Application of the Theory of Planned Behavior to Understand Traveler Behavior Affected by COVID-19: A Qualitative Study

With the pandemic still intact, understanding how COVID-19 affects traveler behaviors may be critical to the tourism industry. While much research has been conducted related to COVID-19’s impact on travel, little research has examined the underlying determinants of changes in behavior, and whether these changes will continue over time. This qualitative study was grounded in the Theory of Planned Behavior (TPB), Plog\u27s travel model, and the concept of constraint negotiation, to better understand the impact of COVID-19 on travel behavior through in-depth interviews. Preliminary results found that travelers\u27 attitudes, social norms, and perceived behavioral control were influenced by COVID-19, and the degree of influence may be related to their tendency toward being allocentric or psychocentric. Travelers\u27 ability to negotiate constraints was also found to be related to their attitudes, which extends the theory of TPB. This study also provides suggestions for tourism companies’ marketing strategies post-pandemic

Direct Growth of Copper Oxide Films on Ti Substrate for Nonenzymatic Glucose Sensors

Copper oxide (CuO) films directly grown on Ti substrate have been successfully prepared via a hydrothermal method and used to construct an amperometric nonenzymatic glucose sensor. XRD and SEM were used to characterize the samples. The electrochemical performances of the electrode for detection of glucose were investigated by cyclic voltammetry and chronoamperometry. The CuO films based glucose sensors exhibit enhanced electrocatalytic properties which show very high sensitivity (726.9 μA mM−1 cm−2), low detection limit (2 μM), and fast response (2 s). In addition, reproducibility and long-term stability have been observed. Low cost, convenience, and biocompatibility make the CuO films directly grown on Ti substrate electrodes a promising platform for amperometric nonenzymatic glucose sensor

Meshless deformable models for LV motion analysis

We propose a novel meshless deformable model for in vivo cardiac left ventricle (LV) 3D motion estimation. As a relatively new technology, tagged MRI (tMRI) provides a direct and noninvasive way to reveal local deformation of the myocardium, which creates a large amount of heart motion data which requiring quantitative analysis. In our study, we sample the heart motion sparsely at intersections of three sets of orthogonal tagging planes and then use a new meshless deformable model to recover the dense 3D motion of the myocardium temporally during the cardiac cycle. We compute external forces at tag intersections based on tracked local motion and redistribute the force to meshless particles throughout the myocardium. Internal constraint forces at particles are derived from local strain energy using a Moving Least Squares (MLS) method. The dense 3D motion field is then computed and updated using the Lagrange equation. The new model avoids the singularity problem of mesh-based models and is capable of tracking large deformation with high efficiency and accuracy. In particular, the model performs well even when the control points (tag intersections) are relatively sparse. We tested the performance of the meshless model on a numerical phantom, as well as in vivo heart data of healthy subjects and patients. The experimental results show that the meshless deformable model can fully recover the myocardium motion in 3D. 1

Determining anomalies in a semilinear elliptic equation by a minimal number of measurements

We are concerned with the inverse boundary problem of determining anomalies associated with a semilinear elliptic equation of the form $-\Delta u+a(\mathbf x, u)=0$, where $a(\mathbf x, u)$ is a general nonlinear term that belongs to a H\"older class. It is assumed that the inhomogeneity of $f(\mathbf x, u)$ is contained in a bounded domain $D$ in the sense that outside $D$, $a(\mathbf x, u)=\lambda u$ with $\lambda\in\mathbb{C}$. We establish novel unique identifiability results in several general scenarios of practical interest. These include determining the support of the inclusion (i.e. $D$) independent of its content (i.e. $a(\mathbf{x}, u)$ in $D$) by a single boundary measurement; and determining both $D$ and $a(\mathbf{x}, u)|_D$ by $M$ boundary measurements, where $M\in\mathbb{N}$ signifies the number of unknown coefficients in $a(\mathbf x, u)$. The mathematical argument is based on microlocally characterising the singularities in the solution $u$ induced by the geometric singularities of $D$, and does not rely on any linearisation technique

Multi-sensor Suboptimal Fusion Student's tt Filter

A multi-sensor fusion Student's $t$ filter is proposed for time-series recursive estimation in the presence of heavy-tailed process and measurement noises. Driven from an information-theoretic optimization, the approach extends the single sensor Student's $t$ Kalman filter based on the suboptimal arithmetic average (AA) fusion approach. To ensure computationally efficient, closed-form $t$ density recursion, reasonable approximation has been used in both local-sensor filtering and inter-sensor fusion calculation. The overall framework accommodates any Gaussian-oriented fusion approach such as the covariance intersection (CI). Simulation demonstrates the effectiveness of the proposed multi-sensor AA fusion-based $t$ filter in dealing with outliers as compared with the classic Gaussian estimator, and the advantage of the AA fusion in comparison with the CI approach and the augmented measurement fusion.Comment: 8 pages, 8 figure

Empirical Pricing of Chinese Defaultable Corporate Bonds Based on the Incomplete Information Model

The default of Suntech Power made the year 2013 in China “the first year of default” of bond markets. People are also clearly aware of the default risk of corporate bonds and find that fair pricing for defaultable corporate bonds is very important. In this paper we first give the pricing model based on incomplete information, then empirically price the Chinese corporate bond “11 super JGBS” from Merton’s model, reduced-form model, and incomplete information model, respectively, and then compare the obtained prices with the real prices. Results show that all the three models can reflect the trend of bond prices, but the incomplete information model fits the real prices best. In addition, the default probability obtained from the incomplete information model can discriminate the credit quality of listed companies

Multi-level Gated Bayesian Recurrent Neural Network for State Estimation

The optimality of Bayesian filtering relies on the completeness of prior models, while deep learning holds a distinct advantage in learning models from offline data. Nevertheless, the current fusion of these two methodologies remains largely ad hoc, lacking a theoretical foundation. This paper presents a novel solution, namely a multi-level gated Bayesian recurrent neural network specifically designed to state estimation under model mismatches. Firstly, we transform the non-Markov state-space model into an equivalent first-order Markov model with memory. It is a generalized transformation that overcomes the limitations of the first-order Markov property and enables recursive filtering. Secondly, by deriving a data-assisted joint state-memory-mismatch Bayesian filtering, we design a Bayesian multi-level gated framework that includes a memory update gate for capturing the temporal regularities in state evolution, a state prediction gate with the evolution mismatch compensation, and a state update gate with the observation mismatch compensation. The Gaussian approximation implementation of the filtering process within the gated framework is derived, taking into account the computational efficiency. Finally, the corresponding internal neural network structures and end-to-end training methods are designed. The Bayesian filtering theory enhances the interpretability of the proposed gated network, enabling the effective integration of offline data and prior models within functionally explicit gated units. In comprehensive experiments, including simulations and real-world datasets, the proposed gated network demonstrates superior estimation performance compared to benchmark filters and state-of-the-art deep learning filtering methods