Investigation of the structure of supersonic nitrogen microjets

Results of research on microjets escaping into an ambient space from nozzles with diameters of 341–10.4 μm are described. A special Pitot microtube is used for studying the structure of supersonic microjets. The main feature of this microtube is a small diameter of the intake hole (12 μm). The main parameters of supersonic underexpanded microjets are identified, including the size of shock cells and the supersonic core length of the microjet. The results show a significant increase in the supersonic core length of microjets compared to macrojets. The Reynolds numbers of the laminar–turbulent transition in microjets are found to be in the range of 1,100–2,100. In addition, a classification of supersonic underexpanded axisymmetric microjets escaping into the ambient space is proposed.     

Global convergence of a reaction–diffusion predator–prey model with stage structure and nonlocal delays

In this paper, a Lotka–Volterra type reaction–diffusion predator–prey model with stage structure for the prey and nonlocal delays due to gestation of the predator is investigated. In the case of a general domain, sufficient conditions are obtained for the global convergence of positive solutions of the proposed problem by using the energy function method. Numerical simulations are carried out to illustrate the main results.     

A Well-Balanced Reconstruction of Wet/Dry Fronts for the Shallow Water Equations

In this paper, we construct a well-balanced, positivity preserving finite volume scheme for the shallow water equations based on a continuous, piecewise linear discretization of the bottom topography. The main new technique is a special reconstruction of the flow variables in wet–dry cells, which is presented in this paper for the one dimensional case. We realize the new reconstruction in the framework of the second-order semi-discrete central-upwind scheme from (Kurganov and Petrova, Commun. Math. Sci., 5(1):133–160, 2007). The positivity of the computed water height is ensured following (Bollermann et al., Commun. Comput. Phys., 10:371–404, 2011): The outgoing fluxes are limited in case of draining cells.     

Effective position–posture control strategy based on switching control for planar three-link underactuated mechanical system

A planar three-link passive–active–active (PAA) underactuated mechanical system is a kind of nonlinear system with a passive first joint. The position–posture control objective for the planar PAA system is to move the end effector from an initial position to a target position with a specified posture. This paper presents a switch control strategy to solve the position–posture control problem. First, a Lyapunov function is constructed based on the system control objective. Then, a set of main controllers based on this Lyapunov function are designed. However, the main controllers may make the system stabilise at one of equilibrium points, which is not the system target position. To avoid the above phenomenon, when the system is about to stabilise at one non-target position, the main controllers are switched to a set of sub-controllers, which are designed according to another Lyapunov function constructed based on the control objective of the active links. When the sub-controllers are running, their design parameters are adjusted to try to keep the derivative of the first Lyapunov function being a non-positive function. Therefore, the switch control between the main controllers and the sub-controllers realises the position–posture control objective of the system. Finally, the simulation results demonstrate the effectiveness of the switch control strategy.     

Existence and Exponential Stability of Anti-periodic Solutions for a High-Order Delayed Cohen–Grossberg Neural Networks with Impulsive Effects

In this paper, a high-order Cohen–Grossberg neural networks with bounded, unbounded delays and impulses is considered. By using differential inequality techniques, some very verifiable criteria on the existence and exponential stability of anti-periodic solutions for the model are obtained. Our results are new and complementary to previously known results. An example is included to illustrate the feasibility and effectiveness of our main results.     

Permanence and global attractivity of the discrete Gilpin–Ayala type population model

In this paper, we propose a discrete Gilpin–Ayala competition model and a discrete Gilpin–Ayala type multispecies competition–predator model. For general nonautonomous case, sufficient conditions which ensure the permanence and the global stability of the system are obtained; For periodic case, sufficient conditions which ensure the existence of an unique globally stable positive periodic solution of the system are obtained. An example together with its numeric simulations shows the feasibility of the main results.     

On the accuracy of the direct discrete simulation of the Landau collision integral by the Boltzmann integral

The Landau (Fokker–Planck) integral of Coulomb collisions is an integral component of the physical and mathematical models of both laboratory and space plasma, in which the intermediate collisionality regime is important. This paper discusses the method for the direct statistical simulation of the Monte Carlo type for a kinetic equation with a nonlinear operator of the Landau–Fokker–Planck (LFP) Coulomb collisions. This method is based on the approximation of the Landau collision integral by the Boltzmann collision integral. The paper has two main objectives: firstly, to obtain numerical estimates of the order of approximation of the Landau collision integral by the Boltzmann integral and, secondly, to explore the possibilities of optimizing the algorithm for multiply charged ions. The results are illustrated by the calculations of the problem on the relaxation of the initial distribution to equilibrium for one and two components.     

Fractional neural observer design for a class of nonlinear fractional chaotic systems

In this paper, a novel observer structure for nonlinear fractional-order systems is presented to estimate the states of fractional-order nonlinear chaotic system with unknown dynamical model. A new fractional error back-propagation learning algorithm is derived to adapt weights of the artificial neural network, by taking advantage of the Lyapunov stability strategy of fractional-order systems which is called Miattag–Leffler stability. The main contribution is the extension of neural observer for fractional dynamics in a way that satisfies Miattag–Leffler conditions. Observer design procedure guarantees the convergence of observer error to the neighborhood of zero. Furthermore, the robustness of the proposed estimator against uncertainties and external disturbances are the main benefits of the proposed method. Simulation results show the effectiveness and capabilities of the proposed observer.

     

Delta-Differentiable Weighted Pseudo-Almost Automorphicity on Time–Space Scales for a Novel Class of High-Order Competitive Neural Networks with WPAA Coefficients and Mixed Delays

In this paper, we consider a novel class of high-order competitive neural networks with mixed delays. Different from the previous literature, we study the existence and exponential stability of weighted pseudo-almost automorphic on time–space scales solutions for the suggested system. Our method is mainly based on the Banach’s fixed point theorem, the theory of calculus on time scales and the Lyapunov–Krasovskii functional method. Moreover, a numerical example is given to show the effectiveness of the main results.     

Superconductivity information extraction from the literature: A new corpus and its evaluations

This paper describes a superconductivity corpus for materials informatics (SC-CoMIcs) tailored for the extraction of superconductive material information from the literature. Currently, few corpora exist for materials informatics in contrast to the situation for biomedical informatics. In particular, there is no sizable corpus that can be used to assist with the search for superconducting materials. The SC-CoMIcs consists of 1,000 abstracts with manually annotated named entities, main materials, and relations/events associated with superconductivity. In particular, the main material is a selling point of our corpus, which can be regarded as a hub that binds implicitly related physical entities and properties in abstracts. We conduct named entity recognition, main material identification, and relation/event extraction experiments to determine the effectiveness of the corpus. The experimental results show that we can obtain F1 scores of approximately 74%–95%, 84% and 73%–97% for named entity recognition, main material identification and relation extraction tasks, respectively. We also demonstrate that the extracted doping information is consistent with that of the well-known Hume–Rothery rules, which implies that the constructed corpus can provide an opportunity to revisit or find physical chemical rules from the literature.     

On initial and boundary value problems for fractional order mixed type functional differential inclusions

In this paper we prove some existence results for initial and boundary value problems for functional differential inclusions of fractional order with both retarded and advanced arguments. The Banach fixed point theorem, the nonlinear alternative of the Leray–Schauder type and the Covitz–Nadler fixed point theorem are the main tools in deriving our proofs.     

Weighted Average Pinning Synchronization for a Class of Coupled Neural Networks with Time-Varying Delays

This paper deals with the pinning synchronization problem for a class of neural networks with time-varying delay and nonsymmetrical coupling. The weighted average of all the node states is selected as the controlled synchronization state. A pinning feedback controller is proposed, and some sufficient conditions are derived to guarantee the global asymptotical synchronization by use of the Lyapunov–Krasovskii function method. Consequently, the adaptive pinning synchronization is also investigated. Finally, a numerical example is given to show the effectiveness of the main results.     

Lattice Boltzmann simulation of water and gas flow in porous gas diffusion layers in fuel cells reconstructed from micro-tomography

The porous gas diffusion layers (GDLs) are key components in hydrogen fuel cells. During their operation the cells produce water at the cathode, and to avoid flooding, the water has to be removed out of the cells. How to manage the water is therefore an important issue in fuel cell design. In this paper we investigated water flow in the GDLs using a combination of the lattice Boltzmann method and X-ray computed tomography at the micron scale. Water flow in the GDL depends on water–air surface tension and hydrophobicity. To correctly represent the water–gas surface tension, the formations of water droplets in air were simulated, and the water–gas surface tension was obtained by fitting the simulated results to the Young–Laplace formula. The hydrophobicity is represented by the water–gas-fabric contact angle. For a given water–gas surface tension the value of the contact angle was determined by simulating the formations of water droplets on a solid surface with different hydrophobicity. We then applied the model to simulate water intrusion into initially dry GDLs driven by a pressure gradient in attempts to understand the impact of hydrophobicity on water distribution in the GDLs. The structures of the GDL were acquired by X-ray micro-tomography at a resolution of 1.7 microns. The simulated results revealed that with an increase in hydrophobicity, water transport in GDLs changes from piston-flow to channelled flow.     

Joint target tracking and classification via RFS-based multiple model filtering

Firstly, a multiple model extension of the random finite set (RFS)-based single-target Bayesian filtering (STBF), referred as MM-STBF, is presented to accommodate the possible target maneuvering behavior in a straightforward manner. This paper is concerned with joint target tracking and classification (JTC) which are closely coupled. In particular, we take into account extraneous target-originated measurements which were not modeled in the existing JTC algorithms. Therefore, the main contribution is that the paper derives a new JTC algorithm based on the MM-STBF, i.e., MM-STBF–JTC. The MM-STBF–JTC is an optimal Bayesian solution, which can simultaneously accommodate unknown data association, miss-detection, clutter and several measurements originated from a target. The MM-STBF–JTC can reduce to a traditional JTC algorithm under some assumptions. The simulation results are provided to demonstrate the tracking and classification performance of the MM-STBF–JTC algorithm.     

Benchmarking distribution centres using Principal Component Analysis and Data Envelopment Analysis: A case study of Serbia

The efficiency of distribution systems is largely affected by the performances of distribution centres. The main objective of this paper is to develop and propose a DEA model for distribution centres efficiency measuring that can help managers in decision making and improving the efficiency. Due to numerous indicators that describe DCs operating, the main problem is indicators selection. In order to improve discriminatory power of classical DEA models PCA–DEA approach is used. This paper analysis the efficiency of distribution centres of one trading company in Serbia. Proposed models integrate operational, quality, energy, utilisation and equipment warehouse and transport indicators. Several hypotheses are tested in this paper. The results showed that small distribution centres are more efficient than large.     

Nonlinearity in Simple and Complex Cells in Early Biological Visual Systems

The nonlinear model put forward in Mahmoodi (J Math Imaging Vis 54(2):138–161, 2016) for early visual systems is investigated in detail in this paper to explain some nonlinear behaviours of complex and some simple cells. Nonlinear cells are modelled as systems with linear–nonlinear structures where the linear sub-unit is constructed by the layers proposed in Mahmoodi (2016) and nonlinear sub-units are the results of an axon (modelled as a transmission line) carrying a series of spikes. In this paper, the nonlinear sub-systems of complex cells are investigated in more detail to show the mechanism by which nonlinear neurons work. Here the nonlinear systems modelling nonlinear sub-units of complex cells are represented by their first- and second-order responses. Our analytical as well as numerical results show good agreements with biological recordings reported in the literature.     

Mixed output feedback of the finite frequency H∞ control for singularly perturbed system

This paper considers the H_∞ control problem for a class of linear singularly perturbed systems in the finite frequency range. A mixed output feedback controller comprising of a static output feedback controller and a dynamic output feedback controller is developed for the system stabilisation. Based on the generalised Kalman–Yakubovich–Popov (GKYP) lemma, the frequency-domain inequalities can be converted into linear matrix inequalities which are numerically tractable. Compared with the existing full frequency approaches, better results are obtained. Moreover, the selection methods of the cut-off frequencies in both low and high frequency ranges are extensively studied with a view to reduce the conservativeness in output feedback control design. Simulation results suggest the asymptotic validity of the main results in this paper.     

Numerical solution of nonlinear delay differential equations of fractional variable-order using a novel shifted Jacobi operational matrix

This paper presents the generalized nonlinear delay differential equations of fractional variable-order. In this article, a novel shifted Jacobi operational matrix technique is introduced for solving a class of multi-terms variable-order fractional delay differential equations via reducing the main problem to an algebraic system of equations that can be solved numerically. The suggested technique is successfully developed for the aforementioned problem. Comprehensive numerical experiments are presented to demonstrate the efficiency, generality, accuracy of proposed scheme and the flexibility of this method. The numerical results compared it with other existing methods such as fractional Adams method (FAM), new predictor–corrector method (NPCM), a new approach, Adams–Bashforth–Moulton algorithm and L1 predictor–corrector method (L1-PCM). Comparing the results of these methods as well as comparing the current method (NSJOM) with the exact solution, indicating the efficiency and validity of this method. Note that the procedure is easy to implement and this technique will be considered as a generalization of many numerical schemes. Furthermore, the error and its bound are estimated.

     

Measuring efficiency and productive performance of colleges at the university of Santo Tomas: a nonparametric approach

This paper estimates relative efficiency and productive performance of 13 colleges at the University of Santo Tomas (UST), using data envelopment analysis (DEA) – Malmquist indices and a multi‐stage model. DEA is a management evaluation tool that assists with identifying the most efficient and inefficient decision‐making units (DMUs) in the best practice frontier. Total factor productivity (TFP) is measured for a sample of 13 colleges at UST over the period 1998–2003. Empirical results show that the main contributing factor to TFP growth is efficiency change. That is, UST colleges are technically operating efficiently in the frontier technology; though there is a downward shift in the technological advancement. Our results further imply that with the use of output–input mix, UST colleges as a whole have recorded a higher level of technical efficiency than innovation. These new findings contribute significantly to the existing literature on efficiency and productive performance in the education sector.