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2022 ◽
Vol 110 ◽
pp. 11-23
Author(s):
Shuai Zhang ◽
Ping Zhou ◽
Yongfang Xie ◽
Tianyou Chai
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2022 ◽
pp. 1-13
Author(s):
James J A Hammond ◽
Francesco Montomoli ◽
Marco Pietropaoli ◽
Richard Sandberg ◽
Vittorio Michelassi
Keyword(s):

Abstract This work shows the application of Gene Expression Programming to augment RANS turbulence closure modelling, for flows through complex geometry designed for additive manufacturing. Specifically, for the design of optimised internal cooling channels in turbine blades. One of the challenges in internal cooling design is the heat transfer accuracy of the RANS formulation in comparison to higher fidelity methods, which are still not used in design on account of their computational cost. However, high fidelity data can be extremely valuable for improving current lower fidelity models and this work shows the application of data driven approaches to develop turbulence closures for an internally ribbed duct. Different approaches are compared and the results of the improved model are illustrated; first on the same geometry, and then for an unseen predictive case. The work shows the potential of using data driven models for accurate heat transfer predictions even in non-conventional configurations and indicates the ability of closures learnt from complex flow cases to adapt successfully to unseen test cases.

Author(s):
Qiulin Guo ◽
Hongjia Ren ◽
Xiaozhi Wu ◽
Zhuangxiaoxue Liu ◽
Yanzhao Wei ◽
...
Keyword(s):

AbstractIn this study, a fractal simulation method for simulating resource abundance is proposed based on the evaluation results of the exploration risk and prediction technology for the spatial distribution of oil and gas resources at home and abroad. In addition, a key technical workflow for simulating resource abundance was developed. Furthermore, the model for predicting resource abundance has been modified, and the objective functions for conditional simulation have been improved. A series of prediction technologies for predicting the spatial distribution of oil and gas resources have been developed, and the difficulties in visualizing the exploration risks and predicting the spatial distribution of oil and gas resources have been solved. Prediction technologies have been applied to the Jurassic Sangonghe Formation in the hinterland of the Junggar Basin, and good results have been obtained. The results indicated that within the known area, taking the known abundance as the constraint condition, the coincidence rate of the simulated quantities of the original model and the improved model with the actual reserves reached 99.98% after the conditional simulation, indicating that the conditional simulation was effective. In addition, with the improved model, the predicted remaining resources are 0.7899$$\times 10^{8}$$ × 10 8 t, which is 65% of the discovered reserves, and the original model predicts that the remaining resources are 3.5033$$\,\times \,10^{8}$$ × 10 8 t, which is 2.89 times greater than the discovered reserves. Compared with the area in the middle stage of exploration, the results of the improved model are more consistent, and the results of the original model are obviously larger, indicating that the improved model has a good predictive effect for the unknown area. Finally, according to the risk probability and remaining resource distribution, the favorable areas for exploration were optimized as follows: the neighborhood of the triangular area formed by Well Lunan1, Well Shimo1, and Well Shi008, the area near Well Mo11, the area east of Well Mo5, the area west of Well Pen7, the area southwest of Well Shidong1, and the surroundings, as well as the area north of Well Fang2. The application results show that these prediction technologies are effective and can provide important reference and decision-making for resource evaluation and target optimization.

2022 ◽
Author(s):
Alise R. Muok ◽
Kurni Kurniyati ◽
Davi R. Ortega ◽
Flory Olsthoorn ◽
...
Keyword(s):

Pathogenic spirochetes can alter their morphologies and behaviors to infect and survive within their hosts. Previous reports demonstrate that the formation of so-called round bodies and biofilms, and chemotaxis are involved in spirochete pathogenesis. Here, in the spirochete Treponema denticola, we report a direct link between these cellular states that involves a new class of protein sensor (CheWS) with hitherto unclear function. Using cryo-EM methods, protein modeling, bioinformatics, genetics methods, and behavioral assays we demonstrate that spirochetes regulate these behaviors in response to the small molecule s-adenosylmethionine (SAM) via a SAM sensor that is anchored to chemotaxis arrays. CheWS influences chemotaxis, biofilm and round body formation under non-stressed conditions by a novel sporulation-like mechanism. Taken together, we establish an improved model for round body formation, we discovered a direct link between this SAM sensor and changes in cellular states, as well as characterized a new sensor class involved in chemotaxis.

2022 ◽
Author(s):
Anatoly Soloviev ◽
Dmitry Peregoudov
Keyword(s):

Abstract In 2019, the WDC for Solar-Terrestrial Physics in Moscow digitized the archive of observations of the Earth’s magnetic field carried out by the Soviet satellites Kosmos-49 (1964) and Kosmos-321 (1970). As a result, the scientific community for the first time obtained access to a unique digital data set, which was registered at the very beginning of the scientific space era. This article sets out three objectives. First, the quality of the obtained measurements is assessed by their comparison with the IGRF reference field model. Secondly, we assess the quality of the models, which at that time were derived from the data of these two satellites and ground-based observations. Thirdly, we propose a new, improved model of the geomagnetic field secular variation based on the scalar measurements of the Kosmos-49 and Kosmos-321 satellites using modern mathematical methods.

2022 ◽
Vol 12 (1) ◽
Author(s):
Priya N. Anandakumaran ◽
Abigail G. Ayers ◽
Pawel Muranski ◽
Remi J. Creusot ◽
Samuel K. Sia
Keyword(s):

AbstractIdentification of cognate interactions between antigen-specific T cells and dendritic cells (DCs) is essential to understanding immunity and tolerance, and for developing therapies for cancer and autoimmune diseases. Conventional techniques for selecting antigen-specific T cells are time-consuming and limited to pre-defined antigenic peptide sequences. Here, we demonstrate the ability to use deep learning to rapidly classify videos of antigen-specific CD8+ T cells. The trained model distinguishes distinct interaction dynamics (in motility and morphology) between cognate and non-cognate T cells and DCs over 20 to 80 min. The model classified high affinity antigen-specific CD8+ T cells from OT-I mice with an area under the curve (AUC) of 0.91, and generalized well to other types of high and low affinity CD8+ T cells. The classification accuracy achieved by the model was consistently higher than simple image analysis techniques, and conventional metrics used to differentiate between cognate and non-cognate T cells, such as speed. Also, we demonstrated that experimental addition of anti-CD40 antibodies improved model prediction. Overall, this method demonstrates the potential of video-based deep learning to rapidly classify cognate T cell-DC interactions, which may also be potentially integrated into high-throughput methods for selecting antigen-specific T cells in the future.

2022 ◽
Vol 2022 ◽
pp. 1-11
Author(s):
Cheng Xu ◽
Hongjun Wu ◽
Yinong Zhang ◽
Songyin Dai ◽
Hongzhe Liu ◽
...
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The Internet of Vehicles and information security are key components of a smart city. Real-time road perception is one of the most difficult tasks. Traditional detection methods require manual adjustment of parameters, which is difficult, and is susceptible to interference from object occlusion, light changes, and road wear. Designing a robust road perception algorithm is still challenging. On this basis, we combine artificial intelligence algorithms and the 5G-V2X framework to propose a real-time road perception method. First, an improved model based on Mask R-CNN is implemented to improve the accuracy of detecting lane line features. Then, the linear and polynomial fitting methods of feature points in different fields of view are combined. Finally, the optimal parameter equation of the lane line can be obtained. We tested our method in complex road scenes. Experimental results show that, combined with 5G-V2X, this method ultimately has a faster processing speed and can sense road conditions robustly under various complex actual conditions.

Author(s):
Željko Čupić ◽
Stevan Maćešić ◽
Slobodan Anić ◽
Ljiljana Kolar-Anić ◽
Ana Ivanović-Šašić ◽
...
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Author(s):
Omid Raja ◽
Hamed Ebrahimian ◽
Keyword(s):

2022 ◽
Vol 5 (1) ◽
Author(s):