Birefringence induced polarization-independent and nearly all-angle transparency through a metallic film

We propose an birefringence route to perfect electromagnetic (EM) wave tunneling through a metallic film which relies on homogeneous birefringent coatings with moderate and positive parameters only. EM transparency is achieved in such an birefringent-metal-birefringent (BMB) structure for both polarizations and over nearly all incident angles. The stringent restrictions in conventional dielectric-metal-dielectric media, i.e., dielectrics with extremely negative permittivity, high magnetic field and polarization dependence (only for TE waves), are not required in our method. The criterion for perfect transmission is obtained by analyzing the effective medium theory and EM fields of such a birefringent structure. The solutions hold for lossless and lossy cases in a quite large frequency range.Comment: 9 page

Direct Bonding SOI Wafer Based Cantilever Resonator for Trace Gas Sensor Application

A thermal driving and piezoresistive sensing MEMS cantilever resonator has been proposed and developed to construct trace gas detection sensors. The problem of integrating vibration structure, transducers and electric elements is the main concern in the design and fabrication of the resonator. In this paper, the parameters and the configuration of the resonator are discussed, the fabrication process and the test results are presented. Finite Element Analysis (FEA) has been carried out to optimize the configuration of the resonator to obtain high sensitivity and efficiency with a uniform temperature distribution that is propitious to the function of the gas sensing material. The fabrication process is based on direct bonding silicon-on-insulator (SOI) wafer and inductive coupled plasma (ICP) etching technology, which conciliate the semiconductor processes and the micromaching processes, and provide precise control of the resonator parameters. The experimental test results of the fabricated resonator agreed well with the calculation and simulation results and demonstrated that the proposed resonator was qualified to construct trace gas detection sensors

Catalytic Iridium-Based Janus Micromotors Powered by Ultralow Levels of Chemical Fuels

We describe catalytic micromotors powered by remarkably low concentrations of chemical fuel, down to the 0.0000001% level. These Janus micromotors rely on an iridium hemispheric layer for the catalytic decomposition of hydrazine in connection to SiO_2 spherical particles. The micromotors are self-propelled at a very high speed (of ∼20 body lengths s^(–1)) in a 0.001% hydrazine solution due to osmotic effects. Such a low fuel concentration represents a 10 000-fold decrease in the level required for common catalytic nanomotors. The attractive propulsion performance, efficient catalytic energy-harvesting, environmentally triggered swarming behavior, and magnetic control of the new Janus micromotors hold considerable promise for diverse practical applications

DCMD: Distance-based Classification Using Mixture Distributions on Microbiome Data

Current advances in next generation sequencing techniques have allowed researchers to conduct comprehensive research on microbiome and human diseases, with recent studies identifying associations between human microbiome and health outcomes for a number of chronic conditions. However, microbiome data structure, characterized by sparsity and skewness, presents challenges to building effective classifiers. To address this, we present an innovative approach for distance-based classification using mixture distributions (DCMD). The method aims to improve classification performance when using microbiome community data, where the predictors are composed of sparse and heterogeneous count data. This approach models the inherent uncertainty in sparse counts by estimating a mixture distribution for the sample data, and representing each observation as a distribution, conditional on observed counts and the estimated mixture, which are then used as inputs for distance-based classification. The method is implemented into a k-means and k-nearest neighbours framework and we identify two distance metrics that produce optimal results. The performance of the model is assessed using simulations and applied to a human microbiome study, with results compared against a number of existing machine learning and distance-based approaches. The proposed method is competitive when compared to the machine learning approaches and showed a clear improvement over commonly used distance-based classifiers. The range of applicability and robustness make the proposed method a viable alternative for classification using sparse microbiome count data.Comment: 27 pages, 3 figure

2-(4,6-Dimethyl­pyrimidin-2-ylsulfan­yl)-N-(4-methyl­pyridin-2-yl)acetamide

The non-H atoms of the title mol­ecule, C14H16N4OS, are coplanar, with an r.m.s. deviation of 0.039 Å. The dihedral angle between the two aromatic rings is 2.4 (2)°. An intra­molecular C—H⋯O hydrogen bond is observed. The mol­ecules exist as N—H⋯N hydrogen-bonded centrosymmetric dimers