Prévenir le feu : la communication et la sensibilisation vers le grand public.

Nous ne le répéterons jamais assez, la mémoire est courte en matière d'incendie de forêt. Même des personnes touchées par les incendies "oublient" de débroussailler leur propriété quelques années après ! De nombreux PV pour usage dangereux du feu ont été dressés en 2003 dans le Var. Cela montre une imprudence notoire de la part de la population inconsciente du risque, et donc l'importance de communiquer de manière permanente sur la question. L'expression "Vivre avec le feu" ne doit pas être synonyme de fatalisme, mais d'une prise de conscience collective du risque feux de forêt

Communiquer après le feu.

International audienc

A Review on Reinforcement Learning in Condition-based Maintenance

In this paper, we examine the implementation of reinforcement learning in the field of condition-based maintenance. It begins with a brief overview of the pertinent areas. In response to the query of why reinforcement learning is becoming an attractive tool in condition-based maintenance, an extensive review of reinforcement learning in this field is provided. Finally, the prospective direction of research is discussed. Condition-based maintenance involves monitoring the condition of a system and performing maintenance only when necessary, as opposed to traditional time-based maintenance, which maintains systems on a regular schedule regardless of their current state. Numerous industrial cases and academic studies have shown that condition-based maintenance yields superior results compared to time-based maintenance in a range of situations. Condition-based maintenance, like other maintenance strategies, seeks to maximize the system's availability while minimizing the maintenance cost. There are different approaches to describing a condition-based maintenance issue. A common one is to apply a parametric structure to the system under consideration. In other words, the maintained system is structured based on parameters such as the preventive maintenance threshold and the interval between inspections. Decision-makers must determine the optimal value of these parameters in order to minimize the average maintenance cost. This strategy has been studied exhaustively over the past several decades. Modularity is a substantial advantage of this method. We can alter the system's underlying degeneration process without altering the problem's overall structure. While it is possible to discover optimal values for the structure's parameters, it is challenging to demonstrate that this structure is the optimal structure for the problem. To surmount this limitation, recent research examines the condition-based maintenance problem as a sequential decision-making problem and formulates it as a Markov decision process and its variations. Besides the renowned dynamic programming method that can be used when the transition function of the MDP is known, reinforcement learning is a modern and flexible tool that can also be used to obtain solutions to MDP problems. Reinforcement learning is a class of machine learning techniques used to solve problems involving sequential decision-making. It is a method founded on rewards that guides a decision-maker to act rationally in a stochastic environment. In contrast to the structural parametric approach, the decision-maker must determine the maintenance action at each state encountered in order to minimize the maintenance cost. In other words, we seek a policy that minimizes the cost of maintenance. It is the key distinction between the parametric approach and the sequential decision-making approach, and it will be analyzed in detail in this paper: in the former, the policy is given at the outset, and optimal parameter values are sought for the parameterized policy, whereas in the latter, the policy must be determined. This new approach relaxes the predetermined structure and permits greater flexibility in the maintenance policy. This review also provides a classification of reinforcement learning algorithms according to the system's characteristics

Comparative pharmacodynamic and pharmacokinetic characteristics of subcutaneous insulin glulisine and insulin aspart prior to a standard meal in obese subjects with type 2 diabetes

Aims: A multinational, randomized, double-blind, two-way crossover trial to compare the pharmacokinetic and pharmacodynamic properties of bolus, subcutaneously administered insulin glulisine (glulisine) and insulin aspart (aspart) in insulin-naÏve, obese subjects with type 2 diabetes

A year in review in Minerva Anestesiologica 2019. Anesthesia, analgesia, and perioperative medicine

Innovations in anesthesia and perioperative pain management have animated the debate the last year

A year in review in Minerva Anestesiologica 2014

Year in revie

Acute mesenteric ischaemia in refractory shock on veno-arterial extracorporeal membrane oxygenation

Background: Acute mesenteric ischaemia is a severe complication in critically ill patients, but has never been evaluated in patients on veno-arterial extracorporeal membrane oxygenation (V-A ECMO). This study was designed to determine the prevalence of mesenteric ischaemia in patients supported by V-A ECMO and to evaluate its risk factors, as well as to appreciate therapeutic modalities and outcome. Methods: In a retrospective single centre study (January 2013 to January 2017), all consecutive adult patients who underwent V-A ECMO were included, with exclusion of those dying in the first 24 hours. Diagnosis of mesenteric ischaemia was performed using digestive endoscopy, computed tomography scan or first-line laparotomy. Results: One hundred and fifty V-A ECMOs were implanted (65 for post-cardiotomy shock, 85 for acute cardiogenic shock, including 39 patients after refractory cardiac arrest). Overall, median age was 58 (48-69) years and mortality 56%. Acute mesenteric ischaemia was suspected in 38 patients, with a delay of four (2-7) days after ECMO implantation, and confirmed in 14 patients, that is, a prevalence of 9%. Exploratory laparotomy was performed in six out of 14 patients, the others being too unstable to undergo surgery. All patients with mesenteric ischaemia died. Independent risk factors for developing mesenteric ischaemia were renal replacement therapy (odds ratio (OR) 4.5, 95% confidence interval (CI) 1.3-15.7, p=0.02) and onset of a second shock within the first five days (OR 7.8, 95% CI 1.5-41.3, p=0.02). Conversely, early initiation of enteral nutrition was negatively associated with mesenteric ischaemia (OR 0.15, 95% CI 0.03-0.69, p=0.02). Conclusions: Acute mesenteric ischaemia is a relatively frequent but dramatic complication among patients on V-A ECMO