Abstract
Besides the extraordinary impact of Business Angels (BAs) as the leading contributors to fund entrepreneurship at the early stage, the value BAs perceive from their investment activity has not yet been fully explored. Based on a new holistic perspective resulting from marketing and consumer behaviour, this article builds on the concept of perceived investment value to create a new instrument that measures the overall value perceived by BAs in their investing activity. Data were collected through a survey questionnaire with 849 BAs from 79 countries. The instrument created was tested through Structural Equation Modelling. The results reveal that: (i) BAs are not purely financial investors and expect more than just money from their activity, as self-esteem, entrepreneurship, emotion and altruism are the main dimensions that explain angel perceived investment value (APIV), with economic and functional dimensions being the least influential; (ii) APIV positively influences their job satisfaction; and (iii) angels satisfied with their jobs are more likely to reinvest their money and engage in positive word-of-mouth. A multigroup comparison was implemented, validating the results for males and females, novices and veterans and light and heavy investors.

Abstract
We consider global models of communicating agents specified as transition systems labelled by interactions in which multiple senders and receivers can participate. A realisation of such a model is a set of local transition systems—one per agent—which are executed concurrently using synchronous communication. Our core challenge is how to check whether a global model is realisable and, if it is, how to synthesise a realisation. We identify and compare two variants to realise global interaction models, both relying on bisimulation equivalence. Then we investigate, for both variants, realisability conditions to be checked on global models. We propose a synthesis method for the construction of realisations by grouping locally indistinguishable states. The paper is accompanied by a tool that implements realisability checks and synthesises realisations.

Abstract
Despite advancements in interaction with immersive Virtual Reality (VR) systems, using hand gestures for all interactions still imposes some challenges, especially in interactions with graphical user interfaces that are usually performed with point-and-click interfaces. Therefore, exploring the use of alternative hands-free methods for selection is essential to overcome usability problems and provide natural interaction for users. The results and insights gained from this exploration can lead to enhanced user experiences in VR applications. This study aims to contribute to the literature with the evaluation of the usability of the most commonly used hands-free methods for selection and system control tasks in immersive VR and their impact on standard and validated experience and usability metrics, namely the sense of presence, cybersickness, system usability, workload, and user satisfaction. A Fitts' selection task was performed using a within-subjects design by nine participants experienced in VR. The methods evaluated were the handheld controllers, the head gaze, eye gaze, and voice commands for pointing at the targets, and dwell time and voice commands to confirm the selections. Results show that the methods provide similar levels of sense of presence and low cybersickness while showing low workload values and high user satisfaction, matching the experience of traditional handheld controllers for non-multimodal approaches. The assisted eye gaze with dwell was the preferred hands-free method and the one with the highest values of usability. Still, developers should minimize the number of gaze movements to reduce fatigue. The evaluation also showed that using a multimodal approach for selections, especially using the voice, decreases user satisfaction and increases users' frustration.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Abstract
In this article, the use of diffuse reflectance (R-d) spectroscopy is explored to evaluate the diffusion properties of water and sucrose in skeletal muscle during optical clearing treatments. Treating muscle samples with sucrose-water solutions with different osmolarities, collimated transmittance (T-c) and R-d measurements were performed to obtain the diffusion time (t) and the diffusion coefficient (D) values that characterize the unique water and sucrose fluxes in the muscle and also the optical clearing mechanisms designated as tissue dehydration and refractive index matching. Considering the R-d measurements, the estimated t and D values for water in the muscle were 63.1s and 1.72x10(-6) cm(2)/s, while the ones estimated for sucrose were 261s and 4.86x10(-7) cm(2)/s. Comparing these values with the ones estimated from the T-c measurements, the relative differences observed for t and D were 1.6% and 2.8% in the case of water and 0.3% and 0.4% in the case of sucrose.

Abstract
Evaluating predictive models is a crucial task in predictive analytics. This process is especially challenging with time series data because observations are not independent. Several studies have analyzed how different performance estimation methods compare with each other for approximating the true loss incurred by a given forecasting model. However, these studies do not address how the estimators behave for model selection: the ability to select the best solution among a set of alternatives. This paper addresses this issue. The goal of this work is to compare a set of estimation methods for model selection in time series forecasting tasks. This objective is split into two main questions: (i) analyze how often a given estimation method selects the best possible model; and (ii) analyze what is the performance loss when the best model is not selected. Experiments were carried out using a case study that contains 3111 time series. The accuracy of the estimators for selecting the best solution is low, despite being significantly better than random selection. Moreover, the overall forecasting performance loss associated with the model selection process ranges from 0.28 to 0.58%. Yet, no considerable differences between different approaches were found. Besides, the sample size of the time series is an important factor in the relative performance of the estimators.

Abstract