Service Response Time Research Articles

Location decision of low-altitude service station for transfer flight based on modified immune algorithm

The location of Low-Altitude Flight Service Station (LAFSS) is a comprehensive decision work, and it is also a multi-objective optimization problem (MOOP) with constraints. As a swarm intelligence search algorithm for solving constrained MOOP, the Immune Algorithm (IA) retains the excellent characteristics of genetic algorithm. Using some characteristic information or knowledge of the problem selectively and purposefully, the degradation phenomenon in the optimization process can be suppressed and the global optimum can be achieved. However, due to the large range involved in the low-altitude transition flight, the geographical characteristics, economic level and service requirements among the candidate stations in the corridor are quite different, and the operational safety and service efficiency are interrelated and conflict with each other. And all objectives cannot be optimal. Therefore, this article proposes a Modified Immune Algorithm (MIA) with two-layer response to solve the constrained multi-objective location mathematical model of LAFSS. The first layer uses the demand track as the cell membrane positioning pattern recognition service response distance to trigger the innate immunity to achieve the basic requirements of security service coverage. In the second layer, the expansion and upgrading of adjacent candidate sites are compared to the pathogen’s effector, and the adaptive immunity is directly or indirectly triggered again through the cloning, mutation and reproduction between candidate sites to realize the multi-objective equilibrium of the scheme. Taking 486,000 km2 of Sichuan Province as an example, MIA for LAFSS is simulated by the MATLAB platform. Based on the Spring open source application framework of Java platform, the cesiumjs map data is called through easyui, and the visualization of site selection scheme is presented with the terrain data of Map World as the background. The experimental results show that, compared with dynamic programming and ordinary immunization, the immune trigger mode of double response and the improved algorithm of operation parameter combination designed by the Taguchi experiment, the total economic cost of location selection is reduced by 26.4%, the service response time is reduced by 25%, the repeat coverage rate is reduced by 29.5% and the effective service area is increased by 17.5%. The security risk, service efficiency and location cost are balanced. The present work is to provide an effective location method for the layout number and location of local transfer flight service stations. For complex scenes with larger scale of low-altitude flight supply and demand and larger terrain changes in the region, the above research methods can be used to effectively split and reduce the dimension.

Application of Internet of Things Technology in Safe Centralized Storage of Big Data Information

To solve the problem of low data processing rate in traditional medical information storage methods, a safe centralized storage method of medical big data information is proposed. The data set storage model is designed through the Internet of Things, and VeritasBackupExee is used as data recovery and backup software, and data recovery and backup are realized by the Internet of Things. Encrypted storage of ciphertext index vector is carried out by using the ciphertext storage algorithm, and the medical data center of Internet of Things middleware is constructed. By sharing storage devices with multiple hosts, highly reliable backup can be realized. The average response time of the method in this paper is different for medical big data services. The average response time for modifying service is 11ms, creating service is 12ms, and migrating service is 13.7 ms. To verify the data service response time of this method, it is necessary to verify the response performance of different methods. The results show that this method has higher data download and upload rates and faster data service response.

Distributed Architecture of Geospatial Data Collection and Processing System based on Web Design Patterns

This article discusses the issues of improving the reactivity and stability of web-based geographic information systems. It proposes a solution based on the integration of well-known web design patterns, which provides high internal and low external coupling of modules in the plugin architecture of the application. A prototype system was developed utilizing web design and programming patterns such as Model-View-Controller pattern and message-based communication between modules. The experiment was conducted on the dataset based on the results of registering the values of the Earths geomagnetic field parameters and its variations. The experiment demonstrates increased performance when using the suggested approach. The results show a reduction in server response time by 47 %.

Effect of snow on the emergency medical service response times of out-of-hospital cardiac arrest in Japan, 2009-2019 (JCS-ReSS)

Effect of snow on the emergency medical service response times of out-of-hospital cardiac arrest in Japan, 2009-2019 (JCS-ReSS)

Optimizing a Drone Network to Respond to Opioid Overdoses.

Introduction:Effective out-of-hospital administration of naloxone in opioid overdoses is dependent on timely arrival of naloxone. Delays in emergency medical services (EMS) response time could potentially be overcome with drones to deliver naloxone efficiently to the scene for bystander use. Our objective was to evaluate a mathematical optimization simulation for geographical placement of drone bases in reducing response time to opioid overdose. Methods: Using retrospective data from a single EMS system from January 2016-February 2019, we created a geospatial drone-network model based on current technological specifications and potential base locations. Genetic optimization was then used to maximize county coverage by drones and the number of overdoses covered per drone base. From this model, we identified base locations that minimize response time and the number of drone bases required. Results: In a drone network model with 2,327 opioid overdoses, as the number of modeled drone bases increased the calculated response time decreased. In a geospatially optimized drone network with four drone bases, response time compared to ambulance arrival was reduced by 4 minutes 38 seconds and covered 64.2% of the county. Conclusion: In our analysis we found that in a mathematical model for geospatial optimization, implementing four drone bases could reduce response time of 9-1-1 calls for opioid overdoses. Therefore, drones could theoretically improve time to naloxone delivery.

The Influence of Cardiac Arrest Floor-Level Location within a Building on Survival Outcomes.

This nationwide, population-based observational study investigated the association between the floor level of out-of-hospital cardiac arrest (OHCA) incidence and survival outcomes in South Korea, notable for its significant high-rise apartment living. Data were collected retrospectively from OHCA patients through the South Korean Out-of-Hospital Cardiac Arrest Surveillance database. The study incorporated cases that included the OHCA's building floor information. The primary outcome assessed was survival to discharge, analyzed using multivariate logistic regression, and the secondary outcome was favorable neurological outcome. Among 36,977 patients, a total of 29,729 patients were included, and 1680 patients were survivors. A weak yet significant correlation between floor level and hospital arrival time was observed. Interestingly, elevated survival rates were noted among patients from higher floors despite extended emergency medical service response times. Multivariate analysis identified age, witnessed OHCA, shockable rhythm, and prehospital return of spontaneous circulation (ROSC) as primary determinants of survival to discharge. The floor level's impact on survival was less substantial than anticipated, suggesting residential emergency response enhancements should prioritize witness interventions, shockable rhythm management, and prehospital ROSC rates. The study underscores the importance of bespoke emergency response strategies in high-rise buildings, particularly in urban areas, and the potential of digital technologies to optimize response times and survival outcomes.

Collaborative on-demand dynamic deployment via deep reinforcement learning for IoV service in multi edge clouds

In vehicular edge computing, the low-delay services are invoked by the vehicles from the edge clouds while the vehicles moving on the roads. Because of the insufficiency of computing capacity and storage resource for edge clouds, a single edge cloud cannot handle all the services, and thus the efficient service deployment strategy in multi edge clouds should be designed according to the service demands. Noticed that the service demands are dynamic in temporal, and the inter-relationship between services is a non-negligible factor for service deployment. In order to address the new challenges produced by these factors, a collaborative service on-demand dynamic deployment approach with deep reinforcement learning is proposed, which is named CODD-DQN. In our approach, the number of service request of each edge clouds are forecasted by a time-aware service demands prediction algorithm, and then the interacting services are discovered through the analysis of service invoking logs. On this basis, the service response time models are constructed to formulated the problem, aiming to minimize service response time with data transmission delay between services. Furthermore, a collaborative service dynamic deployment algorithm with DQN model is proposed to deploy the interacting services. Finally, the real-world dataset based experiments are conducted. The results show our approach can achieve lowest service response time than other algorithms for service deployment.

Understanding the joint effects of internal and external anthropomorphic cues of intelligent customer service bot on user satisfaction

As one specific type of chatbot, Intelligent Customer Service Bot (ICSB) satisfies users by providing uninterrupted customer service and reducing response time. However, users still prefer to interact with humans rather than robots. In previous studies, scholars have focused more on enhancing the anthropomorphic characteristics of chatbot by treating anthropomorphic characteristics as a general concept. Few literatures have studied the influence of both external and internal features on user satisfaction and the possible interactive effects between the two dimensions of chatbot anthropomorphic cues. Therefore, this paper studies the main effects of internal anthropomorphic constructs (cognitive empathy and emotional empathy) and external anthropomorphic constructs (virtual appearance) of ICSB on user satisfaction, as well as the two-way and three-way interaction effects among them. With a scenario-based survey of 331 respondents, the results confirm the hypotheses about the main effects, the two-way and three-way interaction effects in the model.

Production analytics for renewable energy portfolio management: A comparative study of active and passive strategies in digital twin

Developed societies are facing a number of problems due to technological advances, including the increased requirement for power, resulting in small electrical grids utilizing local power sources, also known as a microgrid (MG). The MG is linked to the central electrical network or runs alone in the form of a power island based on the renewable energy sources and dispatchable units. On the other hand, the smart grid concept is built based on the employment of sensors and cloud computing systems to address the problems. It is possible to decrease energy consumption problems by using a smart MG; however, proper implementation is necessary to avoid generating too much information. As a result, the study uses fog computing for energy production and consumption analysis and management by reducing the amount of information passing to the cloud. The proposed model would reduce service response times in an active strategy. To this end, an enhanced particle swarm optimization with Levy Walk is used for solving the optimization problem based on the real-time data receiving from the digital twin of the system. As a result, to evaluate the efficiency and accuracy of the fog in the case, efficiency evaluations based on active and passive strategies are carried out in order to determine whether parameters including the number of residents, the optimization algorithm, moving appliances, and the power resource affect response times and source demands.

Information system prototype for monitoring and content analysis of complaints from smart city residents

One of the current computerization trends is the active development of “smart city” or “Smart city” technologies, in particular, the support of administrative e-services, the development of transport systems, the use of ecologically clean energy sources, the development of ecology, the modernization of the city’s digital equipment, in an improved state. life in the city, completion of all processes of the city infrastructure. All this complex of works is as accessible as possible with the correct use of “Smart city” technology. Smart cities contribute to sustainable development. Smart technologies fully support cities’ growth and increase efficiency to ensure the well-being of citizens and the efficiency of city governance in the years to come. Along with the advantages of Smart city, there are problems that are provided as a result of the wide use of various mobile applications, big data and various technologies. So that the risks do not outweigh the positive benefits, all smart city initiatives must be focused on the needs of consumers. Access to administrative data will increase the involvement of citizens in public and city processes. Opponents of the smart city express their dismay at the possibility of non-compliance with the proper storage of confidential and personal data, ensuring their security. The method of work is the development of an information system of a smart city to increase the speed of response of communal services to various problems in it. Residents of the city should be classified as other extreme situations and actions should be taken to quickly solve this problem. The object of the study is the process of reducing the response time of communal services to various emergency situations in the city. The subject of the study is the analysis of the speed of response to various tests. The practical significance of the obtained results is the development of a software addition to the information system for collecting and analyzing problems in the city.

Anomaly Detection in Microservice-Based Systems

Currently, distributed software systems have evolved at an unprecedented pace. Modern software-quality requirements are high and require significant staff support and effort. This study investigates the use of a supervised machine learning model, a Multi-Layer Perceptron (MLP), for anomaly detection in microservices. The study covers the creation of a microservices infrastructure, the development of a fault injection module that simulates application-level and service-level anomalies, the creation of a system monitoring dataset, and the creation and validation of the MLP model to detect anomalies. The results indicate that the MLP model effectively detects anomalies in both domains with higher accuracy, precision, recovery, and F1 score on the service-level anomaly dataset. The potential for more effective distributed system monitoring and management automation is highlighted in this study by focusing on service-level metrics such as service response times. This study provides valuable information about the effectiveness of supervised machine learning models in detecting anomalies across distributed software systems.

Surgical pit crew: initiative to optimise measurement and accountability for operating room turnover time

Background and objectivesTurnover time (TOT), defined as the time between surgical cases in the same operating room (OR), is often perceived to be lengthy without clear cause. With the aim...

The Relationship between Service Response Time and Waiting Time for Transfer of IGD Patients to Inpatients with Patient Satisfaction

The front line hospital services are the Emergency Room where the services provided are immediate, fast, precise and accurate. The services provided will affect patient satisfaction. One that is closely related to patient satisfaction in the emergency room is service response time and waiting time for patient transfer to inpatient care. This study aims to determine the relationship between service response time and patient transfer time to inpatient care with patient satisfaction at RSUD dr. Abdul Rivai in 2022. The design of this study was an analytic observational Cross Sectional design with a quantitative non-experimental approach to research methods with relationship analysis using the chi-square test. Based on the results of the bivariate test, it was found that P-Value = 0.016 on the relationship between service response time and patient satisfaction, and P-Value = 0.002 on the relationship between transfer time and patient satisfaction. There is a significant relationship between service response time and waiting time for patient transfer to the inpatient room with patient satisfaction.

The impact of dynamic traffic conditions on the sustainability of urban fire service

Frequent fires pose a significant threat to the safety and sustainability of society. The urban fire service is critical for protecting against fire hazards. Traffic congestion increases the response time of fire service seriously, and dynamic traffic conditions can affect the sustainability of urban fire service. In this study, to evaluate the impact of dynamic traffic conditions on fire service comprehensively, we proposed a method for estimating the total effective coverage area of fire service (TECA) based on real-time traffic data extracted from online maps. To validate the proposed method, a fire station (FS-HN) in Hunan Province, China was selected as the study case. In total, 6625 POIs around FS-HN were characterized as the fire service demand points, and 336 estimating scenarios throughout a week were set. By analyzing the extracted 2,226,000 groups of sample data, TECA for FS-HN was estimated as 7.63 km2 with the assumption that the average travel time of fire trucks is 80% of that of ordinary vehicles. Visual results show that traffic congestion and the lack of transportation infrastructure (only one bridge over a river near FS-HN) seriously reduce the service coverage of FS-HN. To improve fire service, it is necessary to improve traffic conditions and build more transportation infrastructures.

The association between paramedic service system hospital offload time and response time.

To address an important care issue in Canada, we tested the association between paramedic system hospital offload and response time, while considering the impact of other system-level factors. Data from Calgary, Alberta (2014-2017), included median offload (exposure) and response (outcome) time aggregated by hour, with covariates paramedic system episodes of care-dispatch and arrival of a response unit-and hospital transport arrivals (collectively called volume), time of day, and season. Analyses used linear regression and modified Poisson models. 301,105 EMS episodes of care over 26,193 1-h periods were included. For any given 1-h period, the median (IQR) across all episodes of care for offload time, response time, episodes of care, and hospital transport arrivals were 55.3 (45.7, 66.3) min, 8.6 (7.6, 9.8) min, 12 (8, 16) episodes, and 8 (5, 10) hospital arrivals, respectively. Multivariable modelling revealed a complex association differing over levels of exposure and covariates, requiring description using "light stress" and "heavy stress" system scenarios. The light scenario was defined as median offload of 30min and volume < 10th percentile (six episodes and four hospital arrivals), in the summer, and the heavy scenario as median offload of 90min and volume > 90th percentile (17 episodes and 13 hospital arrivals), in the winter. An increase is reported in minutes:seconds for median hourly response time between scenarios by time of day: 1:04-4:16 (0000-0559h.), 0:42-2:05 (0600-1159h.), 0:57-3:01 (1200-1759h.), and 0:18-2:21 (1800-2359h.). Increasing offload is associated with increased response time; however the relationship is complex, with a greater impact on response time noted in select situations such as high volume in the winter. These observations illustrate the interdependence of paramedic, ED, and inpatient systems and provide high-yield targets for polices to mitigate the risk to community availability of paramedic resources at times of high offload delay/system stress.

A fault-tolerant adaptive genetic algorithm for service scheduling in internet of vehicles

Over the years, a range of Internet of Vehicles services has emerged, along with improved quality parameters. However, the field still faces several limitations, including resource constraints and the time response requirement. This paper extracts cost, energy, processing power, service management, and resource allocation parameters. Mathematical equations are then defined based on these parameters. To simplify the process complexity and ensure scalability, we propose an algorithm that uses the genetic algorithm for fault and cost management during resource allocation to services. The main concept is to pick resources for services using a genetic algorithm. We discuss the processing and energy costs associated with this function, which is the algorithm’s objective function and is created to optimize cost. Our approach goes beyond the conventional genetic algorithm in two stages. In the first step, services are prioritized, and resources are allocated in accordance with those priorities; in the second step, load balancing in message transmission paths is ensured, and message failures are avoided. The algorithm’s performance is evaluated using various parameters, and it was shown to outperform other metaheuristic algorithms like the classic genetic algorithm, particle swarm, and mathematical models. Different scenarios with various nodes and service variables are defined in various system states, including fault occurrences to various percentages of 10, 20, and 30. To compare methods, we consider different parameters, the most significant being performance success rate. Moreover, the cost optimization has a good convergence after iterations, and the rate of improvement in the big scenario has slowed down after 150 iterations. Besides, it provides acceptable performance in response time for services.

Comparison of out-of-hospital cardiac arrests during the COVID-19 pandemic with those before the pandemic: an updated systematic review and meta-analysis.

The coronavirus disease of 2019 (COVID-19) pandemic, directly and indirectly, affected the emergency medical care system and resulted in worse out-of-hospital cardiac arrest (OHCA) outcomes and epidemiological features compared with those before the pandemic. This review compares the regional and temporal features of OHCA prognosis and epidemiological characteristics. Various databases were searched to compare the OHCA outcomes and epidemiological characteristics during the COVID-19 pandemic with before the pandemic. During the COVID-19 pandemic, survival and favorable neurological outcome rates were significantly lower than before. Survival to hospitalization, return of spontaneous circulation, endotracheal intubation, and use of an automated external defibrillator (AED) decreased significantly, whereas the use of a supraglottic airway device, the incidence of cardiac arrest at home, and response time of emergency medical service (EMS) increased significantly. Bystander CPR, unwitnessed cardiac arrest, EMS transfer time, use of mechanical CPR, and in-hospital target temperature management did not differ significantly. A subgroup analysis of the studies that included only the first wave with those that included the subsequent waves revealed the overall outcomes in which the epidemiological features of OHCA exhibited similar patterns. No significant regional differences between the OHCA survival rates in Asia before and during the pandemic were observed, although other variables varied by region. The COVID-19 pandemic altered the epidemiologic characteristics, survival rates, and neurological prognosis of OHCA patients. Review registration: PROSPERO (CRD42022339435).

APACHE WEB SERVER RESPONSE TIME OPTIMIZATION

This article explores approaches to online optimization of the Apache web server, focusing on the MaxClients parameter (which controls the maximum number of workers). Using both empirical and analytical methods, we show that MaxClients has a concave-upward effect on response time, and hence hill-climbing techniques can be used to determine the optimal value of MaxClients. We investigate two optimizers that use hill climbing—one based on Newton’s method and one based on fuzzy control. A third technique is a heuristic that exploits the relationship between bottleneck utilization and response time minimization. In all cases, online optimization reduces the response time by a factor of 10 or more compared to the static default value. The trade-offs between online schemes are as follows. Newton’s method is well known but does not provide consistent results for highly variable data such as response times. Fuzzy control is more reliable but converges slowly. The heuristic works well in our prototype system, but it can be difficult to generalize because it requires knowledge of resource bottlenecks and the ability to measure their use. This topic is relevant in the context of a rapidly growing number of web applications and high requirements for the performance and efficiency of web servers. This topic explores how to improve the speed and response time of the Apache web server using various techniques and settings, such as optimizing server settings, using caching, data compression, optimizing request routing, and more. The purpose of this research is to improve the performance and response speed of the Apache web server, which can be useful for developers and administrators of web applications and services. The speed and response time of web servers are critical factors in meeting user needs and achieving business goals for web applications and services. Apache is one of the most widely used web servers in the world, so optimizing the response time of the Apache server is an important task for many developers and administrators of web applications and services. This study will examine various approaches and techniques for optimizing the response time of the Apache web server, including configuring server parameters, using caching, data compression, optimizing request routing, and others. The results of the study can be useful for developers and administrators of web applications and services that deal with the use of the Apache web server. Optimizing Apache server response time can significantly improve the performance and efficiency of web applications and services, which in turn can lead to user satisfaction and business goals.

An Intelligent Server load balancing based on Multi-criteria decision-making in SDN

In an environment of rising internet usage, it is difficult to manage network traffic while maintaining a high quality of service. In highly trafficked networks, load balancers are crucial for ensuring the quality of service. Although different approaches to load-balancing have been proposed in traditional networks, some of them require manual reconfiguration of the device to accommodate new services due to a lack of programmability. These problems can be solved through the use of software-defined networks. This research paper presents a dynamic load-balancing algorithm for software-defined networks based on server response time and content mapping. The proposed technique dispatches requests to servers based on real-time server loads. This technique comprises three different modules, such as a request classification module, a server monitoring module, and an optimized dynamic load-balancing module using content-based routing. There are a variety of robust mathematical tools to address complex problems that have multiple objectives. Multi-Criteria Decision-Making is one of them. The performance of the proposed scheme has been validated by applying the Weighted Sum Method of the multi-criteria decision-making technique. The proposed method Server load balancing based on Multi-criteria Decision Making[SDLB-MCDM] is compared with different load-balancing schemes such as round robin, random, load-balancing scheme based on server response time [LBBSRT], and An SDN-aided mechanism for web load- balancing based on server statistics [SD-WLB]. The experimental results of SDLB-MCDM show a significant improvement of 58% when weights are equal and 50% when unequal weights are assigned to various QoS parameters in comparison with the ROUND ROBIN, RANDOM, LBBSRT and SD-WLB techniques.

Effects of urban environmental factors on heat-related emergency medical services (EMS) response time

Due to the time-sensitive nature of heat-related illnesses, disparities in access to heat-related emergency medical services (EMS) services may contribute to urban health disparities. This paper is an empirical study utilizing Austin-Travis County EMS data to estimate the delays in response time due to traffic congestion through spatiotemporal analysis and to conduct the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models to examine the underlying factors affecting delays in peak traffic rush hours. Our results reveal that heat-related EMS is most delayed in the morning and the evening; there are higher clustering patterns of EMS travel time difference in Austin's metropolitan outskirts, notably in the east and west Austin. OLS and GWR analyses suggest that larger EMS counts, longer distances from an EMS station to the scene and from the scene to a hospital, and neighborhoods with a greater black and Hispanic population exacerbate heat-related EMS delays. Road density, average speed limit, and open space growth rate are statistically significant in the OLS model, although GWR findings suggest coefficient signs vary locally, requiring more investigation. Our findings provided additional insights through the spatial patterns of EMS delays to practitioners for their reference to reduce local response times.