Stochastic Analysis and Regeneration of Rough Surfaces

We investigate Markov property of rough surfaces. Using stochastic analysis we characterize the complexity of the surface roughness by means of a Fokker-Planck or Langevin equation. The obtained Langevin equation enables us to regenerate surfaces with similar statistical properties compared with the observed morphology by atomic force microscopy.Comment: 4 pages, 7 figure

Height Fluctuations and Intermittency of V2O5V_2 O_5 Films by Atomic Force Microscopy

The spatial scaling law and intermittency of the $V_2 O_5$ surface roughness by atomic force microscopy has been investigated. The intermittency of the height fluctuations has been checked by two different methods, first, by measuring scaling exponent of q-th moment of height-difference fluctuations i.e. $C_q =$ and the second, by defining generating function $Z(q,N)$ and generalized multi-fractal dimension $D_q$. These methods predict that there is no intermittency in the height fluctuations. The observed roughness and dynamical exponents can be explained by the numerical simulation on the basis of forced Kuramoto-Sivashinsky equation.Comment: 6 pages (two columns), 11 eps. figures, late

Localization of elastic waves in heterogeneous media with off-diagonal disorder and long-range correlations

Using the Martin-Siggia-Rose method, we study propagation of acoustic waves in strongly heterogeneous media which are characterized by a broad distribution of the elastic constants. Gaussian-white distributed elastic constants, as well as those with long-range correlations with non-decaying power-law correlation functions, are considered. The study is motivated in part by a recent discovery that the elastic moduli of rock at large length scales may be characterized by long-range power-law correlation functions. Depending on the disorder, the renormalization group (RG) flows exhibit a transition to localized regime in {\it any} dimension. We have numerically checked the RG results using the transfer-matrix method and direct numerical simulations for one- and two-dimensional systems, respectively.Comment: 5 pages, 4 figures, to appear in Phys. Rev. Let

Highly Designable Protein Structures and Inter Monomer Interactions

By exact computer enumeration and combinatorial methods, we have calculated the designability of proteins in a simple lattice H-P model for the protein folding problem. We show that if the strength of the non-additive part of the interaction potential becomes larger than a critical value, the degree of designability of structures will depend on the parameters of potential. We also show that the existence of a unique ground state is highly sensitive to mutation in certain sites.Comment: 14 pages, Latex file, 3 latex and 6 eps figures are include

Multiscale modeling of polycrystalline graphene: A comparison of structure and defect energies of realistic samples from phase field crystal models

© 2016 American Physical Society. We extend the phase field crystal (PFC) framework to quantitative modeling of polycrystalline graphene. PFC modeling is a powerful multiscale method for finding the ground state configurations of large realistic samples that can be further used to study their mechanical, thermal, or electronic properties. By fitting to quantum-mechanical density functional theory (DFT) calculations, we show that the PFC approach is able to predict realistic formation energies and defect structures of grain boundaries. We provide an in-depth comparison of the formation energies between PFC, DFT, and molecular dynamics (MD) calculations. The DFT and MD calculations are initialized using atomic configurations extracted from PFC ground states. Finally, we use the PFC approach to explicitly construct large realistic polycrystalline samples and characterize their properties using MD relaxation to demonstrate their quality

Geometrical exponents of contour loops on synthetic multifractal rough surfaces: multiplicative hierarchical cascade p-model

In this paper, we study many geometrical properties of contour loops to characterize the morphology of synthetic multifractal rough surfaces, which are generated by multiplicative hierarchical cascading processes. To this end, two different classes of multifractal rough surfaces are numerically simulated. As the first group, singular measure multifractal rough surfaces are generated by using the $p$ model. The smoothened multifractal rough surface then is simulated by convolving the first group with a so-called Hurst exponent, $H^*$ . The generalized multifractal dimension of isoheight lines (contours), $D(q)$, correlation exponent of contours, $x_l$, cumulative distributions of areas, $\xi$, and perimeters, $\eta$, are calculated for both synthetic multifractal rough surfaces. Our results show that for both mentioned classes, hyperscaling relations for contour loops are the same as that of monofractal systems. In contrast to singular measure multifractal rough surfaces, $H^*$ plays a leading role in smoothened multifractal rough surfaces. All computed geometrical exponents for the first class depend not only on its Hurst exponent but also on the set of $p$ values. But in spite of multifractal nature of smoothened surfaces (second class), the corresponding geometrical exponents are controlled by $H^*$, the same as what happens for monofractal rough surfaces.Comment: 14 pages, 14 figures and 6 tables; V2: Added comments, references, table and major correction

Comparative LCA technology improvement opportunities for a 1.5 MW wind turbine in the context of an offshore wind farm

Wind energy is playing an increasingly important role in the development of cleaner and more efficient energy technologies leading to projections in reliability and performance of future wind turbine designs. This paper presents life cycle assessment (LCA) results of design variations for a 1.5 MW wind turbine due to the potential for advances in technology to improve the performance of a 1.5 MW wind turbine. Five LCAs have been conducted for design variants of a 1.5 MW wind turbine. The objective is to evaluate potential environmental impacts per kilowatt hour of electricity generated for a 114 MW onshore wind farm. Results for the baseline turbine show that higher contributions to impacts were obtained in the categories Ozone Depletion Potential, Marine Aquatic Eco-toxicity Potential, Human Toxicity Potential and Terrestrial Eco-toxicity Potential compared to Technology Improvement Opportunities (TIOs) 1 to 4. Compared to the baseline turbine, TIO 1 showed increased impact contributions to Abiotic Depletion Potential, Acidification Potential, Eutrophication Potential, Global Warming Potential and Photochemical Ozone Creation Potential, and TIO 2 showed an increase in contributions to Abiotic Depletion Potential, Acidification Potential and Global Warming Potential. Additionally, lower contributions to all the environmental categories were observed for TIO 3 while increased contributions towards Abiotic Depletion Potential and Global Warming Potential were noted for TIO 4. A comparative LCA study of wind turbine design variations for a particular power rating has not been explored in the literature. This study presents new insight into the environmental implications related with projected wind turbine design advancements

Monotonicity, frustration, and ordered response: an analysis of the energy landscape of perturbed large-scale biological networks

<p>Abstract</p> <p>Background</p> <p>For large-scale biological networks represented as signed graphs, the index of frustration measures how far a network is from a monotone system, i.e., how incoherently the system responds to perturbations.</p> <p>Results</p> <p>In this paper we find that the frustration is systematically lower in transcriptional networks (modeled at functional level) than in signaling and metabolic networks (modeled at stoichiometric level). A possible interpretation of this result is in terms of energetic cost of an interaction: an erroneous or contradictory transcriptional action costs much more than a signaling/metabolic error, and therefore must be avoided as much as possible. Averaging over all possible perturbations, however, we also find that unlike for transcriptional networks, in the signaling/metabolic networks the probability of finding the system in its least frustrated configuration tends to be high also in correspondence of a moderate energetic regime, meaning that, in spite of the higher frustration, these networks can achieve a globally ordered response to perturbations even for moderate values of the strength of the interactions. Furthermore, an analysis of the energy landscape shows that signaling and metabolic networks lack energetic barriers around their global optima, a property also favouring global order.</p> <p>Conclusion</p> <p>In conclusion, transcriptional and signaling/metabolic networks appear to have systematic differences in both the index of frustration and the transition to global order. These differences are interpretable in terms of the different functions of the various classes of networks.</p

A Transposon-Based Genetic Screen in Mice Identifies Genes Altered in Colorectal Cancer

Human colorectal cancers (CRCs) display a large number of genetic and epigenetic alterations, some of which are causally involved in tumorigenesis (drivers) and others that have little functional impact (passengers). To help distinguish between these two classes of alterations, we used a transposon-based genetic screen in mice to identify candidate genes for CRC. Mice harboring mutagenic Sleeping Beauty (SB) transposons were crossed with mice expressing SB transposase in gastrointestinal tract epithelium. Most of the offspring developed intestinal lesions, including intraepithelial neoplasia, adenomas, and adenocarcinomas. Analysis of over 16,000 transposon insertions identified 77 candidate CRC genes, 60 of which are mutated and/or dysregulated in human CRC and thus are most likely to drive tumorigenesis. These genes include APC, PTEN, and SMAD4. The screen also identified 17 candidate genes that had not previously been implicated in CRC, including POLI, PTPRK, and RSPO2

Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications

The success of new scientific areas can be assessed by their potential for contributing to new theoretical approaches and in applications to real-world problems. Complex networks have fared extremely well in both of these aspects, with their sound theoretical basis developed over the years and with a variety of applications. In this survey, we analyze the applications of complex networks to real-world problems and data, with emphasis in representation, analysis and modeling, after an introduction to the main concepts and models. A diversity of phenomena are surveyed, which may be classified into no less than 22 areas, providing a clear indication of the impact of the field of complex networks.Comment: 103 pages, 3 figures and 7 tables. A working manuscript, suggestions are welcome