This paper investigates the use of the Bernstein Basis Function network for reconstructing accurate geometries of bones from medical images. Accurate models of bone geometry are indispensable for biomedical applications, especially in designing customized orthopedic implants. The two-layer neural architecture BBF network uses nonlinear Bernstein polynomials to perform curve and surface fitting, where the generated weights during training act as control points for Bézier curves. The BBF network adjusts the number of basis neurons so that curve fitting accuracy is optimally balanced with smoothness, addressing weaknesses inherent in traditional and earlier neural network methods. The constraints of positional and tangential continuity are incorporated into the learning algorithm to improve geometric consistency. Quantitative analysis has shown that the BBF network significantly improves the precision of curve fitting, reduces the roughness of reconstructions, and outperforms other methods in simulation studies. Experiments in vivo further validate its clinical usability, showing its ability to reproduce complex geometries with high accuracy in bone reproductions. This study also shows that the BBF network can be a crucial innovation in medical imaging where anatomical modeling and personalized medicine can be accomplished robustly. Some limitations include: dependency on certain imaging techniques and dataset biases. As such, the future course of work involves broader validations across various imaging techniques.
Human situation awareness is a critical factor in human-artifact interaction, particularly under high workloads where errors can have catastrophic consequences. This study investigates the dynamics of human interaction with externally provided tasks and their impact on situation awareness. Using the 1995 Columbia high-tech aircraft accident as a case study, the research examines how resource-bounded human cognition interacts with external environments, exploring the implications of design expectation deviations and the role of dynamic simulations in understanding these phenomena. Findings highlight the influence of cognitive-environmental interactions on situation awareness, the significance of adaptive design strategies in mitigating human errors, and the potential of advanced simulations to enhance awareness in complex systems. The study underscores the importance of integrating human factors into system design and proposes recommendations for improving high-tech system reliability. This work contributes to advancing the understanding of human situation awareness and its implications for safer, more effective human-artifact interactions.
This paper investigates the use of the Bernstein Basis Function network for reconstructing accurate geometries of bones from medical images. Accurate models of bone geometry are indispensable for biomedical applications, especially in designing customized orthopedic implants. The two-layer neural architecture BBF network uses nonlinear Bernstein polynomials to perform curve and surface fitting, where the generated weights during training act as control points for Bézier curves. The BBF network adjusts the number of basis neurons so that curve fitting accuracy is optimally balanced with smoothness, addressing weaknesses inherent in traditional and earlier neural network methods. The constraints of positional and tangential continuity are incorporated into the learning algorithm to improve geometric consistency. Quantitative analysis has shown that the BBF network significantly improves the precision of curve fitting, reduces the roughness of reconstructions, and outperforms other methods in simulation studies. Experiments in vivo further validate its clinical usability, showing its ability to reproduce complex geometries with high accuracy in bone reproductions. This study also shows that the BBF network can be a crucial innovation in medical imaging where anatomical modeling and personalized medicine can be accomplished robustly. Some limitations include: dependency on certain imaging techniques and dataset biases. As such, the future course of work involves broader validations across various imaging techniques.
This paper discusses the application of semiotics as a unifying framework to understand and improve creativity in engineering design. The study focuses on how concepts change as signs in design processes and stresses the role of analogy and metaphor in creative reasoning. The study illustrates, with a qualitative methodology, the case studies and thematic analysis, that semiotic processes greatly influence the creative value of designs, filling the gap between human creativity and computational design. Findings indicated that the application of semiotics in various design methodologies would unify several design methodologies in order to reveal how these semiotic systems promote the dynamic development of concepts, innovative ways of solving problems, and the integration of computational tools in creative design. Despite the promise, challenges persist in fully capturing human-like creativity and seamless interdisciplinary integration. The study suggests directions for future research to improve the semiotic theories and further their application in engineering design, which will, in turn, improve creative processes..
This research explores the application of pheromone-inspired concepts in agent-based shop floor control systems to enhance production management. Drawing analogies from coordination mechanisms that are observed in insect colonies, the research investigates the efficacy, adaptability, and scalability of pheromone-based coordination. A qualitative methodology is applied in this research, based on case studies and expert interviews, which reveal how pheromone mechanisms enhance task allocation, resource management, and production efficiency while addressing challenges like integration and scalability. Although there have been shown advantages in adaptability and responsiveness, practical applicability and scalability to very large scenarios are yet to be established. This paper focuses on the possibility of adaptive pheromone models for revolutionizing shop floor management while outlining avenues for further research in that direction towards improving industrial relevance.