Miracle-dz/UniFAR-FTUs huggingface dataset:
Hydromagnetic Jeffery-Hamel Unsteady Flow of a Dissipative Non-Newtonian Fluid with Nonlinear Viscosity

In this paper, the unsteady two-dimensional Jeffery-Hamel flow of an incompressible non-Newtonian fluid, with nonlinear viscosity and skin friction, flowing through a divergent conduit in the presence of a constant applied magnetic field in the direction perpendicular to the fluid motion is studied. The resulting nonlinear partial differential equations governing this flow problem are reduced into a system of nonlinear ordinary differential equations using similarity transformation. The resulting boundary value problem is solved numerically using the collocation method and implemented in MATLAB using the bvp4c inbuilt function to obtain the numerical solution. The effect of varying Reynolds number Re, Hartmann number Ha, Prandtl number Pr , Eckert number Ec, and unsteadiness parameter λ on the fluid velocity, fluid temperature, skin-friction, and heat transfer rate are presented in form of graphs and tables; and are discussed. From the results, it is noted that there are Correspondence author: E-mail: francokech@gmail.com significant effects of pertinent parameters on the flow variables. This study provides useful information for engineering, technological, and industrial applications such as in hydromagnetic power generators. AMS subject classification: 26D15, 34K20, 39B82.
Data Visualization for Deep Neural Networks Based on Interlayer Canonical Correlation Analysis

In this paper, we develop data visualization methods which consider interlayer correlations in deep neural networks (DNN). In general, DNN naturally acquires multiple feature representations corresponding to their intermediate layers through their learning process. In order to understand relationships of those intermediate features which are strongly correlated with each other, we utilize canonical correlation analysis (CCA) to visualize the data distributions of diﬀerent feature layers in a common subspace. Our method can grasp movement of samples between consecutive layers in DNN. By using standard benchmark data sets, we show that our visualization results contain much information that typical visualization methods (such as principal component analysis) usually do not represent.
Two-step inertial method for solving split common null point problem with multiple output sets in Hilbert spaces

In this paper, an algorithm with two-step inertial extrapolation and self-adaptive step sizes is proposed to solve the split common null point problem with multiple output sets in Hilbert spaces. Weak convergence analysis are obtained under some easy to verify conditions on the iterative parameters in Hilbert spaces. Preliminary numerical tests are performed to support the theoretical analysis of our proposed algorithm.
Vibration Analysis of Thin Plate Structures Subjected to a Moving Force Using Frequency-Domain Spectral Element Method

A frequency-domain spectral element method (SEM) is proposed for the vibration analysis of thin plate structures subjected to a moving point force. The thin plate structures may consist of multiple rectangular thin plates with arbitrary boundary conditions that form multispan thin plate structures, such as bridges. The time-domain point force moving on a thin rectangular plate with arbitrary trajectory is transformed into a series of stationary point forces in the frequency domain. The vibration responses induced by the moving point force are then obtained by superposing all vibration responses excited by each stationary point force. For the vibration response of a specific stationary point force, the plate subjected to the specific stationary point force is represented by four spectral finite plate elements, which were developed in the authors’ previous work. The SEM-based vibration analysis technique is first presented for single-span thin plate structures and then extended to the multispan thin plate structures. The high accuracy and computational efficiency of the proposed SEM-based vibration analysis technique are verified by comparison with other well-known solution methods, such as the exact theory, integral transform method, finite element method, and the commercial finite element analysis package ANSYS.
Global dynamics of an epidemic model with relapse and nonlinear incidence

On the basis of a basic SIR epidemic model, we propose and study an epidemic model with nonlinear incidence. The model also incorporates many features of the recovered such as relapse and with/without immunity. A threshold dynamics is established, which is completely determined by the basic reproduction number. The global stability of the disease‐free equilibrium is proved by means of the fluctuation lemma. To prove the global stability of the endemic equilibrium, we need some novel techniques including the transformation of variables, the construction of a new type of Lyapunov functions, and the seeking of an appropriate positively invariant set of the model.
An Idiosyncratic Journey Beyond Mean Field Theory

This chapter contains sections titled: Introduction, Inference, Some Models from Statistical Physics, The Gibbs Free Energy, Mean Field Theory: The Variational Approach, Correcting Mean Field Theory, The Bethe Approximation, Belief Propagation, Kikuchi Approximations and Generalized Belief Propagation, Acknowledgments, References
The role of complex magnetic topologies on stellar spin-down

Abstract The rotational braking of magnetic stars through the extraction of angular momentum by stellar winds has been studied for decades, leading to several formulations. We recently demonstrated that the dependency of the braking law on the coronal magnetic field topology can be taken into account through a simple scalar parameter: the open magnetic flux. The Zeeman-Doppler Imaging technique has brought the community a reliable and precise description of the surface magnetic field of distant stars. The coronal structure can then be reconstructed using a potential field extrapolation, a technique that relies on a source surface radius beyond which all field lines are open, thus avoiding a computationally expensive MHD simulations. We developed a methodology to choose the best source surface radius in order to estimate open flux and magnetic torques. We apply this methodology to five K-type stars from 25 to 584 Myr and the Sun, and compare the resulting torque to values expected from spin evolution models.
A Robust Backstepping Sensorless Control for Interior Permanent Magnet Synchronous Motor Using a Super‐Twisting Based Torque Observer

In order to achieve high‐performance speed regulation for sensorless interior permanent magnet synchronous motors (IPMSMS), a robust backstepping sensorless control is presented in this paper. Firstly, instead of a real mechanical sensor, a robust terminal sliding mode observer is used to provide the rotor position. Then, a new super‐twisting algorithm (STA) based observer is designed to obtain estimates of load torque and speed. The proposed observer ensures finite‐time convergence, maintains robust to uncertainties, and eliminates the common assumption of constant or piece‐wise constant load torque. Finally, a sensorless scheme is designed to realize speed control despite parameter uncertainties, by combining the robust backstepping control with sliding mode actions and the presented sliding mode observers. The stability of the observer and controller are verified by using Lyapunov's second method to determine the design gains. Simulation results show the effectiveness of the proposed approach.
Characterizing the galaxy populations within different environments in the RCS2319 supercluster

Abstract We present the results of a multi-wavelength photometric study of the high redshift supercluster RCS2319+00. RCS2319+00 is a high-redshift (z ~ 0.9) supercluster comprising three spectroscopically confrmed cluster cores discovered in the Red Sequence Cluster Survey (RCS) (Gladders & Yee 2005). Core proximities and merger rates estimate coalescence into a 1015 M ⊙ cluster by z ~ 0.5 (Gilbank et al. 2008). Spectroscopic studies of the system have revealed over 300 supercluster members located in the cores and several infalling groups (Faloon et al. 2013). RCS2319 presents a diverse range of dynamical systems and densities making it an ideal laboratory in which to study the effects of environment on galaxy properties. Imaging in optical and near infrared (griz' from MegaCam, JK s from WIRCam, both at CFHT), as well as 3.6 μm and 4. 5μm from IRAC have enabled the assembly of a large photometric catalogue. Coupled with an extensive spectroscopic survey (Faloon et al. 2013) providing nearly 2400 redshifts across the field, photometric redshifts were determined using the template fitting code EAZY (Brammer et al. 2008). Nearly 80 000 photometric redshifts were measured providing a sample of nearly 3000 cluster members. To investigate effects of global environment, analysis was done utilizing a friend-of-friends group finding algorithm identifying several large and small infalling groups along with the three cluster cores. The cores are found to be dominated by massive, red galaxies and the field galaxies are populated by low mass, blue galaxies, as is the case in the local universe. Interestingly, the large groups exhibit intermediate properties between field and core populations, suggesting possible pre-processing as they are being accreted into the core halos. Relative fifth-nearest neighbour overdensity, log(1+δ5), is used as a proxy for local environment to investigate environmental dependence on galaxy colour. While there is an overall dependence of colour on local density, when controlled for stellar mass the dependence largely disappears. Indeed, galaxy mass is the dominant factor in determining colour, with local density a secondary effect only noticeable in lower mass galaxies at the 3 σ level for both colour and red fraction. RCS2319+00 presents a rare opportunity to probe many different densities and environments all located within the same object. We're able to investigate how galaxy evolution is affected by the environment, from field galaxies to infalling to groups to dense cluster cores, as well as the different density regions within each environment.
AI versus Human: Rethinking the Role of Agent Knowledge in Consumers’ Coping Mechanism Related to Influencer Marketing

Abstract Despite the exponential growth of influencer marketing, little research has examined how consumers’ coping mechanism differs with two types of influencers (AI vs. human). Drawing on the persuasion knowledge model, action identification theory, and construal-level theory, we redefined agent knowledge as a core belief that shapes different autonomy perceptions of the influencers. Employing two single-factor between-subject experiments, Study 1 revealed that consumers perceived AI (vs. human) as a heteronomous agent, which guides a low-level construal devoid of perceived superordinate intentions. Building on these findings, Study 2 showed that consumers’ agent knowledge of AI (vs. human) negatively affected attitudinal and behavioral changes, being sequentially mediated by source credibility and perceived persuasion effectiveness, respectively. Theoretical and practical implications are discussed.
In-orbit verification of microwave humidity sounder spectral channels coregistration using the moon

Abstract. In-orbit verification of the coregistration of channels in a scanning microwave or infrared radiometer can in principle be done during normal in-orbit operation, using the regular events of lunar intrusion in the instrument cold space calibration view. A technique of data analysis based on best fit of data across lunar intrusions has been used to check the mutual alignment of the spectral channels of the microwave humidity sounder (MHS) instrument. MHS is a cross-track scanning radiometer in the millimeter-wave range flying on EUMETSAT and NOAA polar satellites, used operationally for the retrieval of atmospheric parameters in numerical weather prediction and nowcasting. This technique does not require any special operation or maneuver and only relies on the analysis of data from the nominal scanning operation. The coalignment of sounding channels and window channels can be evaluated by this technique, which would not be possible using Earth landmarks, due to the absorption effect of the atmosphere. The analysis reported shows an achievable accuracy below 0.5 mrad against a beam width at 3 dB and spatial sampling interval of about 20 mrad. In-orbit results for the MHS instrument on Metop-B are also compared with the prelaunch instrument characterization, showing a good correlation.
The Role of Customer Loyalty in Mediating Corporate Social Responsibility and the Company's Financial Performance

ABSTRACK Purpose: This study aims to examine and analyze the mediating effect of customer loyalty on the influence of Corporate Social Responsibility toward financial performance.Design/methodology/approach: This research is quantitative study using secondary data taken from Indonesia Stock Exchange 2014-2017. The data analysis technique used is the WarpPLS 6.0 program which is carried out in two testing stages; the direct and indirect impacts of Corporate Social Responsibility variables on financial performance.Findings: The result indicates that customer loyalty variable partially mediates the effect of Corporate Social Responsibility on financial performance. It means customer loyalty does not fully mediate the effect of Corporate Social Responsibility on financial performance.Research limitation: CSR measurement uses an index that calculates CSR based on the quantity not the quality of implementation. Practical implications: This researchprovides an illustration to the company that the implementation of proper and serious CSR has an impact on growing the company's financial performance.Originality/value: This research is a development from previous research by adding consumer loyalty as a mediating variable.Paper type: This is categorized as a research paper. Keywords: customer loyalty, Corporate Social Responsibility, financial performance
Informing a risk prediction model for binary outcomes with external coefficient information

We consider a situation where rich historical data are available for the coefficients and their standard errors in an established regression model describing the association between a binary outcome variable Y and a set of predicting factors X, from a large study. We would like to utilize this summary information for improving estimation and prediction in an expanded model of interest, Y|X,B. The additional variable B is a new biomarker, measured on a small number of subjects in a new data set. We develop and evaluate several approaches for translating the external information into constraints on regression coefficients in a logistic regression model of Y|X,B. Borrowing from the measurement error literature we establish an approximate relationship between the regression coefficients in the models Pr(Y=1|X,β), Pr(Y=1|X,B,γ) and E(B|X,θ) for a Gaussian distribution of B. For binary B we propose an alternative expression. The simulation results comparing these methods indicate that historical information on Pr(Y=1|X,β) can improve the efficiency of estimation and enhance the predictive power in the regression model of interest Pr(Y=1|X,B,γ). We illustrate our methodology by enhancing the high grade prostate cancer prevention trial risk calculator, with two new biomarkers: prostate cancer antigen 3 and TMPRSS2:ERG.
Gravitational disturbances generated by the Sun, Phobos and Deimos in orbital maneuvers around Mars with automatic correction of the semi-major axis

The objective of this work is to analyze orbital maneuvers of a spacecraft orbiting Mars, considering disturbance effects due to the gravitational attraction of the Sun, Phobos and Deimos, beyond the disturbances due to the gravitational potential of Mars. To simulate the trajectory, constructive aspects of the propulsion system were considered. Initially ideal thrusters, capable of applying infinite magnitude of the thrust, were used. Thus, impulsive optimal maneuvers were obtained by scanning the solutions of the Lambert's problem in order to select the maneuver of minimum fuel consumption. Due to the impossibility of applying an impulse, the orbital maneuver must be distributed in a propulsive arc around the position of the impulse given by the solution of the Lambert's problem. However the effect of the propulsive arc is not exactly equivalent to the application of an impulse due to the errors in magnitude and direction of applied thrust. Therefore, the influence of the thrusters’ capacity in the trajectory was evaluated for a more realistic model instead of the ideal case represented by the impulsive approach. Beyond the evaluation of the deviation in the orbital path, was considered an automatic correction of the semi-major axis using continuous low thrust controlled in closed loop to minimize the error in the trajectory after the application of the main thrust.
Extractive Clinical Question-Answering With Multianswer and Multifocus Questions: Data Set Development and Evaluation Study

Extractive question-answering (EQA) is a useful natural language processing (NLP) application for answering patient-specific questions by locating answers in their clinical notes. Realistic clinical EQA can yield multiple answers to a single question and multiple focus points in 1 question, which are lacking in existing data sets for the development of artificial intelligence solutions. This study aimed to create a data set for developing and evaluating clinical EQA systems that can handle natural multianswer and multifocus questions. We leveraged the annotated relations from the 2018 National NLP Clinical Challenges corpus to generate an EQA data set. Specifically, the 1-to-N, M-to-1, and M-to-N drug-reason relations were included to form the multianswer and multifocus question-answering entries, which represent more complex and natural challenges in addition to the basic 1-drug-1-reason cases. A baseline solution was developed and tested on the data set. The derived RxWhyQA data set contains 96,939 QA entries. Among the answerable questions, 25% of them require multiple answers, and 2% of them ask about multiple drugs within 1 question. Frequent cues were observed around the answers in the text, and 90% of the drug and reason terms occurred within the same or an adjacent sentence. The baseline EQA solution achieved a best F1-score of 0.72 on the entire data set, and on specific subsets, it was 0.93 for the unanswerable questions, 0.48 for single-drug questions versus 0.60 for multidrug questions, and 0.54 for the single-answer questions versus 0.43 for multianswer questions. The RxWhyQA data set can be used to train and evaluate systems that need to handle multianswer and multifocus questions. Specifically, multianswer EQA appears to be challenging and therefore warrants more investment in research. We created and shared a clinical EQA data set with multianswer and multifocus questions that would channel future research efforts toward more realistic scenarios.
Yeast biocontrol against green mold in pear orange postharvest

The objective of this study was to evaluate the efficiency of the enzyme β-1, 3-glucanase and the mechanisms of action produced by yeasts in the biological control ofgreen mold in post-harvest oranges of the pear variety. The Fully Casualized Delineation(FCD) was used. The results were submitted to analysis of variance (ANOVA), in triplicate,compared with Tukey's test at a 5% probability level (p < 0.05), using the statisticalsoftware Sisvar. It was found that the killer activity was observed in 85.7% of the yeastsevaluated. The yeast Candida peltata URM4681 showed better production of β-1.3-glucanase, between 1.071 and 5.422 µg mL -1 . A factorial delineation 2 3 was used in amedium composed of yeast extract, soybean extract and meat extract to increaseenzymatic production, reaching an increase of 4.351 times comparing the assay of lowerand higher activity. The in situ assays showed a reduction in the number of infected fruitstreated with the enzymatic extract, being on average 78.5% when compared with thecontrol treatment (distilled water). As for sporulation, the enzymatic extract, which had anaverage of 8.4 spores, did not differ statistically from the IMZ and TBZ fungicides, both in apreventive and curative manner. Regarding the severity of the disease, it was noted thatthe fruits treated with enzymatic extract, had an average damage of 7.57 %, also notdiffering statistically from fungicides, both preventive and curative. Our studies suggestthat C. peltata URM4681, makes this a good candidate for biocontrol against green mold.
Particle Accelerator Physics Basic Principles And Linear Beam Dynamics

Thank you very much for downloading particle accelerator physics basic principles and linear beam dynamics. As you may know, people have look hundreds times for their chosen books like this particle accelerator physics basic principles and linear beam dynamics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious virus inside their laptop.
Adaptive Grid Refinement for Two-Phase Offshore Applications

In the past, the CFD simulation method ComFLOW has been successfully applied in a wide range of offshore applications, involving wave simulations and impact calculations. In many of these calculations the area of interest comprises a small part of the domain and remains fixed in time, which allows for efficient grid refinement by means of grid stretching or static local refinement. However, when trying to accurately resolve the surface dynamics and kinematics of irregular and breaking waves, the resolution requirements are strongly time-dependent and difficult to predict in advance. Efficient grids can only be obtained by means of time-adaptive refinement. A Cartesian block-based refinement approach is followed which allows for efficient grid adaptation, with moderate overhead. An array-based data structure is employed which exploits the semi-structured nature of the Cartesian block grid. Currently we are testing the method with the simulation of lifeboat drops in regular and irregular wave conditions. This poses several challenges such as accurately imposing the incoming waves and modifying the absorbing boundary conditions to support two-phase flow. To reduce the wall-clock time, the simulation method has been parallelized.
An Ensemble of Weighted 1-Nearest Neighbor (1-NN) Classifiers Based on Bimodal Perturbation

Ensemble learning algorithms train multiple component learners and then combine their predictions. Since the generalization ability of an ensemble could be significantly better than that of a single learner, ensemble learning has been a hot topic during the past years. Among the ensemble methods, two methods have been widely used: bagging [2] and random subspace method [4]. In bagging, many different training sets are generated by resampling and a base classifier is trained on each set. On the other hand, in the random subspace method, many different feature subsets are randomly chosen for producing component classifiers.