Dielectric loss and electromagnetic absorption performance study of 1T/2H-MoS2/Mo2S3 nanocomposites with different ratios

High dielectric loss materials have an important application in electromagnetic (EM) absorption fields. In this paper, the ternary nanocomposites: 1T/2H-MoS₂/Mo₂S₃ with heterogeneous interfaces are synthesized by hydrothermal method. XRD, XPS, FTIR, SEM, and TEM measurements are applied to study the structure, morphology, and composition. The frequency spectra of complex permittivity (ε_r-f) are measured in 2–18 GHz by vector network analyzer. The results show that the nanocomposites have higher dielectric loss angle tangents than the reported 2H-MoS₂ absorbers. Based on the ε_r-f spectra, the reflection loss-frequency curves ($RL-f$) are simulated at given thicknesses. An effective absorption bandwidth of 5.2 GHz (12.8–18 GHz) and a $RL$ peak of −29.49 dB are achieved in a thin thickness of 1.62 mm, which are comparable to the reported 2H-MoS₂ absorbers with complex composition, showing that the 1T/2H-MoS₂/Mo₂S₃ nanocomposites have great application potential as an EM wave absorber in the K_u band.     

Digital image correlation in experimental mechanics and image registration in computer vision: Similarities,differences and complements

Digital image correlation and image registration or matching are among the most widely used techniques in the fields of experimental mechanics and computer vision, respectively. Despite their applications in separate fields, both techniques primarily involve detecting the same physical points in two or more images. In this paper, a brief technical comparison of the two techniques is reviewed, and their similarities and differences as well as complements are presented. It is shown that some concepts from the image registration or matching technique can be applied to the digital image correlation technique to substantially enhance its performance, which can help broaden the applications of digital image correlation in scientific research and engineering practice.     

Improved and self-tuned occurrence heuristics

In this note we present three efficient variations of the occurrence heuristic, adopted by many exact string matching algorithms and first introduced in the well-known Boyer–Moore algorithm. Our first heuristic, called improved-occurrence heuristic, is a simple improvement of the rule introduced by Sunday in his Quick-Search algorithm. Our second heuristic, called worst-occurrence heuristic, achieves its speed-up by selecting the relative position which yields the largest average advancement. Finally, our third heuristic, called jumping-occurrence heuristic, uses two characters for computing the next shift. Setting the distance between these two characters optimally allows one to maximize the average advancement. The worst-occurrence and jumping-occurrence heuristics tune their parameters according to the distribution of the characters in the text. Experimental results show that the newly proposed heuristics achieve very good results on average, especially in the case of small alphabets.     

On Default Priors for Robust Bayesian Estimation with Divergences

This paper presents objective priors for robust Bayesian estimation against outliers based on divergences. The minimum γ-divergence estimator is well-known to work well in estimation against heavy contamination. The robust Bayesian methods by using quasi-posterior distributions based on divergences have been also proposed in recent years. In the objective Bayesian framework, the selection of default prior distributions under such quasi-posterior distributions is an important problem. In this study, we provide some properties of reference and moment matching priors under the quasi-posterior distribution based on the γ-divergence. In particular, we show that the proposed priors are approximately robust under the condition on the contamination distribution without assuming any conditions on the contamination ratio. Some simulation studies are also presented.     

Maximum weight edge-constrained matchings

Practical questions arising from (for instance) biological applications can often be expressed as classical optimization problems with specific, new features. We are interested here in the version of the maximum weight matching problem (on a graph G) obtained by (1) defining a set F of pairs of incompatible edges of G and (2) asking that the matching contains at most one edge in each given pair. Such a matching is called an odd matching. The graph T(F)=(V_F,F), where V_F is the set of edges of G occurring in at least one pair of F, is called the trace-graph of G and F.We motivate the introduction of the maximum weight odd-matching (abbreviated as Odd-MWM) problem and study its complexity with respect to two parameters: the type of graph G and the graph class T to which T(F) belongs.Our contribution includes:

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A proof that Odd-MWM is NP-complete for 3-degree bipartite graphs when T(F) is a matching (i.e. when T is the class of 1-regular graphs), even if the weight function is constant.

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A proof that Odd-MWM is NP-complete (for 3-degree bipartite graphs as well as for any larger class) if and only if T is a class of graphs with unbounded induced matching. Otherwise, Odd-MWM is polynomial.

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A (Δ(T(F))+1)-approximate algorithm for Odd-MWM on general graphs. This algorithm becomes a χ(T(F))-approximate algorithm when the graph class T admits a polynomial algorithm for minimum vertex coloring.