Abstract
The proper integration of technology in teaching and learning processes must consider the role of teachers and students, as well as the design of tasks and the context in which they are implemented. Teachers' perceived self-efficacy significantly influences their willingness to integrate educational robotics (ER) into their practice, so initial teacher training should provide opportunities for teachers to participate in structured activities that integrate ER. In this study, a class of pre-service teachers from an initial teacher training programme were provided with their first contact with an ER platform through the use of a simulator. We present the design process of a student exploration guide and teacher guide, developed over three iterative cycles of implementation, assessment and redesign. The analysis of the data collected allowed for improvements in the design of the tasks, the graphic component of the student exploration guide, and more precise indications for the teacher's actions. The main contribution of this study is the chain orchestration between the simulator, student exploration guide and teacher guide, which allowed pre-service teachers to solve a set of challenges of increasing complexity, thereby progressively decreasing their difficulties and contributing to an adequate integration of ER in their future teaching practices.

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Abstract
Context: Low-code development is a concept whose presence has grown both in academia and the software industry and is discussed alongside others, such as model-driven engineering and domain-specific languages. Usability is an important concept in low-code contexts since users of these tools often lack a background in programming. Grey literature articles have also stated that low-code tools have high usability.Objective: This paper examines the current literature about low-code and no-code to discover more about them and their relationship with usability, particularly its quality, which factors are the most relevant, and how users view these tools. This focus on usability aims to provide a different point of view from other works on low-code.Method: We performed a systematic literature review based on a formal protocol for this study. The search protocol returned a total of 207 peer-review articles across five databases, which was supplemented with a snowballing process. These were filtered using inclusion and exclusion criteria, resulting in 38 relevant articles that were analysed, synthesised and reported.Conclusion: Despite growing interest and a strong enterprise presence in academia, we did not find a formal definition of low-code, although common characteristics have been specified. We found that users have a heightened awareness of usability regarding low-code tools, with some authors performing feasibility studies on their implementations or listing factors that influence the user experience in a given tool. Researchers are considering usability factors unconsciously, and the low-code field would grow if research on usability increased. This paper also suggests a definition for low-code development.

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This document presents the case study for the ABZ 2023 conference. The case study introduces a safety critical interactive system called AMAN (Arrival MANager), which is a partly-autonomous scheduler of landing sequences of aircraft in airports. This interactive systems interleaves Air Traffic Controllers activities with automation in AMAN. While some AMAN systems are currently deployed in airports, we consider here only a subset of functions which represent a challenge in modelling and verification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
In the current technological scenario of artificial intelligence growth, especially using machine learning, large datasets are necessary. Recommender systems appear with increasing frequency with different techniques for information filtering. Few large wine datasets are available for use with wine recommender systems. This work presents X-Wines, a new and consistent wine dataset containing 100,000 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1-5 ratings carried out over a period of 10 years (2012-2021) for wines produced in 62 different countries. A demonstration of some applications using X-Wines in the scope of recommender systems with deep learning algorithms is also presented.

Abstract
The study of the evacuation of buildings in emergency fire situations has deserved the attention of researchers for decades, particularly regarding the real-time guiding of occupants in their way to exit the building. However, finding solutions to guide the occupants evacuating a building requires a thorough knowledge of that domain. Using ontological models to model the knowledge of a domain allows the understanding of that domain to be shared. This paper presents an ontological model that pretends to reinforce and deepen knowledge of the domain under study and help develop solutions and systems capable of guiding the occupants during a building evacuation. The ontology was developed following the METHONTOLOGY methodology, and for implementation, the Protégé tool was used. The ontological model was successfully submitted to a thorough evaluation process and is publicly available on the Web.