Coupled Vector Research Articles

Modeling and estimation of CO2 capture by porous liquids through machine learning

Modeling and estimation of CO2 capture by porous liquids through machine learning

Coupled vector gauge fields in teleparallel scalar-kinetic branes

Coupled vector gauge fields in teleparallel scalar-kinetic branes

Coupled vector Gauss-Bonnet theories and hairy black holes

We study vector-tensor theories in which a 4-dimensional vector field Aμ is coupled to a vector quantity Jμ, which is expressed in terms of Aμ and a metric tensor gμν. The divergence of Jμ is equivalent to a Gauss-Bonnet (GB) term. We show that an interacting Lagrangian of the form f(X)AμJμ, where f is an arbitrary function of X=−(1/2)AμAμ, belongs to a scheme of beyond generalized Proca theories. For f(X)=α=constant, this interacting Lagrangian reduces to a particular class of generalized Proca theories. We apply the latter coupling to a static and spherically symmetric vacuum configuration by incorporating the Einstein-Hilbert term, Maxwell scalar, and vector mass term ηX (η is a constant). Under an expansion of the small coupling constant α with η≠0, we derive hairy black hole solutions endowed with nonvanishing temporal and radial vector field profiles. The asymptotic properties of solutions around the horizon and at spatial infinity are different from those of hairy black holes present in scalar-GB theories. We also show that black hole solutions without the vector mass term, i.e., η=0, are prone to ghost instability of odd-parity perturbations.

Conformally and Disformally Coupled Vector Field Models of Dark Energy

Conformally and Disformally Coupled Vector Field Models of Dark Energy

Voxelwise Prediction of Recurrent High-Grade Glioma via Proximity Estimation–Coupled Multidimensional Support Vector Machine

To provide early and localized glioblastoma (GBM) recurrence prediction, we introduce a novel postsurgery multiparametric magnetic resonance-based support vector machine (SVM) method coupling with stem cell niche (SCN) proximity estimation. This study used postsurgery magnetic resonance imaging (MRI) scans from 50 patients with recurrent GBM, obtained approximately 2 months before clinically diagnosed recurrence. The main prediction pipeline consisted of a proximity-based estimator to identify regions with high risk of recurrence (HRRs) and an SVM classifier to provide voxelwise prediction in HRRs. The HRRs were estimated using the weighted sum of inverse distances to 2 possible origins of recurrence-the SCN and the tumor cavity. Subsequently, multiparametric voxels (from T1, T1 contrast-enhanced, fluid-attenuated inversion recovery, T2, and apparent diffusion coefficient) within the HRR were grouped into recurrent (warped from the clinical diagnosis) and nonrecurrent subregions and fed into the proximity estimation-coupled SVM classifier (SVMPE). The cohort was randomly divided into 40% and 60% for training and testing, respectively. The trained SVMPE was then extrapolated to an earlier time point for earlier recurrence prediction. As an exploratory analysis, the SVMPE predictive cluster sizes and the image intensities from the 5 magnetic resonance sequences were compared across time to assess the progressive subclinical traces. On 2-month prerecurrence MRI scans from 30 test cohort patients, the SVMPE classifier achieved a recall of 0.80, a precision of 0.69, an F1-score of 0.73, and a mean boundary distance of 7.49 mm. Exploratory analysis at early time points showed spatially consistent but significantly smaller subclinical clusters and significantly increased T1 contrast-enhanced and apparent diffusion coefficient values over time. We demonstrated a novel voxelwise early prediction method, SVMPE, for GBM recurrence based on clinical follow-up MR scans. The SVMPE is promising in localizing subclinical traces of recurrence 2 months ahead of clinical diagnosis and may be used to guide more effective personalized early salvage therapy.

Towards estimating absorption of major air pollutant gasses in ionic liquids using soft computing methods

• Solubilities of CO 2 , CO, N 2 O, SO 2 , CH 4 and H 2 S gases in ionic liquids are correlated. • Machine learning models of MLP-ANN, Hybrid‐ANFIS, PSO‐ANFIS and CSA-LSSVM are implemented. • 3060 data points for 40 types of ILs were gathered. • Machine learning models can be used to estimate pollutant gas capture by ILs. • CSA-LSSVM model demonstrates the highest accuracy. Capture of air pollutant gases using novel and green solvents is obtaining widespread attention. Accurate estimation of this process is complex. We have estimated the absorption of CO 2 , CH 4 , H 2 S, N 2 O, SO 2 and CO gases in ionic liquids (ILs). We have applied Multilayer Perceptron-Artificial Neural Networks (MLP-ANN), Hybrid-Adaptive Neuro Fuzzy Inference System (Hybrid-ANFIS), Particle Swarm Optimization-Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and Coupled Simulated Annealing-Least Squares Support Vector Machine (CSA-LSSVM). We have gathered 3060 data of 72 IL-Gas mixtures for 40 types of ILs. The inputs of these models are: Temperature ( T ), pressure ( P ), IL molecular weight (Mw IL ), IL critical temperature ( T c , IL ), IL critical pressure ( P c , IL ), IL acentric factor (ω IL ), gas molecular weight ( Mw gas ), gas critical temperature ( T c , gas ), gas critical pressure ( P c , gas ), gas kinetic diameter (d) and the acentric factor ( ω gas ). The CSA-LSSVM model produces best estimation with an Average Absolute Relative Deviation (AARD) of 8.7%. The results suggest the solubilities of the gases in ILs are correlated with structural factors of ILs. Estimation of the equilibrium behaviors in ionic liquids is of importance in simulation and design of solvent-based pollutant gas capture processes.

Prediction of penetration rate by Coupled Simulated Annealing-Least Square Support Vector Machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters

Field information analysis is the main element of reducing costs and improving drilling operations. Therefore, the development of field data analysis tools is one of the ways to improve drilling operations. This paper uses mathematical programming and optimization-based methods to present and review learning models for data classification. A comprehensive multi-objective optimization model is proposed by extracting commonalities and the same philosophy of some of the most popular mathematical optimization models in the last few years. The geometric representation of the model will be to make it easier to understand the characteristics of the proposed model. Then it is shown that a large number of models studied in the past and present are subsets, and exceptional cases of this proposed comprehensive model and how to convert the proposed comprehensive model to these methods will be examined. This seeks to bridge the gap between new multi-objective programming models and the powerful and improved CSA-LSSVM methods presented for classification in data mining and to generalize studies to improve each of these methods.

Coupled vector dark energy

We provide a general framework for studying the evolution of background and cosmological perturbations in the presence of a vector field Aμ coupled to cold dark matter (CDM) . We consider an interacting Lagrangian of the form Q f(X) Tc, where Q is a coupling constant, f is an arbitrary function of X=−AμAμ/2, and Tc is a trace of the CDM energy-momentum tensor. The matter coupling affects the no-ghost condition and sound speed of linear scalar perturbations deep inside the sound horizon, while those of tensor and vector perturbations are not subject to modifications. The existence of interactions also modifies the no-ghost condition of CDM density perturbations. We propose a concrete model of coupled vector dark energy with the tensor propagation speed equivalent to that of light. In comparison to the Q=0 case, we show that the decay of CDM to the vector field leads to the phantom dark energy equation of state wDE closer to −1. This alleviates the problem of observational incompatibility of uncoupled models in which wDE significantly deviates from −1. The maximum values of wDE reached during the matter era are bounded from the CDM no-ghost condition of future de Sitter solutions.

Oil flooded scroll compressors: Predicting the energy performance and evaluating the experimental data

Abstract The annual or seasonal performance evaluation of Oil Flooded Scroll Compressor (OFSC) is essential to maximize its performance and increase vapor compression cycle efficiency. An experimental data bank for detail evaluation of this component in the vapor compression systems has need of a highly equipped laboratory and proper instruments. The previous researchers have developed different theoretical, empirical, and semi-empirical models to solve this problem, which are not highly effective under different situations. We propose a Hybrid-Adaptive Neuro Fuzzy Inference System (Hybrid-ANFIS) for the fast and precise estimation of the discharge temperature, refrigerant mass flow rate, and electrical power of the OFSCs. The performance of the Hybrid-ANFIS is checked against the other well-known models, including the Particle Swarm Optimization-Artificial Neural Network, Coupled Simulated Annealing-Least Square Support Vector Machine, Genetic Algorithm-Least Square Support Vector Machine, and the existing correlations in the open literature. The results indicated that the proposed Hybrid-ANFIS model is more accurate and has the promising potential for estimating the desired parameters by introducing a coefficient of determination higher than 0.998. The application of the suggested model is further illustrated against the experimental data and new test conditions are evaluated. Finally, the Leverage algorithm, which is a novel statistical approach, is implemented to assess the quality of the experimental data and diagnose the probable doubtful data samples. The results of the data analysis and the outlier detection method clarified that the presented Hybrid-ANFIS model is statistically valid and four experimental data samples are doubtful reported for the OFSC.

Estuary salinity prediction using a coupled GA-SVM model: a case study of the Min River Estuary, China

Estuary salinity predictions can help to improve water safety in coastal areas. Coupled genetic algorithm-support vector machine (GA-SVM) models, which adopt a GA to optimize the SVM parameters, have been successfully applied in some research fields. In light of previous research findings, an application of a GA-SVM model for tidal estuary salinity prediction is proposed in this paper. The corresponding model is developed to predict the salinity of the Min River Estuary (MRE). By conducting an analysis of the time series of daily salinity and the results of simulation experiments, the high-tide level, runoff and previous salinity are considered as the major factors that influence salinity variation. The prediction accuracy of the GA-SVM model is satisfactory, with coefficient of determination (R2) of 0.85, Nash–Sutcliffe efficiency of 0.84 and root mean square error of 119 (μS/cm). The proposed model performs significantly better than the traditional SVM model in terms of prediction accuracy and computing time. It can be concluded that the proposed model can successfully predict the salinity of MRE based on the high-tide level, runoff and previous salinity.

量子点中两耦合时间矢量光孤子的碰撞特性

量子点中两耦合时间矢量光孤子的碰撞特性

A Low-pass-equivalent,State-space Model for the Nonlinear Coupling Dynamics in Mechatronic Transducers

The nonlinear analysis of a typical electrical oscillator coupled nonlinearly to a mechanical one, as encountered in mechatronics applications for sensing, actuation and energy harvesting, is approached by using a state-space decomposition inspired by Volterra theory representation. The equation of motion of the mechanical subsystem includes an electromagnetic force directly proportional to the electric current squared. The nonlinear coupled dynamics is investigated systematically by partitioning the coupled system state vector in such a way as to fully exploit the mechanical low-pass and the electrical band-pass intrinsic features of free dynamics. In particular, by employing the Hilbert Transform, a low-pass equivalent system is derived and verified by using standard perturbation analysis. Then, a typical case is investigated thoroughly by means of numerical simulation of the original coupled low and band-pass, real-state-variable system and the low-pass-equivalent, complex-state-variable derived one. The nonlinear model equations considered here pave the way for a systematic investigation of nonlinear feedback control options designed to operate mechatronic transducers in energy harvesting, sensing or actuation modes.

Solving Nonstationary Classification Problems With Coupled Support Vector Machines

Many learning problems may vary slowly over time: in particular, some critical real-world applications. When facing this problem, it is desirable that the learning method could find the correct input-output function and also detect the change in the concept and adapt to it. We introduce the time-adaptive support vector machine (TA-SVM), which is a new method for generating adaptive classifiers, capable of learning concepts that change with time. The basic idea of TA-SVM is to use a sequence of classifiers, each one appropriate for a small time window but, in contrast to other proposals, learning all the hyperplanes in a global way. We show that the addition of a new term in the cost function of the set of SVMs (that penalizes the diversity between consecutive classifiers) produces a coupling of the sequence that allows TA-SVM to learn as a single adaptive classifier. We evaluate different aspects of the method using appropriate drifting problems. In particular, we analyze the regularizing effect of changing the number of classifiers in the sequence or adapting the strength of the coupling. A comparison with other methods in several problems, including the well-known STAGGER dataset and the real-world electricity pricing domain, shows the good performance of TA-SVM in all tested situations.

Yeast-Plant Coupled Vector System for Identification of Nuclear Proteins

Nuclear proteins are involved in many critical biological processes within plant cells and, therefore, are in the focus of studies that usually begin with demonstrating that the protein of interest indeed exhibits nuclear localization. Thus, studies of plant nuclear proteins would be facilitated by a convenient experimental system for identification of proteins that are actively imported into the cell nucleus and visualization of their nuclear accumulation in vivo. To this end, we developed a system of vectors that allows screening for cDNAs coding for nuclear proteins in a simple genetic assay in yeast cells, and verification of nuclear accumulation in planta following one-step transfer and autofluorescent tagging of the identified clones into a multiple cloning site-compatible and reading frame-compatible plant expression vector. In a recommended third experimental step, the plant expression cassette containing the identified clone can be transferred, also by a one-step cloning, into a binary multigene expression vector for transient or stable coexpression with any other proteins.

An Algorithm for Three-way Data Analysis That Alternatively Minimizes Coupled Vector (COV) Resolution Error and PARAFAC Error

A novel algorithm, alternatively minimizing coupled vector (COV) resolution error and PARAFAC error algorithm, is proposed in this paper. This algorithm can overcome the problem of slow convergence and is insensitive to the estimation of component number, such problems are unavoidable while using the traditional parallel factors analysis (PARAFAC) algorithm. In other words, this algorithm is capable of improving the computing speed and providing accurate resolutions provided that the number of factors used in the computation is no less than that of the actual underlying ones. The characteristic performances were demonstrated with a novel fluorescence data array.

3nj-coefficients of su(1,1) as connection coefficients between orthogonal polynomials in n variables

In the tensor product of n+1 positive discrete series representations of su(1,1), a coupled basis vector can be described by a certain binary coupling tree. To every such binary coupling tree, polynomials Rl(k)(x) and Rl(k)(x) are associated. These polynomials are n-variable Jacobi and continuous Hahn polynomials, and are orthogonal with respect to a weight function. The connection coefficients expressing such a polynomial associated with a given binary coupling tree in terms of those polynomials associated with another binary coupling tree are proportional to 3nj-coefficients of su(1,1).

Permanence of Weakly Coupled Vector Fields

Let F1 , . . . , Fk be k dissipative vector fields on finite dimensional Euclidean spaces that preserve the skeleton of the positive orthant. Permanence of all sufficiently weak couplings of these vector fields corresponds to robust permanence of the uncoupled vector field F1 × ... × Fk. A sufficient condition for robust permanence of F1 × ... × Fk involving unsaturated Morse decompositions is provided. In the case of coupled food chain vector fields and coupled two-dimensional vector fields, this sufficient condition is shown to be necessary. As an illustration, these results are applied to weakly coupled logistic-Holling predator-prey systems.

Coupled scalar and vector P N methods for solving multigroup transport problems in multislab geometry

Coupled scalar and vector P N methods for solving multigroup transport problems in multislab geometry

Coupled scalar and vector P N methods for solving multigroup transport problems in multislab geometry

Abstract A spherical-harmonics ( P N ) method that incorporates and couples existent scalar and vector versions of the method is developed. The scalar (group-by-group) version of method is applied to the slowing-down energy range and the vector version to the thermal energy range. The link of coupling between these methods is the slowing-down source to the thermal groups which becomes available once the problems for all of the slowing-down groups are solved with the scalar method. The advantage of using the coupled P N method over the vector method when solving transport problems defined in extended energy range is demonstarted in this work.

Phase Conjugation by Degenerate Four-wave Mixing a General Two-dimensional Theory

A general two-dimensional theory of degenerate four-wave mixing is presented for arbitrary relative angle of incidence between the two sets of counterpropagating waves and for arbitrary polarizations of the waves. Coupled vector differential equations are derived and solved analytically under certain restricted conditions. It is shown that a linearly polarized object wave leads to a linearly polarized image wave but with a rotation of the polarization axis.