Abstract
Serious Games have been used in professional training to increase employee engagement and improve the results of training initiatives. This work intends to investigate the influence of game elements, in adaptable Serious Games, according to the users' interactions, on the increase of engagement in the game itself and, as the main goal, on the learning results and the transfer of the acquired knowledge and practised skills to the daily work activities. Using the Design Science Research - DSR methodology, this study aims to develop a framework for the development and evaluation of Serious Games to improve the user experience, the learning outcomes, the transfer of knowledge to work situations, and the application of the skills practised in the game in real professional scenarios. This paper presents an initial Framework for Adaptive Serious Games derived from a systematic literature review. The next steps in this investigation are pointed out following the DSR methodology.

Abstract
This annual issue embraces articles from three sets of sources: the first covers the topic Virtual Environments and Interaction Design Research at the University of Saint Joseph, Macao SAR, China works selected and guest edited by Carlos Sena Caires and Gerald Estadieu; the second set are three extended articles from the International Conference on Graphics and Interaction (ICGI’2021), selected and guest edited by Daniel Mendes and Nuno Rodrigues; and, finally, two articles from the regular pipeline.

Abstract
Cloud computing and Infrastructure-as-Code (IaC), supported by technologies such as Docker, have shaped how many software systems are built and deployed. Previous research has identified typical issues for some types of IaC specification but not why they come to be, or they have delved into collaboration aspects but not into technical ones. This work aims to characterize the activities around two particular kinds of IaC specification-Dockerfiles and docker-compose.yml files. We seek to know how they can be better supported and therefore study also what approaches and tools practitioners employ. We used an online questionnaire to gather data. The first part of the study reached 68 graduate students from a study program on informatics engineering, and the second one 120 professional software developers. The results show that most of the activities of the process of developing a Dockerfile are perceived as time-consuming, especially when the respondents are beginners with this technology. We also found that solving issues using trial-and-error approaches is very common and that many developers do not use ancillary tools to support the development of Dockerfiles and docker-compose.yml files.

Abstract
We study a variant of the Probabilistic Travelling Salesman Problem arising when retailers crowdsource last-mile deliveries to their own customers, who can refuse or accept in exchange for a reward. A planner must identify which deliveries to offer, knowing that all deliveries need fulfilment, either via crowdsourcing or using the retailer's own vehicle. We formalise the problem and position it in both the literature about crowdsourcing and among routing problems in which not all customers need a visit. We show that to evaluate the objective function of this stochastic problem for even one solution, one needs to solve an exponential number of Travelling Salesman Problems. To address this complexity, we propose Machine Learning and Monte Carlo simulation methods to approximate the objective function, and both a branch-and-bound algorithm and heuristics to reduce the number of evaluations. We show that these approaches work well on small size instances and derive managerial insights on the economic and environmental benefits of crowdsourcing to customers.

Abstract
Cardiac auscultation is the key exam to screen cardiac diseases both in developed and developing countries. A heart sound auscultation procedure can detect the presence of murmurs and point to a diagnosis, thus it is an important first-line assessment and also cost-effective tool. The design automatic recommendation systems based on heart sound auscultation can play an important role in boosting the accuracy and the pervasiveness of screening tools. One such as step, consists in detecting the fundamental heart sound states, a process known as segmentation. A faulty segmentation or a wrong estimation of the heart rate might result in an incapability of heart sound classifiers to detect abnormal waves, such as murmurs. In the process of understanding the impact of a faulty segmentation, several common heart sound segmentation errors are studied in detail, namely those where the heart rate is badly estimated and those where S1/S2 and Systolic/Diastolic states are swapped in comparison with the ground truth state sequence. From the tested algorithms, support vector machine (SVMs) and random forest (RFs) shown to be more sensitive to a wrong estimation of the heart rate (an expected drop of 6% and 8% on the overall performance, respectively) than to a swap in the state sequence of events (an expected drop of 1.9% and 4.6%, respectively).

Abstract
In this paper we describe the implementation of a secure key distribution system based on an ultra-long fiber laser with a bi-directional erbium doped fiber amplifier. The resilience of the system was tested against passive attacks from an eavesdropper, having been observed a similarity in spectrum for both secure configurations of the system.