We show how distinct terminally disposed self-organizing processes can be linked together so that they collectively suppress each other's self-undermining tendency despite also potentiating it to occur in a restricted way. In this way, each process produces the supportive and limiting boundary conditions for the other. The production of boundary conditions requires dynamical processes that decrease local entropy and increase local constraints. Only the far-from-equilibrium dissipative dynamics of self-organized processes produce these effects. When two such complementary self-organizing processes are linked by a shared substrate-the waste product of one that is the necessary ingredient for the other-the co-dependent structure that results develops toward a self-sustaining target state that avoids the termination of the whole, and any of its component processes. The result is a perfectly naturalized model of teleological causation that both escapes the threat of backward influences and does not reduce teleology to selection, chemistry or chance. This article is part of the theme issue 'Thermodynamics 2.0: Bridging the natural and social sciences (Part 1)'.
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http://dx.doi.org/10.1098/rsta.2022.0282 | DOI Listing |
Cells Dev
January 2025
Department of Agri-Production Sciences, College of Agriculture, Tamagawa University, Tokyo, Japan.
Embryonic development is a complex self-organizing process orchestrated by a series of regulatory events at the molecular and cellular levels, resulting in the formation of a fully functional organism. This review focuses on activin protein as a mesoderm-inducing factor and the self-organizing properties it confers. Activin has been detected in both unfertilized eggs and embryos, suggesting its involvement in early developmental processes.
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January 2025
Agricultural Engineering Department, Faculty of Agriculture and Natural Resources, Aswan University, Aswan, Egypt.
The experiments were conducted at different levels of infrared power, airflow, and temperature. The relationships between the input process factors and response factors' physicochemical properties of dried garlic were optimized by a self-organizing map (SOM), and the model was developed using machine learning. Artificial Neural Network (ANN) with 99% predicting accuracy and Self-Organizing Maps (SOM) with 97% clustering accuracy were used to determine the quality characteristics of garlic.
View Article and Find Full Text PDFChemosphere
January 2025
College of Design and Engineering, National University of Singapore, Singapore, 117576, Singapore. Electronic address:
Airborne particulate matter (PM) poses significant environmental and health challenges, particularly in urban areas. This study investigated the characteristics of water-soluble organic compounds (WSOC) in PM (PM with an aerodynamic diameter of 2.5 μm or less) in Singapore, a tropical Asian city-state, over a six-month period.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
Shanghai University, Shanghai, China.
Neurodynamic observations indicate that the cerebral cortex evolved by self-organizing into functional networks, These networks, or distributed clusters of regions, display various degrees of attention maps based on input. Traditionally, the study of network self-organization relies predominantly on static data, overlooking temporal information in dynamic neuromorphic data. This paper proposes Temporal Self-Organizing (TSO) method for neuromorphic data processing using a spiking neural network.
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January 2025
Faculty of Education, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia.
To improve the scientific accuracy and precision of children's physical fitness evaluations, this study proposes a model that combines self-organizing maps (SOM) neural networks with cluster analysis. Existing evaluation methods often rely on traditional, single statistical analyses, which struggle to handle the complexity of high-dimensional, nonlinear data, resulting in a lack of precision and personalization. This study uses the SOM neural network to reduce the dimensionality of high-dimensional health data.
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