Compound interest

Summary

Compound interest is the addition of interest to the principal sum of a loan or deposit, or in other words, interest on principal plus interest. It is the result of reinvesting interest, or adding it to the loaned capital rather than paying it out, or requiring payment from borrower, so that interest in the next period is then earned on the principal sum plus previously accumulated interest. Compound interest is standard in finance and economics. Compound interest is contrasted with simple interest, where previously accumulated interest is not added to the principal amount of the current period, so there is no compounding. The simple annual interest rate is the interest amount per period, multiplied by the number of periods per year. The simple annual interest rate is also known as the nominal interest rate (not to be confused with the interest rate not adjusted for inflation, which goes by the same name). Compounding frequency The compounding frequency is the number

Official source

https://en.wikipedia.org/wiki/Compound_interest

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District scale prediction of subsurface waste heat flows

Due to increasing urbanization and scarcity of land, the need to include the underground in urban planning is growing, especially in cities. The human-made underground structures generate an anthropogenic heat flow which has a significant impact on the ground temperature and leads to the creation of urban underground heat islands (UUHI). In parallel with the urbanization, the consumption of energy increases and the interest in sustainable energy production from thermal energy sources is growing. The UUHI have a high geothermal potential and are therefore of great interest for energy production. A sustainable evaluation of the geothermal potential in cities depends however on many factors and is therefore a complex procedure in which different scenarios must be assessed. This master thesis proposes a machine learning (ML) based approach for the evaluation of the underground waste heat flow of buildings. ML techniques are suitable to very quick and accurate modeling and can therefore be of great interest in the field of complex geothermal potential evaluation. In this project we will focus on the ground temperature change due to heat losses of basements. To this effect, 2D heat maps are created for a given depth and a given simulation time period. In this study, a comprehensive validation of the proposed ML model (random forest algorithm) is presented for different small scale scenarios, using various heat source geometries, locations, numbers and thermal loads. Furthermore, we study the heat maps for diverse depths and simulation time periods. To do so, we assume only a conductive heat flow mode and constant boundary conditions. The proposed methodology involves having access to fully solved numerical simulation to create a data set which is used to train and test the ML algorithm. For this purpose, the finite element software package COMSOL is used. We evaluate and discuss the accuracy of the proposed ML approach on each studied scenario. The results promise a big potential all while requiring only few solved FE models. We obtain a root mean squared error of always less than 5 to 10% depending on the scenario. At the end of this paper a district scale application of the proposed ML technique is presented on the Loop district in Downtown Chicago, US. It will be shown that 25% of the total Loop data from the FE model is sufficient to learn the ML model and to predict the whole district with a root mean squared error of less than 5%.

2020

During the last twenty years we have witnessed the steady rise in academic research and practitioner interest and inquiry on the topic of business models. The topic is still in its infancy, and thus a lot of literature hitherto has focused on the basics: definitions and conceptualizations have dominated in academic literature, providing little support to practitioners who are trying to design, develop, and deploy new business models. Whereas innovative market entrants have grabbed the limelight for academics and business leaders, incumbent firms have had scarce attention, especially large, complex multinationals. There is much to be learned from these businesses in terms of the nature of the components of a business model, the dimensions of analysis, and the relationship between their strategies and business models. The goal of this body of research is to shed light on the mechanics of business model reconfiguration in complex enterprises. As its main research contribution, this dissertation introduces the Business Model Beacon: a new framework with which to characterize business models: defining the complementarity of the strategy and business model, mapping the central and peripheral components of the business model at different dimensions of analysis of the firm. To understand the mechanics of business model reconfiguration, this dissertation presents a case study on the North American unit of consumer products giant Unilever, portrayed through the lens of the Beacon framework.

EPFL2016

Suivi environnemental d’herbicides en Valais par échantillonnage passif sur POCIS et évaluation de l’emploi de PRC

Robin Udrisard

Les capteurs passifs comme les POCIS (Polar Organic Chemical Integrative Sampler) sont des outils de choix pour le contrôle des quantités de micropolluants dans les cours d’eau. Le caractère continu de l’échantillonnage par capteurs passifs permet par ailleurs des comparaisons spatio-temporelles ce qui les rend particulièrement intéressants pour la recherche de sources de pollution. Dans ce projet, une campagne a été réalisée pour suivre l’évolution des quantités de six herbicides dans deux cours d’eau en Valais (La Morge et le canal Sion-Riddes). En parallèle à cette campagne, l’utilisation de quatre PRC (Performance Reference Compound) a été évaluée. Le but des PRC est d’atténuer les effets de divers facteurs environnementaux (vitesse, température, pH, biofouling) sur les mesures, ceci afin d’améliorer les estimations de la concentration dans l’eau d’une part, et de valider les différences éventuelles entre deux points de mesure d’autre part. Pour évaluer l’utilisation des PRC, des taux d’échantillonnage (Rs) ont été déterminés par calibration in situ (Rs in situ) dans la Morge et dans une autre rivière (Venoge). Les Rs pour une série de capteurs donnée ont été adaptés avec les PRC pour estimer les Rs d’autres séries. Ces estimations ont finalement été comparées avec les Rs in situ. Dans les deux cours d’eau en Valais, le diuron, le linuron, la simazine, la terbuthylazine et dans une moindre mesure l’atrazine étaient présents. Le métolachlor n’a pas été détecté. Une tendance à une plus forte pollution durant le mois d’avril a en outre été mise en évidence. Les Rs calculés par calibration in situ étaient variables entre différents sites et différentes semaines sur un même site. Trois des quatre composés testés comme PRC (isoproturon-d6, terbutryne-d5 et irgarol-d9) ne se sont pas désorbés sur une immersion de 21 jours et sont donc inutilisables. La DIA-d5 a quant à elle montré une désorption mais des résultats mitigés pour la correction de taux d’échantillonnage. Son utilisation comme PRC nécessite donc encore des recherches.

2013

District scale prediction of subsurface waste heat flows

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2013