计算机模式识别法对甲状腺机能亢进症微量元素谱的研究

据Mahlanobis距离判别法，利用计算机多元统计判别程序，对14例甲亢思者和21例健康人的血清中锌、铁、铜、锰、镁和锶等元素浓度差异进行分类判别研究。选择锌、铁、钙、锶作判别特征参量时，患者和健康人的分类准确率为100％，服碘前与服碘后的甲亢患者分类准确率为92.7％，分类研究指出，甲亢患者血清中的铁和锶浓度高于健康人，经服碘治疗后，患者血清中上述元素浓度降低与健康人相近。     

Cyclodextrins as chiral stationary phases in capillary gas chromatography. Part VII: Cyclodextrins with an inverse substitution pattern – synthesis and enantioselectivity

Transferring the site of specific substitution of dipentylated cyclodextrins with methyl or acyl residues from the secondary 3-hydroxyl group to the primary 6-hydroxyl group was expected to provide new information on the mechanism of chiral recognition. The 3-position points towards and the 6-position points away from the cyclodextrin cavity which via inclusion complex formation is supposed to play a major role in chiral separation. The “inverse” 6-O-acyl-2,3-di-O-pentyl-cyclodextrins displayed almost no enantioselectivity but the corresponding 6-O-methyl derivatives are a versatile supplement to the chiral capillary GC phases nowadays available. Among the compounds that could be enantiomerically resolved are alcohols, amino acids, alkyl halides, bicyclic ethers, acetals, olefins, other hydrocarbons and chiral pharmaceuticals.     

X-ray, Raman and FTIRS studies of the microstructural evolution of zirconia particles caused by the thermal treatment

Genesis of the structure of zirconia particles prepared by precipitation of amorphous hydrated zirconia by ammonia from the ZrO(NO₃)₂ solution followed by a mild hydrothermal treatment (HTT) of precipitate, washing and calcination under air up to 1000 °C has been studied by X-ray diffraction (XRD), Raman and FTIRS. As revealed by FTIRS of lattice modes, the local structure of amorphous zirconia subjected to HTT is close to that in m-ZrO₂. This helps to obtain nearly single-phase monoclinic nanozirconia (particle size 5-15 nm) already after a mild calcination at 500 °C. Stability of this phase with nanoparticles sizes below the critical value determined by thermodynamic constraints is due to its excessive hydroxylation demonstrated by FTIRS. Dehydroxilation and sintering of these nanoparticles at higher (600-650 °C) temperatures of calcination leads to reappearance of the (111) “cubic” reflection in XRD patterns. Modeling of XRD patterns revealed that this phenomenon could be explained by polysynthetic (001) twinning earlier observed by HRTEM.     

用电子散斑干涉法测量材料热膨胀系数

 利用双光束电子散斑干涉法（ESPI）对试件受热变形进行了实时观测，针对一次实验过程中得到的图片较多（300～500幅）的特点，在图像处理时摒弃了以往的手动识别等位移线的办法，用MATLAB语言编写了批处理程序，能够在采集的大量散斑图片中自动快速准确地标定等位移线，得到相应的位移和应变，并结合实时测量的温度值，获得了45钢和LY12铝合金在不同温升率下的热膨胀系数及其随温度的变化。实验结果表明，在涉及的温升率范围内，温升率的改变对材料热膨胀系数的影响不明显，材料的热膨胀系数随温度的升高略有上升。     

Alpha-Beta Hybrid Quantum Associative Memory Using Hamming Distance

This work presents a quantum associative memory (Alpha-Beta HQAM) that uses the Hamming distance for pattern recovery. The proposal combines the Alpha-Beta associative memory, which reduces the dimensionality of patterns, with a quantum subroutine to calculate the Hamming distance in the recovery phase. Furthermore, patterns are initially stored in the memory as a quantum superposition in order to take advantage of its properties. Experiments testing the memory’s viability and performance were implemented using IBM’s Qiskit library.     

A Novel Method for Pattern Recognition of GIS Partial Discharge via Multi-Information Ensemble Learning

Partial discharge (PD) is the main feature that effectively reflects the internal insulation defects of gas-insulated switchgear (GIS). It is of great significance to diagnose the types of insulation faults by recognizing PD to ensure the normal operation of GIS. However, the traditional diagnosis method based on single feature information analysis has a low recognition accuracy of PD, and there are great differences in the diagnosis effect of various insulation defects. To make the most of the rich insulation state information contained in PD, we propose a novel multi-information ensemble learning for PD pattern recognition. First, the ultra-high frequency and ultrasonic data of PD under four typical defects of GIS are obtained through experiment. Then the deep residual convolution neural network is used to automatically extract discriminative features. Finally, multi-information ensemble learning is used to classify PD types at the decision level, which can complement the shortcomings of the independent recognition of the two types of feature information and has higher accuracy and reliability. Experiments show that the accuracy of the proposed method can reach 97.500%, which greatly improves the diagnosis accuracy of various insulation defects.     

Spiking patterns of a hippocampus model in electric fields

We develop a model of CA3 neurons embedded in a resistive array to mimic the effects of electric fields from a new perspective. Effects of DC and sinusoidal electric fields on firing patterns in CA3 neurons are investigated in this study. The firing patterns can be switched from no firing pattern to burst or from burst to fast periodic firing pattern with the increase of DC electric field intensity. It is also found that the firing activities are sensitive to the frequency and amplitude of the sinusoidal electric field. Different phase-locking states and chaotic firing regions are observed in the parameter space of frequency and amplitude. These findings are qualitatively in accordance with the results of relevant experimental and numerical studies. It is implied that the external or endogenous electric field can modulate the neural code in the brain. Furthermore, it is helpful to develop control strategies based on electric fields to control neural diseases such as epilepsy.     

Fraunhofer Diffraction Pattern by a Slit Aperture between a Knife Edge and a Metal Cylinder

The Fraunhofer diffraction pattern of an aperture formed between a knife edge and a smooth surface of a turned metal cylinder or half cylinder is different from the diffraction pattern of an ideal slit aperture. The diffraction pattern is simulated by adding the light reflected by the cylinder surface to the pattern formed by a slit, and is measured using a cylinder that has been partly cut away so that it can represent either a full or half cylinder. The theoretical and measured patterns agree well with each other.     

Gaussian pre-filtering for uncertainty minimization in digital image correlation using numerically-designed speckle patterns

This paper discusses the effect of pre-processing image blurring on the uncertainty of two-dimensional digital image correlation (DIC) measurements for the specific case of numerically-designed speckle patterns having particles with well-defined and consistent shape, size and spacing. Such patterns are more suitable for large measurement surfaces on large-scale specimens than traditional spray-painted random patterns without well-defined particles. The methodology consists of numerical simulations where Gaussian digital filters with varying standard deviation are applied to a reference speckle pattern. To simplify the pattern application process for large areas and increase contrast to reduce measurement uncertainty, the speckle shape, mean size and on-center spacing were selected to be representative of numerically-designed patterns that can be applied on large surfaces through different techniques (e.g., spray-painting through stencils). Such “designer patterns” are characterized by well-defined regions of non-zero frequency content and non-zero peaks, and are fundamentally different from typical spray-painted patterns whose frequency content exhibits near-zero peaks. The effect of blurring filters is examined for constant, linear, quadratic and cubic displacement fields. Maximum strains between ±250 and ±20,000 µε are simulated, thus covering a relevant range for structural materials subjected to service and ultimate stresses. The robustness of the simulation procedure is verified experimentally using a physical speckle pattern subjected to constant displacements. The stability of the relation between standard deviation of the Gaussian filter and measurement uncertainty is assessed for linear displacement fields at varying image noise levels, subset size, and frequency content of the speckle pattern. It is shown that bias error as well as measurement uncertainty are minimized through Gaussian pre-filtering. This finding does not apply to typical spray-painted patterns without well-defined particles, for which image blurring is only beneficial in reducing bias errors.     

Biochemical study for the effect of henna (Lawsonia inermis) on Escherichia coli

Leaf samples of Lawsonia inermis (Li) were examined for their antimicrobial potential. Broth extracts in different concentrations were prepared and bioassayed in vitro for the growth of Escherichia coli. The growth of E. coli pathogen was inhibited to various degrees by increasing the concentration of the herbal powder. In addition to the observed alterations which were detected electrophoretically in the protein pattern, were activities of the amylase enzyme and glycoprotein fractions. The protein pattern has one common band of Rf 0.47 and two characteristic bands of Rf 0.36 and Rf 0.42 for E. coli sample. The quantitative mutation was observed in the bacteria with different concentrations of L. inermis compared with the control. Some types of proteins in E. coli completely disappeared upon being S.I affected. The amylase pattern showed one common band with Rf 0.037 and two characteristic bands with Rf 0.18 and Rf 0.37 for E. coli sample. The obvious quantitative mutation observed in bacteria with different concentrations of L. inermis compared with E. coli. The glycoprotein pattern recorded one common band at R₁ with Rf 0.94 for E. coli sample and bacteria inoculated with different concentrations of L. inermis. These results confirmed the antibacterial activity of henna leaves and supported the traditional use of the plant in therapy of bacterial infections and disturbances that occurred at the biochemical level. The broth extract of the L. inermis leaves showed obvious antibacterial activity against E. coli.