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https://www.selleckchem.com/products/AR-42-HDAC-42.html 13% that of the anonymous LOB dataset. The contributions of this study are twofold. First, a machine learning-based tool for finance researchers is proposed to quantitatively measure the price predictability of LOB features, and the results of the impact of LOB transparency on traders' profitability are novel as this study is empirical. Second, the empirical result strongly suggests that the broker ID queues in the LOB consist of significant information content for price prediction, and thus, the study provides insights for regulators to determine the appropriate degree of LOB transparency to guarantee a fair market for all investors.Purpose This study investigated the use of a new software feature, namely, dynamic text with speech output, on the acquisition of single-word reading skills by six children with developmental disabilities during shared e-book reading experiences with six typically developing peers. Method A single-subject, multiple-probe design across participants was used to evaluate the effects of the software intervention. Six children with developmental delays were the primary focus for intervention, while six children with typical development participated as peer partners in intervention activities. e-Books were created with the new software feature, in which a child selects a picture from the e-book and the written word is presented dynamically and then spoken out. -books were then used in shared reading activities with dyads including a child with a disability and a peer with typical development. Participants engaged in the shared reading activity for an average of 13 sessions over a 6-week time period, an average of 65 min of intervention for each dyad. Results Participants with disabilities acquired an average of 73% of the words to which they were exposed, a gain of 4.3 words above the baseline average of 1.7 correct responses. The average effect size (Tau-U) was .94, evidence of a very l

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