A numerically efficient variational algorithm to solve a fractional nonlinear elastic string equation

Numerical Algorithms - In this work, we propose a fractional extension of the one-dimensional nonlinear vibration problem on an elastic string. The fractional problem is governed by a hyperbolic...     

On Sizes of 1-Cross Intersecting Set Pair Systems

Siberian Mathematical Journal - Let $ \{(A_{i},B_{i})\}_{i=1}^{m} $ be a set pair system. Füredi, Gyárfás, and Király called it $ 1 $ -cross intersecting if $...     

Nonwandering continuum possessing the Wada property

Theoretical and Mathematical Physics - Dynamic systems acting on the plane and possessing the Wada property have been observed. There exist only two topological types, symmetric and antisymmetric,...     

Invariant Spaces of Entire Functions

Mathematical Notes - Subspaces of the space of analytic functions on a convex domain in the complex plane that are invariant with respect to the differentiation operator are studied. The problem of...     

Periodicity of Two-Dimensional Bonding of Main Group Elements

In the periodic table the position of each atom follows the ‘aufbau’ principle of the individual electron shells. The resulting intrinsic periodicity of atomic properties determines the overall behavior of atoms in two-dimensional (2D) bonding and structure formation. Insight into the type and strength of bonding is the key in the discovery of innovative 2D materials. The primary features of 2D bonding and the ensuing monolayer structures of the main-group II–VI elements result from the number of valence electrons and the change of atom size, which determine the type of hybridization. The results reveal the tight connection between strength of bonding and bond length in 2D networks. The predictive power of the periodic table reveals general rules of bonding, the bonding-structure relationship, and allows an assessment of published data of 2D materials.     

Carbohydrate-based nanostructured catalysts: applications in organic transformations

The requirement of green and sustainable materials to prepare heterogeneous catalysts has intensified for practical reasons over the past few decades. Carbohydrates are possibly the most plentiful and renewable organic materials in nature with inimitable physiochemical properties, plausible low-cost and large-scale production, and sustainability features could be exploited in the generation of nanostructured heterogeneous catalysts. This review article outlines the organic transformations catalyzed by diverse carbohydrate-based nanostructured catalysts in greener and environmentally friendly processes. Selected examples are highlighted for a variety of organic reactions exploiting the proposed catalysts’ reactivity and reusability, and interactions with the intrinsic nature of the applied carbohydrate supports; advantages and speculated challenges of the introduced catalysts are deliberated as well.     

High-fidelity Recognition of Organotrifluoroborate Anions (R−BF3−) as Designer Guest Molecules

The recognition of boron compounds is well developed as boronic acids but untapped as organotrifluoroborate anions (R−BF₃⁻). We are exploring the development of these and other designer anions as anion-recognition motifs by considering them as substituted versions of the parent inorganic ion. To this end, we demonstrate strong and reliable binding of organic trifluoroborates, R−BF₃⁻, by cyanostar macrocycles that are size-complementary to the inorganic BF₄⁻ progenitors. We find that recognition is modulated by the substituent's sterics and that the affinities are retained using the common K⁺ salts of R−BF₃⁻ anions.     

Ensemble relation network with multi-level measure

Fine-grained few-shot learning is a difficult task in image classification. The reason is that the discriminative features of fine-grained images are often located in local areas of the image, while most of the existing few-shotlearning image classification methods only use top-level features and adopt a single measure. In that way, the localfeatures of the sample cannot be learned well. In response to this problem, ensemble relation network with multi-level measure (ERN-MM) is proposed in this paper. It adds the relation modules in the shallow feature space tocompare the similarity between the samples in the local features, and finally integrates the similarity scores from thefeature spaces to assign the label of the query samples. So the proposed method ERN-MM can use local details andglobal information of different grains. Experimental results on different fine-grained datasets show that the proposedmethod achieves good classification performance and also proves its rationality.     

Graphene Saturable Absorber Mirror for Mode-Locked Orange Pulse Laser

Journal of Russian Laser Research - We demonstrate a passively-mode-locked Pr3+: LuLiF4 laser operated at the 604 nm orange wave-length, using monolayer graphene as a saturable absorber. The...     

Epidemic Models of Malicious-Code Propagation and Control in Wireless Sensor Networks: An Indepth Review

Wireless Personal Communications - Besides anti-malware usage for the eradication of malicious attacks, researchers have developed epidemic models in order to gain more insights into the spread...