Under-reporting remains a persistent challenge in aviation safety, as pilots may withhold information about incidents, fatigue, and medical issues for various reasons. Although widely studied internationally, the existing literature has largely examined individual reporting domains or specific pilot groups, leaving a gap in the integrated assessment of under-reporting across different aviation populations. This study developed and validated the Aviation Under-Reporting Scale – Hellenic version (AURS-H), the first instrument to systematically measure Greek pilots' under-reporting tendency across incident, fatigue, and medical domains. Building on a prior qualitative study, items were generated and refined through confirmatory factor analysis on a sample of (N = 676) military, commercial, and general aviation pilots, representing a previously unexamined EASA-regulated pilot population. The final 23-item scale demonstrated adequate model fit within a three-factor structure, with measurement invariance confirmed across pilot groups. Results revealed that risk-related concerns, particularly those associated with medical reporting, constitute the most significant barriers, whereas inconvenience-related obstacles and lack of motivation were less influential. The AURS-H extends existing approaches by providing a multidimensional instrument applicable across pilot groups and reporting domains, while also enabling systematic comparison within an EASA-regulated context. Based on the results, targeted managerial interventions and long-term organizational strategies are proposed to mitigate under-reporting, foster trust, and promote a Just Culture. Beyond its national significance, the scale provides opportunities for cross-cultural adaptation and replication, contributing to broader European and global aviation safety strategies.
This study critically distinguishes between AI Agents and Agentic AI, offering a structured conceptual taxonomy, application mapping, and challenge analysis to clarify their divergent design philosophies and capabilities. We begin by outlining the search strategy and foundational definitions, characterizing AI Agents as modular systems driven by Large Language Models (LLMs) and Large Image Models (LIMs) for narrow, task-specific automation. Generative AI is positioned as a precursor, with AI Agents advancing through tool integration, prompt engineering, and reasoning enhancements. In contrast, Agentic AI systems represent a paradigmatic shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy. Through a sequential evaluation of architectural evolution, operational mechanisms, interaction styles, and autonomy levels, we present a comparative analysis across both paradigms. Application domains such as customer support, scheduling, and data summarization are contrasted with Agentic AI deployments in research automation, robotic coordination, and medical decision support. We further examine unique challenges in each paradigm including hallucination, brittleness, emergent behavior, and coordination failure and propose targeted solutions such as ReAct loops, RAG, orchestration layers, and causal modeling. This work aims to provide a definitive roadmap for developing robust, scalable, and explainable AI agent and Agentic AI-driven systems. >AI Agents, Agent-driven, Vision-Language-Models, Agentic AI Decision Support System, Agentic-AI Applications
Circular economy (CE) claims in fashion aim to mobilize consumer participation in reuse and recycling, yet the interpretative flexibility of "circular" language can also enable vague messaging and skepticism. This study investigates how consumers assess CE fashion claims in terms of (a) claim substantiation quality (CSQ) and (b) claim support credibility (CSC), and how these assessments influence perceived green authenticity (PGA), green trust (GTR), and circular purchase intention (CPI) in Greece and the United Kingdom. A cross-national online stimulus-based survey utilizing standardized e-commerce product-card claims for a fictitious circular fashion brand gathered data from Greece (n = 640) and the UK (n = 572). PLS-SEM and multi-group analysis evaluated a model distinguishing CSQ and CSC as complementary message properties. In the overall sample, both CSQ and CSC exhibited a positive correlation with CPI, whereas PGA and GTR emerged as the most significant proximal predictors, with authenticity demonstrating the most substantial impact. Indirect-effect tests showed that CSQ affected CPI through both authenticity and trust. On the other hand, CSC was only effective through authenticity, and there was no clear pathway for CSC trust intention. The multi-group results also showed context sensitivity: Greece exhibited a stronger trust-based path to intention, while the UK had a stronger authenticity-based path to intention. Overall, the results support a dual-route theory of CE claim persuasion. Additionally, they suggest that effective CE fashion communication should combine clear, specific content with credible, externally checkable support cues.
Childhood cancer is a rare but serious disease that affects approximately 400,000 children and adolescents worldwide each year. One of the major problems faced by children with cancer is pain, which affects an estimated 70% of patients. Effective pain management is a central aspect of supportive care for children with cancer.The World Health Organization recommends an analgesic ladder consisting of three levels: For mild pain, non-opioid drugs are used; for moderate pain, mild opioids such as codeine are prescribed, often in combination with non-opioid medications; and for severe pain, strong opioids such as morphine are administered. While this ladder is useful, the complexity of pediatric pain often requires adjustments to treatment. Non-pharmacological interventions such as hypnosis, relaxation techniques, and meditation have proven beneficial in reducing pain and enhancing the mental well-being of children. Pain management in pediatric cancer requires a holistic approach that combines pharmacological and non-pharmacological interventions, with opioids being the cornerstone of analgesic treatment in this population.
PurposeThis study examines the sector-level herding and herding spillover across 11 US-listed Real Estate Investment Trust (REIT) sectors. Design/methodology/approachWe examine herding behaviour of REITs employing returns-based methods in the context of the standard linear model, along with extensions that capture any time-varying component of herding. FindingsA standard linear model shows no herding behaviour for all sectors, except for the lodging and resorts sector; whereas, a more robust quantile regression reveals significant herding in all 11 sectors and for the overall market at the lower tails of the distribution of cross-sectional return dispersion. The time-varying parameter ordinary least squares approach demonstrates spasmodic switches between herding and anti-herding behaviours during the sample period across all sectors and the overall market. A spillover analysis highlights significant and original herding spillover effects across REIT sectors. Practical implicationsOur results could be useful for investment management purposes since herding can drive asset price volatility to a higher level and undermine the effects of portfolio diversification. Thus, investors should pay attention to sectors that are involved in significant herding spillovers for the sake of portfolio and risk management inferences in the US REIT sectors. Regulators should monitor the developments and deploy effective policies to mitigate the effects of herding since it is widely known that herding could ultimately pose a threat to market stability. Originality/valueThis study contributes to the dynamic nature of behavioural biases of investors in US equity REITs and enhances our understanding of contagious effects of herding across sectors.
