平行平板横向剪切干涉仪装调误差的矫正方法研究

对平行平板双光路横向剪切干涉仪的装调进行了研究,提出了一种矫正两个平行平板之间角度误差的方法.输出激光的波前采用Zernike多项式拟合,经过理论推导,发现两个方向差分波前求解出的倾斜像散之差与平行平板的角度误差存在线性关系,利用两个方向倾斜像散之差来矫正两个平行平板之间的角度误差.在平行平板横向剪切干涉仪的装调过程中使两个方向差分波前的倾斜像散之差为零即可以使两个方向的平行平板之间的角度误差值为零.进一步地从实验上证明了这个线性关系,对于所用的实验系统,当离焦像差为-3.224 7±0.001 8,两个方向差分波前的倾斜像散之差波动范围为±2.0×10^-3时,平行平板的角度误差可以控制在8.82″之内,高阶像差对平行平板的角度误差调节精度的影响约为1.63″.该方法具有装调简单、精确度高,易于流程化操作的优点.     

Escape from the quantum pigeon conundrum

It has recently been argued in Aharonov et al. (2016) that quantum mechanics violates the Pigeon Counting Principle (PCP) which states that if one distributes three pigeons among two boxes there must be at least two pigeons in one of the boxes. However, this conclusion cannot be justified by rigorous theoretical arguments. The issue is further complicated by experimental confirmation of the transition amplitudes predicted in this paper that nevertheless do not support the conclusion of PCP violation. Here we prove via a set of operator identities that the PCP is not violated within quantum mechanics, regardless of interpretation.     

The Application of Fractal Transform and Entropy for Improving Fault Tolerance and Load Balancing in Grid Computing Environments

This paper applies the entropy-based fractal indexing scheme that enables the grid environment for fast indexing and querying. It addresses the issue of fault tolerance and load balancing-based fractal management to make computational grids more effective and reliable. A fractal dimension of a cloud of points gives an estimate of the intrinsic dimensionality of the data in that space. The main drawback of this technique is the long computing time. The main contribution of the suggested work is to investigate the effect of fractal transform by adding R-tree index structure-based entropy to existing grid computing models to obtain a balanced infrastructure with minimal fault. In this regard, the presented work is going to extend the commonly scheduling algorithms that are built based on the physical grid structure to a reduced logical network. The objective of this logical network is to reduce the searching in the grid paths according to arrival time rate and path’s bandwidth with respect to load balance and fault tolerance, respectively. Furthermore, an optimization searching technique is utilized to enhance the grid performance by investigating the optimum number of nodes extracted from the logical grid. The experimental results indicated that the proposed model has better execution time, throughput, makespan, latency, load balancing, and success rate.     

Prediction and simulation of wear response of Linz–Donawitz (LD) slag filled glass–epoxy composites using neural computation

This article reports on the implementation of a soft computing technique based on artificial neural networks (ANNs) in analyzing the wear performance of a new class of hybrid composites filled with Linz–Donawitz slag (LDS). LDS is a major solid waste generated in huge quantities during steel making. It comes from slag formers such as burned lime/dolomite and from oxidizing of silica, iron etc. while refining the iron into steel in the LD furnace. In this work, hybrid composites consisting of short glass fiber (SGF) reinforced epoxy filled with different LDS content (0, 7.5, 15 and 22.5 wt%) are prepared by simple hand lay‐up technique. Solid particle erosion trials, as per ASTM G 76 test standards, are conducted on the composite samples following a well‐planned experimental schedule based on Taguchi design of experiments. Significant process parameters predominantly influencing the rate of erosion are identified. The study reveals that the LDS content is the most significant among various factors influencing the wear rate of these composites. Further, a model based on ANN for the prediction of erosion performance of these composites is implemented. The ANN prediction profiles for the characteristic wear properties exhibit very good agreement with the measured results demonstrating that a well‐trained network has been created. The simulated results explaining the effect of significant process variables on the wear rate indicate that the trained neural network possesses enough generalization capability of predicting wear rate even beyond the experimental range. Copyright © 2014 John Wiley & Sons, Ltd.     

“Parallel factor analysis of multi-excitation ultraviolet resonance Raman spectra for protein secondary structure determination”

Protein secondary structural analysis is important for understanding the relationship between protein structure and function, or more importantly how changes in structure relate to loss of function. The structurally sensitive protein vibrational modes (amide I, II, III and S) in deep-ultraviolet resonance Raman (DUVRR) spectra resulting from the backbone C–O and N–H vibrations make DUVRR a potentially powerful tool for studying secondary structure changes. Experimental studies reveal that the position and intensity of the four amide modes in DUVRR spectra of proteins are largely correlated with the varying fractions of α-helix, β-sheet and disordered structural content of proteins. Employing multivariate calibration methods and DUVRR spectra of globular proteins with varying structural compositions, the secondary structure of a protein with unknown structure can be predicted. A disadvantage of multivariate calibration methods is the requirement of known concentration or spectral profiles. Second-order curve resolution methods, such as parallel factor analysis (PARAFAC), do not have such a requirement due to the “second-order advantage.” An exceptional feature of DUVRR spectroscopy is that DUVRR spectra are linearly dependent on both excitation wavelength and secondary structure composition. Thus, higher order data can be created by combining protein DUVRR spectra of several proteins collected at multiple excitation wavelengths to give multi-excitation ultraviolet resonance Raman data (ME-UVRR). PARAFAC has been used to analyze ME-UVRR data of nine proteins to resolve the pure spectral, excitation and compositional profiles. A three factor model with non-negativity constraints produced three unique factors that were correlated with the relative abundance of helical, β-sheet and poly-proline II dihedral angles. This is the first empirical evidence that the typically resolved “disordered” spectrum represents the better defined poly-proline II type structure.     

Enhanced conformational sampling method for proteins based on the TaBoo SeArch algorithm: Application to the folding of a mini‐protein,chignolin

The conformational samplings are indispensible for obtaining reliable canonical ensembles, which provide statistical averages of physical quantities such as free energies. However, the samplings of vast conformational space of biomacromolecules by conventional molecular dynamics (MD) simulations might be insufficient, due to their inadequate accessible time‐scales for investigating biological functions. Therefore, the development of methodologies for enhancing the conformational sampling of biomacromolecules still remains as a challenging issue in computational biology. To tackle this problem, we newly propose an efficient conformational search method, which is referred as TaBoo SeArch (TBSA) algorithm. In TBSA, an inverse energy histogram is used to select seeds for the conformational resampling so that states with high frequencies are inhibited, while states with low frequencies are efficiently sampled to explore the unvisited conformational space. As a demonstration, TBSA was applied to the folding of a mini‐protein, chignolin, and automatically sampled the native structure (C_α root mean square deviation < 1.0 Å) with nanosecond order computational costs started from a completely extended structure, although a long‐time 1‐µs normal MD simulation failed to sample the native structure. Furthermore, a multiscale free energy landscape method based on the conformational sampling of TBSA were quantitatively evaluated through free energy calculations with both implicit and explicit solvent models, which enable us to find several metastable states on the folding landscape. © 2015 Wiley Periodicals, Inc.     

High throughput N-modular redundancy for error correction design of memristive stateful logic

Memristive stateful logic is one of the most promising candidates to implement an in-memory computing system that computes within the storage unit. It can eliminate the costs for the data movement in the traditional von Neumann system. However, the instability in the memristors is inevitable due to the limitation of the current fabrication technology, which incurs a great challenge for the reliability of the memristive stateful logic. In this paper, the implication of device instability on the reliability of the logic event is simulated. The mathematical relationship between logic reliability and redundancy has been deduced. By combining the mathematical relationship with the vector-matrix multiplication in a memristive crossbar array, the logic error correction scheme with high throughput has been proposed. Moreover, a universal design paradigm has been put forward for complex logic. And the circuit schematic and the flow of the scheme have been raised. Finally, a 1-bit full adder (FA) based on the NOR logic and NOT logic is simulated and the mathematical evaluation is performed. It demonstrates the scheme can improve the reliability of the logic significantly. And compared with other four error corrections, the scheme which can be suitable for all kinds of R-R logics and V-R logics has the best universality and throughput. Compared with the other two approaches which also need additional complementary metal-oxide semiconductor (CMOS) circuits, it needs fewer transistors and cycles for the error correction.     

大学化学云计算学习环境与协作学习模型的构建

"云计算"在教育教学中的应用构建了"云计算"辅助教学的概念。21世纪的教学方式是以学为主、以教为辅,学习的方式也从个人学习变成了协作学习,这正是"云计算"辅助教学的核心。本文回顾了计算机辅助教学的发展,对"云计算"学习环境与协作学习模型的构建进行了分析探讨,并以"大学化学云学堂"为案例解析了"云计算"学习环境与协作学习模型建立的实际应用,阐述了其设计思路和实现方法,以期为"云计算"辅助学习与教学研究提供一些帮助。     

Parallelizing Nonlinear Least-Squares Regression with Application to Analyses of Microalgae

Nonlinear least-squares regression is a valuable tool for gaining chemical insights into complex systems. Yet, the success of nonlinear regression as measured by residual sum of squares (RSS), correlation, and reproducibility of fit parameters strongly depends on the availability of a good initial solution. Without such, iterative algorithms quickly become trapped in an unfavorable local RSS-minimum. For determining an initial solution, a high-dimensional parameter space needs to be screened, a process that is very time-consuming but can be parallelized. Another advantage of parallelization is equally important: After determining initial solutions, the used processors can be tasked to each optimize an initial guess. Even if several of these optimizations become stuck in a shallow local RSS-minimum, other processors continue and improve the regression outcome. A software package for parallel processing-based constrained nonlinear regression (RegressionLab) has been developed, implemented, and tested on a variety of hardware configurations. As proof-of-principle, microalgae to environment interactions have been studied by infrared attenuated total reflection spectroscopy. Additionally, light microscopy has been used to monitor cell production. It is shown that spectroscopic data sets with 10,000?s of data points and >1000 nonlinear model parameters as well as imaging data with 100,000s of data points and >2000 nonlinear model parameters may now be investigated by constrained nonlinear regression. Acceleration factors of up to 8.1 have been obtained which is of high practical relevance when computations take weeks on single-processor machines. Solely using parallel processing, the RSS values may be improved up to a factor of 5.5.