Schaeferodom6212

We have also observed a new type of bicritical point, which involves two different sets of harmonic oscillations. The effects of variation of Q and Pr on the threshold Rao and critical wavenumber ko are also investigated.The fundamental problem of adhesion in the presence of surface roughness and its effect on the prediction of friction has been a hot topic for decades in numerous areas of science and engineering, attracting even more attention in recent years in areas such as geotechnics and tectonics, nanotechnology, high-value manufacturing and biomechanics. In this paper a new model for deterministic calculation of the contact mechanics for rough surfaces in the presence of adhesion is presented. The contact solver is an in-house boundary element method that incorporates fast Fourier transform for numerical efficiency. The adhesive contact model considers full Lennard-Jones potentials and surface integration at the asperity level and is validated against models in the literature. click here Finally, the effect of surface roughness on the adhesion between surfaces was studied, and it was shown that the root mean square gradient of surface roughness can change the adhesive pressures irrespective of the root mean square surface roughness. We have tested two adhesion parameters based on Johnson's modified criteria and Ciavarella's model. We showed that Civarella's model introduces the most reasonable criteria suggesting that the RMS roughness and large wavelength of surfaces roughness are the important parameters of adhesion between rough surfaces.The primary questions motivating this report are Are there ways to increase coherence and delocalization of excitation among many molecules at moderate electronic coupling strength? Coherent delocalization of excitation in disordered molecular systems is studied using numerical calculations. The results are relevant to molecular excitons, polaritons, and make connections to classical phase oscillator synchronization. In particular, it is hypothesized that it is not only the magnitude of electronic coupling relative to the standard deviation of energetic disorder that decides the limits of coherence, but that the structure of the Hamiltonian-connections between sites (or molecules) made by electronic coupling-is a significant design parameter. Inspired by synchronization phenomena in analogous systems of phase oscillators, some properties of graphs that define the structure of different Hamiltonian matrices are explored. The report focuses on eigenvalues and ensemble density matrices of various structured, random matrices. Some reasons for the special delocalization properties and robustness of polaritons in the single-excitation subspace (the star graph) are discussed. The key result of this report is that, for some classes of Hamiltonian matrix structure, coherent delocalization is not easily defeated by energy disorder, even when the electronic coupling is small compared to disorder.Wireless connectivity is no longer limited to facilitating communications between individuals, but is also required to support diverse and heterogeneous applications, services and infrastructures. Internet of things (IoT) systems will dominate future technologies, allowing any and all devices to create, share and process data. If artificial intelligence resembles the brain of IoT, then high-speed connectivity forms the nervous system that connects the devices. For IoT to safely operate autonomously, it requires highly secure and reliable wireless links. In this article, we shed light on the potential of optical wireless communications to provide high-speed secure and reliable ubiquitous access as an enabler for fifth generation and beyond wireless networks.We introduce and study a new canonical integral, denoted I + - ε , depending on two complex parameters α1 and α2. It arises from the problem of wave diffraction by a quarter-plane and is heuristically constructed to capture the complex field near the tip and edges. We establish some region of analyticity of this integral in C 2 , and derive its rich asymptotic behaviour as |α1 | and |α2 | tend to infinity. We also study the decay properties of the function obtained from applying a specific double Cauchy integral operator to this integral. These results allow us to show that this integral shares all of the asymptotic properties expected from the key unknown function G+- arising when the quarter-plane diffraction problem is studied via a two-complex-variables Wiener-Hopf technique (see Assier & Abrahams, SIAM J. Appl. Math., in press). As a result, the integral I + - ε can be used to mimic the unknown function G+- and to build an efficient 'educated' approximation to the quarter-plane problem.In this work, we develop a framework for shape analysis using inconsistent surface mapping. Traditional landmark-based geometric morphometr- ics methods suffer from the limited degrees of freedom, while most of the more advanced non-rigid surface mapping methods rely on a strong assumption of the global consistency of two surfaces. From a practical point of view, given two anatomical surfaces with prominent feature landmarks, it is more desirable to have a method that automatically detects the most relevant parts of the two surfaces and finds the optimal landmark-matching alignment between these parts, without assuming any global 1-1 correspondence between the two surfaces. Our method is capable of solving this problem using inconsistent surface registration based on quasi-conformal theory. It further enables us to quantify the dissimilarity of two shapes using quasi-conformal distortion and differences in mean and Gaussian curvatures, thereby providing a natural way for shape classification. Experiments on Platyrrhine molars demonstrate the effectiveness of our method and shed light on the interplay between function and shape in nature.From epidemiology to economics, there is a fundamental need of statistically principled approaches to unveil spatial patterns and identify their underpinning mechanisms. Grounded in network and information theory, we establish a non-parametric scheme to study spatial associations from limited measurements of a spatial process. Through the lens of network theory, we relate spatial patterning in the dataset to the topology of a network on which the process unfolds. From the available observations of the spatial process and a candidate network topology, we compute a mutual information statistic that measures the extent to which the measurement at a node is explained by observations at neighbouring nodes. For a class of networks and linear autoregressive processes, we establish closed-form expressions for the mutual information statistic in terms of network topological features. We demonstrate the feasibility of the approach on synthetic datasets comprising 25-100 measurements, generated by linear or nonlinear autoregressive processes.