A qualitative case study approach to define and identify perceived challenges of knowledge management for casino hotel industry

The overall goal of this study is to analyze and establish the importance of KM applications in the hotel industry, with two purposes in mind. The first is to explore hotel organizations\u27 awareness and perceptions of KM applications. The second purpose is to identify casino hotel executives\u27 perceived challenges of KM. Qualitative case studies and descriptive research designs with interviews were applied to accomplish the study objectives; Case study subjects\u27 perceptions regarding KM definitions, KM functions, and expected benefits of KM were identified and discussed. Casino hotel executives perceive knowledge sharing, change resistance, and knowledge transfer major challenges of KM. A KM definition for the hotel industry has also been proposed

Particle Size Effects of TiO 2

Large particle sizes having a strong light scattering lead to a significantly decreased surface area and small particle sizes having large surface area lack light-scattering effect. How to combine large and small particle sizes together is an interesting work for achieving higher solar efficiency. In this work, we investigate the solar performance influence of the dye-sensitized solar cells (DSSCs) by the multiple titanium oxide (TiO2) layers with different particle sizes. It was found that the optimal TiO2 thickness depends on the particle sizes of TiO2 layers for achieving the maximum efficiency. The solar efficiency of DSSCs prepared by triple TiO2 layers with different particle sizes is higher than that by double TiO2 layers for the same TiO2 thickness. The choice of particle size in the bottom layer is more important than that in the top layer for achieving higher solar efficiency. The choice of the particle sizes in the middle layer depends on the particle sizes in the bottom and top layers. The mixing of the particle sizes in the middle layer is a good choice for achieving higher solar efficiency

Particle Size Effects of TiO2 Layers on the Solar Efficiency of Dye-Sensitized Solar Cells

Large particle sizes having a strong light scattering lead to a significantly decreased surface area and small particle sizes having large surface area lack light-scattering effect. How to combine large and small particle sizes together is an interesting work for achieving higher solar efficiency. In this work, we investigate the solar performance influence of the dye-sensitized solar cells (DSSCs) by the multiple titanium oxide (TiO2) layers with different particle sizes. It was found that the optimal TiO2 thickness depends on the particle sizes of TiO2 layers for achieving the maximum efficiency. The solar efficiency of DSSCs prepared by triple TiO2 layers with different particle sizes is higher than that by double TiO2 layers for the same TiO2 thickness. The choice of particle size in the bottom layer is more important than that in the top layer for achieving higher solar efficiency. The choice of the particle sizes in the middle layer depends on the particle sizes in the bottom and top layers. The mixing of the particle sizes in the middle layer is a good choice for achieving higher solar efficiency

Optimal QoE Scheduling in MPEG-DASH Video Streaming

DASH is a popular technology for video streaming over the Internet. However, the quality of experience (QoE), a measure of humans’ perceived satisfaction of the quality of these streamed videos, is their subjective opinion, which is difficult to evaluate. Previous studies only considered network-based indices and focused on them to provide smooth video playback instead of improving the true QoE experienced by humans. In this study, we designed a series of click density experiments to verify whether different resolutions could affect the QoE for different video scenes. We observed that, in a single video segment, different scenes with the same resolution could affect the viewer’s QoE differently. It is true that the user’s satisfaction as a result of watching high-resolution video segments is always greater than that when watching low-resolution video segments of the same scenes. However, the most important observation is that low-resolution video segments yield higher viewing QoE gain in slow motion scenes than in fast motion scenes. Thus, the inclusion of more high-resolution segments in the fast motion scenes and more low-resolution segments in the slow motion scenes would be expected to maximize the user’s viewing QoE. In this study, to evaluate the user’s true experience, we convert the viewing QoE into a satisfaction quality score, termed the Q-score, for scenes with different resolutions in each video segment. Additionally, we developed an optimal segment assignment (OSA) algorithm for Q-score optimization in environments characterized by a constrained network bandwidth. Our experimental results show that application of the OSA algorithm to the playback schedule significantly improved users’ viewing satisfaction

Bayesian Belief Network Model Quantification Using Distribution-Based Node Probability and Experienced Data Updates for Software Reliability Assessment

Since digital instrumentation and control systems are expected to play an essential role in safety systems in nuclear power plants (NPPs), the need to incorporate software failures into NPP probabilistic risk assessment has arisen. Based on a Bayesian belief network (BBN) model developed to estimate the number of software faults considering the software development lifecycle, we performed a pilot study of software reliability quantification using the BBN model by aggregating different experts' opinions. In this paper, we suggest the distribution-based node probability table (D-NPT) development method which can efficiently represent diverse expert elicitation in the form of statistical distributions and provides mathematical quantification scheme. Besides, the handbook data on U.S. software development and V&V and testing results for two nuclear safety software were used for a Bayesian update of the D-NPTs in order to reduce the BBN parameter uncertainty due to experts' different background or levels of experience. To analyze the effect of diverse expert opinions on the BBN parameter uncertainties, the sensitivity studies were conducted by eliminating the significantly different NPT estimates among expert opinions. The proposed approach demonstrates a framework that can effectively and systematically integrate different kinds of available source information to quantify BBN NPTs for NPP software reliability assessment

Plasma fatty acids and the risk of metabolic syndrome in ethnic Chinese adults in Taiwan

<p>Abstract</p> <p>Background</p> <p>Evidence of predictive power of various fatty acids on the risk of metabolic syndrome was scanty. We evaluated the role of various fatty acids, including saturated fat, monounsaturated fat, transfat, n-6 fatty acid, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), for the risk of the metabolic syndrome in Taiwan.</p> <p>Results</p> <p>A nested case-control study based on 1000 cases of metabolic syndrome and 1:1 matched control subjects. For saturated fat, monounsaturated fat and transfat, the higher the concentration the higher the risk for metabolic syndrome: participants in the highest quintile had a 2.22-fold (95% confidence interval [CI], 1.66 to 2.97) higher risk of metabolic syndrome. In addition, the participants in higher EPA quintiles were less likely to have the risk of metabolic syndrome (adjusted risk, 0.46 [0.34 to 0.61] for the fifth quintile). Participants in the highest risk group (low EPA and high transfat) had a 2.36-fold higher risk of metabolic syndrome (95% CI, 1.38 to 4.03), compared with those in the lowest risk group (high EPA and low transfat). For prediction power, the area under ROC curves increased from 0.926 in the baseline model to 0.928 after adding fatty acids. The net reclassification improvement for metabolic syndrome risk was substantial for saturated fat (2.1%, <it>P </it>= 0.05).</p> <p>Conclusions</p> <p>Plasma fatty acid components improved the prediction of the metabolic syndrome risk in Taiwan.</p

Clinical meaning of age-related expression of fecal cytokeratin 19 in colorectal malignancy

<p>Abstract</p> <p>Background</p> <p>Colorectal cancer (CRC) is one of the leading causes of malignant death worldwide. Because young age of onset is often considered a poor prognostic factor for CRC, it is important to identify the poor outcomes of CRC in a younger population and to consider an aggressive approach by implementing early treatment. Our aim was to specifically quantify the fecal cytokeratin 19 (CK19) transcript from CRC patients and investigate its correlation with clinical stage, tumor malignancy, and age.</p> <p>Methods</p> <p>The quantitation of fecal CK19 transcript was determined by a quantitative real-time reverse transcription polymerase chain in 129 CRC patients (45 younger than 60 years at diagnosis) and 85 healthy controls. The levels of CK19 protein were examined both in colonic cell lines and tissues.</p> <p>Results</p> <p>The analysis of 45 younger CRC patients (age ≤ 60 years) revealed that patients at the M1 stage had significantly higher expression levels of fecal CK19 mRNA when compared with healthy controls (<it>p </it>< 0.001) and patients at the M0 stage (<it>p </it>= 0.004). Additionally, the degree of consistency between the mean level of fecal CK19 mRNA and the distant metastatic rate in each age interval was up to 89% (<it>p </it>= 0.042).</p> <p>Conclusion</p> <p>These results indicate that high levels of fecal CK19 mRNA represent a potential marker for colorectal malignancy and for aggressive treatment of younger CRC patients.</p