Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of WENO-extended two-fluid modelling in compressible multiphase flows

ABSTRACT The two-fluid modelling based on an advection-upwind-splitting-method (AUSM)-family numerical flux function, AUSM+-up, following the work by Chang and Liou [Journal of Computational Physics 2007;225: 840–873], has been successfully extended to the fifth order by weighted-essentially-non-oscillatory (WENO) schemes. Then its performance is surveyed in several numerical tests. The results showed a desired performance in one-dimensional benchmark test problems: Without relying upon an anti-diffusion device, the higher-order two-fluid method captures the phase interface within a fewer grid points than the conventional second-order method, as well as a rarefaction wave and a very weak shock. At a high pressure ratio (e.g. 1,000), the interpolated variables appeared to affect the performance: the conservative-variable-based characteristic-wise WENO interpolation showed less sharper but more robust representations of the shocks and expansions than the primitive-variable-based counterpart did. In two-dimensional shock/droplet test case, however, only the primitive-variable-based WENO with a huge void fraction realised a stable computation.
A Theoretical Approach on Face Recognition with Single Sample Per Class using CS-LBP and Gabor Magnitude and Phase

Objectives: To develop a theoretical model in order to understand how to recognize acquainted faces and the relationship between face processing acknowledgement with other aspects in face. Greater Performance is achieved in face recognition by local appearance based methods. Methods: The Centre Symmetric local binary pattern with gabor magnitude phase have been proposed in this paper to provide an expression, illumination and pose invariant in a single sample problem for face recognition approach with local spatial, scale and directional discriminate and low dimensional face representation based on features. The proposed methodology was compared with PCA and LBP. Findings: Gabor magnitude and Gabor phase tracks the texture boundaries of textured regions accurately. The evaluated Face features from CS-LBP and gabor magnitude phase has better performance. The Photometric descriptors are used in recent years, proven successful for computing regions which are in interest. In this approach the strength of SIFT descriptors are used in combination with LBP texture operator collectively called CS-LBP descriptor. This nullified illumination changes, strengthening flat image areas, and proficiency in computation. Improvements: For images with severe illumination variations SIFT descriptor is outperformed by CSLBP descriptor this was proved experimentally. The face recognition rate is increased by selective local texture feature Gabor Magnitude and Phase CS-LBP when compared with LBP method and Gabor filter.
Turbulent wake behind side-by-side flat plates: computational study of interference effects

The complex wake behind two side-by-side flat plates placed normal to the inflow direction has been explored in a direct numerical simulation study. Two gaps, $g=0.5d$ and $1.0d$ , were considered, both at a Reynolds number of 1000 based on the plate width $d$ and the inflow velocity. For gap ratio $g/d=0.5$ , the biased gap flow resulted in an asymmetric flow configuration consisting of a narrow wake with strong vortex shedding and a wide wake with no periodic near-wake shedding. Shear-layer transition vortices were observed in the wide wake, with characteristic frequency 0.6. For $g/d=1.0$ , two simulations were performed, started from a symmetric and an asymmetric initial flow field. A symmetric configuration of Kármán vortices resulted from the first simulation. Surprisingly, however, two different three-dimensional instability features were observed simultaneously along the span of the upper and lower plates. The spanwise wavelengths of these secondary streamwise vortices, formed in the braid regions of the primary Kármán vortices, were approximately $1d$ and $2d$ , respectively. The wake bursts into turbulence some $5d$ – $10d$ downstream. The second simulation resulted in an asymmetric wake configuration similar to the asymmetric wake found for the narrow gap $0.5d$ , with the appearance of shear-layer instabilities in the wide wake. The analogy between a plane mixing layer and the separated shear layer in the wide wake was examined. The shear-layer frequencies obtained were in close agreement with the frequency of the most amplified wave based on linear stability analysis of a plane mixing layer.
Existence of solutions for a non-isothermal Navier-Stokes-Allen-Cahn system with thermo-induced coefficients

This article aims to study the existence of solutions for a non-isothermalNavier-Stokes-Allen-Cahn system with thermo-induced coefficients.The system can be considered as a model describing the motion of a mixture of two viscous incompressible fluids with viscosity, thermal conductivity and interfacial thickness being temperature-dependent. This is a more general system than the previous ones considered in the literature, involving temperature dependence on all main coefficients. The strong non-linear couplings between those equations because of the temperature dependence brings new mathematical difficulties that only allows working in two dimensions.
Health-Related Quality of Life Improvement in Residential Care Facilities’ Elders through a Physical Activity Program

Epidemiological data show that people are living longer. This change carries opportunities for people but also the increased need of residential care facilities (RCFs) for older adults who require care that cannot be taken at home. The entry in RCFs is a delicate moment, in which maintaining autonomy is essential, along with promoting physical and mental health. The aim of this work was to evaluate the perceived quality of life (QoL)-related variables in RCFs residents at the baseline and after a 6-week physical activity (PA) intervention, to explore its possible role in improving the older adults’ perceived QoL. Overall, 108 residents belonging to seven Italian RCFs were enrolled. The Short-Form 12 (SF-12) questionnaire was used to assess the Physical Component Score (PCS) and Mental Component Score (MCS). To evaluate the residents’ attendance at PA, questions about weekly frequency of PA performed were included, analyzing data also considering the presence of a kinesiologist as a member of the RCF team. After baseline assessments, a 6-week PA intervention conducted by a kinesiologist was implemented in 2 RCFs and then the same variables considered at the baseline were reevaluated. As expected, the residents’ PCS resulted inversely related to their age. The individual PA attendance and the MCS were greater in older adults living in RCFs where a kinesiologist was present. Moreover, the higher adherence at individual and group PA was related to higher MCS only. After the 6-week PA intervention, a greater RCFs residents’ attendance at individual PA and a high frequency in group activities were shown. Furthermore, the program improved both MCS and PCS of the participants. Data suggest the strong impact of PA, structured by an expert, in increasing the health-related variables in RCFs’ older residents, especially the mental sphere of the perceived QoL. These beneficial effects were shown particularly with the twice-a-week program.
The fluctuation–dissipation theorem

Abstract A system in equilibrium will in general exhibit microscopic fluctuations about the equilibrium state. The fluctuation–dissipation theorem relates the spectrum of these fluctuations to a solution of the macroscopic equation describing the approach to equilibrium from a non-equilibrium state. The aim here is to show exactly what the theorem is and how it is to be used. An account of the quantum version of the theorem is given in three parts, depending on the solution of the macroscopic equation used to express the fluctuations: the relaxation function, the response function or the Green function for continuous systems. Each part is illustrated with an example: charge fluctuations in an RLC circuit for the first two and electric field fluctuations in vacuum for the last.
Stability Analysis and Voltage Control in the Power System Based on the Hybrid Automata Model

This paper proposes a systematic method using the hybrid automata (HA) model for supervisory management design of power systems to increase stability. The HA model monitors the behavior of the power system and derives a model from which the system’s stability is evaluated. In the modeling framework, the hybrid system formalism regulates continuous dynamics and discrete switching behavior. The proposed method extracts a system’s control graph, state space, and transfer function, facilitating voltage stability evaluation. A discrete event system (DES) is employed to manage disturbances resulting from load surges, generator outages, capacitor banks, and under-load tap changer (ULTC) transformers. The ULTC transformer and the capacitor bank are utilized to examine the feasibility and effectiveness of the proposed supervisory controller design approach in the presence of distributed generation (DG). Simulation results showed the effectiveness of the HA-based supervisory controller in improving the control and voltage stability, as demonstrated by the generator rotor angle and bus voltage responses, power system eigenvalues, and the stability theorem of linear switching systems. The proposed method provides a promising solution for managing power system stability.
A Dual-Reduct Approach to Generate Core Rules

In this paper, we propose a Dual-Reduct method to generate core rules from original data sets for decision making. We rank the rules by rule usefulness after the step of first reduct. Then we take the useful rules as condition attribute and construct another new decision table. After the step of second reduct we generate core rules from the new constructed decision table. In our approach the generation process is straightforward and objective. At the same time, our approach can significantly reduce the number of rules comparing to the traditional generation approach because we adopt rule usefulness as a measure of core rules. We also provide theoretical proofs and deductions. Our approach is proved to be feasible and effective in a production security system.
A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials

Almost all of the major applications in the specific Fields of Communication used a well-known device called Linear Feedback Shift Register. Usually LFSR functions in a Galois Field GF(2n), meaning that all the operations are done with arithmetic modulo n degree Irreducible and especially Primitive Polynomials. Storing data in Galois Fields allows effective and manageable manipulation, mainly in computer cryptographic applications. The analysis of functioning for Primitive Polynomials of 16th degree shows that almost all the obtained results are in the same time distribution.
parabolic bipolynomial fractional Dirichlet-Laplace problem

We derive existence results for a parabolic bipolynomial abstract and classical problems containing fractional powers of the Dirichlet-Laplace operator on a bounded domain, in the sense of the Stone-von Neumann operator calculus. The main tools are theorems on the existence and uniqueness of a weak solutions to an abstract problem, due to Friedman, and a general theorem on the equivalence of weak and strong solutions to some operator equation.
Decomposing global solar radiation into its diffuse and direct normal radiation

ABSTRACT This work presents a model based on Radial Basis Function (RBF) to estimate the diffused solar radiation (DSR) and direct normal radiation (DNR) fractions of solar radiation from global solar radiation in a semiarid area in Algeria based on a database measured between 2013 and 2015. The data has been collected at Applied Research Unit for Renewable Energies, (URAER) at Ghardaia city situated in the south of Algeria. The experimental results show that RBF model estimates DNR and DSR with high performance. The difference between the measured and the predicted values show a normalised Root Mean Square Error (nRMSE) of 0.033 and 0.065 for DNR and DSR, respectively. The obtained values of Determination Coefficient (R²) and Correlation Coefficient (R) are: 97.3%, 98.60%, respectively for DNR and 88.89%, 91.12% For DSR. However, the obtained results are very plausible and showed that RBF model estimates the DSR and DNR with good accuracy.
Decision making under risk: prospect theory approach and cognitive reflection test

Behavioral finance aims to understand the reasoning patterns of investors, constrained by emotional processes, and how they influence the decision-making. This motivated the work, whose purpose is to evaluate individuals' decision-making behavior under risk/uncertainty. The methodological procedure adopted exploratory research through a questionnaire grounded by the prospect theory and the cognitive reflection test with 329 responses from individuals in Portugal. The results are aligned with the Kahneman and Tversky's study. Moreover, they support different behaviors between non-investors and investors, and consistency between non-familiar and familiar with the behavioral finance. Although the ratio of correct answers to the cognitive reflection test is low, it is higher in the group of investors and the male gender. The combination of the results identified groups of high and low cognitive aptitude with different responses to the effects of the prospect theory. Overall, individuals' decision-making seems to be influenced by cognitive biases and the intuitive system. The findings are important because they highlight the importance of strategic financial literacy plans
Probing Nuclear Spin Bath Dynamics in a Self-Assembled Quantum Dot with Optically Detected NMR

This thesis describes experiments using optically detected nuclear magnetic resonance (NMR) techniques to probe and control the nuclear spin bath evolution in single self-assembled InGaAs quantum dots at large external magnetic fields. Strong non-resonant optical pumping is used to create spin bath polarisations & 60% in dynamic nuclear polarisation. Changes of the nuclear magnetisation under resonant radio frequency (rf) excitation of nuclear Zeeman transitions are detected in lowpower photoluminescence spectroscopy measurements of the hyperfine shifts of the exciton transition lines. Continuous wave rf excitation shows strong inhomogeneous broadening of the NMR spectra in a quantum dot owing to strain-induced quadrupolar interactions. Hahn echo measurements reveal comparatively long nuclear spin phase memory times T M,n ∼ 1 − 4 ms which are attributed to strong suppression of nuclear spin fluctuations due to the inhomogeneous broadening. Further progress towards full control of the spin bath evolution is made using a set of new multiple pulse NMR sequences combining features of Hahn and solid echoes. Measurements demonstrate that spin bath coherence times of up to ∼ 20 ms can be achieved within the experimental limitations of this work. Use of these NMR sequences in combination with electron/hole spin control techniques is expected to increase central spin coherence times significantly. The equilibrium spin bath coherence properties are probed using a novel, weaklyinvasive rf frequency comb NMR method. Frequency combs with varying tooth spacing are used to determine the homogeneous NMR lineshapes. The sensitivity of the homogeneous linewidths to fluctuations in the spin bath is used to probe the bath dynamics. Few-second-long spin flip-flop correlation times are revealed, demonstrating the potential of self-assembled InGaAs quantum dots to serve as a highly stable host system for spin qubits.
The V c – σ c relation in high and low surface brightness galaxies

We investigated the correlation between the circular velocity Vc and the central velocity dispersion of the spheroidal component σc. We measured Vc and σc for a sample of 42 high surface brightness disc galaxies (hereafter HSB), 11 low surface brightness spiral galaxies (hereafter LSB), and 24 elliptical galaxies characterized by flat rotation curves. We find that the Vc–σc correlation for the HSB and elliptical galaxies is consistent with a linear law out to velocity dispersions as low as σc≈ 50 km s−1. We analyzed the location of the elliptical galaxies with Vc based on H I data and LSB galaxies in the Vc–σc plane. These types of galaxies were not considered in previous studies. The data points corresponding to the elliptical galaxies with H I-based Vc follow the same relation as the HSB galaxies and the elliptical galaxies with Vc based on dynamical models. On the contrary, the LSB galaxies follow a different Vc–σc relation, since most of them show either higher Vc (or lower σc) with respect to their HSB counterparts. If the Vc–σc relation is equivalent to one between the mass of central supermassive black hole and that of the dark matter halo, these results suggest that the LSB galaxies host a supermassive black hole with a smaller mass compared to HSB galaxies. Moreover, the finding that at a given dark matter mass (as traced by Vc) the central σc of LSB galaxies is smaller than in their HSB counterparts, would argue against the relevance of baryon collapse in the radial density profile of the dark matter haloes of LSB galaxies.
On Comparison of Local Polynomial Regression Estimators for P=0 and P=1 in a Model Based Framework

This article discusses the local polynomial regression estimator for and the local polynomial regression estimator for in a finite population. The performance criterion exploited in this study focuses on the efficiency of the finite population total estimators. Further, the discussion explores analytical comparisons between the two estimators with respect to asymptotic relative efficiency. In particular, asymptotic properties of the local polynomial regression estimator of finite population total for are derived in a model based framework. The results of the local polynomial regression estimator for are compared with those of the local polynomial regression estimator for studied by Kikechi et al (2018). Variance comparisons are made using the local polynomial regression estimator for and the local polynomial regression estimator for which indicate that the estimators are asymptotically equivalently efficient. Simulation experiments carried out show that the local polynomial regression estimator outperforms the local polynomial regression estimator in the linear, quadratic and bump populations.
Incorporating couplings into collaborative filtering

Recommender Systems (RS) have been proposed to help users tackle information overload by suggesting potentially interesting items to users. A typical RS usually has a set of users and items with various rating preferences. The key task of RS is to predict an unknown rating or to recommend relevant items to a given user. Many existing recommendation methods such as Collaborative Filtering (CF), Content-based Recommendation, and Hybrid Filtering often assume that users, items and their attributes are identically and independently distributed. In the real world, however, these objects and their attributes are often coupled with each other through explicit or implicit relations. On one hand, users are often connected through social or trust relations, and items are interacted with linkage or citation relations. On the other hand, the attributes of users or items are also more or less coupled with each other. These dependent relations clearly demonstrate that the users, items, and their attributes in RS are not identically and independently distributed (non-IID), which is rarely considered in most existing recommendation methods. The non-IID RS have emerged with the consideration of non-IID characteristics into RS. A main challenge in non-IID RS is to analyse and model the coupling relations between users and between items. In this dissertation, we aim to improve recommendation effectiveness by incorporating the coupling relations into RS. The main contributions of the dissertation are summarized as follows: (1) We propose three novel neighbourhood-based CF methods including cou-
Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models

Gaussian processes are powerful nonparametric distributions over continuous functions that have become a standard tool in modern probabilistic machine learning. However, the applicability of Gaussian processes in the large-data regime and in hierarchical probabilistic models is severely limited by analytic and computational intractabilities. It is, therefore, important to develop practical approximate inference and learning algorithms that can address these challenges. To this end, this dissertation provides a comprehensive and unifying perspective of pseudo-point based deterministic approximate Bayesian learning for a wide variety of Gaussian process models, which connects previously disparate literature, greatly extends them and allows new state-of-the-art approximations to emerge. We start by building a posterior approximation framework based on Power-Expectation Propagation for Gaussian process regression and classification. This framework relies on a structured approximate Gaussian process posterior based on a small number of pseudo-points, which is judiciously chosen to summarise the actual data and enable tractable and efficient inference and hyperparameter learning. Many existing sparse approximations are recovered as special cases of this framework, and can now be understood as performing approximate posterior inference using a common approximate posterior. Critically, extensive empirical evidence suggests that new approximation methods arisen from this unifying perspective outperform existing approaches in many real-world regression and classification tasks. We explore the extensions of this framework to Gaussian process state space models, Gaussian process latent variable models and deep Gaussian processes, which also unify many recently developed approximation schemes for these models. Several mean-field and structured approximate posterior families for the hidden variables in these models are studied. We also discuss several methods for approximate uncertainty propagation in recurrent and deep architectures based on Gaussian projection, linearisation, and simple Monte Carlo. The benefit of the unified inference and learning frameworks for these models are illustrated in a variety of real-world state-space modelling and regression tasks.
Stokes parameters and DOA estimation of polarised sources with unknown number of sources

In this study, the problem of Stokes parameters and direction-of-arrival estimation of polarised sources is addressed based on the block-sparsity reconstruction, in case of an unknown number of sources. Since Stokes parameters have four components, the block-sparsity model of polarised sources is introduced by employing the difference coarray of the coprime array with cross-dipole sensors. In case of an unknown number of sources, a novel estimate approach is proposed by combining the block orthogonal matching pursuit (BOMP) algorithm with the deterministic maximum likelihood (DML). In the proposed approach, the DML test step and refining grid step are added in each iteration of BOMP to identify the number of sources and to reduce the estimation error incurred by the grid mismatch. This approach has low computational complexity and is suitable for both the completely polarised and partially polarised sources. Simulations are used to verify the performance of the proposed approach.