Explore the vast range of titles available.

Diffusion models for simple two-choice decision making have achieved prominence in psychology and neuroscience. The standard model views decision making as a process in which noisy evidence is accumulated until one of the two response criteria is reached, at which point the associated response is made. The criteria represent the amount of evidence needed to make a decision, and they reflect the decision maker's response biases and speed-accuracy trade-off settings. In this article, we review the regularities in experimental data that a model must explain. These include the relation between accuracy and mean response times, the shapes of response-time distributions for correct and error responses and how they change with experimental variables, and individual differences in response time and accuracy. These relations are sometimes overlooked by researchers, but, taken together, they provide extremely strong tests of models.

Evidence accumulations models (EAMs) have become the dominant modeling framework within rapid decision-making, using choice response time distributions to make inferences about the underlying decision process. These models are often applied to empirical data as “measurement tools”, with different theoretical accounts being contrasted within the framework of the model. Some method is then needed to decide between these competing theoretical accounts, as only assessing the models on their ability to fit trends in the empirical data ignores model flexibility, and therefore, creates a bias towards more flexible models. However, there is no objectively optimal method to select between models, with methods varying in both their computational tractability and theoretical basis. I provide a systematic comparison between nine different model selection methods using a popular EAM—the linear ballistic accumulator (LBA; Brown & Heathcote, Cognitive Psychology 57 (3), 153–178 2008 )—in a large-scale simulation study and the empirical data of Dutilh et al. (Psychonomic Bulletin and Review, 1–19 2018 ). I find that the “predictive accuracy” class of methods (i.e., the Akaike Information Criterion [AIC], the Deviance Information Criterion [DIC], and the Widely Applicable Information Criterion [WAIC]) make different inferences to the “Bayes factor” class of methods (i.e., the Bayesian Information Criterion [BIC], and Bayes factors) in many, but not all, instances, and that the simpler methods (i.e., AIC and BIC) make inferences that are highly consistent with their more complex counterparts. These findings suggest that researchers should be able to use simpler “parameter counting” methods when applying the LBA and be confident in their inferences, but that researchers need to carefully consider and justify the general class of model selection method that they use, as different classes of methods often result in different inferences.

Air temperature measurements in naturally ventilated thermometer screens underpin the instrumental climate record. Increasing automation is, however, revealing limitations. One is through thermometer time response, especially in light winds or calm conditions, often at the daily temperature minimum. The exponential time response τ63 ${\\tau }_{63}$ for thermometers enclosed within a Stevenson screen is a key parameter, but poorly known. Here, τ63 ${\\tau }_{63}$ is evaluated in a practical experimental situation against the World Meteorological Organization (WMO)'s recommended sensor τ63≤20 ${\\tau }_{63}\\le 20$ s. We find τ63 ${\\tau }_{63}$ increases with sensor diameter d $d$, with only a d $d$ = 2 mm sensor meeting WMO expectations, even then requiring ambient wind speeds ≥3ms−1 ${\\ge} 3\\,\\mathrm{m}{\\mathrm{s}}^{-1}$. Typical d $d$ = 4 mm sensors never meet the criterion when either force‐ or naturally ventilated, with τ63≥20 ${\\tau }_{63}\\ge 20$ mins in a naturally ventilated arrangement under calm conditions. Inadequate τ63 ${\\tau }_{63}$ will lead to underestimation of the diurnal temperature range or other local measures derived from daily temperature maxima and minima.

Fog computing (FC) designates a decentralized computing structure placed among the devices that produce data and cloud. Such flexible structure empowers users to place resources to increase performance. However, limited resources and low delay services obstruct the application of new virtualization technologies in the task scheduling and resource management of fog computing. Scheduling and load balancing (LB) in the cloud computing have been widely studied. However, countless efforts in LB have been proposed in the fog architectures. This presents some enticing challenges to solve the problem of how tasks are routed between different physical devices between fog nodes and cloud. Within fog, due to its mass and heterogeneity of devices, the scheduling is very difficult. There are still few studies that have been conducted. LB is a very interesting and important study area in FC as it aims to achieve high resource utilization. There are various challenges in LB such as security and fault tolerance. The main objective of this paper is to introduce an effective dynamic load balancing technique (EDLB) using convolutional neural network and modified particle swarm optimization, which is composed of three main modules, namely: (i) fog resource monitor (FRM), (ii) CNN-based classifier (CBC), and (iii) optimized dynamic scheduler (ODS). The main purpose of EDLB is to achieve LB in FC environment via dynamic real-time scheduling algorithm. This paper studies the FC architecture for Healthcare system applications. The FRM is responsible for monitoring each server resource and save the server's data into table called fog resources table. The CNN-based classifier (CBC) is responsible for classifying each fog server to suitable or not suitable. The optimized dynamic scheduler (ODS) is responsible for assigning the incoming process to the most appropriate server. Comparing EDLB with other previous LB algorithms, it reduces the response time and achieves high resource utilization. Hence, it is an efficient way to ensure the continuous service. Accordingly, EDLB is simple and efficient in real-time systems in fog computing such as in the case of healthcare system. Although several methods in LB for FC have been introduced, they have many limitations. EDLB overcomes these limitations and achieves high performance in various scenarios. It achieved better makespan, average resource utilization and load balancing level as compared to previously mentioned LB algorithms.

Background Emergency Medical Services (EMS) response times are critical for optimizing patient outcomes, particularly in time-sensitive emergencies. This study explores the multifaceted determinants of EMS response times, leveraging machine learning (ML) techniques to identify key factors such as urgency levels, environmental conditions, and geographic variables. The findings aim to inform strategies for enhancing resource allocation and operational efficiency in EMS systems. Methods A retrospective analysis was conducted using over one million EMS missions from Stockholm, Sweden, between 2017 and 2022. Advanced ML techniques, including Gradient Boosting models, were applied to evaluate the influence of diverse variables such as call handling times, travel times, weather patterns, and resource availability. Feature engineering was employed to extract meaningful insights, and statistical models were used to validate the relationships between key predictors and response times. Results The study revealed a complex interplay of factors influencing EMS response times, aligning with the study’s aim to deepen the understanding of these determinants. Key drivers of response time variability included weather conditions, call priority, and resource constraints. ML models, particularly Gradient Boosting, proved effective in quantifying these impacts and provided robust predictions of response times across scenarios. By providing a comprehensive evaluation of these influences, the results support the development of adaptive resource allocation models and evidence-based policies aimed at enhancing EMS efficiency and equity across all call priorities. Conclusions This study underscores the potential of ML-driven insights to revolutionize EMS resource allocation strategies. By integrating real-time data on weather, call types, and workload, EMS systems can transition to adaptive deployment models, reducing response times and enhancing equity across priority levels. The research provides a blueprint for implementing predictive analytics in EMS operations, paving the way for evidence-based policies that improve emergency care efficiency and outcomes. Clinical trial number Not applicable.

Continuous, wide field-of-view, high-efficiency, and fast-response beam steering devices are desirable in a plethora of applications. Liquid crystals (LCs)—soft, bi-refringent, and self-assembled materials which respond to various external stimuli—are especially promising for fulfilling these demands. In this paper, we review recent advances in LC beam steering devices. We first describe the general operation principles of LC beam steering techniques. Next, we delve into different kinds of beam steering devices, compare their pros and cons, and propose a new LC-cladding waveguide beam steerer using resistive electrodes and present our simulation results. Finally, two future development challenges are addressed: Fast response time for mid-wave infrared (MWIR) beam steering, and device hybridization for large-angle, high-efficiency, and continuous beam steering. To achieve fast response times for MWIR beam steering using a transmission-type optical phased array, we develop a low-loss polymer-network liquid crystal and characterize its electro-optical properties.

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors, hinge upon the validity of the models’ parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants’ behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these \"modeler’s degrees of freedom\" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.

Over the course of the COVID-19 progress, reports from many locations around the world indicated major increases in EMS call volume, which imposed great pressure on EMS dispatch centers (EMSDC) globally. No studies yet have been done to examine this phenomenon. This paper examines the interrelated effects of the unprecedented global increase of EMS call, the effect of the COVID-19 crisis on responding to non-COVID-19 emergencies, and the concurrent effects of having overwhelmed dispatch centers. It tries to explain the current evidence of the bottleneck of EMS calls during the early phase of the worldwide pandemic. We examine the numbers of EMS calls internationally between March and June 2020, derived from published literature and news media. Only articles in English were selected, with certain keywords related to EMS calls, ambulance delay, stroke and cardiac arrest. Google Scholar was the main searching source. After applying the selection criteria, a total of 29 citations were chosen, and a pattern of knowledge resulted in the emergence of five themes: EMS calls during COVID-19, Reduced EMS operator response time, Ambulance response delays, Collateral mortality and morbidity among non-COVID-19 cases, and Total ambulance call time. Over the course of COVID-19 progress, there was a global phenomenon of exponential increases in EMS calls, which is expected to impose a great pressure on EMS dispatch centers. Several factors contributing to the bottleneck of EMS calls are identified and explained.

Fog computing (FC) can be considered as a computing paradigm which performs Internet of Things (IoT) applications at the edge of the network. Recently, there is a great growth of data requests and FC which lead to enhance data accessibility and adaptability. However, FC has been exposed to many challenges as load balancing (LB) and adaptation to failure. Many LB strategies have been proposed in cloud computing, but they are still not applied effectively in fog. LB is an important issue to achieve high resource utilization, avoid bottlenecks, avoid overload and low load, and reduce response time. In this paper, a LB and optimization strategy (LBOS) using dynamic resource allocation method based on Reinforcement learning and genetic algorithm is proposed. LBOS monitors the traffic in the network continuously, collects the information about each server load, handles the incoming requests, and distributes them between the available servers equally using dynamic resource allocation method. Hence, it enhances the performance even when it’s the peak time. Accordingly, LBOS is simple and efficient in real-time systems in fog computing such as in the case of healthcare system. LBOS is concerned with designing an IoT-Fog based healthcare system. The proposed IoT-Fog system consists of three layers, namely: (1) IoT layer, (2) fog layer, and (3) cloud layer. Finally, the experiments are carried out and the results show that the proposed solution improves the quality-of-service in the cloud/fog computing environment in terms of the allocation cost and reduce the response time. Comparing the LBOS with the state-of-the-art algorithms, it achieved the best load balancing Level (85.71%). Hence, LBOS is an efficient way to establish the resource utilization and ensure the continuous service.

Pressure-sensitive paint (PSP) is an optical sensor that can measure global pressure distribution by using the oxygen quenching of dye molecules. In particular, anodized aluminum pressure-sensitive paint (AA-PSP) exhibits a fast time response. AA-PSP has been used in unsteady measurements at supersonic and transonic speeds, such as on the surface of a transonic free-flying sphere or the wall of a shock tube when the shock wave passes. To capture such ultrafast phenomena, the frame rate of the camera must be sufficiently fast, and the exposure time must be sufficiently short. Therefore, it is desirable that the AA-PSP exhibits bright luminescence, high-pressure sensitivity, and fast response time. This study focused on pyrene-based AA-PSPs and investigated their characteristics, such as luminescence intensity and pressure sensitivity, at different anodization times, dipping solvents, and dipping concentrations. Furthermore, a time-response test using a shock tube was conducted on the brightest AA-PSP. Consequently, the time for a 90% rise in pressure was 2.2 μs.