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5 clés pour comprendre et gérer le syndrome métabolique

I.3. SYNDROME METABOLIQUE (SM) I.3.1. Définition Le syndrome métabolique (SM), comme son nom l‘indique n’est pas une maladie spécifique mais un syndrome. Un syndrome est un ensemble reconnu de symptômes sans cause évidente. Les composantes du syndrome coexistent assez régulièrement pour que leur apparition ne soit pas attribuée au hasard. Lorsque la cause est clairement […]

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5 clés pour comprendre l’index glycémique des aliments

I.1. GENERALITES SUR LES PRINCIPES NUTRITIFS I.3.1. Définition L’index glycémique est la mesure qui permet de décrire l’influence de la consommation de glucides sur la glycémie (taux de glucose dans le sang). Il est utilisé pour décrire le type de glucides contenus dans un aliment et susceptibles d’avoir une influence sur l’augmentation du taux de

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5 astuces pour une alimentation équilibrée et saine

Chapitre I : revue de la littérature I.1. Généralités sur les principes nutritifs Les principes nutritifs désignent l’ensemble des substances utiles au développement d’un organisme. Ils sont classés en deux grandes catégories dont les principes nutritifs énergétiques constitués des protides, lipides, glucides, l’alcool et les principes nutritifs non énergétiques comprenant les matières minérales (l’eau et

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Evaluating risk with precision: How SERAT outperforms traditional models in project management

VALIDATION AND DISCUSSION Sensitivity analysis of the model To get a better level of confidence about the model that we designed, a simple sensitivity analysis has been made. In order to evaluate the value of sensitivity analysis first the model needs to be tested by validation against other results elsewhere. So we choose as a

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Decoding complex risks: a step-by-step guide to final risk level interpretation and management strategies

Step 5: Final risk level and Interpretation In this step, the decision maker will appreciate the final value of the project risk, and give some explanations about it. For each value of the risk corresponds a suitable interpretation that will be used further in the overall risk management. Final risk level The system output variable

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Mastering project risks: 3-step guide to effective measurement and management

Step 3: Risk level measurement Projects with high-impact/low- probability risks need to be continually monitored to make sure the risk probabilities don’t change, Taylor says. If the probability of a high-impact event increases, there should be no debate as to what that means for the project. This is why this step is somehow undefeatable for

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Enhancing project risk assessment: a 6-step fuzzy model approach

AN IMPROVED FUZZY MODEL FOR PROJECT RISK ASSESSMENT A brief Summary of the proposed Approach The purpose of this study is to design and develop a Fuzzy decision making support model to assist project managers in identifying potential risk factors and evaluating the corresponding development risks. Different approaches have been created to assess the various

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Navigating risks: 5 key strategies for decision making in solar energy projects

CURRENT APPROACHES FOR DECISION MAKING IN SOLAR ENERGY PROJECT RISK ASSESSMENT General situation of solar energy risk assessment The use of photovoltaic (PV) solar systems for the generation of electric power has increased dramatically All over the world in recent years. The reason for this has been, on the one hand, the recent development of

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Demystifying fuzzy logic: A concise guide to mastering uncertainty

Fuzzy logic theory and systems Introduction to fuzzy logic Background To deal with vagueness in human thought, Lotfi A. Zadeh (1965) first introduced the fuzzy set theory, which has the capability to represent and manipulate data and information possessing based on non-statistical uncertainties. Moreover fuzzy set theory has been designed to mathematically represent uncertainty and

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Enhancing solar project risk management: a fuzzy logic approach

This thesis develops a fuzzy logic–based decision-making model to improve project risk assessment, focusing on solar energy projects. By using MATLAB’s Fuzzy Logic Toolbox, the research proposes a computer-assisted tool for project managers to evaluate risks under uncertainty. The approach enhances early risk identification, prioritization, and decision-making, offering cost savings, improved accuracy, and practical application

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