Abstract
Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care.

Abstract
Artificial intelligence (AI) is a strategy for global economic development due to its economic potential. However, the need for more transparency in AI applications generates mistrust because of the complexity of the algorithms. AI has transformed the service industry along with the development and challenge of human-AI interactions. This interaction can elicit negative feelings in consumers, creating communities to voice their disapproval and hatred of brands. Research in this area needs to be improved, and this study aims to understand the negative feelings that result from human-AI interaction in online communities (Reddit). Using sentiment analysis techniques and a qualitative approach, we aimed to identify the predominant negative emotions generated by this interaction. This study also hopes to understand the emotional effects of this interaction better, thus filling in a gap in the literature. The insights obtained can help develop more effective interaction strategies between humans and AI that can benefit brands and society. The results show a sizable presence of negative feelings such as hate anger and frustration. It is, therefore, essential to understand the negative interactions between consumers, brands and AI and the need to develop strategies to mitigate these feelings. Contributions from the academic and corporate fields emphasise the importance of monitoring feelings and promoting more positive interactions between brands and consumers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
This research revolves around the potential challenges, opportunities, and strategies associated with human-centered generative artificial intelligence (AI) in the music compositional practice, emphasizing the role of metaphorical design in shaping musicians' expectations toward the adoption of generative AI in their everyday creative activities. Through a human-computer interaction (HCI) lens, this paper aims to discuss the cultural implications of the human-AI metaphorical design space for the seamless integration of intelligent algorithmic experiences in a manner that aligns with cultural values and realistic expectations of music creators while promoting informed policies, sociotechnical imaginaries, and culturally sensitive generative AI design strategies with focus on user-friendly interfaces that resonate with diverse music creation groups.

Abstract
The advantages of Virtual Reality (VR) over traditional training, together with the development of VR technology, have contributed to an increase in the body of literature on training professionals with VR. However, there is a gap in the literature concerning the comparison of training in a Virtual Environment (VE) with the same training in a Real Environment (RE), which would contribute to a better understanding of the capabilities of VR in training. This paper presents a study with firefighters (N = 12) where the effect of a firefighter training exercise in a VE was evaluated and compared to that of the same exercise in a RE. The effect of environments was evaluated using psychophysiological measures by evaluating the perception of stress and fatigue, transfer of knowledge, sense of presence, cybersickness, and the actual stress measured through participants' Heart Rate Variability (HRV). The results showed a similar perception of stress and fatigue between the two environments; a positive, although not significant, effect of the VE on the transfer of knowledge; the display of moderately high presence values in the VE; the ability of the VE not to cause symptoms of cybersickness; and finally, obtaining signs of stress in participants' HRV in the RE and, to a lesser extent, signs of stress in the VE. Although the effect of the VE was shown to be non-comparable to that of the RE, the authors consider the results encouraging and discuss some key factors that should be addressed in the future to improve the results of the training VE.

Abstract
Context. A low-mass companion potentially in the brown dwarf mass regime was discovered on a similar to 12 yr orbit (similar to 5.5 au) around HD 167665 using radial velocity (RV) monitoring. Joint RV-astrometry analyses confirmed that HD 167665B is a brown dwarf with precisions on the measured mass of similar to 4-9%. Brown dwarf companions with measured mass and luminosity are valuable for testing formation and evolutionary models. However, its atmospheric properties and luminosity are still unconstrained, preventing detailed tests of evolutionary models. Aims. We further characterize the HD 167665 system by measuring the luminosity and refining the mass of its companion and reassessing the stellar age. Methods. We present new high-contrast imaging data of the star and of its close-in environment from SPHERE and GRAVITY, which we combined with RV data from CORALIE and HIRES and astrometry from HIPPARCOS and Gaia. Results. The analysis of the host star properties indicates an age of 6.20 +/- 1.13 Gyr. GRAVITY reveals a point source near the position predicted from a joint fit of RV data and HIPPARCOS-Gaia proper motion anomalies. Subsequent SPHERE imaging confirms the detection and reveals a faint point source of contrast of Delta H2 = 10.95 +/- 0.33 mag at a projected angular separation of similar to 180 mas. A joint fit of the high-contrast imaging, RV, and HIPPARCOS intermediate astrometric data together with the Gaia astrometric parameters constrains the mass of HD 167665B to similar to 1.2%, 60.3 +/- 0.7 M-J. The SPHERE colors and spectrum point to an early or mid-T brown dwarf of spectral type T4(-2)(+1). Fitting the SPHERE spectrophotometry and GRAVITY spectrum with synthetic spectra suggests an effective temperature of similar to 1000-1150 K, a surface gravity of similar to 5.0-5.4 dex, and a bolometric luminosity log(L/L-circle dot)=-4.892(-0.028)(+0.024) dex. The mass, luminosity, and age of the companion can only be reproduced within 3 sigma by the hybrid cloudy evolutionary models of Saumon & Marley (2008, ApJ, 689, 1327), whereas cloudless evolutionary models underpredict its luminosity.

Abstract
The notable expansion of technologies related to automated processes has been observed in recent years, largely driven by the significant advantages they provide across diverse industries. Concurrently, there has been a rise in simulation technologies aimed at replicating these complex systems. Nevertheless, in order to fully leverage the potential of these technologies, it is crucial to ensure the highest possible resemblance of simulations to real-world scenarios. In brief, this work consists of the development of a data acquisition and processing pipeline allowing a posterior search for the optimal physical parameters in MuJoCo simulator to obtain a more accurate simulation of a dexterous robotic hand. In the end, a Random Search optimization algorithm was used to validate this same pipeline.