A Parameterized Class of Complex Nonsymmetric Algebraic Riccati Equations

In this paper, by introducing a definition of parameterized comparison matrix of a given complex square matrix, the solvability of a parameterized class of complex nonsymmetric algebraic Riccati equations (NAREs) is discussed. The existence and uniqueness of the extremal solutions of the NAREs is proved. Some classical numerical methods can be applied to compute the extremal solutions of the NAREs, mainly including the Schur method, the basic fixed-point iterative methods, Newton's method and the doubling algorithms. Furthermore, the linear convergence of the basic fixed-point iterative methods and the quadratic convergence of Newton's method and the doubling algorithms are also shown. Moreover, some concrete parameter selection strategies in complex number field for the doubling algorithms are also given. Numerical experiments demonstrate that our numerical methods are effective.     

Generalized Coupled Fixed Point Results on Complex Partial Metric Space Using Contractive Condition

In this paper, we obtain coupled fixed point results on complex partial metric space under contractive condition. An example to support our result is presented.     

一类输出耦合混沌复杂动态网络不稳定平衡点的控制

研究节点输出耦合混沌复杂动态网络不稳定平衡点的控制问题,基于输出控制思想,提出网络节点不稳定平衡点的全局控制方法以及牵制控制方法,将混沌复杂动态网络的所有节点镇定到其平衡点.利用李稚普诺夫稳定性理论,得到控制器参数选择条件,以蔡氏混沌电路作为网络节点动态进行仿真研究,证明该方法的有效性.     

Geometric, variational discretization of continuum theories

This study derives geometric, variational discretization of continuum theories arising in fluid dynamics, magnetohydrodynamics (MHD), and the dynamics of complex fluids. A central role in these discretizations is played by the geometric formulation of fluid dynamics, which views solutions to the governing equations for perfect fluid flow as geodesics on the group of volume-preserving diffeomorphisms of the fluid domain. Inspired by this framework, we construct a finite-dimensional approximation to the diffeomorphism group and its Lie algebra, thereby permitting a variational temporal discretization of geodesics on the spatially discretized diffeomorphism group. The extension to MHD and complex fluid flow is then made through an appeal to the theory of Euler-Poincaré systems with advection, which provides a generalization of the variational formulation of ideal fluid flow to fluids with one or more advected parameters. Upon deriving a family of structured integrators for these systems, we test their performance via a numerical implementation of the update schemes on a cartesian grid. Among the hallmarks of these new numerical methods are exact preservation of momenta arising from symmetries, automatic satisfaction of solenoidal constraints on vector fields, good long-term energy behavior, robustness with respect to the spatial and temporal resolution of the discretization, and applicability to irregular meshes.     

The dielectric relaxation study of 2(2-alkoxyethoxy)ethanol-water mixtures using time domain reflectometry

Complex permittivity spectra of 2(2-alkoxyethoxy)ethanol-water mixtures for the entire concentration range and at 25 °C have been measured using time domain reflectometry technique up to 30 GHz. The relaxation processes have been obtained by fitting the complex permittivity spectra of the binary mixtures in 2-Debye equation. In glycol ether-water mixtures except for pure water and its rich region the double relaxation times, primary process (τ₁) and secondary process (τ₂) have been observed. The primary relaxation process (τ₁) may be due to cooperative relaxation of the H-bond network of mixture constituents and the secondary relaxation process (τ₂) may be due to its flexible parts attached to the carbon chain. The intra and intermolecular hydrogen bonding of 2(2-alkoxyethoxy)ethanol in pure form as well as in binary mixtures of water have been discussed using Kirkwood correlation factor, Excess dielectric properties and the Bruggeman factor.     

Pulsed laser ablation of zinc selenide in nitrogen ambience: Formation of zinc nitride films

Zinc nitride (Zn₃N₂) thin films are prepared using pulsed laser deposition (PLD) from zinc selenide (ZnSe) target at different nitrogen ambient pressures viz. 1, 3, 5, 7 and 10 Pa. The films prepared with nitrogen pressures 1 and 3 Pa are amorphous in nature, whereas the films prepared at 5, 7 and 10 Pa exhibit the presence of cubic bixbyite Zn₃N₂ structure with lattice parameter very close to bulk of Zn₃N₂. The particle size calculated by Debye Scherrer's formula is in the nano regime. Surface morphology of the films is studied by SEM and AFM analysis. Optical parameters such as band gap, refractive index and porosity of the films are calculated. Moreover, the present study confers an outlook about how do various factors such as substrate temperature, reactive supplementing gas and laser-target interaction influence the film developing process during pulsed lased deposition.     

Magnetic hyperfine field at s-p impurities on Laves phase compounds

Recent experimental results for the magnetic hyperfine field B_hf at the nuclei of s-p impurities such as ¹¹⁹Sn in intermetallic Laves phases RM₂ (R=Gd, Tb, Dy, Ho, Er; M=Fe, Co) and ¹¹¹Cd in R Co₂, the impurity occupying a R site indicate that the ratio B_hf/μ_3d exhibits different behavior when one goes from RFe₂ to RCo₂. In this work, we calculate these local moments and the magnetic hyperfine fields. In our model, B_hf has two contributions: one arising from the R ions, and the other arising from magnetic 3d-elements; these separate contributions allow the identification of the origin of different behavior of the ratio mentioned above. For ¹¹¹Cd in RCo₂ we present also the contributions for B_hf in the light rare earth Pr, Nd, Pm, Sm compounds. For the sake of comparison we apply also the model to ¹¹¹Cd diluted in R Ni₂. Our self-consistent magnetic hyperfine field results are in good agreement with those recent experimental data.     

Metabolic networks classification and knowledge discovery by information granulation

Graphs are powerful structures able to capture topological and semantic information from data, hence suitable for modelling a plethora of real-world (complex) systems. For this reason, graph-based pattern recognition gained a lot of attention in recent years. In this paper, a general-purpose classification system in the graphs domain is presented. When most of the information of the available patterns can be encoded in edge labels, an information granulation-based approach is highly discriminant and allows for the identification of semantically meaningful edges. The proposed classification system has been tested on the entire set of organisms (5299) for which metabolic networks are known, allowing for both a perfect mirroring of the underlying taxonomy and the identification of most discriminant metabolic reactions and pathways. The widespread diffusion of graph (network) structures in biology makes the proposed pattern recognition approach potentially very useful in many different fields of application. More specifically, the possibility to have a reliable metric to compare different metabolic systems is instrumental in emerging fields like microbiome analysis and, more in general, for proposing metabolic networks as a universal phenotype spanning the entire tree of life and in direct contact with environmental cues.     

Identifying essential proteins using modified-monkey algorithm (MMA)

Proteins are the most essential macromolecules needed for the normal flow of life. Essential proteins play a key role to control other proteins in an interaction network for the growth and understanding of the molecular mechanism of cellular life. Though there are many computational algorithms for essential drug discovery depending on nature of essential proteins, but still lots of improvements and optimizations are required. In this work, modified-Monkey algorithm (MMA) is proposed for the identification of essential proteins in protein-protein interaction network (PPIN). Nature of a monkey can be distinctly described in three processes like climb, watch-jump, and somersault in different problem spaces. These processes of monkey traversal are plotted in PPIN with objective to find out essential proteins. Performance of MMA is assessed with other existing essential protein prediction methodologies, including Eigenvector Centrality (EC), Betweenness Centrality (BC), Network Centrality (NC) and others also. The proposed methodology has achieved higher success rates in comparison to the existing state-of-art model.     

A simple approach to reduce dimensionality from comprehensive two-dimensional liquid chromatography coupled with a multichannel detector

Comprehensive two-dimensional liquid chromatographic (LC × LC) systems play an ever increasing role in separation and characterization of complex samples. When coupled with multichannel detectors, such as the diode array detector, these LC × LC systems become especially useful for non-target analysis and identification of patterns based on the information extracted from those complex samples. Nevertheless, due to the large amount of data generated by these systems, the extraction of useful information for the identification of patterns still is one of the major drawbacks for a wider application of this technique. As a preliminary step in data treatment, we have developed a simple and fast way to deal with this large amount of multi-dimensional data by identifying the three-dimensional (3D) regional maxima of each chromatographic peak generated in a LC × LC–DAD system: retention times at the peak maximum in the first- and second-dimensions and the wavelength of the maximum UV absorption. This dataset is then used to build a 3D fingerprinting of the given sample, which alongside the 3D fingerprinting of other samples, can be used to identify different patterns associated with the specific properties of every sample under study. The applicability of the developed methodology was further assessed by performing a non-target LC × LC–DAD analysis of four Portuguese red wine samples.