By using forest inventory data in combination with plot measurement, the characteristics of carbon density, stock, and partitioning in artificial forest ecosystem in Sichuan Province of China were studied. The results showed that the carbon density in this forest ecosystem was averagely 161.16 Mg C x hm(-2), being ranked in the order of soil layer (141.64 Mg C x hm(-2)) >tree layer (17.95 Mg C x hm(-2)) >litter layer (1.06 Mg C x hm(-2)) >shrub layer (0.52 Mg C x hm(-2)), and the total carbon stock was 573.57 Tg C, with 63.88 Tg C, 1.836 Tg C, 3.764 Tg C, and 504.09 Tg C, accounting for 11.14%, 0.32%, 0.66%, and 87.88% of the total in tree layer, shrub layer, litter layer, and soil layer, respectively. The carbon density and stock in different artificial forest ecosystems varied from 75.50 Mg C x hm(-2) to 251.74 Mg C x hm(-2) and from 1.21 Tg C to 99.44 Tg C, with the highest and lowest values observed in soil layer and shrub layer, respectively. Comparing with other regions in China, Sichuan Province had a lower carbon density in the tree layer of artificial forest ecosystem, due to the higher proportion of young and middle age forest stands, which implied that a proper management of artificial forest could increase the carbon sequestration in forest ecosystem of Sichuan. To monitor the carbon stock in artificial forest ecosystem at ecosystem level could be helpful to the improvement of the precision of forest carbon sequestration evaluation.
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Eur Radiol
January 2025
Department of Radiology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
Objectives: Adenomatous colorectal polyps require endoscopic resection, as opposed to non-adenomatous hyperplastic colorectal polyps. This study aims to evaluate the effect of artificial intelligence (AI)-assisted differentiation of adenomatous and non-adenomatous colorectal polyps at CT colonography on radiologists' therapy management.
Materials And Methods: Five board-certified radiologists evaluated CT colonography images with colorectal polyps of all sizes and morphologies retrospectively and decided whether the depicted polyps required endoscopic resection.
J Clin Exp Neuropsychol
January 2025
Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
Introduction: Diagnostic evaluations for attention-deficit/hyperactivity disorder (ADHD) are becoming increasingly complicated by the number of adults who fabricate or exaggerate symptoms. Novel methods are needed to improve the assessment process required to detect these noncredible symptoms. The present study investigated whether unsupervised machine learning (ML) could serve as one such method, and detect noncredible symptom reporting in adults undergoing ADHD evaluations.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, China.
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Forest Biometrics and Remote Sensing Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USA.
Developing the capacity to monitor species diversity worldwide is of great importance in halting biodiversity loss. To this end, remote sensing plays a unique role. In this study, we evaluate the potential of Global Ecosystem Dynamics Investigation (GEDI) data, combined with conventional satellite optical imagery and climate reanalysis data, to predict in situ alpha diversity (Species richness, Simpson index, and Shannon index) among tree species.
View Article and Find Full Text PDFJ Clin Med
January 2025
Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal.
Several artificial intelligence systems based on large language models (LLMs) have been commercially developed, with recent interest in integrating them for clinical questions. Recent versions now include image analysis capacity, but their performance in gastroenterology remains untested. This study assesses ChatGPT-4's performance in interpreting gastroenterology images.
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