Transactions of the Department of Agriculture of the State of Illinois with reports from county and district agricultural organizations for the year ...

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The Natural history of Enewetak Atoll / edited by Dennis M. Devaney ... [et al.] ; prepared by Office of Scientific and Technical Information, U.S. Department of Energy.

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Ecotopes, Natural Infection and Trophic Resources of Triatoma brasiliensis (Hemiptera, Reduviidae, Triatominae)

Triatoma brasiliensis is considered as one of the most important Chagas disease vectors in the northeastern Brazil. This species presents chromatic variations which led to descriptions of subspecies, synonymized by Lent and Wygodzinsky (1979). In order to broaden bionomic knowledge of these distinct colour patterns of T. brasiliensis, captures were performed at different sites, where the chromatic patterns were described: Caicó, Rio Grande do Norte (T. brasiliensis brasiliensis Neiva, 1911), it will be called the "brasiliensis population"; Espinosa, Minas Gerais (T. brasiliensis melanica Neiva & Lent 1941), the "melanica population" and Petrolina, Pernambuco (T. brasiliensis macromelasoma, Galvão 1956), the "macromelasoma population". A fourth chromatic pattern was collected in Juazeiro, Bahia the darker one in overall cuticle coloration, the "Juazeiro population". At the sites of Caicó, Petrolina and Juazeiro, specimens were captured in peridomiciliar ecotopes and in wilderness. In Espinosa the specimens were collected only in wilderness, even though several exhaustive captures have been performed in peridomicile at different sites of this municipality. A total of 298 specimens were captured. The average registered infection rate was 15% for "brasiliensis population" and of 6.6% for "melanica population". Specimens of "macromelasoma" and of "Juazeiro populations" did not present natural infection. Concerning trophic resources, evaluated by the precipitin test, feeding eclecticism for the different colour patterns studied was observed, with dominance of goat blood in household surroundings as well as in wilderness

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Innovative Applications : Open Educational Resources and Mobile Resources Repository for the Instruction of Educational Researchers in Mexico

The movement of Open Educational Resources (OER) is one of the most important trends that are helping education through the Internet worldwide. "Tecnológico de Monterrey" (http://tecvirtual.itesm.mx/) in Mexico, with other Mexican higher education institutions, is creating an Internet/web based repository of OERs and Mobile Resources for the instruction and development of educational researchers at undergraduate, Master's and Doctoral level. There is a lack of open educational resources and material available at the Internet that can help and assist the development and education of educational researchers in Spanish speaking countries. This OER repository is part of a project that is experimenting new technology for the delivery of OERs from one repository (http://catedra.ruv.itesm.mx/) through an indexed OER catalog (http://www.temoa.info/) to mobile devices (Ipod, Iphone, MP3, MP4). This paper presentation will describe and comment about this project: outcomes, best practices, difficulties and technological constraints.

Analytical expressions for the flame front speed in the downward combustion of thin solid fuels and comparison to experiments

We derive analytical expressions for the propagation speed of downward combustion fronts of thin solid fuels with a background flow initially at rest. The classical combustion model for thin solid fuels that consists of five coupled reaction-convection-diffusion equations is here reduced into a single equation with the gas temperature as the single variable. For doing so we apply a two-zone combustion model that divides the system into a preheating region and a pyrolyzing region. The speed of the combustion front is obtained after matching the temperature and its derivative at the location that separates both regions.We also derive a simplified version of this analytical expression expected to be valid for a wide range of cases. Flame front velocities predicted by our analyticalexpressions agree well with experimental data found in the literature for a large variety of cases and substantially improve the results obtained from a previous well-known analytical expression

Hardin County Sanitary Solid Waste Disposal Commission, June 30, 2002

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Delaware County Solid Waste Disposal Commission, June 30, 2001 & June 30, 2002

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Crawford County Area Solid Waste Agency Commission, June 30, 2001 & June 30, 2002

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