Efficient implementation of the implicit error correction nVoD schema

Implicit Error Correction is a near Video-on-Demand (nVoD) schema that trades bandwidth utilization for initial playback delay while potentially allowing an infinite number of users. Additionally, it provides error protection without further bandwidth increase by exploiting the implicit redundancy of nVoD protocols, using linear combinations of the segments transmitted in a given time slot. This paper introduces a new implementation that outperforms the original schema by adding two new mechanisms: redundancy channels and feedback error correction.     

Modeling the reliability of a group membership protocol for dual-scheduled time division multiple access networks

We present reliability models for a group membership protocol designed for TDMA networks such as FlexRay, a protocol that is likely to become the de facto standard for next generation automotive networks. The models are based on discrete-time Markov chains and consider a comprehensive set of fault scenarios. Furthermore, they are parametric allowing for a sensitivity analysis. The results, obtained by a numeric solution of the models using the PRISM model-checker, show that they are computationally practical for realistic configurations and that the GMP can achieve reliability levels in the range required for safety critical applications.     

Effect of extra virgin olive oil on glycaemia in healthy young subjects

In this approach we studied the glycaemia levels in 20 healthy young volunteers (26 ± 2 years), before and after a 30‐day intake of 50 mL of extra virgin olive oil (EVOO). We selected an oil rich in phenolic compounds (523 mg/L) with a high content of secoiridoidic derivatives (over 94.5%). The findings from our study reveal a significant decrease of glycaemia from 89.6 ± 6.8 to 82.7 ± 10.3 mg/dL (p<0.05), related to a long term daily intake of the study EVOO, as the only added fat. A significant increment of the HDL cholesterol, from 68.7 ± 11.5 to 75.2 ± 4.9 mg/dL, was also highlighted. Total cholesterol, LDL, VLDL, triglycerides, and blood pressure did not show significant variation after the 30‐day consumption of this EVOO. So far, few articles have described the influence of EVOO consumption, on plasma glucose levels in humans. This effect is observed in a group of healthy young humans. Moreover, we confirm that the level of free hydroxytyrosol (OH‐Tyr) in plasma increased up to fourfold (p<0.05) after the 30‐day intake of this EVOO. In addition, the excretion in urine of the main metabolite of OH‐Tyr, homovanillic acid (HVA), significantly increased.     

In vitro digestibility of phenolic compounds from edible fruits: could it be explained by chemometrics?

The health benefits of phenolic compounds depend on the ingested amount, molecular diversity and gastrointestinal digestibility. The phenolic profile of eight fruits (blackberry, blueberry, strawberry, raspberry, mulberry, pomegranate, green and red globe grapes) was chemometrically associated with their in vitro digestibility (oral, gastric, intestinal). Extractable phenols, flavonoids and anthocyanins strongly correlated with each other (r ≥ 0.84), proanthocyanidins with anthocyanins (r = 0.62) and hydrolysable phenols with both extractable phenols (r = 0.45) and proanthocyanidins (r = ?0.54). Two principal components explained 93% of the variance [61% (free‐phenols), 32% (bounded‐phenols)], and four clusters were confirmed by hierarchical analysis, based in their phenolic richness (CLT 1‐4: low to high) and molecular diversity. In vitro digestibility of extractable phenols and flavonoids was blackberry (CLT‐4)> raspberry (CLT‐2)> red grape (CLT‐1) related to their phenolic richness (r ≥ 0.96; P < 0.001), but anthocyanins’ digestibility was pH‐dependent. Chemometrics is useful to predict the in vitro digestibility of phenolic compounds in the assayed fruits.     

Improving retention rate and response quality in Web-based surveys

This study centers on three parameters that can influence responses to Web-based surveys: personalization, the periodicity of follow-up mailings and incentives based on prize draws. The results show the need to send a lower number of reminders with personalized e-mail messages when the aim is for respondents to complete the full questionnaire. In contrast, the use of post-incentives based on prize draws was not found to have a significant effect on retention rate when used alone or in combination with personalized messages and/or a lower number of reminders. Moreover, none of the above factors, except personalization, improves response quality when used separately or in conjunction.     

Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction

The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density (MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool. Although most automated methods rely on supervised approaches based on a global labeling of the mammogram, the proposed method relies on pixel-level labeling, allowing better tissue classification and density measurement on a continuous scale. The fully automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case–control dataset consisting of 230 mammograms. The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC = 0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC = 0.838. In the case–control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessments present a good correlation. Both the methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available.     

Dynamic affinity-based classification of multi-class imbalanced data with one-versus-one decomposition: a fuzzy rough set approach

Although the relevance problem in recommender systems, which typically refers to the similarity between the preference of the user and the items the system recommends, has been well studied, the issue of making recommendations in right orders has barely been mentioned before. The order defines the way in which items should be better consumed in relation to each other. The analysis of real-world data sets demonstrates strong item consumption patterns in the degree of order. In this paper, we propose a novel method to tackle the right-order recommendation problem based on a graph structure that incorporates the item consumption orders. The proposed method can combine relevance and order effects in recommendations. We attempt to recommend the relevant items in the consecutive steps within a user session so that the user’s selected items in the consecutive steps can be stringed together in a right order. The experimental evaluation conducted on three real-world data sets shows that the recommendation accuracy is improved by considering item consumption orders compared with the baseline recommendation methods. In addition, the study on right-order recommendation contributes to the exploration on recommendation appropriateness besides relevance.     

An experimental analysis on the evolution of the transient tip penetration in reacting Diesel sprays

Schlieren imaging has helped deeply characterize the behavior of Diesel spray when injected into an oxygen-free ambient. However, when considering the transient penetration of the reacting spray after autoignition, i.e. the Diesel flame, few studies have been found in literature. Differences among optical setups as well as among experimental conditions have not allowed clear conclusions to be drawn on this issue. Furthermore, soot radiation may have a strong effect on the image quality, which cannot be neglected.