Improving EBSD precision by orientation refinement with full pattern matching

We present a comparison of the precision of different approaches for orientation imaging using electron backscatter diffraction (EBSD) in the scanning electron microscope. We have used EBSD to image the internal structure of WC grains, which contain features due to dislocations and subgrains. We compare the conventional, Hough-transform based orientation results from the EBSD system software with results of a high-precision orientation refinement using simulated pattern matching at the full available detector resolution of 640 × 480 pixels. Electron channelling contrast imaging (ECCI) is used to verify the correspondence of qualitative ECCI features with the quantitative orientation data from pattern matching. For the investigated sample, this leads to an estimated pattern matching sensitivity of about 0.5 mrad (0.03°) and a spatial feature resolution of about 100 nm. In order to investigate the alternative approach of postprocessing noisy orientation data, we analyse the effects of two different types of orientation filters. Using reference features in the high-precision pattern matching results for comparison, we find that denoising of orientation data can reduce the spatial resolution, and can lead to the creation of orientation artefacts for crystallographic features near the spatial and orientational resolution limits of EBSD.     

Group sequential crossover trial designs with strong control of the familywise error rate

Crossover designs are an extremely useful tool to investigators, and group sequential methods have proven highly proficient at improving the efficiency of parallel group trials. Yet, group sequential methods and crossover designs have rarely been paired together. One possible explanation for this could be the absence of a formal proof of how to strongly control the familywise error rate in the case when multiple comparisons will be made. Here, we provide this proof, valid for any number of initial experimental treatments and any number of stages, when results are analyzed using a linear mixed model. We then establish formulae for the expected sample size and expected number of observations of such a trial, given any choice of stopping boundaries. Finally, utilizing the four-treatment, four-period TOMADO trial as an example, we demonstrate that group sequential methods in this setting could have reduced the trials expected number of observations under the global null hypothesis by over 33%.     

Centrifuge-aided micromolding of micron- and submicron-sized patterns

Developed from soft lithography, replica molding has been proven to be a good method to prepare micron- and submicron-sized features. However, the fidelity of the features can be compromised by incomplete feature cavity filling and feature shrinkage during the forming process. In this study, centrifuge-aided micromolding is developed to prepare micron- and submicron-sized ZnO features. By introducing a centrifugal force, the shear-thinning behavior of the suspensions is utilized, and the cavity filling process and the diffusion of trapped air out of the features are accelerated. The drying shrinkage is decreased by increasing the density of the wet nanoparticle packing from the centrifugal process. The centrifugal force improves the fidelity of all the designed features. ZnO ridges from 0.4 μm to 2 μm size and rods of 1.6 μm size are prepared successfully. The wide applicability of this strategy has been demonstrated by preparing ZrO₂ features via the same method.     

Surface properties and surface patterning of UV-curable coating using perfluorosilane-treated nanosilica

In this work, a one-step film formation method is demonstrated to obtain the patterned surface of an acrylate photocuring coating using nano-silica particles treated with a perfluoroalkoxysilane ((heptadecafluoro-1,1,2,2-tetra hydrodecyl) triethoxysilane) as a nanoadditive. Nanosilica particles were treated with perfluoroalkoxysilane and used in a UV-curable matrix. The typical patterns on the surface of the UV-cured films are revealed in AFM images. The surface properties of the cured films were investigated by measuring water contact angle and surface energy. The degree of conversion of the samples was obtained by FTIR analysis and pendulum hardness was measured using a Konig hardness meter. Scratch resistance of cured films was measured by standard scratch measuring pens. The characteristics obtained from AFM analysis showed that rough surface patterns in this system are controlled linearly by changes in the treated nano-particle concentration. The subtraction of surface energy of the cured film was clear and the water contact angle showed a 60% increase with the addition of a fluorinated nano-particle concentration. Surface hardness decreased and scratch resistance increased as the concentration of treated nanoparticles increased, while the final degree of conversion of the film remained unchanged.     

一种基于Spark的不确定数据集频繁模式挖掘算法

如何在海量不确定数据集中提高频繁模式挖掘性能是目前研究的热点.传统算法大多是以期望、概率或者权重等单一指标为数据项集支持度，在大数据背景下，同时考虑概率和权重支持度的算法难以兼顾其执行效率.为此，本文提出一种基于Spark的不确定数据集频繁模式挖掘算法（UWEFP），首先，为了同时兼顾数据项的概率和权重，计算一项集的最大概率权重值并进行剪枝；然后，为了减少对数据集的多次扫描，结合Spark框架的优点，设计了一种具有FP-tree特征的新颖的UWEFP-tree结构进行模式树的构建及挖掘；最后在Spark环境下，以UCI数据集进行实验验证.实验结果表明本文的方法在保证挖掘结果的同时，提高了效率.     

新形势下煤矿企业外包工程安全管控模式研究

针对煤矿企业外包工程安全生产问题严重、事故多发的特点，通过对新的煤矿企业外包工程的安全管控模式研究，分析煤矿安全监管、技术和装备水平、安全培训等存在的问题，提出了实施煤矿外包工程队准入制度，创新外包安全管控模式，找到对外包工程安全状况进行管理的行之有效的办法。     

Bootstrap procedures for detecting multiple persistence shifts in heteroskedastic time series

This article proposes new bootstrap procedures for detecting multiple persistence shifts in a time series driven by non-stationary volatility. The assumed volatility process can accommodate discrete breaks, smooth transition variation as well as trending volatility. We develop wild bootstrap sup-Wald tests of the null hypothesis that the process is either stationary [I(0)] or has a unit root [I(1)] throughout the sample. We also propose a sequential procedure to estimate the number of persistence breaks based on ordering the regime-specific bootstrap p-values. The asymptotic validity of the advocated procedures is established both under the null of stability and a variety of persistence change alternatives. A comparison with existing tests that assume homoskedasticity illustrates the finite sample improvements offered by our methods. An application to OECD inflation rates highlights the empirical relevance of the proposed approach and weakens the case for persistence change relative to existing procedures.     

Metacarpophalangeal joint patterns based personal identification system

This paper introduces a novel approach for identity authentication system based on metacarpophalangeal joint patterns (MJPs). A discriminative common vector (DCV) based method is utilized for feature selection. In the literature, there is no study using whole MJP for identity authentication, exceptionally a work (Ferrer et al., 2005) using the hand knuckle pattern which is some part of the MJP draws the attention as a similar study. The originality of this approach is that: whole MJP is firstly used as a biometric identifier and DCV method is firstly applied for extracting the feature set of MJP. The developed system performs some basic tasks like image acquisition, image pre-processing, feature extraction, matching, and performance evaluation. The feasibility and effectiveness of this approach is rigorously evaluated using the k-fold cross validation technique on two different databases: a publicly available database and a specially established database. The experimental results indicate that the MJPs are very distinctive biometric identifiers and can be securely used in biometric identification and verification systems, DCV method is successfully employed for obtaining the feature set of MJPs and proposed MJP based authentication approach is very successful according to state of the art techniques with a recognition rate of between 95.33% and 100.00%.     

Time series forecasting with a neuro-fuzzy modeling scheme

Time series forecasting concerns the prediction of future values based on the observations previously taken at equally spaced time points. Statistical methods have been extensively applied in the forecasting community for the past decades. Recently, machine learning techniques have drawn attention and useful forecasting systems based on these techniques have been developed. In this paper, we propose an approach based on neuro-fuzzy modeling for time series prediction. Given a predicting sequence, the local context of the sequence is located in the series of the observed data. Proper lags of relevant variables are selected and training patterns are extracted. Based on the extracted training patterns, a set of TSK fuzzy rules are constructed and the parameters involved in the rules are refined by a hybrid learning algorithm. The refined fuzzy rules are then used for prediction. Our approach has several advantages. It can produce adaptive forecasting models. It works for univariate and multivariate prediction. It also works for one-step as well as multi-step prediction. Several experiments are conducted to demonstrate the effectiveness of the proposed approach.