Vitamin D is an essential nutrient to be consumed in the habitual dietary intake, whose deficiency is associated with various disturbances. This study represents a validation of vitamin D status estimation using a semi-quantitative FFQ, together with data from additional physical activity and lifestyle questionnaires. This information was combined to forecast the serum vitamin D status. Different statistical methods were applied to estimate the vitamin D status using predictors based on diet and lifestyle. Serum vitamin D was predicted using linear regression (with leave-one-out cross-validation) and random forest models. Intraclass correlation coefficients, Lin’s agreement coefficients, Bland–Altman plots and other methods were used to assess the accuracy of the predicted v. observed serum values. Data were collected in Spain. A total of 220 healthy volunteers aged between 18 and 78 years were included in this study. They completed validated questionnaires and agreed to provide blood samples to measure serum 25-hydroxyvitamin D (25(OH)D) levels. The common final predictors in both models were age, sex, sunlight exposure, vitamin D dietary intake (as assessed by the FFQ), BMI, time spent walking, physical activity and skin reaction after sun exposure. The intraclass correlation coefficient for the prediction was 0·60 (95 % CI: 0·52, 0·67; P < 0·001) using the random forest model. The magnitude of the correlation was moderate, which means that our estimation could be useful in future epidemiological studies to establish a link between the predicted 25(OH)D values and the occurrence of several clinical outcomes in larger cohorts.

Studies exploring the accuracy of equations calculating resting energy expenditure (REE) in patients with Crohn’s disease (CD) are lacking. The aim of this study was to investigate the accuracy of REE predictive equations against indirect calorimetry in CD patients. REE was measured using indirect calorimetry (mREE) after an overnight fasting. Fourteen predictive equations, with and without body composition analysis parameters, were compared with mREE using different body weight approaches. Body composition analysis was performed using dual X-ray absorptiometry. One hundred and eighty-six CD outpatients (102 males) with mean age 41·3 (sd 14·1) years and 37·6 % with active disease were evaluated. Mean mREE in the total sample was 7255 (sd 1854) kJ/day. All equations underpredicted REE and showed moderate correlations with mREE (Pearson’s r or Spearman’s rho 0·600–0·680 for current weight, all P-values < 0·001). Accuracy was low for all equations at the individual level (28–42 and 25–40 % for current and adjusted body weight, respectively, 19–33 % for equations including body composition parameters). At the group level, accuracy showed wide limits of agreement and proportional biases. Accuracy remained low when sample was studied according to disease activity, sex, BMI and medication use. All predictive equations underestimated REE and showed low accuracy. Indirect calorimetry remains the best method for estimating REE of patients with CD.

A new aerofoil parameterisation method is put forward to represent an aerofoil by combining the leading edge modification class/shape function transformation (LEM CST) method and improved Hicks–Henne bump function’s method. The new class/shape function transformation (NEW CST) method has two additional basis functions comparing the original CST method. In order to confirm these two basis functions, the radial basis functions neural network (RBF) model is trained by some samples which are generated by the Latin hypercube design (LHD) method and Genetic Algorithm (GA) is proposed to achieve the basis functions of the NEW CST method. The NEW CST method has been evaluated in fitting precision of 1,545 aerofoils by comparison with the LEM CST method and the original CST method. And the improved ability of the NEW CST at the leading edge and trailing edge is verified by a series of complex aerofoil case studies within 1,545 aerofoils. The results indicate that the NEW CST method can represent the whole aerofoils and possesses the intuitive property as well as the original CST. Moreover, the number of control parameters (NCP) to parameterise aerofoils is the fewest among these three methods. Furthermore, when the NCP of the NEW CST and LEM CST is the same, the NEW CST method has the higher accuracy and smaller root mean square errors (RMSE) especially at the leading edge and trailing edge.

Within the framework of the AUSPICIO (AUtomatic Scaling of Polar Ionograms and Co-operative Ionospheric Observations) project, a limited sample of ionograms recorded mostly in 2001 and 2009, and to a lesser extent in 2006–07 and 2012–15, at the ionospheric observatories of Hobart and Macquarie Island (mid-latitude), Comandante Ferraz and Livingstone Island (high latitude), and Casey, Mawson, Davis and Scott Base (inside the Antarctic Polar Circle (APC)) were considered to study the capability of the NeQuick2 and IRI2012 models for predicting the behaviour of the ionosphere at mid- and high latitudes and over the Antarctic area. The applicability of NeQuick2 and IRI2012 was evaluated as i) climatological models taking as input the F10.7 solar activity index and ii) assimilative models ingesting the foF2 and hmF2 measurements obtained from the electron density profiles provided by the Adaptive Ionospheric Profiler (AIP). The statistical analysis results reveal that the best description of the ionosphere’s electron density is achieved when the AIP measurements are ingested into the NeQuick2 and IRI2012 models. Moreover, NeQuick2 performance is far better than IRI2012 performance outside the APC. Conversely, the IRI2012 model performs better than the NeQuick2 model inside the APC.