Variable Dimension Research Articles

Hafnium-Oxide 3-D Nanofilms via the Anodizing of Al/Hf Metal Layers

Hafnium-oxide films with self-organized nanostructured 3-D architectures and variable dimension (10 to 400 nm) are synthesized via the high-current anodizing of thin aluminum-on-hafnium layers in phosphoric, malonic, and oxalic acid electrolytes. In the approach, the self-organized growth of a porous anodic alumina (PAA) film is immediately followed by the fast PAA-assisted reanodizing of the hafnium underlayer. The PAA-dissolved films consist of arrays of upright-standing hafnium-oxide nanorods held on the substrate by the tiny needle-like “nanoroots” widespread over a continuous hafnium-oxide bottom layer. The roots are amorphous Hf2O3, while the rods are amorphous HfO2–Hf2O3–Al2O3 mixed oxides, the bottom layer being, however, highly textured nanocrystalline HfO2. The calculated transport numbers for O2– and Hf4+(3)+ ions are, respectively, ∼0.55 and ∼0.45, which is a unique situation for anodic hafnium oxide, which normally grows by O2– transport only. Annealing the films in air at 600 °C oxidizes the remaining Hf metal to polycrystalline HfO2, still leaving the roots and rods amorphous. The annealing in vacuum results in partial oxide reduction and crystallization of the roots and rods to stable orthorhombic and monoclinic HfO2 phases. A model of the anodic film growth and solid-state ionic transport is proposed and experimentally justified. Potential applications of the 3-D hafnium-oxide nanofilms are in advanced electronic, photonic, or magnetic micro- and nanodevices.

Assessment of Location of the Mandibular Canal: Correlation Between Panoramic and Cone Beam Computed Tomography Measurements.

To compare panoramic and cone beam computed tomography (CBCT) determinations of implant-to-mandibular canal (MC) dimensions in mandibular regions posterior to the mental foramen and to investigate whether factors such as gender, age, region, and vertical dimension influence correlation between the two techniques. A retrospective analysis was carried out in 64 consecutive adult patients (42 females, 22 males; average age 57.1 ± 13.3 years) in whom 126 implants were positioned in the posterior segment of the mandible. Implant sites (first premolar, second premolar, first molar, and second molar) were assessed on each panoramic and CBCT radiograph by measuring the distance from the inferior border of the implant to the superior border of the MC. Binary logistic regression analysis was used to compute the odds ratios (ORs) of each implant site for underestimation vs nonunderestimation. Linear regression analysis was performed with CBCT dimension as the dependent variable and panoramic dimension, gender, and age as the independent variables. The mean implant-to-MC dimension was 2.50 ± 1.31 mm in panoramic radiography and 2.91 ± 1.62 mm in CBCT. The OR that an implant at the second molar region belonged to the underestimation group was strong (15.1:1) and highly significant (P = .011). If a predictive value of .95 was demanded, the implant-to-MC dimensions had to be overestimated by 2 mm compared to the predicted CBCT dimension. This study provides evidence of an underestimation of available vertical bone dimensions for implants in the posterior regions of the mandible when assessed by panoramic radiography. Use of CBCT is therefore recommended for all implant size estimations in this region.

An analytic approach for interpretable predictive models in high-dimensional data in the presence of interactions with exposures.

ABSTRACTPredicting a phenotype and understanding which variables improve that prediction are two very challenging and overlapping problems in the analysis of high‐dimensional (HD) data such as those arising from genomic and brain imaging studies. It is often believed that the number of truly important predictors is small relative to the total number of variables, making computational approaches to variable selection and dimension reduction extremely important. To reduce dimensionality, commonly used two‐step methods first cluster the data in some way, and build models using cluster summaries to predict the phenotype. It is known that important exposure variables can alter correlation patterns between clusters of HD variables, that is, alter network properties of the variables. However, it is not well understood whether such altered clustering is informative in prediction. Here, assuming there is a binary exposure with such network‐altering effects, we explore whether the use of exposure‐dependent clustering relationships in dimension reduction can improve predictive modeling in a two‐step framework. Hence, we propose a modeling framework called ECLUST to test this hypothesis, and evaluate its performance through extensive simulations. With ECLUST, we found improved prediction and variable selection performance compared to methods that do not consider the environment in the clustering step, or to methods that use the original data as features. We further illustrate this modeling framework through the analysis of three data sets from very different fields, each with HD data, a binary exposure, and a phenotype of interest. Our method is available in the eclust CRAN package.

Hotelling's T<SUP align="right">2</SUP> control chart with adaptive sampling interval and variable dimensional

T2 control chart with variable dimension is useful when there is a set of p1 variables that are easy to monitor or whose measurement is cheap, against a set of p2 variables, p = p1 + p2, that are d...

A PH multiplier-based PSO method for metal balance in nonferrous metal industry

Aiming at the problem of metal balance management in metallurgical enterprises, this paper presents an improved PSO method for data reconciliation technology. According to the actual production condition of the copper company, a least squares data reconciliation model is established, and a PH multiplier-based PSO optimization method is proposed for the characteristics of the model variable dimension and the constraint condition. This method first reduced the dimension of the problem due to the large size of the variable dimension, transformed the search of a large number of variables into several multipliers. And then the multipliers are searched by using PSO algorithm with faster search speed. The experiment shows that the method is efficient and the coordination result accords with the actual production.

Hotelling's T<SUP align="right">2</SUP> control chart with adaptive sampling interval and variable dimensional

T2 control chart with variable dimension is useful when there is a set of p1 variables that are easy to monitor or whose measurement is cheap, against a set of p2 variables, p = p1 + p2, that are difficult and/or expensive to monitor. However, the information that these p2 variables provide is important to detect quickly the process quality shift. Therefore, there are cases when controlling the whole set of p variables may be difficult or expensive, but controlling sometimes p1 variables, and only when the process seems to have a problem controlling the full set of p variables, may be a cheaper option on average and very efficient. However, its not effective if the shift size is small. For obtaining good performance in detecting such shifts, we propose the application of the variable sampling interval technique to the variable dimensional T2 (VDT2) control chart, resulting in the VSIVDT2 control chart.

Convolutional Bidirectional Long Short-Term Memory for Deception Detection With Acoustic Features

Despite the widespread use of multi-physiological parameters for deception detection, they have been severely restricted due to the high degree of cooperation in contacting-detection. Therefore, a non-contacting method is proposed for deception detection using acoustic features as an input and convolutional bidirectional long short-term memory (LSTM) as a classifier. The algorithm extracts frame-level acoustic features whose dimension dynamically varies with the length of speech, in order to preserve the temporal information in the original speech. Bidirectional LSTM was applied to match temporal features with variable dimension in order to learn the context dependences in speech. Furthermore, the convolution operation replaces multiplication in the traditional LSTM, which is used to excavate time–frequency mixed data. The average accuracy of the experiment on Columbia–SRI–Colorado corpus reaches 70.3%, which is better than the previous works with non-contacting modes.

A surrogate model based on Non-Uniform Rational B-Splines hypersurfaces

This study aims at providing an original metamodeling technique based on the Non-Uniform Rational B-Splines (NURBS) formalism. The proposed approach is able to fit general non-convex sets of target points (TPs) by extending the NURBS formalism to the N-dimensional (N-D) case, getting in this way a general NURBS hypersurface. The shape of such a hypersurface is tuned by several parameters: the number of control points (CPs), their coordinates and related weights, the degrees of the blending functions and the knot-vector components defined along each direction. The goal of the proposed strategy is to find the best NURBS hypersurface approximating a given set of TPs. To this purpose the problem is formulated as an unconstrained least-square distance problem wherein the optimisation variables are all the parameters tuning the shape of the NURBS hypersurface. Nevertheless, when the number of CPs and the degrees of the basis functions are included among the design variables the resulting problem is defined over a space having a variable dimension. To deal with this aspect, a special genetic algorithm, able to solve problems characterised by a variable number of design variables, is considered to determine automatically (i.e. without the user’s intervention) the optimum value of both the design space size (related to the integer variables of the NURBS hypersurface) and the NURBS hypersurface continuous parameters. The effectiveness of the proposed approach is proven by means of a meaningful benchmark.

Successive ESPRIT Algorithm for Joint DOA-Range-Polarization Estimation With Polarization Sensitive FDA-MIMO Radar

Polarization sensitive frequency and frequency diversity array (FDA)-MIMO (PSFDA-MIMO) hybrid radar is an emerging technology. The existing algorithms for parameter estimation with PSFDA-MIMO radar need multiple-dimensional searches, whose computational complexity is high. To reduce the computational complexity, in this paper, a search-free algorithm is proposed to estimate 4-D parameters with PSFDA-MIMO radar. First, direction of arrival (DOA) is estimated with the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm using the rotational invariance of the receiving array. Second, based on the estimated DOA parameter, range estimation is obtained with the ESPRIT algorithm using the rotational invariance of the transmitting array. Finally, polarization parameters are estimated with the ESPRIT algorithm using the rotational invariance of the polarization domain. Thus, the proposed algorithm is termed the successive ESPRIT algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm.

Use of fuzzy logic for measuring practices and performances of supply chain

The objective of this research is to show an innovative way for processing the collected data and measurement of practice and performances of supply chain by means of transformation of the obtained linguistic values, using the appropriate fuzzy methods, into crisp values of research variable dimensions.The methodology was applied for the measurement of the influence of an independent supply chain practice variable on the dependent supply chain performance variable and the research included the food industry companies. In order to apply the multivariate analysis methods, it is necessary to have the dimensions of the variable, but not the claims. Fuzzy logic enables the weighting of each claim and determining its weight, and determining the research variable dimension value in order to be able to use the multivariate analysis methods. The use of this methodology enables realistic evaluation of the dimensions and the results obtained show a statistically significant influence proving to be suitable for further testing using various statistical methods. The hypotheses about the relationships between supply chain practices and supply chain performances are confirmed.

Optimal design of the variable sampling size and sampling interval variable dimension T2 control chart for monitoring the mean vector of a multivariate normal process

ABSTRACTThis paper develops the algorithm for the optimization designs of the adaptive T2 Control Chart for Monitoring the Mean Vector of a Multivariate Normal Process. It includes the variable sample size, variable sampling interval and variable dimensional chart. The VDT2 control chart performs well for moderate and large shifts in the mean vector. However, its performance for small shifts is poor. To improve the chart's performance in detecting such shifts, we propose the application of the variable sample size and sampling interval technique to the VDT2 control chart, resulting in the VSSIVDT2 control chart.

A General Hybrid Optimization Strategy for Curve Fitting in the Non-uniform Rational Basis Spline Framework

In this paper, a general methodology to approximate sets of data points through Non-uniform Rational Basis Spline (NURBS) curves is provided. The proposed approach aims at integrating and optimizing the full set of design variables (both integer and continuous) defining the shape of the NURBS curve. To this purpose, a new formulation of the curve fitting problem is required: it is stated in the form of a constrained nonlinear programming problem by introducing a suitable constraint on the curvature of the curve. In addition, the resulting optimization problem is defined over a domain having variable dimension, wherein both the number and the value of the design variables are optimized. To deal with this class of constrained nonlinear programming problems, a global optimization hybrid tool has been employed. The optimization procedure is split in two steps: firstly, an improved genetic algorithm optimizes both the value and the number of design variables by means of a two-level Darwinian strategy allowing the simultaneous evolution of individuals and species; secondly, the optimum solution provided by the genetic algorithm constitutes the initial guess for the subsequent gradient-based optimization, which aims at improving the accuracy of the fitting curve. The effectiveness of the proposed methodology is proven through some mathematical benchmarks as well as a real-world engineering problem.

Scheduling meets n-fold integer programming

Scheduling problems are fundamental in combinatorial optimization. Much work has been done on approximation algorithms for NP-hard cases, but relatively little is known about exact solutions when some part of the input is a fixed parameter. In 2014, Mnich and Wiese initiated a systematic study in this direction. In this paper we continue this study and show that several additional cases of fundamental scheduling problems are fixed parameter tractable for some natural parameters. Our main tool is n-fold integer programming, a recent variable dimension technique which we believe to be highly relevant for the parameterized complexity community. This paper serves to showcase and highlight this technique. Specifically, we show the following four scheduling problems to be fixed-parameter tractable, where p max is the maximum processing time of a job and w max is the maximum weight of a job: - Makespan minimization on uniformly related machines $(Q||C_{max} )$ parameterized by $p_{max}$, - Makespan minimization on unrelated machines $(R||C_{max} )$ parameterized by $p_{max}$ and the number of kinds of machines, - Sum of weighted completion times minimization on unrelated machines $(R|| \sum w_i C_i )$ parameterized by $p_{max} + w_{max}$ and the number of kinds of machines, - The same problem, $(R|| \sum w_i C_i),$ parameterized by the number of distinct job times and the number of machines.

Evaluation of NaFeTiO4 as an electrode for energy storage application

We report a polycrystalline NaFeTiO4 prepared via conventional solid-state reaction route. X-ray diffraction (XRD) results and Rietveld refinement confirmed single-phase NaFeTiO4 having an orthorhombic unit cell with lattice parameters a = 9.17051 A, b = 2.96310 A, and c = 10.73676 A and Pnma space group (No. 62). Energy dispersive spectrum (EDS) yielded sample stoichiometry that agrees well with its molecular formula. The surface morphology indicated a cylindrical rod-like microstructure comprising well-defined grains having variable dimension, i.e., diameter ~ 250 to 350 nm and length ~ 1 to 5 μm. Vibrational spectroscopy (FTIR/Raman) results indicated presence of FeO6 and TiO6 octahedra in good agreement with crystallographic study. Brunner-Emmet-Teller (BET) surface area measurement yielded a specific surface area as high as ~ 4.28 m2 g−1. Electrical impedance spectrum indicated presence of grains separated by well-defined grain boundaries in agreement with microstructural analysis. Electrical conductivity of the material was estimated to be ~ 6.05 × 10−6 S cm−1. The structural model obtained using XRD and vibrational spectrum results suggest layered tunnel/cage structure of cage dimension ~ 4.65 A, along [010] direction in the xz plane, which is larger than the size of Na+ ion (0.98 A). So, easier Na+ migration feasibility exists in NaFeTiO4 crystal lattice making it a good candidate for electrode applications.

Generalization of the Filippov method for systems with a large periodic input

Using the Filippov method, the stability of a nominal cyclic steady state of a nonlinear dynamic system (a buck dc–dc converter) is investigated. A common approach to this study is based upon a complete clock period, and assumes that the input is from a regulated dc power supply. In reality, this is usually not the case: converters are mostly fed from a rectified and filtered source. This dc voltage will then contain ripples (i.e. the peak-to-peak input voltage is not zero). Therefore, we consider the input as a sinusoidal voltage. Its frequency is chosen as a submultiple T of the converter’s clock and our objective is to analyze, clarify and predict some of the nonlinear behaviors that these circuits may exhibit, when the input voltage frequency changes in time. This input frequency’s parameter T determines the number of the switching instants over a whole clock cycle, obtained as Newton–Raphson solutions. Then, for the considered buck converter, we develop a mathematical model in a compact form of the Jacobian matrix with a variable dimension proportional to the input voltage harmonics. Finally, the Floquet multipliers of the monodromy matrix are used to predict the system stability. Numerical examples illustrate how these multipliers cross the unit cycle causing various bifurcations.

Investigation of flocculation dynamics under changing hydrodynamic forcing on an intertidal mudflat

In situ floc size and turbulent shear stress were measured together with suspended sediment concentration to investigate the floc properties under changing hydrodynamic forcing over the intertidal mudflat. A tripod system was established in the field for a period of approximately one month, including ~6days of stormy conditions in the middle of the investigation period. Mean floc size exhibited strong temporal variations within a tidal cycle, and inverse relationship was found between mean floc size and shear stress. Suspended sediment concentration (SSC) can modulate the flocculation dynamics when shear stress decreases down to enhancing flocculation. Asymmetrical behaviors of floc sizes between flood and ebb phases were identified, with overall larger floc sizes in flood than in ebb tide under the same shear stresses. Floc structure showed different properties under calm and stormy conditions, and the variable fractal dimension and variable primary particle size were more convincing in simulating the variation of floc effective density with mean floc size during the storm period, which was inferred to be related to the resuspension of bed sediment as well as organic matter. A total of 110mm bed erosion was measured during the storm, and erosion events occurred only around low water, due to the high current-wave combined bed shear stress and off-shore current. After the storm, ~40% of the erosion recovered within one week, and the fast settling of large flocs around high water plays significant role in the deposition process, leading to ~60% of the recovery.

Rapid identification of milk samples by high and low frequency unfolded partial least squares discriminant analysis combined with near-infrared spectroscopy

A high and low frequency unfolded partial least squares discriminant analysis (HLFUPLS-DA) for building a pattern recognition model of near-infrared (NIR) spectra is proposed to identify milk samples. In the approach, the spectra are decomposed into different frequency components by empirical mode decomposition (EMD) at first. Then the former high frequency components are summed as a high frequency matrix and vice versa. Thirdly, the high and low frequency matrices are extended to an extended matrix in the variable dimension. Finally, PLS-DA model is built between the extended matrix and the target vectors. Coupled with NIR spectroscopy, HLUPLS-DA is applied to identify milk samples of different qualities. Comparing with PLS-DA and other signal processing techniques combined with PLS-DA, the proposed method is proved to be a promising tool for spectral qualitative analysis of complex samples.

A prediction scheme of tropical cyclone frequency based on lasso and random forest

This study aims to propose a novel prediction scheme of tropical cyclone frequency (TCF) over the Western North Pacific (WNP). We concerned the large-scale meteorological factors inclusive of the sea surface temperature, sea level pressure, the Nino-3.4 index, the wind shear, the vorticity, the subtropical high, and the sea ice cover, since the chronic change of these factors in the context of climate change would cause a gradual variation of the annual TCF. Specifically, we focus on the correlation between the year-to-year increment of these factors and TCF. The least absolute shrinkage and selection operator (Lasso) method was used for variable selection and dimension reduction from 11 initial predictors. Then, a prediction model based on random forest (RF) was established by using the training samples (1978–2011) for calibration and the testing samples (2012–2016) for validation. The RF model presents a major variation and trend of TCF in the period of calibration, and also fitted well with the observed TCF in the period of validation though there were some deviations. The leave-one-out cross validation of the model exhibited most of the predicted TCF are in consistence with the observed TCF with a high correlation coefficient. A comparison between results of the RF model and the multiple linear regression (MLR) model suggested the RF is more practical and capable of giving reliable results of TCF prediction over the WNP.

PENGARUH BRAND IMAGE DAN KUALITAS PELAYANAN TERHADAP KEPUASAN WISATAWAN DALAM MEMILIH TAKSI BLUE BIRD DI BALI

In Bali, many types of taxi can be encountered starting from the airport until city and tourist destination, one of them is Blue Bird Taxi. Blue Bird taxi has a brand image and good service quality which can be seen from many awards, some of them are Mark Plus Wow Service Excellence Award 2015 and Indonesian Leading Taxi/Limousine Company. Along with the technology development, giving significant impact to competition among land transportation for instance online based taxi. The technique of determining the respondents is purposive sampling technique with the number of respondents were 170 respondents. Furthermore, in analysing the data, this study using multiple regression analysis techniques. Results of this study mention that variable dimension dominate on brand image is strength. In partially brand image give positive and significant impact on tourist satisfaction with tcount of 2,465 and significant value of 0,015. And variable dimension dominate on service quality is tangible. Service quality give positive and significant impact on tourist satisfaction with tcount of 9,219 and significant value of 0,000. Simultaneously, it can be concluded that brand image and service quality give positive and significant impact on tourist satisfaction with Fcount of 105,731 and significant value of 0,000.

Elastic Net based sparse feature learning and classification for Alzheimer's disease identification.

Magnetic resonance imaging (MRI) has been successfully applied to investigate neuron pathological changes. Since the high dimension of observation data, sparse feature learning plays an important role in overcoming the challenge of high variable dimension and low sample size problem among the disease identification. In this paper, sparse Elastic Net (EN) was used to extract low dimension features and to identify the Alzheimer's disease (AD). Compare with principal component analysis (PCA) method, the EN method can solve the problems of less samples and high correlations between variables. For those variables sharing the same biological phenomenon, it selected whole groups into the model automatically once one variable among them was selected. Unlike other subspace learning methods, the proposed method used less man-made feature setting. The problems of dimension reduction and classification were conducted into a similar formulation. Experimental results illustrated the effectiveness of the proposed method.