This work is concerned with the multiscale prediction of the transport and sound absorption properties associated with industrial glass wool samples. In the first step, an experimental characterization is performed on various products using optical granulometry and porosity measurements. A morphological analysis, based on scanning electron imaging, is further conducted to identify the probability density functions associated with the fiber angular orientation. The key morphological characterization parameters of the microstructure, which serve as input parameters of the model, include the porosity, the weighted volume diameter accounting for both lengths and diameters of the analyzed fibers (and therefore the specific surface area of the random fibrous material), and the preferred out-of-plane fiber orientation generated by the manufacturing process. A computational framework is subsequently proposed and allows for the reconstruction of an equivalent fibrous network. A fully stochastic microstructural model, parameterized by the probability laws inferred from the database, is also proposed herein. Multiscale simulations are carried out to estimate transport properties and sound absorption. With no adjustable parameter, the results accounting for ten different samples obtained with various processing parameters are finally compared with the experimental data and used to assess the relevance of the reconstruction procedures and the multiscale computations.
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http://dx.doi.org/10.1121/1.5040479 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48104.
In ecology, Alan Turing's proposed activation-inhibition mechanism has been abstracted as corresponding to several ecological interaction types to explain pattern formation in ecosystems. Consumer-resource interactions have strong theoretical arguments linking them to both the Turing mechanism and pattern formation, but there is little empirical support to demonstrate these claims. Here, we connect several lines of evidence to support the proposition that consumer-resource interactions can create empirically observed spatial patterns through a mechanism similar to Turing's theory.
View Article and Find Full Text PDFEcol Appl
January 2025
Division of Natural Resources, Park Operations Department, Cleveland Metroparks, Cleveland, Ohio, USA.
Human-caused conversion of natural habitat areas to developed land cover represents a major driver of habitat loss and fragmentation, leading to reorganization of biological communities. Although protected areas and urban greenspaces can preserve natural systems in fragmented landscapes, their efficacy has been stymied by the complexity and scale-dependency underlying biological communities. While migratory bird communities are easy to-study and particularly responsive to anthropogenic habitat alterations, prior studies have documented substantial variation in habitat sensitivity across species and migratory groups.
View Article and Find Full Text PDFBiophys J
January 2025
Department of Physics and Astronomy, Department of Chemistry, NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, California, USA. Electronic address:
In this work we present a minimal structure-based model of protein diffusional search along local DNA amid protein binding and unbinding events on the DNA, taking into account protein-DNA electrostatic interactions and hydrogen-bonding (HB) interactions or contacts at the interface. We accordingly constructed the protein diffusion-association/dissociation free energy surface and mapped it to 1D as the protein slides along DNA, maintaining the protein-DNA interfacial HB contacts that presumably dictate the DNA sequence information detection. Upon DNA helical path correction, the protein 1D diffusion rates along local DNA can be physically derived to be consistent with experimental measurements.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu 641032 India.
Cross subject Electroencephalogram (EEG) emotion recognition refers to the process of utilizing electroencephalogram signals to recognize and classify emotions across different individuals. It tracks neural electrical patterns, and by analyzing these signals, it's possible to infer a person's emotional state. The objective of cross-subject recognition is to create models or algorithms that can reliably detect emotions in both the same person and several other people.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computer Science, University of Jaén, Jaén, Spain.
In the production sector, the usefulness of predictive systems as a tool for management and decision-making is well known. In the agricultural sector, a correct economic balance of the farm depends on making the right decisions. For this purpose, having information in advance on crop yields is an extraordinary help.
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