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Only a few studies on large sports tourism explore motorsport events such as the Moto Grand Prix. The present study addresses this gap by examining the factors influencing visitors’ satisfaction and intention to revisit related to the large sports event of MotoGP, Indonesia. The data were collected from visitors who had attended the MotoGP event in Indonesia using an online questionnaire with purposive sampling. The partial Least Square-Structural Equation Modeling (PLS-SEM) analysis was applied in this study to analyze the data. The findings indicate that hospitality, accessibility, and attraction significantly influenced visitor satisfaction at the MotoGP sports event in Mandalika, Indonesia. However, the factors of hospitality, infrastructure, accessibility, and attraction are insignificant where it concerns visitors’ intention to revisit. Moreover, satisfaction successfully influences visitors to revisit the MotoGP event in Indonesia. This study contributes to research on sports event management in large sports events by examining the factors that influence satisfaction and intention to revisit in the context of the MotoGP sports event, and contributes both theoretically and practically to research on sports events and tourism.

A systematic review and meta-analysis is an important step in evidence synthesis. The current paradigm for meta-analyses requires a presentation of the means under a random-effects model; however, a mean with a confidence interval provides an incomplete summary of the underlying heterogeneity in meta-analysis. Prediction intervals show the range of true effects in future studies and have been advocated to be regularly presented. Most commonly, prediction intervals are estimated assuming that the underlying heterogeneity follows a normal distribution, which is not necessarily appropriate. In this article, we provide a simple method with a ready-to-use spreadsheet file to estimate prediction intervals and predictive distributions nonparametrically. Simulation studies show that this new method can provide approximately unbiased estimates compared with the conventional method. We also illustrate the advantage and real-world significance of this approach with a meta-analysis evaluating the protective effect of vaccination against tuberculosis. The nonparametric predictive distribution provides more information about the shape of the underlying distribution than does the conventional method.

Digital light processing additive manufacturing (DLP‐AM) technology has received a lot of attention in the field of biomedical engineering due to its high precision and customizability. However, some photoinitiators, as one of the key components in DLP‐AM, may present toxicity and limit the application of DLP‐AM toward biomedical applications. In order to gain further insights into the correlation between biocompatibility and photoinitiators in photoresins, a study on the selection of photoinitiators used in DLP‐AM is conducted. The light absorbance range and cytocompatibility of four photoinitiators, vitamin B2 combined with triethanolamine (B2/TEOA), diphenyl(2,4,6‐trimethylbenzoyl)phosphine oxide (TPO), 2‐dimethoxy‐2‐phenylacetophenone (DMPA), and 2‐hydroxy‐4‐(2‐hydroxyethoxy)‐2‐methylpropiophenone (I2959), are characterized. Each photoinitiator is then combined with poly(glycerol sebacate) acrylate (PGSA) and poly(ε‐caprolactone) diacrylate (PCLDA), to evaluate their miscibility and film formation ability through photopolymerization. The mechanical properties, in vitro and in vivo biocompatibility studies on bulk films are investigated. It is found that B2/TEOA and TPO exhibit a wider light absorbance range than I2959 and DMPA. PGSA films with B2/TEOA (PGSA‐B2/TEOA) is capable of sustaining cell proliferation up to 10 days and showing low immune responses after 14 days post implantation, proving its biocompatibility. Although B2/TEOA requires longer photopolymerization time, the mechanical strength of PGSA‐B2/TEOA is comparable to PGSA films with TPO and DMPA, and this combination is 3D‐printable through DLP‐AM at the rate of 100 s per layer. In summary, B2/TEOA is a promising photoinitiator for 3D printing. [ABSTRACT FROM AUTHOR]

Random-effects meta-analysis is one of the mainstream methods for research synthesis. The heterogeneity in meta-analyses is usually assumed to follow a normal distribution. This is actually a strong assumption, but one that often receives little attention and is used without justification. Although methods for assessing the normality assumption are readily available, they cannot be used directly because the included studies have different within-study standard errors. Here we present a standardization framework for evaluation of the normality assumption and examine its performance in random-effects meta-analyses with simulation studies and real examples. We use both a formal statistical test and a quantile-quantile plot for visualization. Simulation studies show that our normality test has well-controlled type I error rates and reasonable power. We also illustrate the real-world significance of examining the normality assumption with examples. Investigating the normality assumption can provide valuable information for further analysis or clinical application. We recommend routine examination of the normality assumption with the proposed framework in future meta-analyses. [ABSTRACT FROM AUTHOR]