Flutter Derivatives Identification and Uncertainty Quantification for Bridge Decks Based on the Artificial Bee Colony Algorithm and Bootstrap Technique

This paper presents a novel parameter identification and uncertainty quantification method for flutter derivatives estimation of bridge decks. The proposed approach is based on free-decay vibration records of a sectional model in wind tunnel tests, which consists of parameter identification by a heuristic optimization algorithm in the sense of weighted least squares and uncertainty quantification by a bootstrap technique. The novel contributions of the method are on three fronts. Firstly, weighting factors associated with vertical and torsional motion in the objective function are determined more reasonably using an iterative procedure rather than preassigned. Secondly, flutter derivatives are identified using a hybrid heuristic and classical optimization method, which integrates a modified artificial bee colony algorithm with the Powell’s algorithm. Thirdly, a statistical bootstrap technique is used to quantify the uncertainties of flutter derivatives. The advantages of the proposed method with respect to other methods are faster and more accurate achievement of the global optimum, and refined uncertainty quantification in the identified flutter derivatives. The effectiveness and reliability of the proposed method are validated through noisy data of a numerically simulated thin plate and experimental data of a bridge deck sectional model.

Principal Components of Genetic Sequences: Correlations and Significance

Any numerical series can be decomposed into principal components using singular spectral analysis. We have recently proposed a new analysis method ‒ PCA-Seq, which allows calculating numerical principal components for a sequence of elements of any type. In particular, the sequence may be composed of nucleotide base pairs or amino acid residues. Two questions inevitably arise about interpretation of the obtained principal components and about the assessment of their reliability. For interpretation of the symbolic sequence principal components, it is reasonable to evaluate their correlations with numerical characteristics of the sequence elements. To assess the significance of correlations between sequences, one should bear in mind that standard significance criteria are based on the assumption of independence of observations, which, as a rule, is not fulfilled for real sequences. The article discusses the use of an anchor bootstrap technique for these purposes also previously developed by the authors of the article. In this approach it is assumed, that points of a metric space can represent the objects. When taken together they make up some fixed structure in it, in particular, a sequence. The objects are assigned the same random integer weights as in the classical bootstrap. This is sufficient to obtain the bootstrap distribution of the correlation coefficients and assess their significance. The coding sequence of the SLC9A1 gene (synonyms APNH, NHE1, PPP1R143) were taken as an example of use the anchor bootstrap technique in the genetic sequence analysis. Significant correlations of the first principal component were revealed with the hydrophobicity/“transmembraneity” of the corresponding fragments of the amino acid sequence, the phenylalanine content in them, as well as the difference in the T- and A-content in the corresponding nucleotide fragments. Earlier a similar pattern was found by other authors for other genes. Very likely, that it is of a more general nature.

Statistical techniques for predicting rupture risk in abdominal aortic aneurysms: A contribution based on bootstrap

The morphometry of abdominal aortic aneurysms (AAA) has been recognized as one of the main factors that may predispose them to rupture. The need to quantify the morphometry of AAA on a patient-specific basis constitutes a valuable tool for assisting in rupture risk prediction. Previous results of this research group have determined the correlations between hemodynamic stresses and aneurysm morphometry by means of the Pearson coefficient. The present work aims to find how the AAA morphology correlates with the hemodynamic stresses acting on the arterial wall. To do so, the potential of the bootstrap technique has been explored. Bootstrap works appropriately in applications where few data are available (13 patient-specific AAA models were simulated). The methodology developed can be considered a contribution to predicting the hemodynamic stresses from the size and shape indices. The present work explores the use of a specific statistical technique (the bootstrap technique) to predict, based on morphological correlations, the patient-specific aneurysm rupture risk, provide greater understanding of this complex phenomenon that can bring about improvements in the clinical management of aneurysmatic patients. The results obtained using the bootstrap technique have greater reliability and robustness than those obtained by regression analysis using the Pearson coefficient, thus allowing to obtain more reliable results from the characteristics of the samples used, such as their small size and high variability. Additionally, it could be an indicator that other indices, such as AAA length, deformation rate, saccular index, and asymmetry, are important.

Demography of Thrips palmi (Thysanoptera: Thripidae) Reared on Brassica oleracea (Brassicales: Brassicaceae) and Phaseolus vulgaris (Fabales: Fabaceae) With Discussion on the Application of the Bootstrap Technique in Life Table Research

In order to contrast the fitness of Thrips palmi Karny reared on two of its hosts plants, Brassica oleracea L. leaves and Phaseolus vulgaris L. beans, life tables were constructed for T. palmi individuals reared on the two hostplants at 25°C using the age–stage, two-sex life table. The durations of the egg, first instar larva, pupa, and total immature stages of T. palmi reared on P. vulgaris were significantly longer than those reared on B. oleracea, whereas the adult preoviposition period, female adult longevity, and number of oviposition days of T. palmi reared on B. oleracea were significantly longer/greater than those on P. vulgaris. The fecundity (F) of T. palmi reared on B. oleracea (159.71 offspring/female) was significantly higher than in females reared on P. vulgaris (F = 122.98 offspring/female) despite the higher proportion of female adults that emerged on P. vulgaris. Although there were no significant differences in the intrinsic rate of increase (r), finite rate (λ), net reproductive rate (R0), and mean generation time (T) between the two populations, computer projection showed that the T. palmi population will increase faster when reared on B. oleracea than on P. vulgaris. Our results demonstrate that population projection based on life table data is potentially an important tool in pest management. Furthermore, we used the bootstrap technique to demonstrate the variable effect that survival rate and fecundity at different ages can have on population parameters.

DEA Performance Measurements in Cotton Production of Harran Plain, Turkey: A Single and Double Bootstrap Truncated Regression Approaches

A single and a double bootstrap of data envelopment analysis examines Harran Plain cotton farming in Turkey. The single bootstrap technique was employed to derive the bias-corrected efficiency values under both constant returns to scale (CRS) and versus variable returns to scale (VRS) technologies while discriminating between the two technologies using a smoothed bootstrap test statistic. Results indicated that the farms operated under VRS technology. Given that VRS technology prevailed across Harran Plain cotton farmers sampled, we then determined factors affecting the bias-corrected technical efficiencies using the double bootstrap technique. Another important finding in the single bootstrap analysis is that cotton farmers in the region have a U-shaped technical efficiency based on the input and output scale. Thus, small-scale farmers tend to use their resources more efficiently in cotton farming than that of both medium- and large-scale farmers. Interestingly, the medium-scale farmers with resource inefficiency are at the forefront of the other two types of farmers (i.e., small-scale and large-scale) on the Harran Plain in Turkey. The results also showed that most of the farm and farmer specific as well as economic factors play a significant role in explaining the technical efficiency values.