El oso feroz.

Este artículo gira entorno a la obra de A. A. Milne (Alan Alexander Milne, Londres, 1882-Hartgield, 1956), autor de Winnie the Pooh. Su influencia y su trascendencia ha sido eclipsada por su versión cinematográfica de la compañía Disney, incluso llegando a anular su autoría y concediéndosela a la Disney.

Mystery history of a viking longboat.

Los vikingos eran un pueblo guerrero, pero también de comerciantes, pescadores y exploradores. El orgullo de la flota vikinga fue el Longboat que podía ser transportado por aguas poco profundas e incluso por tierra. Construyeron barcos preparados para diferentes tareas y hacer largas travesías por el océano que les permitió llegar a América.

I didn't know that chimps use tools.

Observa la diferencia entre los monos y simios describiendo lo que comen, cómo se comunican y muestran las emociones, cómo cuidan a los hijos, cuales son sus enemigos, y otros aspectos de su comportamiento. Texto con varios niveles de dificultad de comprensión lectora y con distintos tamaños de letra.

Desarrollo de un programa de ciencia-literatura.

Un programa de ciencia-literatura podría proporcionar una atalaya diferente para el estudio de la ciencia. Esto podrá ayudar a los estudiantes a meditar seriamente las cosas y a aceptar lo menos posible de buena fe. Los profesores también deberían darse cuenta de que un programa de ambas ciencias podría mejorar la enseñanza de las ciencias. El asunto de la ciencia como de la literatura se ocupa, a cierto nivel, de postulados y hechos. El enfoque de ciencia-literatura ofrece materia de estudio en la cual los estudiantes pueden aplicar la lógica inductiva y deductiva a la naturaleza del orden y organización en sistemas vivos. Además, estas dos áreas pueden ayudar a que los estudiantes se vuelvan más críticos frente a arbitrariedades y sistemas de creencias preestablecidas. Pueden desarrollarse puntos de referencia científicos y literarios que supongan las condiciones necesarias para la supervivencia de sistemas vivos. Durante el Renacimiento, la filosofía natural o historia natural, de la cual surgió la ciencia, influía en la literatura. Durante el siglo XVII, la nueva ciencia puede haber desempeñado un rol importante en cambiar el estilo de la prosa en Inglaterra. Más tarde, la teoría de la evolución, de Darwin, la teoría de la relatividad, de Einstein, y otras motivaron a novelistas, poetas, dramaturgos y críticos literarios a cambiar su visión del hombre y del mundo. Entre los poetas y novelistas más recientes cuyas obras fueron influidas por acontecimientos, se cuentan: Walt Whitman, James Russel lowell, James Joyce, Virginia Wolf y Marcel Proust. Ciencia y literatura pueden referirse a los mismos fenómenos, pero desde distintos puntos de vista. Las distintas disciplinas se complementan entre sí. Ninguna disciplina aislada agota el tema.

Análisis de El Rey León : la disneylandización social.

El trabajo se ha desarrollado a partir de una metodología de estudio por proyectos de carácter colaborativo a través de Internet. El artículo forma parte de un monográfico sobre La comunicación humana. Retos en los umbrales del milenio

Discurso y socialización en producciones cinematográficas infantiles.

El artículo forma parte de una sección de la revista dedicada a investigación.- Resumen tomado parcialmente de la revista

Fenómenos televisivos 'teenagers' : prototipias adolescentes en series vistas en España.

El artículo pertenece a una sección de la revista dedicada a investigación.

El color en los spots infantiles : prevalencia cromática y relación con el logotipo de marca.

Resumen tomado de la publicación. - El artículo forma parte de una sección de la revista dedicada a: Investigaciones

An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest

Canopy leaf area index (LAI), defined as the single-sided leaf area per unit ground area, is a quantitative measure of canopy foliar area. LAI is a controlling biophysical property of vegetation function, and quantifying LAI is thus vital for understanding energy, carbon and water fluxes between the land surface and the atmosphere. LAI is routinely available from Earth Observation (EO) instruments such as MODIS. However EO-derived estimates of LAI require validation before they are utilised by the ecosystem modelling community. Previous validation work on the MODIS collection 4 (c4) product suggested considerable error especially in forested biomes, and as a result significant modification of the MODIS LAI algorithm has been made for the most recent collection 5 (c5). As a result of these changes the current MODIS LAI product has not been widely validated. We present a validation of the MODIS c5 LAI product over a 121 km2 area of mixed coniferous forest in Oregon, USA, based on detailed ground measurements which we have upscaled using high resolution EO data. Our analysis suggests that c5 shows a much more realistic temporal LAI dynamic over c4 values for the site we examined. We find improved spatial consistency between the MODIS c5 LAI product and upscaled in situ measurements. However results also suggest that the c5 LAI product underestimates the upper range of upscaled in situ LAI measurements.

Comparison of MODIS broadband albedo over an agricultural site with ground measurements and values derived from Earth observation data at a range of spatial scales

Land surface albedo is dependent on atmospheric state and hence is difficult to validate. Over the UK persistent cloud cover and land cover heterogeneity at moderate (km-scale) spatial resolution can also complicate comparison of field-measured albedo with that derived from instruments such as the Moderate Resolution Imaging Spectrometer (MODIS). A practical method of comparing moderate resolution satellite-derived albedo with ground-based measurements over an agricultural site in the UK is presented. Point measurements of albedo made on the ground are scaled up to the MODIS resolution (1 km) through reflectance data obtained at a range of spatial scales. The point measurements of albedo agreed in magnitude with MODIS values over the test site to within a few per cent, despite problems such as persistent cloud cover and the difficulties of comparing measurements made during different years. Albedo values derived from airborne and field-measured data were generally lower than the corresponding satellite-derived values. This is thought to be due to assumptions made regarding the ratio of direct to diffuse illumination used when calculating albedo from reflectance. Measurements of albedo calculated for specific times fitted closely to the trajectories of temporal albedo derived from both Systeme pour l'Observation de la Terre (SPOT) Vegetation (VGT) and MODIS instruments.

Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?

Attempts to estimate photosynthetic rate or gross primary productivity from remotely sensed absorbed solar radiation depend on knowledge of the light use efficiency (LUE). Early models assumed LUE to be constant, but now most researchers try to adjust it for variations in temperature and moisture stress. However, more exact methods are now required. Hyperspectral remote sensing offers the possibility of sensing the changes in the xanthophyll cycle, which is closely coupled to photosynthesis. Several studies have shown that an index (the photochemical reflectance index) based on the reflectance at 531 nm is strongly correlated with the LUE over hours, days and months. A second hyperspectral approach relies on the remote detection of fluorescence, which is a directly related to the efficiency of photosynthesis. We discuss the state of the art of the two approaches. Both have been demonstrated to be effective, but we specify seven conditions required before the methods can become operational.

Impact of land cover uncertainties on estimates of biospheric carbon fluxes

Large-scale bottom-up estimates of terrestrial carbon fluxes, whether based on models or inventory, are highly dependent on the assumed land cover. Most current land cover and land cover change maps are based on satellite data and are likely to be so for the foreseeable future. However, these maps show large differences, both at the class level and when transformed into Plant Functional Types (PFTs), and these can lead to large differences in terrestrial CO2 fluxes estimated by Dynamic Vegetation Models. In this study the Sheffield Dynamic Global Vegetation Model is used. We compare PFT maps and the resulting fluxes arising from the use of widely available moderate (1 km) resolution satellite-derived land cover maps (the Global Land Cover 2000 and several MODIS classification schemes), with fluxes calculated using a reference high (25 m) resolution land cover map specific to Great Britain (the Land Cover Map 2000). We demonstrate that uncertainty is introduced into carbon flux calculations by (1) incorrect or uncertain assignment of land cover classes to PFTs; (2) information loss at coarser resolutions; (3) difficulty in discriminating some vegetation types from satellite data. When averaged over Great Britain, modeled CO2 fluxes derived using the different 1 km resolution maps differ from estimates made using the reference map. The ranges of these differences are 254 gC m−2 a−1 in Gross Primary Production (GPP); 133 gC m−2 a−1 in Net Primary Production (NPP); and 43 gC m−2 a−1 in Net Ecosystem Production (NEP). In GPP this accounts for differences of −15.8% to 8.8%. Results for living biomass exhibit a range of 1109 gC m−2. The types of uncertainties due to land cover confusion are likely to be representative of many parts of the world, especially heterogeneous landscapes such as those found in western Europe.

Scaling up data mining techniques to large datasets using parallel and distributed processing

Advances in hardware and software technology enable us to collect, store and distribute large quantities of data on a very large scale. Automatically discovering and extracting hidden knowledge in the form of patterns from these large data volumes is known as data mining. Data mining technology is not only a part of business intelligence, but is also used in many other application areas such as research, marketing and financial analytics. For example medical scientists can use patterns extracted from historic patient data in order to determine if a new patient is likely to respond positively to a particular treatment or not; marketing analysts can use extracted patterns from customer data for future advertisement campaigns; finance experts have an interest in patterns that forecast the development of certain stock market shares for investment recommendations. However, extracting knowledge in the form of patterns from massive data volumes imposes a number of computational challenges in terms of processing time, memory, bandwidth and power consumption. These challenges have led to the development of parallel and distributed data analysis approaches and the utilisation of Grid and Cloud computing. This chapter gives an overview of parallel and distributed computing approaches and how they can be used to scale up data mining to large datasets.