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Purpose: Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net based deformable image registration (ConvUNet-DIR) method using unsupervised learning to establish correspondence between baseline pre-operative and follow-up MRI scans of patients with brain glioma., Methods: This study involved multi-parametric brain MRI scans (T1, T1-contrast enhanced, T2, FLAIR) acquired at pre-operative and follow-up time for 160 patients diagnosed with glioma, representing the BraTS-Reg 2022 challenge dataset. ConvUNet-DIR, a deep learning-based deformable registration workflow using 3D U-Net style architecture as a core, was developed to establish correspondence between the MRI scans. The workflow consists of three components: (1) the U-Net learns features from pairs of MRI scans and estimates a mapping between them, (2) the grid generator computes the sampling grid based on the derived transformation parameters, and (3) the spatial transformation layer generates a warped image by applying the sampling operation using interpolation. A similarity measure was used as a loss function for the network with a regularization parameter limiting the deformation. The model was trained via unsupervised learning using pairs of MRI scans on a training data set (n = 102) and validated on a validation data set (n = 26) to assess its generalizability. Its performance was evaluated on a test set (n = 32) by computing the Dice score and structural similarity index (SSIM) quantitative metrics. The model's performance also was compared with the baseline state-of-the-art VoxelMorph (VM1 and VM2) learning-based algorithms., Results: The ConvUNet-DIR model showed promising competency in performing accurate 3D deformable registration. It achieved a mean Dice score of 0.975 ± 0.003 and SSIM of 0.908 ± 0.011 on the test set (n = 32). Experimental results also demonstrated that ConvUNet-DIR outperformed the VoxelMorph algorithms concerning Dice (VM1: 0.969 ± 0.006 and VM2: 0.957 ± 0.008) and SSIM (VM1: 0.893 ± 0.012 and VM2: 0.857 ± 0.017) metrics. The time required to perform a registration for a pair of MRI scans is about 1 s on the CPU., Conclusions: The developed deep learning-based model can perform an end-to-end deformable registration of a pair of 3D MRI scans for glioma patients without human intervention. The model could provide accurate, efficient, and robust deformable registration without needing pre-alignment and labeling. It outperformed the state-of-the-art VoxelMorph learning-based deformable registration algorithms and other supervised/unsupervised deep learning-based methods reported in the literature., (© 2024. The Author(s).)

This study presented a unique, miniaturised asymmetric interconnected vertical stripe (IVS) design for terahertz (THz) frequency applications. Therefore, this research aimed to achieve a frequency response of 0 to 10 THz using a 5 × 5 µm 2 Silicon (Si) substrate material. Meanwhile, various parametric examinations were conducted to investigate variations in the performance. For example, the unit cell selection process was carefully examined by using various design structures and modifying the structure by adding split gaps and connecting bars between vertical stripes. Furthermore, the proposed sandwich structure design was used to compute the absorbance and reflectance properties. All the analytical examinations were executed utilising the Computer Simulation Technology (CST) 2019 software. The introduced IVS metamaterial exhibits negative index behaviour and has a single resonance frequency of 5.23 THz with an acceptable magnitude of - 24.38 dB. Additionally, the quadruple-layer IVS structure exhibits optimised transmission coefficient behaviour between 3 and 6 THz and 7 to 9 THz, respectively. However, the magnitude of the transmission coefficient increased with the number of material layers. Besides that, the absorbance study shows that using a quadruple-layer structure obtains unique and promising results. Overall, the proposed asymmetric IVS metamaterial design achieves the required performance by using a compact structure rather than extending the dimensions of the design., (© 2024. The Author(s).)

This study developed a metamaterial-inspired split-ring resonator (SRR) based inversion symmetry-shaped structure for airport surveillance radar and local area wireless network applications. The proposed device exhibited suitability for S- and C-band applications, featuring distinct resonance peaks at 2.8 and 4.9 GHz, respectively. The two-layer double negative metamaterial unit cell comprises a copper-based resonator, patch, and a low-loss substrate material known as Rogers RT5800 with a thickness of 1.575 mm. The 8 × 8 mm 2 structure unit cell was identified with an effective medium ratio (EMR) of 13.4 at the resonance peak of 2.8 GHz. With the alteration of the metamaterial unit cell structure, the electric field, surface current distribution, magnetic field, and design evolution were observed, analysed, and investigated in this study. Meanwhile, the retrieved data from the reflection and transmission coefficients from CST Microwave Studio were validated using the Ansys High-Frequency Structure Simulator (HFSS) software. A Vector Network Analyzer (VNA) further measured the numerical results. Based on the findings, the proposed novel double negative metamaterial device is suitable for radar communication and satellite applications, especially airport surveillance radar (ASR) and wireless local area network (WLAN), due to its high EMR at the desired resonance frequency., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

In the present paper, a double layer advanced model was used to investigate the adsorption process putatively involved in the olfactory perception of sotolone and abhexone molecules on the human olfactory receptor OR8D1. The number of adsorbed molecules or the fraction of adsorbed molecule per site, n, informed that the two odorants molecules are docked on OR8D1 binding sites with mixed parallel and nonparallel anchorages. Furthermore, the estimated molar adsorption energy (-ΔE 1 and -ΔE 2 ) were inferior to 40 kJ/mol for the two adsorption systems, which confirmed the physical nature and the exothermic character of the adsorption process. In addition, stereographic characterizations of the receptor sites surface were carried out through the determination of the receptor site size distribution (RSDs) via Kelvin equation, which spread out from 0.05 to 1.5 nm. The adsorption energy distributions (AEDs) via Polayni equation show an adsorption band spectrum localized between 17 kJ/mol and 22.5 kJ/mol for sotolone and abhexone molecules respectively. A molecular docking calculation was performed. The results indicate that the binding affinities are belonging to the spectrum of the energy band of the molecules sotolone and abhexone, with values 19.66 kJ/mol and 19.24 kJ/mol., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)