A Study on Taiwan Consumers’ Adoption of Online Financial Services

Despite Taiwan financial institutions’ huge investments in online financial services systems, Taiwan consumers’ adoption of online financial services has been slower than anticipated. So far, online financial services research in Taiwan is still in its infancy, hence receiving little academic attention. This suggests a need to understand Taiwan Internet users’ adoption behavior of online financial services and to identify the potential factors that may motivate or impede Taiwan Internet users’ acceptance of online financial services. The research framework of this study is constituted by the extended version of the Technology Acceptance Model (TAM2). Other variables, which have proven academically important in influencing consumers’ intentions to use information technology, were added to the conceptual framework. The results strongly support that the extended TAM (TAM2) is a valid model to predict Taiwan consumers’ intention to use online financial services and to explain the intention difference between adopters and non-adopters. The results also demonstrated that perceived privacy protection, perceived security, and consumer innovativeness not only have a significant, positive relationship with Taiwan consumers’ intention to use online financial services but also can significantly predict who is more likely to be an online financial service adopter in Taiwan. The research findings may help Taiwan financial institutions and other interested parties to formulate appropriate marketing strategies and design effective online financial services systems and accelerate the diffusion of online financial services in the future

Graphic-Card Cluster for Astrophysics (GraCCA) -- Performance Tests

In this paper, we describe the architecture and performance of the GraCCA system, a Graphic-Card Cluster for Astrophysics simulations. It consists of 16 nodes, with each node equipped with 2 modern graphic cards, the NVIDIA GeForce 8800 GTX. This computing cluster provides a theoretical performance of 16.2 TFLOPS. To demonstrate its performance in astrophysics computation, we have implemented a parallel direct N-body simulation program with shared time-step algorithm in this system. Our system achieves a measured performance of 7.1 TFLOPS and a parallel efficiency of 90% for simulating a globular cluster of 1024K particles. In comparing with the GRAPE-6A cluster at RIT (Rochester Institute of Technology), the GraCCA system achieves a more than twice higher measured speed and an even higher performance-per-dollar ratio. Moreover, our system can handle up to 320M particles and can serve as a general-purpose computing cluster for a wide range of astrophysics problems.Comment: Accepted for publication in New Astronom

Lowly Expressed Ribosomal Protein S19 in the Feces of Patients with Colorectal Cancer

Colorectal cancer (CRC) has become one of the most common fatal cancers. CRC tumorigenesis is a complex process involving multiple genetic changes to several sequential mutations or molecular alterations. P53 is one of the most significant genes; its mutations account for more than half of all CRC. Therefore, understanding the cellular genes that are directly or indirectly related to p53 is particularly crucial for investigating CRC tumorigenesis. In this study, a p53-related ribosomal protein, ribosomal protein S19 (RPS19), obtained from the feces of CRC patients is evaluated by using specifically quantitative real-time PCR and knocked down in the colonic cell line by gene silencing. This study found that CRC patients with higher expressions of RPS19 in their feces had a better prognosis and consistent expressions of RPS19 and BAX in their colonic cells. In conclusion, the potential mechanism of RPS19 in CRC possibly involves cellular apoptosis through the BAX/p53 pathway, and the levels of fecal RPS19 may function as a prognostic predictor for CRC patients

Semantic Segmentation Using Super Resolution Technique as Pre-Processing

Combining high-level and low-level visual tasks is a common technique in the field of computer vision. This work integrates the technique of image super resolution to semantic segmentation for document image binarization. It demonstrates that using image super-resolution as a preprocessing step can effectively enhance the results and performance of semantic segmentation

Molecular and clinical analyses of 84 patients with tuberous sclerosis complex

BACKGROUND: Tuberous sclerosis complex (TSC) is an autosomal dominant disease characterized by the development of multiple hamartomas in many internal organs. Mutations in either one of 2 genes, TSC1 and TSC2, have been attributed to the development of TSC. More than two-thirds of TSC patients are sporadic cases, and a wide variety of mutations in the coding region of the TSC1 and TSC2 genes have been reported. METHODS: Mutational analysis of TSC1 and TSC2 genes was performed in 84 Taiwanese TSC families using denaturing high-performance liquid chromatography (DHPLC) and direct sequencing. RESULTS: Mutations were identified in a total of 64 (76 %) cases, including 9 TSC1 mutations (7 sporadic and 2 familial cases) and 55 TSC2 mutations (47 sporadic and 8 familial cases). Thirty-one of the 64 mutations found have not been described previously. The phenotype association is consistent with findings from other large studies, showing that disease resulting from mutations to TSC1 is less severe than disease due to TSC2 mutation. CONCLUSION: This study provides a representative picture of the distribution of mutations of the TSC1 and TSC2 genes in clinically ascertained TSC cases in the Taiwanese population. Although nearly half of the mutations identified were novel, the kinds and distribution of mutation were not different in this population compared to that seen in larger European and American studies

Emotion and Concentration Integrated System: Applied to the Detection and Analysis of Consumer Preference

With the expansion of consumer market, the appearance becomes an important issue when consumers make decisions under the situation of similar qualities and contents. Accordingly, to attract consumers, companies cost and take much attention on product appearance. Compared to using questionnaires individually, obtaining humans’ thoughts directly from their brains can accurately grasp the actual preference of consumers, which can provide effective and precious decisions for companies. \ In this study, consumers’ brainwaves which are related to concentration and emotion are extracted by wearing a portable and wireless Electroencephalography (EEG) device. The extracted EEG data are then trained by using perceptron learning algorithm (PLA) to make the judgments of concentration and emotion work well with each subject. They are then applied to the detection and analysis of consumer preference. Finally, the questionnaires are also performed and used as the reference on training process. They are integrated with brainwaves data to create one prediction model which can improve the accuracy significantly. The Partial Least Squares is used to compare the correlation between different factors in the model, to ensure the test can accurately meet consumers’ thoughts

Aciculatin inhibits lipopolysaccharide-mediated inducible nitric oxide synthase and cyclooxygenase-2 expression via suppressing NF-κB and JNK/p38 MAPK activation pathways

<p>Abstract</p> <p>Objectives</p> <p>Natural products have played a significant role in drug discovery and development. Inflammatory mediators such as inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) have been suggested to connect with various inflammatory diseases. In this study, we explored the anti-inflammatory potential of aciculatin (8-((2<it>R</it>,4<it>S</it>,5<it>S</it>,6<it>R</it>)-tetrahydro-4,5-dihydroxy-6-methyl-2<it>H</it>-pyran-2-yl)-5-hydroxy-2-(4-hydroxyphenyl)-7-methoxy-4<it>H</it>-chromen-4-one), one of main components of <it>Chrysopogon aciculatis</it>, by examining its effects on the expression and activity of iNOS and COX-2 in lipopolysaccharide (LPS)-activated macrophages.</p> <p>Methods</p> <p>We used nitrate and prostaglandin E₂(PGE₂) assays to examine inhibitory effect of aciculatin on nitric oxide (NO) and PGE₂levels in LPS-activated mouse RAW264.7 macrophages and further investigated the mechanisms of aciculatin suppressed LPS-mediated iNOS/COX-2 expression by western blot, RT-PCR, reporter gene assay and confocal microscope analysis.</p> <p>Results</p> <p>Aciculatin remarkably decreased the LPS (1 μg/mL)-induced mRNA and protein expression of iNOS and COX-2 as well as their downstream products, NO and PGE₂respectively, in a concentration-dependent manner (1-10 μM). Such inhibition was found, via immunoblot analyses, reporter gene assays, and confocal microscope observations that aciculatin not only acts through significant suppression of LPS-induced NF-κB activation, an effect highly correlated with its inhibitory effect on LPS-induced IκB kinase (IKK) activation, IκB degradation, NF-κB phosphorylation, nuclear translocation and binding of NF-κB to the κB motif of the iNOS and COX-2 promoters, but also suppressed phosphorylation of JNK/p38 mitogen-activated protein kinases (MAPKs).</p> <p>Conclusion</p> <p>Our results demonstrated that aciculatin exerts potent anti-inflammatory activity through its dual inhibitory effects on iNOS and COX-2 by regulating NF-κB and JNK/p38 MAPK pathways.</p

CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization

To efficiently extract the textual information from color degraded document images is an important research topic. Long-term imperfect preservation of ancient documents has led to various types of degradation such as page staining, paper yellowing, and ink bleeding; these degradations badly impact the image processing for information extraction. In this paper, we present CCDWT-GAN, a generative adversarial network (GAN) that utilizes the discrete wavelet transform (DWT) on RGB (red, green, blue) channel splited images. The proposed method comprises three stages: image preprocessing, image enhancement, and image binarization. This work conducts comparative experiments in the image preprocessing stage to determine the optimal selection of DWT with normalization. Additionally, we perform an ablation study on the results of the image enhancement stage and the image binarization stage to validate their positive effect on the model performance. This work compares the performance of the proposed method with other state-of-the-art (SOTA) methods on DIBCO and H-DIBCO ((Handwritten) Document Image Binarization Competition) datasets. The experimental results demonstrate that CCDWT-GAN achieves a top two performance on multiple benchmark datasets, and outperforms other SOTA methods