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ObjectiveWe investigated the correlation between nontyphoidal Salmonella (NTS) infection and systemic lupus erythematosus (SLE) risk.MethodsThis case-control study comprised 6,517 patients with newly diagnosed SLE between 2006 and 2013. Patients without SLE were randomly selected as the control group and were matched at a case-control ratio of 1:20 by age, sex, and index year. All study individuals were traced from the index date back to their NTS exposure, other relevant covariates, or to the beginning of year 2000. Conditional logistic regression analysis was used to analyze the risk of SLE with adjusted odds ratios (aORs) and 95% confidence intervals (CIs) between the NTS and control groups.ResultsThe mean age was 37.8 years in the case and control groups. Females accounted for 85.5%. The aOR of having NTS infection were significantly increased in SLE relative to controls (aOR, 9.20; 95% CI, 4.51-18.78) in 1:20 sex-age matching analysis and (aOR, 7.47; 95% CI=2.08-26.82) in propensity score matching analysis. Subgroup analysis indicated that the SLE risk was high among those who dwelled in rural areas; had rheumatoid arthritis, multiple sclerosis, or Sjogren’s syndrome; and developed intensive and severe NTS infection during admission.ConclusionsExposure to NTS infection is associated with the development of subsequent SLE in Taiwanese individuals. Severe NTS infection and other autoimmune diseases such as rheumatoid arthritis, multiple sclerosis, or Sjogren’s syndrome also contributed to the risk of developing SLE.

Recently, expanded metal mesh has been used on the facades of many buildings in Taiwan. Therefore, in this study, we evaluated the impact of expanded metal mesh on natural lighting and energy consumption in office buildings. First, the compatibility of EnergyPlus and DIVA simulation software with expanded metal mesh was verified using field measurements. The results show a high correlation between simulation and measurement, except for some periods of direct sunlight. Then, we evaluated the effects of window-to-wall ratio (WWR), glass, and expanded metal mesh on energy consumption and lighting. The results show that WWR has a significant influence on both lighting and energy consumption. The greater the WWR, the greater the energy saving potential of the expanded metal mesh and glass. If the SHGC of the glass is lower, the potential of the expanded metal mesh to save air conditioning energy consumption is smaller, and, as a result, the expanded metal mesh may increase the total energy consumption. Of the 36 simulation cases performed, three cases met the LEED lighting standard. The case with minimum energy consumption is achieved when SHGC = 50%, using laminated clear glass and expanded metal mesh with a 21% perforated ratio.

Incubator care is essential for premature infants during early hospitalization. As the infants’ conditions improve, incubator weaning becomes necessary. This retrospective study aimed to evaluate the effect of body weight gain and status of intake-calorie gain on the incubator weaning process for very low birth weight (VLBW) premature infants. The study included 127 VLBW premature neonates. We analyzed data on clinical characteristics potentially associated with the weaning period and the end-weaning body weight (EWBW), including body weight gain status, intake-calorie gain status, and disease conditions. The neonates were weaned from the incubators at a mean postmenstrual age (PMA) of 35.1 ± 1.3 weeks; postnatal days, 37.7 ± 18.2 days; and body weight, 1882.8 ± 157.1 g. The total weaning period was 3.5 ± 3.1 days. Regarding the weaning period, there was a strong positive relationship only in the end-weaning PMA and the daily body weight within 3 days before incubator weaning. Further, regarding the factors associated with EWBW, only the end-weaning PMA and necrotizing enterocolitis had a significant positive impact. Body weight gain and the status of intake-calorie gain showed no association with either the weaning period or the EWBW and, thus, were not related to the incubator weaning process.

In Taiwan, activity centers such as school auditoriums and gymnasiums are common multi-functional spaces that are often used for performances, singing, and speeches. However, most cases are designed using only Sabine’s equation for architectural acoustics. Although that estimation formula is simple and fast, the calculation process ignores many details. Furthermore, while more accurate analysis can be obtained through acoustics simulation software, it is more complicated and time-consuming and thus is rarely used in practical design. The purpose of this study is to use machine learning to propose a predictive model of acoustic indicators as a simple evaluation tool for the architectural design and interior decoration of multi-functional activity centers. We generated 800 spaces using parametric design, adopting Odeon to obtain acoustic indicators. The machine learning model was trained with basic information of the space. We found that through GBDT and ANN algorithms, almost all acoustic indicators could be predicted within JND ± 2, and the JND of C50, C80, STI, and the distribution of SPL could reach within ±1. Through machine learning methods, we established a convenient, fast, and accurate prediction model and were able to obtain various acoustic indicators of the space without 3D-modeling or simulation software.

Recently, micro-perforated panels (MPP) have become a popular sound absorbing material in the field of architectural acoustics. However, the cost of MPP is still high for the commercial market in Taiwan, and MPP is still not very popular compared to other sound absorbing materials and devices. The objective of this study is to develop a prediction model for MEMM via a machine learning approach. An experiment including 14 types of MEMM was first carried out in a reverberation room based on ISO 354. To predict the sound absorption coefficient of the MEMM, the capability of three conventional models and three machine learning (ML) models of the supervised learning method were studied for the development of the prediction model. The results showed that in most conventional models, the sound absorption coefficient of using an equivalent perimeter had the best agreement compared with other parameters, and the root mean square error (RMSE) between prediction models and experimental data were around 0.2~0.3. However, the RMSE of all ML models was less than 0.1, and the RMSE of the gradient boost model was 0.033 in the training sets and 0.062 in the testing sets, which showed the best agreement with the experiment data.