Abstract
In the last 30 years, several academic and commercial projects have explored the context-awareness concept in multiple domains. Ubiquitous computing and ambient intelligence are features associated with the 4th generation industry empowering space to interact and respond appropriately according to context. In the scope of Industry 4.0, context-aware systems aim to improve productivity in smart factories and offer assistance to workers through services, applications, and devices, delivering functionalities and contextualised content. This article, through descriptive research, discusses the concepts related to context, presents and analyses projects related to ubiquitous computing and associated with Industry 4.0, and discusses the main challenges in systems and applications development to support intelligent environments for increased productivity, supporting informed decision-making in the factories of the future. The study results indicate that many research questions regarding the analysed projects remain the same, leading the research in the context-aware systems area to minimise issues related to context-aware features, improving the incorporation of Industry 4.0 paradigm concepts.

Abstract
The increasing integration of Distributed Energy Resources (DER) in the distribution network has brought more importance to Peer-to-Peer (P2P) markets. However, energy traded in P2P markets can lead to voltage and congestion constraints in distribution networks operated by Distribution System Operators (DSOs). At the same time, Transmission System Operators (TSOs) may need to solve system problems, requesting the participation of DERs in frequency regulation services. To ensure competitive participation in P2P markets, as well as to ensure a correct operation of distribution networks and to contribute to mitigate problems at the system level, coordination mechanisms between the P2P market and the System Operators (SOs) are required. This paper introduces a set of mathematical models considering P2P flexibility trading at the distribution system, while assisting the DSO and TSO in solving the congestion, voltage and frequency problems, respectively. The models are assessed on an IEEE 37bus distribution network with high DER penetration. The first and second models are based on product differentiation to avoid violating the lines' thermal limits and the nodes' voltage limits, respectively. The second model also considers reactive power control in order to impact voltage constraints. The third model uses a virtual load, connected to the TSO network (before the power transformer), to model frequency regulation services. The last model proposes the integration of all methods. Results showed that each model was effective in solving its constraint. However, they do not dismiss the use of the peers' flexibility assets to assure an overall feasible techno-economic solution. The use of the methodology proposed in the present paper can significantly facilitate the adoption of full P2P markets as well as the confidence of the system operators in the integration of these markets.& COPY; 2023 Elsevier Ltd. All rights reserved.

Abstract
The high costs associated with implementing and maintaining a training program based on immersive Virtual Reality (VR) technologies are a barrier to its adoption and widespread. This paper presents an Immersive VR training platform that intends to overcome such barriers. The Immersive VR platform was developed based on a real-usage case study conducted with an industry adopter. The case scenario focuses on training in the operation and maintenance of excavator machines. The industry partner has participated in the whole Immersive VR platform creation process, from conceptualization to its evaluation and validation. The Immersive VR training platform comprises two main modules: an authoring tool for an easy creation/update of training scenarios that supports industry-standard 3D models to ensure that they are continually updated when new products are released to the market and the training simulators that allow running training sessions regarding operation and maintenance of forestry machines. An exploratory usability evaluation of the training simulators created with the Authoring Tool revealed them as viable, validating the immersive VR platform. Limitations and future research directions are discussed to pave the way in this application field.

Abstract
Identifying the types of transportation modes that people use is a central problem in transportation research. Effective feature construction plays a crucial role in developing a successful machine learning model. In this study, we demonstrate an approach to identify commuters' transportation modes solely using raw GPS trajectory data. First, we transform the representation of location data points into a vector of motion features in the time domain. Next, we create fixed-length instances in the time domain. Subsequently, we transform the instances time-domain features into frequency-domain features using the fast Fourier transform. This results in a pool of features for the instances in both the time and frequency domains. We use the Sequential Forward Floating Selection technique to select the most informative features to train our models. We evaluate our approach using two distinct real-world GPS trajectory datasets. Our results show that the random forest classifier achieved an ROC-AUC scores of 79% and 89% on the respective datasets.

Abstract
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Abstract
Nowadays, Virtual Reality (VR) and Augmented Reality (AR) systems are not exclusively associated with the gaming industry. Their potential is also useful for other business areas such as healthcare, automotive, and educational domains. Companies need to accompany technological advances and enhance their business processes and thus, the adoption of VR or AR technologies could be advantageous in reducing resource usage or improving the overall efficiency of processes. However, before implementing these technologies, companies must be aware of potential cyberattacks and security risks to which these systems are subject. This study presents a survey of attacks related to VR and AR scenarios and their risk assessment when considering healthcare, automation, education, and gaming industries. The main goal is to make companies aware of the possible cyberattacks that can affect the devices and their impact on their business domain.