This study presents a methodology for optimizing key parameters of a fused deposition modeling (FDM) printer to minimize energy consumption (EC) while exceeding a specified tensile strength (TS) threshold. Employing Design of Experiments (DoE) with Taguchi and Response Surface analysis, we identify influential parameters affecting TS and EC. A Mixed-Integer Nonlinear Multi-Objective Optimization model is then utilized to balance TS and EC, resulting in optimal parameter values. Validation using fabricated specimens demonstrates less than 5 % error in Tensile Strength and less than 2 % error in Energy Consumption, confirming the efficacy of the proposed methodology.

A bstract: The integration of Artificial Intelligence (AI) into medical education has emerged as a transformative element in the modern healthcare educational system. With the exponential growth of medical knowledge and the increasing complexity of healthcare systems, AI offers innovative solutions to enhance learning outcomes, facilitate personalized education pathways, and improve clinical decision-making skills among medical professionals. This literature review explores the transformative role of AI in the training of healthcare providers, focusing on advancements in medical education, medical diagnostics, and emergency care training. Additionally, it addresses the readiness of healthcare professionals to employ AI technologies, analyzing their current knowledge, attitudes, and the training provided. By synthesizing findings from multiple studies, we aim to highlight AI’s potential to enhance medical education, address challenges, and propose future directions for integrating AI into healthcare training.

Current emotion research utilizes two primary frameworks: categorical and dimensional. Each framework offers unique insight, but also faces challenges in capturing the full spectrum of emotional experience. This study compares and integrates categorical and dimensional approaches to better understand their applications and limitations in emotion research, particularly in enhancing human-centered designs. Using Plutchik’s Wheel and the Self-Assessment Manikin (SAM), 238 participants rated their emotional responses to AI- generated images of various driving scenarios. Subsequently, the categories of emotions were mapped into the valence and arousal space. The integration of categorical and dimensional models revealed significant variability in individual interpretations of different emotions, highlighting the complexity and non-uniformity of emotional experience. These findings suggest that emotions may require more than two dimensions for full representation. A multifaceted approach that combines both frameworks can offer a comprehensive understanding of emotions and is essential for developing effective, empathetic, human-centered applications.

Advanced driver assistance systems (ADAS) can enhance driving safety, yet their effectiveness depends on drivers’ accurate understanding of system capabilities, limitations, and interactions. This study proposes a structured evaluation framework that combines state diagrams, Cognitive Task Analysis (CTA), and behavioral modeling to assess drivers’ mental models of overlapping ADAS functionalities. Twelve participants operated a vehicle equipped with multiple longitudinal and lateral features while providing concurrent verbal protocols. Behavioral indicators—such as pedal hovers and response delays—were recorded and analyzed to identify patterns of confusion and automation misinterpretation. Preliminary findings highlight specific system transitions and interface inconsistencies that contribute to cognitive mismatches and inappropriate driver responses. By linking observed behaviors to conceptual misunderstandings, this framework supports the systematic identification of usability challenges in ADAS design. The approach provides actionable insights for refining automation logic, improving human-machine interfaces, and informing educational strategies aimed at promoting accurate mental models and safer interactions with automated driving technologies.

The increasing integration of automation into transportation systems has raised critical questions about how humans interact with and rely on advanced vehicle technologies. SAE Level 3 (L3) automated vehicles represent a transitional stage, where control alternates between the driver and the system depending on operational boundaries. This dynamic introduces distinct trust-related challenges, as drivers must transition between passive monitoring and active control. To clarify how trust is formed, measured, and influenced in L3 driving, this systematic literature review synthesizes 80 empirical studies identified through a PRISMA-guided search. The analysis reveals six key categories shaping trust: automation features, environmental context, driver characteristics, communication and HMI design, non-driving task engagement, and training or familiarization. Most studies rely on simulator environments and self-report measures, limiting ecological validity and comparability. Few explore how trust evolves over time or across contexts. The review underscores the need for standardized, multimodal trust measurement approaches and more longitudinal, real-world research.