Prigogine’s 1978 concept of dissipative structures, drawing parallels with living systems, forms the basis for exploring life’s unique traits. However, these identified similarities prove insufficient in capturing the entirety of life. To address this gap, our proposed modeling approach emphasizes the distinctive ability of living organisms to observe other systems—an attribute intricately tied to quantum mechanics’ "measurement" processes, as highlighted by Howard Pattee. This article introduces a comprehensive mathematical model centered on quantum dynamical dissipative systems, portraying living systems as entities defined by their observational capacities within this framework. The exploration extends to the core dynamics of these systems and the intricacies of biological cells, including the impact of membrane potentials on protein states. Within this theoretical structure, the model is expanded to multicellular living systems, revealing how cells observe quantum dynamical systems through protein state changes influenced by membrane potentials. The conclusion acknowledges the current theoretical status of the model, underscoring the crucial need for experimental validation, particularly regarding the superposition state of membrane proteins under the influence of an electric field.

This study presents a novel approach for automating the body condition scoring of dairy cows, leveraging advancements in 3D imaging technology and deep neural networks. The primary objective was to design and implement a system capable of accurately and efficiently assessing the body condition of dairy cows, a critical metric in livestock management for optimizing health and productivity. To achieve this, a 3D camera was employed to capture detailed point cloud data, reconstructing the three-dimensional morphology of individual cows. The obtained data were then fed into a deep neural network, specifically tailored for the task of ranking body condition. The neural network was trained on a diverse dataset of annotated body condition score representing varying degrees of body condition, ensuring robust performance across different physiological states. The results demonstrate the efficacy of the proposed system in automatically and objectively scoring the body condition of dairy cows. The automated process not only expedites the assessment but also reduces the subjectivity associated with manual scoring methods. This innovative approach holds promise for improving the efficiency of dairy farm management by providing timely and accurate body condition assessments. The integration of 3D imaging and deep learning techniques paves the way for future advancements in precision livestock farming, contributing to enhanced animal welfare, optimized production, and sustainable agriculture practices.