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In this paper, a novel inter-layer exchange coupled (IEC) based 3-input full adder design methodology is proposed and subsequently the architecture has been implemented on the widely accepted micromagnetic OOMMF platform. The impact of temperature on the IEC coupled full-adder design has been analyzed up to Curie temperature. [https://www.selleckchem.com/products/p7c3.html NAMPT activator] It was observed that even up to Curie temperature the IEC based adder design was able to operate at sub-50 nm as contrast to dipole coupled adder design which failed at 5 K for sub 50 nm. Simulation results obtained from OOMMF micromagnetic simulator shows, the IEC based adder design was at a lower energy state as compared to the dipole coupled adder indicating a more stable system and as the temperature of the design was increased, the total energy increased resulting in reduced stability. Potential explanation for the thermodynamic stability of IEC model lies in its energetically favored architecture, such that the total energy was lower than its dipole coupled counterparts. IEC architecture demonstrates supremacy in reliability and strength enabling NML to march towards beyond CMOS devices.Pancreas segmentation in medical imaging is of great significance for clinical pancreas diagnostics and treatment. However, the large population variations in the pancreas shape and volume cause enormous segmentation difficulties, even for state-of-the-art algorithms utilizing fully convolutional neural networks (FCNs). Specifically, pancreas segmentation suffers from the loss of statement temporal information in 2D methods, and the high computational cost of 3D methods. To alleviate these problems, we propose a probabilistic-map-guided bi-directional recurrent UNet (PBR-UNet) architecture, which fuses intra-slice information and inter-slice probabilistic maps into a local 3D hybrid regularization scheme, which is followed by a bi-directional recurrent optimization scheme. The PBR-UNet method consists of an initial estimation module for efficiently extracting pixel-level probabilistic maps and a primary segmentation module for propagating hybrid information through a 2.5D UNet architecture. Specifically, local 3D information is inferred by combining an input image with the probabilistic maps of the adjacent slices into multi-channel hybrid data, and then hierarchically aggregating the hybrid information of the entire segmentation network. Besides, a bi-directional recurrent optimization mechanism is developed to update the hybrid information in both the forward and the backward directions. This allows the proposed network to make full and optimal use of the local context information. Quantitative and qualitative evaluation was performed on the NIH Pancreas-CT and MSD pancreas dataset, and our proposed PBR-UNet method achieved similar segmentation results with less computational cost compared to other state-of-the-art methods.Monoclinic scheelite bismuth vanadate is an efficient photocatalyst for water splitting. In this paper, we perform DFT + Ucalculations to investigate the structural, electronic, and optical properties, water adsorption and the oxygen evolution reaction processes on BiVO4(001) and BiVO4(110) surfaces in acidic medium both in the gas and solution (water) phases. The structural, electronic, optical, and water adsorption properties reveal that BiVO4(001) surface is energetically more stable than BiVO4(110) surface in vacuum. On other hand, the water oxidation mechanisms reveal that BiVO4(110) surface in water and in strained form in vacuum is energetically more stable than BiVO4(001) surface in water and in strained form in vacuum bothU = 0 and 2.1 V. The free energy of adsorption for all systems atU = 2.1 V reduce about 2 times than that atU = 0 V. Such analyzes provide important insights into the role of different facets on BiVO4surface for photocatalytic reactions.Organ delineation is crucial to diagnosis and therapy, while it is also labor-intensive and observer-dependent. Dual energy CT (DECT) provides additional image contrast than conventional single energy CT (SECT), which may facilitate automatic organ segmentation. This work aims to develop an automatic multi-organ segmentation approach using deep learning for head-and-neck region on DECT. We proposed a mask scoring regional convolutional neural network (R-CNN) where comprehensive features are firstly learnt from two independent pyramid networks and are then combined via deep attention strategy to highlight the informative ones extracted from both two channels of low and high energy CT. To perform multi-organ segmentation and avoid misclassification, a mask scoring subnetwork was integrated into the Mask R-CNN framework to build the correlation between the class of potential detected organ's region-of-interest (ROI) and the shape of that organ's segmentation within that ROI. We evaluated our model on DECT imagescilitate the current head-and-neck cancer radiation therapy workflow in treatment planning.Cellular metabolites play a crucial role in promoting and regulating cellular activities, but it has been difficult to monitor these cellular metabolites in living cells and in real time. Over the past decades, iterative development and improvements of fluorescent probes have been made, resulting in the effective monitoring of metabolites. In this review, we highlight recent progress in the use of fluorescent probes for tracking some key metabolites, such as adenosine triphosphate, cyclic adenosine monophosphate, cyclic guanosine 5'-monophosphate, Nicotinamide adenine dinucleotide (NADH), reactive oxygen species, sugar, carbon monoxide, and nitric oxide for both whole cell and subcellular imaging.In vitroexperiments show significant reduction in the survival fraction of cells under irradiation treatments assisted with high-Znanoparticles (NPs). In order to predict the radiosensitization effect of NPs, a modification of the local effect model (LEM), in which the energy deposition from NPs is assessed by Monte Carlo (MC) radiation transport codes, has been employed in the past. In this work, a combined framework that splits the consideration of the radiosensitization effect into two steps is proposed. The first step is the evaluation of the radial dose distribution (RDD) around a single NP ionized by a photon beam with given energy spectrum using MC simulation. Thereafter, an analytical approach based of the LEM and the calculated RDD is used for evaluation of the average dose and the average number of lethal lesions in a cell target due to a set of ionized NPs. The explicit expressions were derived for the case of a spherical cell target and the RDD describing by the power law function. RDDs around gold NPs (GNPs) of different radii were simulated using the MC technique and fitted by a power law function.