Abstract

Abstract
Similar to the case of biotechnology industry, companies providing devices in the biomedicine industry face several challenges, and to stand out from competitors need to know how to get to the right customer. Potential customers (i.e., individuals and organizations) may choose to adopt or reject an innovative product and will later confirm that decision or not. Such decision is of utmost importance to the success of innovative products and, therefore, of the company that provides them. The aim of this study is to understand how perceptions formed about a biomedical product can influence its adoption intention and behavior and, hereafter, influence the decision of other potential adopters. Findings from a multiple case study provide a clear definition of the adoption process of a specific biomedical product, combining two existing theories - the Diffusion of Innovations Theory and the Technology Acceptance Model - and including the feedback created by interactions between current users of the product and potential users, to understand what influences potential adopters' decisions. © 2020 IEEE.

Abstract
[No abstract available]

Abstract
The aim of this study was to test the accuracy of a new automated computer software tool for the assessment of passive hip laxity. The hip laxity was estimated using the dedicated computer software by two blinded evaluators, one previously trained and one without specific training for distraction index measurement, in two independent sessions using 230 hip joints from 115 dogs that underwent screening for passive hip laxity using the distraction view. Previously, all of these radiographs were sent to PennHIP Analysis Center for an official distraction index record. The measurement repeatability of the two sessions was adequate for both evaluators. The reproducibility of the official distraction index measurement, mean distraction index +/- standard deviation 0.44 +/- 0.15, was adequate (P>0.05) for the trained evaluator, 0.44 +/- 0.15, and non-adequate (P<0.05), for the non-trained evaluator 0.47 +/- 0.17. The distraction index measurement tool proposed can be used with confidence for hip laxity evaluation by trained evaluators, as it provided good repeatability and reproducibility of official reports. The simplicity of the process described leads to a less time-consuming and more affordable procedure.

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This article presents Clava, a Clang-based source-to-source compiler, that accepts scripts written in LARA, a JavaScript-based DSL with special constructs for code queries, analysis and transformations. Clava improves Clang's source-to-source capabilities by providing a more convenient and flexible way to analyze, transform and generate C/C++ code, and provides support for building strategies that capture run-time behavior. We present the Clava framework, its main capabilities, and how it can been used. Furthermore, we show that Clava is sufficiently robust to analyze, instrument and test a set of large C/C++ application codes, such as GCC. (C) 2020 The Authors. Published by Elsevier B.V.

Abstract
Performance estimation aims at estimating the loss that a predictive model will incur on unseen data. This process is a fundamental stage in any machine learning project. In this paper we study the application of these methods to time series forecasting tasks. For independent and identically distributed data the most common approach is cross-validation. However, the dependency among observations in time series raises some caveats about the most appropriate way to estimate performance in this type of data. Currently, there is no consensual approach. We contribute to the literature by presenting an extensive empirical study which compares different performance estimation methods for time series forecasting tasks. These methods include variants of cross-validation, out-of-sample (holdout), and prequential approaches. Two case studies are analysed: One with 174 real-world time series and another with three synthetic time series. Results show noticeable differences in the performance estimation methods in the two scenarios. In particular, empirical experiments suggest that blocked cross-validation can be applied to stationary time series. However, when the time series are non-stationary, the most accurate estimates are produced by out-of-sample methods, particularly the holdout approach repeated in multiple testing periods.