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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
This article provides a framework for coordinating the operation of multiple microgrids with hydrogen systems in a distribution network considering the uncertainties of wind and solar power generation as well as load demands. The model is based upon a bilevel stochastic programming problem. On the upper level, the distribution system is the leader with a profit-maximization goal, and the microgrids are followers with cost-minimization goals on the lower level. The problem is solved by transforming the model to a single-level model using Karush-Kuhn-Tucker (KKT) conditions and linearized using McCormick's relaxation and Fortuny-Amat techniques. Unlike previous studies, both levels are modeled as scenario-based stochastic problems. Moreover, the scenarios associated with uncertain variables are obtained from a real data set. After preparing the data set, scenarios are reduced using a machine learning-based clustering approach. An application of the coordinated operation model is developed for a distribution network containing several microgrids. By solving the problem, the optimal amount of power exchange and the clearing price between microgrids and distribution systems are determined. Moreover, the proposed bilevel model made 13% more profit for the distribution system than the centralized model. Also, the effects of integrating hydrogen systems with microgrids on increasing the flexibility of operators are investigated.

Abstract
Clinical decision support systems have been increasingly utilized in the healthcare industry to improve patient outcomes and enhance clinical decision-making, taking advantage of the growing digital medical data. Despite their potential, there are still obstacles in an extensive adoption of these systems, such as low usability and human factors. In this systematic review, several articles describing clinical decision support systems with clinical validation are used to address some of the gaps, as well as to map the current academic landscape for the given context. The selected articles are observed through a Human-Computer Interaction perspective, aiming to identify the state-of-the-art, as well as barriers to the application of these principles. From an initial database search resulting in 121 articles, 16 articles were selected that fulfilled the chosen criteria: (1) article must be available and written in English, (2) article must report experimental work, (3) the reported system must be clinically validated. The research strategy followed the PRISMA framework. We highlight the need for clinical validation, a standardized clinical decision support taxonomy and the evaluation of these tools across multiple variables. Based on the found results, a list of recommendations can be formed to aid the development of future CDSS, or the improvement of current ones.

Abstract
The harbour infrastructures have some structures that still need regular inspection. However, the nature of this environment presents a number of challenges when it comes to determining an accurate vehicle position and consequently performing successful image similarity detection. In addition, the underwater environment is highly dynamic, making place recognition harder because the appearance of a place can change over time. In these close-range operations, the visual sensors have a major impact. There are some factors that degrade the quality of the captured images, but image preprocessing steps are increasingly used. Therefore, in this paper, a purely visual similarity detection with enhancement technique is proposed to overcome the inherent perceptual problems in a port scenario. Considering the lack of available data in this context and to facilitate the variation of environmental parameters, a harbour scenario was simulated using the Stonefish simulator. The experiments were performed on some predefined trajectories containing the poor visibility conditions typical of these scenarios. The place recognition approach improves the performance by up to 10% compared to the results obtained with captured images. In general, it provides a good balance in coping with turbidity and light incidence at low computational cost and achieves a performance of about 80%.

Abstract