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.
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.
•A collection of previous work for the characterization of real-world scenarios by fusing 3D geometry with thermal, multispectral and hyperspectral data.•Description of main processes to map ...remote-sensing data on 3D models, considering critical aspects such as occlusion issues, spatial resolution, performance and automation level.•Review of the most used sensors and platforms for the acquisition of UAV-based imagery and the reconstruction of 3D environments.•Presentation and discussion of main applications in the field of agriculture and forestry that benefit from the 3D representation of remote sensing datasets.
Three-dimensional (3D) image mapping of real-world scenarios has a great potential to provide the user with a more accurate scene understanding. This will enable, among others, unsupervised automatic sampling of meaningful material classes from the target area for adaptive semi-supervised deep learning techniques. This path is already being taken by the recent and fast-developing research in computational fields, however, some issues related to computationally expensive processes in the integration of multi-source sensing data remain. Recent studies focused on Earth observation and characterization are enhanced by the proliferation of Unmanned Aerial Vehicles (UAV) and sensors able to capture massive datasets with a high spatial resolution. In this scope, many approaches have been presented for 3D modeling, remote sensing, image processing and mapping, and multi-source data fusion. This survey aims to present a summary of previous work according to the most relevant contributions for the reconstruction and analysis of 3D models of real scenarios using multispectral, thermal and hyperspectral imagery. Surveyed applications are focused on agriculture and forestry since these fields concentrate most applications and are widely studied. Many challenges are currently being overcome by recent methods based on the reconstruction of multi-sensorial 3D scenarios. In parallel, the processing of large image datasets has recently been accelerated by General-Purpose Graphics Processing Unit (GPGPU) approaches that are also summarized in this work. Finally, as a conclusion, some open issues and future research directions are presented.
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