Evaluation of neighborhood impacts caused by companies of the high-tech industrial cluster of São Carlos, SP, Brazil

The purpose of this study was to develop a methodology for evaluating neighborhood impacts using a Geographic Information System (GIS) and to apply the procedures to the companies of the High-Technology Industrial Cluster of São Carlos. To this end, an evaluation was made of the neighborhood impacts on the physical environment, urban components, quality of life, and urban infrastructure using impact matrices, and the impacts were assigned scores according to type, order, magnitude and duration. Fifty one companies were examined based on data provided by the companies themselves and on field surveys. The impacts are represented spatially in proportional symbols maps, based on the spatial distribution of the companies in the urban area of the city of São Carlos and the areas of influence of each company. The application of the proposed methodology served to validate it and indicated that the neighborhood impacts caused by the companies of this study are related to each company's type of activity, its size, and its occupation of the area. © 2008 Journal of Urban and Environmental Engineering (JUEE). All rights reserved.

El cluster del salmón: ¿un caso de desarrollo incluyente?

Incluye bibliografía

¿Es posible un cluster en torno al bauxita en el Estado de Pará?

Incluye Bibliografía

Aglomeraciones pesqueras en América Latina: ventajas asociadas al enfoque de cluster

Incluye Bibliografía

Evolución y complejidad en el desarrollo de encadenamientos productivos en México: los desafíos de la construcción del cluster aeroespacial en Querétaro

Incluye bibliografía

La formación de un cluster en torno al turismo de naturaleza sustentable en Bonito, Brasil

Incluye Bibliografía

Formación y desarrollo de un cluster globalizado: el caso de la industria del salmón en Chile

Incluye Bibliografía

¿A Chilean wine cluster? governance and upgrading in the phase of internationalization

Includes bibliography

Una estrategia de desarrollo basada en recursos naturales: análisis 'cluster' del complejo de cobre de la Southern Perú

Incluye Bibliografía

El cluster del ecoturismo en San Pedro

Incluye Bibliografía

El cluster del ecoturismo en Bonito, MS

Incluye Bibliografía

Hacia un cluster ecoturístico en Aysén

Incluye Bibliografía

Desarrollo local y alternativas de desarrollo productivo: el impulso de un cluster eco-turístico en la región de Aysén

Incluye Bibliografía

Cluster analysis and artificial neural network on the superovulatory response prediction in mice

Complex biological systems require sophisticated approach for analysis, once there are variables with distinct measure levels to be analyzed at the same time in them. The mouse assisted reproduction, e.g. superovulation and viable embryos production, demand a multidisciplinary control of the environment, endocrinologic and physiologic status of the animals, of the stressing factors and the conditions which are favorable to their copulation and subsequently oocyte fertilization. In the past, analyses with a simplified approach of these variables were not well succeeded to predict the situations that viable embryos were obtained in mice. Thereby, we suggest a more complex approach with association of the Cluster Analysis and the Artificial Neural Network to predict embryo production in superovulated mice. A robust prediction could avoid the useless death of animals and would allow an ethic management of them in experiments requiring mouse embryo.

QK-Means: A clustering technique based on community detection and K-Means for deployment of cluster head nodes

Wireless Sensor Networks (WSN) are a special kind of ad-hoc networks that is usually deployed in a monitoring field in order to detect some physical phenomenon. Due to the low dependability of individual nodes, small radio coverage and large areas to be monitored, the organization of nodes in small clusters is generally used. Moreover, a large number of WSN nodes is usually deployed in the monitoring area to increase WSN dependability. Therefore, the best cluster head positioning is a desirable characteristic in a WSN. In this paper, we propose a hybrid clustering algorithm based on community detection in complex networks and traditional K-means clustering technique: the QK-Means algorithm. Simulation results show that QK-Means detect communities and sub-communities thus lost message rate is decreased and WSN coverage is increased. © 2012 IEEE.