In this study, the unfolding of the plastic scintillator spectrum was undertaken using the artificial neural networks tools of MATLAB. To this purpose, the response matrix of the plastic scintillator was generated for 145 energy groups and in 512 pulse-height channels using the MCNPX2.6 code. The results confirmed that the relative error in the gamma-ray energy unfolding with artificial neural networks is less than 3.8%.
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http://dx.doi.org/10.1016/j.apradiso.2022.110265 | DOI Listing |
iScience
January 2025
Cognitive Neuroimaging Unit U992, CNRS, INSERM, CEA, DRF/Institut Joliot, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France.
The need for attention to enable statistical learning is debated. Testing individuals with impaired consciousness offers valuable insight, but very few studies have been conducted due to the difficulties inherent in such studies. Here, we examined the ability of patients with varying levels of disorders of consciousness (DOC) to extract statistical regularities from an artificial language composed of randomly concatenated pseudowords by measuring frequency tagging in EEG.
View Article and Find Full Text PDFArch Bone Jt Surg
January 2025
Orthopedic Research Center, Department of Orthopedic Surgery, Mashhad University of Medical Sciences, Mashhad, Iran.
Artificial Intelligence (AI) is rapidly transforming healthcare, particularly in orthopedics, by enhancing diagnostic accuracy, surgical planning, and personalized treatment. This review explores current applications of AI in orthopedics, focusing on its contributions to diagnostics and surgical procedures. Key methodologies such as artificial neural networks (ANNs), convolutional neural networks (CNNs), support vector machines (SVMs), and ensemble learning have significantly improved diagnostic precision and patient care.
View Article and Find Full Text PDFHealthc Technol Lett
January 2025
Artificial Intelligence Research Center, The National Institute of Advanced Science and Technology Tsukuba Japan.
3D measurement for endoscopic systems has been largely demanded. One promising approach is to utilize active-stereo systems using a micro-sized pattern-projector attached to the head of an endoscope. Furthermore, a multi-frame integration is also desired to enlarge the reconstructed area.
View Article and Find Full Text PDFRSC Adv
January 2025
Department of Chemistry, College of Science, King Saud University P.O. Box 2455 Riyadh 11451 Saudi Arabia.
In this study, the specific capacitance characteristics of a carbon nanotube (CNT) supercapacitor was predicted using different machine learning algorithms, such as artificial neural network (ANN), random forest regression (RFR), -nearest neighbors regression (KNN), and decision tree regression (DTR), based on experimental studies. The results of the simulation verified the accuracy of the ANN algorithm with respect to the data derived from the specific capacitance of the supercapacitor module. It was observed that there was a strong correlation between the experimental results and the predictions made by the ANN algorithm.
View Article and Find Full Text PDFACS Nano
January 2025
State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (Nanjing Tech), 30 South Puzhu Road, Nanjing 211816, China.
Neuromorphic vision sensors capable of multispectral perception and efficient recognition are highly desirable for bioretina emulation, but their realization is challenging. Here, we present a cocrystal strategy for preparing an organic nanowire retinamorphic vision sensor with UV-vis-NIR perception and fast recognition. By leveraging molecular-scale donor-acceptor interpenetration and charge-transfer interfaces, the cocrystal nanowire device exhibits ultrawide photoperception ranging from 350 to 1050 nm, fast photoresponse of 150 ms, high specific detectivity of 8.
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