Abstract
Collaboration in extended reality (XR) environments presents complex challenges that revolve around how users perceive the presence, intentions, and actions of their collaborators. This paper delves into the intricate realm of group awareness, focusing specifically on workspace awareness and the innovative visual cues designed to enhance user comprehension. The research begins by identifying a spectrum of collaborative situations drawn from an analysis of XR prototypes in the existing literature. Then, we describe and introduce a novel classification for workspace awareness, along with an exploration of visual cues recently employed in research endeavors. Lastly, we present the key findings and shine a spotlight on promising yet unexplored topics. This work not only serves as a reference for experienced researchers seeking to inform the design of their own collaborative XR applications but also extends a welcoming hand to newcomers in this dynamic field.

Abstract
This article proposes the Artificial Intelligence Models Switching Mechanism (AIMSM), a novel approach to optimize system resource utilization by allowing systems to switch AI models during runtime in dynamic environments. Many real-world applications utilize multiple data sources and various AI models for different purposes. In many of those applications, every AI model doesn’t have to operate all the time. The AIMSM strategically allows the system to activate and deactivate these models, focusing on system resource optimization. The switching of each AI model can be based on any information, such as context or previous results. In the case study of an autonomous mobile robot performing computer vision tasks, the AIMSM helps the system to achieve a significant increment in performance, with a 50% average increase in frames per second (FPS) rate, for this specific case study, assuming that no erroneous switching occurred. Experimental results have demonstrated that the AIMSM can improve system resource utilization efficiency when properly implemented, optimize overall resource consumption, and enhance system performance. The AIMSM presented itself as a better alternative to permanently loading all the models simultaneously, improving the adaptability and functionality of the systems. It is expected that using the AIMSM will yield a performance improvement that is particularly relevant to systems with multiple AI models of a complex nature, where such models do not need to be all continuously executed or systems that will benefit from lower resource usage. Code is available at https://github.com/BrunoGeorgevich/AIMSM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
ObjectivesThere is a growing interest in mindfulness-based programs. Yet, research in the area is limited, and little is known about the factors that moderate the effects of these programs. The two-fold aim of this study was (1) to examine the effects of a mindfulness-based program on dispositional mindfulness, inattention and emotional lability, handwriting fluency, spelling accuracy, and composing quality, as well as school achievement; and (2) to evaluate the moderating role of lesson absences, intervention-related knowledge, and social validity.MethodUsing a quasi-experimental design, 257 fourth graders were assigned to an experimental group receiving a mindfulness-based program (n = 130) or an active control group receiving a health-based program (n = 127). Both programs were implemented in the classroom for 8 weekly units, which included two 30-min sessions delivered by psychologists, followed by three 5-min sessions delivered by teachers. All children were evaluated before and after the programs.ResultsCompared to the control condition, the mindfulness-based program resulted in higher levels of internal and external awareness, and decentering and nonreactivity, as well as better composing quality and mathematics grades. Lesson absences, intervention-related knowledge, and social validity did not moderate the effects of the mindfulness-based program.ConclusionsThese findings support the integration of mindfulness practices in primary school as a means to improve children's academic-related skills and ability to be mindful.

Abstract
Haptic feedback in Virtual Reality is commonly provided through wearable or grounded devices adapted to specific scenarios and situations. Shape-changing devices allow for the physical representation of different virtual objects but are still a minority, complex, and usually have long transformation times. We present Shape-a-getti, a novel ungrounded, non-wearable, and graspable haptic device that can quickly change between different radially symmetrical shapes. It uses a single actuator to rotate several identical poles distributed along a radius to render the different shapes. The format of the poles defines the possible shapes, and in our prototype, we used one that could render concave, straight, and convex shapes with different radii. We conducted a user evaluation with 21 participants asking them to recognize virtual objects by grasping the Shape-a-getti. Despite having difficulties distinguishing between some objects with very similar shapes, participants could successfully identify virtual objects with different shapes rendered by our device. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Abstract
Virtual reality (VR) has the potential to significantly boost productivity in professional settings, especially those that can benefit from immersive environments that allow a better and more thorough way of visualizing information. However, the physical demands of mid-air movements make it difficult to use VR for extended periods. DeskVR offers a solution that allows users to engage in VR while seated at a desk, minimizing physical exhaustion. However, developing appropriate motion techniques for this context is challenging due to limited mobility and space constraints. This work focuses on object manipulation techniques, exploring touch-based and mid-air-based approaches to design a suitable solution for DeskVR, hypothesizing that touch-based object manipulation techniques could be as effective as mid-air object manipulation in a DeskVR scenario while less physically demanding. Thus, we propose Scaled Indirect Touch 6-DOF (SIT6), an indirect touch-based object manipulation technique incorporating scaled input mapping to address precision and out-of-reach manipulation issues. The implementation of our solution consists of a state machine with error-handling mechanisms and visual indicators to enhance interaction. User experiments were conducted to compare the SIT6 technique with a baseline mid-air approach, revealing comparable effectiveness while demanding less physical exertion. These results validated our hypothesis and established SIT6 as a viable option for object manipulation in DeskVR scenarios. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).