Constructing an active health and safety performance questionnaire in the food manufacturing industry.

To design a comprehensive health and safety management performance system, extant literature on the health and safety performance indicators of and management systems for the application of occupational health and safety management systems was reviewed; additionally, the provisions of occupational health and safety laws were examined with a total of three main categories, including 28 active safety and health management performance categories. In the present study, health and safety management performance was evaluated by food manufacturing industry employees. An active performance evaluation questionnaire was developed by adopting the Delphi method to seek professional and expert opinion. With food manufacturing workers as participants, an in-depth discussion was conducted regarding the status of active health and safety performance indicators. Six active health and safety performance indicators were determined: emergency response; change management; procurement management; communication; prevention management; security behavior. These performance indicators have not been sufficiently implemented and require improvement.

Using Internet Search Trends and Historical Trading Data for Predicting Stock Markets by the Least Squares Support Vector Regression Model.

Historical trading data, which are inevitably associated with the framework of causality both financially and theoretically, were widely used to predict stock market values. With the popularity of social networking and Internet search tools, information collection ways have been diversified. Instead of only theoretical causality in forecasting, the importance of data relations has raised. Thus, the aim of this study was to investigate performances of forecasting stock markets by data from Google Trends, historical trading data (HTD), and hybrid data. The keywords employed for Google Trends are collected from three different ways including users' definitions (GTU), trending searches of Google Trends (GTTS), and tweets (GTT) correspondingly. The hybrid data include Internet search trends from Google Trends and historical trading data. In addition, the correlation-based feature selection (CFS) technique is used to select independent variables, and one-step ahead policy is adopted by the least squares support vector regression (LSSVR) for predicting stock markets. Numerical experiments indicate that using hybrid data can provide more accurate forecasting results than using single historical trading data or data from Google Trends. Thus, using hybrid data of Internet search trends and historical trading data by LSSVR models is a promising alternative for forecasting stock markets.