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The purpose of this study was to investigate the differences in night eating status according to adolescents’ body mass index (BMI).

This was a cross-sectional study that included a total of 688 middle-school students. The subjects were categorized as underweight, normal weight and overweight according to their BMI, and their night eating intake patterns, night eating menu preference and intake frequency were compared and analyzed.

With regard to their night eating frequency, 39.8 per cent replied almost never, while 24.3 per cent replied once a week and less and 22.5 per cent replied two to three times a week. Among 11 night eating menus, the preference for fast foods, confectioneries, street foods and noodles was significantly higher in the underweight group than in the overweight group. The intake frequency of night eating menus such as fast foods, confectioneries and breads increased in the following order: overweight, normal and underweight group. The underweight group had a higher frequency of night eating, and they preferred to eat snacks more frequently from their night eating menu.

In conclusion, it is necessary to form positive dietary habits including nighttime eating for proper dietary management of adolescents.

Limited geometric accuracy is one of the major challenges that hinder the wider application of additive manufacturing (AM). This paper aims to predict in-plane shape deviation for online inspection and compensation to prevent error accumulation and improve shape fidelity in AM.

A sequence-to-sequence model with an attention mechanism (Seq2Seq+Attention) is proposed and implemented to predict subsequent layers or the occluded toolpath deviations after the multiresolution alignment. A shape compensation plan can be performed for the large deviation predicted.

The proposed Seq2Seq+Attention model is able to provide consistent prediction accuracy. The compensation plan proposed based on the predicted deviation can significantly improve the printing fidelity for those layers detected with large deviations.

Based on the experiments conducted on the knee joint samples, the proposed method outperforms the other three machine learning methods for both subsequent layer and occluded toolpath deviation prediction.

This work fills a research gap for predicting in-plane deviation not only for subsequent layers but also for occluded paths due to the missing scanning measurements. It is also combined with the multiresolution alignment and change point detection to determine the necessity of a compensation plan with updated G-code.

Fierce competition in the crowdfunding market has resulted in high failure rates. Owing to their dedication and efforts, many founders have relaunched failed campaigns as a second attempt. Despite the need for a better understanding, the success of campaign relaunches has not been well-researched. To fill this research gap, this study first theorizes how founders’ learning may enhance their competencies and influence investors’ attribution of entrepreneurial failure. The study then empirically documents the extent and conditions under which such learning efforts impact campaign relaunch performance.

This study examines 5,798 Kickstarter-relaunched campaigns. The founders’ learning efforts are empirically captured by key changes in campaign design that deviate from past business practices. Word movers’ distances and perceptual hashing algorithms (pHash) are used separately to measure differences in campaign textual descriptions and pictorial designs.

Differences in textual descriptions and pictorial designs during campaign failure–relaunch are positively associated with campaign relaunch success. The impacts are further amplified when the previous failures are more severe.

This study is one of the first to examine the success of a campaign relaunch after an initial failure. This study contributes to a better understanding of founders’ learning in crowdfunding contexts and provides insights into the strategies founders can adopt to reap performance benefits.