Phobias are a type of anxiety disorder characterized by severe fear of objects and situations. In recent years, virtual reality exposure therapy has emerged as a safer and more convenient method of treating phobias, with the same rate of success as classical therapy, the in vivo exposure to stimuli. Following extensive research concerning the available technologies and after studying the state of the art in the field, we developed the PhoVR (Phobia therapy through Virtual Reality) system during a 2-year long project that involved the participation of a technical university, a biology faculty, and an industry partner specialized in designing 3D applications. Our final prototype incorporates scenarios for acrophobia, claustrophobia, and fear of public speaking therapy, in which the user is required to perform gamified tasks and navigate virtual reality environments with biophysical data acquisition (electrodermal activity and heart rate), automatic anxiety level classification, biofeedback integrated into the scenes’ elements, the dynamic adaptation of the virtual environments, and relaxation techniques made available at any moment. The control panel is an application dedicated to psychotherapists for managing patients’ profiles and therapy sessions. The feedback obtained in a qualitative survey with subjects and psychotherapists who evaluated the prototype validated the system and provided suggestions for refinement.

This paper examines the effectiveness of virtual reality (VR) in training public speaking skills. The fear of public speaking (FPS) is a common problem that can have a significant impact on an individual’s professional and personal life. Traditional therapies for public speaking anxiety have been shown to be effective, but there is growing interest in the use of VR as an alternative or supplement to these therapies. This study aims to investigate the VR medium in improving public speaking skills and to explore the potential mechanisms underlying this effect. A framework was developed with the aim to investigate the possibility of improving public speaking skills through VR. Key features of this framework include the ability to adjust the audience size and alter the dimensions of the room. Additionally, it allows for the modification of initial audience behaviors. One of the innovative aspects is the inclusion of an evolving attention span in the virtual audience, adding a dynamic element to the VR experience. The framework excels in tracking various metrics in real time and has the audience react dynamically based on them. These metrics include movement and voice parameters. The system is designed to present this data as immediate feedback to the user, but also as a summary after a presentation has concluded. After an extensive two-phased testing, the results are discussed. These findings suggest that VR can be an effective means for improving public speaking skills and potentially helping in alleviating fear of public speaking.

Background: Immersive virtual reality (VR) has emerged as a promising tool to enhance neuroplasticity, motivation, and engagement during post-stroke motor rehabilitation. However, evidence on its feasibility and data-driven integration into clinical practice remains limited. Objective: This pilot study aimed to evaluate the feasibility, usability, and short-term motor outcomes of an immersive VR-assisted rehabilitation program using the Travee-VR system. Methods: Fourteen adults with post-stroke upper-limb paresis completed a 10-day hybrid rehabilitation program combining conventional therapy with immersive VR sessions. Feasibility and tolerability were assessed through adherence, adverse events, the System Usability Scale (SUS), and the Simulator Sickness Questionnaire (SSQ). Motor outcomes included active and passive range of motion (AROM, PROM) and a derived GAP index (PROM–AROM). Correlations between clinical changes and in-game performance metrics were explored to identify potential digital performance metrics of recovery. Results: All participants completed the program without adverse events. Usability was rated as high (mean SUS = 79 ± 11.3), and cybersickness remained mild (SSQ < 40). Significant improvements were observed in shoulder abduction (+7.3°, p < 0.01) and elbow flexion (+5.8°, p < 0.05), with moderate-to-large effect sizes. Performance gains in the Fire and Fruits games correlated with clinical improvement in shoulder AROM (ρ = 0.45, p = 0.041). Cluster analysis identified distinct responder profiles, reflecting individual variability in neuroplastic adaptation. Conclusions: The Travee-VR system proved feasible, well tolerated, and associated with measurable short-term improvements in upper-limb function. By linking clinical outcomes with real-time kinematic data, this study supports the role of immersive, feedback-driven VR as a catalyst for data-informed neuroplastic recovery. These results lay the groundwork for adaptive, clinic-to-home rehabilitation models integrating clinical and exploratory digital performance metrics.

Co-simulation can bring improvements to the development of cyber–physical perceptive systems (CPPS) in critical fields, allowing uninterrupted system operation and flexibility to use both real-time sensor data and non-real-time data. This paper proposes a co-simulation approach that integrates physical systems and communication systems, including both hardware and software components. This study demonstrates how systems of different natures with discrete or continuous events can be simulated using three methods: time stepped, global event driven, and variable stepped. Through two case studies from the medical and energy fields, CPPS and co-simulation reveal their importance for the future by improving precision and efficiency, which leads to more accurate diagnoses and personalized treatments in the medical field and increases the stability of energy networks.

Background: Stroke is the third leading cause of disability. Virtual reality (VR) has shown promising results in post-stroke rehabilitation. The VR TRAVEE system was designed for the neuromotor rehabilitation of the upper limb after a stroke and offers the ability to track limb movements by providing auditory feedback and visual augmentation. The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0), aligned with the International Classification of Functioning, Disability, and Health (ICF) principles, is a valid tool for measuring disability regardless of its cause. This study aimed to investigate the feasibility of the VR TRAVEE system in upper limb rehabilitation for stroke patients. Methods: A total of 14 stroke patients with residual hemiparesis were enrolled in the study. They underwent a 10-day program combining conventional therapy (CnvT) with VR rehabilitation. At baseline (T0), the upper limb was assessed using the Modified Ashworth Scale (MAS), active range of motion (AROM), and the Numeric Rating Scale (NRS) for pain. These assessments were repeated after the 10-day rehabilitation program (T1). Additionally, disability was measured using WHODAS 2.0 at T0 and again 30 days after completing the program. Results: Significant improvements were observed in AROM and MAS scores for the shoulder, elbow, wrist, and metacarpophalangeal joints, as well as in the reduction in shoulder pain (p ˂ 0.001). WHODAS scores decreased across all six domains, with a statistically significant improvement in the Cognition domain (p = 0.011). Conclusions: Combining CnvT with VR as a rehabilitation approach enhances motor function in the upper limb. This method has the potential to reduce disability scores and promote neuroplasticity.

Nowadays, technology allows people to connect and communicate with each other even from miles away, no matter the distance. With the increased use of social networks that were rapidly adopted in human beings’ lives, they can chat and share different media files. While the intent for which they have been created may be positive, they can be abused and utilized in a negative way. One form in which they can be maliciously used is represented by cyberbullying. This is a form of bullying where an aggressor shares, posts, or sends false, harmful, or negative content about someone else by electronic means. In this paper, we propose a solution for bullying detection in GIFs. We employ a hybrid architecture that comprises a Convolutional Neural Network (CNN) and three Recurrent Neural Networks (RNNs). For the feature extractor, we used the DenseNet-121 model that was pre-trained on the ImageNet-1k dataset. The obtained results give an accuracy of 99%.