Predicting cetane number,kinematic viscosity,density and higher heating value of biodiesel from its fatty acid methyl ester composition

Biodiesel is a renewable bio-fuel derived from natural fats or vegetable oils, and it is considered as a promising alternative to substitute diesel fuels. Cetane number, viscosity, density, and higher heating value are important properties to affect the utilization of biodiesel fuels, because they are involved in the definition of fuel quality and are required as input data for predictive engine combustion models. This work presents the characterization of two biodiesel samples made from beef tallow and soybean oil through their fatty acid methyl esters (FAMEs) profile. Empirical equations were developed to estimate four physical properties of methyl esters; and an average absolute deviation (AAD) of 5.95%, 2.57%, 0.11% and 0.21% for the cetane number, kinematic viscosity, density, and higher heating value were founded. Cetane number, viscosity, and higher heating value increases because of the increase of molecular weight and these physical properties decrease as the number of double bonds increases. Unlike that of above properties, density decreases as molecular weight increases and density increases as the degree of unsaturation increases. Two general mixing rules and five biodiesel samples were used to study the influence of FAMEs over the physical properties of biodiesel. The prediction of the cetane number, kinematic viscosity, density and higher heating value of biodiesel is very close to the experimental values.     

Air and HST Multimodal Products. A Segmentation Analysis for Policy Makers

Different solutions for the integration of high-speed rail (HSR) and air transport could be implemented, ranging from very basic integration to more sophisticated systems that include ticket and handling integration. This paper uses two statistical techniques, name cluster analysis and discrete choice models in order to investigate how different market segments have a determining influence in being more proactive to change to HSR for the second leg in multimodal trips. A discrete choice experiment is conducted to better understand passengers’ preferences. We obtain a number of clusters and estimate flexible choice models, taking into account the panel nature of stated preference data. We obtain a range of willingness-to-pay values for service quality attributes, finding results that can be used to infer policy conclusions about the real attractiveness of the Air-HSR integrated alternative. In this respect, clusters and schedule coordination, which reduces connecting time, are crucial to explain HSR attractiveness.     

Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. However, the performance of most of the algorithms still falls far below expert expectations. In this paper, we review the main approaches to automated MS lesion segmentation. The main features of the segmentation algorithms are analysed and the most recent important techniques are classified into different strategies according to their main principle, pointing out their strengths and weaknesses and suggesting new research directions. A qualitative and quantitative comparison of the results of the approaches analysed is also presented. Finally, possible future approaches to MS lesion segmentation are discussed.     

Short‐term load forecasting based on support vector regression and load profiling

The article proposes a methodology to forecast the electric load for the 24 h of the following day based on support vector regression. The study considers 24 distinct models, one for each predicted hour, where each individual model is treated independently. Its objective is to find the optimal combination of support vector machine parameters that could generalize low forecasting errors, using simulated annealing as a metaheuristic. The adopted methodology is compared to concurrent methods based on neural networks when applied to a simulated load diagram (to illustrate a distribution feeder supplying a sample of 740 consumers). The results have proven its effectiveness with mean absolute percentage errors being less than 5% for testing samples. The study also focuses on evaluating the potential benefits of adopting load profiling information as input in support vector regression, giving a consistent proof of its importance. Copyright © 2013 John Wiley & Sons, Ltd.     

Detection of Channel Variations to Improve Channel Estimation Methods

In current digital communication systems, channel information is typically acquired by supervised approaches that use pilot symbols included in the transmit frames. Given that pilot symbols do not convey user data, they penalize throughput spectral efficiency, and transmit energy consumption of the system. Unsupervised channel estimation algorithms could be used to mitigate the aforementioned drawbacks although they present higher computational complexity than that offered by supervised ones. This paper proposes a simple decision method suitable for slowly varying channels to determine whether the channel has suffered a significant variation, which requires to estimate the matrix of the recently changed channel. Otherwise, a previous estimate is used to recover the transmitted symbols. The main advantage of this method is that the decision criterion is only based on information acquired during the time frame synchronization, which is carried out at the receiver. We show that the proposed criterion provides a considerable improvement of computational complexity for both supervised and unsupervised methods, without incurring in a penalization in terms of symbol error ratio. Specifically, we consider systems that make use of the popular Alamouti code. Performance evaluation is accomplished by means of simulated channels as well as making use of indoor wireless channels measured using a testbed.     

Understanding reductions in water consumption in tourist areas: a case study of the Costa Brava,Spain

With nearly three million tourists per year, the Costa Brava (Spain) is one of the most frequently visited tourist areas on the Mediterranean coast. The aim of this article is to understand its hydrosocial cycle and to identify why water consumption per capita is decreasing in the region. Overall, this decrease is not related to a single factor. Instead, many factors, including the incorporation of technological and institutional innovations in water management, increasing levels of social awareness related to saving water, the introduction of alternative water resources, and increasing water prices, must be considered together.