An intelligent framework for performance optimisation of integrated management system and resilience engineering in pharmaceutical plants

An integrated approach for evaluating and improving the performance of surgical theaters with resilience engineering

Operating rooms are among the most high-risk and vital parts of a hospital. Therefore, one of the most fundamental tasks of risk management is maintaining the safety of operating rooms. Resilience engineering (RE) can be introduced as a model for overcoming problems, and it seeks ways to raise success rates by focusing on and addressing complexities. To this end, an RE-based framework is presented to evaluate the performance of operating rooms. First, the RE indicators are identified, and the relative importance of each is calculated via the best-worst method (BWM). Subsequently, the required data are collected from operating room experts using a standard questionnaire. Next, a data envelopment analysis (DEA) method is employed to evaluate the performance of operating rooms in the study case. Lastly, drawing upon the sensitivity analysis and statistical tests, the effect of each RE indicator is examined on the surgical department. Accordingly, some improvement approaches are proposed. Besides, SWOT (strengths, weaknesses, opportunities, and threats) analysis is used to extract appropriate strategies to improve performance. To the best of our knowledge, this paper is the first to evaluate the performance of operating rooms quantitatively in terms of RE indicators, and the framework presented in this paper can have practical applications in different operating rooms.

How Technological Innovation Affect China's Pharmaceutical Smart Manufacturing Industrial Upgrading

In recent years, a new generation of information technology has provided sufficient technical support for the smart manufacturing industry. In order to promote the upgrading of China's pharmaceutical smart manufacturing industry, the direction of industrial upgrading and transformation will be discussed from the perspective of technological innovation. According to the input and output data of technological innovation in China's pharmaceutical manufacturing industry from 2007 to 2019, the DEA method is used to analyze the allocation of innovative resources in China's pharmaceutical manufacturing industry in recent years. The study found that the efficiency of technological innovation in China's pharmaceutical manufacturing industry fluctuated greatly from 2007 to 2019, with a low overall level and varying degrees of wasted resources. On this basis, an in-depth analysis of the system architecture of the pharmaceutical smart manufacturing industry under the Industry 4.0 environment was performed. Finally, four paths for the digital transformation of China's pharmaceutical manufacturing industry are proposed. Chinese pharmaceutical manufacturing companies need to use new technologies to carry out comprehensive intelligent upgrading and digital transformation to improve innovation efficiency.

Public service hot issue discovery with binary differential evolution algorithm based on fuzzy system theory

Social media is becoming more and more closely related to the real life. More and more netizens choose to obtain news and publish notice through social networks. Such huge amount of social media information generated by these users contains a lot of information related to hot topics and events. At the same time, problem of information overload has posed a challenge for people to use the information. It has become an important research issue to discover and track hot events and topics automatically from mass social media data. On the one hand, the short, highly noisy and real-time features of the social media data bring challenges to the discovery and tracking methods of traditional hot issues. On the other hand, the social media data contains abundant information of geography, time, and social relations, which brings great convenience to relevant researches. Based on these features of the social media data, this paper makes a deep study on the discovery, extraction, and tracking of hot issues in the social media based on fuzzy system theory and the word vector semantic clustering.