Modeling and simulation of nanofluid flows is crucial for applications ranging from the cooling of electronic devices to solar water heating systems, particularly when compared to the high expense of ...experimental studies. Accurate simulation of a thermal-fluid system requires a deep understanding of the underlying physical phenomena occurring in the system. In the case of a complex nanofluid-based system, suitable simplifying approximations must be chosen to strike a balance between the nano-scale and macro-scale phenomena. Based on these choices, the computational approach – or set of approaches – to solve the mathematical model can be identified, implemented and validated. In Part I of this review (Mahian et al., 2019), we presented the details of various approaches that are used for modeling nanofluid flows, which can be classified into single-phase and two-phase approaches. Now, in Part II, the main computational methods for solving the transport equations associated with nanofluid flow are briefly summarized, including the finite difference, finite volume, finite element, lattice Boltzmann methods, and Lagrangian methods (such as dissipative particle dynamics and molecular dynamics). Next, the latest studies on 3D simulation of nanofluid flow in various regimes and configurations are reviewed. The numerical studies in the literature mostly focus on various forms of heat exchangers, such as solar collectors (flat plate and parabolic solar collectors), microchannels, car radiators, and blast furnace stave coolers along with a few other important nanofluid flow applications. Attention is given to the difference between 2D and 3D simulations, the effect of using different computational approaches on the flow and thermal performance predictions, and the influence of the selected physical model on the computational results. Finally, the knowledge gaps in this field are discussed in detail, along with some suggestions for the next steps in this field. The present review, prepared in two parts, is intended to be a comprehensive reference for researchers and practitioners interested in nanofluids and in the many applications of nanofluid flows.
Objective: To assess the sensitivity, specificity and accuracy of a digital algorithm based on convolutional neural networks used for restoring the lost surface of the skull bones.
Materials and ...methods. The neural network was trained over 6,000 epochs on 78,000 variants of skull models with artificially generated skull injuries. The key parameters of the algorithm were assessed on 222 series of multislice computed tomography (MSCT) of patients with defects of the skull bones, presented in DICOM format.
Results. For the group as a whole, the sensitivity, specificity, and accuracy rates were 95.3%, 85.5%, and 79.4%, respectively. Multiple experiments were conducted with a step-by-step elimination of 3D models in order to find the underlying cause of unsatisfactory outcomes of the skull lost surface restoration. Incorrect identification of the defect zone most often occurred in the area of the facial skeleton. After excluding series with the presence of artifacts, the mean increase in metrics was 2.6%. Conclusion. The accuracy of identifying the reference points (specificity) on a 3D model of the skull by the algorithm had the greatest impact on the ultimate accuracy of repairing the lost surface. The maximum accuracy of the algorithm allowing the use of the resulting surfaces without additional processing in a 3D modeling environment was achieved in series without the presence of artifacts in computed tomography (83.5%), as well as with defects that did not extend to the base of the skull (79.5%).
In January 2024, a targeted conference, ‘CellVis2’, was held at Scripps Research in La Jolla, USA, the second in a series designed to explore the promise, practices, roadblocks, and prospects of ...creating, visualizing, sharing, and communicating physical representations of entire biological cells at scales down to the atom.
In January 2024, a targeted conference, ‘CellVis2’, was held at Scripps Research in La Jolla, USA, the second in a series designed to explore the promise, practices, roadblocks, and prospects of creating, visualizing, sharing, and communicating physical representations of entire biological cells at scales down to the atom.
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•Structure from motion (SFM) − based 3D reconstruction setup was designed to achieve a global point cloud of tomato fruit.•Particles were applied as a filme on the sample prior to ...imaging.•SFM models the physical characteristics such as volume, surface, and color indexes.•The suggested procedure has a high-accuracy 3D model for tomato fruit with RMSEs of 0.35 to 2.44.•SFM can model the 3D shape and achieve geometric characteristics of objects with reflecting surfaces.
Besides the importance of internal quality, the textural quality properties of the product have a remarkable effect on consumer satisfaction and market price. Mainly, the three-dimensional (3D) modeling technique is applied to evaluate the quality of agricultural products. This study aimed to employ the structure from motion (SFM) method along with the particle film technique to predict the physical features of tomatoes. Reconstructing 3D models of tomatoes is challenging due to their shiny skin. In this study, a new method based on particle films was developed to address this issue. Fifty samples of tomatoes were randomly selected with various sizes and four ripeness levels with no-external damage. The SFM technique applied with kaolin particle film was a suitable approach for assessing the surface area, volume, and color index, with a correlation coefficient of 0.76, 0.88, and 0.67, respectively. Also, 3D model predictions achieved these textural properties by obtaining RMSEs of 2.44, 2.30, and 0.35, respectively. Finally, the proposed SFM method’s results showed an error of less than 5%. This technique has proven to be a cable technique for the 3D reconstruction of products with a glossy surface.
Given the increasing attention to environmental preservation and sustainable development, the digitization of rural landscapes stands out as a pivotal strategy for effective environmental management ...and sustainability, land use planning, and preservation of cultural heritage. This work proposes a novel methodology for generating 3D models of rural landscapes by integrating multiscale data sources. Although Unmanned Aerial Vehicles (UAV) simplify the acquisition of multi-source data, their coverage is typically restricted to small landscapes due to their limited range and flight time. On the other hand, although the use of aerial images provides a broader view of the terrain, it is important to note that the low resolution of these images interferes with the task of accurate 3D modeling. Given these challenges, we propose a methodology that combines UAV data and high-resolution aerial imagery provided by the Spanish National Orthophoto Program (PNOA). This multi-source data integration is crucial to generating detailed and accurate 3D models of rural environments. The proposed methodology involves three steps: (1) semantic segmentation of aerial images identifying features such as vegetation, ground, and human-made structures, (2) estimation of the Digital Elevation Model (DEM), and (3) 3D modeling of rural environments using the point clouds generated from UAV images. The conducted experiments demonstrate the effectiveness of our approach identifying and representing previously mentioned features. Thus, this work presents advances in 3D representation techniques for real scenarios, contributing to the coordination of land utilization and environmental sustainability in rural landscapes.
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•Multiscale Data Integration: Combines UAV data with high-resolution imagery from PNOA, enhancing coverage and resolution.•Four-Step Process for Accurate 3D Models: Includes semantic segmentation, DEM estimation, and 3D modeling from UAV point clouds.•Contribution to Environmental Management and Sustainability: Advanced 3D models support land use planning and conservation, aligning with global sustainability goals.
The SARS-CoV-2 betacoronavirus uses its highly glycosylated trimeric Spike protein to bind to the cell surface receptor angiotensin converting enzyme 2 (ACE2) glycoprotein and facilitate host cell ...entry. We utilized glycomics-informed glycoproteomics to characterize site-specific microheterogeneity of glycosylation for a recombinant trimer Spike mimetic immunogen and for a soluble version of human ACE2. We combined this information with bioinformatics analyses of natural variants and with existing 3D structures of both glycoproteins to generate molecular dynamics simulations of each glycoprotein both alone and interacting with one another. Our results highlight roles for glycans in sterically masking polypeptide epitopes and directly modulating Spike-ACE2 interactions. Furthermore, our results illustrate the impact of viral evolution and divergence on Spike glycosylation, as well as the influence of natural variants on ACE2 receptor glycosylation. Taken together, these data can facilitate immunogen design to achieve antibody neutralization and inform therapeutic strategies to inhibit viral infection.
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•Site-specific N-linked microheterogeneity is defined at 22 sites of SARS-CoV-2 Spike•Six sites of N-linked microheterogeneity of human ACE2 receptor are described•Molecular dynamics simulations of Spike and ACE2 show essential roles for glycosylation•We uncover roles for variants in protein-glycan and glycan-glycan interactions
Combining glycomics-informed glycoproteomics and bioinformatic analyses of variants with molecular dynamics simulations, Zhao et al. detail a role for glycan-protein and glycan-glycan interactions in the SARS-CoV-2 viral Spike protein-ACE2 human receptor complex.
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