Influence of ion bombardment on the properties and microstructure of unbalanced magnetron deposited niobium coatings

The effect of the ion bombardment to unbalanced magnetron deposited, approximately 1.5 and 4.5 μm thick, Nb coatings have been investigated as the bias voltage was varied from U_B=−75 to −150 V. Increasing bias voltage increased the hardness of the coating from 4.5 to 8.0 GPa. This was associated with residual stress and Ar incorporation into the Nb lattice. Strong {110} texture developed in the samples deposited at low bias voltages, while beyond U_B=−100 V a {111} texture became dominant. However, strong {111} texture was observed only with the thicker 3Nb coatings. Secondary electron microscopy investigation of the coating topography showed fewer defects in the thicker coatings. All coatings exhibited good corrosion resistance, with the thicker coatings clearly outperforming the thinner ones. Excessive bias voltages (U_B=−150 V) was found to lead to poor adhesion and loss of corrosion resistance.     

A Study of AlN Films Prepared by Arc Ion Plating

Hexagonal AlN films have been obtained by arc ion plating at different negative biases ,X-ray diffraction and scanning electron microscopy results show that AlN films with smooth surfaces and (002) preferred orientation are obtained at low biases ,whereas those with coarse surfaces and (100) preferred orientation are obtained at high biases,The formation mechanism of AlN is analyzed and the experiment results are discussed,The effect of bias on adhesion strength has also been examined.     

Advancements in the design and validation of an air pollution integrated assessment model for Spain

This paper describes the design and application of the Atmospheric Evaluation and Research Integrated model for Spain (AERIS). Currently, AERIS can provide concentration profiles of NO₂, O₃, SO₂, NH₃, PM, as a response to emission variations of relevant sectors in Spain. Results are calculated using transfer matrices based on an air quality modelling system (AQMS) composed by the WRF (meteorology), SMOKE (emissions) and CMAQ (atmospheric-chemical processes) models. The AERIS outputs were statistically tested against the conventional AQMS and observations, revealing a good agreement in both cases. At the moment, integrated assessment in AERIS focuses only on the link between emissions and concentrations. The quantification of deposition, impacts (health, ecosystems) and costs will be introduced in the future. In conclusion, the main asset of AERIS is its accuracy in predicting air quality outcomes for different scenarios through a simple yet robust modelling framework, avoiding complex programming and long computing times.     

Magnesium interlayered diamond coating on silicon

Diamond thin films have been deposited using hot filament chemical vapour deposition technique on manually scratched p-Si(1 0 0) substrate, with and without magnesium interlayer. In spite of magnesium melting point being lower (T_m = 649 °C) than the growth temperature of the substrate (T_s  750 °C) used in these experiments, it was found that high quality diamond films could be grown on Mg covered substrate. A liquid substrate is probably generated during the diamond film growth. Raman spectroscopy analysis exhibited only the triply degenerate, zone centre optical phonon peak at 1333 cm⁻¹ indicating that nearly stress free crystallites were present. Broadening of the Raman peak (11.76 cm⁻¹) indicates that some small crystallites also are present. Scanning electron and atomic force microscopy accompanied by X-ray diffraction analysis where used to compare the details of diamond film growth directly on scratched Si(1 0 0) and Mg interlayered scratched Si(1 0 0) substrates.     

Confirmation Bias in Online Searches: Impacts of Selective Exposure Before an Election on Political Attitude Strength and Shifts

Impacts of Internet use on political information seeking and subsequent processes have been subject to much debate. A 2‐session online field study presented online search results on political topics to examine selective exposure and its attitudinal impacts. Session 1 captured attitudes, including their accessibility. Session 2 tracked what online search results participants selected and how long they read them; participants then reported attitudes again. The study represented a 4x8x2x2 within‐subjects design: 4 topics, 8 browsing intervals each, with articles presenting opposing stances, with low versus high source credibility. Attitude‐consistent messages and messages from high‐credibility sources were preferred. Exposure to attitude‐consistent search results increased attitude accessibility and reinforced attitudes, whereas exposure to attitude‐discrepant content had opposite effects, regardless of messages' source credibility.     

Image segmentation and bias correction via an improved level set method

Intensity inhomogeneity causes considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus bias field estimation is a necessary pre-processing step before quantitative analysis of MR data. This paper presents a variational level set approach for bias correction and segmentation for images with intensity inhomogeneities. Our method is based on the observation that local intensity variations in relatively smaller regions are separable, despite the inseparability of the whole image. In the beginning we define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. Generally the local intensity variations are described by the Gaussian distributions with different means and variances. In this work the objective functions are integrated over the entire domain with local Gaussian distribution of fitting energy, ultimately analyzing the data with a level set framework. Our method is able to capture bias of quite general profiles. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results.     

Satellite image-based maps: Scientific inference or pretty pictures?

The scientific method has been characterized as having two distinct components, Discovery and Justification. Discovery emphasizes ideas and creativity, focuses on conceiving hypotheses and constructing models, and is generally regarded as lacking a formal logic. Justification begins with the hypotheses and models and ends with a valid scientific inference. Unlike Discovery, Justification has a formal logic whose rules must be rigorously followed to produce valid scientific inferences. In particular, when inferences are based on sample data, the rules of the logic of Justification require assessments of bias and precision. Thus, satellite image-based maps that lack such assessments for parameters of populations depicted by the maps may be of little utility for scientific inference; essentially, they may be just pretty pictures. Probability- and model-based approaches are explained, illustrated, and compared for producing inferences for population parameters using a map depicting three land cover classes: non-forest, coniferous forest, and deciduous forest. The maps were constructed using forest inventory data and Landsat imagery. Although a multinomial logistic regression model was used to classify the imagery, the methods for assessing bias and precision can be used with any classification method. For probability-based approaches, the difference estimator was used, and for model-based inference, a bootstrap approach was used.     

Hyper least squares fitting of circles and ellipses

This work extends the circle fitting method of Rangarajan and Kanatani (2009) to accommodate ellipse fitting. Our method, which we call HyperLS, relies on algebraic distance minimization with a carefully chosen scale normalization. The normalization is derived using a rigorous error analysis of least squares (LS) estimators so that statistical bias is eliminated up to second order noise terms. Numerical evidence suggests that the proposed HyperLS estimator is far superior to the standard LS and is slightly better than the Taubin estimator. Although suboptimal in comparison to maximum likelihood (ML), our HyperLS does not require iterations. Hence, it does not suffer from convergence issues due to poor initialization, which is inherent in ML estimators. In this sense, the proposed HyperLS is a perfect candidate for initializing the ML iterations.     

The relation between compressive strength of carbon/epoxy fabrics and micro-tow geometry with various bias angles

In this paper compressive tests of carbon/epoxy (plain weave, 3k) fabric composites were performed to investigate the relation between compressive strength and various bias angles and shear angles. Compressive properties such as chord modulus and maximum strength of the fabric composite materials are essential to analyze the drape behaviour and estimate the quality of the final products. Various specimens with different bias and shear angles which were fabricated by using autoclave de-gassing moulding process were prepared to estimate the strength and chord modulus with respect to the bias and shear angle variations. The stacking sequences of the compressive test specimens are [0]_10T, [15]_10T, [30]_10T and [45]_10T for bias specimens and [±37]_10T, [±32]_10T, [±28]_10T, [±22]_10T for sheared specimens. Micro-tow structures were observed to correlate the fabric compressive strength with crimp angle. Anti-buckling rig was involved to prevent specimens from buckling during the compressive tests. The compressive test was performed with 1.3 mm/min strain rates. Compressive test results were compared with calculation results. Facture modes which were classified in two different modes were analyzed using microscopic observation.     

实现快速乘法的几种改进贝斯算法

分析并评价了在传统贝斯算法基础上改进的几种算法,它们分别是完全冗余贝斯算法、局部冗余贝斯算法,以及有偏差的贝斯算法。通过将仿真结果与传统贝斯算法的比较,证明局部冗余贝斯算法有与传统贝斯算法几乎相同的速度,但其面积和功耗节省不多。完全冗余算法比传统贝斯算法速度慢,但其面积和功耗节省较多。有偏差的冗余算法是完全冗余算法和局部冗余算法的综合,兼容了两种算法的优点,所以其面积和功耗效率均较高,运算速度也较快。综合来讲,有偏差的贝斯算法是其中最好的一种算法。