Discrete choice experiments (DCEs) are frequently used to estimate and forecast the behavior of an individual's choice. DCEs are based on stated preference; therefore, underlying experimental designs are required for this type of study. According to psychologists, DCE designs consist of a small number of choice sets with a limited size in the number of alternatives within a choice set to increase the response efficiency in the questionnaire. Even though algorithmic constructions (known as efficient designs) become quite common for practitioners, optimal designs (sometimes so-called orthogonal designs) continue to be used in choice experiment studies, particularly in the case that prior information about the extent of the population preference is not available. Various approaches have been developed to construct DCE designs with fewer choice sets. However, the question in many practitioners' minds is which techniques perform better (i.e. given small designs with high efficiency) in a given circumstance. In this paper and to address these concerns, we conducted an overview of the constructions of discrete choice experiments in the literature for models with only main effects. The various ways of constructing optimal and near-optimal designs were compared in terms of their ability to minimize the number of choice sets in the survey. Our findings shed light on the optimal sample sizes needed for efficient experimentation which then can help the researchers to design more effective experiments in this area.

Autonomous vehicles (AVs) will transform transport, but public opinion will play a key role in decisions on how widely and quickly they are adopted. The purpose of the study presented here was to investigate community’s views on that transition. As a method for primary data collection on public awareness, attitudes, and readiness to use autonomous cars, survey was conducted in Saudi Arabia. Following that, we used statistical tools to analyse responses. Our findings indicate that the participants are largely receptive to using new technologies and had favourable attitudes towards the transition. Ordinal logistic regression model showed a wide variation in public opinion regarding the expected benefits that may accompany the transition. Our findings reveal that awareness of AVs’ benefits is positively correlated with the age of participants. Perceived costs on one side, and convenience and safety on the other, were found to have had a substantial impact on the opinions of the participants. Investigation presented here shows a sample of the public’s perception of AVs in Saudi Arabia. This can guide the development of AVs and their deployment in that region as well as worldwide.

Autonomous vehicles (AV)s will transform transport, but public opinion will play a key role in the decision how widely and quickly, they will be adopted. The purpose of the study presented here, was to investigate community’s views on the transition. As a method for primary data collection on public awareness, attitudes, and readiness to use autonomous cars, survey was conducted in Saudi Arabia. Following that, we have used statistical tools to analyse responses. Our findings indicate that the participants are mainly open to use new technology and had favourable attitude towards transition. Ordinal logistic regression model showed a wide variation in public opinion regarding the expected benefits that may accompany the transition, despite an average high score on this factor. Our findings reveal that awareness of AVs' benefits is more positively correlated to the age of participants. Perceived cost, on one side, and convenience, and safety, on the other side, have substantial impact on opinions. Investigation, presented here, shows how AVs are seen in Saudi Arabia. This can guide the development of AVs and deployment in that region.

Aim: The aim of this study was to examine Saudi Arabian nursing students' level of pain management knowledge and attitudes. Design: A cross‐sectional design was used in this study. Methods: The ‘Knowledge and Attitudes Survey Regarding Pain’ tool was used to measure students' knowledge of and attitudes towards pain management. Data were collected from four universities between November 2023 and January 2024 and analysed using descriptive statistics. Results: A total of 393 online surveys were submitted. The mean ‘Knowledge and Attitudes Survey Regarding Pain’ score was 44% (SD = 8.8), indicating generally low levels of knowledge and attitudes towards pain management. There were significant differences in scores across universities. Conclusion: The results of this study suggest that many of the participants had inadequate levels of knowledge and poor attitudes towards pain management. There is a need for further research to explore the factors that impact students' pain management knowledge and attitudes. No Patient or Public Contribution: No patient or public contribution in the study.

The SAMEWMA–VSS chart combines support-vector-regression-based adaptive smoothing with a variable sample size within a Mahalanobis-form multivariate EWMA statistic, but neither this mechanism nor the classical multivariate charts it builds on were designed for contaminated data: a single gross measurement error can distort the estimated shift magnitude that drives the chart’s adaptive smoothing constant and sample size, and classical Phase-I moment estimates are well known to suffer from masking under contamination. This paper proposes MCD–Huber SAMEWMA–VSS, which replaces the classical Phase-I mean and covariance with robust minimum covariance determinant (MCD) estimates and replaces each subgroup’s raw contribution with a Huber-weighted one, while retaining the original chart’s SVR-based adaptive smoothing and variable-sample-size mechanisms. An extensive Monte Carlo study compares the proposed chart against the original SAMEWMA–VSS chart and against an Isolation-Forest-weighted (IF-weighted) alternative across clean shifts, gross-error contamination at varying rates and magnitudes, and a range of correlation structures. The results show a clear contrast between the two robust alternatives: the IF-weighted chart’s more aggressive weighting substantially improves contamination resistance but masks genuine large shifts, never signaling at all for the largest shifts examined, whereas MCD–Huber SAMEWMA–VSS achieves a substantial but partial gain in contamination resistance — the in-control average run length under 10% gross-error contamination rises from 27.19 for the original chart to 67.42, still well below the clean target of 370—while retaining detection speed close to the original chart for small and moderate clean shifts (within about 13% up to δ = 1.5), at the cost of slower detection of large shifts (δ ≥ 3), where its ARL levels off near 7. An illustrative NASA IMS bearing feature-based simulation, using real Phase-I vibration-feature statistics as the reference model with simulated Phase-II shifts and contamination, demonstrates the proposed chart’s behavior under both clean and contaminated conditions. Supplementary analyses examine the run-length distribution, the precision of the control limits, the Huber cutoff, the Phase-I sample size and contamination, the delayed-shift metrics FAP_τ and CDD_τ, dimensions up to 20, serial correlation and computing time.