Abstract
HARMONI is the first light visible and near-IR integral field spectrograph for the ELT. It covers a large spectral range from 450nm to 2450nm with resolving powers from 3500 to 18000 and spatial sampling from 60mas to 4mas. It can operate in two Adaptive Optics (AO) modes - SCAO (including a High Contrast capability) and LTAO - or with NOAO. The project is preparing for Final Design Reviews. The SCAO system for HARMONI is based on a pyramid wavefront sensor (PWFS) operating in the visible (700 - 1000 nm). Previous implementations on very large telescopes have demonstrated the challenges associated with optimising PWFS performance on-sky, particularly when operated at visible wavelengths. ELT operation will pose further challenges for AO systems, particularly related to the segmentation of the telescope and the control of badly seen 'etal modes'. In this paper we investigate these challenges in the context of the HARMONI SCAO system. We present the results of end-to-end simulations of our baseline approach, using a coupled control basis to avoid the runaway development of petal modes in the control loop. The impact of key parameters are investigated and methods for optical gain compensation and optimisation of the control basis are presented. We discuss recent updates to the control algorithms and demonstrate the possibility of improving performance using a form of super resolution. Finally, we report on the expected performance across a range of conditions.

Abstract

Abstract
Throughout this article the execution of the motion planning for a robotic manipulator by means of Reinforcement Learning methods is studied. Towards this, an implementation based on a Wire and loop game is used as an example case to be solved. The loop is controlled in a single plane as the endeffector of the manipulator. The modeling of the problem and the process of training the agent is detailed. This allowed for the verification of the capacity of a learning based method, having produced, under the considered abstractions, satisfying results by gaining the capability of completing the path imposed by the wire in 23 seconds.

Abstract
Managing company knowledge and using it effectively is more than ever a strong competitive advantage in the business world. The scientific area of knowledge management and knowledge management systems have been intensively studied in the last years; however, we still see the unstructured implementation of knowledge management systems in organizations, the misalignment of knowledge management systems from the business model and the frustration non-use, lack of systems integration and/or non-return on investment made either in technology or spent on heavy implementation processes. The state-of-the-art conducted during this study, showed that most knowledge management systems alignment models in the business context have a strong focus on the organizational dimension, e.g., culture, organizational processes, organizational structure, and leadership, having been identified only three models that also cover, simultaneous, the technological and strategic dimension. Our final objective in this study is, following the research survey methodology, to develop a proposed framework for the strategic alignment of knowledge management systems that can support company managers in their decision-making, and to contribute to the development of scientific knowledge in this area.

Abstract
The special issue on Digital Media Artefacts, “Hybrid Praxis”, is a post-congress collective reflection about technology, science, and art. The invited authors participated in ARTeFACTo 2020 or ARTECH 2021; two key congresses were exploring state-of-the-art digital media arts. These encounters gather experiences from the academic world, practitioner world, and hybrid praxis, with art practice-based research as the common thread. If we reflect on contemporary technology, science, and art, we can see that technology is pushing more profound changes in sciences and arts than the other way around. Just take your eyes off the screen, look around you, and compare your daily life to what it was twenty or even ten years ago. Digital technology has profoundly changed your everyday life, relationships, possibilities, and how you express, explore, research, share, and do. We could therefore look exclusively into the digital domain to frame the relationship between the elements of this triad as it now stands.

Abstract
Traffic flow forecasting is an essential component of an intelligent transportation system to mitigate congestion. Recurrent neural networks, particularly gated recurrent units and long short-term memory, have been the stateof-the-art traffic flow forecasting models for the last few years. However, a more sophisticated and resilient model is necessary to effectively acquire long-range correlations in the time-series data sequence under analysis. The dominant performance of transformers by overcoming the drawbacks of recurrent neural networks in natural language processing might tackle this need and lead to successful time-series forecasting. This article presents a multi-head attention based transformer model for traffic flow forecasting with a comparative analysis between a gated recurrent unit and a long-short term memory-based model on PeMS dataset in this context. The model uses 5 heads with 5 identical layers of encoder and decoder and relies on Square Subsequent Masking techniques. The results demonstrate the promising performance of the transform-based model in predicting long-term traffic flow patterns effectively after feeding it with substantial amount of data. It also demonstrates its worthiness by increasing the mean squared errors and mean absolute percentage errors by (1.25 - 47.8)% and (32.4 - 83.8)%, respectively, concerning the current baselines.