac arrest (OHCA). A retrospective study was used to collect the clinical data of OHCA patients admitted to the emergency center of Zhengzhou People's Hospital from July 2016 to July 2019 based on the pre-hospital emergency database of Utstein model. Patients' gender, age, cardiac arrest (CA) etiology, presence or absence of bystander cardiopulmonary resuscitation (CPR), emergency response time, initial cardiac rhythm, ventilation method, use of epinephrine, defibrillation and ROSC were included. The patients were divided into elderly and young groups according to whether age ≥ 60 years old, and the differences in various indicators between the two groups were compared. Univariate Logistic regression analysis was used to analyze the relationship between emergency response time and ROSC in elderly patients and multivariate Logistic regression analysis was used to identify factors influencing ROSC in elderly patients. A total of 3 429 adult OHCA patients were enrolled in this study, including 2 105 elderly patients (61.39%), and 1 324 young and middle-aged patients (38.61%). Compared to the young group, the proportion of females, non-cardiac causes and asystole as the initial rhythm was higher in the elderly group, the emergency response time was shorter, the rate of defibrillation and tracheal intubation, and the success rate of ROSC were lower (all P < 0.05). Among them, the proportion of cardiac arrest as the initial rhythm in elderly male patients was significantly higher than that of young male patients (P < 0.05); the proportion of non-cardiac causes in elderly female patients was significantly higher than that of young female patients, and the proportion of defibrillation was significantly lower than that of young female patients (all P < 0.05). Multivariate Logistic regression analysis showed that cardiac arrest as the initial rhythm was strongly associated with ROSC in elderly male patients [odds ratio (OR) = 0.126, 95% confidence interval (95%CI) was 0.045-0.352, P < 0.05]. Univariate Logistic regression analysis of the relationship between emergency response time and ROSC in elderly patients showed that although there was no significant difference in the ROSC success rate among elderly patients with various emergency response times, an emergency response time within 10 minutes was beneficial for ROSC in elderly patients. The success rate of ROSC is very low in elderly OHCA patients aged ≥60 years. Although the CPR-related indicators of elderly patients are significantly different from those of young patients, there are gender differences. The association between the elderly male patients and cardiac arrest as the initial rhythm is stronger, while OHCA caused by non-cardiac diseases is more common and defibrillable rhythm is less common in elderly female patients. It may be more beneficial for elderly patients to shorten the emergency response time and increase bystander CPR.