Aportes de Frederick W. Taylor : Dos caras de una misma moneda

La escuela de la administración científica es el punto de partida de la administración contemporánea. Uno de sus principales exponentes fue Frederick W. Taylor con su obra Principios de Administración Científica. Sus estudios comienzan en el año 1880, en un contexto político de corte totalmente autoritario; en lo social, la legislación laboral y sindical era muy escasa y en cuanto a lo económico, el desarrollo de la tecnología cambiaba la realidad de las organizaciones. Una de las críticas que los manuales de administración hacen al autor es que trató al hombre como a un engranaje más de la maquinaria. Si bien es cierto que en la obra se hace referencia a la productividad del hombre y de la máquina y que hay párrafos a través de los cuales podría inferirse esta asimilación hombre-máquina, no puede dejar de mencionarse que Taylor estudió al hombre y sus capacidades, con los recursos intelectuales de que dispuso, en ese contexto histórico y social al que se hace referencia. En ese marco el objetivo del presente trabajo es analizar y reflexionar sobre la mencionada obra de Taylor, plasmando diferentes perspectivas, intentando una crítica constructiva que promueva una mirada más amplia respecto de sus aportes

A high-resolution record of the atmospheric CO2 concentration from 60-20 kyr BP from the Taylor Dome ice core, Antarctica

A high-resolution record of the atmospheric CO2 concentration from 60 to 20 thousand years before present (kyr BP) based on measurements on the ice core of Taylor Dome, Antarctica is presented. This record shows four distinct peaks of 20 parts per million by volume (ppmv) on a millennial time scale. Good correlation of the CO2 record with temperature reconstructions based on stable isotope measurements on the Vostok ice core (Antarctica) is found.

Freshwater runoff for Taylor Slough to Florida Bay for 1965 to 2000 for the FATHOM model

The Florida Bay ecosystem supports a number of economically important ecosystem services, including several recreational fisheries, which may be affected by changing salinity and temperature due to climate change. In this paper, we use a combination of physical models and habitat suitability index models to quantify the effects of potential climate change scenarios on a variety of juvenile fish and lobster species in Florida Bay. The climate scenarios include alterations in sea level, evaporation and precipitation rates, coastal runoff, and water temperature. We find that the changes in habitat suitability vary in both magnitude and direction across the scenarios and species, but are on average small. Only one of the seven species we investigate (Lagodon rhomboides, i.e., pinfish) sees a sizable decrease in optimal habitat under any of the scenarios. This suggests that the estuarine fauna of Florida Bay may not be as vulnerable to climate change as other components of the ecosystem, such as those in the marine/terrestrial ecotone. However, these models are relatively simplistic, looking only at single species effects of physical drivers without considering the many interspecific interactions that may play a key role in the adjustment of the ecosystem as a whole. More complex models that capture the mechanistic links between physics and biology, as well as the complex dynamics of the estuarine food web, may be necessary to further understand the potential effects of climate change on the Florida Bay ecosystem.

Prediction of PM10 concentrations using Fuzzy c-Means and ANN

Salamanca has been considered among the most polluted cities in Mexico. The vehicular park, the industry and the emissions produced by agriculture, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Particulate Matter less than 10 μg/m3 in diameter (PM10). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM10. Before the prediction, Fuzzy c-Means clustering algorithm have been implemented in order to find relationship among pollutant and meteorological variables. These relationship help us to get additional information that will be used for predicting. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results shown that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours