The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

Ras activates a series of downstream effectors, including the mitogen-activated protein kinase pathway and the Rac/Rho pathway after insulin stimulation. Mutations in Ras are found in approximately 30% of all human cancers and are critical factors in tumor initiation and maintenance. There are four Ras proteins with 80-90% amino acid sequence homology with major differences in the carboxyl termini. Ras proteins undergo farnesylation on their carboxyl termini catalyzed by the enzyme protein farnesyltransferase (FTase), which facilitates localization of Ras proteins to the inner surface of the plasma membrane. Because inhibition of FTase would prevent Ras from processing into its active form, FTase is viewed as a potential therapeutic target. A variety of FTase inhibitors have showed great potency against tumor cells in preclinical studies. Although many farnesyltransferase inhibitors have been developed, their adverse effects on the mitogenic and metabolic actions of insulin are not completely understood. Here we show that YH3096, a farnesyltransferase inhibitor, inhibits insulin-mediated DNA synthesis in HIRc-B cells without affecting c-Jun expression and membrane ruffling in HIRc-B cells. Moreover, YH3096 and its derivatives did not affect insulin-induced glucose uptake in 3T3-L1 adipocytes. Our results provide a laboratory evaluation of the effects of Ras inhibitors on insulin functions.

Purpose: The problem of housing poverty among young people is a very important problem for the nation. Therefore, the main purpose of this paper is to identify the problems of the government's housing support policy for young people. And it is in presenting specific solutions by fully reflecting the opinions of experts. Research design, data and methodology: This study consisted of analyzing the following three research topics: 1) the differences of youth residential support housing policy impact on young adults’ housing stability, 2) the problems and solutions of youth housing support policy, and 3) the differences of experts' opinions on the impact of government policy on youth housing stability. The subject of this study is the government's seven housing policies for young people. The targets include Happy Public Rental Housing (Happiness Housing), Station Area Rental Housing for youth (Station Area 2030), Public Dormitory for College Students (Public Dormitory & Hope Dormitory), Jeonse Rental Housing for College Students (Subject Lease Rental Housing for College Students), Social Housing for Young People, and Share House. The data was organized through expert surveys from 1st to 30th June 2020. The experts surveyed include professors & researchers, public officer & public institutions staff, and private developers of young adults’ housing. The methodology of analysis on the problem and the solution of government policy was Frequency analysis. And analysis methods on differences of experts’ opinion were ANOVA, Levene’ test, and Schefe test. Results: Problems in Government's youth residential support housing policy include high rents, lack of supply, difficulty in acquiring rental housing, inconvenience in using shared spaces, conflicts with cohabitants, and invasion of privacy. Solutions include expanding supply to urban areas, establishing long-term plans, securing privacy, diversifying business methods, establishing platforms for rental housing transactions, and expanding various public support (financial support, etc). Conclusions: There was a difference in perception among groups of experts on the impact of public rental housing (called ‘happiness housing’) in youth housing stability. It is very urgent to come up with the most reasonable policy to support youth housing. This requires in-depth discussions by experts to narrow their differences.