Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO2) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction

Immunological modulation of neural stem cells in the adult brain

Breast cancer is one of the leading causes of female mortality in the world, and early detection is an important means of reducing the mortality rate. The presence of microcalcification clusters has been considered as a very important indicator of malignant types of breast cancer, and its detection is important to prevent and treat the disease. This paper presents an effective approach, in order to detect microcalcification clusters in digitized mammograms, based on the synergy of image processing and partitional (hard and fuzzy) clustering techniques. Mathematical morphology has been used for image processing, and is used in this work as a first step, with the purpose of enhancing the contrast of microcalcifications. Image segmentation is an important task in the field of image processing, in order to identify regions with the same features. In the second step, we use image segmentation, using three partitional, hard and fuzzy clustering algorithms, such as k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means, in order to make a comparison of the advantages and drawbacks offered by these algorithms, and which should help to improve the detection of microcalcification clusters in digitized mammograms. " 2011 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.",,,,,,"10.1016/j.scient.2011.04.009",,,"http://hdl.handle.net/20.500.12104/42064","http://www.scopus.com/inward/record.url?eid=2-s2.0-80054825067&partnerID=40&md5=0044bc7f9f846b89ca71010d076c0f97",,,,,,"3 D",,"Scientia Iranica",,"58

Air pollution analysis with a PFCM clustering algorithm applied in a real database of Salamanca (Mexico)

Over the last ten years, Salamanca has been considered among the most polluted cities in México. Nowadays, there is an Automatic Environmental Monitoring Network (AEMN) which measures air pollutants (Sulphur Dioxide (SO2), Particular Matter (PM10), Ozone (O3), etc.), as well as environmental variables (wind speed, wind direction, temperature, and relative humidity), and it takes a sample of the variables every minute. The AEM Network is mainly based on three monitoring stations located at Cruz Roja, DIF, and Nativitas. In this work, we use the PFCM (Possibilistic Fuzzy c Means) clustering algorithm as a mean to get a combined measure, from the three stations, looking to provide a tool for better management of contingencies in the city, such that local or general action can be taken in the city according to the pollution level given by each station and the combined measure. Besides, we also performed an analysis of correlation between pollution and environmental variables. The results show a significative correlation between pollutant concentrations and some environmental variables. So, the combined measure and the correlations can be used for the establishment of general contingency thresholds. Zapotitlán2010 IEEE