Scaling of the elastic contribution to the surface free energy of a nematic liquid crystal on a sawtoothed substrate

We characterize the elastic contribution to the surface free energy of a nematic liquid crystal in the presence of a sawtooth substrate. Our findings are based on numerical minimization of the Landau-de Gennes model and analytical calculations on the Frank-Oseen theory. The nucleation of disclination lines (characterized by non-half-integer winding numbers) in the wedges and apexes of the substrate induces a leading order proportional to q ln q to the elastic contribution to the surface free-energy density, with q being the wave number associated with the substrate periodicity.

Die Surface Designer System

This paper defines Die Surface Designer (DSD) System for fast draw die in the product development feasibility phase on surfaces coming from styling. We propose a CAD integration, for better support the design process in industry, particularly on the development of new products in automotive sector. The DSD system intends to reduce the lead time by providing and integrating flexible and efficient capabilities for testing early concepts from surface analysis points of view in automotive product development.

Chaos and crises in a model for cooperative hunting: A symbolic dynamics approach

In this work we investigate the population dynamics of cooperative hunting extending the McCann and Yodzis model for a three-species food chain system with a predator, a prey, and a resource species. The new model considers that a given fraction sigma of predators cooperates in prey's hunting, while the rest of the population 1-sigma hunts without cooperation. We use the theory of symbolic dynamics to study the topological entropy and the parameter space ordering of the kneading sequences associated with one-dimensional maps that reproduce significant aspects of the dynamics of the species under several degrees of cooperative hunting. Our model also allows us to investigate the so-called deterministic extinction via chaotic crisis and transient chaos in the framework of cooperative hunting. The symbolic sequences allow us to identify a critical boundary in the parameter spaces (K, C-0) and (K, sigma) which separates two scenarios: (i) all-species coexistence and (ii) predator's extinction via chaotic crisis. We show that the crisis value of the carrying capacity K-c decreases at increasing sigma, indicating that predator's populations with high degree of cooperative hunting are more sensitive to the chaotic crises. We also show that the control method of Dhamala and Lai [Phys. Rev. E 59, 1646 (1999)] can sustain the chaotic behavior after the crisis for systems with cooperative hunting. We finally analyze and quantify the inner structure of the target regions obtained with this control method for wider parameter values beyond the crisis, showing a power law dependence of the extinction transients on such critical parameters.

Pricing intraday dynamics across EUAS and CERS markets

The relative contribution of European Union Allowances (EUAs) and Certified Emission Reductions (CERs) to the price discovery of their common true value has been empirically studied using daily data with inconclusive results. In this paper, we study the short-run and long-run price dynamics between EUAs and CERs future contracts using intraday data. We report a bidirectional feedback causality relationship both in the short-run and in the long-run, with the EUA's market being the leader.

Comparison of deposited surface area of airborne ultrafine particles generated from two welding processes

This article describes work performed on the assessment of the levels of airborne ultrafine particles emitted in two welding processes metal-active gas (MAG) of carbon steel and friction-stir welding (FSW) of aluminium in terms of deposited area in alveolar tract of the lung using a nanoparticle surface area monitor analyser. The obtained results showed the dependence from process parameters on emitted ultrafine particles and clearly demonstrated the presence of ultrafine particles, when compared with background levels. The obtained results showed that the process that results on the lower levels of alveolar-deposited surface area is FSW, unlike MAG. Nevertheless, all the tested processes resulted in important doses of ultrafine particles that are to be deposited in the human lung of exposed workers.

Nanofiltration of surface water for the removal of endocrine disruptors

The assessment of surface water nanofiltration (NF) for the removal of endocrine disruptors (EDs) Nonylphenol Ethoxylate (IGEPAL), 4-Nonylphenol (NP) and 4-Octylphenol (OP) was carried out with three commercial NF membranes - NF90, NF200, NF270. The permeation experiments were conducted in laboratory flat-cell units of 13.2 x 10(-4) m(2) of surface area and in a DSS Lab-unit M20 with a membrane surface area of 0.036 m2. The membranes hydraulic permeabilities ranged from 3.7 to 15.6 kg/h/m(2)/bar and the rejection coefficients to NaCl, Na2SO4 and Glucose are for NF90: 97%, 99% and 97%, respectively; for NF200: 66%, 98% and 90%, respectively and for NF270: 48%, 94% and 84%, respectively. Three sets of nanofiltration experiments were carried out: i) NF of aqueous model solutions of NP, IGEPAL and OP running in total recirculation mode; ii) NF of surface water from Rio Sado (Settibal, Portugal) running in concentration mode; iii) NF of surface water from Rio Sado inoculated with NP, IGEPAL and OP running in concentration mode. The results of model solutions experiments showed that the EDs rejection coefficients are approximately 100% for all the membranes. The results obtained for the surface water showed that the rejection coefficients to natural organic Matter (NOM) are 94%, 82% and 78% for NF90, NF200 and NF 270 membranes respectively, with and without inoculation of EDs. The rejection coefficients to EDs in surface water with and without inoculation of EDs are 100%, showing that there is a fraction of NOM of high molecular weight that retains the EDs in the concentrate and that there is a fraction of NOM of low molecular weight that permeates through the NF membranes free of EDs.

Autoinhibition of TBCB regulates EB1-mediated microtubule dynamics

Tubulin cofactors (TBCs) participate in the folding, dimerization, and dissociation pathways of the tubulin dimer. Among them, TBCB and TBCE are two CAP-Gly domain-containing proteins that together efficiently interact with and dissociate the tubulin dimer. In the study reported here we showed that TBCB localizes at spindle and midzone microtubules during mitosis. Furthermore, the motif DEI/M-COO− present in TBCB, which is similar to the EEY/F-COO− element characteristic of EB proteins, CLIP-170, and α-tubulin, is required for TBCE–TBCB heterodimer formation and thus for tubulin dimer dissociation. This motif is responsible for TBCB autoinhibition, and our analysis suggests that TBCB is a monomer in solution. Mutants of TBCB lacking this motif are derepressed and induce microtubule depolymerization through an interaction with EB1 associated with microtubule tips. TBCB is also able to bind to the chaperonin complex CCT containing α-tubulin, suggesting that it could escort tubulin to facilitate its folding and dimerization, recycling or degradation.

Symbolic dynamics and renormalization of non-autonomous k periodic dynamical systems

The purpose of this paper was to introduce the symbolic formalism based on kneading theory, which allows us to study the renormalization of non-autonomous periodic dynamical systems.

An Extension of Gompertzian Growth Dynamics Weibull and Frechet Models

In this work a new probabilistic and dynamical approach to an extension of the Gompertz law is proposed. A generalized family of probability density functions, designated by Beta* (p, q), which is proportional to the right hand side of the Tsoularis-Wallace model, is studied. In particular, for p = 2, the investigation is extended to the extreme value models of Weibull and Frechet type. These models, described by differential equations, are proportional to the hyper-Gompertz growth model. It is proved that the Beta* (2, q) densities are a power of betas mixture, and that its dynamics are determined by a non-linear coupling of probabilities. The dynamical analysis is performed using techniques of symbolic dynamics and the system complexity is measured using topological entropy. Generally, the natural history of a malignant tumour is reflected through bifurcation diagrams, in which are identified regions of regression, stability, bifurcation, chaos and terminus.

Topological Complexity and Predictability in the Dynamics of a Tumor Growth Model with Shilnikov's Chaos

Dynamical systems modeling tumor growth have been investigated to determine the dynamics between tumor and healthy cells. Recent theoretical investigations indicate that these interactions may lead to different dynamical outcomes, in particular to homoclinic chaos. In the present study, we analyze both topological and dynamical properties of a recently characterized chaotic attractor governing the dynamics of tumor cells interacting with healthy tissue cells and effector cells of the immune system. By using the theory of symbolic dynamics, we first characterize the topological entropy and the parameter space ordering of kneading sequences from one-dimensional iterated maps identified in the dynamics, focusing on the effects of inactivation interactions between both effector and tumor cells. The previous analyses are complemented with the computation of the spectrum of Lyapunov exponents, the fractal dimension and the predictability of the chaotic attractors. Our results show that the inactivation rate of effector cells by the tumor cells has an important effect on the dynamics of the system. The increase of effector cells inactivation involves an inverse Feigenbaum (i.e. period-halving bifurcation) scenario, which results in the stabilization of the dynamics and in an increase of dynamics predictability. Our analyses also reveal that, at low inactivation rates of effector cells, tumor cells undergo strong, chaotic fluctuations, with the dynamics being highly unpredictable. Our findings are discussed in the context of tumor cells potential viability.

Strong and weak Allee effects and chaotic dynamics in Richards' growths

In this paper we define and investigate generalized Richards' growth models with strong and weak Allee effects and no Allee effect. We prove the transition from strong Allee effect to no Allee effect, passing through the weak Allee effect, depending on the implicit conditions, which involve the several parameters considered in the models. New classes of functions describing the existence or not of Allee effect are introduced, a new dynamical approach to Richards' populational growth equation is established. These families of generalized Richards' functions are proportional to the right hand side of the generalized Richards' growth models proposed. Subclasses of strong and weak Allee functions and functions with no Allee effect are characterized. The study of their bifurcation structure is presented in detail, this analysis is done based on the configurations of bifurcation curves and symbolic dynamics techniques. Generically, the dynamics of these functions are classified in the following types: extinction, semi-stability, stability, period doubling, chaos, chaotic semistability and essential extinction. We obtain conditions on the parameter plane for the existence of a weak Allee effect region related to the appearance of cusp points. To support our results, we present fold and flip bifurcations curves and numerical simulations of several bifurcation diagrams.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.