Technological innovation has led to the development of increasingly efficient and complex industrial plants. To manage this complexity, it is necessary to define an integrated vision of the socio-technological system that includes: technological, human and organizational component. Petrochemicals can be considered one of the most complex socio-technical systems that deserve special attention to high risk management, especially during the emergency conditions. Traditional safety management models only consider static systems, while new resilience engineering models evaluate the performance variability developed between different actions. One of the recent development methods is the Functional Resonance Analysis Method (FRAM) that identifies the pairs between the functions. FRAM unfortunately is a qualitative model, this research integrates this model with the Performance Shaping Factors (PSFs) and with the Bayesian approach to identify the performance variability of the system. The analysis aims to develop a system that improves safety analysis. The proposed model is applied in a case study of an emergency in a petrochemical company.

The history of Artificial Intelligence (AI) development dates to the 40s. The researchers showed strong expectations until the 70s, when they began to encounter serious difficulties and investments were greatly, reduced. With the introduction of the Industry 4.0, one of the techniques adopted for AI implementation is Machine Learning (ML) that focuses on the machines ability to receive data series and learn on their own. Given the considerable importance of the subject, researchers have completed many studies on ML to ensure that machines are able to replace or relieve human tasks. This research aims to analyze, systematically, the literature on several aspects, including publication year, authors, scientific sector, country, institution, keywords. Analyzing existing literature on AI is a necessary stage to recommend policy on the matter. The analysis has been done using Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software have been used to complete them. Literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by USA and the increasing interest after the birth of Industry 4.0.

Although conventional chemotherapeutic drugs are the first line of treatment for cancer, they have numerous side effects. One of the emerging challenges in cancer treatment is drug resistance. Nat-ural compounds have proven effective against various hallmarks of cancer for their multi-target inhibition properties, but especially for their ability to synergistically bypass low bioavailability. Methods: The present study investigated the in vivo antitumor effects of a combination of two natural dietary agents, epigallocatechin-3-gallate (EGCG) from Camelia Sinensis and curcumin, a component of turmeric (Curcuma longa). We aimed to compare, for the first time in vivo, the antiangiogenic and antitumor effects of sunitinib with the combination of curcumin and EGCG in a mouse model of renal cell carcinoma (ccRCC). It was shown that they are able to inhibit cell survival, proliferation of several type of cancer, including renal cell carcinoma (ccRCC), by mod-ulating different signaling pathways and that their combination respect to single compound syn-ergistically decreased angiogenesis. Results: Herein, we highlighted that these compounds inhib-ited the growth of xenografted renal cancer in nude mice by significant inhibition of tumor vol-ume, tumor weight and CD31 expression with no signs of hepatic toxicity. Moreover, mice treated with these natural compounds showed a significant reduction in angiogenesis and an improve-ment in survival rate with p<0.05. Finally, pretreatment of mice with a diet containing 0.6% curcumin before injection of tumor cells showed a significant inhibition of tumor engraftment in 60% of mice with respect to controls and other groups. Conclusions: Taken together, our data in-dicate, for the first time, that the combination of curcumin and EGCG acts in a synergistic manner to inhibit the growth and angiogenesis of ccRCC and with less toxicity than sunitinib and provide an important rationale for future clinical development for chemoprevention and treatment of re-nal cancer.