An Empirical Study on Consumption Intention of Virtual Tour Streaming

This study employs the social interaction motivation of the audience to explore the social capital dual-model relationship generated by the audience of “Virtual Tour Streaming,” a term that describes virtual tour streaming’s nascent digital economy. This is situated in a virtual tour streaming platform to ascertain how it influences the intention of the audience and to use “Swift Guanxi” as the interaction variable to actual intention behavior. This is done to understand the contributions of virtual tour streaming adoption in a direct dial platform of different audience levels and their consumption behavior. The remaining sections discuss the theoretical and practical implications of the study

PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge Distillation

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed pre-impact fall detection systems using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms)

Investigation of morphological changes in the tamsui river estuary using an integrated coastal and estuarine processes model

© 2020 by the authors. This study is to investigate morphological changes in the Tamsui River Estuary in Taiwan driven by multiple physical processes, such as river flows, tides, waves, and storm surges, and then to study the impacts of sediment flushing operated at the Shihmen reservoir upstream on the river estuary. An integrated coastal and estuarine processes model (CCHE2D-Coast) (Center for Computational Hydroscience and Engineering Two-Dimensional-Coast) was validated by simulating these physical processes in the estuary driven by three historical typhoons in 2008. The site-specifically validated model was then applied to simulate morphological changes in the estuary in response to reservoir sediment flush scenarios from the upstream. For the impact assessment of sediment flushing, a synthetic hydrological event was designed by including a historical typhoon and a typical monsoon. It was found that during the typhoon, the sediments will be mostly deposited in the estuarine river reach of Tamsui and the Wazihwei sandy beach. During the monsoon period, most of the sediments tend to be deposited in the second fishing port of Tamsui, the northern breakwater, and the estuary, while the Wazihwei sandy beach in the river mouth would be scoured by backflow. Simulations of the complex flow fields and morphological changes will facilitate the best practice of sediment management in the coastal and estuarine regions

Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a "long-lie". Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD

Antioxidant activity and growth inhibition of human colon cancer cells by crude and purified fucoidan preparations extracted from Sargassum cristaefolium

AbstractFucose-containing sulfated polysaccharides, also termed “fucoidans”, which are known to possess antioxidant, anticoagulant, anticancer, antiviral, and immunomodulating properties, are normally isolated from brown algae via various extraction techniques. In the present study, two methods (SC1 and SC2) for isolation of fucoidan from Sargassum cristaefolium were compared, with regard to the extraction yields, antioxidant activity, and inhibition of growth of human colon cancer cells exhibited by the respective extracts. SC1 and SC2 differ in the number of extraction steps and concentration of ethanol used, as well as the obtained sulfated polysaccharide extracts, namely, crude fucoidan preparation (CFP) and purified fucoidan preparation (PFP), respectively. Thin layer chromatography, Fourier transform infrared analysis, and measurements of fucose and sulfate contents revealed that the extracts were fucoidan. There was a higher extraction yield for CFP, which contained less fucose and sulfate but more uronic acid, and had weaker antioxidant activity and inhibition of growth in human colon cancer cells. In contrast, there was a lower extraction yield for PFP, which contained more fucose and sulfate but less uronic acid, and had stronger antioxidant activity and inhibition of growth in human colon cancer cells. Thus, since the difference in bioactive activities between CFP and PFP was not remarkable, the high extraction yield of SC1 might be favored as a method in industrial usage for extracting fucoidan

Postchemoradiotherapy Pathologic Stage Classified by the American Joint Committee on the Cancer Staging System Predicts Prognosis of Patients with Locally Advanced Esophageal Squamous Cell Carcinoma

IntroductionTo determine whether the postchemoradiotherapy (post-CRT) pathologic stage predicts the outcomes of patients with locally advanced esophageal squamous cell carcinoma (ESCC) undergoing preoperative CRT followed by surgery.MethodsFrom three phase II trials of preoperative CRT for locally advanced ESCC, 140 patients were included. Preoperative CRT comprised twice weekly paclitaxel and cisplatin-based regimens and 40-Gy radiotherapy in 20 fractions. The post-CRT pathologic stage was classified according to the American Joint Committee on Cancer, 7th edition staging system. The prognostic effects of clinicopathologic factors were analyzed using Cox regression.ResultsWith a median follow-up of 61.9 months, the median progression-free survival (PFS) and overall survival (OS) of the entire cohort were 24.5 and 30.9 months, respectively. The post-CRT pathologic stage was 0 in 34.5%, I in 12.9%, II in 29.3%, III in 13.6%, and ypT0N1-2 in 6.4% of the patients. The median PFS was 47.2, 25.9, 16.0, 9.4, and 15.1 months, and the median OS was 57.4, 34.1, 26.2, 14.1, and 17.6 months for patients with post-CRT pathologic stage 0, I, II, III, and ypT0N1-2, respectively. In multivariate analysis, performance status (p < 0.001), tumor location (p = 0.016), and extranodal extension (p = 0.024) were independent prognostic factors for PFS, whereas performance status (p < 0.001) and post-CRT pathologic stage (p = 0.027) were independent prognostic factors for OS.ConclusionsThe post-CRT pathologic stage classified by American Joint Committee on Cancer, 7th edition staging system predicted the survival of locally advanced ESCC patients who underwent preoperative paclitaxel and cisplatin-based CRT followed by esophagectomy

Detection of SARS-associated Coronavirus in Throat Wash and Saliva in Early Diagnosis

Early detection of SARS-CoV in throat wash and saliva suggests that these specimens are ideal for SARS diagnosis