Fractal Behavior in the Clarification Process of Cane Sugar Production

Cane sugar production is an important industrial process. One of the most important steps in cane sugar production is the clarification process, which provides high-quality, concentrated sugar syrup crystal for further processing. To gain fundamental understanding of the physical and chemical processes associated with the clarification process and help design better approaches to improve the clarification of the mixed juice, we explore the fractal behavior of the variables pertinent to the clarification process. We show that the major variables in this key process all show persistent long-range correlations, for time scales up to at least a few days. Persistent long-range correlations amount to unilateral deviations from a preset target. This means that when the process is in a desired mode such that the target variables, color of the produced sugar and its clarity degree, both satisfy preset conditions, they will remain so for a long period of time. However, adversity could happen, in the sense that when they do not satisfy the requirements, the adverse situation may last quite long. These findings have to be explicitly accounted for when designing active controlling strategies to improve the quality of the produced sugar

Cross-Perspective Human Behavior Recognition Based on a Joint Sparse Representation and Distributed Adaptation Algorithm Combined with Wireless Optical Transmission

Traditional human behavior recognition needs many training samples. Signal transmission of images and videos via visible light in the body is crucial for detecting specific actions to accelerate behavioral recognition. Joint sparse representation techniques improve identification accuracy by utilizing multi-perspective information, while distributional adaptive techniques enhance robustness by adjusting feature distributions between different perspectives. Combining both techniques enhances recognition accuracy and robustness, enabling efficient behavior recognition in complex environments with multiple perspectives. In this paper, joint sparse representation has been combined with distributed adaptation algorithm to recognize human behavior under the fusion algorithm, and verify the feasibility of the fusion algorithm through experimental analysis. The research objective of this article is to explore the use of the combination of joint sparse representation technology and distributed adaptive technology in the recall and accuracy of human detection, combined with the cross perspective human behavior recognition of wireless optical transmission. The experimental results showed that in the process of human detection, the recall and precision of the fusion algorithm in this paper reached 92% and 90% respectively, which are slightly higher than the comparison algorithm. In the experiment of recognition accuracy of different actions, the recognition accuracy of the fusion algorithm in this paper was also higher than that of the control algorithm. It can be seen that the fusion of joint sparse representation and distributed adaptation algorithms, as well as wireless communication light technology, are of great significance for human behavior recognition

Tailoring Nanostructure Morphology for Enhanced Targeting of Dendritic Cells in Atherosclerosis

Atherosclerosis, a leading cause of heart disease, results from chronic vascular inflammation that is driven by diverse immune cell populations. Nanomaterials may function as powerful platforms for diagnostic imaging and controlled delivery of therapeutics to inflammatory cells in atherosclerosis, but efficacy is limited by nonspecific uptake by cells of the mononuclear phagocytes system (MPS). MPS cells located in the liver, spleen, blood, lymph nodes, and kidney remove from circulation the vast majority of intravenously administered nanomaterials regardless of surface functionalization or conjugation of targeting ligands. Here, we report that nanostructure morphology alone can be engineered for selective uptake by dendritic cells (DCs), which are critical mediators of atherosclerotic inflammation. Employing near-infrared fluorescence imaging and flow cytometry as a multimodal approach, we compared organ and cellular level biodistributions of micelles, vesicles (<i>i</i>.<i>e</i>., polymersomes), and filomicelles, all assembled from poly­(ethylene glycol)-<i>bl</i>-poly­(propylene sulfide) (PEG-<i>bl</i>-PPS) block copolymers with identical surface chemistries. While micelles and filomicelles were respectively found to associate with liver macrophages and blood-resident phagocytes, polymersomes were exceptionally efficient at targeting splenic DCs (up to 85% of plasmacytoid DCs) and demonstrated significantly lower uptake by other cells of the MPS. In a mouse model of atherosclerosis, polymersomes demonstrated superior specificity for DCs (<i>p</i> < 0.005) in atherosclerotic lesions. Furthermore, significant differences in polymersome cellular biodistributions were observed in atherosclerotic compared to naïve mice, including impaired targeting of phagocytes in lymph nodes. These results present avenues for immunotherapies in cardiovascular disease and demonstrate that nanostructure morphology can be tailored to enhance targeting specificity