Implementation of Web-Based Interactive Interface as Software Execution Environment

Statistical modeling of emergency medical services’ response and rescue times to road traffic crashes in the Kingdom of Saudi Arabia

The use of process data, such as response time (RT) in psychometrics, has generally focused on the relationship between speed and accuracy. The potential relationships between RT and item discrimination remain less explored. In this study, we propose a model for simultaneously estimating the relationships between RT and item discrimination at the person, item, and person-by-item (residual) levels and illustrate our approach through an item-level meta-analysis of 40 empirical data sets comprising 1.84 million item responses. We find no evidence of average differences in item discrimination between items of different time intensity or persons of different average RT, while residual RT strongly and negatively predicts item discrimination (pooled coef. = -.27% per 1% difference in RT, SE = .04, = .17). While heterogeneity is high, we find little evidence of moderation by overall data set characteristics. Flexible generalized additive models show that the relationship between residual RT and item discrimination is generally curvilinear, with discrimination maximized just below average RT and minimized at the extremes. Our results suggest that RT data can provide insights into the measurement properties of educational and psychological assessments, but that the relationships between RT and item discrimination are highly variable.

<p><b>The client-centric multi-cloud has become a popular cloud ecosystem because it allows enterprise users to share the workload across multiple cloud providers to achieve high-quality services with lower operation costs and higher application resilience. From the perspective of application providers, the location of cloud resources for application deployment significantly impacts the deployment cost and performance of applications, e.g., request response time. This gives rise to the problem of location-aware application deployment in multi-cloud to select suitable cloud resources from widely distributed multi-cloud data centers to balance the cost and performance. Existing research works did not pay full attention to the key impact of the location for application deployment. Therefore, it is urgent to study the problem both theoretically and in practice. In this thesis, innovative optimization methods and machine learning techniques are proposed for three common scenarios, namely composite application deployment, application replication and deployment, and elastic application deployment.</b></p> <p>First, this thesis studies the composite application deployment problem with the goal to minimize the average response time of composite applications subject to a budget constraint. We propose a Hybrid Genetic Algorithm (GA)-based approach, i.e., H-GA, for solving the problem with an extremely large search space. H-GA features a newly designed and domain-tailored service clustering algorithm, repair algorithm, solution representation, population initialization, and genetic operators. Experiments show that H-GA can outperform significantly several state-of-the-art approaches, achieving up to about 8% performance improvement in terms of response time, and 100% budget satisfaction in the meantime.</p> <p>Second, this thesis studies the application replication and deployment problem with the goal to minimize the total deployment cost of all application replicas subject to a stringent requirement on average response time. We propose two approaches under different optimization frameworks to solve the problem. With user requests dispatched to the closest application replicas, we develop an approach under a GA framework for Application Replication and Deployment (ARD), i.e., GA-ARD. GAARD features problem-specific solution representation, fitness measurement, and population initialization, which are effective to optimize the deployment of application replicas in multi-cloud. The experiments show that GA-ARD outperforms common application replication and placement strategies in the industry. With user requests flexibly dispatched among different application replicas, we develop another approach under a two-stage optimization framework, i.e., MCApp. MCApp can optimize both replica deployment and request dispatching by combining mixed-integer linear programming with domain-tailored large neighborhood search. Our experiments show that MCApp can achieve up to 25% reduction in total deployment cost compared with several recently developed approaches.</p> <p>Third, this thesis studies the elastic application deployment problem to minimize the deployment cost over a time span such as a billing day while satisfying the constraint on average response time. The goal of adapting resources for application deployment in response to dynamic and distributed workloads motivates us to adopt deep reinforcement learning (DRL) techniques. The proposed approach, namely DeepScale, applies a deep Q-network (DQN) to capture the optimal scaling policy that can perform online resource scaling. DeepScale also includes a long short-term memory-based prediction model to allow the DQN to consider predicted future requests while making cost-effective scaling decisions. Besides, we design a penalty-based reward function and a safety-aware action executor to ensure that any scaling decisions made by DRL can satisfy the response time constraint. The experiments show that DeepScale can significantly reduce the deployment cost of applications compared with the state-of-the-art baselines, including Amazon auto-scaling service and recently proposed RL-based algorithms. In the meanwhile, DeepScale can effectively satisfy the constraint on the average response time.</p> <p>In summary, this thesis studies three new problems for location-aware application deployment in multi-cloud. We propose four novel approaches under different optimization and machine learning frameworks, i.e., HGA, GA-ARD, MCApp, and DeepScale, for solving these problems. New constraint handling techniques are developed to satisfy the practical deployment requirements of enterprise applications.</p>

The problems of security computing are becoming increasingly relevant in connection with the integration of systems into a unified global Internet. Elbrus secure computing technology allows protecting the system from the internal program errors as well as external threats. The article describes the hardware and software components of a secure execution environment for critical applications in embedded systems based on computing means of the Elbrus family. The authors describe the basis of the Elbrus secure computing concept - the idea of a descriptor and external tags. They consider the revision of the main parts of the C language library and the kernel of the Linux operating system necessary for the implementation of this environment. The study investigates the issue of passing parameters to system calls from applications running in a secure environment. For such applications, the authors consider the process of dynamic binding of the modules loaded into memory. The method for detecting cases of the use of hung links due to the use of descriptors and external tags is presented in relation to the mechanism of orders and the release of dynamic memory. The authors suggest the container virtualization technology to detect memory leaks in a safe execution environment.

With the surge in data volume and dimensions, existing decision-making systems are difficult to capture nonlinear relationships and cope with the complexity and diversity of data, which leads to decision lag. To solve this problem, this article studies an intelligent decision-making system. The system integrates multi-layer feedforward neural networks and decision tree algorithms, and designs a distributed computing architecture based on the Apache Spark platform. Through the integration of the big data platform, the system integrates and processes multiple data sources; then, a three-layer feedforward neural network model is adopted, and a specific mechanism is used to prevent overfitting. After that, the decision tree algorithm is used to deeply explore the complex relationships in the data; Apache Spark is applied to accelerate the calculation process and efficiently query and analyze the stored data. The system also supports online learning strategies to continuously update the model to adapt to new data changes. The research results show that in the field of financial risk analysis, the average accuracy of the intelligent decision-making system in this article is 92.29 %, and the average response time is only 0.013 seconds; the average accuracy and response time of the traditional system are 82.54 % and 0.034 seconds respectively; the system in this article is significantly better than the traditional system. According to the study, an intelligent decision-making system not only increases decision-making's efficiency and accuracy but also offers solid base for more intelligent decisions in the future.

The detection of early-warning signals of performance deterioration can help technical support teams in taking swift remedial actions, thus ensuring rigor in production support operations of large scale software systems. Performance anomalies or deterioration, if left unattended, often result in system slowness and unavailability. In this paper, we presents a simple, intuitive and low-overhead technique for recognizing the early warning signs in near real time before they impact the system The technique is based on the inverse relationship which exists between throughput and average response time in a closed system. Because of this relationship, a significant increase in the average system response time causes an abrupt fall in system throughput. To identify such occurrences automatically, Individuals and Moving Range (XmR) control charts are used. We also provide a case study from a real-world production system, in which the technique has been successfully used. The use of this technique has reduced the occurrence of performance related incidents significantly in our daily operations. The technique is tool agnostic and can also be easily implemented in popular system monitoring tools by building custom extensions.

Effects of the whole-body vibration direction on the cognitive response of standing subjects

Response time is one of the important indicators in fire operations. It is crucial to have a continuous assessment of response time to monitor the performance level of the firefighter. This paper conducted the initial assessment of fire response time on selected fire stations from categories A, B, C and D throughout Malaysia from 2018 until 2020. A 10 minutes standard response time was used as a benchmark to evaluate the fire stations performance. Most of the fire stations show positive development in terms of average response time within the three years. The implementation of movement control order in 2020 has significantly reduced traffic congestion hence improving the average response time. Category C and D fire stations show a high average distance travelled and response time due to the local topographic. Constant development in these semi-rural areas might require a new risk profiling to minimise the fire risk. The new addition of fire stations in these areas will cater for the demand from the community.

Emergency medical service rescue times in Riyadh

This exploratory study used archived hospital data to determine whether the call light use rate and the average call light response time contribute to the fall and the injurious fall rates in acute care settings. Inpatients often use call lights to seek nurses' attention and assistance. Although implied in patient safety, no studies have examined data related to the call light use or the response time to call lights collected via existing tracking mechanisms to monitor nursing practice. The study was conducted in a Michigan community hospital and used archived hospital data for analyses for the period from February 2007-June 2008. The unit of analysis was unit-week. The call light use rate per patient-day was calculated based on information retrieved from the call light tracking system. The average response time in seconds was used as generated from the tracking system. The fall and injurious fall rates per 1000 patient-days were calculated based on the fall incident reports. SPSS was used for data analyses. One-way ANOVA and correlation analyses were conducted. More calls for assistance related to less fall-related patient harm. Surprisingly, longer response time to call lights also related to fewer total falls and less fall-related patient harm. Generally speaking, more call light use related to longer response times. This study's findings challenged the appropriateness of targeting the goals of reducing the frequency of call light use and the fall rates as two outcome indicators of conducting hourly patient rounds. Encouraging call light use is a key to reducing injurious fall rates. Unit managers should routinely monitor the trend of the call light use rate and ensure that the call light use rate is maintained at least above the mean rate.

With the rapid development of artificial intelligence technology and big data analysis technology, the use of online learning has become more technical premises. It has the advantages of convenient data update, simple real-time communication, and high stability and flexibility. These have laid a good technical cornerstone for the development of online learning platforms. In order to solve the problems such as the lack of teaching materials for physical gymnastics in colleges and the lack of technical support for teaching, this study aims to study the design of college sports gymnastics online learning system based on BP neural network. On the basis of analyzing the introduction of BP neural network, the status quo of gymnastics teaching in Chinese colleges and universities and the principles of system design and adopting the principles of reliability, stability, and maintainability as the method of system design, the gymnastics online learning system based on BP neural network is carried out. Finally, the performance of the constructed system is tested. The test results show that, as the number of concurrent users continues to increase, the response time is gradually increasing. When the number of concurrent users is 3000, the maximum response time is 1.832 s, the minimum response time is 1.473 s, and the average response time is 1.512 s, which can meet the needs of users when the amount of concurrency is large.

Forty subjects responded to a set of 64 different combinations of linear displays and rotary controls presented by photographic slides. The subject's task was to rotate a control to increase the numerical value on the display. It was expected that response time for an arrangement having a strong stereotype would be faster than one with a weaker stereotype. Data showed there a strong relationship between these two measures of compatibility for horizontal displays with controls either on the top or bottom of the display; there was no significant relationship for any of the vertical layouts. Comparing horizontal and vertical displays, the average response times were 1.25 and 1.55 seconds and average stereotype strengths were .86 and .73, respectively. Thus on both criteria, horizontal displays were superior to vertical displays. Response time was found to be dependent on the magnitude of the component principle making the greatest contribution to the strength of the overall stereotype. In the case of horizontal displays this was the clockwise-to-right principle; for vertical displays it was Warrick's principle or, if this was not applicable, the scale-side principle.

This study compared the response time, response rate, and cost of two types of survey administration techniques: e-mail/web-based versus conventional postal mail. The main aim of the survey was to collect descriptive information on the existence of Acute Care for Elders units and their characteristics by surveying geriatric division chiefs. Two randomized cohorts of geriatric division chiefs were formed to receive a survey either by electronic mail (n = 57) or by conventional postal mail (n = 57). If there was no response to the initial mailing, two follow-up mailings were sent to both groups using the original modality; a third follow-up was performed using the alternative modality. For each group, response rate and response time were calculated. The average total cost was computed and compared across two groups. The aggregate response rate was 58% (n = 31) for the e-mail group versus 77% (n = 44) for the postal mail group. The overall average response time was shorter in the e-mail group, 18 days compared with 33 days for the conventional postal mailing group. The cost comparison showed that average cost was $7.70 for the e-mail group, compared to $10.50 per response for the conventional mail group. It appears that although the web-based technology is gaining popularity and leads to lower cost per response, the conventional postal method of surveying continues to deliver a better response rate among the geriatric medicine division chiefs. The web-based approach holds promise given its lower costs and acceptable response rate combined with the shorter response time.

BackgroundIn collaboration with the Mobile Monitoring of Cognitive Change (M2C2; 1‐U2C‐AG060408‐01) Project, our team developed a tablet‐based electronic Stroop (eStroop) task. Initial development explored direct translation of the paper‐Stroop (pStroop) often used in clinical settings, and found that this resulted in ceiling effects. Herein we report on initial validation of the eStroop task from the iPACES v3 clinical trial.MethodInitial validation examined eStroop data (n = 59 sessions) serially collected from ten MCI and caregiver participants in the iPACES v3 clinical trial (mean age = 70.2, mean years of education = 16.9). Included in analyses were eStroop data for both traditional metrics (i.e., overall accuracy by block condition), response time, as well as typically constructed difference/ratio scores (i.e., AC ratio). After a baseline tele‐neuropsychological evaluation in which paper Stroop was administered, at predetermined study intervals (typically weekly), participants were automatically prompted to complete the eStroop on their iPACES Android tablet. In the eStroop, participants were presented with four blocks of 40 trials: (1) color words in black font, (2) congruent word and font color; (3) incongruent words with font color; (4) mixed congruent and incongruent trials.ResultThe paper Stroop AC Ratio (total seconds to complete paper A, divided by time to complete paper C), was moderately negatively correlated (r = ‐0.52, p < .001) with average response time on block three of eStroop (incongruent words with font color). The pStroop AC Ratio was also significantly negatively correlated with number of errors on block one (r = ‐0.42, p < .001), two (r = ‐0.26, p = 0.04), three (r = ‐0.39, p = 0.002), and four (r = ‐0.26, p = .049). A weak, significant positive correlation (r = 0.44, p < .001) was observed between the AC ratio from pStroop and eStroop. Very weak non‐significant correlations between paper Stroop AC Ratio and average response time on blocks one, two, and four of eStroop were observed.ConclusionThese pilot data provide preliminary evidence that the eStroop can serve as a remotely and self‐administered digital test for serial assessment of executive function and will be further evaluated in our current iPACES study.

A three-dimnensional vision system for on-line operation that aids a collision avoidance system for an industrial robot is developed. Because of the real-time requirement, the process that locates and describes the obstacles must be fast. To satisfy the safety requirement, the obstacle model should always contain the physical obstacle entirely. This condition leads to the bounding box description of the obstacle, which is simple for the computer to process. The image processing is performed by a Machine Intelligence Corporation VS-100 machine vision system. The control and object perception is performed by the developed software on a host Digital Equipment Corporation VAX 11/780 Computer. The resultant system outputs a file of the locations and bounding descriptions for each object found. When the system is properly calibrated, the bounding descriptions always completely envelop the obstacle. The response time is data-dependent. When using two cameras and processed on UNIX time sharing mode, the average response time will be less than 2 s if eight or fewer objects are present. When using all three cameras, the average response time will be less than 4 s if eight or fewer objects are present.