Partitioned solution of an unsteady adjoint for strongly coupled fluid-structure interactions and application to parameter identification of a one-dimensional problem

Unsteady fluid-structure interaction (FSI) simulations are generally time-consuming. Gradient-based methods are preferred to minimise the computational cost of parameter identification studies (and more in general optimisation) with a high number of parameters. However, calculating the cost function’s gradient using finite differences becomes prohibitively expensive for a high number of parameters. Therefore, the adjoint equations of the unsteady FSI problem are solved to obtain this gradient at a cost almost independent of the number of parameters. Here, both the forward and the adjoint problems are solved in a partitioned way, which means that the flow equations and the structural equations are solved separately. The application of interest is the identification of the arterial wall’s stiffness by comparing the motion of the arterial wall with a reference, possibly obtained from non-invasive imaging. Due to the strong interaction between the fluid and the structure, quasi-Newton coupling iterations are applied to stabilise the partitioned solution of both the forward and the adjoint problem.     

Improved desorption temperature of magnesium hydride via multi-layering Mg/Fe thin film

Hydrogen sorption property of Mg in Pd-capped thin film nanoconfined with Fe is investigated. Two methods of depositing the thin films were utilised, i.e., resistive heating method and pulsed laser deposition (PLD) method. In the thinnest Mg film prepared by resistive heating, hydrogen content was observed to be the highest among all samples and the hydrogen desorption temperature is 230 °C. Using pulsed laser deposition method, Mg/Fe nanoconfined multilayers are easily prepared. The hydrogen desorption temperature of Mg film with 12 Mg/Fe layers prepared via PLD method was significantly reduced to 155 °C, and the hydrogen storage capacity is improved as compared to the Mg/Fe with only several layers of same overall thickness. This study showed that the desorption temperatures correlate with the film thickness, thinner films react with hydrogen at lower temperatures. In addition, multi-layering Mg with Fe improves the desorption temperatures and hydrogen capacity, due to the higher grain boundary density, which acts as diffusion pathways for Pd in hydrogenation and dehydrogenation process.     

A Commonly Used Biocide 2-N-octyl-4-isothiazolin-3-oneInduces Blood–Brain Barrier Dysfunction via Cellular Thiol Modification and Mitochondrial Damage

Isothiazolinone (IT) biocides are potent antibacterial substances commonly used as preservatives or disinfectants, and 2-n-Octyl-4-isothiazolin-3-one (OIT; octhilinone) is a common IT biocide that is present in leather products, glue, paints, and cleaning products. Although humans are exposed to OIT through personal and industrial use, the potentially deleterious effects of OIT on human health are still unknown. To investigate the effects of OIT on the vascular system, which is continuously exposed to xenobiotics through systemic circulation, we treated brain endothelial cells with OIT. OIT treatment significantly activated caspase-3-mediated apoptosis and reduced the bioenergetic function of mitochondria in a bEnd.3 cell-based in vitro blood–brain barrier (BBB) model. Interestingly, OIT significantly altered the thiol redox status, as evidenced by reduced glutathione levels and protein S-nitrosylation. The endothelial barrier function of bEnd.3 cells was significantly impaired by OIT treatment. OIT affected mitochondrial dynamics through mitophagy and altered mitochondrial morphology in bEnd.3 cells. N-acetyl cysteine significantly reversed the effects of OIT on the metabolic capacity and endothelial function of bEnd.3 cells. Taken together, we demonstrated that the alteration of the thiol redox status and mitochondrial damage contributed to OIT-induced BBB dysfunction, and we hope that our findings will improve our understanding of the potential hazardous health effects of IT biocides.     

Enhanced Li-storage performance of In-doped Li1.21[Mn0.54Ni0.125Co0.125]O2 as Li- and Mn-rich cathode materials for lithium-ion batteries

Journal of Applied Electrochemistry - Lithium manganese-rich nickel–manganese–cobalt oxides (LMR-NMCs) are promising candidates for cathodes in Li-ion batteries (LIBs) due to their high...     

Identity verification using palm print microscopic images based on median robust extended local binary pattern features and k-nearest neighbor classifier

Automatic identity verification is one of the most critical and research-demanding areas. One of the most effective and reliable identity verification methods is using unique human biological characteristics and biometrics. Among all types of biometrics, palm print is recognized as one of the most accurate and reliable identity verification methods. However, this biometrics domain also has several critical challenges: image rotation, image displacement, change in image scaling, presence of noise in the image due to devices, region of interest (ROI) detection, or user error. For this purpose, a new method of identity verification based on median robust extended local binary pattern (MRELBP) is introduced in this study. In this system, after normalizing the images and extracting the ROI from the microscopic input image, the images enter the feature extraction step with the MRELBP algorithm. Next, these features are reduced by the dimensionality reduction step, and finally, feature vectors are classified using the k-nearest neighbor classifier. The microscopic images used in this study were selected from IITD and CASIA data sets, and the identity verification rate for these two data sets without challenge was 97.2% and 96.6%, respectively. In addition, computed detection rates have been broadly stable against changes such as salt-and-pepper noise up to 0.16, rotation up to 5°, displacement up to 6 pixels, and scale change up to 94%.     

A Novel Hybrid Fault Tolerance Architecture in the Internet of Things

Wireless Personal Communications - A series of eight new current mode sinusoidal oscillators employing a versatile and less explored active element, i.e., voltage differencing current conveyor, is...     

LAPTAS: lightweight anonymous privacy-preserving three-factor authentication scheme for WSN-based IIoT

Wireless Networks - Nowadays, wireless sensor networks (WSNs) are essential for monitoring and data collection in many industrial environments. Industrial environments are usually huge. The...     

Poly(vinylidene difluoride) Membrane Assisted by Modified ZnO/ZIF Nanoparticles for Membrane Distillation

Poly(vinylidene difluoride) (PVDF) polymeric membranes incorporated by synthesized hydrophobic agents were applied for membrane distillation. ZnO nanoparticles were modified with a silane coupling agent (n‐octyltriethoxysilane) and coupled by zeolite imidazole framework (ZIF)‐8 crystals. In particular, hydrophobic and porous membranes were prepared with ZIF crystals and modified ZnO/ZIF‐coupled nanoparticles. The influences of feed concentration and temperature of NaCl solution and seawater during filtration were evaluated. The hydrophobic modified membranes showed a higher permeate flux than pure PVDF membranes at different feed concentrations and feed temperatures. After modification, the porosity increased significantly and the contact angle became larger.     

A Fast Panoptic Segmentation Network for Self-Driving Scene Understanding

In recent years, a gain in popularity and significance of science understanding has been observed due to the high paced progress in computer vision techniques and technologies. The primary focus of computer vision based scene understanding is to label each and every pixel in an image as the category of the object it belongs to. So it is required to combine segmentation and detection in a single framework. Recently many successful computer vision methods has been developed to aid scene understanding for a variety of real world application. Scene understanding systems typically involves detection and segmentation of different natural and manmade things. A lot of research has been performed in recent years, mostly with a focus on things (a well-defined objects that has shape, orientations and size) with a less focus on stuff classes (amorphous regions that are unclear and lack a shape, size or other characteristics Stuff region describes many aspects of scene, like type, situation, environment of scene etc. and hence can be very helpful in scene understanding. Existing methods for scene understanding still have to cover a challenging path to cope up with the challenges of computational time, accuracy and robustness for varying level of scene complexity. A robust scene understanding method has to effectively deal with imbalanced distribution of classes, overlapping objects, fuzzy object boundaries and poorly localized objects. The proposed method presents Panoptic Segmentation on Cityscapes Dataset. Mobilenet-V2 is used as a backbone for feature extraction that is pre-trained on ImageNet. MobileNet-V2 with state-of-art encoder-decoder architecture of DeepLabV3+ with some customization and optimization is employed Atrous convolution along with Spatial Pyramid Pooling are also utilized in the proposed method to make it more accurate and robust. Very promising and encouraging results have been achieved that indicates the potential of the proposed method for robust scene understanding in a fast and reliable way.