Abstract

Abstract
The rapid production of mobile and wearable devices along with the wireless applications boom is continuing to evolve everyday. This motivates network operators to integrate and exploit wireless spectrum across multiple radio access networks to cope with such intensive demand, while improving quality of service. However, it is crucial to develop innovative network selection techniques that consider heterogeneous networks characteristics, while meeting applications' quality requirements. Thus, this paper develops an optimal network selection with resource allocation scheme over heterogeneous networks that aims to optimize the latency, cost, and energy consumption, while accounting for data compression at the edge. Indeed, our framework could significantly enhance the performance of wireless healthcare systems by enabling data transfer from patients edge nodes to the cloud in cost-effective and energy-efficient manner, while maintaining strict Quality of Service (QoS) requirements of health applications. Our simulation results depict that our solution significantly outperforms state-of- the-art techniques in terms of energy consumption, latency, and cost.

Abstract
AbstractThis chapter aims to identify the supply chain (SC) issues that can be considered “horizontal”, as they are cross–sectorial and faced by most companies operating both in production and distribution sectors, and to propose a set of policy recommendations that can support public and private organisations to promote and foster innovation and competitiveness of future European SCs. The definition of the Key Horizontal Issues (KHI) is the basis for developing 12 policy recommendations regarding infrastructure requirements, technological and organisational improvements and regulatory developments needed to set the stage for the European SCs for the future. Specifically, the policy recommendations entail assuring appropriate standards and legislation for European SCs; educating and training professionals for the future SCs; drafting of international agreements aiming at future European SCs; supporting and fostering incentives and funding schemes; promoting reference bodies for European SCs; and establishing infrastructure for fostering of future European SCs.

Abstract
The carsharing market has never been as competitive as it is now, and during the last years, we have been witnessing a boom in the number of carsharing organizations that appear, often accompanied by an also booming number of companies that disappear. Designing a viable carsharing system is challenging and often depends on local conditions as well as on a myriad of operational decisions that need to be supported by suitable decision support systems. Therefore, carsharing is being increasingly studied in the Operations Management (OM) literature. Nevertheless, often due to the limited transparency of this highly competitive sector and the recency of this business, there is still a "gap of understanding" of the scientific community concerning the business practices and contexts, often resulting in over-simplifications and relevant problems being overlooked. In this paper, we aim to close this "gap of understanding" by describing, conceptualizing, and analyzing the reality of 34 business to-consumer carsharing organizations. With the data collected, we propose a detailed description of the current business practices, such as the ones concerning pricing. From this, we highlight relevant "research insights" and structure all collected data organized by different OM topics, enabling knowledge to be further developed in this field.

Abstract
This paper presents a systematic literature review (SLR) about realistic simulators that can be applied in an educational robotics context. These simulators must include the simulation of actuators and sensors, the ability to simulate robots and their environment. During this systematic review of the literature, 559 articles were extracted from six different databases using the Population, Intervention, Comparison, Outcomes, Context (PICOC) method. After the selection process, 50 selected articles were included in this review. Several simulators were found and their features were also analyzed. As a result of this process, four realistic simulators were applied in the review's referred context for two main reasons. The first reason is that these simulators have high fidelity in the robots' visual modeling due to the 3D rendering engines and the second reason is because they apply physics engines, allowing the robot's interaction with the environment.

Abstract
Chest radiography is one of the most ubiquitous imaging modalities, playing an essential role in screening, diagnosis and disease management. However, chest radiography interpretation is a time-consuming and complex task, requiring the availability of experienced radiologists. As such, automated diagnosis systems for pathology detection have been proposed aiming to reduce the burden on radiologists and reduce variability in image interpretation. While promising results have been obtained, particularly since the advent of deep learning, there are significant limitations in the developed solutions, namely the lack of representative data for less frequent pathologies and the learning of biases from the training data, such as patient position, medical devices and other markers as proxies for certain pathologies. The lack of explainability is also a challenge for the adoption of these solutions in clinical practice.Generative adversarial networks could play a significant role as a solution for these challenges as they allow to artificially create new realistic images. This way, new synthetic chest radiography images could be used to increase the prevalence of less represented pathology classes and decrease model biases as well as improving the explainability of automatic decisions by generating samples that serve as examples or counter-examples to the image being analysed, ensuring patient privacy.In this study, a few-shot generative adversarial network is used to generate synthetic chest radiography images. A minimum Fréchet Inception Distance score of 17.83 was obtained, allowing to generate convincing synthetic images. Perceptual validation was then performed by asking multiple readers to classify a mixed set of synthetic and real images. An average accuracy of 83.5% was obtained but a strong dependency on reader experience level was observed. While synthetic images showed structural irregularities, the overall image sharpness was a major factor in the decision of readers. The synthetic images were then validated using a MobileNet abnormality classifier and it was shown that over 99% of images were classified correctly, indicating that the generated images were correctly interpreted by the classifier. Finally, the use of the synthetic images during training of a YOLOv5 pathology detector showed that the addition of the synthetic images led to an improvement of mean average precision of 0.05 across 14 pathologies.In conclusion, the usage of few-shot generative adversarial networks for chest radiography image generation was shown and tested in multiple scenarios, establishing a baseline for future experiments to increase the applicability of generative models in clinical scenarios of automatic CXR screening and diagnosis tools.