On stratified variable thermal conductivity stretched flow of Walter-B material subject to non-Fourier flux theory

The objective here is to examine the characteristics of non-Fourier flux theory in flow induced by a nonlinear stretched surface. Constitutive expression for an incompressible Walter-B liquid is taken into account. Consideration of thermal stratification and variable thermal conductivity characterizes the heat transfer process. The concept of boundary layer is adopted for the formulation purpose. Modern methodology for the computational process is implemented. Surface drag force is computed and discussed. Salient features of significant variables on the physical quantities are reported graphically. It is explored that velocity is enhanced for a larger ratio of rate constants. The increasing values of thermal relaxation factor correspond to less temperature.

     

Dynamics of a neuron exposed to integer- and fractional-order discontinuous external magnetic flux

We propose a modified Fitzhugh-Nagumo neuron (MFNN) model. Based on this model, an integer-order MFNN system (case A) and a fractional-order MFNN system (case B) were investigated. In the presence of electromagnetic induction and radiation, memductance and induction can show a variety of distributions. Fractional-order magnetic flux can then be considered. Indeed, a fractional-order setting can be acceptable for non-uniform diffusion. In the case of an MFNN system with integer-order discontinuous magnetic flux, the system has chaotic and non-chaotic attractors. Dynamical analysis of the system shows the birth and death of period doubling, which is a sign of antimonotonicity. Such a behavior has not been studied previously in the dynamics of neurons. In an MFNN system with fractional-order discontinuous magnetic flux, different attractors such as chaotic and periodic attractors can be observed. However, there is no sign of antimonotonicity.

     

Anomaly detection in heterogeneous bibliographic information networks using co-evolution pattern mining

Detecting evolution-based anomalies have emerged as an effective research topic in many domains, such as social and information networks, bioinformatics, and diverse security applications. However, the majority of research has focused on detecting anomalies using evolutionary behavior among objects in a network. The real-world networks are omnipresent, and heterogeneous in nature, while, in these networks, multiple types of objects co-evolve together with their attributes. To understand the anomalous co-evolution of multi-typed objects in a heterogeneous information network (HIN), we need an effective technique that can capture abnormal co-evolution of multi-typed objects. For example, detecting co-evolution-based anomalies in the heterogeneous bibliographic information network (HBIN) can depict better the object-oriented semantics than just scrutinizing the co-author or citation network alone. In this paper, we introduce the novel notion of a co-evolutionary anomaly in the HBIN, detect anomalies using co-evolution pattern mining (CPM), and study how multi-typed objects influence each other in their anomalous declaration by following a special type of HIN called star networks. The influence of three pre-defined attributes namely paper-count, co-author, and venue over target objects is measured to detect co-evolutionary anomalies in HBIN. The anomaly scores are calculated for each 510 target objects and individual influence of attributes is measured for two top target objects in case-studies. It is observed that venue has the most influence on the target objects discussed as case studies, however, about the rest of anomalies in the list, the most anomalous influential attribute could be rather different than the venue. Indeed, the CABIN algorithm constructs the way to find out the most influential attributes in co-evolutionary anomaly detection. Experiments on bibliographic dataset validate the effectiveness of the model and dominance of the algorithm. The proposed technique can be applied on various HINs such as Facebook, Twitter, Delicious to detect co-evolutionary anomalies.     

Base fluids with CNTs as nanoparticles through non-Darcy porous medium in convectively heated flow: A comparative study

Comparative analysis for flow of CNTs nanofluids is discoursed in the presence of non-Darcy porous medium. The consequences of homogeneous/heterogeneous process and heat transfer through convection are employed. The flow induced is due to non-linear stretching sheet of variable thickness. The bottom of the variable thickness sheet is heated by convective processes from a heated fluid. The velocity, temperature and concentration functions are formulated for the stretched flow problem. Convergence control variables and square residual errors for series solutions are obtained through OHAM (Optimal Homotopy Analysis Method). Biot number corresponds to larger temperature distribution in case of MWCNT than SWCNT. Comparison of nanoparicles SWCNT and MWCNT for the CNTs nanofluid fluids is highlighted. Water and engine oil CNTs fluids have higher magnitude of Nusselt number when compared with kerosene oil CNT fluid. The heat transfer rate in the presence of MWCNT is higher than SWCNT. Comparison of present study with previous published data is made. The outcomes are found in favorable agreement.     

Moving data window gradient‐based iterative algorithm of combined parameter and state estimation for bilinear systems

The combined iterative parameter and state estimation problem is considered for bilinear state‐space systems with moving average noise in this paper. There are the product terms of state variables and control variables in bilinear systems, which makes it difficult for the parameter and state estimation. By designing a bilinear state estimator based on the Kalman filtering, the states are estimated using the input‐output data. Furthermore, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. A state estimator‐based MDW gradient‐based iterative (MDW‐GI) algorithm is proposed to estimate the unknown states and parameters jointly. Moreover, given the extended gradient‐based iterative (EGI) algorithm as a comparison, the MDW‐GI algorithm can reduce the impact of noise to parameter estimation and improve the parameter estimation accuracy. The numerical simulation examples validate the effectiveness of the proposed algorithm.     

Recursive Identification Methods for Multivariate Output-error Moving Average Systems Using the Auxiliary Model

This paper studies the parameter identification problems of multivariate output-error moving average systems. An auxiliary model based extended stochastic gradient algorithm and based recursive extended least squares algorithm are proposed for estimating the parameters of the multivariate output-error moving average systems. By using the multi-innovation identification theory, an auxiliary model based multi-innovation extended stochastic gradient algorithm is derived for improving the parameter estimation accuracy. Finally, the simulation results indicate that the proposed algorithms can work well.     

Magnetohydrodynamic stagnation point flow of third-grade liquid toward variable sheet thickness

Neural Computing and Applications - The current study examines the boundary layer stagnation point flow of third-grade fluid toward a stretching surface with variable thickness. Electrically...     

Recursive least squares identification methods for multivariate pseudo-linear systems using the data filtering

This paper concerns the parameter identification methods of multivariate pseudo-linear autoregressive systems. A multivariate recursive generalized least squares algorithm is presented as a comparison. By using the data filtering technique, a multivariate pseudo-linear autoregressive system is transformed into a filtered system model and a filtered noise model, and a filtering based multivariate recursive generalized least squares algorithm is developed for estimating the parameters of these two models. The proposed algorithm achieves a higher computational efficiency than the multivariate recursive generalized least squares algorithm, and the simulation results prove that the proposed method is effective.     

Data interpretation for spectral sensors with correlated bands

New classes of spectral sensors are emerging that have significant overlap in the band spectral response functions. While conventional sensors such as the Multispectral Thermal Images (MTI) or Landsat may have responses with a few percent overlap between adjacent bands, some of the emerging sensors can have more than 50% correlation among all spectral bands. The traditional geometrical models used to describe spectral data fail when such high levels of correlation exist. In this paper we present a generalized geometrical model that relies on functional analysis. We define a sensor space and a scene space that can be used to characterize the suitability of a sensor for a particular spectral sensing task. We demonstrate that classifiers based on first-order distance and angle metrics fail for sensors with highly correlated bands unless appropriate preprocessing is carried out. We further show that second-order statistical classifiers are largely immune to many of the problems introduced by the correlated band responses.