Utilize XR as a sustainable service design for Nordic tourism

In the rapidly evolving field of tourism, integrating advanced technologies is crucial for promoting sustainable service design. This study investigates the transformative potential of Extended Reality XR technologies in enhancing sustainable and immersive tourist experiences. Combining service design principles with XR's immersive capabilities, it proposes novel frameworks to boost visitor engagement, environmental consciousness, and cultural preservation. An extensive literature review identifies existing knowledge gaps, which this research aims to fill through a mix of qualitative and quantitative methods, including user experience studies and environmental impact assessments. The findings highlight XR's role in encouraging eco-friendly tourism practices, reducing carbon footprints, and increasing cultural appreciation. Furthermore, the study offers actionable recommendations for industry stakeholders, advocating XR's integration into tourism services. Contributing both theoretical insights and practical strategies, this research underscores XR's significance in shaping future tourism experiences, promoting immersive, sustainable, and culturally rich services

Molecular detection of Torque teno virus in different breeds of swine

<p>Abstract</p> <p>Background</p> <p>Torque teno virus (TTV), of the <it>Anelloviridae </it>family, <it>Iotatorquevirus </it>genus, is a non-enveloped, single-stranded, and negative sense DNA (ssDNA) virus infecting human and many domestic animals including swines. Very little information is known about the investigations of TTV prevalence in different swine breeds so far.</p> <p>Methods</p> <p>In this study, 208 serum samples collected from seven swine breeds (<it>Rongchang pig</it>, <it>Chenghua pig</it>, <it>Zibet pig</it>, <it>Wild boar</it>, <it>Duroc</it>, <it>Landrace</it>, <it>Large Yorkshire</it>) from two independent farms were detected to determine the prevalence of two swine TTV genogroups, TTV1 and TTV 2, by nested polymerase chain reaction methods, and to analyse prevalence difference among these breeds.</p> <p>Results</p> <p>The results showed that the prevalence of TTV in the seven breeds was 92%-100%. No significant difference (p > 0.05) in TTV infection was observed between different breeds. Interestingly, significantly higher prevalence for TTV1 in <it>Rongchang </it>boars (90%) and for TTV2 in <it>Rongchang </it>sows (95%) were detected, while co-infection rate (43.8%) was lower than other breeds. Sequence analysis showed that the homology of TTV1 and TTV2 were over 90.9% and 86.4% in these breeds, respectively.</p> <p>Conclusions</p> <p>The results indicated that TTV was widely distributed in the seven swine breeds. The prevalence of both TTV genogroups associated with swine breeds and genders. This study also respented the first description of swine TTV prevalence in different swine breeds. It was vitally necessary to further study swine TTV pathogenicity.</p

Mutual-Guided Dynamic Network for Image Fusion

Image fusion aims to generate a high-quality image from multiple images captured under varying conditions. The key problem of this task is to preserve complementary information while filtering out irrelevant information for the fused result. However, existing methods address this problem by leveraging static convolutional neural networks (CNNs), suffering two inherent limitations during feature extraction, i.e., being unable to handle spatial-variant contents and lacking guidance from multiple inputs. In this paper, we propose a novel mutual-guided dynamic network (MGDN) for image fusion, which allows for effective information utilization across different locations and inputs. Specifically, we design a mutual-guided dynamic filter (MGDF) for adaptive feature extraction, composed of a mutual-guided cross-attention (MGCA) module and a dynamic filter predictor, where the former incorporates additional guidance from different inputs and the latter generates spatial-variant kernels for different locations. In addition, we introduce a parallel feature fusion (PFF) module to effectively fuse local and global information of the extracted features. To further reduce the redundancy among the extracted features while simultaneously preserving their shared structural information, we devise a novel loss function that combines the minimization of normalized mutual information (NMI) with an estimated gradient mask. Experimental results on five benchmark datasets demonstrate that our proposed method outperforms existing methods on four image fusion tasks. The code and model are publicly available at: https://github.com/Guanys-dar/MGDN.Comment: ACMMM 2023 accepte

Neural Degradation Representation Learning for All-In-One Image Restoration

Existing methods have demonstrated effective performance on a single degradation type. In practical applications, however, the degradation is often unknown, and the mismatch between the model and the degradation will result in a severe performance drop. In this paper, we propose an all-in-one image restoration network that tackles multiple degradations. Due to the heterogeneous nature of different types of degradations, it is difficult to process multiple degradations in a single network. To this end, we propose to learn a neural degradation representation (NDR) that captures the underlying characteristics of various degradations. The learned NDR decomposes different types of degradations adaptively, similar to a neural dictionary that represents basic degradation components. Subsequently, we develop a degradation query module and a degradation injection module to effectively recognize and utilize the specific degradation based on NDR, enabling the all-in-one restoration ability for multiple degradations. Moreover, we propose a bidirectional optimization strategy to effectively drive NDR to learn the degradation representation by optimizing the degradation and restoration processes alternately. Comprehensive experiments on representative types of degradations (including noise, haze, rain, and downsampling) demonstrate the effectiveness and generalization capability of our method

Study of subjective and objective quality assessment of night-time videos

With the widespread usage of video capture devices and social media videos, videos are dominating the multimedia landscape. There is an emerging need for video quality assessment (VQA) that forms the backbone of advanced video systems. Night-time videos play an important role in user capturing, hence being able to accurately assess their quality is critical. However, the characteristics of night-time videos differ from those of general in-capture videos; and VQA algorithms that have been developed for general-purpose videos cannot accurately assess the quality of night-time videos. Research is needed to gain a better understanding of how humans perceive the quality of night-time videos, and use this new understanding to develop reliable VQA algorithms. To this end, we construct a large-scale night-time VQA database, namely Mobile In-capture Night-time Database for Video Quality (MIND-VQ), containing 1181 night-time videos, 435 subjects, and over 130000 opinion scores. We perform thorough analyses to reveal subjective quality assessment behaviors of night-time videos. Furthermore, we propose a new VQA model, namely Visibility-based Night-time Video Quality Assessment Network, VINIA. Spatial and temporal visibility-aware components are characterized to reflect properties of human perception of night-time VQA task. A series of experiments are conducted to compare our VINIA with other existing IQA/VQA algorithms using our new MIND-VQ database and other public VQA databases. Experimental results show that our subjective VQA database provides new insights and our new VINIA model achieves superior performance in accessing night-time video quality

A Blockchain-Based Access Control Scheme for Smart Grids

At present, the access control schemes in the power grid are centralized. In the centralized system, the data of the network sensor nodes is transmitted by centralized nodes, and the data itself may be illegally tamped with or lost, which can lead to reduced system reliability. For this feature, we apply blockchain technology to the design of access control schemes. In this paper, we propose a blockchain-based access control scheme that is suitable for multiple scenarios in the smart grid. Our access control scheme is based on an identity-based combined encryption, signature and signcryption scheme. In addition, we design a consensus algorithm in the power system for the consortium blockchain architecture to solve the key escrow problem of the untrusted third parties. Our scheme also ensures the confidentiality, integrity, authentication and non-repudiation of the data. Compared with the existing work, our scheme can use the same key pair to encrypt, sign and signcrypt the message, which has lower computation and communication costs in multiple scenarios of smart grids

Severe Fever With Thrombocytopenia Syndrome Virus-Induced Macrophage Differentiation Is Regulated by miR-146

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging hemorrhagic fever with a high mortality rate in humans, which is caused by SFTS virus (SFTSV), a novel phlebovirus in the Bunyaviridae family, is tick borne and endemic in Eastern Asia. Previous study found that SFTSV can infect and replicate in macrophages in vivo and in vitro. However, the role of macrophages in virus replication and the potential pathogenic mechanisms of SFTSV in macrophage remain unclear. In this study, we provided evidence that the SFTSV infection drove macrophage differentiation skewed to M2 phenotype, facilitated virus shedding, and resulted in viral spread. We showed evidence that miR-146a and b were significantly upregulated in macrophages during the SFTSV infection, driving the differentiation of macrophages into M2 cells by targeting STAT1. Further analysis revealed that the elevated miR-146b but not miR-146a was responsible for IL-10 stimulation. We also found that SFTSV increased endogenous miR-146b-induced differentiation of macrophages into M2 cells mediated by viral non-structural protein (NSs). The M2 skewed differentiation of macrophages may have important implication to the pathogenesis of SFTS

Resilient Delayed Impulsive Control for Consensus of Multiagent Networks Subject to Malicious Agents

Impulsive control is widely applied to achieve the consensus of multiagent networks (MANs). It is noticed that malicious agents may have adverse effects on the global behaviors, which, however, are not taken into account in the literature. In this study, a novel delayed impulsive control strategy based on sampled data is proposed to achieve the resilient consensus of MANs subject to malicious agents. It is worth pointing out that the proposed control strategy does not require any information on the number of malicious agents, which is usually required in the existing works on resilient consensus. Under appropriate control gains and sampling period, a necessary and sufficient graphic condition is derived to achieve the resilient consensus of the considered MAN. Finally, the effectiveness of the resilient delayed impulsive control is well demonstrated via simulation studies

Incorporation of Extranodal Metastasis of Gastric Carcinoma into the 7th Edition UICC TNM Staging System

BACKGROUND: To assess the clinical significance and prognostic impact of extranodal metastasis (EM) in gastric carcinoma and establish an optimal classification in the staging system. METHODOLOGY/PRINCIPAL FINDINGS: A total of 1343 patients with gastric carcinoma who underwent surgical resection were recruited to determine the frequency and prognostic significance of EMs. EMs were divided into two groups (EM1 and EM2) and then incorporated into the 7(th) edition UICC TNM staging system. EMs was detected in 179 (13.3%) of 1343 patients who underwent radical resection. Multivariate analysis identified EMs as an independent prognostic factor (HR = 1.412, 95%CI = 1.151-1.731, P<0.001). After curative operation, the overall survival rate were worse in patients with ≥3 cases of EM (EM2) than those with the number of 1 and 2 cases (EM1) (P<0.001). Survival of patients with EM1 was found almost comparable to that of N3 stage (P = 0.437). Survival of patients with EM2 showed similar to that of stage IV patients (P = 0.896). By using the linear trend X(2), likelihood ratio X(2), and Akaike information criterion (AIC) test, EM1 treated as N3 stage and EM2 treated as M1 stage performed higher linear trend X(2) scores, likelihood ratio X(2) scores, and lower AIC value than the 7(th) edition UICC TNM staging system, which represented the optimum prognostic stratification, together with better homogeneity, discriminatory ability, and monotonicity of gradients. CONCLUSIONS/SIGNIFICANCE: EMs might be classified based on their number and prognostic information and should incorporate into the TNM staging system