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A technique to enhance the reconstruction quality and consequently the accuracy of the velocity vector field obtained in Tomo-PIV experiments is presented here. The methodology involves detecting and eliminating spurious outliers in the reconstructed intensity field (ghost particles). A simulacrum matching-based reconstruction enhancement (SMRE) technique is proposed, which utilizes the characteristic shape and size of actual particles to remove ghost particles in the reconstructed intensity field. An assessment of SMRE is performed by a quantitative comparison of Tomo-PIV simulation results and DNS data, together with a comparison to Tomo-PIV experimental data measured in a turbulent channel flow at a matched Reynolds number (Reτ = 937) to the DNS study. For the simulation data, a comparative study is performed on the reconstruction quality based on an ideal reconstruction determined from known particle positions. The results suggest that a significant improvement in the reconstruction quality and flow statistics is achievable at typical seeding densities used in Tomo-PIV experiments. This improvement is further amplified at higher seeding densities, enabling the use of up to twice the typical seeding densities currently used in Tomo-PIV experiments. A reduction of spurious vectors present in the velocity field is also observed based on a median outlier detection criterion. The application of SMRE to Tomo-PIV experimental data shows an improvement in flow statistics, comparable to the improvement seen in simulations. Finally, due to the non-iterative nature of SMRE, the increase in processing time is marginal since only a single pass of the reconstruction algorithm is required. [ABSTRACT FROM AUTHOR]

Simulations of tomographic particle image velocimetry (Tomo-PIV) are performed using direct numerical simulation data of a channel flow at Reynolds number of Re = 934, to investigate the influence of experimental parameters such as camera position, seeding density, interrogation volume size and spatial resolution. The simulations employ camera modelling, a Mie scattering illumination model, lens distortion effects and calibration to realistically model a tomographic experiment. Results are presented for camera position and orientation in three-dimensional space, to obtain an optimal reconstruction quality. Furthermore, a quantitative analysis is performed on the accuracy of first and second order flow statistics, at various voxel sizes normalised using the viscous inner length scale. This enables the result to be used as a general reference for wall-bounded turbulent experiments. In addition, a ratio relating seeding density and the interrogation volume size is proposed to obtain an optimal reference value that remains constant. This can be used to determine the required seeding density concentration for a certain interrogation volume size. [ABSTRACT FROM AUTHOR]

Polyphenolic compounds are among the most widely researched compounds for various therapeutic applications. However, naturally occurring phenylethanoid glycosides are least explored under this class of compounds. One such phenylethanoid glycoside, verbascoside (Vb), abundantly found among 200 species of 23 families, has gained recent attention due to its wide-spectrum therapeutic properties such as antioxidant, antimicrobial, anti-inflammatory, neuroprotective, cardioprotective, skin-protective, and anti-cancer. Despite having multiple therapeutic benefits, due to its large size, the compound has poor bioavailability for oral and topical applications. To meet these limitations, current research on Vb focuses on delivering it through nanoformulations. Presently, most developed formulations are liposome based for various applications, such as corneal epithelial wound healing, anti-neuropathic, anti-wrinkle, anti-hyperalgesia, atopic dermatitis, alopecia, and cutaneous wound healing. Multiple studies have confirmed the least acute and sub-acute toxicity for Vb. Few clinical studies have been performed for the therapeutic application of Vb to manage COVID-19, nephropathy, platelet aggregation, chronic primary glomerulonephritis, and acute hepatitis. Recent studies have shown the immense therapeutic potential of Vb in wound healing, dermatitis, neuroprotection, and anti-cancer activities, which creates a need for developing novel formulations for their respective uses. Long-term toxicity studies and techniques for scaling up Vb production by biotechnological approaches should be emphasized., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Anomalies are infrequent in nature, but detecting these anomalies could be crucial for the proper functioning of any system. The rarity of anomalies could be a challenge for their detection as detection models are required to depend on the relations of the datapoints with their adjacent datapoints. In this work, we use the rarity of anomalies to detect them. For this, we introduce the reversible instance normalized anomaly transformer (RINAT). Rooted in the foundational principles of the anomaly transformer, RINAT incorporates both prior and series associations for each time point. The prior association uses a learnable Gaussian kernel to ensure a thorough understanding of the adjacent concentration inductive bias. In contrast, the series association method uses self-attention techniques to specifically focus on the original raw data. Furthermore, because anomalies are rare in nature, we utilize normalized data to identify series associations and employ non-normalized data to uncover prior associations. This approach enhances the modelled series associations and, consequently, improves the association discrepancies.