Endoscopic nasopharyngectomy represents a significant intervention for recurrent nasopharyngeal carcinoma (NPC). Various surgical techniques, including transnasal and transoral approaches, are employed. However, the i...
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Endoscopic nasopharyngectomy represents a significant intervention for recurrent nasopharyngeal carcinoma (NPC). Various surgical techniques, including transnasal and transoral approaches, are employed. However, the impact of these procedures on nasal airflow dynamics is not well understood. This computational fluid dynamics (CFD) study aimed to investigate alterations in nasal airflow and air conditioning following endoscopic nasopharyngectomy. A 55-year-old male patient with recurrent NPC was selected, whose CT data were utilized for image reconstruction. A preoperative model and two postoperative models, including the transnasal and transoral approach models, were established. The airflow patterns and various CFD parameters were analyzed. In the postoperative models, the high-speed airflow went along the soft palate and into the nasopharyngeal outlet, and there was the low-speed turbulence in the expanded nasopharyngeal cavity. Compared to the preoperative model, the postoperative models exhibited reductions in surface-to-volume ratio, nasal resistance, airflow velocity and proportion of high wall shear stress regions in nasopharynx. The changing trends of nasopharyngeal air temperature and humidity in the preoperative and transoral models were consistent. The heating and humidification efficiency decreased in the transnasal model compared to the transoral model. The endoscopic nasopharyngectomy for recurrent NPC affects the nasal airflow and warming and humidification function. The transoral approach has less influence on aerodynamics of the upper airway compared to the transnasal approach. From a CFD perspective, the endoscopic nasopharyngectomy does not increase the risk of postoperative complications, including the empty nose syndrome and the carotid blowout syndrome.
With the global population steadily increasing, ensuring food security is a major concern, especially in agriculture. Detecting plant diseases early can make farming more efficient and prevent significant food losses....
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The modern times have led to the adoption of distinctive meta-heuristic procedures for solving distinct class of optimization-problems. The meta-heuristics procedures have benefit above conventional algorithms because...
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Human Activity Recognition (HAR) is a trading area in computer vision and deep learning. However, boosting the performance of deep learning models often necessitates increasing their size or capacity, which raises com...
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Anomalous diffusion plays a crucial rule in understanding molecular-level dynamics by offering valuable insights into molecular interactions, mobility states and the physical properties of systems across both biologic...
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Anomalous diffusion plays a crucial rule in understanding molecular-level dynamics by offering valuable insights into molecular interactions, mobility states and the physical properties of systems across both biological and materials sciences. Deep-learning techniques have recently outperformed conventional statistical methods in anomalous diffusion recognition. However, deep-learning networks are typically trained by data with limited distribution, which inevitably fail to recognize unknown diffusion models and misinterpret dynamics when confronted with out-of-distribution (OOD) scenarios. In this work, we present a general framework for evaluating deep-learning-based OOD dynamics-detection methods. We further develop a baseline approach that achieves robust OOD dynamics detection as well as accurate recognition of in-distribution anomalous diffusion. We demonstrate that this method enables a reliable characterization of complex behaviors across a wide range of experimentally diverse systems, including nicotinic acetylcholine receptors in membranes, fluorescent beads in dextran solutions and silver nanoparticles undergoing active endocytosis.
With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for indi...
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Recent years have seen the rise of big data workflow management solutions as widespread data analytic platforms for handling massive amounts of data in the cloud. However, keeping information private and ensuring the ...
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computational modeling and optimization were utilized to design the metastructure of a polymer nanocomposite system. The goal is to minimize the reflection loss (RL) and broaden the absorption bandwidth. Truncated con...
This article provides a detailed analysis of the main difficulties and limitations of traditional BI (Business Intelligence) systems, and combines the latest AI technology to verify the possibility of applying Artific...
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In recent years, neural networks in deep reinforce-ment learning (D RL) have achieved success in various tasks, owing to their high expressiveness and flexibility. Neural networks are expected to be increasingly used ...
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