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Internet of Things (IoT) is one of the most important emerging technologies that supports Metaverse integrating process, by enabling smooth data transfer among physical and virtual domains. Integrating sensor devices, wearables, and smart gadgets into Metaverse environment enables IoT to deepen interactions and enhance immersion, both crucial for a completely integrated, data-driven Metaverse. Nevertheless, because IoT devices are often built with minimal hardware and are connected to the Internet, they are highly susceptible to different types of cyberattacks, presenting a significant security problem for maintaining a secure infrastructure. Conventional security techniques have difficulty countering these evolving threats, highlighting the need for adaptive solutions powered by artificial intelligence (AI). This work seeks to improve trust and security in IoT edge devices integrated in to the Metaverse. This study revolves around hybrid framework that combines convolutional neural networks (CNN) and machine learning (ML) classifying models, like categorical boosting (CatBoost) and light gradient-boosting machine (LightGBM), further optimized through metaheuristics optimizers for leveraged performance. A two-leveled architecture was designed to manage intricate data, enabling the detection and classification of attacks within IoT networks. A thorough analysis utilizing a real-world IoT network attacks dataset validates the proposed architecture's efficacy in identification of the specific variants of malevolent assaults, that is a classic multi-class classification challenge. Three experiments were executed utilizing data open to public, where the top models attained a supreme accuracy of 99.83% for multi-class classification. Additionally, explainable AI methods offered valuable supplementary insights into the model's decision-making process, supporting future data collection efforts and enhancing security of these systems. [ABSTRACT FROM AUTHOR]

Background/Objectives: A properly functioning musculoskeletal system is imperative for human well-being at every stage of life, including at an older age. This study's aim was to assess the relationship between sociodemographic and physical functioning variables and the presence of individual musculoskeletal disorders (MSDs), MSD comorbidity, and multimorbidity, as well as to determine factors that are independent predictors of the presence of MSDs in people over 65 years old. Methods: This population-based study included 3701 participants aged 65 years and older. Data on individual MSDs addressed cervical and lumbosacral spine regions and degenerative joint disease (arthrosis). The subjects were categorized into four groups: those without any diseases; those with one MSD; those with two MSDs (comorbidities); and those with three MSDs (multimorbidities). The sociodemographic and physical functioning variables were analyzed. Results: Females were more likely to have MSDs (two: OR 1.95 and three: OR 2.25) than men. Elderly people aged 75 and above were 1.49 times more likely to have three MSDs. Elderly people with elementary school education were more likely to have MSDs (two: OR 1.34 and three: OR 2.06) than those with high school/university education. The low-income population was 2.47 times more likely to have three MSDs. Individuals with partial activity limitations because of health problems had greater chances of having one, two, or three MSDs (OR 1.60, 1.59, and 1.94, respectively), and elderly individuals with severe limitations had an OR of 1.43, 2.17, and 4.12, respectively. Individuals with some/many difficulties in walking up or down 12 steps were more likely to have MSDs (two: OR 2.26 and three: OR 2.28). Conclusions: The significant predictors of experiencing a single MSD, MSD comorbidity, or MSD multimorbidity include residing in the Serbian capital city and having limitations in activities due to health problems. A significant predictor of having a single MSD or MSD comorbidity is residing in the northern region of Serbia. A significant predictor of MSD comorbidity is residing in the southeastern region of Serbia. Significant predictors of MSD comorbidity or MSD multimorbidity include female gender, an elementary school educational level, and experiencing difficulty in walking up or down 12 steps. Significant predictors of MSD multimorbidity are being 75 years of age and above and having a lower income. [ABSTRACT FROM AUTHOR]

It has been estimated that 9 to 13% of children and adolescents have a mental disorder that causes significant functioning impairment and that only one fifth of those who need mental health services actually receive them. The majority of children and adolescents are enrolled in schools, where they spend a considerable amount of time, and this is followed by the increasing efforts to establish collaboration between mental health professionals and school personnel. The role of mental health professionals is crucial in improving children's mental health, as well as for providing better response to parents, educational staff and other agencies. There are several ways for a child psychiatrist to reach children and adolescents in a school setting: through individual student consultations (case consultations), through consultations to schools on general mental issues (systems consultations) and through promotion of mental health by creating and demonstrating evidence-based programmes for children, parents, school staff and others who are involved in child's care. In order to achieve these goals, we need to establish partnerships and to define roles among organisations and individuals in the wider school environment, such as schools and school management, municipal authorities and administration, media, police, social welfare centres, health centres, parents and youth associations.