Technical product harvesting (TEPHA) is a newly developing interdisciplinary approach in which bio-based production is investigated from a technical and ecological perspective. Society's demand for ecologically produced and sustainably operable goods is a key driver for the substitution of conventional materials like metals or plastics through bio-based alternatives. Technical product harvesting of near net shape grown components describes the use of suitable biomass for the production of technical products through influencing the natural shape of plants during their growth period. The use of natural materials may show positive effects on the amount of non-renewable resource consumption. This also increases the product recyclability at the end of its life cycle. Furthermore, through the near net shape growth of biomass, production steps can be reduced. As a consequence such approaches may save energy and the needed resources like crude oil, coal or gas. The derived near net shape grown components are not only considered beneficial from an environmental point of view. They can also have mechanical advantages through an intrinsic topology optimization in contrast to common natural materials, which are influenced in their shape after harvesting. In order to prove these benefits a comprehensive, interdisciplinary scientific strategy is needed. Here, both mechanical investigations and life cycle assessment as a method of environmental evaluation are used.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5648737 | PMC |
http://dx.doi.org/10.1186/s12302-017-0125-x | DOI Listing |
PLoS Comput Biol
January 2025
Electrical and Computer Engineering Department, Concordia University, Montreal, Canada.
Astrocytes critically shape whole-brain structure and function by forming extensive gap junctional networks that intimately and actively interact with neurons. Despite their importance, existing computational models of whole-brain activity ignore the roles of astrocytes while primarily focusing on neurons. Addressing this oversight, we introduce a biophysical neural mass network model, designed to capture the dynamic interplay between astrocytes and neurons via glutamatergic and GABAergic transmission pathways.
View Article and Find Full Text PDFMicroscopy (Oxf)
January 2025
Department of Biomedical Data Science, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan.
Large-scale reconstitution of neuronal circuits from volumetric electron microscopy images is a remarkable research goal in neuroanatomy. However, the large-scale reconstruction is a result of automatic segmentation using convolutional neural networks (CNNs), which is still challenging for general researchers to perform. This review focuses on two representative CNNs for dense neuronal segmentation: flood-filling networks (FFN) and local shape descriptors (LSD)-predicting U-Net (LSD network).
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Information Engineering, China University of Geosciences, Beijing 100083, China.
Extracting fragmented cropland is essential for effective cropland management and sustainable agricultural development. However, extracting fragmented cropland presents significant challenges due to its irregular and blurred boundaries, as well as the diversity in crop types and distribution. Deep learning methods are widely used for land cover classification.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Deep learning-based morphometric analysis of zebrafish is widely utilized for non-destructively identifying abnormalities and diagnosing diseases. However, obtaining discriminative and continuous organ category decision boundaries poses a significant challenge by directly observing zebrafish larvae from the outside. To address this issue, this study simplifies the organ areas to polygons and focuses solely on the endpoint positioning.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Master's Program in Information and Computer Science, Doshisha University, Kyoto 610-0394, Japan.
The semantic segmentation of bone structures demands pixel-level classification accuracy to create reliable bone models for diagnosis. While Convolutional Neural Networks (CNNs) are commonly used for segmentation, they often struggle with complex shapes due to their focus on texture features and limited ability to incorporate positional information. As orthopedic surgery increasingly requires precise automatic diagnosis, we explored SegFormer, an enhanced Vision Transformer model that better handles spatial awareness in segmentation tasks.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!