This article attempts to examine the factors affecting residential electricity consumption through two approaches, a socioeconomic perspective and a direct use perspective. The results of this study find that gross domestic product (GDP), employment rates, residential space, and the implementation of energy labeling schemes provide significant impacts on residential electricity consumption. However, the impacts of electricity price and the energy efficiency standards do not receive significant support. The analysis of the direct use approach finds that air conditioners consume the largest portion of electricity, amounting to 1470 kWh for each household and accounting for 26.81% except for lighting. Refrigerators and rice cookers follow, consuming 815.83 kWh (14.88%) and 343.85 kWh (6.27%) of electricity. The correlation analysis reveals that GDP keeps a high relationship with the installation of electrical appliances and eventually leads to an increase in residential electricity consumption. By integrating the results obtained from these two approaches, this paper also discusses some improvement strategies. The results, derived from the case example of Taiwan’s residential electricity consumption, may provide valuable information for policy-making involving green labeling, energy standards, and electricity prices.

Taiwan’s Environmental Protection Administration (EPA) has enacted a food waste recycling policy since 2003 as an alternative of landfill and incineration for the final disposal of municipal solid waste. Recycled food waste is currently seen as a valuable material, especially when appropriate technology is developed. This paper conducts a cost/benefit analysis based on six cases of food waste composting plants in Taiwan, finding that (1) the composting of food waste may yield the most net benefit compared to other applications of today; (2) the production cost of compost ranges from NT$ 2897–23,117/tonne; (3) the adoption of more automatic technology may reduce operation costs and, thus, a closed composting system with mechanical aeration may be more cost effective; (4) the output is a determinant of affecting production costs and private firms are more competitive in production costs than government-affiliated composting units; (5) all of the government-affiliated composting units face a negative profit and thus they are required to make use of the market value of the produced compost to achieve economic viability; and (6) a subsidy to the compost producer is needed to expand the market demand as the food waste recycled can save the disposal cost of municipal solid waste (MSW) incineration.

Although vaccination is carried out worldwide, the vaccination rate varies greatly. As of 24 May 2021, in some countries, the proportion of the population fully vaccinated against COVID-19 has exceeded 50%, but in many countries, this proportion is still very low, less than 1%. This article aims to explore the impact of vaccination on the spread of the COVID-19 pandemic. As the herd immunity of almost all countries in the world has not been reached, several countries were selected as sample cases by employing the following criteria: more than 60 vaccine doses per 100 people and a population of more than one million people. In the end, a total of eight countries/regions were selected, including Israel, the UAE, Chile, the United Kingdom, the United States, Hungary, and Qatar. The results find that vaccination has a major impact on reducing infection rates in all countries. However, the infection rate after vaccination showed two trends. One is an inverted U-shaped trend, and the other is an L-shaped trend. For those countries with an inverted U-shaped trend, the infection rate begins to decline when the vaccination rate reaches 1.46–50.91 doses per 100 people.

An all-inclusive and accurate prediction of outcomes for patients with acute ischemic stroke (AIS) is crucial for clinical decision-making. This study developed extreme gradient boosting (XGBoost)-based models using three simple factors—age, fasting glucose, and National Institutes of Health Stroke Scale (NIHSS) scores—to predict the three-month functional outcomes after AIS. We retrieved the medical records of 1848 patients diagnosed with AIS and managed at a single medical center between 2016 and 2020. We developed and validated the predictions and ranked the importance of each variable. The XGBoost model achieved notable performance, with an area under the curve of 0.8595. As predicted by the model, the patients with initial NIHSS score > 5, aged over 64 years, and fasting blood glucose > 86 mg/dL were associated with unfavorable prognoses. For patients receiving endovascular therapy, fasting glucose was the most important predictor. The NIHSS score at admission was the most significant predictor for those who received other treatments. Our proposed XGBoost model showed a reliable predictive power of AIS outcomes using readily available and simple predictors and also demonstrated the validity of the model for application in patients receiving different AIS treatments, providing clinical evidence for future optimization of AIS treatment strategies.

Type 2 diabetes mellitus (T2DM) is a prevalent health challenge faced by countries worldwide. In this study, we propose a novel large language multimodal models (LLMMs) framework incorporating multimodal data from clinical notes and laboratory results for diabetes risk prediction. We collected five years of electronic health records (EHRs) dating from 2017 to 2021 from a Taiwan hospital database. This dataset included 1,420,596 clinical notes, 387,392 laboratory results, and more than 1505 laboratory test items. Our method combined a text embedding encoder and multi-head attention layer to learn laboratory values, and utilized a deep neural network (DNN) module to merge blood features with chronic disease semantics into a latent space. In our experiments, we observed that integrating clinical notes with predictions based on textual laboratory values significantly enhanced the predictive capability of the unimodal model in the early detection of T2DM. Moreover, we achieved an area greater than 0.70 under the receiver operating characteristic curve (AUC) for new-onset T2DM prediction, demonstrating the effectiveness of leveraging textual laboratory data for training and inference in LLMs and improving the accuracy of new-onset diabetes prediction.

The major purpose of this paper was to examine the transmission of COVID-19 and the associated factors that affect the transmission. A qualitative analysis was conducted by comparing the COVID-19 transmission of six countries: China, Korea, Japan, Italy, the USA, and Brazil. This paper attempted to examine the mitigation effectiveness for the transmission of COVID-19 and the pandemic severity. Time to reach the peak of daily new confirmed cases and the maximum drop rate were used to measure the mitigation effectiveness, while the proportion of confirmed cases to population and the mortality rate were employed to evaluate the pandemic severity. Based on the mitigation effectiveness, the pandemic severity, and the mortality rate, the six sample countries were categorized into four types: high mitigation effectiveness vs. low pandemic severity, middle mitigation effectiveness vs. low pandemic severity, high mitigation effectiveness vs. high pandemic severity, and low mitigation effectiveness vs. high pandemic severity. The results found that Korea and China had relatively higher mitigation effectiveness and lower pandemic severity, while the USA and Brazil had the opposite. This paper suggests that viral testing together with contacts tracing, strict implementation of lockdown, and public cooperation play important roles in achieving a reduction in COVID-19 transmission.