Dynamic stability of a class of fractional-order nonlinear systems via fixed point theory

In this paper, the problem of the uniform stability for a class of fuzzy fractional-order genetic regulatory networks with random discrete delays, distributed delays, and parameter uncertainties is studied. Although there is a portion of literature on using fixed point theorems to study the stability of fractional neural networks, most of them required the fractional order to be in $\left(\frac{1}{2},1\right)$. However, the case of the fractional-order belonging to $(0,\frac{1}{2})$ has not been discussed. To solve it, this work proposes a novel idea of using fixed point theory to study the stability of fuzzy (0,1) order neural networks, the problem of the uniqueness of the solution of the considered genetic regulatory networks is resolved, and a novel sufficient condition to guarantee the uniform stability of above genetic regulatory networks is also derived. Eventually, an example is given to demonstrate that the obtained result is effective.     

Model selection based on Lorenz and concentration curves,Gini indices and convex order

In order to determine an appropriate amount of premium, statistical goodness-of-fit criteria must be supplemented with actuarial ones when assessing performance of a given candidate pure premium. In this paper, concentration curves and Lorenz curves are shown to provide actuaries with effective tools to evaluate whether a premium is appropriate or to compare two competing alternatives. The idea is to compare the premium income for sub-portfolios gathering low risks (identified as low by means of the premiums under consideration) to the true one, or equivalently, to the actual losses. Numerical illustrations performed on hypothetical data and real ones demonstrate the usefulness of the proposed approach.     

Learning extended tree augmented naive structures

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). We enhance our procedure with a new score function that only takes into account arcs that are relevant to predict the class, as well as an optimization over the equivalent sample size during learning. These ideas may be useful for structure learning of Bayesian networks in general. A range of experiments shows that we obtain models with better prediction accuracy than naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator (AODE). We release our implementation of ETAN so that it can be easily installed and run within Weka.     

Mean field limits for nonlinear spatially extended Hawkes processes with exponential memory kernels

We consider spatially extended systems of interacting nonlinear Hawkes processes modeling large systems of neurons placed in ${\mathbb{R}}^d$ and study the associated mean field limits. As the total number of neurons tends to infinity, we prove that the evolution of a typical neuron, attached to a given spatial position, can be described by a nonlinear limit differential equation driven by a Poisson random measure. The limit process is described by a neural field equation. As a consequence, we provide a rigorous derivation of the neural field equation based on a thorough mean field analysis.     

Understanding the Reshaping of Fluorinated Polyester Vitrimers by Kinetic and DFT Studies of the Transesterification Reaction

Vitrimers are a third class of polymers gathering the mechanical properties and solvent resistance of 3D thermosets and the reprocessability of thermoplastics. This unique behaviour is due to the triggering of certain covalent exchange reactions that allow the network to rearrange upon application of a stimulus. The constitutive feature of vitrimers is the adoption of a glass-like viscosity during the rearrangement of the network, often due to an associative mechanism for the exchange reaction. Transesterification networks are one of the most studied type of vitrimers that usually require the incorporation of a catalyst, implying the associated drawbacks. Following up on a recent report on catalyst-free transesterification vitrimers in which the ester functions are particularly reactive thanks to the presence of fluorine atoms in α- or β-position, parallel DFT calculations and an experimental kinetic study on model molecules are presented in order to quantitatively assess the effect of neighbouring fluorinated groups on the transesterification reaction rate.     

Strain rate effects on compressive behavior of covalently bonded CNT networks

In this study, strain rate effects on the compressive mechanical properties of randomly structured carbon nanotube (CNT) networks were examined. For this purpose, three-dimensional atomistic models of CNT networks with covalently-bonded junctions were generated. After that, molecular dynamics (MD) simulations of compressive loading were performed at five different strain rates to investigate the basic deformation characteristic mechanisms of CNT networks and determine the effect of strain rate on stress–strain curves. The simulation results showed that the strain rate of compressive loading increases, so that a higher resistance of specimens to deformation is observed. Furthermore, the local deformation characteristics of CNT segments, which are mainly driven by bending and buckling modes, and their prevalence are strongly affected by the deformation rate. It was also observed that CNT networks have superior features to metal foams such as metal matrix syntactic foams (MMSFs) and porous sintered fiber metals (PSFMs) in terms of energy absorbing capabilities.     

Side chain engineering of naphthalene diimide–bithiophene‐based polymer acceptors in all‐polymer solar cells

Two novel polymeric acceptors based on naphthalene diimide (NDI) and 2.2′‐bithiophene, named as P(NDI2THD‐T2) and P(NDI2TOD‐T2), were designed and synthesized for all polymer solar cells application. The structural and electronic properties of the two acceptors were modulated through side‐chain engineering of the NDI units. The optoelectronic properties of the polymers and the morphologies of the blend films composed of the polymer acceptors and a donor polymer PTB7‐Th were systemically investigated. With thiophene groups introduced into the side chains of the NDI units, both polymers showed wider absorption from 350 nm to 900 nm, compared with the reference polymer acceptor of N2200. No redshift of absorption spectra from solutions to films indicated reduced aggregation of the polymers due to the steric hindrance effect of thiophene rings in the side chains. The photovoltaic performance were characterized for devices in a configuration of ITO/PEDOT:PSS/PTB7‐Th:acceptors/2,9‐bis(3‐(dimethylamino)propyl)anthra[2,1,9‐def:6,5,10‐def]diisoquinoline‐1,3,8,10(2H,9H)‐tetraone (PDIN)/Al. With the addition of diphenyl ether as an additive, the power conversion efficiencies (PCEs) of 2.73% and 4.75% for P(NDI2THD‐T2) and P(NDI2TOD‐T2) based devices were achieved, respectively. The latter showed improved J_sc, Fill Factor (FF), and PCE compared with N2200 based devices. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2017 , 55, 3679–3689