About the oscillatory possibilities of the dynamical systems

This paper attempts to make feasible the evolutionary emergence of novelty in a supposedly deterministic world which behavior is associated with those of the mathematical dynamical systems. The work was motivated by the observation of complex oscillatory behaviors in a family of physical devices, for which there is no known explanation in the mainstream of nonlinear dynamics. The paper begins by describing a nonlinear mechanism of oscillatory mode mixing explaining such behaviors and establishes a generic dynamical scenario with extraordinary oscillatory possibilities, including expansive growing scalability. The relation of the scenario to the oscillatory behaviors of turbulent fluids and living brains is discussed. Finally, by considering the scenario as a dynamic substrate underlying generic aspects of both the functioning and the genesis of complexity in a supposedly deterministic world, a theoretical framework covering the evolutionary development of structural transformations in the time evolution of that world is built up.Comment: 40 pages, 12 figures, to appear in Physica

Design of a Novel Antenna Array Beamformer Using Neural Networks Trained by Modified Adaptive Dispersion Invasive Weed Optimization Based Data

A new antenna array beamformer based on neural networks (NNs) is presented. The NN training is performed by using optimized data sets extracted by a novel Invasive Weed Optimization (IWO) variant called Modified Adaptive Dispersion IWO (MADIWO). The trained NN is utilized as an adaptive beamformer that makes a uniform linear antenna array steer the main lobe towards a desired signal, place respective nulls towards several interference signals and suppress the side lobe level (SLL). Initially, the NN structure is selected by training several NNs of various structures using MADIWO based data and by making a comparison among the NNs in terms of training performance. The selected NN structure is then used to construct an adaptive beamformer, which is compared to MADIWO based and ADIWO based beamformers, regarding the SLL as well as the ability to properly steer the main lobe and the nulls. The comparison is made considering several sets of random cases with different numbers of interference signals and different power levels of additive zero-mean Gaussian noise. The comparative results exhibit the advantages of the proposed beamformer

Electronic structure of few-electron concentric double quantum rings

The ground state structure of few-electron concentric double quantum rings is investigated within the local spin density approximation. Signatures of inter-ring coupling in the addition energy spectrum are identified and discussed. We show that the electronic configurations in these structures can be greatly modulated by the inter-ring distance: At short and long distances the low-lying electron states localize in the inner and outer rings, respectively, and the energy structure is essentially that of an isolated single quantum ring. However, at intermediate distances the electron states localized in the inner and the outer ring become quasi-degenerate and a rather entangled, strongly-correlated system is formed.Comment: 16 pages (preprint format), 6 figure

Nucleation in dilute 3He-4He liquid mixtures at low temperatures

We present a study of phase separation from supersaturated 3He-4He liquid mixtures at low temperatures addressing both the degree of critical supersaturation Dx and the thermal-to-quantum crossover temperature T* for the nucleation process. Two different nucleation seeds are investigated, namely 3He droplets and 4He vortex lines with cores filled with 3He. We have found that the experimental T* is reproduced when we consider that nucleation proceeds from 3He droplets, whereas Dx is reproduced when we consider 4He vortex lines filled with 3He. However, neither nucleation configuration is able to simultaneously reproduce the current experimental information on Dx and T*.Comment: To appear in J. of Low Temp. Physic

Direct distribution with side sway for multi-story one bay frame

Call number: LD2668 .R4 1963 P57

The Effect of Varied Gender Groupings on Argumentation Skills among Middle School Students in Different Cultures

The purpose of this mixed-methods study was to explore the effect of varied gender groupings on argumentation skills among middle school students in Taiwan and the United States in a project-based learning environment that incorporated a graph-oriented computer-assisted application (GOCAA). A total of 43 students comprised the treatment condition and were engaged in the collaborative argumentation process in same-gender groupings. Of these 43 students, 20 were located in the U.S. and 23 were located in Taiwan. A total of 40 students comprised the control condition and were engaged in the collaborative argumentation process in mixed-gender groupings. Of these 40 students, 19 were in the U.S. and 21 were in Taiwan. In each country, verbal collaborative argumentation was recorded and the students’ post essays were collected. Among females in Taiwan, one-way analysis of variance (ANOVA) indicated that statistically a significant gender-grouping effect was evident on the total argumentation skills outcome, while MANOVA indicated no significant gender-grouping effect on the combined set of skill outcomes. Among females in the U.S., MANOVA indicated statistically significant gender-grouping effect on the combined set of argumentation skills outcomes Specifically, U.S. female students in mixed-gender groupings (the control condition) significantly outperformed female students in single-gender groupings (the treatment condition) in the counterargument and rebuttal skills. No significant group differences were observed among males. A qualitative analysis was conducted to examine how the graph-oriented computer-assisted application supported students’ development of argumentation skills in different gender groupings in both countries. In each country, all teams in both conditions demonstrated a similar pattern of collaborative argumentation with the exception of three female teams in the U.S. Female teams, male teams, (the treatment condition) and mixed-gender teams (the control condition) demonstrated metacognition regulation skills in different degrees and with different scaffolding

Bayesian optimization for materials design

We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian process regression, which allows predicting the performance of a new design based on previously tested designs. After providing a detailed introduction to Gaussian process regression, we introduce two Bayesian optimization methods: expected improvement, for design problems with noise-free evaluations; and the knowledge-gradient method, which generalizes expected improvement and may be used in design problems with noisy evaluations. Both methods are derived using a value-of-information analysis, and enjoy one-step Bayes-optimality