Abstract
This research's aim is to check if computer engineers have an appropriate set of rules on how to develop eXtended Reality (XR) applications for users with Autism Spectrum Disorder (ASD). In order to answer this question, a literature review has been performed on the main computer science association publishing avenues: the Digital Library of the Association for Computer Machinery (ACM DL), and the Institute of Electrical and Electronics Engineers (IEEE). The research findings are synthesized in 12 recommendations, but a gap is identified in established methodologies and consolidated knowledge.

Abstract
The fourth industrial revolution is based on the production process’s digitisation to promote traceability, reduction of waste and decision support. This transition from the physical to the information domain requires, besides the process’s digital representation, sensors and transducers capable of capturing the system states. In the case of agricultural processes, due to the heterogeneity of production conditions, as well as the large area in which it takes place, physical characterisation often requires a large number of sensors spread over several hectares. This fact makes the economic costs associated with the digitisation of agriculture a very relevant aspect. In particular, if robust and precise sensors are to be deployed. Within this reference frame, this paper describes the approach taken to develop low-cost sensors capable of measuring soil moisture at three different depths. Robustness is achieved through the use of materials with high mechanical strength and corrosion resistance and both accuracy and repeatability are accomplished by using a signal conditioning strategy based on a programmable analogue front-end. Details about the methodology used are presented throughout the article. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
Over the last decades, district heating has been under development, especially the technologies like heat pumps, solar thermal and cogeneration. However, there is still a long way to go regarding regulation, legislation and market liberalization, which varies across countries and regions. The objective of this work is to investigate the potential benefits of decentralized district heating systems in residential areas. By studying a case study of EnergyLab Nordhavn, a residential area in Copenhagen, Denmark, the paper compares the market outcomes of decentralized systems such as community markets to the centralized pool market currently in practice, under the EMB3Rs platform. The study focuses on key market outputs such as dispatched production, revenues, and daily consumption patterns. Additionally, the paper examines the impact of advanced features such as flexible heat consumption and network awareness in the market. The results of this research suggest that decentralized district heating systems have the potential to improve market outcomes and increase energy efficiency in residential areas.

Abstract
Left ventricle (LV) segmentation of 2-D echocardiography images is an essential step in the analysis of cardiac morphology and function and-more generally-diagnosis of cardiovascular diseases (CVD). Several deep learning (DL) algorithms have recently been proposed for the automatic segmentation of the LV, showing significant performance improvement over the traditional segmentation algorithms. However, unlike the traditional methods, prior information about the segmentation problem, e.g., anatomical shape information, is not usually incorporated for training the DL algorithms. This can degrade the generalization performance of the DL models on unseen images if their characteristics are somewhat different from those of the training images, e.g., low-quality testing images. In this study, a new shape-constrained deep convolutional neural network (CNN)-called B-spline explicit active surface (BEAS)-Net-is introduced for automatic LV segmentation. The BEAS-Net learns how to associate the image features, encoded by its convolutional layers, with anatomical shape-prior information derived by the BEAS algorithm to generate physiologically meaningful segmentation contours when dealing with artifactual or low-quality images. The performance of the proposed network was evaluated using three different in vivo datasets and was compared with a deep segmentation algorithm based on the U-Net model. Both the networks yielded comparable results when tested on images of acceptable quality, but the BEAS-Net outperformed the benchmark DL model on artifactual and low-quality images.

Abstract
Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) in adults can present challenges in learning and work environments, by impacting focus, organization, social interaction, and self-esteem. The aim of this study is the potential of Human-Computer Interaction (HCI) in the empowerment of adults with ADHD and ASD. Specific difficulties faced in educational and professional settings were found through qualitative interviews with six participants. HCI seems to offer a pathway towards a more inclusive future, as educational technology solutions built on HCI principles can create better and alternative learning environments with fewer distractions and gamification for increased engagement. Assistive technologies can address challenges related to focus and organization (like task management apps, time tracking tools). Additionally, features promoting social interaction and communication can empower individuals with ASD. Technologies arising nowadays like Augmented and Virtual Reality (AR/VR) can create interactive learning experiences. Through the use of HumanComputer Interaction principles, more inclusive learning and work environments that empower individuals with ADHD and ASD can originate, while improving engagement and efficiency for all.

Abstract
Sectorization problems refer to dividing a large set, area or network into smaller parts concerning one or more objectives. A decision support system (DSS) is a relevant tool for solving these problems, improving optimisation procedures, and finding feasible solutions more efficiently. This paper presents a new web-based Decision Support System for Sectorization (D3S). D3S is designed to solve sectorization problems in various areas, such as school and health districting,planning sales territories and maintenance operations zones, or political districting. Due to its generic design, D3S bridges the gap between sectorization problems and a state-of-the-art decision support tool. The paper aims to present the generic and technical attributes of D3S by providing detailed information regarding the problem-solution approach (based on Evolutionary Algorithms), objectives (most common in sectorization), constraints, structure and performance.