Towards a new intelligent traffic system based on deep learning and data integration

Time series forecasting is an important technique to study the behavior of temporal data in order to forecast the future values, which is widely applied in intelligent traffic systems (ITS). In this paper, several deep learning models were designed to deal with the multivariate time series forecasting problem for the purpose of long-term predicting traffic volume. Simulation results showed that the best forecasts are obtained with the use of two hidden long short-term memory (LSTM) layers: the first with 64 neurons and the second with 32 neurons. Over 93% of the forecasts were made with less than ±2.0% error. The analysis of variances is mainly due to peaks in some extreme conditions. For this purpose, the data was then merged between two different sources: electromagnetic loops and cameras. Data fusion is based on a calibration of the reliability of the sources according to the visibility conditions and time of the day. The integration results were then compared with the real data to prove the improvement of the prediction results in peak periods after the data fusion step.

Validity of instruction leaflets for parents to measure their child's weight and height at home: results obtained from a randomised controlled trial

Objectives: To compare the validity of parent-reported height, weight and body mass index (BMI) values of children (aged 4-10years), when measured at home by means of newly developed instruction leaflets in comparison with simple estimated parental reports. Design: Randomised controlled trial with control and intervention group using simple randomisation. Setting: Belgian children and their parents recruited via schools (multistage cluster sampling design). Participants: 164 Belgian children (53% male; participation rate 62%). Intervention: Parents completed a questionnaire including questions about the height and weight of their child. Parents in the intervention group received instruction leaflets to measure their child's weight and height. Classes were randomly allocated to the intervention and control groups. Nurses measured height and weight following standardised procedures up to 2weeks after parental reports. Outcome measures: Weight, height and BMI category of the child were derived from the index measurements and the parental reports. Results: Mean parent-reported weight was slightly more underestimated in the intervention group than in the control group relative to the index weights. However, for all three parameters (weight, height and BMI), correlations between parental reports and nurse measurements were higher in the intervention group. Sensitivity for underweight and overweight/obesity was respectively, 75% and 60% in the intervention group, and 67% and 43% in the control group. Weighed for classifying children in the correct BMI category was 0.30 in the control group and was 0.51 in the intervention group. Conclusions: Although mean parent-reported weight was slightly more underestimated in the intervention than in the control group, correlations were higher and there was considerably less misclassification into valid BMIcategories for the intervention group. This pattern suggests that most of the parental deviations from the index measurements were probably due to random errors of measurement and that diagnostic measures could improve by encouraging parents to measure their children's weight and height at home by means of instruction leaflets

Optimizing polymer-stabilized raw earth composite with plant fibers reinforcement for historic building rehabilitation

This research focuses on the optimization of formulation, characterization, and damage analysis of plant fiber-reinforced polyester resin composites (jute and date palm). To better understand the characteristics and mechanical behavior of these materials, this study investigates the influence of resin content and plant fibers on the physico-mechanical behavior of the resin composites. Resinous composites consisting of polyester resin and raw earth were studied using a novel formulation based on an empirical method that follows the principle of earth saturation with polyester resin. Saturation was achieved with a 28% content of polyester resin, which appeared to be an optimal blend for the earth–resin composite. Plant fibers were randomly incorporated as reinforcement in the composites at various percentages (1%, 2%, and 3%) and lengths (0.5 cm, 1 cm, and 1.5 cm). Mechanical tests including bending, compression, and indentation were conducted to evaluate the mechanical properties of the composites. Analysis of fracture morphology revealed that the deformation and rupture mechanisms in bending, compression, and indentation of these composites differ from those of traditional concrete and cement mortar. The obtained results indicate that the composites exhibit acceptable performance and could be favorably employed in the rehabilitation of historic buildings

Repeated 24-hour recalls versus dietary records for estimating nutrient intakes in a national food consumption survey

The methodology used in the first Belgian food consumption survey followed to a large extent the instructions of the European Food Consumption (EFCOSUM) reports, where repeated 24-hour recalls (24HR) using EPIC-SOFT were recommended.To evaluate the relative validity of two non-consecutive 24HR using EPIC-SOFT by comparison with 5-day estimated dietary records (EDR). To assess misreporting in energy for both methods by comparing energy intake with energy expenditure from accelerometery in a subsample.A total of 175 subjects (aged 15 and over) were recruited to participate in the study. Repeated 24HR were performed with an interval of 2–8 weeks. After completion of the second interview, subjects were instructed to keep an EDR. Dietary intakes were adjusted for within-person variability to reflect usual intakes. A Student's t-test was calculated to assess differences between both methods. Spearman and Kappa correlation coefficients were used to investigate agreement.In total, 127 subjects completed the required repeated 24HR, as well as the five record days. From 76 participants, accelerometer data were available. In both methods, about 35% of participants had ratios of Energy Intake/Total Energy Expenditure (EI/TEE) above or below 95% confidence intervals for EI/TEE, suggesting misreporting of energy. Significant differences between the two dietary intake methods were found for total energy, total fat, fatty acids, cholesterol, alcohol, vitamin C, thiamine, riboflavin and iron. In general, intakes from 24HR were higher compared to EDR. Correlation coefficients for all nutrients ranged from 0.16 for thiamine to 0.70 for water.The results from this study show that in the context of nutritional surveillance, duplicate 24HR can be used to asses intakes of protein, carbohydrates, starch, sugar, water, potassium and calcium

Trends of serum phospholipid fatty acids over time in rural Uganda: evidence of nutritional transition?

Non-communicable diseases are projected to become the most common causes of death in Africa by 2030. The impact on health of epidemiological and nutritional transitions in sub-Saharan Africa remains unclear. To assess the trends of dietary fatty acids over time in Uganda, we examined fatty acids in serum collected from individuals in rural south-west Uganda, at three time points over two decades. Independent cross-sectional samples of 915 adults and children were selected from the general population cohort in 1990 (n 281), 2000 (n 283) and 2008 (n 351). Serum phospholipid fatty acids were measured by GC. Multivariate regression analyses were performed to compare the geometric means of fatty acids by time period. Serum fatty acid profiling showed high proportions of SFA, cis-MUFA and industrial trans-fatty acids (iTFA), likely to be biomarkers of high consumption of palm oil and hydrogenated fats. In contrast, proportions of n-6 and n-3 PUFA from vegetable oils and fish were low. From 1990 to 2008, serum phospholipids showed increases in absolute amounts of SFA (17·3 % increase in adults and 26·4 % in children), MUFA (16·7 % increase in adults and 16·8 % in children) and n-6:n-3 PUFA (40·1 % increase in adults and 39·8 % in children). The amount of elaidic acid, iTFA from hydrogenated fats, increased in children (60·1 % increase). In this rural Ugandan population, we show evidence of unfavourable trends over time of dietary fatty acids

Subjects’ perception in quantifying printed and digital photos of food portions

Although digital photos have the potential to improve the precision of reported portions in dietary assessment, there are few studies investigating its accuracy in comparison to printed photos. The aim of this study was to evaluate the perception of adults in quantifying food portion sizes using printed and digital photos, displayed on computer-screens and tablets. In total, 1165 evaluations were performed for 60 photos of portion sizes in Brazil. Each participant (n = 58) attended two sessions in the study center, with an interval of at least one week. In each session, twelve food portions were prepared and randomly evaluated by each participant in its printed and digital forms. The mean error (difference between the estimated and true portions) was not significantly different between the printed photos (2.1g ± 47.2) and the digital ones (-6.4g ± 53.7). The agreement on using the printed and digital photos was 91% and 90%, respectively. Furthermore, the use of the tablet was more prone to underestimation when compared to printed and computer-screen photos (p < 0.001). Overall, participants did not present major difficulties in perceiving the portion sizes using the printed and digital photos, but the use of tablets led to less accurate results, indicating that this needs to be further evaluated

Studies of jet quenching within a partonic transport model

Background: Finite mixture models posit the existence of a latent categorical variable and can be used for probabilistic classification. The authors illustrate the use of mixture models for dietary pattern analysis. An advantage of this approach is taking classification uncertainty into account. Methods: Participants were a random sample of women from the European Prospective Investigation into Cancer. Food consumption was measured using dietary questionnaires. Mixture models identified latent classes in food consumption data, which were interpreted as dietary patterns. Results: Among various assumptions examined, models allowing the variance of foods to vary within and between classes fit better than alternatives assuming constant variance (the K-means method of cluster analysis also makes the latter assumption). An eight-class model was best fitting and five patterns validated well in a second random sample. Patterns with lower classification uncertainty tended to be better validated. One pattern showed low consumption of foods despite being associated with moderate body mass index. Conclusion: Mixture modelling for dietary pattern analysis has advantages over both factor and cluster analysis. In contrast to these other methods, it is easy to estimate pattern prevalence, to describe patterns and to use patterns to predict disease taking classification uncertainty into account. Owing to substantial error in food consumptions, any analysis will usually find some patterns that cannot be well validated. While knowledge of classification uncertainty may aid pattern evaluation, any method will better identify patterns from food consumptions measured with less error. Mixture models may be useful to identify individuals who under-report food consumption