Myers Briggs Type Indicator

Résumé

Le Myers Briggs Type Indicator (MBTI) est un outil d'évaluation psychologique déterminant le type psychologique d'un sujet parmi seize types différents, suivant une méthode proposée en 1962 par Isabel Briggs Myers et Katherine Cook Briggs. Il sert comme outil dans les identifications des dominantes psychologiques des personnes dans des cadres liés au management ou aux problèmes dans le cadre des relations interpersonnelles. Bien que le MBTI s'apparente à certaines théories psychologiques, il a été qualifié de pseudoscience et n'est jamais utilisé par les chercheurs universitaires dans le domaine de la psychologie, ni par l'immense majorité des praticiens. L'indicateur présente d'importantes lacunes scientifiques (psychométriques), notamment :

une faible validité (c'est-à-dire ne pas mesurer ce qu'il est censé mesurer, ne pas avoir de pouvoir prédictif ou ne pas avoir d'items qui peuvent être généralisés) ;
une mauvaise fiabilité (donnant des résultats différents pour la même per

Source officielle

https://fr.wikipedia.org/wiki/Myers_Briggs_Type_Indicator

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Publications associées (14)

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Concepts associés (14)

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Cours associés (3)

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Séances de cours associées (3)

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RNA velocity of single cells

Gioele La Manno, Zehua Liu

RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput 1 . However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

2018

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SCCER-FURIES - Definition of optimal control of DWH for self-consumption strategies

Lionel Benjamin Bloch, Jordan Holweger

The massive integration of renewable energy, and the electrification of the building heating systems are required in order to meet the Paris agreement’s CO2 emission target. Heat pumps and PV is expected to penetrate quickly in the market come with significant investment costs. To maximize the profit and advantage of both technologies without increasing their management complexity, novel algorithms guaranteeing close-to-optimal operations should be demonstrated. In this work, a novel heuristic control algorithm is presented. The algorithm optimizes an indicator comparing the variation of the operating cost and the generated heat. It is versatile and agnostic of the building modeling complexity. The benchmark against a mixed-integer linear formulation of the energy management shows that the proposed algorithm performs closely to the optimal solution. The differences in terms of operating costs and temperature deviations are negligible. The simplicity of the algorithm makes it suitable for implementing in a micro-controller as a state machine.

2020

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Much effort has been invested in developing snow models over several decades, resulting in a wide variety of empirical and physically based snow models. For the most part, these models are built on similar principles. The greatest differences are found in how each model parameterizes individual processes (e.g., surface albedo and snow compaction). Parameterization choices naturally span a wide range of complexities. In this study, we evaluate the performance of different snow model parameterizations for hydrological applications using an existing multimodel energy-balance framework and data from two well-instrumented alpine sites with seasonal snow cover. We also include two temperature-index snow models and an intensive, physically based multilayer snow model in our analyses. Our results show that snow mass observations provide useful information for evaluating the ability of a model to predict snowpack runoff, whereas snow depth data alone are not. For snow mass and runoff, the energy-balance models appear transferable between our two study sites, a behavior which is not observed for snow surface temperature predictions due to site-specificity of turbulent heat transfer formulations. Errors in the input and validation data, rather than model formulation, seem to be the greatest factor affecting model performance. The three model types provide similar ability to reproduce daily observed snowpack runoff when appropriate model structures are chosen. Model complexity was not a determinant for predicting daily snowpack mass and runoff reliably. Our study shows the usefulness of the multimodel framework for identifying appropriate models under given constraints such as data availability, properties of interest and computational cost.

American Geophysical Union2015

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