Resident Attitudes toward Dark Tourism, a Perspective of Place-based Identity Motives

Place-based identity theories prove to be valid in better understanding resident attitudes towards support for tourism. Yet, its effectiveness is not verified in the context of dark tourism and resident attitudes towards dark tourism remains unknown. Based on a survey of 526 local residents in China’s Yingxiu, the epicentre of the Great Wenchuan Earthquake, the authors examined the relationships between the local residents’ place-based identity motives and their attitudes towards support for dark tourism development. Results show that the motive of ‘belonging/meaning’ is one of the most important determinants; residents’ involvement in dark tourism and bereavement affect their identity motives and attitudes towards support for dark tourism. The theoretical contributions and managerial implications are discussed

Reflectance and Carbon Isotopes of Kerogen in Lower Cambrian Black Shales of Zunyi and Zhangjiajie, Southwest China : Indicators to the Source of Au-Ag-PGE

China University of GeosciencesInstitute of Geochemistry, Academia SinicaScedule:17-18 March 2003, Vemue: Kanazawa, Japan, Kanazawa Citymonde Hotel, Project Leader : Hayakawa, Kazuichi, Symposium Secretariat: XO kamata, Naoto, Edited by:Kamata, Naoto

A Variational Bayesian Superresolution Approach Using Adaptive Image Prior Model

The objective of superresolution is to reconstruct a high-resolution image by using the information of a set of low-resolution images. Recently, the variational Bayesian superresolution approach has been widely used. However, these methods cannot preserve edges well while removing noises. For this reason, we propose a new image prior model and establish a Bayesian superresolution reconstruction algorithm. In the proposed prior model, the degree of interaction between pixels is adjusted adaptively by an adaptive norm, which is derived based on the local image features. Moreover, in this paper, a monotonically decreasing function is used to calculate and update the single parameter, which is used to control the severity of penalizing image gradients in the proposed prior model. Thus, the proposed prior model is adaptive to the local image features thoroughly. With the proposed prior model, the edge details are preserved and noises are reduced simultaneously. A variational Bayesian inference is employed in this paper, and the formulas for calculating all the variables including the HR image, motion parameters, and hyperparameters are derived. These variables are refined progressively in an iterative manner. Experimental results show that the proposed SR approach is very efficient when compared to existing approaches

Resilience-oriented repair crew and network reconfiguration coordinated operational scheduling for post-event restoration

This paper introduces a post-disaster load restoration approach for the distribution grid, utilizing network reconfiguration (NR) and dispatching of repair crews (RCs) to significantly enhance grid resilience. We propose an RC–NR coordinated model that leverages diverse flexible resources within the active distribution network (ADN), aimed at not only enhancing the grid’s resilience level but also efficiently mending the fault lines. The model introduces fault repairing and sequential NR coupled constraints to devise an optimal resilience strategy within temporal domain cooperation, focusing on minimizing repair and penalty costs associated with the restoration process. To tackle the challenge of computational complexity, the nonlinear model is reformulated into a mixed-integer second-order cone programming model. The efficacy of the approach is validated through case studies on an IEEE 33-bus system, in which simulation results demonstrate a considerable improvement in grid resilience, achieving optimal load recovery with reduced restoration time and costs. The proposed approach outperforms traditional methods with optimal repair sequence and RC scheduling, aligned with NR efforts, and contributes to an improved system resilience level

Advanced age is associated with increased adverse outcomes in patients undergoing middle cerebral artery stenting

PurposeThis study tried to evaluate whether advanced age has an increased incidence of major complications in patients undergoing MCA stenting.MethodsA total of 348 patients who underwent MCA stenting were reviewed from a prospectively maintained database. Ninety-day ischemic stroke, intracerebral hemorrhage, and death outcomes were compared among the young (≤40 years old), middle (41–60 years old) and old (≥61 years old) groups. Univariate analysis and multivariable logistic regression analysis were used to investigate different variables associated with 90-day major adverse events. Kaplan–Meier analysis was performed to determine long-term outcomes during follow-up.ResultsThe incidence of 90-day ischemic stroke was 9.26% in the old group, 2.86% in the middle group, and 0% in the young group (P = 0.024). The incidence of all 90-day major adverse events was 3.33% in patients ≤40 years old, 19.90% in patients 41–60 years old, and 24.07% in patients ≥61 years old, with statistical significance (P = 0.04). Advanced age was associated with increased 90-day ischemic stroke (OR = 1.074, 95% CI: 1.019–1.132, P = 0.007; adjusted OR: 1.071, 95% CI: 1.008–1.138, P = 0.026) and 90-day death (OR = 1.072, 95% CI: 1.012–1.135, P = 0.018; adjusted OR: 1.095, 95% CI: 1.015–1.182, P = 0.018). Meanwhile, advanced age was also associated with decreased long-term survival and ischemic stroke-free survival during follow-up.ConclusionOur data indicated that MCA stenting in elderly patients is associated with a high risk of adverse events and should be cautiously considered

Protein functional module identification method combining topological features and gene expression data

Article conducting an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. The experimental results show that the ECTG algorithm can detect protein functional modules better

Controlled synthesis of monodisperse gold nanorods with different aspect ratios in the presence of aromatic additives

This paper reports the synthesis of monodisperse gold nanorods (GNRs) via a simple seeded growth approach in the presence of different aromatic additives, such as 7-bromo-3-hydroxy-2-naphthoic acid (7-BrHNA), 3-hydroxy-2-naphthoic acid (HNA), 5-bromosalicylic acid (5-BrSA), salicylic acid (SA) or phenol (PhOH). Effects of the aromatic additives and hydrochloric acid (HCl) on the structure and optical properties of the synthesized GNRs were investigated. The longitudinal surface plasmon resonance (LSPR) peak wavelength of the resulting GNRs was found to be dependent on the aromatic additive in the following sequence: 5-BrSA (778 nm) > 7-BrHNA (706 nm) > SA (688 nm) > HNA (676 nm) > PhOH (638 nm) without addition of HCl, but this was changed to 7-BrHNA (920 nm) > SA (890 nm) > HNA (872 nm) > PhOH (858 nm) > 5-BrSA (816 nm) or 7-BrHNA (1005 nm) > PhOH (995 nm) > SA (990 nm) > HNA (980 nm) > 5-BrSA (815 nm) with the addition of HCl or HNO3 respectively. The LSPR peak wavelength was increased with the increasing concentration of 7-BrHNA without HCl addition, however, there was a maximum LSPR peak wavelength when HCl was added. Interestingly, the LSPR peak wavelength was also increased with amount of HCl added. The results presented here thus established a simple approach to synthesize monodisperse GNRs of different LSPR wavelength

Multiframe Superresolution of Vehicle License Plates Based on Distribution Estimation Approach

Low-resolution (LR) license plate images or videos are often captured in the practical applications. In this paper, a distribution estimation based superresolution (SR) algorithm is proposed to reconstruct the license plate image. Different from the previous work, here, the high-resolution (HR) image is estimated via the obtained posterior probability distribution by using the variational Bayesian framework. To regularize the estimated HR image, a feature-specific prior model is proposed by considering the most significant characteristic of license plate images; that is, the target has high contrast with the background. In order to assure the success of the SR reconstruction, the models representing smoothness constraints on images are also used to regularize the estimated HR image with the proposed feature-specific prior model. We show by way of experiments, under challenging blur with size 7 × 7 and zero-mean Gaussian white noise with variances 0.2 and 0.5, respectively, that the proposed method could achieve the peak signal-to-noise ratio (PSNR) of 22.69 dB and the structural similarity (SSIM) of 0.9022 under the noise with variance 0.2 and the PSNR of 19.89 dB and the SSIM of 0.8582 even under the noise with variance 0.5, which are 1.84 dB and 0.04 improvements in comparison with other methods