The workplace, and more particularly the healthcare sector, has recently experienced a staggering increase in violence. These aggressive behaviors are resulting in considerable consequences on healthcare workers, both in terms of mental and physical health. In light of this observation, this study aims to provide a quantitative analysis of the potential causes leading to violence in Algerian hospitals, which have become the place where tensions arise, especially during these uncertain times caused by the COVID-19 pandemic. Therefore, we started with conducting a field survey, in order to highlight the main causes behind this violence, as well as the strategy in terms of how it is managed as a risk. Then we used ISHIKAWA diagrams to classify predefined causes into several categories and anticipate the likelihood of such violent behaviors. Finally, the results of this study revealed that working conditions were the main cause of violence in Algerian hospitals. In order to remedy this gap, we recommend improving the healthcare staff well-being, as well as prioritizing proactive measures preventing violent behaviors.

The increasing complexity of most industrial processes always tends to create problems in monitoring and supervision systems. Detection and early fault diagnosis are the best way to manage and solve these problems. Artificial neural networks (ANNs), by their ability to learn and store a large volume of information, are tools particularly suitable for diagnostic support systems. Effectiveness of ANNs for fault diagnosis in milk pasteurization process is presented in this paper. The initial data base used for fault diagnosis is constructed using data extracted from FMEA (Failure Modes and Effects Analysis) tables of milk pasteurization process. Indeed, this analysis makes it possible to establish the links of cause and effect between the faulty components and the observed symptoms. Three models of ANNs, namely Feed-Forward Back Propagation (FFBP), Radial Basis Function based Neural Network (RBNN), and Generalized Regression Neural Networks (GRNN) are developed and compared. The determination coefficient (R2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) statistics were used as evaluation criteria of all the models. The comparison results indicate that the performances of GRNN model are better than the FFBP and RBNN models. The same neuronal models can be extended to any technical system by considering appropriate parameters and defects.

The decision process is considered as the key element for the smooth behavior of systems. For non-complex or linear systems, this process usually follows clear rules or requirements, so that the decision will be easy to make without any difficulty. In the case of complex (non-linear) systems, the decision-making process is repeatedly challenged problems because of the interactions of several factors at the same time and because of the hazards characterizing these factors. This situation will worsen further if the system goes out of its normal operating range to fall into uncertainty. In this paper, we will try to make our contribution, to arrive at simplifying the decision-making process, while playing on human factors (ability, anticipation, risk-taking, etc.). As this process can be improved by the development of intelligent decision support tools.

Malaysian construction industry is considered as substantial power for the economy of the country. The value of construction work is increasing year per year. Malaysia's economy experienced a commendable growth the recent years; basically, the construction industry is statistically significant with economic development and has strong linkages with other industry sectors. However, the Malaysian construction industry is facing many challenges especially the fatal accidents. These last came first on top of the construction sector compared all other industries. This article describes the importance of the construction sector for Malaysia's economy and its efficient contribution across all states of Malaysia. Furthermore, detailed statistics and analysis were carried out to describe the severity of fatal accidents occurred in construction industry of Malaysia. Besides, recommendations were listed in order to better manage the construction industry without involving in fatal accidents. By taking into account the significant influence of the construction industry on Malaysian economy, full attention should be given to the construction field by the Malaysian government and improve further the construction management in general and the construction safety in particular.