Response Time Analysis Research Articles

The effect of ambulance response time on patient and transfer to hospital

This study investigates the critical relationship between ambulance response time and its impact on patient outcomes during the transfer to the hospital. Timely and efficient ambulance responses are fundamental in emergency medical services, significantly influencing patient morbidity and mortality rates. The research employs a retrospective analysis of ambulance response times and patient transfer data collected over a specific period. Key variables include response time intervals, patient demographics, medical conditions, and outcomes during hospital transfer. Statistical analyses, including regression modeling, are utilized to assess the correlation between response times and patient outcomes, considering potential confounding factors. Preliminary findings indicate a noteworthy association between extended ambulance response times and adverse patient outcomes, emphasizing the urgency of swift emergency medical interventions. This research contributes valuable insights to emergency medical service providers, policymakers, and healthcare professionals, aiming to enhance pre-hospital care strategies and improve overall patient well-being. The implications of these findings extend beyond statistical correlations, emphasizing the imperative need for efficient emergency response systems to optimize patient care and outcomes. Keywords: Ambulance response time, Patient care, Emergency medical service, Hospital.

Reform and Innovation of Dance Education in Colleges and Universities in the Internet Era

Abstract This paper firstly proposes the reform strategy and innovation direction of college dance education based on the Internet background of the era and adopts Webwork+Spring+Hibernate to build the overall architecture of the dance education system and designs the functional modules of the dance education system. Then, establish the dance teaching model based on association rules, use the association rule data mining algorithm to mine specific laws to improve the prediction model, and measure the standard of association rules by T test. Finally, the dance education system is tested for operation, compatibility, response time, and application analysis. The results show that the average score of students’ final exam before applying the dance teaching system is 75.23, and the average score of students’ final exam after applying it is 86.12, which is an improvement of 10.89 points, which shows that the dance education system constructed in this paper is able to improve the student’s academic performance, and promote the reform of dance education in colleges and universities effectively, and provide a new direction for the development of dance education in colleges and universities.

What Can We Learn from a Semiparametric Factor Analysis of Item Responses and Response Time? An Illustration with the PISA 2015 Data.

It is widely believed that a joint factor analysis of item responses and response time (RT) may yield more precise ability scores that are conventionally predicted from responses only. For this purpose, a simple-structure factor model is often preferred as it only requires specifying an additional measurement model for item-level RT while leaving the original item response theory (IRT) model for responses intact. The added speed factor indicated by item-level RT correlates with the ability factor in the IRT model, allowing RT data to carry additional information about respondents' ability. However, parametric simple-structure factor models are often restrictive and fit poorly to empirical data, which prompts under-confidence in the suitablity of a simple factor structure. In the present paper, we analyze the 2015 Programme for International Student Assessment mathematics data using a semiparametric simple-structure model. We conclude that a simple factor structure attains a decent fit after further parametric assumptions in the measurement model are sufficiently relaxed. Furthermore, our semiparametric model implies that the association between latent ability and speed/slowness is strong in the population, but the form of association is nonlinear. It follows that scoring based on the fitted model can substantially improve the precision of ability scores.

Working memory and attention in choice.

We study the role of attention and working memory in choices where options are presented sequentially rather than simultaneously. We build a model where a costly attention effort is chosen, which can vary over time. Evidence is accumulated proportionally to this effort and the utility of the reward. Crucially, the evidence accumulated decays over time. Optimal attention allocation maximizes expected utility from final choice; the optimal solution takes the decay into account, so attention is preferentially devoted to later times; but convexity of the flow attention cost prevents it from being concentrated near the end. We test this model with a choice experiment where participants observe sequentially two options. In our data the option presented first is, everything else being equal, significantly less likely to be chosen. This recency effect has a natural explanation with appropriate parameter values in our model of leaky evidence accumulation, where the decline is stronger for the option observed first. Analysis of choice, response time and brain imaging data provide support for the model. Working memory plays an essential role. The recency bias is stronger for participants with weaker performance in working memory tasks. Also activity in parietal areas, coding the stored value in working, declines over time as predicted.

Workload-Aware Scheduling of Multiple- Criticality Real-Time Applications in Vehicular Edge Computing System

In this article, we study the problem of designing an adaptive scheduling scheme for dynamic multiple-criticality real-time applications in vehicular edge computing systems. This scheduling problem is formulated as a mixed-integer nonlinear problem. We propose a workload-aware scheduling approach that not only guarantees the applicaitons' mixed-criticality schedulability but also adaptively manages their execution depending on their released frequencies at runtime. In particular, we first present the response time analysis for multiple-criticality applications in the edge computing system with different computing capability servers. Then, we derive a state-transition equation that makes the dynamic programming applicable to building an excellent schedule for one specific criticality-level mode. Such an approach is extended to the system's different criticality-level modes. For the purpose of increasing the quality-of-service toward low-critical applications, we leave low-critical applications to execute as much as possible at runtime, depending on their predicted frequencies. Extensive experimental results show the superiority of our proposed approaches in terms of improving the schedulability success rate and reducing the number of suspended applications online

Improving Worst-case TSN Communication Times of Large Sensor Data Samples by Exploiting Synchronization

Higher levels of automated driving also require a more sophisticated environmental perception. Therefore, an increasing number of sensors transmit their data samples as frame bursts to other applications for further processing. As a vehicle has to react to its environment in time, such data is subject to safety-critical latency constraints. To keep up with the resulting data rates, there is an ongoing transition to a Time-Sensitive Networking (TSN)-based communication backbone. However, the use of TSN-related industry standards does not match the automotive requirements of large timely sensor data transmission, nor it offers benefits on time-critical transmissions of single control data packets. By using the full data rate of prioritized IEEE 802.1Q Ethernet, giving time guarantees on large data samples is possible, but with strongly degraded results due to data collision. Resolving such collisions with time-aware shaping comes with significant overhead. Hence, rather than optimizing the parameters of the existing protocol, we propose a system design that synchronizes the transmission times of sensor data samples. This limits network protocol complexity and hardware requirements by avoiding tight time synchronization and time-aware shaping. We demonstrate that individual sensor data samples are transmitted without significant interference, exclusively at full Ethernet data rate. We provide a synchronous event model together with a straightforward response time analysis for synchronous multi-frame sample transmissions. The results show that worst-case latencies of such sample communication, in contrast to non-synchronized approaches, are close to their theoretical minimum as well as to simulative results while keeping the overall network utilization high.

Chronotype and Time of Day Effects on a Famous Face Recognition Task with Dynamic Stimuli.

Chronotype and Time of Day (ToD) can modulate several aspects of cognitive performance. However, there is limited evidence about the effect of these variables on face recognition performance, so the aim of the present study is to investigate this influence. For this, 274 participants (82.5% females; age 18-49 years old, mean = 27.2, SD = 1.82) were shown 20 short videoclips, each gradually morphing from a general identity unfamiliar face to a famous face. Participants should press the spacebar to stop each video as soon as they could identify the famous face, and then provide the name or an unequivocal description of the person. Analysis of response times (RT) showed that evening-types recognised the faces faster than morning-types. Considering different ToD windows, the effect of chronotype was only significant in the 13h-17h and in the 21h-6h time-windows. Altogether, results suggest an advantage of evening-types on famous face recognition using dynamic stimuli with morning-types, being particularly slower during their non-optimal period.

CANASTA: Controller Area Network Authentication Schedulability Timing Analysis

The Controller Area Network (CAN) dominates in-vehicle networking systems in modern vehicles. CAN was designed with low-latency and reliability as key features. Authenticity of a CAN frame was not considered in the design, thus, most in-vehicle network nodes inherently trust received messages as coming from a legitimate source. As a result, it is trivial to program (or hack) a network node to spoof traffic. Authentication is challenging for CAN and related protocols, such as SAE J1939, due to limited frame sizes and high bus utilization. Adding a message authentication code (MAC) as a separate message can unduly stress the real-time delivery of safety-critical messages. Although this stressor is well-known, the impact of authentication protocols on real-time message delivery in CAN has not yet been thoroughly examined. In this paper, we provide the first comprehensive analysis of realtime schedulability analysis applied to authentication schemes for CAN, CAN Flexible Data-rate (CAN FD), and CAN extra long payload (CAN XL). We formulate the response time analysis for addition of MACs and periodic transmission of MACs, and we examine their impact on two case studies and through evaluation with randomized schedulability experiments over a wide range of message sets.

Server load estimation by Burr distribution mixture analysis of TCP SYN response time

Server load estimation is key in balancing traffic between servers when optimizing data center resources. Intrusive methods are sometimes difficult or impossible to implement. Therefore, non-intrusive estimation methods are the best alternative in these cases. The objective of this paper is to present a server load estimation method based on external network traffic measurements obtained in a vantage point close to the server. Statistical distributions of TCP SYN response time, that is, the time from SYN to SYN+ACK segments at the server side, are used to fit Burr Type XII heavy tail distribution mixtures. The fitting algorithm, based on maximum likelihood estimation, is developed in detail in this paper. Experimental data shows that the median of the fitted distribution correlates within the 95% confidence interval of the server load figures and, thus, it can be used as a non-intrusive and accurate method to measure it. This new method can be applied to almost any existing load balancing algorithm, as it does not make any assumption about the server, which is considered a black box.

Feasibility investigation of non-equilibrium condensing nitrogen droplets as tracer particles in cryogenic wind tunnels

Application of high-precision non-intrusive flow field measurement techniques in transonic cryogenic wind tunnels (CWT) is confined to the lack of suitable tracer particles. The present study explored the feasibility of utilizing non-equilibrium condensing nitrogen droplets as tracer particles. A numerical model of the nitrogen non-equilibrium condensation was established. Accordingly, the non-equilibrium condensation process of nitrogen droplets and feasibility analysis of tracing were investigated. The results reveal that the occurrence of nitrogen droplets as well as the droplet concentration and mean radius can be regulated in the nucleation zone and droplet growth zone by varying gas temperature or pressure. Furthermore, the feasibility of employing nitrogen droplets as tracer particles in the working conditions of CWT is confirmed through the analysis of tracking capability, response time, concentration, and duration of droplet traceability. Formulas correlating used to calculate duration of droplet traceability have been proposed. This study proved the potential application of condensing nitrogen droplets as tracer particles in CWT.

Processing of attentional and emotional stimuli depends on retrospective response of foot pressure: Conceptualizing neuron-cognitive distribution in human brain

Cognitive function of human brain requires temporal execution of emotional or attentional tasks, or their inter-dependence influences. Smooth execution of such tasks requires spontaneous distribution of cognitive load at specific regions of brain based on its classification. A strong connectivity between peripheral sensors and central nervous system is thought to assist the cognitive load distribution effectively. Novelty of current study evaluates the modulation of foot pressure and its mapping with distributed cognitive load while executing attentional biased emotional tasks. Emotional stimulus in form of happy and sad faces with attentional paradigm drawn on them were used in the study. Behavioral results were measured with respect to the analysis of response time (RT) and response accuracy (%). Neurological signals were acquired using 10-channel EEG data acquisition system, whereas, another 6 channels were used to measure foot pressure in the left and right feet at three different locations of foot. Acquired signals were further analyzed in time and frequency domains to interpret the cognitive load distribution, and the influence of foot pressure on distribution of cognitive loads. We found that the foot pressure accelerated the response accuracy rate in attending the local scope of attention, which was not in the case of global scope of attention. This means that the global attention does not require any pressure from peripheral sensory neurons. Our event related potential (ERP) results revealed that the early sensory negative N100 characterized the processing of global scope of attention coupled with high-foot pressure. However, the late positive peak of P300 and P600 associated with local scope of attention along with high-foot pressure. The global scope of attention with low-foot pressure modulates delta and theta oscillations. These results largely contribute to the literature on cognitive neuroscience of attention and it corelation with the peripheral sensory foot pressure.

Identifying Problem-Solving Solution Patterns Using Network Analysis of Operation Sequences and Response Times

ABSTRACT Process data from educational assessments enhance the understanding of how students answer cognitive items. However, effectively making use of these data is challenging. We propose an approach to identify solution patterns from operation sequences and response times by generating networks from process data and defining network features that extract essential information from them. With these features, we group respondents to a problem-solving task from PISA 2012 using Gaussian mixture models. The results indicate the presence of two and four clusters for groups defined by failure and success on the task, respectively. We interpret the clusters as less-able, low-effort, adaptable, back-and-forth, deliberate, and trial-and-error clusters by considering the cluster-specific feature statistics. The proposed approach sheds light on students’ problem-solving mental processes, which can aid item development and facilitate individualized feedback to students. The method is applicable to many computer-based problems, but a limitation is that the feature definitions can be task-dependent.

Partial-order reduction in reachability-based response-time analyses of limited-preemptive DAG tasks

Response-time analysis (RTA) has been a means to evaluate the temporal correctness of real-time systems since the 1970 s. While early analyses were successful in capturing the exact upper bound on the worst-case response-time (WCRT) of systems with relatively simple computing platforms and task activation models, nowadays we see that most existing RTAs either become pessimistic or do not scale well as systems become more complex (e.g., parallel tasks running on a multicore platform). To make a trade-off between accuracy and scalability, recently, a new reachability-based RTA, called schedule-abstraction graph (SAG), has been proposed. The analysis is at least three orders of magnitude faster than other exact RTAs based on UPPAAL. However, it still has a fundamental limitation in scalability as it suffers from state-space explosion when there are large uncertainties in the timing parameters of the input jobs (e.g., large release jitters or execution-time variations). This could impede its applicability to large industrial use cases, or to be integrated with automated tools that explore alternative design choices. In this paper, we improve the scalability of the SAG analysis by introducing partial-order reduction rules that avoid combinatorial exploration of all possible scheduling decisions. We include systems with dependent and independent task execution models (i.e., with and without precedence constraint). Our empirical evaluations show that the proposed solution is able to reduce the runtime by five orders of magnitude and the number of explored states by 98% in comparison to the original SAG analysis. These achievements come only at a negligible cost of an over-estimation of 0.1% on the actual WCRT. We applied our solution on an automotive case study showing that it is able to scale to realistic systems made of hundreds of tasks for which the original analysis fails to finish.

Analyses of response time data in the same–different task.

The Same-Different task presents two stimuli in close succession and participants must indicate whether they are completely identical or if there are any attributes that differ. While the task is simple, its results have proven difficult to explain. Notably, response times are characterized by a fast-same effect whereby Same responses are faster than Different responses even though identical stimuli should be exhaustively processed to be accurate. Herein, we examine a little more than a quarter million response times (N = 255,744) obtained from 327 participants who participated in one of 14 variants of the task involving minor changes in the stimuli or their durations. We performed distribution fitting and analyzed estimated parameters stemming from the ex-Gaussian, lognormal, and Weibull distributions to infer the cognitive processing characteristics underlying this task. The results exclude serial processing of the stimuli and do not support dual-route processing. The fast-same effect appears only through a shift of the entire response time distributions, a feature impossible to detect solely with mean response time analyses. An attention-modulated process driven by entropy may be the most adequate model of the fast-same effect. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

In-Vehicle Network Average Response Time Analysis for CAN-FD and Automotive Ethernet

In-vehicle network (IVN) technologies are changing corresponding to the rapidly increasing demand in autonomous driving and vehicle electrification. Due to the increase in real-time data generated from the control modules and sensors, controller area networks (CANs) have a heavier burden to satisfy the strict delay bounds. Therefore, CAN with flexible data rate (CAN-FD) and Automotive Ethernet (AE) have drawn attention as promising IVNs. This paper provides an overview of the IVN transmission protocols, time delay characteristics, and analysis methods. Most of the existing IVN timing analysis studies assume the worst case and consider only the occurrence of periodic or aperiodic messages, however for more accurate control, the average time delay needs to be accurately estimated. In this paper, CAN, CAN-FD, and AE analytical models based on queueing analysis are proposed to estimate the average response time for mixed traffic. For CAN and CAN-FD systems, the network synchronization statuses are considered and AE with 1-level preemption to support deterministic delays is analyzed. The improved accuracy of the newly derived analytical models is verified based on comparison with existing CAN queueing models through simulation experiments.

Sensory substitution can improve decision-making

New technologies are often considered direct competitors to humans in the realm of decision-making. This paper explores a novel approach to augmenting human decision-making through technology. Specifically, drawing on the brain's unique ability to learn from sensory experiences, we introduce sensory substitution, the encoding of information in an alternative sensory modality, as a method to improve decision-making. In a within-subject design (N = 48), we show that translating numerical information into sensory experiences (i.e., tactile stimulation administered to a person's body) results in higher decision accuracy in a multiple-cue learning task. Response time analyses, participants' self-reports, and cognitive modeling all suggest that the benefits afforded by sensory substitution are the result of a shift from explicit rule abstraction to configural learning. That is, rather than deliberately inferring decision rules, participants develop intuitive, perceptual strategies to accurately predict outcomes. Together, our findings suggest that sensory substitution could enhance decision-making by training “gut instincts” rather than deliberate decision-making skills.

Lazy Load Scheduling for Mixed-criticality Applications in Heterogeneous MPSoCs

Newly emerging multiprocessor system-on-a-chip (MPSoC) platforms provide hard processing cores with programmable logic (PL) for high-performance computing applications. In this article, we take a deep look into these commercially available heterogeneous platforms and show how to design mixed-criticality applications such that different processing components can be isolated to avoid contention on the shared resources such as last-level cache and main memory. Our approach involves software/hardware co-design to achieve isolation between the different criticality domains. At the hardware level, we use a scratchpad memory (SPM) with dedicated interfaces inside the PL to avoid conflicts in the main memory. At the software level, we employ a hypervisor to support cache-coloring such that conflicts at the shared L2 cache can be avoided. In order to move the tasks in/out of the SPM memory, we rely on a DMA engine and propose a new CPU-DMA co-scheduling policy, called Lazy Load , for which we also derive the response time analysis. The results of a case study on image processing demonstrate that the contention on the shared memory subsystem can be avoided when running with our proposed architecture. Moreover, comprehensive schedulability evaluations show that the newly proposed Lazy Load policy outperforms the existing CPU-DMA scheduling approaches and is effective in mitigating the main memory interference in our proposed architecture.

Productive Asset Management Methodology Technique in Virtual Machines to Increase CPU Utilization

One of the technologies with the quickest growth is cloud computing, which is utilized in many applications where customers can access virtual machines (VMs) that are offered by cloud service providers in data centers. The ideal distribution should meet both the needs of users and service providers. A2OA, an adaptive Archimedes optimization algorithm, is the suggested mechanism for allocating resources in cloud computing. The optimization problem will be solved and user tasks will be distributed using this adaptive approach. The adaptive approach will combine the Seagull Optimization Algorithm with the Archimedes Optimization Algorithm (AOA) (SOA). The update procedure in the AOA will be improved. in cooperation with the SOA. The tasks will be distributed to the user optimally based on the proposed A2OA. In MATLAB, the suggested methodology will be put into practice, and results will be examined. Performance metrics such make span, load standard deviation, load ratio, user provider satisfaction level, response time, and convergence analysis will be used to assess how well the suggested methodology performs. The suggested approach will be contrasted with the traditional approaches, including the Genetic Algorithm (GA), Particle Swarm Algorithm (PSO), and Whale Optimization Algorithm (WOA), respectively. Key Word: Virtual machines, adaptive Archimedes optimization algorithm (A2OA), Seagull Optimization Algorithm (SOA), Genetic Algorithm (GA), maximum power, load ratio

Sulfonamide functionalized silica nano-composite: characterization and fluorescence "turn-on" detection of Fe3+ ions in aqueous samples.

We have synthesized novel sulfonamide-based nano-composite (SAN) for selective and sensitive detection of Fe3+ ions in aqueous samples. Morphological characterization of SAN was carried out with TGA, FT-IR, UV-Vis, ninhydrin assay, FE-SEM, pXRD, BET, EDX, and elemental analysis. The sensing nature, effect of pH, sensor concentration and response time analysis were accomplished with the help of emission spectral studies and SAN was assessed as "turn-on" emission detector for the biologically important Fe3+ ions. Here, the LOD and LOQ were computed to be 26.68nM and 88.93nM, respectively, and it was found to be much lower than the permissible limit of Fe3+ ions in drinking water. The accuracy of the sensor (SAN) was determined by testing the aqueous samples spiked with known concentrations of Fe3+ ions and results demonstrated 98.00-99.66% recovery, which made SAN a reliable, selective and sensitive chemosensor for the quantification of Fe3+ ions in fully aqueous media.

Analyzing the influence of changes in land use and management practices on the lag time of peak flows for tropical watersheds of Ethiopia

AbstractChanges in peak flow response time governed by rainfall and physical configurations of watersheds have been the topic of many studies, but other factors are also important. We aimed to analyze the dynamics in the lag time (TL) of peak flow for a tropical watershed (Kecha) and its sub‐watersheds (Dokmit, Zenjero Maderia, and Wotit Minch) in Ethiopia, as influenced by land‐use changes from 1982 to 2017 and the implementation of soil and water conservation (SWC) practices since 2011. Layered maps of land use, soil type, and SWC practices were used to determine the curve number, and the Natural Resource Conservation Service hydrologic model was used to estimate TL. We compared the estimated values against measured median values of TL for 30 rainfall–runoff events. The estimated TL of 1982, 2005, and 2017 varied from 9 to 19 min, 8 to 18 min, and 10 to 22 min, respectively, in the Dokmit, Zenjero Maderia, and Wotit Minch sub‐watersheds. The smallest (8 min) and greatest (22 min) values of TL were observed during 2005 and 2017, respectively. These results are likely attributable to the increased amount of cultivated area at the expense of bushland and forest in 2005 as compared to 1982, and the implementation of SWC practices in 2011–2017. Dokmit had the lowest TL values of the three sub‐watersheds, most likely because of its greater coverage of grazing and cultivated lands and degraded Nitic Luvisol. The variation in TL values among the sub‐watersheds was related to changes in both land use and SWC practices but also to the presence of degraded areas inherited from past human activities. Overall, such spatiotemporal flow response time analysis can provide useful information for the proper design of sustainable development strategies for particular niches in tropical regions of Ethiopia and elsewhere.