Contact angle of spherical drops inside a smooth and homogeneous cylindrical capillary with hemispherical head

To investigate the wettability of spherical drops in a smooth and homogeneous cylindrical capillary with hemispherical head, based on Gibbs’s method of dividing surface and Rusanov’s concept of dividing line, the contact angle of spherical droplets has been successfully derived considering the effects of the line tension. Additionally, under the condition of ignoring the line tension, the equation describing the contact angle is simplified as the classical Young equation

The valley filter efficiency of monolayer graphene and bilayer graphene line defect model

In addition to electron charge and spin, novel materials host another degree of freedom, the valley. For a junction composed of valley filter sandwiched by two normal terminals, we focus on the valley efficiency under disorder with two valley filter models based on monolayer and bilayer graphene. Applying the transfer matrix method, valley resolved transmission coefficients are obtained. We find that: i) under weak disorder, when the line defect length is over about $15\rm nm$, it functions as a perfect channel (quantized conductance) and valley filter (totally polarized); ii) in the diffusive regime, combination effects of backscattering and bulk states assisted intervalley transmission enhance the conductance and suppress the valley polarization; iii) for very long line defect, though the conductance is small, polarization is indifferent to length. Under perpendicular magnetics field, the characters of charge and valley transport are only slightly affected. Finally we discuss the efficiency of transport valley polarized current in a hybrid system.Comment: 6 figure

Online Regularization for High-Dimensional Dynamic Pricing Algorithms

We propose a novel \textit{online regularization} scheme for revenue-maximization in high-dimensional dynamic pricing algorithms. The online regularization scheme equips the proposed optimistic online regularized maximum likelihood pricing (\texttt{OORMLP}) algorithm with three major advantages: encode market noise knowledge into pricing process optimism; empower online statistical learning with always-validity over all decision points; envelop prediction error process with time-uniform non-asymptotic oracle inequalities. This type of non-asymptotic inference results allows us to design safer and more robust dynamic pricing algorithms in practice. In theory, the proposed \texttt{OORMLP} algorithm exploits the sparsity structure of high-dimensional models and obtains a logarithmic regret in a decision horizon. These theoretical advances are made possible by proposing an optimistic online LASSO procedure that resolves dynamic pricing problems at the \textit{process} level, based on a novel use of non-asymptotic martingale concentration. In experiments, we evaluate \texttt{OORMLP} in different synthetic pricing problem settings and observe that \texttt{OORMLP} performs better than \texttt{RMLP} proposed in \cite{javanmard2019dynamic}

(2′-Amino-4,4′-bi-1,3-thia­zol-2-aminium-κ2 N,N′)aqua­[citrato(4−)-κ3 O,O′,O′′)chromium(III) dihydrate

In the title compound, [Cr(C6H7N4S2)(C6H4O7)(H2O)]·2H2O, the CrIII atom is in a distorted octa­hedral environment, coordinated by one water mol­ecule, two N atoms from a protonated diamino­bithia­zole ligand and three O atoms from a citrate(4−) anion. The complex is zwitterionic, with the H atom from the uncoordinated carboxyl­ate group of the citrate anion transferred to one amino group of the diamino­bithia­zole ligand. O—H⋯O and N—H⋯O hydrogen bonds link the complexes into layers including the two uncoordinated water mol­ecules

Research on Wavelet Based Autofocus Evaluation in Micro-vision

AbstractThis paper presents the construction of two kinds of focusing measure operators defined in wavelet domain. One mechanism is that the Discrete Wavelet Transform (DWT) coefficients in high frequency subbands of in-focused image are higher than those of defocused one. The other mechanism is that the autocorrelation of an in-focused image filtered through Continuous Wavelet Transform (CWT) gives a sharper profile than blurred one does. Wavelet base, scaling factor and form to get the sum of high frequency energy are the key factors in constructing the operator. Two new focus measure operators are defined through the autofocusing experiments on the micro-vision system of the workcell for micro-alignment. The performances of two operators can be quantificationally evaluated through the comparison with two spatial domain operators Brenner Function (BF) and Squared Gradient Function (SGF). The focus resolution of the optimized DWT-based operators is 14% higher than that of BF and its computational cost is 52% approximately lower than BF's. The focus resolution of the optimized CWT-based operators is 41% lower than that of SGF whereas its computational cost is approximately 36% lower than SGF's. It shows that the wavelet based autofocus measure functions can be practically used in micro-vision applications