Abstract
Purpose This study aims to explore worldwide innovative solutions that have been proposed to mitigate the effects of COVID-19 pandemic on people's mental health. Design/methodology/approach A qualitative methodology is adopted, which performs an exploratory study considering the innovative projects identified by the Observatory for Public Sector Innovation framework. Additionally, the analysis of the relevance and characteristics of these projects are explored considering a multidimensional framework composed of five dimensions: novelty level; social need; improvement of society; sector neutrality; and level of emergence. Findings The findings reveal that the number of projects in the field of mental health is low, despite their strong relevance to their communities. These projects arise from a strong social need to protect especially the most vulnerable groups in this pandemic and involve a large number of partners in the public sector, business and civil society. The role of volunteering in the revitalization and growth of these initiatives is also recognized. Originality/value This study is relevant in both the theoretical and practical dimensions. It allows the exploration of these projects considering the dimensions of social innovation and offers practical implications that allow these projects to be replicated in other countries and regions.

Abstract
The performance of shared-resource multi-core hard-ware platforms in complex cyber-physical systems (CPSs), e.g., automotive industry, can be improved using task-based parallelism through OpenMP. However, most CPS require certain level of predictability, which challenges the efficient implementation of the task-to-thread mapping process. This exploratory work build on the fact that existing mapping methods mostly use elementary or heuristic algorithms, and the idea that artificial intelligence (AI) algorithms can be used to enhance the efficiency of such processes. Accordingly, this paper (1) evaluates the suitability of AI-based tech-niques in improving the performance of task-to-thread mapping in the OpenMP framework, and (2) proposes a hypothesis to perform an intelligent mapping using fuzzy logic for multi-queue schedulers to improve the predictability of the system. © 2025 Elsevier B.V., All rights reserved.

Abstract
Background and Aim: Papillomaviruses (PVs) infections have been documented in numerous animal species across different regions worldwide. They often exert significant impacts on animal health and livestock production. Scientists have studied natural products for over half a century due to their diverse chemical composition, acknowledging their value in fighting cancer. Acorns (Quercus ilex) are believed to have several unexplored pharmacological properties. This study aimed to evaluate the in vivo safety and cancer chemopreventive activity of an infusion extract of Q. ilex in a transgenic mouse model of human PV (HPV)-16, which developed squamous cell carcinomas through a multistep process driven by HPV16 oncogenes. Materials and Methods: Q. ilex extract was prepared by heating in water at 90 degrees C and then characterized by mass spectrometry. Phenolic compounds from this extract were administered in drinking water to female mice in three different concentrations (0.03, 0.06, and 0.09 g/mL) over a period of 28 consecutive days. Six groups (n = 6) were formed for this study: group 1 (G1, wildtype [WT], water), group 2 (G2, HPV, water), group 3 (G3, WT, 0.09 g/mL), group 4 (G4, HPV, 0.03 g/mL), group 5 (G5, HPV, 0.06 g/ mL), and group 6 (G6, HPV, 0.09 g/mL). Throughout the experiment, humane endpoints, body weight, food intake, and water consumption were recorded weekly. Following the experimental period, all mice were sacrificed, and blood, internal organs, and skin samples were collected. Blood was used to measure glucose and microhematocrit and later biochemical parameters, such as creatinine, urea, albumin, alanine aminotransferase, and total proteins. Histological analysis was performed on skin and organ samples. Results: The administration of Q. ilex extract resulted in a statistically significant increase in relative organ weight among HPV transgenic animals, indicating adaptive biological response to the tested concentrations. Moreover, a reduction in characteristic skin lesions was observed in animals treated with the 0.06 and 0.09 g/mL extract. Conclusion: These results provide a favorable chemopreventive profile for Q. ilex extract at concentrations of 0.06 and 0.09 g/mL. This study highlights the potential of Q. ilex extract as a safe and effective therapeutic strategy against HPV16associated lesions in transgenic mouse models. The limitation of our study was the durability of transgenic animals. As a more sensitive species, we must always be careful with the durability of the test. We intend to study concentrations of 0.06 and 0.09 g/mL for longer to further investigate their possible effects.

Abstract
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Abstract
Various relevant aspects of our lives relate to the places we visit and our daily activities. The movement of individuals between regular places, such as work, school, or other important personal locations is getting increasing attention due to the pervasiveness of geolocation devices and the amount of data they generate. This paper presents an approach for personal location prediction using a probabilistic model and data mining techniques over mobility data streams. We extract the individuals’ locations from relevant events in a data stream to build and maintain a Markov Chain over the important places. We evaluate the method over 3 real-world datasets. The results show the usefulness of the proposal in comparison with other well-known approaches.

Abstract
Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. Consequently, this paper addresses those problems by proposing an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (i) incremental profiling, (ii) data drift detection & adaptation, and (iii) identification of spam reviews employing Machine Learning. The explainable mechanism displays a visual and textual prediction explanation in a dashboard. The best results obtained reached up to 87% spam F-measure.