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Abstract
In recent years, there has been an increasing trend towards innovative and interactive learning approaches. Serious games have emerged as a promising solution in health education, offering engaging and immersive learning experiences. This article presents the development steps of a mobile application to promote knowledge of nursing assessment and intervention in the family. A prototype was developed for Android devices using React Native technology and Firebase database, incorporating gamification elements. It was then evaluated by potential users. The results showed that the proposed solution successfully enhanced nurses’ learning about family issues and dynamics, receiving positive feedback from users regarding its effectiveness and usability. By leveraging the power of mobile technology and gamification, this research work seeks to bridge an existing gap, contributing to the advancement of game-based educational approaches in the health field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Glaucoma is a condition that affects the optic nerve, with loss of retinal nerve fibers, increased excavation of the optic nerve, and a progressive decrease in the visual field. It is the leading cause of irreversible blindness in the world. Manual classification of glaucoma is a complex and time-consuming process that requires assessing a variety of ocular features by experienced clinicians. Automated detection can assist the specialist in early diagnosis and effective treatment of glaucoma and prevent vision loss. This study developed a deep learning model based on vision transformers, called ViT-BRSET, to detect patients with increased excavation of the optic nerve automatically. ViT-BRSET is a neural network architecture that is particularly effective for computer vision tasks. The results of this study were promising, with an accuracy of 0.94, an F1-score of 0.91, and a recall of 0.94. The model was trained on a new dataset called BRSET, which consists of 16,112 fundus images of patients with increased excavation of the optic nerve. The results of this study suggest that ViT-BRSET has the potential to improve early diagnosis through early detection of optic nerve excavation, one of the main signs of glaucomatous disease. ViT-BRSET can be used to mass-screen patients, identifying those who need further examination by a doctor. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract
Almond cultivation is of great socio-economic importance worldwide. With the demand for almonds steadily increasing due to their nutritional value and versatility, optimizing the management of almond orchards becomes crucial to promote sustainable agriculture and ensure food security. The present systematic literature review, conducted according to the PRISMA protocol, is devoted to the applications of remote sensing technologies in almond orchards, a relatively new field of research. The study includes 82 articles published between 2010 and 2023 and provides insights into the predominant remote sensing applications, geographical distribution, and platforms and sensors used. The analysis shows that water management has a pivotal focus regarding the remote sensing application of almond crops, with 34 studies dedicated to this subject. This is followed by image classification, which was covered in 14 studies. Other applications studied include tree segmentation and parameter extraction, health monitoring and disease detection, and other types of applications. Geographically, the United States of America (USA), Australia and Spain, the top 3 world almond producers, are also the countries with the most contributions, spanning all the applications covered in the review. Other studies come from Portugal, Iran, Ecuador, Israel, Turkey, Romania, Greece, and Egypt. The USA and Spain lead water management studies, accounting for 23% and 13% of the total, respectively. As far as remote sensing platforms are concerned, satellites are the most widespread, accounting for 46% of the studies analyzed. Unmanned aerial vehicles follow as the second most used platform with 32% of studies, while manned aerial vehicle platforms are the least common with 22%. This up-to-date snapshot of remote sensing applications in almond orchards provides valuable insights for researchers and practitioners, identifying knowledge gaps that may guide future studies and contribute to the sustainability and optimization of almond crop management.

Abstract
As advancements in the health sector continue to improve, people are living longer and increasingly aging in place. However, aging is often accompanied by disabilities and mobility issues. Whether these issues develop gradually or suddenly, many homes are not equipped to accommodate such changes, resulting in significant mobility barriers. This document presents a systematic review focusing on three key areas: “Home Barriers and Modification”, “Accessibilities and Disabilities”, and “Gamification and Assistive Technologies”. The aim is to synthesize existing knowledge and explore the interconnections among these topics. The primary objective of this review is to examine how gamification can be utilized to identify barriers within the homes of individuals with disabilities. Despite numerous advancements and available technologies, the review reveals a paucity of research on the application of gamification in this context, highlighting a promising area for future investigation. Additionally, the review underscores the benefits of home modifications to enhance accessibility, emphasizing the potential for significant improvements in the quality of life for individuals with disabilities. © 2024 The Authors

Abstract
Building rehabilitation is a reality, and all phases of rehabilitation work need to be efficiently sustainable and promote healthy places to live in. Current procedures for assessing construction conditions are time-consuming, laborious and expensive and pose threats to the health and safety of engineers, especially when inspecting locations that are not easy to access. In the initial step, a survey of the condition of the building is carried out, which subsequently implies the elaboration of a report on existing pathologies, intervention solutions, and associated costs. This survey involves an inspection of the site (through photographs and videos). Also, biological growth can threaten the humans inhabiting the houses. The World Health Organization states that the most important effects are increased prevalences of respiratory symptoms, allergies and asthma, as well as perturbation of the immunological system. This work aims to alert to this fact and contribute to detecting and locating biological growth (BG) defects automatically in images of the facade of buildings. To make this possible, we need a dataset of images of building components with and without biological growths. At this moment, that database doesn't exist. So, we need to construct that dataset to use deep learning models in the future. This paper also identifies the steps to do that work and presents some real cases of building façades with BG and solutions to repair those defects. The conclusions and the future works are identified. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.