Assessing the solid wood content is crucial when acquiring stacked roundwood. A frequently used method for this is to multiply determined conversion factors by the measured gross volume. However, the conversion factors are influenced by several log and stack parameters. Although these parameters have been identified and studied, their individual influence has not yet been analyzed using a broad statistical basis. This is due to the considerable financial resources that the data collection entails. To overcome this shortcoming, a 3D-simulation model was developed. It generates virtual wood stacks of randomized composition based on one individual data set of logs, which may be real or defined by the user. In this study, the development and evaluation of the simulation model are presented. The model was evaluated by conducting a sensitivity and a quantitative analysis of the simulation outcomes based on real measurements of 405 logs of Norway spruce and 20 stacks constituted with these. The results of the simulation outcomes revealed a small overestimation of the net volume of real stacks: by 1.2% for net volume over bark and by 3.2% for net volume under bark. Furthermore, according to the calculated mean bias error (MBE), the model underestimates the gross volume by 0.02%. In addition, the results of the sensitivity analysis confirmed the capability of the model to adequately consider variations in the input parameters and to provide reliable outcomes.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8926204 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0264414 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
Spherical harmonics texture extraction for versatile analysis of biological objects.
PLoS Comput Biol
January 2025
European Molecular Biology Laboratory, Cell Biology and Biophysics Unit, Heidelberg, Germany.
The characterization of phenotypes in cells or organisms from microscopy data largely depends on differences in the spatial distribution of image intensity. Multiple methods exist for quantifying the intensity distribution - or image texture - across objects in natural images. However, many of these texture extraction methods do not directly adapt to 3D microscopy data.
View Article and Find Full Text PDFQuantification of training-induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females.
Physiol Rep
February 2025
Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
View Article and Find Full Text PDFDosimetric Planning Comparison for Left Ventricle Avoidance in Non-small Cell Lung Cancer Radiotherapy.
Cureus
December 2024
Physics and Engineering, London Regional Cancer Program, London, CAN.
Introduction: Radiation may unintentionally injure myocardial tissue, potentially leading to radiation-induced cardiac disease (RICD), with the net benefit of non-small cell lung cancer (NSCLC) radiotherapy (RT) due to the proximity of the lung and heart. RTOG-0617 showed a greater reduction in overall survival (OS) comparing higher doses to standard radiation doses in NSCLC RT. VHeart has been reported as an OS predictor in the first- and fifth-year follow-ups.
View Article and Find Full Text PDFTowards contrast-agnostic soft segmentation of the spinal cord.
Med Image Anal
January 2025
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Québec, Canada; Mila - Québec Artificial Intelligence Institute, Montréal, Québec, Canada; Functional Neuroimaging Unit, CRIUGM, University of Montreal, Montreal, Québec, Canada; Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Québec, Canada. Electronic address:
Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This is partly due to the varying appearance of the boundary between the spinal cord and the cerebrospinal fluid that depends on the sequence and acquisition parameters.
View Article and Find Full Text PDFThe Added Effect of Artificial Intelligence in CT Assessment of Abdominal Lymphadenopathy.
Lymphology
January 2025
Medical Biophysics Department, Medical Research Institute, Alexandria University, Alexandria, Egypt.
Lymphadenopathy is associated with lymph node abnormal size or consistency due to many causes. We employed the deep convolutional neural network ResNet-34 to detect and classify CT images from patients with abdominal lymphadenopathy and healthy controls. We created a single database containing 1400 source CT images for patients with abdominal lymphadenopathy (n = 700) and healthy controls (n = 700).
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!