A Hybrid Modeling Approach for Strategy Optimization of E-business Values

Value proposition, creation, and maximization are essential corporate objectives in e-business planning and operations, and thus constitute central tasks of the ebusiness strategic management. The goal of this paper is to provide a hybrid modeling approach that integrates the dynamic programming and the balanced scorecard models for strategy optimization of e-business values. Values from the market, supply chain, business organization, and customer perspectives are identified first based on a generic e-business model framework. In the subsequent value-based strategic planning stage, strategies with objectives and metrics for value creation in different perspectives are outlined. In the mean time, a multiperiod, multi-dimensional dynamic programming model is formulated for optimizing the expected total business value. In the value-based performance measurement stage, an adapted balanced scorecard model is developed to hold a balanced view for evaluating strategy performances regarding all value perspectives. The proposed modeling approach aims at providing e-business firms with clear and well-structured guidelines for efficiently and effectively handling complex decision and management activities including business model design, value identification, strategy formulation, as well as performance measurement

Antibacterial effect of water-soluble chitosan on representative dental pathogens Streptococcus mutans and Lactobacilli brevis

Dental caries is still a major oral health problem in most industrialized countries. The development of dental caries primarily involves Lactobacilli spp. and Streptococcus mutans. Although antibacterial ingredients are used against oral bacteria to reduce dental caries, some reports that show partial antibacterial ingredients could result in side effects. OBJECTIVES: The main objective is to test the antibacterial effect of water-soluble chitosan while the evaluation of the mouthwash appears as a secondary aim. MATERIAL AND METHODS: The chitosan was obtained from the Application Chemistry Company (Taiwan). The authors investigated the antibacterial effects of water-soluble chitosan against oral bacteria at different temperatures (25-37ºC) and pH values (pH 5-8), and evaluated the antibacterial activities of a self-made water-soluble chitosan-containing mouthwash by in vitro and in vivo experiments, and analyzed the acute toxicity of the mouthwashes. The acute toxicity was analyzed with the pollen tube growth (PTG) test. The growth inhibition values against the logarithmic scale of the test concentrations produced a concentrationresponse curve. The IC50 value was calculated by interpolation from the data. RESULTS: The effect of the pH variation (5-8) on the antibacterial activity of water-soluble chitosan against tested oral bacteria was not significant. The maximal antibacterial activity of water-soluble chitosan occurred at 37ºC. The minimum bactericidal concentration (MBC) of water-soluble chitosan on Streptococcus mutans and Lactobacilli brevis were 400 µg/mL and 500 µg/mL, respectively. Only 5 s of contact between water-soluble chitosan and oral bacteria attained at least 99.60% antibacterial activity at a concentration of 500 µg/mL. The water-soluble chitosan-containing mouthwash significantly demonstrated antibacterial activity that was similar to that of commercial mouthwashes (>;99.91%) in both in vitro and in vivo experiments. In addition, the alcohol-free mouthwash exhibited no cytotoxicity and no oral stinging. To the best of our knowledge, this was the first study to combine in vitro and in vivo investigations to analyze the antibacterial properties of water-soluble chitosan-containing mouthwash. CONCLUSIONS: This study illustrated that water-soluble chitosan may be a viable alternative to commercial mouthwashes 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

Developing Strategy Maps for the Formulation of Digital Divides Strategies

Prior investigations commented that almost no country is completely ready to bridge digital divide due to the absence of the balance between strategizing, coordination and action. In the e-government sector, the links among strategic objectives, action plans, and performance measures related to strategies for reducing digital divides had been constantly overlooked. This paper aims at adopting and combining the concepts of strategy map and the balanced scorecard to fill up the absences. A generic model of digital divide strategy maps is presented and the steps of developing strategy maps are illustrated in detail as well

Therapeutic and Radiosensitizing Effects of Armillaridin on Human Esophageal Cancer Cells

Background. Armillaridin (AM) is isolated from Armillaria mellea. We examined the anticancer activity and radiosensitizing effect on human esophageal cancer cells. Methods. Human squamous cell carcinoma (CE81T/VGH and TE-2) and adenocarcinoma (BE-3 and SKGT-4) cell lines were cultured. The MTT assay was used for cell viability. The cell cycle was analyzed using propidium iodide staining. Mitochondrial transmembrane potential was measured by DiOC6(3) staining. The colony formation assay was performed for estimation of the radiation surviving fraction. Human CE81T/VGH xenografts were established for evaluation of therapeutic activity in vivo. Results. AM inhibited the viability of four human esophageal cancer cell lines with an estimated concentration of 50% inhibition (IC50) which was 3.4–6.9 μM. AM induced a hypoploid cell population and morphological alterations typical of apoptosis in cells. This apoptosis induction was accompanied by a reduction of mitochondrial transmembrane potential. AM accumulated cell cycle at G2/M phase and enhanced the radiosensitivity in CE81T/VGH cells. In vivo, AM inhibited the growth of CE81T/VGH xenografts without significant impact on body weight and white blood cell counts. Conclusion. Armillaridin could inhibit growth and enhance radiosensitivity of human esophageal cancer cells. There might be potential to integrate AM with radiotherapy for esophageal cancer treatment

Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS

Development of a short and universal learning self-efficacy scale for clinical skills

Background Learning self-efficacy, defined as learners’ confidence in their capability to learn specific subjects, is crucial for the enhancement of academic progress, because it is positively correlated with academic achievements and effective learning strategy use. In this study, we developed a universal scale called the Learning Self-Efficacy Scale (L-SES) for Clinical Skills for undergraduate medical students and validated it through item analysis and content validity index (CVI) calculation. Design The L-SES was developed based on the framework of Bloom’s taxonomy, and the questions were generated through expert consensus and CVI calculation. A pilot version of the L-SES was administered to 235 medical students attending a basic clinical skills course. The collected data were then examined through item analysis. Results The first draft of the L-SES comprised 15 questions. After expert consensus and CVI calculation, 3 questions were eliminated; hence, the pilot version comprised 12 questions. The CVI values of the 12 questions were between .88 and 1, indicating high content validity. Moreover, the item analysis indicated that the quality of L-SES reached the qualified threshold. The results showed that the L-SES scores were unaffected by gender (t = −0.049; 95% confidence interval [−.115, .109], p > .05). Conclusion The L-SES is a short, well-developed scale that can serve as a generic assessment tool for measuring medical students’ learning self-efficacy for clinical skills. Moreover, the L-SES is unaffected by gender differences. However, additional analyses in relevant educational settings are needed

Are You A Tourist? Tourists’ Self-identification and the Definition of Tourism

This study empirically investigates the question of who is a tourist, building on the literature of what is tourism, how to measure tourism, and the negative characterization of the term tourist. The study’s main contribution is that it contrasts the distance-based practical definitions with tourists’ self-identification and characterizes those who define themselves as tourists. Data came from 1,619 responses to a visitor survey, conducted at a midwestern destination. A surprisingly high proportion of the respondents self-identified as tourists suggested that while it might still exist, the negative connotation of the term “tourist” is not always as dominant as suggested by the literature. The statistical analyses (chi-square test, Marascuilo procedure, and a binary logistic model) suggest that the propensity to self-identify as tourist is positively related to the distance traveled and first-visit status, and it is lower among visitors whose trip purpose was to visit friends or relatives. These findings on how travelers might feel about the role of distance in the definition of tourism could assist policy makers who use distance to define and measure tourism. The characterization of those who self-identify as tourists has important implications for CVBs and DMOs who wish to better address the negative connotation of the term “tourist” in their communication