Abstract
Temporary transnational relocation is a growing type of migration. However, travel behavior adaptation of highly skilled temporary residents and its urban impacts have largely been ignored. This study extends the knowledge of mobility biographies, mobility cultures, and mobility of millennials by examining how temporary residents adapt their intra-urban travel behavior in response to a transnational relocation. The data used here comes from semi-structured interviews with students and researchers of nine different nationalities, aged between 19 and 31 years, temporarily living in Portugal (Lisbon or Porto). We found supporting evidence for the occurrence of residential self-selection, although prior information on study/workplace combined with low knowledge on neighborhood-level make it somewhat specific. Given their shortterm perspective, temporary residents are more prone to rely on public transport and non-motorized modes, having a low likelihood of purchasing vehicles. Thus, measures aimed at improving and facilitating the use of active modes can have an immediate effect on this group's travel behavior and contribute to reaching critical mass for these sustainable alternatives. Temporary residents are also a potentially interesting market segment for public transportation operators for increases in revenues, as they tend to display a relatively higher travel intensity and a wider diversity of activities and destinations. Finally, technology usage was found to reduce the stress-related to traveling to unfamiliar places by increasing the perceived spatial orientation, having the downside of generating a feeling of confidence that decreases the internalization of information. Providing timely and persuasive information at the very beginning of temporary residents' stay can help induce their travel behavior decisions.

Abstract
Deformation monitoring is a common practice in most of dams to ensure their structural health and safety status. Systematic monitoring is frequently carried out by means of geotechnical sensors and geodetic techniques that, although very precise an accurate, can be time-consuming and economically costly. Remote sensing techniques are proved to be very effective in assessing deformation. Changes in the structure, shell or associated infrastructures of dams, including adjacent slopes, can be efficiently recorded by using satellite Synthetic Aperture Radar Inteferometry (InSAR) techniques, in particular, Muti-Temporal InSAR time-series analyses. This is a mature technology nowadays but not very common as a routine procedure for dam monitoring. Today, thanks to the availability of spaceborne satellites with high spatial resolution SAR images and short revisit times, this technology is a powerful cost-effective way to monitor millimeter-level displacements of the dam structure and its surroundings. What is more, the potential of the technique is increased since the Copernicus C-band SAR Sentinel-1 satellites are in orbit, due to the high revisit time of 6 days and the free data availability. ReMoDams is a Spanish research project devoted to provide the deformation monitoring of several embankments dams using advances time-series InSAR techniques. One of these dams is The Arenoso dam, located in the province of Cordova (southern Spain). This dam has been monitored using Sentinel-1 SAR data since the beginning of the mission in 2014. In this paper, we show the processing of 382 SLC SAR images both in ascending and descending tracks until March 2019. The results indicate that the main displacement of the dam in this period is in the vertical direction with a rate in the order of -1 cm/year in the central part of the dam body. (C) 2020 The Authors. Published by Elsevier B.V.

Abstract
Lung cancer is the type of cancer with highest mortality worldwide. Low-dose computerized tomography is the main tool used for lung cancer screening in clinical practice, allowing the visualization of lung nodules and the assessment of their malignancy. However, this evaluation is a complex task and subject to inter-observer variability, which has fueled the need for computer-aided diagnosis systems for lung nodule malignancy classification. While promising results have been obtained with automatic methods, it is often not straightforward to determine which features a given model is basing its decisions on and this lack of explainability can be a significant stumbling block in guaranteeing the adoption of automatic systems in clinical scenarios. Though visual malignancy assessment has a subjective component, radiologists strongly base their decision on nodule features such as nodule spiculation and texture, and a malignancy classification model should thus follow the same rationale. As such, this study focuses on the characterization of lung nodules as a means for the classification of nodules in terms of malignancy. For this purpose, different model architectures for nodule characterization are proposed and compared, with the final goal of malignancy classification. It is shown that models that combine direct malignancy prediction with specific branches for nodule characterization have a better performance than the remaining models, achieving an Area Under the Curve of 0.783. The most relevant features for malignancy classification according to the model were lobulation, spiculation and texture, which is found to be in line with current clinical practice.

Abstract
Proper evaluation of realism in immersive virtual experiences is crucial to ensure optimisation of resources. This way, we can take better decisions while designing realistic immersive experiences, prioritising factors that have a higher impact on the perceived realism of the virtual experience. This systematic review aims to provide readers with an overview of methodologies used throughout the literature to evaluate realism in immersive virtual, augmented and mixed reality. A total of 79 from 1300 gathered articles met the eligibility criteria and were analysed. Results have shown that virtual reality is by far the platform where realism studies were performed. Head-mounted displays are by far the preferred equipment for such studies. Visual realism is the most researched, followed by audiovisual. The majority of methodologies consisted of subjective, as well as a combination of objective and subjective measures. The most used evaluation instrument is questionnaires where many of which are custom and non-validated. Presence questionnaires are the most used ones and are often used to evaluate the presence, perceived realism and involvement. Cybersickness evaluation is consistently assessed by one self-report questionnaire.

Abstract
Management 3.0 is a new concept that intends to revolutionize the way managers and leaders act within companies to offer a more happy, collaborative, and productive work environment. This paper aims to analyze the management 3.0 phenomenon and establish a research agenda in the field. A systematic review was conducted considering 215 published studies in the field between 2010 and 2019. The findings reveal that management 3.0 is an emerging area and one that has grown in 2019 and involves multidisciplinary research teams from management, leadership, information technology, and psychology.

Abstract
The problem of electricity load forecasting has emerged as an essential topic for power systems and electricity markets seeking to minimize costs. However, this topic has a high level of complexity. Over the past few years, convolutional neural networks (CNNs) have been used to solve several complex deep learning challenges, making substantial progress in some fields and contributing to state of the art performances. Nevertheless, CNN architecture design remains a challenging problem. Moreover, designing an optimal architecture for CNNs leads to improve their performance in the prediction process. This article proposes an effective approach for the electricity load forecasting problem using a deep neuroevolution algorithm to automatically design the CNN structures using a novel modified evolutionary algorithm called enhanced grey wolf optimizer (EGWO). The architecture of CNNs and its hyperparameters are optimized by the novel discrete EGWO algorithm for enhancing its load forecasting accuracy. The proposed method is evaluated on real time data obtained from datasets of Australian Energy Market Operator in the year 2018. The simulation results demonstrated that the proposed method outperforms other compared forecasting algorithms based on different evaluation metrics.