Abstract: Background: Several studies have reported alarming rates of mental health issues and sleep problems among nurses even in the post-pandemic era. Objectives: To investigate the prevalence of stress, anxiety, depression, burnout and insomnia among nurses in Greece one year after the end of the pandemic and to construct a mediation model evaluating the impact of stress on insomnia, the chain mediating roles of depression and burnout, as well as the moderating role of anxiety in the model. Methods: This cross-sectional study was conducted online in July 2024 and included 380 hospital nurses who completed the Depression Anxiety Stress Scale (DASS-21), the Copenhagen Burnout Inventory (CBI) and the Athens Insomnia Scale (AIS). Results: The prevalence rates of stress, anxiety, depression, burnout and insomnia were 33.9%, 33.3%, 35%, 46.8% and 56.1%, respectively. Multiple regression analysis indicated that the Depression subscale of the DASS-21 explained 40.6% of the variance in AIS, while an additional 7.6% was explained by CBI and another 1.3% rate by the Stress subscale of the DASS-21. Mediation analysis revealed that stress affected insomnia both directly and indirectly through the chain mediating roles of depression and burnout. Anxiety moderated the chain mediation path by enhancing the negative impact of stress on depression. Conclusions: The proposed moderated chain mediation model introduces certain factors influencing insomnia and explains how changes in any one of these factors effectuate changes in the other factors, offering insights for individualized interventions.

Abstract: Creativity has been widely regarded throughout history to be unique to humanity alone. However, the recent rise of sophisticated generative artificial intelligence (AI) models, with profound applications across limitless fields, poses the question of whether AI has gained the potential to aid humans in creative endeavors. Our study investigates this novel question by assessing the creative capabilities of human participants as compared to various large language models specifically prompted to impersonate each participant. Using objective measures such as the AUT, TTCT, and RAT, we determine if AI can be of assistance to humans in regard to creativity.

Abstract: Organizations in the Third sector operating in non-Western contexts need to have enhanced governance models that match the need to ensure ethical and efficient commercial operations within their mission. The key research question of present article is how the implementation of GCG in Slovenian NGOs will contribute to strengthening their legitimacy, stakeholder trust, and effectiveness in achieving social goals. The article develops a conceptual model which should include all key variables, mediators, moderators and latent constructs in itself, serving as a theoretical basis for future empirical studies and best practices. The main finding is that the GCG should be organized as a single-track administrative body of public institutes in the future. Better single-truck GCG regulations, which would presuppose amendment of legislation, can be created and executed in practice only by working together, with the public, regulatory agencies, academic community, NGOs, and prominent intellectuals.

Abstract: This study focuses on the phenomenon of recurring specific failures in the aviation industry, despite maintaining high safety levels through advanced technology and sophisticated safety management systems. To overcome the limitations of the current Safety-I centered reactive safety management approach, which only addresses surface-level causes and solutions of failures, this study proposes a new integrated approach combining Safety-I and Safety-II. To achieve this, we applied the HEAR (Human Error Analysis and Reduction) framework, which has been validated in the railway sector, to analyze three types of failure cases in aviation - FMS (Flight Management System) operation errors, turbulence-related accidents, and aircraft energy management issues. The analysis revealed that organizational factors (87.1%) were more significant causes of failures than individual/task factors (12.9%). Furthermore, by combining Safety-II methodologies such as AA LIT (American Airlines' Learning and Improvement Team) and FSF LAO (Flight Safety Foundation's Learning from All Operations), we developed specific methods to transform failure cases into resilient success cases. This study emphasizes strengthening safety managers' capabilities and establishing systematic training programs, proposing a paradigm shift in aviation safety management from 'failure prevention' to 'success expansion.' This integrated approach is expected to contribute to improving aviation safety culture and operational performance.

Abstract: The use of augmented reality, assistive technology (AT) and augmentative and alternative communication (AAC) presents an opportunity to significantly enhance the general reading abilities of students with specific learning disorders (SLD) by providing effective learning tools. This study aimed to assess students’ learning experiences to understand the effectiveness of AT and AAC in language skills development and identify the AT tools and devices used in classroom-settings, to better inform practitioners. A literature search was performed using various databases. The review included 22 articles, focusing on multiple implications of AT and AAC. Common factors associated with AT use in teaching students with SLD were identified, and a thematic investigation revealed recurring data on the impact of AT solutions on students with SLD. The findings indicate notable improvements in language skills among students with SLD. However, two articles reported limited effects or no effects on language skills, self-efficacy, and self-esteem. This review shows that AT and AAC effectively support language skills development and outcomes (vocabulary, spelling, orthography, phonological awareness, and reading comprehension) for students with SLD. Given the limited number of studies and the complexity of explored factors, this conclusion should be interpreted with caution.

Abstract: Artificial Intelligence, Big Data and the Internet of Things ( IoT ) have been in constant development within various fields of study for their application, especially within Traffic Management Systems and sustainable urban transport, the above was born with the need to reduce, mitigate and control large traffic flows, polluting emissions from vehicles, long travel times and traffic accidents that occur within cities, especially cities in underdeveloped countries, such as the Latin American zone. It is important to generate development strategies that consider a multidimensional and long-term perspective, the success of urban development requires strategic governance and the construction of public projects and the construction of the habitat as a public matter with social inclusion. Coordinated action is needed for environmental protection, territorial planning and public policies. Therefore, the purpose of this research is to analyze the implementation of Artificial Intelligence, Big Data and IoT within Traffic Management systems, specifically, within the Ibero-American zone, likewise, to examine the most effective artificial intelligence technologies and algorithms such as Predictive Models that provide traffic flows helping to have better control within the roads, these use Neural Networks to improve the statistical models used in these models, Machine Learning and Deep Learning among others, which have shown to have positive results in predicting demand and reducing vehicular congestion and accidents within cities and identify the challenges and limitations found in the implementation of these solutions within cities. For the above, a bibliographic review and a bibliometric analysis were carried out, which was performed using the Scopus database, supported by Bibliometrix and VOSviewer for bibliometrics, analyzing information on data, trends and characteristics of research publication, most preferred and productive journals, author, journal and productive countries, thematic evaluation and co-occurrence of words. The results show a low relevance of Ibero-America within the field of study despite having several investigations developed within it. The knowledge obtained in this research will be valuable for young researchers, industry professionals, transport policy makers and government entities in their search to identify solutions to road congestion.

Abstract: The recent paradigm shift within cognitive neuroscience toward predictive processing appearsto align with many psychoanalytic conceptualizations of the mind. In this article, we arguethat several psychoanalytic concepts, such as projection, transference, wish-fulfillment, andperceptual identity, are particularly compatible with the current neuroscientific conception ofthe brain as a prediction machine. Specifically, we propose that the concept of projection asused in modern psychoanalysis to explore subjective experience and fantasies is closelyanalogous to the concept of prediction as it is used to explain the fundamental cognitivefunctions of the brain. We discuss the implications of this parallelism for understanding therole of homeostasis in psychoanalysis and cognitive neuroscience, and we also discuss theparallels between insight and surprise in these two fields of mental science. Limitations indrawing parallels between projection and prediction are also addressed. By integrating thesetwo fields, we envision the possibility of tackling subjectivity scientifically.

Abstract:

Sri Lanka is exploring sustainable energy solutions to meet increasing energy demands through mitigating the impact of climate change. The present heavy reliance on imported fossil fuels by the country makes it vulnerable to global price fluctuations and supply disruptions in global geopolitical changes. As a pre-merging economy, Sri Lanka stands to benefit significantly from adopting the regulatory frameworks, including the sound policies focused on green hydrogen, despite it being in the initial stage of basic research in the country but having the potential to climb to the R&D economic contribution on a long-term basis. Promoting a hydrogen economy within a robust policy framework and incentives can enable job creation, attract direct foreign investment, and thereby foster innovation in clean energy technologies. Transitioning to green hydrogen has a strong promise to position Sri Lanka as a proactive player in the global shift towards sustainability in the economies. The implications of sound policies to harness the green hydrogen spillovers and spinoffs are not only a strategic imperative but also a pathway to ensuring a resilient and prosperous future for the nation.

Abstract: Blended learning has become an innovative approach in English language education, integrating modern technology with traditional methods to create a flexible and effective learning experience. This article explores strategies that can support the implementation of blended learning, including the use of interactive videos, game-based learning applications, and online formative assessments. The research was conducted through a qualitative approach with a case study method in educational institutions that have implemented this model. The results showed that blended learning increases student engagement, learning flexibility, and the development of 21st-century skills. However, the success of this approach is highly dependent on teacher readiness, technology infrastructure, and institutional policy support. In addition, it is important to maintain a balance between online learning and face-to-face interactions to ensure students' mastery of communication skills. With careful planning and integrated support from all parties, blended learning can be an effective solution in teaching English in the digital era. This article offers important insights for educators and policy makers to optimize the potential of blended learning.

Abstract: The integration of ChatGPT, a sophisticated AI chatbot by OpenAI, has introduced transformative possibilities in language education. Distinguished by its ability to generate diverse text outputs, answer queries, and provide instant translations, ChatGPT represents a valuable asset in modern AI applications. However, its specific role in enhancing students' language learning remains underexplored. This study addresses this gap by examining students' interactions with ChatGPT and their perceptions of its influence on language acquisition. A qualitative methodology was adopted, utilizing semi-structured interviews with ten EFL students at Islamic Azad University, Varamin-Pishva Branch, Iran. The findings indicate that ChatGPT effectively supports language learning by assisting students with diverse tasks and fostering personalized learning experiences. Participants exhibited critical evaluative skills, adapting prompts to optimize outcomes and addressing challenges related to academic integrity. These skills are essential in navigating the ethical dimensions of AI-assisted learning. The present study contributes to extant research by providing empirical insights into ChatGPT's utility from learners' perspectives, highlighting its capacity for immediate feedback and autonomous learning facilitation. The implications for pedagogy include the development of AI-enhanced language support systems, the promotion of learner autonomy, and the cultivating of critical thinking for evaluating AI-generated content. As educational institutions continue to integrate technological advancements, ChatGPT demonstrates significant potential in reshaping teaching and learning practices, paving the way for innovative approaches in language education.