Object-space model optimization (OSMO) has been proven to be a simple and high-accuracy approach for additive manufacturing of tomographic reconstructions compared with other approaches. In this paper, an improved OSMO algorithm is proposed in the context of OSMO. In addition to the two model optimization steps in each iteration of OSMO, another two steps are introduced: one step enhances the target regions' in-part edges of the intermediate model, and the other step weakens the target regions' out-of-part edges of the intermediate model to further improve the reconstruction accuracy of the target boundary. Accordingly, a new quality metric for volumetric printing, named 'Edge Error', is defined. Finally, reconstructions on diverse exemplary geometries show that all the quality metrics, such as VER, PW, IPDR, and Edge Error, of the new algorithm are significantly improved; thus, this improved OSMO approach achieves better performance in convergence and accuracy compared with OSMO.
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http://dx.doi.org/10.3390/mi14071362 | DOI Listing |
Adv Sci (Weinh)
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Tissue Engineering and Organ Manufacturing (TEOM) Lab, Department of Biomedical Engineering, Wuhan University TaiKang Medical School (School of Basic Medical Sciences), Wuhan, 430071, China.
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January 2025
University of Ghana, P.O. Box 134, Legon-Accra, Ghana.
Sentiment analysis has become a difficult and important task in the current world. Because of several features of data, including abbreviations, length of tweet, and spelling error, there should be some other non-conventional methods to achieve the accurate results and overcome the current issue. In other words, because of those issues, conventional approaches cannot perform well and accomplish results with high efficiency.
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January 2025
North Carolina School of Science and Mathematics, Durham, NC, 27705, USA.
Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.
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January 2025
Department of Endodontics, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
The preservation of the original configurations of root canals during endodontic preparation is crucial for treatment success. Nickel-titanium (NiTi) rotary systems have been refined to optimize canal shaping while minimizing iatrogenic errors. This study aimed to evaluate and compare the shaping efficacy of the novel R-Motion (RM) and the established WaveOne Gold (WG) systems using micro-computed tomography (micro-CT).
View Article and Find Full Text PDFCalcif Tissue Int
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Univ Angers, Nantes Université, ONIRIS, Inserm, RMeS, UMR 1229, 49000, Angers, France.
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