Ash deposition during the co-firing of bituminous coal with pine sawdust and olive stones in a laboratory furnace

This article describes an experimental study on ash deposition during the co-firing of bituminous coal with pine sawdust and olive stones in a laboratory furnace. The main objective of this study was to relate the ash deposit rates with the type of biomass burned and its thermal percentage in the blend. The thermal percentage of biomass in the blend was varied between 10% and 50% for both sawdust and olive stones. For comparison purposes, tests have also been performed using only coal or only biomass. During the tests, deposits were collected with the aid of an air-cooled deposition probe placed far from the flame region, where the mean gas temperature was around 640 degrees C. A number of deposit samples were subsequently analyzed on a scanning electron microscope equipped with an energy dispersive X-ray detector. Results indicate that blending sawdust with coal decreases the deposition rate as compared with the firing of unblended coal due to both the sawdust low ash content and its low alkalis content. The co-firing of coal and sawdust yields deposits with high levels of silicon and aluminium which indicates the presence of ashes with high fusion temperature and, thus, with less capacity to adhere to the surfaces. In contrast, in the co-firing of coal with olive stones the deposition rate increases as compared with the firing of unblended coal and the deposits produced present high levels of potassium, which tend to increase their stickiness.

Reward-risk efficiency in proportional reinsurance with different risk measures

We have studied, in particular under normality of the implied random variables, the connections between different measures of risk such as the standard deviation, the W-ruin probability and the p-V@R. We discuss conditions granting the equivalence of these measures with respect to risk preference relations and the equivalence of dominance and efficiency of risk-reward criteria involving these measures. Then more specifically we applied these concepts to rigorously face the problem of finding the efficient set of de Finetti’s variable quota share proportional reinsurance.

Nutritional status influences generic and disease-specific quality of life measures in haemodialysis patients

Background: Poor nutritional status and worse health-related quality of life (QoL) have been reported in haemodialysis (HD) patients. The utilization of generic and disease specific QoL questionnaires in the same population may provide a better understanding of the significance of nutrition in QoL dimensions. Objective: To assess nutritional status by easy to use parameters and to evaluate the potential relationship with QoL measured by generic and disease specific questionnaires. Methods: Nutritional status was assessed by subjective global assessment adapted to renal patients (SGA), body mass index (BMI), nutritional intake and appetite. QoL was assessed by the generic EuroQoL and disease specific Kidney Disease Quality of Life-Short Form (KDQoL-SF) questionnaires. Results: The study comprised 130 patients of both genders, mean age 62.7 ± 14.7 years. The prevalence of undernutrition ranged from 3.1% by BMI ≤ 18.5 kg/m2 to 75.4% for patients below energy and protein intake recommendations. With the exception of BMI classification, undernourished patients had worse scores in nearly all QoL dimensions (EuroQoL and KDQoL-SF), a pattern which was dominantly maintained when adjusted for demographics and disease-related variables. Overweight/obese patients (BMI ≥ 25) also had worse scores in some QoL dimensions, but after adjustment the pattern was maintained only in the symptoms and problems dimension of KDQoL-SF (p = 0.011). Conclusion: Our study reveals that even in mildly undernourished HD patients, nutritional status has a significant impact in several QoL dimensions. The questionnaires used provided different, almost complementary perspectives, yet for daily practice EuroQoL is simpler. Assuring a good nutritional status, may positively influence QoL.

Measuring and evaluating the impacts of TOD measures: searching for evidence of TOD characteristics in Azambuja train line

TOD (Transit Oriented Development) is typically defined as a high density mixed area (residential and commercial) within easy walking distance of a high capacity public transport station (typically within an 800m buffer area). TOO is viewed as a set of strategies to increase the use of public transport, increasing walking activity, containing urban sprawl, and creating more liveable places. It is believed that this type of combined strategies will improve sustainable growth. This work is an exploratory work for evidence of TOD characteristics in train station areas in Azambuja train line, setting further methodologies to evaluate the success of TOD areas.

Approximate entropy normalized measures for analyzing social neurobiological systems

When considering time series data of variables describing agent interactions in social neurobiological systems, measures of regularity can provide a global understanding of such system behaviors. Approximate entropy (ApEn) was introduced as a nonlinear measure to assess the complexity of a system behavior by quantifying the regularity of the generated time series. However, ApEn is not reliable when assessing and comparing the regularity of data series with short or inconsistent lengths, which often occur in studies of social neurobiological systems, particularly in dyadic human movement systems. Here, the authors present two normalized, nonmodified measures of regularity derived from the original ApEn, which are less dependent on time series length. The validity of the suggested measures was tested in well-established series (random and sine) prior to their empirical application, describing the dyadic behavior of athletes in team games. The authors consider one of the ApEn normalized measures to generate the 95th percentile envelopes that can be used to test whether a particular social neurobiological system is highly complex (i.e., generates highly unpredictable time series). Results demonstrated that suggested measures may be considered as valid instruments for measuring and comparing complexity in systems that produce time series with inconsistent lengths.

Text classification using compression-based dissimilarity measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.