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We use a low-dimensional, agent-based bubble model to study the changes in the global dynamics of fluidized beds in response to changes in the frequency of the rising bubbles. The computationally based bifurcation analysis shows that at low frequencies, the global dynamics is attracted towards a fixed point since the bubbles interact very little with one another. As the frequency of injection increases, however, the global dynamics undergoes a series of bifurcations to new behaviors that include highly periodic orbits, chaotic attractors, and intermittent behavior between periodic orbits and chaotic sets. Using methods from time-series analysis, we are able to approximate nonlinear models that allow for long-term predictions and the possibility of developing control algorithms.

The active synchronous deformation in the arc length of an airfoil employed in a flapping wing can improve its energy extraction efficiency. The present study seeks to understand the underlying physics of this energy extraction by conducting transient numerical simulations of a novel arc-deformable flapping foil design based on dynamic mesh technology and a relative heaving motion reference system. The influence of the flapping frequency and the pitching amplitude on the energy extraction efficiency of the flapping foil modeled under a constant arc length is investigated. The effects of the deformation magnitude β and the position of the deformation center on the energy extraction efficiency are also examined at a constant flapping frequency and pitching amplitude. The results show that active synchronous arc deformation can greatly improve the energy extraction efficiency of a flapping foil compared to the efficiency of a conventional non-deformable flapping foil design. In addition, the results provide sets of optimal flapping frequencies and pitching amplitudes for the deformable flapping foil design with fixed deformation parameters and the non-deformable foil design that obtains the highest energy extraction efficiencies. A single high efficiency zone is obtained for the deformable foil design at a relatively high flapping frequency. In contrast, relatively high efficiency zones are obtained for the non-deformable foil design at both a relatively low flapping frequency and a high flapping frequency. The energy extraction efficiency of the deformable flapping foil first increases with increasing β up to a maximum value of β = 0.25 and then decreases with a further increase in β. The energy extraction efficiency of the deformable flapping foil is also demonstrated to increase as the deformation center moves from the leading edge of the foil to the trailing edge, attaining a maximum value when the deformation center coincides with the center of the pitching axis, and then decreases.

BackgroundEstrogen receptors (ERs) are nuclear transcription factors that are involved in the regulation of many complex physiological processes in humans. ERs have been validated as important drug targets for the treatment of various diseases, including breast cancer, ovarian cancer, osteoporosis, and cardiovascular disease. ERs have two subtypes, ER-α and ER-β. Emerging data suggest that the development of subtype-selective ligands that specifically target ER-β could be a more optimal approach to elicit beneficial estrogen-like activities and reduce side effects.MethodsHerein, we focused on ER-β and developed its in silico quantitative structure-activity relationship models using machine learning (ML) methods.ResultsThe chemical structures and ER-β bioactivity data were extracted from public chemogenomics databases. Four types of popular fingerprint generation methods including MACCS fingerprint, PubChem fingerprint, 2D atom pairs, and Chemistry Development Kit extended fingerprint were used as descriptors. Four ML methods including Naïve Bayesian classifier, k-nearest neighbor, random forest, and support vector machine were used to train the models. The range of classification accuracies was 77.10% to 88.34%, and the range of area under the ROC (receiver operating characteristic) curve values was 0.8151 to 0.9475, evaluated by the 5-fold cross-validation. Comparison analysis suggests that both the random forest and the support vector machine are superior for the classification of selective ER-β agonists. Chemistry Development Kit extended fingerprints and MACCS fingerprint performed better in structural representation between active and inactive agonists.ConclusionThese results demonstrate that combining the fingerprint and ML approaches leads to robust ER-β agonist prediction models, which are potentially applicable to the identification of selective ER-β agonists.