Excitation and propagation of surface plasmon polaritons on a non-structured surface with a permittivity gradient.

Accompanied by the rise of plasmonic materials beyond those based on noble metals and the development of advanced materials processing techniques, it is important to understand the plasmonic behavior of materials with large-scale inhomogeneity (such as gradient permittivity materials) because they cannot be modeled simply as scatterers. In this paper, we theoretically analyze the excitation and propagation of surface plasmon polaritons (SPPs) on a planar interface between a homogeneous dielectric and a material with a gradient of negative permittivity. We demonstrate the following: (i) free-space propagating waves and surface waves can be coupled by a gradient negative-permittivity material and (ii) the coupling can be enhanced if the material permittivity variation is suitably designed. This theory is then verified by numerical simulations. A direct application of this theory, rainbow trapping, is also proposed, considering a realistic design based on doped indium antimonide. This theory may lead to various applications, such as ultracompact spectroscopy and dynamically controllable generation of SPPs

Error compensation method of large size steel sheet measurement based on control field

Aiming at the problem of low accuracy of large size sheet edge, a method of sheet size error measurement based on control field is proposed. Firstly, an error compensation model of the measuring system based on control field is established by analyzing the causes of the errors of the measuring system. A grid standard plate is designed and the error distribution on the grid line is obtained by using the measurement results of the standard plate. Secondly, the error curve is established according to the distribution, and the trig function theorem is used to project the curve into the image pixel coordinate system. Finally, the control field of the whole measurement area is reconstructed by linear interpolation, and the measurement results are compensated and corrected. The steel sheet is measured in the measuring area of 1.2m x 2.6m on the basis of these theories and technologies. The experiment shows that the precision of the measuring system can reach 1mm per meter, which satisfies the accuracy and speed requirements of large-size steel sheet measurement in industry, and has high application value

The Energy Crisis in CPT II Variant Fibroblasts

Carnitine palmitoyltransferase II (CPT II) deficiency is one of the most common causes of fatty acid oxidation metabolism disorders. However, the molecular mechanism between CPT2 gene polymorphisms and metabolic stress has not been fully clarified. We previously reported that a number of patients show a thermal instable phenotype of compound hetero/homozygous variants of CPT II. To understand the mechanism of the metabolic disorder resulting from CPT II deficiency, the present study investigated CPT II variants in patient fibroblasts, [c.1102 G>A (p.V368I)] (heterozygous), [c.1102 G>A (p.V368I)] (homozygous), and [c.1055 T>G (p.F352C)] (heterozygous) + [c.1102 G>A (p.V368I)] (homozygous) compared with fibroblasts from healthy controls. CPT II variants exerted an effect of dominant negative on the homotetrameric proteins that showed thermal instability, reduced residual enzyme activities and a short half-life. Moreover, CPT II variant fibroblasts showed a significant decrease in fatty acid β-oxidation and adenosine triphosphate generation, combined with a reduced mitochondrial membrane potential, resulting in cellular apoptosis. Collectively, our data indicate that the CPT II deficiency induces an energy crisis of the fatty acid metabolic pathway. These findings may contribute to the elucidation of the genetic factors involved in metabolic disorder encephalopathy caused by the CPT II deficiency

Entropy-driven Sampling and Training Scheme for Conditional Diffusion Generation

Denoising Diffusion Probabilistic Model (DDPM) is able to make flexible conditional image generation from prior noise to real data, by introducing an independent noise-aware classifier to provide conditional gradient guidance at each time step of denoising process. However, due to the ability of classifier to easily discriminate an incompletely generated image only with high-level structure, the gradient, which is a kind of class information guidance, tends to vanish early, leading to the collapse from conditional generation process into the unconditional process. To address this problem, we propose two simple but effective approaches from two perspectives. For sampling procedure, we introduce the entropy of predicted distribution as the measure of guidance vanishing level and propose an entropy-aware scaling method to adaptively recover the conditional semantic guidance. For training stage, we propose the entropy-aware optimization objectives to alleviate the overconfident prediction for noisy data.On ImageNet1000 256x256, with our proposed sampling scheme and trained classifier, the pretrained conditional and unconditional DDPM model can achieve 10.89% (4.59 to 4.09) and 43.5% (12 to 6.78) FID improvement respectively. The code is available at https://github.com/ZGCTroy/ED-DPM.Comment: 24 pages, 8 figure