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This research contributes to a innovative approach to address the increasing issues of workplace mental health and stress, particularly in high-pressure environments like assembly lines which also affects workers performance and companies productivity. Recognizing the harmful effects of stress on worker productivity, this study introduce stress-monitoring model using advanced machine learning techniques. Proposed model integrates Internet of Things (IoT) technology and machine learning techniques, utilizing a wearable watch to gather open-source physiological data indicative of workers stress in assembly lines. Key physiological markers, such as heart rate, respiration rate, and skin conductance, are analyzed. Based on these physiological indicators, the primary objective is to develop and validate framework that can accurately predict worker stress levels using IoT and machine learning models. The empirical results of the proposed approach demonstrate that the most effective model achieves an impressive accuracy score, with the XGBoost model providing 99% accuracy and Matthew’s correlation coefficient (MCC) of 0.99, surpassing the performance of Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (kNN), and Support Vector Machine (SVM). The practical implications of these findings suggest a significant potential for implementing such technology in high-stress work settings, offering a proactive tool for stress management and contributing to enhanced worker well-being and productivity.

Mainly, the prognostics and health management (PHM) process is based on three processes: the data acquisition and health assessment process in which sensors signals are acquired and processed, the diagnostic and prognostic process in which the source of failure is detected and the remaining useful life (RUL) is predicted and finally the decision- making process that refers to the term management in prognostics and health management. This paper reviews in the literature about the different aspects of decision-making in the context of PHM. The selected papers are subject to con- tent assessment and grouped according to the decision type. Additionally, this paper presents a synthesis of the previous works that helps identify new trends and deficiencies in the decision-making process. The synthesis can guide efforts for future work.

As the need for cloud services has been growing steadily, the size and energy consumption of datacenters have increased significantly over the past years. Due to economic and environmental constraints, energy efficiency in datacenters and greenhouse emissions have become a major concern. Renewable energy is widely seen as a promising solution to supply datacenters using local energy, without greenhouse gas emissions. However, the intermittent power generation resulting from the use of renewable energy imposes a paradigm change in the way energy and computation activities are managed. On the one hand, service placement and scheduling may be used on the IT (information technologies) side to adapt to the available power. On the other hand, the storage units may be used to lessen power generation variations. Existing literature and actual deployment mainly design optimization algorithms including the entire system (from cloud service to electrical management, the latter often being neglected or simplified). Conversely to these approaches, we propose a solution where each side optimizes its own objectives, both interacting through a negotiation loop process to reach a common agreement. In this paper, we present DATAZERO, a project developing this idea to ensure high availability of IT services, avoiding unnecessary redundancies, under the constraints due to the intermittent nature of electrical and cloud services flows.

Prognostics and health management have become increasingly important in recent years. Many research studies focus on a crucial phase consisting of predicting the remaining useful life of equipment or a component. However, this step is often carried out without taking into account the decisions that will be taken later. This article aims to propose a modification of the existing PHM framework to combine the prognostics and decision-making phases in a closed loop. In this paper, the presented framework is described and some elements for its implementation are proposed. A simplified example is developed to illustrate the presented methodology of post-prognostic decision enhancement.

Prognostic and Health Management (PHM) benefits are strongly tied to the decision-making that follows the assimilation and interpretation of prognostics information. Hence, we deal in this study with the post prognostic decision making in order to improve system safety and avoid downtime and inopportune maintenance spending. We investigate the problem of scheduling production jobs in a single multifunctional machine subjected to predictive maintenance based on PHM results. For this reason, we propose a new interpretation of PHM outputs to define the machine degradation corresponding to each job. We develop a Mixed Integer Linear Programming (MILP) model to find the best integrated scheduling that optimizes the total maintenance cost. Unfortunately, the MILP is not able to compute the optimal solution for large instances. Therefore, we design a Prognostic based Genetic Algorithm (Pro-GA). Computational results of different benchmarks setup show the efficiency and robustness of our scheme with an average deviation of about 0.2% over a newly proposed lower bound.