Myers–Briggs Type Indicator

In personality typology, the Myers–Briggs Type Indicator (MBTI) is an introspective self-report questionnaire indicating differing psychological preferences in how people perceive the world and make decisions. It enjoys popularity despite being widely regarded as pseudoscience by the scientific community. The test attempts to assign a binary value to each of four categories: introversion or extraversion, sensing or intuition, thinking or feeling, and judging or perceiving. One letter from each category is taken to produce a four-letter test result, such as "ISTJ" or "ENFP". The MBTI was constructed by two Americans: Katharine Cook Briggs and her daughter Isabel Briggs Myers, who were inspired by the book Psychological Types by Swiss psychiatrist Carl Jung. Isabel Myers was particularly fascinated by the concept of introversion and she typed herself as an INFP. However, she felt the book was too complex for the general public, and therefore she tried to organize the Jungian cognitive

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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

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

Evaluating snow models with varying process representations for hydrological applications

Tobias Jonas, Nander Wever

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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