Abstract
The identification of land cover changes on a continental scale is a laborious and time-consuming process. A new methodology is proposed based exclusively on SPOT VGT data, illustrated for the African Continent using GLC2000 as reference to select 26 distinct land cover types (classes). For each class, the normalized difference vegetation index (NDVI) time-series are extracted from SPOT VGT images and a hierarchical aggregation is done using two different methods: one that preserves the initial signatures throughout the hierarchical process, and another that recalculates the signatures for each aggregation level. The average classification agreement was above 89% using 26 classes. Reducing the number of classes improves classification agreement. In order to study the influence of temporal variability in the classification results, the methodology was applied on data from 1999, 2001, 2008, and 2010. With 26 classes, the best average classification agreement obtained was 94.5% with annual data, against 74.1% with interannual data.

Abstract
BACKGROUND/OBJECTIVES: The elderly constitute a population group with a high prevalence of non-communicable chronic diseases and high risk of malnutrition. The aim of this study was to identify factors associated to nutritional risk in free-living European elderly. SUBJECTS/METHODS: The sample included 644 European citizens, free living in the community, aged 65 years or more. The sample was quota controlled for age groups (65-74, >= 75 years), gender (male/female) and living circumstances (living alone/with others). Logistic regression was performed to identify factors associated with nutritional risk. RESULTS: Several variables regarding socio-demographic characteristics, food choice, health status and the satisfaction with food-related life were included in the analysis. According to the recoded score of the 'Determine your nutritional health' (NSI checklist), 53% of the elderly were at nutritional risk. Nutritional risk was more likely to occur in elderly who considered that it was more important to choose foods 'easy to chew'; with lower average number of fruit and vegetables (F&V) intake episodes and lower score for general health. It was also found in nod-married participants; those that did not identify changes in their appetite; and those that felt changes in health status. In this sample, the lowest nutritional risk was found for body mass index (BMI) around 18.5 kg/m(2). Country of residence, gender and age were not found to have a significant effect on nutritional risk. CONCLUSIONS: Attention should be drawn to the living circumstances, changes in appetite or health, the general heath perception, F&V intake, choice of foods easy to chew and having a low or high BMI.

Abstract

Abstract
It is expected that in the near future the number of Plug-in Electric Vehicles (PEV) could increase significantly due to their low pollution emissions, high fuel economy, and mitigation of security issues related to oil technical and economic management. Many works have dealt with the impact of PEV on the power and distribution grids, and on other particular aspects, but very few perform more general cost benefit analyses of their global impact. This paper does it in two steps. First, a hydro-thermal unit commitment for a full year simulation provides electricity and reserve prices for different charging strategies. Then, the model computes economic estimations for the costs of the charging infrastructure, specific PEV costs and main externalities (emissions, health benefits and energetic dependence). The model is intended to provide meaningful results on the global economic balance of PEV penetration, helping for example in feed-in tariffs, fuel taxes redesign or other regulatory analyses. © 2013 IEEE.

Abstract
With the objective to study the variation of optical properties of rat muscle during optical clearing, we have performed a set of optical measurements from that kind of tissue. The measurements performed were total transmittance, collimated transmittance, specular reflectance and total reflectance. This set of measurements is sufficient to determine diffuse reflectance and absorbance of the sample, also necessary to estimate the optical properties. All the performed measurements and calculated quantities will be used later in inverse Monte Carlo (IMC) simulations to determine the evolution of the optical properties of muscle during treatments with ethylene glycol and glucose. The results obtained with the measurements already provide some information about the optical clearing treatments applied to the muscle and translate the mechanisms of turning the tissue more transparent and sequence of regimes of optical clearing.

Abstract
The deployment of efficient measurement solutions to assist network management tasks without interfering with normal network operation assumes a prominent role in today's high-speed networks attending to the huge amounts of traffic involved. From a myriad of proposals for traffic measurement, sampling techniques are particularly relevant contributing effectively for this purpose as only a subset of the overall traffic volume is handled for processing, preserving ideally the correct estimation of network statistical behavior. In this context, this paper proposes MuST - a multiadaptive sampling technique based on linear prediction, aiming at reducing significantly the measurement overhead and still assuring that traffic samples reflect the statistical characteristics of the global network traffic under analysis. Conversely to current sampling techniques, MuST is a multi and self-adaptive technique as both the sample size and interval between samples are self-adjustable parameters according to the ongoing network activity and the accuracy of prediction achieved. The tests carried out demonstrate that the proposed sampling technique is able to achieve accurate network estimations with reduced overhead, using throughput as reference parameter. The evaluation results, obtained resorting to real traffic traces representing wired and wireless aggregated traffic scenarios and actual network services, prove that the simplicity, flexibility and self-adaptability of the proposed technique can be successfully explored to improve network measurements efficiency over distinct traffic conditions. For optimization purposes, this paper also includes a study of the impact of varying the order of prediction, i.e., of considering different degrees of past memory in the self-adaptive estimation mechanism. The significance of the obtained results is demonstrated through statistical benchmarking.