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Browsing by Author "Hu, Rui"

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    Blended learning versus face-to-face learning in an undergraduate nursing health assessment course: a quasi-experimental study
    (2021) Berga, Keri-Ann; Vadnais, Elisha; Nelson, Jody; Johnston, Sharon; Buro, Karen; Hu, Rui
    Background: Blended learning, which integrates face-to-face and online instruction, is increasingly being adopted. A gap remains in the literature related to blended learning, self-efficacy, knowledge and perceptions in undergraduate nursing. Objectives: To investigate outcomes of self-efficacy, knowledge and perceptions related to the implementation of a newly blended course. Design: This was a quasi-experimental pre-post test design. Setting: This study was conducted at an undergraduate university in Alberta, Canada. Participants: A total of 217 second-year undergraduate nursing students participated and 187 participants completed all study components. Methods: A convenience sampling method was used. Data were collected at the start and end of the semesters. Data were analyzed using descriptive and inferential statistics using R(3.4.3) and R-Studio(1.1.423). Results: There were no significant differences in self-efficacy scores between groups or in the pre-post surveys (p > 0.100) over time. There was no significant difference in knowledge between the blended online and face-to-face groups (p > 0.100). For students in the blended course, perceptions of the online learning environment were positive. Conclusion: Blended learning has the potential to foster innovative and flexible learning opportunities. This study supports continued use and evaluation of blended learning as a pedagogical approach.
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    Embeddings of function spaces via the Caffarelli–Silvestre extension, capacities and Wolff potentials
    (2022) Li, Pengtao; Shi, Shaoguang; Hu, Rui; Zhai, Zhichun
    Let Pαf (x, t) be the Caffarelli–Silvestre extension of a smooth function f (x) Rn → Rn+1+ := Rn × (0, ∞). The purpose of this article is twofold. Firstly, we want to characterize a nonnegative measure μ on Rn+1 + such that f (x) → Pαf (x, t) induces bounded embeddings from the Lebesgue spaces Lp(Rn) to the Lq (Rn+1 + , μ). On one hand, these embeddings will be characterized by using a newly introduced Lp−capacity associated with the Caffarelli–Silvestre extension. In doing so, the mixed norm estimates of Pαf (x, t), the dual form of the Lp−capacity, the Lp−capacity of general balls, and a capacitary strong type inequality will be established, respectively. On the other hand, when p > q >1, these embeddings will also be characterized in terms of the Hedberg–Wolff potential of μ. Secondly, we characterize a nonnegative measure μ on Rn+1 + such that f (x) → Pαf (x, t) induces bounded embeddings from the homogeneous Sobolev spaces ̇W β,p(Rn) to the Lq (Rn+1+ , μ) in terms of the fractional perimeter of open sets for endpoint cases and the fractional capacity for general cases.
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    Fractional Besov trace/extension-type inequalities via the Caffarelli–Silvestre extension
    (2022) Li, Pengtao; Hu, Rui; Zhai, Zhichun
    Let u(·, ·) be the Caffarelli–Silvestre extension of f . The first goal of this article is to establish the fractional trace-type inequalities involving the Caffarelli–Silvestre extension u(·, ·) of f . In doing so, firstly, we establish the fractional Sobolev/logarithmic Sobolev/Hardy trace inequalities in terms of ∇(x, t)u(x, t). Then, we prove the fractional anisotropic Sobolev/logarithmic Sobolev/Hardy trace inequalities in terms of ∂tu(x, t) or (−Δ)−γ /2u(x, t) only. Moreover, based on an estimate of the Fourier transform of the Caffarelli–Silvestre extension kernel and the sharp affine weighted L p Sobolev inequality, we prove that the H˙ −β/2(Rn) norm of f (x) can be controlled by the product of the weighted L p-affine energy and the weighted L p-norm of ∂tu(x, t). The second goal of this article is to characterize non-negative measures μ on Rn+1+ such that the embeddings u(·, ·)Lq0,p0 μ (Rn+1) f Λ˙ p,q β (Rn ) hold for some p0 and q0 depending on p and q which are classified in three different cases: (1) p = q ∈ (n/(n + β), 1]; (2) (p, q) ∈ (1, n/β) × (1,∞); (3) (p, q) ∈ (1, n/β) × {∞}. For case (1), the embeddings can be characterized in terms of an analytic condition of the variational capacity minimizing function, the iso-capacitary inequality of open balls, and other weak-type inequalities. For cases (2) and (3), the embeddings are characterized by the iso-capacitary inequality for fractional Besov capacity of open sets.
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    A modified susceptible-infected-recovered epidemiological model
    (2022) Bica, Ion; Zhai, Zhichun; Hu, Rui
    Objectives This paper proposes an infectious disease model incorporating two new model compartments, hospitalization, and intensive care unit. Methods The model dynamics are analyzed using the local and global stability theory of nonlinear systems of ordinary differential equations. For the numerical simulations, we used the Rosenbrock method for stiff initial value problems. We obtained numerical simulations using MAPLE software. The returned MAPLE procedure was called only for points inside the range on which the method evaluated the numerical solution of the system with specied initial conditions. Results We proposed a new model to describe the dynamics of microparasitic infections. Numerical simulations revealed that the proposed model fitted with the expected behaviour of microparasitic infections with "acute epidemicity." The numerical simulations showed consistency in the behaviour of the system. Conclusions The model proposed has "robust" dynamics, supported by the global stability of its endemic state and the consistency of the numerical simulations regarding the model's time evolution behaviour. The introduction of the hospitalization and intensive care unit compartments in the proposed model revealed that it is essential to consider such policies in the case of "acute epidemicity" of microparasitic infections.
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    Robust design for the estimation of a threshold probability
    (2018) Hu, Rui
    We consider the construction of robust sampling designs for the estimation of threshold probabilities in spatial studies. A threshold probability is a probability that the value of a stochastic process at a particular location exceeds a given threshold. We propose designs such that the estimation of threshold probabilities is robust to two possible model uncertainties: misspecified regression responses and covariance structures. To address these two uncertainties of this stochastic process, we average the mean squared error of the predicted values relative to the true values, over all possible covariance structures in a neighbourhood of the experimenter's nominal choice, and then maximize it over a neighbourhood of the fitted model. Finally, the maximum is minimized to obtain the robust designs.
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    Robust optimal design when missing data happen at random
    (2023) Hu, Rui; Bica, Ion; Zhai, Zhichun
    In this article, we investigate the robust optimal design problem for the prediction of response when the fitted regression models are only approximately specified, and observations might be missing completely at random. The intuitive idea is as follows: We assume that data are missing at random, and the complete case analysis is applied. To account for the occurrence of missing data, the design criterion we choose is the mean, for the missing indicator, of the averaged (over the design space) mean squared errors of the predictions. To describe the uncertainty in the specification of the real underlying model, we impose a neighborhood structure on the regression response and maximize, analytically, the Mean of the averaged Mean squared Prediction Errors (MMPE), over the entire neighborhood. The maximized MMPE is the “worst” loss in the neighborhood of the fitted regression model. Minimizing the maximum MMPE over the class of designs, we obtain robust “minimax” designs. The robust designs constructed afford protection from increases in prediction errors resulting from model misspecifications.
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    Sound signature detection by probability density function of normalized amplitudes
    (2019) Bica, Ion; Zhai, Zhichun; Hu, Rui; Melnyk, Mickey H.
    In this paper, we propose to use the probability density function of normalized amplitudes (PDFNA) to detect distinctive sounds in classical music. Based on data sets generated by waveform audio files (WAV files), we use the kernel method to estimate the probability density function. The confidence interval of the kernel density estimator is also given. In order to illustrate our method, we used the audio data collected from recordings of three composers; Johann Sebastian Bach (1686-1750), Ludwig van Beethoven (1770-1827) and Franz Schubert (1797-1828).
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    Unexplored territory: measuring self-efficacy, student knowledge and satisfaction in a blended health assessment in nursing course
    (2017) Berga, Keri-Ann; Vadnais, Elisha; Nelson, Jody; Johnston, Sharon; Mitchell, Agnes; Hu, Rui; Olaiya, Bo
    Notable gaps exist within the literature on Blended Learning (BL) in undergraduate nursing curricula (McCutcheon et al., 2015), and research has suggested that newly developed BL modules should be tested repeatedly to identify differences and facilitate the development of effective BL in nursing education (Hsu & Hsieh, 2011). This current research further explores BL in the undergraduate nursing context, through a comparison of BL and traditional face-to-face learning in health assessment education.