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We perform a multi-scale analysis of the geometric structure of the network of X (Twitter at the time of data collection) interactions surrounding the Italian snap general elections of September 25th 2022. We identify within it the communities related to the major Italian political parties and after it we analyse both the large-scale structure of interactions between different parties, showing that it resembles the coalitions formed in the run-up to the elections and the internal structure of each community. We observe that some parties have a very centralised communication with the major leaders clearly occupying the central role, while others have a more horizontal communication strategy, with many accounts playing an important role. We observe that this can be characterized by checking whether the network in the community has a strongly connected giant component or not.
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http://dx.doi.org/10.1038/s41598-024-65564-6 | DOI Listing |
PLoS One
September 2025
School of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Computer-aided diagnostic (CAD) systems for color fundus images play a critical role in the early detection of fundus diseases, including diabetes, hypertension, and cerebrovascular disorders. Although deep learning has substantially advanced automatic segmentation techniques in this field, several challenges persist, such as limited labeled datasets, significant structural variations in blood vessels, and persistent dataset discrepancies, which continue to hinder progress. These challenges lead to inconsistent segmentation performance, particularly for small vessels and branch regions.
View Article and Find Full Text PDFAnal Chim Acta
November 2025
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, PR China; Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, 313001, PR China; Laboratory for Microwave Spatial Inte
Background: X-ray fluorescence (XRF) technology is a promising method for estimating the metal element content in ores, which helps in understanding ore composition and optimizing mining and processing strategies. However, due to the presence of a large number of redundant features in XRF spectra, traditional quantitative analysis models struggle to effectively capture the nonlinear relationship between element concentration and spectral information of XRF, making it more difficult to accurately predict metal element concentrations. Thus, analyzing ore element concentrations by XRF remains a significant challenge.
View Article and Find Full Text PDFComput Methods Biomech Biomed Engin
September 2025
School of Computer and Information, Anhui Normal University, Wuhu, China.
Heart disease is a leading global cause of death, making early prediction critical. This study proposes a multi-scale convolution-enhanced Swin Transformer (MSCST) model for heart disease risk assessment. The model employs a multi-branch convolutional network with channel attention to extract and optimize multi-scale features.
View Article and Find Full Text PDFCarbohydr Polym
November 2025
State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Collaborative innovation center of food safety and quality control in Jiangsu Province, Jiangnan University, Wuxi, Jiangsu 214122, China. Electronic address:
The objective of this study is to investigate the behavior of starch entanglement and to elucidate the starch gelation process from the entanglement perspective. This study established a starch entanglement system based on overlap, entanglement, and concentrated concentrations in aqueous solution. Select the temperature at which starch is prone to form gel (4 °C) and the temperature at which starch begins to gelatinize (60 °C) for entanglement.
View Article and Find Full Text PDFNeuroscience
September 2025
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China.
Achieving a deep understanding of brain mechanisms requires multi-scale perspectives to capture the architecture of complex networks. In this study, we focused on patients with cognitive impairment and constructed individual brain networks from neuroimaging data. We introduced a Significant Edges Selection (SES) method, which effectively extracts the most informative connections while suppressing noise.
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