In this paper, we study the plasmonic thermal conductance of ordered stacks of metallic nanorings in a host material. Using second quantized formalism of the Random Phase Approximation, we first determine the dispersion relations of surface plasmon waves on the stacks of nanorings. Then, using Landauer-Buttiker formalism, we determine the coefficient of plasmonic thermal conductance and heat current through the stack as a function of temperature, radius, spacing of rings and dielectric constant of host material. Our results indicate that ordered stacks of metallic nanorings have potential plasmonic thermal properties for heat transfer in nanostructures.

Migration is a process that reconstructs an image of the earth's reflecting structure from elastic wavefield energy recorded at the surface in seismic traces. In seismic imaging, conventional processing is concentrates on producing a stacked section from common mid point gathers and is followed by a poststack migration, while in a prestack migration, first the events are migrated one by one, then those are stacked and the energy the common reflection point gathers goes to the surface of the earth. In this paper the effects of both post-and pre-stack time migration methods are compared by use of the Kirchhoff summation method on a seismic line in the central Iran area. The method of data collection is offend and the foldage is 48. The source of energy in acquisition is dynamite. The work is implemented by using PROMAX 6.0 software on ULTRA SUN system. The results are as follows: Continuity of reflectors will be improved after prestack migration. Velocity analysis after prestack migration is improved and provides a better harmony of the velocities. Because of the increasing of the reflector's amplitudes continuity, some geology structures appeared after prestack migration.      

This research aims to optimize a 6-transistor SRAM cell based on combination of Schmitt trigger not gate and force stack methods. Considering previous studies, a 12-transistor circuit; single-ended is proposed with 22nm technology at voltage 0. 5 volt. There is an opportunity to use this circuit as a Force stack in hold state and in reading mode the cell will be single ended, So Schmitt trigger not gate will inter the circuit, prevents the reading error. The purpose of this study is to optimize the parameters including speed, power and static noise margin. Finally, the effects of replacing CNTFET transistors on parameters has been investigated using simulation. Simulations have been performed by Hspice software at 25° C. Simulation results have shown that the suggested circuit based on CNTs, increases HSNM about 214mV due to using force stack method. Also, in reading mode RSNM increases more than 131mV considering the not Schmitt trigger’ s gate. Since Nano-tube transistors were used, leakage power decrease from nW to pw and circuit’ s delay is optimized.

Over the past few years, there has been a significant increase in patent applications, which has resulted in a heavier workload for examination offices in examining and prosecuting these inventions. To adequately perform this legal process, examiners must thoroughly analyze patents by manually identifying the semantic information such as problem description and solutions. The process of manually annotating is both tedious and time-consuming. To solve this issue, we have introduced a deep ensemble model for semantic paragraph-level pattern classification based on the semantic content of patents. Specifically, our proposed model classifies the paragraphs into semantic categories to facilitate the annotation process. The proposed model employs stack generalization as an ensemble method for combining various deep models such as Long Short-Term Memories (LSTM), bidirectional LSTM (BiLSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and the pre-trained BERT model. We compared the proposed model with several baselines and state-of-the-art deep models on the PaSA dataset containing 150000 USPTO patents classified into three classes of 'technical advantages', 'technical problems', and 'other boilerplate text'. The results of extensive experiments show that the proposed model outperforms both traditional and state-of-the-art deep models significantly.

Spectral decomposition of time series has a significant role in seismic data processing and interpretations. Since the earth acts as a low-pass filter, it changes the frequency content of the passing seismic waves. Conventional methods of representing signals in a time domain and frequency domain cannot show the time information and the frequency information simultaneously. Time-frequency transforms an upgraded spectral decomposition to a new step and can show time and frequency information simultaneously.Time-frequency transforms generate a high volume of spectral components, which contain useful information about the reservoir and can be decomposed into single frequency volumes. These single frequency volumes can overload the limited space of a computer hard disk and are not easy for an interpreter to investigate them individually; therefore, it is important to use methods to decrease the volume without losing information. The frequency slices are thus separated from these volumes and used for an interpretation.In this study, three different methods were used to represent a buried channel. In the first method, the numbers of the single frequency slices were investigated, variations of the frequency amplitudes in the slices were observed, and an expert interpreter could obtain some information about the channel content and lateral variation. Since different frequencies contain different types of information (low frequencies are sensible to channel content and high frequencies are sensible to channel boundaries), none of the slices were able to show all information simultaneously. In the next two methods using a color stacking method, the RGB plots were constructed which, due to the different frequency content, resulted in more information than the frequency slice representation method.An RGB image, sometimes referred to as a true color image, is an image that defines red, green, and blue color components for each individual pixel and has an intensity between 0 and 1. In this study, RGB plots were constructed in two different manners, RGB plots based on conventional RGB plot methods and RGB plots using basis functions. In the conventional method, three different frequency slices were mapped against the red, green and blue components. Although this method obviates some drawbacks of the single frequency plots, it uses only three slices and practically ignores a big part of information. Using basis functions and defining windows, the interpreter was able to introduce some frequency intervals and plot them against the primary components and use the total bandwidth or its major part. Three simple raised cosine functions having different frequency centers and different periods were chosen. The image quality strongly depended on these two parameters. Longer window widths will introduce longer frequency widths into every primary component and resulted in smoother color combinations for images and very short periods had the same results as the conventional RGB plot method. Different centers showed different details. Low frequency centers showed channel content properties, and high frequency centers showed channel boundaries and fine branches.In this study, the spectral decomposition was first performed on land seismic data from an oil field in Iran using a short time Fourier (STFT) transform and an S transform. Then three demonstration methods were applied for channel detection. Finally it was shown that how RGB color stacking method represented buried channels in more precise images and how a basis function based RGB represents better results than the conventional RGB method.

The conventional velocity analysis sums the amplitudes of events along hyperbolic trajectories and converges the energy in the corresponding intercept time and slowness or velocity. This makes the velocity analysis as one of the most time consuming seismic data processing steps. On the other hand, this algorithm suffers from low resolution due to several reasons. In this paper, we use the Butterfly algorithm to calculate the forward and adjoint operators of the hyperbolic Radon transform in a much faster way, compared to the conventional integration in the time domain. Moreover, by applying it to fast iterative shrinkage-thresholding algorithm (FISTA), a high-resolution velocity panel is obtained.

As one of the most clinically relevant parameters in proton radiotherapy, the range of incident particles can be measured either by counting the number of protons or through depth-dose evaluation in the target. In the latter, the range is defined as the depth in the target at the distal 80% point of the Bragg peak. In this work, a highly accurate analytical model was employed to predict depth-dose distribution, and hence the range, in a desired target. Aiming to study the effect of energy spread on the range, proton beams with initial Gaussian distributions have been considered. For our arbitrary tested energies, the results show that the more the width of energy distribution increases, the more the Bragg peaks shift in depth, by about-0. 25% to-25%, compared with those of monoenergetic beams. Furthermore, it was found that for different widths of initial energy spectrum, keeping the mean energy the same, the range remains unchanged. It was also shown that the results corresponding to utilizing analytical range determination for proton beams of different incident energies in stack of materials deviate from those of Monte Carlo simulations by less than 1. 7%. The results are encouraging, although accurate modeling of analytical proton dose distribution in the presence of tissue inhomogeneities is still an unsolved problem.