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Objective: Delayed emergency responses in patients with large vessel occlusion stroke (LVOS) are associated with reduced access to timely reperfusion therapy and worse clinical outcomes. The present study was aimed at identifying modifiable factors contributing to delays before hospital arrival in LVOS patients undergoing endovascular treatment (EVT). Methods: In this retrospective analysis of prospectively collected data, consecutive acute LVOS patients undergoing EVT at two comprehensive stroke centers between December 2020 and December 2021 were enrolled. Neurologists administered a standardized questionnaire to patients or their caregivers within 24 h after the procedure. Emergency response delay was defined as onset to groin (OTG) time, measured from symptom onset or last known normal to groin puncture, exceeding 6 h. Baseline characteristics, process times, and clinical data were collected for all enrolled patients, and factors influencing the emergency process and outcomes were analyzed. Results: Of the 366 patients initially considered, 14 with in‐hospital stroke were excluded, leaving 352 patients for analysis. The median age was 70 years (63, 76), and 135 patients (38.4%) experienced treatment delays. The median National Institutes of Health Stroke Scale (NIHSS) score was 14 (11, 18), and the median Alberta Stroke Program Early CT Score (ASPECTS) was 9 (7.85, 10). Multivariate analysis identified the main modifiable factors associated with reduced emergency response delay as early calling of emergency services (odds ratio [OR] = 0.41, 95% confidence interval [CI]: 0.22–0.76), initial consultation with a neurologist (OR = 0.35, 95% CI: 0.20–0.62), and stroke awareness (OR = 0.51, 95% CI: 0.29–0.89). Among elderly patients and those whose stroke onset occurred during sleep, early contact with emergency services (120) significantly reduced prehospital delays (OR = 0.48, 95% CI: 0.21–0.94 and OR = 0.30, 95% CI: 0.10–0.86). Conclusion: Emergency physician involvement, stroke awareness, and early calling of emergency services (120) are modifiable factors that can reduce delays in the emergency response process. For patients eligible for EVT, minimizing prehospital delays may require prioritizing both community education on stroke recognition and system‐level improvements to ensure rapid emergency activation and timely neurological assessment.

Computer-based interactive items have become prevalent in recent educational assessments. In such items, detailed human–computer interactive process, known as response process, is recorded in a log file. The recorded response processes provide great opportunities to understand individuals’ problem solving processes. However, difficulties exist in analyzing these data as they are high-dimensional sequences in a nonstandard format. This paper aims at extracting useful information from response processes. In particular, we consider an exploratory analysis that extracts latent variables from process data through a multidimensional scaling framework. A dissimilarity measure is described to quantify the discrepancy between two response processes. The proposed method is applied to both simulated data and real process data from 14 PSTRE items in PIAAC 2012. A prediction procedure is used to examine the information contained in the extracted latent variables. We find that the extracted latent variables preserve a substantial amount of information in the process and have reasonable interpretability. We also empirically prove that process data contains more information than classic binary item responses in terms of out-of-sample prediction of many variables.

This study explores how test-takers process multiple-choice questions in a reading comprehension test using eye-tracking data. A total of 159 participants completed a 10-item English test, while their eye movements were recorded with the Smart Eye Aurora eye tracker. The study first examined item processing patterns using latent profile analysis and then compared test performance across these groups. To identify processing patterns, latent profile models (ranging from one to four classes) were tested for each item based on average log process times across defined Areas of Interest (AOIs) for text lines and answer choices. Results showed that a two-class model (fast- and slow-pacing) provided the best fit for five items, while a three-class model (fast-, moderate-, and slow-pacing) best fit the remaining items. Items with two subgroups were typically moderately difficult (short) or easy (long), while items with three subgroups varied in difficulty and length. Additionally, test-takers in fast-pacing group were more likely to answer items correctly and achieved higher total scores than those in other groups, particularly for highly discriminating and moderately difficult or easy items. Overall, these findings highlight the importance of examining item processing patterns to better understand how individuals interact with multiple-choice test items. Item characteristics—such as difficulty, discrimination, and length—play a crucial role in shaping processing behaviors, providing deeper insights into the cognitive aspects of eye movement patterns during test-taking. This study examined how test-takers process multiple-choice items in a reading comprehension test by using eye-tracking technology. A total of 159 students completed a 10-item English test while their eye movements were recorded. The goal was to identify distinct processing patterns and investigate how these patterns relate to test performance. Results showed that test-takers could be grouped into fast-, moderate-, and slow-pacing profiles depending on how they allocated their time across each item. Fast-pacing test-takers were generally more successful, particularly on items that were easier or moderately difficult and highly discriminating. In contrast, slow-pacing test-takers tended to perform less well. These findings highlight the importance of examining response processes to better understand how individuals interact with test items and how item characteristics shape their processing. They offer valuable insights for improving test design and instructional strategies.

Response process data from computer-based problem-solving items describe respondents’ problem-solving processes as sequences of actions. Such data provide a valuable source for understanding respondents’ problem-solving behaviors. Recently, data-driven feature extraction methods have been developed to compress the information in unstructured process data into relatively low-dimensional features. Although the extracted features can be used as covariates in regression or other models to understand respondents’ response behaviors, the results are often not easy to interpret since the relationship between the extracted features, and the original response process is often not explicitly defined. In this paper, we propose a statistical model for describing response processes and how they vary across respondents. The proposed model assumes a response process follows a hidden Markov model given the respondent’s latent traits. The structure of hidden Markov models resembles problem-solving processes, with the hidden states interpreted as problem-solving subtasks or stages. Incorporating the latent traits in hidden Markov models enables us to characterize the heterogeneity of response processes across respondents in a parsimonious and interpretable way. We demonstrate the performance of the proposed model through simulation experiments and case studies of PISA process data.

There recently have been many studies examining conditional dependence between response accuracy and response times in cognitive tests. While most previous research has focused on revealing a general pattern of conditional dependence for all respondents and items, it is plausible that the pattern may vary across respondents and items. In this paper, we attend to its potential heterogeneity and examine the item and person specificities involved in the conditional dependence between item responses and response times. To this end, we use a latent space item response theory (LSIRT) approach with an interaction map that visualizes conditional dependence in response data in the form of item–respondent interactions. We incorporate response time information into the interaction map by applying LSIRT models to slow and fast item responses. Through empirical illustrations with three cognitive test datasets, we confirm the presence and patterns of conditional dependence between item responses and response times, a result consistent with previous studies. Our results further illustrate the heterogeneity in the conditional dependence across respondents, which provides insights into understanding individuals’ underlying item-solving processes in cognitive tests. Some practical implications of the results and the use of interaction maps in cognitive tests are discussed.

The item response tree (IR-tree) model is increasingly used in the field of personality assessments. The IR-tree model allows researchers to examine the cognitive decision-making process using a tree structure and evaluate conceptual ideas in accounting for individual differences in the response process. Recent research has shown the feasibility of applying IR-tree models to personality data; however, these studies have been exclusively focused on an honest or incumbent sample rather than a motivated sample in a high-stakes situation. The goal of our research is to elucidate the internal mechanism behind how respondents in different testing situations (i.e., honest and motivated test conditions) experience decision-making processes through the three-process IR-tree model (Böckenholt, 2012). Our findings generally corroborate the response mechanism of the direction–extremity–midpoint sequence in both honest and motivated test settings. Additionally, samples in motivated test settings generally exhibit stronger directional and extreme response preferences but weaker preferences of midpoint selection than samples in unmotivated test settings. Furthermore, for actual job applicants, social desirability had a substantial effect on all directional, extreme, and midpoint response preferences. Our findings will aid researchers and practitioners in developing a nuanced understanding of the decision-making process of test-takers in motivated test environments. Furthermore, this research will help researchers and practitioners develop more fake-resistant personality assessments.

This study analyzed supply chain response frameworks (SCRFs). An SCRF that applies to any situation involving supply chain responses (SCRs) and facilitates the understanding of SCR as a process was proposed. The SCRF was designed based on a systematic review of the literature and thematic synthesis. Thirty-seven documents related to the SCR and SCRFs were selected for the literature review. The thematic synthesis identified the contexts in which the frameworks were designed, supply chain (SC) aspects comprising the framework, and coherence between the SCR definition and framework components. Consequently, a new SCRF based on the stimulus–organism–response (SOR) model was proposed. The stimuli that an SC responds to and the results it achieves with the response, response strategy, decision types, time horizon, relationship facilitators, and response feedback are the chain aspects comprising the framework. The new SCRF makes it easier for SC managers to identify the aspects encompassing the response to a stimulus in which managers should be trained to provide a better response. In addition, it extends the theory on the SCR.Using the systematic literature review and the stimulus-organism-response model, this paper proposes a framework for planning the supply chain response to a stimulus. The response is given following a process that begins with the identification and detection of the stimulus, continues with the management of the response, then the results to be obtained are identified, and finally, the response process is evaluated, and feedback is provided. The framework is a guide so that the response to the stimulus, on the one hand, facilitates the achievement of the objectives of the chain and, on the other hand, satisfies the requirements of those who receive it.

Electric discharge machining (EDM) is a material removal process that is especially useful for difficult-to-cut materials with complex shapes and is widely used in aerospace, automotive, surgical tools among other fields. EDM is one of the most efficient manufacturing processes and is used to achieve highly accurate production. It is a non-contact thermal energy process used to machine electrically conductive components irrespective of the material’s mechanical properties. Studies related to the EDM have shown that the process performance can be considerably improved by properly selecting the process material and operating parameters. This paper reviews research studies on the application of EDM to different grades of stainless steel materials and describes experimental and theoretical studies of EDM that have attempted to improve the process performance, by considering material removal rate, surface quality and tool wear rate, amongst others. In addition, this paper examines evaluation models and techniques used to determine the EDM process conditions. This review also presents a discussion on developments in EDM and outlines the likely trend for future research.

Purpose This paper aims to examine the cognitive processes involved in answering survey questions. It also briefly discusses how the cognitive viewpoint has been challenged by other approaches (such as conversational analysis). Design/methodology/approach The paper reviews the major components of the response process and summarizes work examining how each of these components can contribute to measurement errors in surveys. Findings The Cognitive Aspects of Survey Methodology (CASM) model of the survey response process is still generating useful research, but both the satisficing model and the conversational approach provide useful supplements, emphasizing motivational and social sources of error neglected in the CASM approach. Originality/value The paper provides an introduction to the cognitive processes underlying survey responses and how these processes can explain why survey responses may be inaccurate.

Microparameter calibration is an important problem that must be solved in the discrete element method. The Gaussian process (GP) response surface methodology was proposed to calibrate the microparameters based on the Bayesian principle in machine-learning methods, which addresses the problems of uncertainty, blindness, and repeatability in microparameter calibration methods. Using the particle flow code (PFC) as an example, the effects of the microparameters on the macroparameters were evaluated using the control-variable method, and the range of the microparameters was determined based on the macroparameters. The uniform design (UD) method and numerical calculation were used to obtain training samples, and a GP response surface methodology suitable for multifactor, multilevel, and nonlinear processes was used to establish the response surface relationships for macro–micro parameters of rock-like materials in discrete element method. According to the macroparameters obtained from the uniaxial experiments conducted on rock specimens, the microparameters were calibrated using the GP response surfaces. Numerical calculations of uniaxial compression and Brazilian splitting were performed using microparameters, and the results were compared with laboratory experiments for verification. The results showed that the relative errors of the GP response surface and laboratory test values were 5.3% for the modulus of elasticity, −7.8% for compressive strength, and −2.6% for tensile strength. The nonlinear GP response surface considered the characteristics of multiple interacting factors, and the established nonlinear response surface relationship between the microparameters and macroparameters can be used for the calibration of microparameters. The accuracy of the microparameters was verified according to the stress–strain curve and failure morphology of the rock specimens. The method of using the GP response surface to establish the macro–micro parameter relationship in the discrete element method can also be extended to other numerical simulation methods and can provide a basis for accurately analysing the microdamage mechanism of rock materials under complex loading conditions.