Magnetoplasmons in gapped graphene in a periodically modulated magnetic field

Motivated by recent experiments on long‐lived magnetoplasmons in the presence of a perpendicular magnetic field, we investigate the dynamical dielectric response function of graphene in contact with a substrate using the random phase approximation. We add a periodically modulated magnetic field within the graphene plane and address both the inter and intra Landau band magnetoplasmons. Verification of the predicted magnetic modulation effects is possible by experiments analogous to those for the zero gap limit.     

A real-time monitoring approach for bivariate event data

Early detection of changes in the frequency of events is an important task in many fields, such as disease surveillance, monitoring of high-quality processes, reliability monitoring, and public health. This article focuses on detecting changes in multivariate event data by monitoring the time-between-events (TBE). Existing multivariate TBE charts are limited because they only signal after an event occurred for each of the individual processes. This results in delays (i.e., long time-to-signal), especially when we are interested in detecting a change in one or a few processes with different rates. We propose a bivariate TBE chart, which can signal in real-time. We derive analytical expressions for the control limits and average time-to-signal performance, conduct a performance evaluation and compare our chart to an existing method. Our findings showed that our method is an effective approach for monitoring bivariate TBE data and has better detection ability than the existing method under transient shifts and is more generally applicable. A significant benefit of our method is that it signals in real-time and that the control limits are based on analytical expressions. The proposed method is implemented on two real-life datasets from reliability and health surveillance.     

Characterization of brush type discharge electrodes and impact of enhanced corona discharge on operation of electrostatic precipitators

This project presents the results of investigation of current/voltage characteristics of brush type discharge electrodes (BTDE) in tube type electrostatic precipitators and the effect on operation. Experimental investigations were conducted with discharge electrodes of different wire diameter and different brush diameter. The effect of electrode geometry on current/voltage behavior was recorded. Corona current with brush type discharge electrodes was modeled and compared with experimental data. Brush type discharge electrodes produce an enhanced corona current compared with wire type discharge electrodes. Limited enhanced corona has improving effect on collection efficiency. An adjusted correlation was therefore deduced from experimentally obtained current/voltage data with BTDE.     

Characterization of ion irradiated silicon surfaces ablated by laser-induced breakdown spectroscopy

Low energy metallic ions,generated by a Q-switched Nd:YAG laser(1064-nm wavelength,10-mJ energy,9-nm～12-ns-pulse width,10~(11)W/cm~2intensity)irradiated on a silicon substrate to modify various properties,such as electrical,morphological,and structural modifications.Thomson parabola technique is used to calculate the energy of these metallic ions whereas the electrical conductivity is calculated with the help of Four-point probe.Interestingly circular tracks forming chain like damage trails are produced via these energetic ions which are carefully examined by optical microscopy.It is observed that excitation,ionization,and cascade collisions are responsible for surface modifications of irradiated samples.Four-point probe analysis revealed that the electrical conductivity of substrate has reduced with increasing trend of atomic number of irradiated metallic ions(Al,Ti,Cu,and Au).The x-ray diffraction analysis elucidated the crystallographic changes leading to reduction of grain size of N-type silicon substrate,which is also associated with the metallic ions used.The decreasing trend of conductivity and grain size is due to thermal stresses,scattering effect,structural imperfections,and non-uniform conduction of energy absorbed by substrate atoms after the ion irradiation.     

Temperature effects on the magnetoplasmon spectrum of a weakly modulated graphene monolayer

In this work, we determine the effects of temperature on the magnetoplasmon spectrum of an electrically modulated graphene monolayer as well as a two-dimensional electron gas (2DEG). The intra-Landau band magnetoplasmon spectrum within the self-consistent field approach is investigated for both the aforementioned systems. Results obtained not only exhibit Shubnikov-de Haas (SdH) oscillations but also commensurability oscillations (Weiss oscillations). These oscillations are periodic as a function of inverse magnetic field. We find that both the magnetic oscillations, SdH and Weiss, have a greater amplitude and are more robust against temperature in graphene compared to a conventional 2DEG. Furthermore, there is a π phase shift between the magnetoplasmon oscillations in the two systems which can be attributed to Dirac electrons in graphene acquiring a Berry's phase as they traverse a closed path in a magnetic field.     

Gold-surface binding of molecular switches studied by M?ssbauer spectroscopy

The nonanuclear coordination compound [Mo^IV{(CN)Fe^III(3-methyl-saldptn)}₈]Cl₄ exhibits multiple spin transitions (3-methyl-saldptn = N,N′-bis(3′′-methyl-2′′-hydroxy-benzyliden)-1,7-diamino-4-azaheptane). This spin crossover cluster is bound via a self-assembled monolayer onto a two dimensional array gold surface. M?ssbauer spectroscopy indicates that the thermally and optically induced spin crossover of the compound is maintained. Thereby, the foundation for its potential practical application (e.g. in the field of information storage) was laid.     

CT and MRI Medical Image Fusion Using Noise-Removal and Contrast Enhancement Scheme with Convolutional Neural Network

Medical image fusion (MIF) has received painstaking attention due to its diverse medical applications in response to accurately diagnosing clinical images. Numerous MIF methods have been proposed to date, but the fused image suffers from poor contrast, non-uniform illumination, noise presence, and improper fusion strategies, resulting in an inadequate sparse representation of significant features. This paper proposes the morphological preprocessing method to address the non-uniform illumination and noise by the bottom-hat–top-hat strategy. Then, grey-principal component analysis (grey-PCA) is used to transform RGB images into gray images that can preserve detailed features. After that, the local shift-invariant shearlet transform (LSIST) method decomposes the images into the low-pass (LP) and high-pass (HP) sub-bands, efficiently restoring all significant characteristics in various scales and directions. The HP sub-bands are fed to two branches of the Siamese convolutional neural network (CNN) by process of feature detection, initial segmentation, and consistency verification to effectively capture smooth edges, and textures. While the LP sub-bands are fused by employing local energy fusion using the averaging and selection mode to restore the energy information. The proposed method is validated by subjective and objective quality assessments. The subjective evaluation is conducted by a user case study in which twelve field specialists verified the superiority of the proposed method based on precise details, image contrast, noise in the fused image, and no loss of information. The supremacy of the proposed method is further justified by obtaining 0.6836 to 0.8794, 0.5234 to 0.6710, and 3.8501 to 8.7937 gain for QFAB, CRR, and AG and noise reduction from 0.3397 to 0.1209 over other methods for objective parameters.     

生物催化进展:从计算到代谢工程（英文）

经过数次技术研究和超常创新战略的大发展,生物催化逐渐达到工业化水平,从而受到人们特别的关注.基于酶值,通过生物途径生产高附加值化合物和精细工业化学品成为人们最感兴趣的领域之一.更广泛的众多生物化学路线可由酶催化来实现,其中还有一些酶尚未被人们发现.另一方面,由于非同源底物和某些化学过程所必需的苛刻条件,导致酶催化过程的效率低、稳定性差,因而限制了生物催化的应用.因此,开发具有多催化特征、更高效率和稳定性的绿色催化剂,成为生物催化的重中之重.计算科学、代谢工程、合成生物,以及机器学习路线的运用为新催化剂的工程化提供了新方法.本文重点介绍了合成生物学和代谢工程在催化中的作用,讨论了用于催化的机器学习算法和如何选择一种预测蛋白质-配体相互作用的算法;为了预测键合和催化功能,综述了分子对接的重要性;最后给出了结束语、未来挑战和前景展望.