Background: Premature births account for around 11% of the world's live births. With the improvements in survival that have been achieved in recent years, the neurological outcomes of these infants have attracted greater attention. The aim of this study was to evaluate the association between postnatal weight loss and neurodevelopment outcomes of very low birth weight premature infants. Methods: This was a prospective cohort study that was conducted in a tertiary referral center. Premature infants of birth weight less than 1500 g born between October 2015 and January 2017 were enrolled. Perinatal-demographic characteristics, medical interventions, and nutrition records were collected. The Bayley III tests performed by licensed child psychiatrists at corrected ages 6, 12, and 24 months old were adopted as outcome measurements. Results: In total, 52 infants were enrolled. The mean birth weight was 1071 g and the mean gestational age was 29.0 weeks. According to the univariate analysis, the duration of postnatal weight loss had a significantly negative impact on motor outcomes at 12 and 24 months old. The negative impact remained robust after adjusting for confounding factors by multiple linear regression models. The effect of repeated measurement was further considered by generalized estimating equation (GEE) models. GEE models also demonstrated a negative association between the duration of body weight loss and motor scores.

Plastic injection moulding is a typical complex manufacturing process. Its product quality is difficult to assure because of the nonuniform material shrinkage. This research introduces a prognostic concept for predicting the product quality (four edge shrinkages). The prognostic model is developed by path analysis through LISREL which can define the model's reliability when only few sensory data are retrieved from the smoothly running system. The prognostic model is validated by 25 test runs. In each run, each of the nine manufacturing conditions is randomly set at the extreme limit, i.e., either the lower limit or the upper limit. By comparing the actual edge shrinkages defined by the finite element software Moldflow with the results predicted by the prognostic model, this research concludes the prognostic model can successfully predict the quality of products and prevent the production of defective products.

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This paper proposes an intelligent monitoring system to adapt motorized high speed spindle on machine tools to meet the requirement of Industries 4.0, based on the techniques of power electronic engineering, mechatronics and reliability engineering. In this paper, a force-sensor-integrated spindle is proposed to monitor the bearing stress during operation. The implementation of charge amplifier and mechanical design are described as well. As a result, raw data is transmitted through a filter to create fruitful information and allow the remote monitoring system to operate effectively. The information supports predictive maintenance plans and improves the quality of after-sales service.