Random Walks,Bisections and Gossiping in Circulant Graphs

Circulant graphs are regular graphs based on Cayley graphs defined on the Abelian group \(\mathbb{Z}_{n}\) . They are popular network topologies that arise in distributed computing. Using number theoretical tools, we first prove two main results for random directed k-regular circulant graphs with n vertices, when n is sufficiently large and k is fixed. First, for any fixed ε>0, n=p prime and L≥p ^1/k (logp)^1+1/k+ε , walks of length at most L terminate at every vertex with asymptotically the same probability. Second, for any n, there is a polynomial time algorithm that for almost all undirected 2r-regular circulant graphs finds a vertex bisector and an edge bisector, both of size less than n ^1?1/r+o(1). We then prove that the latter result also holds for all (rather than for almost all) 2r-regular circulant graphs with n=p, prime, vertices, while, in general, it does not hold for composite n. Using the bisection results, we provide lower bounds on the number of rounds required by any gossiping algorithms for any n. We introduce generic distributed algorithms to solve the gossip problem in any circulant graphs. We illustrate the efficiency of these algorithms by giving nearly matching upper bounds of the number of rounds required by these algorithms in the vertex-disjoint and the edge-disjoint paths communication models in particular circulant graphs.     

Mullite–Nickel Magnetic Nanocomposite Fibers Obtained from Electrospinning Followed by Thermal Reduction

Mullite–nickel nanocomposite fibers with Ni nanoparticles of controllable size, dispersion, and consequent magnetic properties were fabricated using sol–gel/electrospinning method, followed by thermal reduction. The fibers were electrospun from an aqueous solution containing sol–gel mullite precursor and nickel nitrate. These fibers were then heat treated in the reducing atmosphere between 550°C and 750°C to achieve fine‐dis persed metallic Ni nanoparticles (NPs). After the Ni²⁺ was reduced to Ni NPs at 750°C for 10 h, the fibers were then directly transformed to the mullite fibers at 1000°C without the undesirable intermediate spinel phase. In many high‐temperature applications, mullite is the desired phase than spinel. If not fully reduced, the Ni²⁺ cations induce early precipitation of spinel phase before mullite can be formed. This spinel phase was a solid solution between Al₂NiO₄ and Al‐Si spinels, which later reacted with the residual silica and formed a mixture of mullite and spinel at 1400°C. The formation of spinel phase was suppressed or fully eliminated with chemically reducing Ni²⁺ to metal NPs. The average size of nickel NPs within the fibers was ~20 nm, insensitive of the Ni concentration and reducing temperature. However, the Ni NPs on the fiber surface grew as large as ~80 nm due to fast surface diffusion. The magnetic nanocomposites exhibited ferromagnetism with saturation magnetization (M_s) close to pure nickel of the same nominal weight, but coercivity (H_c) much smaller than the bulk nickel, indicating the nature of bimodal magnetic nanoparticle distributions. The majority of small Ni NPs (~20 nm) within the fibers exhibited superparamagnetism, while the minor portion of relatively large NPs (50–80 nm) showed ferromagnetism.     

New equipment for microwave electric field visualization

We present the operation and design of newly developed, fully automatic equipment for the visualization of microwave electric fields. This equipment enables the observation of microwave field patterns around different objects including metamaterial prototypes and to study the field patterns of various microwave antennas and other objects that have been developed and that interact with a surrounding microwave electromagnetic field. Moreover, the developed prototypes whose interaction with an incident electromagnetic wave is crucial for practical applications can be investigated using size scaling, and hence our equipment can be used for the testing of antennas and other devices that interact with electromagnetic radiation, not only at microwave frequencies, but also at radio frequencies. The performance of our innovative equipment was demonstrated through the investigation of the metamaterial cloak. The frequency behavior of the metamaterial cloak revealed frequency bands with maximum cloaking efficiencies.     

The General Explanation Method with NMR Spectroscopy Enables the Identification of Metabolite Profiles Specific for Normal and Tumor Cell Lines

Machine learning models in metabolomics, despite their great prediction accuracy, are still not widely adopted owing to the lack of an efficient explanation for their predictions. In this study, we propose the use of the general explanation method to explain the predictions of a machine learning model to gain detailed insight into metabolic differences between biological systems. The method was tested on a dataset of ¹H NMR spectra acquired on normal lung and mesothelial cell lines and their tumor counterparts. Initially, the random forests and artificial neural network models were applied to the dataset, and excellent prediction accuracy was achieved. The predictions of the models were explained with the general explanation method, which enabled identification of discriminating metabolic concentration differences between individual cell lines and enabled the construction of their specific metabolic concentration profiles. This intuitive and robust method holds great promise for in‐depth understanding of the mechanisms that underline phenotypes as well as for biomarker discovery in complex diseases.     

Design Strategies of Metal Complexes Based on Chelating Polymer Ligands and Their Application in Nanomaterials Science

The advances and problems associated with the synthesis, properties and structure of metal complexes based on chelating polymer ligands are presented and assessed. The polymeric metal chelates (PMCs) are divided into molecular, intracomplex, macrocyclic and polynuclear types, which in turn are grouped according to the nature of the donor atoms (N,N-, N,О-, N,S-, О,О-, O,S-, S,S-, Р,Р-chelates, etc.). The main attention is focused on the similarities and differences between low and high molecular weight metal chelates, as well as the spatial organization of PMCs. The most important areas of research in the field of such compounds are highlighted. Thermal transformations of metal complexes based on chelating polymer ligands leading to the formation of nanostructured materials are considered. The bibliography includes the articles published after 2010.     

Recursive fuzzy c-means clustering for recursive fuzzy identification of time-varying processes

In this paper we propose a new approach to on-line Takagi-Sugeno fuzzy model identification. It combines a recursive fuzzy c-means algorithm and recursive least squares. First the method is derived and than it is tested and compared on a benchmark problem of the Mackey-Glass time series with other established on-line identification methods. We showed that the developed algorithm gives a comparable degree of accuracy to other algorithms. The proposed algorithm can be used in a number of fields, including adaptive nonlinear control, model predictive control, fault detection, diagnostics and robotics. An example of identification based on a real data of the waste-water treatment process is also presented.     

Effect of plasma treatment on the release kinetics of a chemotherapy drug from biodegradable polyester films and polyester urethane films

Investigation was made into the effect of plasma treatment on the release kinetics of the drug Temozolomide (TMZ) from thin, biodegradable polyester films, comprising polylactic acid (PLA) and polyester urethane. The authors utilized two systems to achieve this, the first being diffuse coplanar surface barrier discharge, applying air as the gaseous medium, while the other involved capacitively coupled radio frequency discharge plasma under an argon atmosphere with hexamethyldisiloxane. Results showed that both forms of plasma treatment positively reduced the undesirable burst effect and benefited the release rate of TMZ. The hydrolytic degradability of the materials was slightly enhanced following hydrophilization, whereas the same diminished after hydrophobization had taken place. This was especially true for PLA due to modification of its wettability.     

Superhydrophobic Polyester/Cotton Fabrics Modified by Barrier Discharge Plasma and Organosilanes

In this paper, diffuse coplanar surface barrier discharge plasma at atmospheric pressure has been used for surface modification of polyester/cotton (PESc) fabric, which was subsequently modified by sol–gel process using suitable organofunctional silanes to enhance its hydrophobicity. Modified PESc fabric surfaces were conditioned during the gelling process to obtain the permanent hydrophobicity. The contact angle of water before washing of plasma and sol–gel pretreated PESc fabric was found to be 154° and 151° after standardized washings. It was also demonstrated that the process is applicable at the industrial scale.     

Commodification of accelerations for the Karp and Miller Construction

Karp and Miller’s algorithm is based on an exploration of the reachability tree of a Petri net where, the sequences of transitions with positive incidence are accelerated. The tree nodes of Karp and Miller are labeled with ω-markings representing (potentially infinite) coverability sets. This set of ω-markings allows us to decide several properties of the Petri net, such as whether a marking is coverable or whether the reachability set is finite. The edges of the Karp and Miller tree are labeled by transitions but the associated semantic is unclear which yields to a complex proof of the algorithm correctness. In this work we introduce three concepts: abstraction, acceleration and exploration sequence. In particular, we generalize the definition of transitions to ω-transitions in order to represent accelerations by such transitions. The notion of abstraction makes it possible to greatly simplify the proof of the correctness. On the other hand, for an additional cost in memory, which we theoretically evaluated, we propose an “accelerated” variant of the Karp and Miller algorithm with an expected gain in execution time. Based on a similar idea we have accelerated (and made complete) the minimal coverability graph construction, implemented it in a tool and performed numerous promising benchmarks issued from realistic case studies and from a random generator of Petri nets.

     

Are genetic algorithms useful for the parameter estimation of FM signals?

The estimation of polynomial-phase signals (PPSs) is a multiparameter problem, and the maximum likelihood (ML) optimization functions have numerous local optima, making the application of gradient techniques impossible. The common solution to this problem is based on the phase differentiation (PD) techniques that reduce the number of dimensions but, at the same time, reduce the accuracy and generate additional difficulties such as spurious components and error propagation. Here we show that genetic algorithms (GAs) can serve as a powerful alternative to the PD techniques. We investigate the limits of accuracy of the ML technique, and of some alternatives such as the high-order cubic phase function (HO-CPF) and high-order Wigner distribution (HO-WD). The ML approach combined with the proposed GA setup is limited up to the fifth-order PPS, which is not sufficient in many applications. However, the HO-CPF and HO-WD techniques coupled with the GA are able to accurately estimate phase parameters up to the tenth-order PPS. They significantly improve the accuracy with respect to the high-order ambiguity function (HAF) and product HAF (PHAF) and, for higher-order PPSs, they are much simpler and more efficient than the integrated generalized ambiguity function (IGAF).