Anisotropic Elastic-Viscoplastic Properties at Finite Strains of Injection-Moulded Low-Density Polyethylene

Injection-moulding is one of the most common manufacturing processes used for polymers. In many applications, the mechanical properties of the product is of great importance. Injection-moulding of thin-walled polymer products tends to leave the polymer structure in a state where the mechanical properties are anisotropic, due to alignment of polymer chains along the melt flow direction. The anisotropic elastic-viscoplastic properties of low-density polyethylene, that has undergone an injection-moulding process, are therefore examined in the present work. Test specimens were punched out from injection-moulded plates and tested in uniaxial tension. Three in-plane material directions were investigated. Because of the small thickness of the plates, only the in-plane properties could be determined. Tensile tests with both monotonic and cyclic loading were performed, and the local strains on the surface of the test specimens were measured using image analysis. True stress vs. true strain diagrams were constructed, and the material response was evaluated using an elastic-viscoplasticity law. The components of the anisotropic compliance matrix were determined together with the direction-specific plastic hardening parameters.     

Neural Estimator of Information for Time-Series Data with Dependency

Novel approaches to estimate information measures using neural networks are well-celebrated in recent years both in the information theory and machine learning communities. These neural-based estimators are shown to converge to the true values when estimating mutual information and conditional mutual information using independent samples. However, if the samples in the dataset are not independent, the consistency of these estimators requires further investigation. This is of particular interest for a more complex measure such as the directed information, which is pivotal in characterizing causality and is meaningful over time-dependent variables. The extension of the convergence proof for such cases is not trivial and demands further assumptions on the data. In this paper, we show that our neural estimator for conditional mutual information is consistent when the dataset is generated with samples of a stationary and ergodic source. In other words, we show that our information estimator using neural networks converges asymptotically to the true value with probability one. Besides universal functional approximation of neural networks, a core lemma to show the convergence is Birkhoff’s ergodic theorem. Additionally, we use the technique to estimate directed information and demonstrate the effectiveness of our approach in simulations.     

Optimization in forestry

 Optimization models and methods have been used extensively in the forest industry. In this paper we describe the general wood-flow in forestry and a variety of planning problems. These cover planning periods from a fraction of a second to more than one hundred years. The problems are modelled using linear, integer and nonlinear models. Solution methods used depend on the required solution time and include for example dynamic programming, LP methods, branch & bound methods, heuristics and column generation. The importance of modelling and qualitative information is also discussed. Received: January 15, 2003 / Accepted: April 24, 2003 Published online: May 28, 2003 Key words. Forestry – modelling – routing – transportation – production planning Mathematics Subject Classification (2000): 20E28, 20G40, 20C20     

Dimensions and Lyapunov exponents from exchange rate series

Detecting the presence of deterministic chaos in economic time series is an important problem that may be solved by measuring the largest Lyapunov exponent. In this paper we present estimates of the largest Lyapunov exponent in daily data for the Swedish Krona vs Deutsche Mark, ECU, U.S. Dollar and Yen exchange rates. In order to estimate the dimension of the systems producing these exchange rate series, we also present estimates of the correlation dimension. We found indications of deterministic chaos in all exchange rate series. However, the estimates for the largest Lyapunov exponents are not reliable, except in the Swedish Krona-ECU case, because of the limited number of data points. In the Swedish Krona-ECU case, we found indications of a low-order chaotic dynamical system.     

A continuation approach to mode-finding of multivariate Gaussian mixtures and kernel density estimates

This paper presents a novel method of multi-objective optimization by learning automata (MOLA) to solve complex multi-objective optimization problems. MOLA consists of multiple automata which perform sequential search in the solution domain. Each automaton undertakes dimensional search in the selected dimension of the solution domain, and each dimension is divided into a certain number of cells. Each automaton performs a continuous search action, instead of discrete actions, within cells. The merits of MOLA have been demonstrated, in comparison with a multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm II (NSGA-II), on eleven multi-objective benchmark functions and an optimal problem in the midwestern American electric power system which is integrated with wind power, respectively. The simulation results have shown that MOLA can obtain more accurate and evenly distributed Pareto fronts, in comparison with MOEA/D and NSGA-II.     

Separating Characters by Blocks

We investigate the problem of finding a set of prime divisorsof the order of a finite group, such that no two irreduciblecharacters are in the same p-block for all primes p in the set.Our main focus is on the simple and quasi-simple groups. Forresults on the alternating and symmetric groups and their doublecovers, some combinatorial results on the cores of partitionsare proved, which may be of independent interest. We also studythe problem for groups of Lie type. The sporadic groups (andtheir relatives) are checked using GAP.     

High electrical conductivity for the partially oxidised d8 Ru–Ru bonded chain [Ru(bpy)(CO)2]n (bpy = 2,2′-bipyridine) and its wide range redox control

High, electrochemically controlled electrical conductivity (0.2–0.3 S cm^?1) was measured for the first time for the metal–metal bonded polymeric compound [Ru(bpy)(CO)₂]_n (bpy = 2,2′-bipyridine). The conductivity depended on the degree of partial oxidation of the initially zero-valent Ru to Ru^I in the chains and was found to increase by four orders of magnitude after 5% oxidative doping. Linear conductance vs. doping level dependence was found for low levels and the conductivity of films could repeatedly be switched on and off. The conduction process is ascribed to charge delocalisation on the Ru chains in analogy to that of other similar chains formed by stacking of planar transition metal d⁸ complexes.     

Theoretical study of the antioxidant properties of pyridoxine

Molecules acting as antioxidants capable of scavenging reactive oxygen species (ROS) are of utmost importance in the living cell. The antioxidative properties of pyridoxine (vitamin B6) have recently been discovered. In this study, we have analyzed the reactivity of pyridoxine toward the ROS (.-)OH, (.-)OOH, and (.-)O(2)- at the density functional theory level (functionals B3LYP and MPW1B95). Two reaction types have been studied as follows: addition to the aromatic ring atoms and hydrogen/proton abstraction. Our results show that (.-)OH is the most reactive species, while (.-)OOH displays low reactivity and (.-)O2(-) does not react at all with pyridoxine. The most exergonic reactions are those where (.-)H is removed from the CH(2)OH groups or the ring-bound OH group and range from -33 to -39 kcal/mol. The most exergonic addition reactions occur by attacking the carbon atoms bonded to nitrogen but with an energy gain of only 6 kcal/mol.