Abstract
Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive syn-thesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increas-ingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and rec-ommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.

Abstract
The massive industrialization of products in a factory environment requires testing the product at a stage before its exportation to the sales market. For example, the end-of-line tests at Continental Advanced Antenna contribute to the validation of an antenna's functionality, a product manufactured by this organization. In addition, the storage of information from the testing process allows the data manipulation through automated machine learning algorithms in search of a beneficial contribution. Studies in this area (automatic learning/machine learning) lead to the search and development of tools designed with objectives such as preventing anomalies in the production line, predictive maintenance, product quality assurance, forecast demand, forecasting safety problems, increasing resources, proactive maintenance, resource scalability, reduced production time, and anomaly detection, isolation, and correction. Once applied to the manufacturing environment, these advantages make the EOL system more productive, reliable, and less time-consuming. This way, a tool is proposed that allows the visualization and previous detection of trends associated with faults in the antenna testing system. Furthermore, it focuses on predicting failures at Continental's EOL.

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
This research focuses on developing a wearable device that aims to enhance problem-solving and communication abilities within the context of Industry 4.0. The wearable is being developed in the Continental Advanced Antenna, and it allows operators to notify material shortages on the manufacturing line and helps minimize workflow disturbance. The wearable gives a list of missing materials using context-aware computing, allowing operators to identify and prioritize the missing item quickly. We used the Quick and Dirty usability testing approach to ensure the device's usability and efficacy, allowing quick feedback and iterative modifications throughout the development process. Experienced consultants of project participated initial tests on the device and found that it has the potential to improve efficiency and communication in an industrial setting. However, further testing involving end users is necessary to optimize the device for the unique demands of the production environment. This paper offers valuable insights into the lessons learned from the project and proposes potential future research directions.

Abstract
Games with purposes beyond entertainment, the so-called serious games, have been useful tools in professional training, especially in engaging participants. However, their evaluation and, also, their adaptable characteristics to different scenarios, audiences and contexts remain challenges. This paper examines the application of serious games in professional training, their results and adaptable ways to achieve certain goals. Using the Design Science Research (DSR) methodology, a framework was built to develop and evaluate serious games to improve user experience, learning outcomes, knowledge transfer to work situations, and the application of the skills practised in the game in real professional settings. At this stage, the investigation presents a framework regarding the triangulation of data collected from a systematic literature review, focus groups and interviews. Following the DSR methodology, the next steps of this investigation, listed at the end of the paper, are the demonstration of the framework in serious game development and the evaluation and validation of this artefact. © 2023 ITMA.

Abstract
Computer architecture is a prevalent topic of study in Informatics and Electrical Engineering courses, though students’ overall grasp of this subject’s concepts is many times hampered, mainly due to the lack of educational tools that can intuitively represent the internal behaviour of a CPU. With the evolution of the ARM architecture and its adoption in higher education institutions, the demand for this sort of tool has increased. Educational tools, specifically developed for the ARMv8 processor, are scarce and inadequate for what is necessary in an academic context. In order to contribute towards solving this problem, eduARM, a practical and interactive web platform that simulates how a ARMv8 CPU functions, was developed and is presented through this paper. Since this tool’s main purpose is to aid computer architecture students, contributing to an improvement in their learning experience, it comprises varied concepts of computer architecture and organization in a simple and intuitive manner, such as the internal structure of a CPU, in both its unicycle and pipelined versions, and the effects of executing a set of instructions. As to better understand its value, the developed tool was then validated through a case study with the participation of computer architecture students. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)