Resistencia de un concreto con cemento sustituido parcialmente por lodo de papel y concha de abanico

La siguiente investigación estuvo enfocada en determinar y comparar la resistencia a compresión de un concreto F´c = 210kg/cm², con otros donde se sustituirá al cemento por la combinación de ceniza de lodo de papel de la empresa Trupal S.A. (ubicada en Santiago de Cao ? La Libertad) y polvo de concha de abanico (extraída de Chimbote - Ancash), en porcentajes de 10% y 15%. Ambos materiales sustituyentes del cemento, después de ser activados térmicamente, obtuvieron gran capacidad cementante por sus altos contenidos de silicio, aluminio y calcio. Consiguientemente se elaboraron las probetas de concreto patrón y las probetas de concreto experimentales, siendo estas sometidas a ensayo de compresión a los 7, 14 y 28 días de curado, determinándose que cuando el cemento fue sustituido en un 10%, la resistencia final fue mayor, alcanzando una F´c = 236.76 Kg/cm2 que representa un 112.74%; mientras que cuando el cemento fue sustituido en un 15% la resistencia final fue menor, alcanzando una F´c = 207.31 Kg/cm2 que representa un 98.72% ,ambas en comparación con el concreto patrón.Tesi

Evolutionary dynamics of group formation

This is an open access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Group formation is a quite ubiquitous phenomenon across different animal species, whose individuals cluster together forming communities of diverse size. Previous investigations suggest that, in general, this phenomenon might have similar underlying reasons across the interested species, despite genetic and behavioral differences. For instance improving the individual safety (e.g. from predators), and increasing the probability to get food resources. Remarkably, the group size might strongly vary from species to species, e.g. shoals of fishes and herds of lions, and sometimes even within the same species, e.g. tribes and families in human societies. Here we build on previous theories stating that the dynamics of group formation may have evolutionary roots, and we explore this fascinating hypothesis from a purely theoretical perspective, with a model using the framework of Evolutionary Game Theory. In our model we hypothesize that homogeneity constitutes a fundamental ingredient in these dynamics. Accordingly, we study a population that tries to form homogeneous groups, i.e. composed of similar agents. The formation of a group can be interpreted as a strategy. Notably, agents can form a group (receiving a ‘group payoff’), or can act individually (receiving an ‘individual payoff’). The phase diagram of the modeled population shows a sharp transition between the ‘group phase’ and the ‘individual phase’, characterized by a critical ‘individual payoff’. Our results then support the hypothesis that the phenomenon of group formation has evolutionary roots.Peer reviewedFinal Published versio

Resistencia de un concreto con cemento sustituido parcialmente por lodo de papel y concha de abanico

TesisLa siguiente investigación estuvo enfocada en determinar y comparar la resistencia a compresión de un concreto F´c = 210kg/cm², con otros donde se sustituirá al cemento por la combinación de ceniza de lodo de papel de la empresa Trupal S.A. (ubicada en Santiago de Cao ? La Libertad) y polvo de concha de abanico (extraída de Chimbote - Ancash), en porcentajes de 10% y 15%. Ambos materiales sustituyentes del cemento, después de ser activados térmicamente, obtuvieron gran capacidad cementante por sus altos contenidos de silicio, aluminio y calcio. Consiguientemente se elaboraron las probetas de concreto patrón y las probetas de concreto experimentales, siendo estas sometidas a ensayo de compresión a los 7, 14 y 28 días de curado, determinándose que cuando el cemento fue sustituido en un 10%, la resistencia final fue mayor, alcanzando una F´c = 236.76 Kg/cm2 que representa un 112.74%; mientras que cuando el cemento fue sustituido en un 15% la resistencia final fue menor, alcanzando una F´c = 207.31 Kg/cm2 que representa un 98.72% ,ambas en comparación con el concreto patrón

Machine learning within commercial real estate : Prediction of future vacancies

The purpose of this thesis is to investigate the possibilities of predicting vacancies in the real estate market by using machine learning models in terms of classification. These models were mainly based on data from contracts between a Swedish real estate company and their tenants. Attributes such as annual renting cost and rental area for each contract were supplemented with additional data regarding financial and geographical information about the tenants. The data was stored in three different formats with the first having binary classes which aim is to predict if the tenant is moving out within a year or more. The format of the second and third version were both multi classification problems that aims to classify if the tenants might terminate their contract within a specific interval with the length of three and six months. Based on the results from Microsoft Azure Machine Learning Studio, it is discovered that the multi classification problems perform rather poorly due to the classes being unbalanced. Regarding the  performance of the binary model, a more satisfying result was obtained but not to the extend to say that the model can be used to determine a vacancy with high accuracy. It should rather be used as a risk analysis tool to detect if a tenant is showing tendencies that could result in a future vacancy. A major pitfall of this thesis was the lack of data and the financial information not being specific enough. The performance of the models will likely increase with a larger dataset and more accurate financial information.

Machine learning within commercial real estate : Prediction of future vacancies

The purpose of this thesis is to investigate the possibilities of predicting vacancies in the real estate market by using machine learning models in terms of classification. These models were mainly based on data from contracts between a Swedish real estate company and their tenants. Attributes such as annual renting cost and rental area for each contract were supplemented with additional data regarding financial and geographical information about the tenants. The data was stored in three different formats with the first having binary classes which aim is to predict if the tenant is moving out within a year or more. The format of the second and third version were both multi classification problems that aims to classify if the tenants might terminate their contract within a specific interval with the length of three and six months. Based on the results from Microsoft Azure Machine Learning Studio, it is discovered that the multi classification problems perform rather poorly due to the classes being unbalanced. Regarding the  performance of the binary model, a more satisfying result was obtained but not to the extend to say that the model can be used to determine a vacancy with high accuracy. It should rather be used as a risk analysis tool to detect if a tenant is showing tendencies that could result in a future vacancy. A major pitfall of this thesis was the lack of data and the financial information not being specific enough. The performance of the models will likely increase with a larger dataset and more accurate financial information.

Tintype - Mr. & Mrs. Drope and Son Harold Drope, Niagara Falls

A tintype of Mr. and Mrs. Drop with infant son Harold Drope. The caption on reverse reads "Grampa Drope, Nannie (Mrs. W. S. Drope), Harold Drope. at Niagara Falls? 1897?". There is a second copy of the tintype that includes the name of the company that created the tintype, "Johnsons Union Tin Type Gallery, Niagara Falls, N.Y. Falls, Whirlpool, Rapids, Bridge, Etc. For Background, P.S. Finest Tin Types at Niagara"