Carbon nanotubes (CNTs) have attracted considerable interest due to their unique physical, chemical, optical and electrical properties opening avenues for a large number of industrial applications. They have shown potential as fire retardant additives in polymers, reducing heat release rate and increasing time to ignition in a number of polymers. Relevant work on the types, properties and applications has been reviewed particularly considering their application in fire situations. There are concerns over the health risks associated with CNTs and many papers have likened CNTs to the health problems associated with asbestos. There are contradictions relating to the toxicity of CNTs with some papers reporting that they are toxic while others state the opposite. Directly comparing various studies is difficult because CNTs come in many combinations of size, type, purity levels and source. CNTs can potentially be released from polymers during the combustion process where human exposure may occur. While this review has shed some light regarding issues relating to toxicity under different fire scenarios much more thorough work is needed to investigate toxicity of CNTs and their evolution from CNT-polymer nanocomposites in order to reach firm conclusions.
This article comprehensively explores the critical interplay between executive functions (EFs), self-regulation, and social media in promoting a peaceful and inclusive educational environment. The study emphasizes the significance of EFs in regulating cognitive processes and coping with new challenges, focusing on the prefrontal cortex (PFC) and its interconnected neural circuits. Addressing the relevance of EFs in conflict resolution, emotional regulation, and metacognitive practices, the research investigates how they contribute to constructive conflict-resolution, emotional maturity, and responsible online behavior in educational settings. Furthermore, the bidirectional relationship between prosocial behaviors and EFs is explored, revealing their mutual reinforcement during early childhood and beyond. The article discusses the benefits and challenges of social media in educational contexts, highlighting the importance of mindful usage to promote positive interactions and empathy among students, educators, and stakeholders. Adopting a holistic approach, the study examines successful self-regulation practices and approaches that empower individuals to navigate negative emotions and cultivate positive relationships within groups. Additionally, the review sheds light on the potential implications of EFs and self-regulation in neurodevelopmental disorders, such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). It identifies shared genetic bases and neurobiological underpinnings, offering valuable insights for developing targeted interventions to enhance executive function skills in affected populations. The findings have practical implications for professionals in education, parents, and policymakers, emphasizing the importance of nurturing emotional regulation, promoting metacognitive practices, and ensuring responsible social media use to create a harmonious and inclusive educational environment. The article aims to provide valuable insights for researchers and practitioners seeking to cultivate empathy, support positive conflict resolution, and facilitate the holistic development of students and stakeholders in educational and intimate learning environments.
In this work, we proposed a sparse version of the Support Vector Regression (SVR) algorithm that uses regularization to achieve sparsity in function estimation. To achieve this, we used an adaptive L0 penalty that has a ridge structure and, therefore, does not introduce additional computational complexity to the algorithm. In addition to this, we used an alternative approach based on a similar proposal in the Support Vector Machine (SVM) literature. Through numerical studies, we demonstrated the effectiveness of our proposals. We believe that this is the first time someone discussed a sparse version of Support Vector Regression (in terms of variable selection and not in terms of support vector selection).
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