Abstract
The use of 3D animations in medical education is becoming increasingly popular. Indeed, animations are an efficient way to present complex information, reducing time spent reading textbooks. Thus, in the educational contexts, animations can help students learn more efficiently, retain and better understand information. In addition to improving the learning experience, medical education is a highly important and necessary endeavor, as it can directly affect the lives of patients. These videos can be useful in emergency care instructions and provide information about how to administer CPR to a patient or help in forensic reconstructions; a doctor might explain a medical term to a patient in a friendly way, and they can also help patients understand complex procedures. We find it important to understand if students and schools, when challenged, take a role in their community preparedness for major health problems. Projects led by schools are addressed within educational scenarios focused on STEM education and developed under a relevant public health issue through their continuous engagement in open schooling approach. By implementing an educational scenario with a focus on 3D animation, and thus potentiate the use of this technology, we intend to help raise awareness on the public health theme.

Abstract
The VR2Care project aims to create age-friendly virtual environments fostering interactive technologies for promoting physical activity, rehabilitation, and social interaction. Smart living environments have intelligent interfaces in a single or multi-user context. The VR2Care ecosystem combines virtual reality technologies for supervised exercise with natural interaction techniques, enhancing social virtualization with gamification aspects in the practice of physical activity in a metaverse context. This paper presents the infrastructure and the high-level functional architecture of the VR2Care ecosystem which provides the necessary tools to construct the communication bridges to bring together people sharing common interests, objectives, or ambitions.

Abstract
The COVID-19 pandemic highlighted the inadequate readiness of numerous nations to address diseases that could potentially evolve into epidemics or pandemics, posing risks to health systems and supply chains. Statistical analysis and predictive models were developed to manage COVID-19 and other diseases that harm public health. However, few public-policy decision-support tools are documented in the literature, although several governments have created them. In line with the previous developments, this tool uses socioeconomic features to model the COVID-19 province's mortality rates. This paper presents a tool to predict the mortality rate of a province using supervised learning techniques, named CuraZone. This tool was validated using 196 provinces in Peru for training and considering 31 characteristics. The tool displays the dataset's most essential characteristics, shows the country's mean square error (MSE), and predicts a province's mortality rate. In addition, the tool contributes to the field of Explainable AI (XAI), as it shows the importance of each feature. Highlighted contributions of this work include the support for the decision-making of governments or stakeholders in epidemics, providing the source code in an open and reproducible way, and the estimated mortality rate for specific populations of a neighborhood, city, or country.

Abstract
Extreme weather events, such as windstorms, hurricanes, and heat waves, exert a significant impact on global natural catastrophes and pose substantial challenges for weather forecasting systems. To enhance the accuracy and preparedness for extreme weather events, this study explores the potential of using expert crowdsourcing in storm forecasting research through the application of stigmergic collaboration. We present the development and implementation of an expert Crowdsourcing for Semantic Annotation of Atmospheric Phenomena (eCSAAP) system, designed to leverage the collective knowledge and experience of meteorological experts. Through a participatory co-creation process, we iteratively developed a web-based annotation tool capable of capturing multi-faceted insights from weather data and generating visualizations for expert crowdsourcing campaigns. In this context, this article investigates the intrinsic coordination among experts engaged in crowdsourcing tasks focused on the semantic annotation of extreme weather events. The study brings insights about the behavior of expert crowds by considering the cognitive biases and highlighting the impact of existing annotations on the quality of data gathered from the crowd and the collective knowledge generated. The insights regarding the crowdsourcing dynamics, particularly stigmergy, offer a promising starting point for utilizing stigmergic collaboration as an effective coordination mechanism for weather experts in crowdsourcing platforms but also in other domains requiring expertise-driven collective intelligence.

Abstract
Augmented reality (AR) is a field of knowledge being developed since the middle of the last century. Its use has been spreading because of its usefulness, but more recently because of mobile platforms being widespread and accessible. AR has been applied in several fields of activity, and also in the field of Education and Training, because AR has several advantages over other teaching methods. In this paper, we search and analyze surveys and reviews of AR to present a brief history and its definition. We also present a classification of our sample under a scheme we developed in past work, and present also examples of technologies and applications of AR in each field. Finally, we do a deeper analysis over the publications of Education and Training, advantages and issues of AR in this field, and some research trends.

Abstract
When crowd workers provide their contributions in a shared working environment, they may be influenced by the inputs of other contributors in implicit ways. Stigmergy in crowdsourcing consists of tracking changes in work activities to guide crowd workers based on the digital traces left by other workers. In such scenarios, there is no direct communication between the contributors. Still, the traceable changes they left during their actions act as a mediating element that clearly affects the final work product. From a behavior analysis perspective, the properties recorded in event logs can be of practical value in observing the behavioral traces produced by crowd workers when performing microtasks. This form of task fingerprinting has been explored for over a decade to better understand performance-related data and user navigational behavior in crowdsourcing markets. In line with this, the goal of this paper is to study the feasibility of task fingerprinting alongside the stigmergic effect occurring in a crowdsourcing setting through a user event logger. To this end, a case study was conducted using a real-world scenario of extreme weather phenomena represented on interactive maps. Each user could observe the traces of other crowd members while providing annotations. Twelve experts in weather forecasting were recruited to participate in this study to annotate extreme weather events. The results indicate that it is possible to use task fingerprinting for tracking the stigmergic effect in such activities with gains in terms of implicit coordination. Furthermore, the task fingerprinting allowed to map participants with similar behavioral traces, suggesting an increase in the accuracy of annotation clusters.