Curriculum laboratory libraries in major universities and state colleges across the United States

The Curriculum Laboratory at Kansas State College of Pittsburg is a well-equipped and well-staffed center. This study grew from a question that the researcher asked himself: How equipped is our facility compared to other state colleges and universities? In pondering this question, others came to mind: What do their facilities consist of as compared to those of Kansas State College of Pittsburg? What are the materials that make up their laboratory? Who funds a curriculum laboratory? Where are they located on campus? Inquiries revealed the fact that little has been written concerning this type of facility. Francis L. Drag, who made a nation-wide survey of curriculum laboratories said, “Reference to curriculum laboratories in the professional educational literature are comparatively few and sketchy.” Most of the pamphlets and papers which were referenced concerning this type of facility were general in their description of the organization of the library and the types of materials which should be included. The description of a curriculum laboratory, as stated in the Standards for State Approval of Teacher Education, 1971, was found to be the most adequate

Computational framework to analyze agrometeorological, climate and remote sensing data: challenges and perspectives.

In the past few years, improvements in the data acquisition technology have decreased the time interval of data gathering. Consequently, institutions have stored huge amounts of data such as climate time series and remote sensing images. Computational models to filter, transform, merge and analyze data from many different areas are complex and challenging. The complexity increases even more when combining several knowledge domains. Examples are research in climatic changes, biofuel production and environmental problems. A possible solution to the problem is the association of several computational techniques. Accordingly, this paper presents a framework to analyze, monitor and visualize climate and remote sensing data by employing methods based on fractal theory, data mining and visualization techniques. Initial experiments showed that the information and knowledge discovered from this framework can be employed to monitor sugar cane crops, helping agricultural entrepreneurs to make decisions in order to become more productive. Sugar cane is the main source to ethanol production in Brazil, and has a strategic importance for the country economy and to guarantee the Brazilian self-sufficiency in this important, renewable source of energy.CSBC 2009

Data Clustering Using Self-organizing Maps Segmented By Mathematic Morphology And Simplified Cluster Validity Indexes: An Application In Remotely Sensed Images

This paper presents a cluster analysis method which automatically finds the number of clusters as well as the partitioning of a data set without any type of interaction with the user. The data clustering is made using the Self-Organizing (or Kobonen) Map (SOM). Different partitions of the trained SOM are obtained from different segmentations of the U-matrix (a neuron-distance image) that are generated by means of mathematical morphology techniques. The different partitions of the trained SOM produce different partitions for the data set which are evaluated by cluster validity indexes. To reduce the computational cost of the cluster analysis process this work also proposes the simplification of cluster validity indexes using the statistical properties of the SOM. The proposed methodology is applied in the cluster analysis of remotely sensed images. © 2006 IEEE.44214428Richards, J.A., Analysis of remotely sensed data: The formative decades and the future (2005) IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 422-432. , MarchSun, Z., Huang, D., Cheung, Y., Liu, J., Huang, G., Using FCMC, FVS, and PCA techniques for feature extraction of Multispectral images (2005) IEEE Geosc. and Rem. Sens. Lett, 2 (2), pp. 108-112. , AprilXu, R., Wunsch, D., Survey of clustering algorithms (2005) IEEE Trans. on Neural Networks, 16 (3), pp. 645-678. , MayTran, T.N., Wehrens, R., Buydes, L.M.C., Clustering multispectral images: A tutorial (2005) Chemometrics and Intelligent Laboratory Systems, 77, pp. 3-17Ball, G.H., Hall, D.J., (1965) ISODATA, a novel method of data analysis and pattern classification, , Stanford Research Institute, Menlo Park, CA, NTIS Report AD 699616Haykin, S., (1999) Neural Network: A Comprehensive Foundation, , New York: Prentice-Hall, 2nd editionCosta, J.A.F., Netto, M.L.A., Estimating the number of clusters in multivariate data by self-organizing maps (1999) International Journal of Neural Systems, 9, pp. 195-202J. A. F. Costa and M. L. A. Netto, Clustering of complex shaped data sets via kohonen maps and mathematical morphology, in Proceedings of the SPIE, Data Mining and Knowledge Discovery. B. Dasarathy (Ed.), 2001, 4384, pp. 16-27Gonçalves, M.L., Netto, M.L.A., Zullo, J., A neural architecture for the classification of remote sensing imagery with advanced learning algorithms (1998) Proc. of the IEEE Signal Processing Society Workshop, pp. 577-585M. L. Gonçalves, M. L. A. Netto, J. A. F. Costa and J. Zullo, Automatic remotely sensed data Clustering by tree-structured self-organizing, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS'05, Korea, 2005Kohonen, T., (1997) Self-Organizing Maps, , 2nd Edition, Berlim: Springer VerlagUltsch, A., Self-organizing neural networks for visualization and classification (1993) Information and Classification, pp. 307-313. , O. Opitz et al, Eds, Berlin, Springer-Verlag, ppLampinen, J., Oja, E., Clustering properties of hierarchical self-organizing maps (1992) J. of Math. Im. and Vis, 2, pp. 261-272Murtagh, F., Interpreting the kohonen self-organizing feature map using contiguity-constrained clustering (1995) Pattern Recognition Letters, 16, pp. 399-408Kiang, M.Y., Extending the kohonen self-organizing map networks for clustering analysis (2001) Computational Statistics & Data Analysis, 38, pp. 161-180Vesanto, J., Alhoniemi, E., Clustering of the self-organizing map (2000) IEEE Trans. on Neural Networks, 11, pp. 586-600. , MayWu, S., Chow, T.W.S., Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intracluster density (2004) Pattern Recognition, 37, pp. 175-188Bleau, A., Leon, L.J., Watershed-based segmentation and region merging (2000) Comp. Vis. Image Underst, 77, pp. 317-370Bezdek, J.C., Pal, N.R., Some new indexes of cluster validity (1998) IEEE Trans. on Syst, Man and Cybern, 28, pp. 301-315Davies, D., Bouldin, D., A cluster separation measure (1979) IEEE Trans. Patt. Rec. and Mach. Intell, PAM1-1, pp. 224-227Pakhira, M.K., Bandyopedhyay, S., Maulik, U., Validity index for crisp and fuzzy clusters (2003) Pattern Recog, 37, pp. 487-501Halkidi, M., Vazirgiannis, M., Clustering validity assessment using multi representatives (2002) Proceedings of SETN Conference, , Thessaloniki, GreeceJI, M., Using fuzy sets to improve cluster labeling in unsupervised classification (2003) Int. J. of Remote Sensing, 24, pp. 657-67

Climatic Changes Impact In Agroclimatic Zonning Of Coffee In Brazil [impacto Das Mudanças Climáticas No Zoneamento Agroclimático Do Café No Brasil]

According to the last report of the Intergovernmental Panel on Climate Change (IPCC), the global temperature is supposed to increase 1°C to 5.8°C and the rainfall 15% in the Tropical area. This paper analyses the effect that these possible scenarios would have in the agroclimatic zoning of the arabic coffee (Coffea arabica L.) main plantation areas in Brazil. The results indicated a reduction of suitable areas greater than 95% in Goiás, Minas Gerais and São Paulo and about 75% for Paraná in the case of a temperature increase of 5.8°C. These results presume that all the physiological characteristics of the crop will be the same for the varieties analyzed and that the ideal climatic condition for economic development is mean annual temperatures between 18°C and 23°C.391110571064Assad, E.D., Evangelista, B., Silva, F.A.M., Cunha, S.A.R., Alves, E.R., Lopes, T.S.S., Pinto, H.S., Zullo Jr., J., Zoneamento agroclimático para a cultura do café (Coffea arabica L.) no Estado de Goiás e sudoeste do Estado da Bahia (2001) Revista Brasileira de Agrometeorologia, 9, pp. 510-518. , Número especial Zoneamento AgrícolaAssad, E.D., Pinto, H.S., Caramori, P.H., (2001) Zoneamento Do Café, , Brasília: Consórcio Brasileiro de pesquisas do CaféEmbrapa, CD-ROMBieto, J.A., Talon, M., (1996) Fisiologia Y Bioquimica Vegetal, pp. 537-553. , Madrid: InteramericanaMcGraw-HillCamargo, A.P.C., Clima e a cafeicultura no Brasil (1985) Informe Agropecuário, (126), pp. 13-26Camargo, M.B.P., Pedro Jr., M.J., Alfonsi, R.R., Probabilidade de ocorrências de temperaturas absolutas mensais e anual no Estado de São Paulo (1993) Bragantia, 52, pp. 161-168Caramori, P.H., Caviglione, J.H., Wrege, M.S., Gonçalves, S.L., Faria, R.T., Androcioli Filho, A., Sera, T., Koguishi, M.S., Zoneamento de riscos climáticos para a cultura de café (Coffea arabica L.) no Estado do Paraná (2001) Revista Brasileira de Agrometeorologia, 9, pp. 486-494. , Número especial Zoneamento AgrícolaDrinnan, J.E., Menzel, C.M., Temperature affects vegetative growth and flowering of coffee (Coffea arabica L.) (1995) Journal of Horticultural Science, 70, pp. 25-34(2001), http://edcdaac.usgs.gov/gtopo30/getopo30.asphtml, Department of the Interior. Geological Survey. USGS EROS Data Center. Distributed Active Archive CenterClimate Change 2001: Working Group II: Impacts, Adaptations and Vulnerability, , http://www.grida.no/climate/ipcc_tar/wg2/005.htmlKalnay, E., Cai, M., Impact of urbanization and land-use change on climate (2003) Nature, 423, pp. 528-531Marengo, J.A., Impactos das condições climáticas e da variabilidade e mudanças do clima sobre a produção e os preços agrícolas: Ondas de frio e seu impacto sobre a cafeicultura nas regiões sul e sudeste do Brasil (2001) Mudanças Climáticas Globais e a Agropecuária Brasileira, pp. 97-123. , LIMA, M.A.CABRAL, O.M.R.MIGUEZ, J.D.G. (Org.). Jaguariúna: Embrapa Meio Ambiente, cap.4Moraes, O.C.C., Ameaça na floresta submersa (2004) Scientific American, (24). , www2.uol.com.br/sciam/conteudo/materia/materia_44htmlPinto, H.S., Alfonsi, R.R., (1974) Estimativa Das Temperaturas Médias, Máximas e Mínimas Mensais No Estado Do Paraná Em Função Da Altitude e Latitude, 30p. , São Paulo: USP, Caderno de Ciências da Terra, 52Pinto, H.S., Ortolani, A.A., Alfonsi, R.R., (1972) Estimativa Das Temperaturas Médias Mensais Do Estado de São Paulo Em Função Da Altitude e Latitude, 20p. , São Paulo: USP, Caderno Ciências da Terra, 23Pinto, H.S., Zullo Jr., J., Assad, E.D., Brunini, O., Alfonsi, R.R., Coral, G., Zoneamento de riscos climáticos para a cafeicultura do Estado de São Paulo (2001) Revista Brasileira de Agrometeorologia, 9, pp. 495-500. , Número especial Zoneamento AgrícolaSalati, E., Dos Santos, A.A., Nobre, C., As Mudanças Climáticas Globais e Seus Efeitos Nos Ecossistemas Brasileiros, , www.comciencia.br/reportagens/clima/clima14.htmSediyama, G.C., Melo Jr., J.C., Santos, A.R., Ribeiro, A., Costa, M.H., Hamakawa, P.J., Costa, J.M.N., Costa, L.C., Zoneamento agroclimático do cafeeiro (Coffea arabica L.) para o Estado de Minas Gerais (2001) Revista Brasileira de Agrometeorologia, 9, pp. 501-509. , Número especial Zoneamento AgrícolaThomas, C.D., Cameron, A., Green, R.E., Bakkenes, M., Beaumont, L.J., Collingham, C.Y., Erasmus, B.F.N., Willians, S.E., Extinction risk from climate change (2004) Nature, 427, pp. 145-148Thornthwaite, C.W., Mather, J.R., The water balance (1955) Publications in Climatology, 8 (1), 104p. , New Jersey: Centerto