The Influence Of Knowledge Management On Market-Related Performance Through Business Process Effectiveness: An Empirical Investigation Of Hospitals And Financial Firms

Knowledge-based resources are critical in service sectors for facing the challenges of dynamic markets and helping organizations manage changes in consumer preference. Knowledge application is needed to improve the business process in order to attain superior market-related performance because there is the unperfected imitation coming from causal ambiguity. However, there is a lack of empirical study in examining the effect of KM and the effect of the business process within the scope of service sectors. This study examines how KM infrastructure supports and KM capabilities influence market-related performance through business processes effectiveness. Data collections of two studies are from 166 hospitals and 106 financial firms. The findings indicate a positive relationship between KM infrastructure and KM capability, and that they have a positive influence on market-related performance through business process effectiveness. For improving this process, the effect of KM infrastructure is greater than the effect of KM capabilities in hospitals. But the effect of KM capabilities is greater than the effect of KM infrastructure in financial firms. The implications of these findings for research and practices in hospitals and financial firms are also discussed

Value of high-sensitivity C-reactive protein assays in predicting atrial fibrillation recurrence: a systematic review and meta-analysis

Objectives: We performed a systematic review and meta-analysis of studies on high-sensitivity C-reactive protein (hs-CRP) assays to see whether these tests are predictive of atrial fibrillation (AF) recurrence after cardioversion. Design: Systematic review and meta-analysis. Data sources PubMed, EMBASE and Cochrane databases as well as a hand search of the reference lists in the retrieved articles from inception to December 2013. Study eligibility criteria This review selected observational studies in which the measurements of serum CRP were used to predict AF recurrence. An hs-CRP assay was defined as any CRP test capable of measuring serum CRP to below 0.6 mg/dL. Primary and secondary outcome measures We summarised test performance characteristics with the use of forest plots, hierarchical summary receiver operating characteristic curves and bivariate random effects models. Meta-regression analysis was performed to explore the source of heterogeneity. Results: We included nine qualifying studies comprising a total of 347 patients with AF recurrence and 335 controls. A CRP level higher than the optimal cut-off point was an independent predictor of AF recurrence after cardioversion (summary adjusted OR: 3.33; 95% CI 2.10 to 5.28). The estimated pooled sensitivity and specificity for hs-CRP was 71.0% (95% CI 63% to 78%) and 72.0% (61% to 81%), respectively. Most studies used a CRP cut-off point of 1.9 mg/L to predict long-term AF recurrence (77% sensitivity, 65% specificity), and 3 mg/L to predict short-term AF recurrence (73% sensitivity, 71% specificity). Conclusions: hs-CRP assays are moderately accurate in predicting AF recurrence after successful cardioversion

The North System for Formosa Speech Recognition Challenge 2023

This report provides a concise overview of the proposed North system, which aims to achieve automatic word/syllable recognition for Taiwanese Hakka (Sixian). The report outlines three key components of the system: the acquisition, composition, and utilization of the training data; the architecture of the model; and the hardware specifications and operational statistics. The demonstration of the system has been made public at https://asrvm.iis.sinica.edu.tw/hakka_sixian

A Reinforcement Learning Approach for the Multichannel Rendezvous Problem

In this paper, we consider the multichannel rendezvous problem in cognitive radio networks (CRNs) where the probability that two users hopping on the same channel have a successful rendezvous is a function of channel states. The channel states are modelled by two-state Markov chains that have a good state and a bad state. These channel states are not observable by the users. For such a multichannel rendezvous problem, we are interested in finding the optimal policy to minimize the expected time-to-rendezvous (ETTR) among the class of {\em dynamic blind rendezvous policies}, i.e., at the $t^{th}$ time slot each user selects channel $i$ independently with probability $p_i(t)$, $i=1,2, \ldots, N$. By formulating such a multichannel rendezvous problem as an adversarial bandit problem, we propose using a reinforcement learning approach to learn the channel selection probabilities $p_i(t)$, $i=1,2, \ldots, N$. Our experimental results show that the reinforcement learning approach is very effective and yields comparable ETTRs when comparing to various approximation policies in the literature.Comment: 5 pages, 9 figures. arXiv admin note: text overlap with arXiv:1906.1042

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