Modelling and parameter identification for a two-stage fractional dynamical system in microbial batch process

In this paper, we consider mathematical modelling and parameter identification problem in bioconversion of glycerol to 1,3-propanediol by Klebsiella pneumoniae. In view of the dynamic behavior with memory and heredity and experimental results in batch culture, a two-stage fractional dynamical system with unknown fractional orders and unknown kinetic parameters is proposed to describe the fermentation process. For this system, some important properties of the solution are discussed. Then, taking the weighted least-squares error between the computational values and the experimental data as the performance index, a parameter identification model subject to continuous state inequality constraints is presented. An exact penalty method is introduced to transform the parameter identification problem into the one only with box constraints. On this basis, we develop a parallel Particle Swarm Optimization algorithm to find the optimal fractional orders and kinetic parameters. Finally, numerical results show that the model can reasonably describe the batch fermentation process, as well as the effectiveness of the developed algorithm. Keywords: fractional dynamical system, parameter identification, parallel optimization

Preparation of graphene film reinforced CoCrFeNiMn high-entropy alloy matrix composites with strength-plasticity synergy via flake powder metallurgy method

Inspired by the design principle of pearl structure, a bottom-up flake powder self-assembly arrangement strategy, flake powder metallurgy, is used to prepare graphene films (GFs) reinforced CoCrFeNiMn high-entropy alloy (HEA) matrix composites with a pearl laminated structure. Flaky HEA powder was prepared by ball milling method and homogeneously mixed with Ni plated GFs. Vacuum hot-press sintering (VHPS) technique was carried out to solidify the mixed powders to obtain composites with uniform distribution of GFs(Ni) and flaky HEA. The results show that the bottom-up preparation strategy can effectively fabricate bionic laminated HEA matrix composites, and the composites have a distinct pearly laminated structure. The tensile strength of the composites with 5 vol% GFs(Ni) content reached 834.04 MPa, and the elongation reached 26.58 %. The compressive strength in parallel and perpendicular laminar directions reached 2069.66 MPa and 2418.45 MPa at 50 % strain, respectively. The laminated GFs(Ni)/HEA matrix composites possessed excellent strength and maintained good plasticity. In this study, the strengthening and toughening mechanism of the laminated GFs(Ni)/HEA matrix composites is discussed in detail, and the results show that the laminated structure and GFs(Ni) are favorable for the hardening and strengthening of the HEA matrix

Dynamic Optimization for Switched Time-Delay Systems with State-Dependent Switching Conditions

This paper considers a dynamic optimization problem for a class of switched systems characterized by two key attributes: (i) the switching mechanism is invoked automatically when the state variables satisfy certain switching conditions; and (ii) the subsystem dynamics involve time-delays in the state variables. The decision variables in the problem, which must be selected optimally to minimize system cost, consist of a set of time-invariant system parameters in the initial state functions. To solve the dynamic optimization problem, we first show that the partial derivatives of the system state with respect to the system parameters can be expressed in terms of the solution of a set of variational switched systems. Then, on the basis of this result, we develop a gradient-based optimization algorithm to determine the optimal parameter values. Finally, we validate the proposed algorithm by solving an example problem arising in the production of 1,3-propanediol

Multi-objective optimization of nonlinear switched time-delay systems in fed-batch process

© 2016 Elsevier Inc.Maximization of productivity and minimization of consumption are two top priorities for biotechnological industry. In this paper, we model a fed-batch process as a nonlinear switched time-delay system. Taking the productivity of target product and the consumption rate of substrate as the objective functions, we present a multi-objective optimization problem involving the nonlinear switched time-delay system and subject to continuous state inequality constraints. To solve the multi-objective optimization problem, we first convert the problem into a sequence of single-objective optimization problems by using convex weighted sum and normal boundary intersection methods. A gradient-based single-objective solver incorporating constraint transcription technique is then developed to solve these single-objective optimization problems. Finally, a numerical example is provided to verify the effectiveness of the numerical solution approach. Numerical results show that the normal boundary intersection method in conjunction with the developed single-objective solver is more favourable than the convex weighted sum method

Gender-Based Deep Learning Firefly Optimization Method for Test Data Generation.

Software testing is a widespread validation means of software quality assurance in industry. Intelligent optimization algorithms have been proved to be an effective way of automatic test data generation. Firefly algorithm has received extensive attention and been widely used to solve optimization problems because of less parameters and simple implement. To overcome slow convergence rate and low accuracy of the firefly algorithm, a novel firefly algorithm with deep learning is proposed to generate structural test data. Initially, the population is divided into male subgroup and female subgroup. Following the randomly attracted model, each male firefly will be attracted by another randomly selected female firefly to focus on global search in whole space. Each female firefly implements local search under the leadership of the general center firefly, constructed based on historical experience with deep learning. At the final period of searching, chaos search is conducted near the best firefly to improve search accuracy. Simulation results show that the proposed algorithm can achieve better performance in terms of success coverage rate, coverage time, and diversity of solutions

Electrostatic Electrochemistry at Insulators

The identity of charges generated by contact electrification on dielectrics has remained unknown for centuries and the precise determination of the charge density is also a long-standing challenge. Here, electrostatic charges on Teflon (polytetrafluoroethylene) produced by rubbing with Lucite (polymethylmethacrylate) were directly identified as electrons rather than ions by electrochemical (analogous to electrogenerated chemiluminescence). Moreover, copper deposition could be amplified by depositing Pd first in a predetermined pattern, followed by electroless deposition to produce Cu lines. This process could be potentially important for microelectronic and other applications because Teflon has desirable properties including a low dielectric constant and good thermal stability. Charge density was determined using Faraday's law and the significance of electron transfer processes on charged polymers and potentially other insulators have been demonstrated. Although both contact electrification of insulating materials (dielectrics), such as Teflon and glass 1 , and electrochemistry at electronic conductors, such as metals and semiconductors 2 , deal with charged interfaces, they have largely remained distinct fields. The possible chemical effects of electrostatic charge have not been widely studied. Despite its long history 3 , the charge identity (electron or ion) on rubbed insulators is still poorly understood. Whereas Harper recognized the role of an electron transfer mechanism for metals and semiconductors 4,5 , on the basis of their relative Fermi level energies, he favoured an ion transfer mechanism for insulators 11 . Experiments designed to test if ion transfer occurred during contact electrification were not successful Experiments were carried out by immersion of charged Teflon into an acidic solution to note any change in pH and formation of hydrogen gas. After 37 pieces of Teflon septa were rubbed with Lucite discs and then briefly immersed in 3 ml of a 0.1 mM HCl solution one after another, the solution pH increased from 4 to 6.2. In another experiment, the pH of 3 ml of an HCl solution changed from 3.1 to 4.1, 5.2 and 7.3 after consecutive contact with charged Teflon tapes. However, this result alone does not prove that the negative charges on Teflon were electrons instead of ions, because H + could also adsorb on charged Teflon or an adsorbed anion, such as hydroxide, transferred to the surface during charging 15 could leach into the solution and cause a pH change. However, if hydrogen gas was produced, the charge carriers on Teflon must be electrons because there is no known way for adsorbed ions to generate hydrogen. Indeed, hydrogen was detected by ultrahigh vacuum (UHV) mass spectrometry. In this case, D 2 O was used and samples were prepared inside a glove box. Charged Teflon tape was introduced through a Teflon tube into a glass reactor with 50 ml D 2 O solution containing 1.5 ml DCl (35%). The reactor, which was equipped with a metal joint, was then connected to a stainlesssteel tube sealed with a valve. Note that some tape stayed above the DCl solution; careful shaking and tilting of the reactor were necessary for them to fully contact the solution. The reactor was then taken out and connected to a UHV system (1.5 × 10 −9 torr). Liquid nitrogen was used to freeze the reactor solution and the gas was first introduced into a sample transfer chamber before it reached the main UHV chamber. A clear D 2 peak appeared in the mass spectrum, whereas a control experiment carried out under the same conditions without contact to charged Teflon showed only a flat baseline. Hydrogen generation clearly shows that energetic electrons were present on the Teflon surface and caused a reduction process that should be faradaic as in conventional electrochemistry (2H + + 2e → H 2 ). In this process, as opposed to that of a typical two-electrode electrochemical cell, the solution becomes negatively charged with an excess of anions. If all of the pH change can be ascribed to the proton reduction, the observed pH change could be used as an accurate way to measure the electrostatic charge density on an insulator. Indeed, when the total number of H + ions removed from the solution is divided by the geometric nature materials ADVANCE ONLINE PUBLICATION www.nature.com/naturematerials

Syntax Tree Constrained Graph Network for Visual Question Answering

Visual Question Answering (VQA) aims to automatically answer natural language questions related to given image content. Existing VQA methods integrate vision modeling and language understanding to explore the deep semantics of the question. However, these methods ignore the significant syntax information of the question, which plays a vital role in understanding the essential semantics of the question and guiding the visual feature refinement. To fill the gap, we suggested a novel Syntax Tree Constrained Graph Network (STCGN) for VQA based on entity message passing and syntax tree. This model is able to extract a syntax tree from questions and obtain more precise syntax information. Specifically, we parse questions and obtain the question syntax tree using the Stanford syntax parsing tool. From the word level and phrase level, syntactic phrase features and question features are extracted using a hierarchical tree convolutional network. We then design a message-passing mechanism for phrase-aware visual entities and capture entity features according to a given visual context. Extensive experiments on VQA2.0 datasets demonstrate the superiority of our proposed model

Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path

Networks found in the real-world are numerous and varied. A common type of network is the heterogeneous network, where the nodes (and edges) can be of different types. Accordingly, there have been efforts at learning representations of these heterogeneous networks in low-dimensional space. However, most of the existing heterogeneous network embedding methods suffer from the following two drawbacks: (1) The target space is usually Euclidean. Conversely, many recent works have shown that complex networks may have hyperbolic latent anatomy, which is non-Euclidean. (2) These methods usually rely on meta-paths, which require domain-specific prior knowledge for meta-path selection. Additionally, different down-streaming tasks on the same network might require different meta-paths in order to generate task-specific embeddings. In this paper, we propose a novel self-guided random walk method that does not require meta-path for embedding heterogeneous networks into hyperbolic space. We conduct thorough experiments for the tasks of network reconstruction and link prediction on two public datasets, showing that our model outperforms a variety of well-known baselines across all tasks.Comment: In proceedings of the 35th AAAI Conference on Artificial Intelligenc