Control of GaN inverted pyramids growth on c-plane patterned sapphire substrates

Growth of gallium nitride(GaN)inverted pyramids on c-plane sapphire substrates is benefit for fabricating novel devices as it forms the semipolar facets.In this work,GaN inverted pyramids are directly grown on c-plane patterned sapphire substrates(PSS)by metal organic vapor phase epitaxy(MOVPE).The influences of growth conditions on the surface morphol-ogy are experimentally studied and explained by Wulff constructions.The competition of growth rate among{0001},{1011},and{1122}facets results in the various surface morphologies of GaN.A higher growth temperature of 985°C and a lowerⅤ/Ⅲratio of 25 can expand the area of{}facets in GaN inverted pyramids.On the other hand,GaN inverted pyramids with almost pure{}facets are obtained by using a lower growth temperature of 930℃,a higherⅤ/Ⅲratio of 100,and PSS with pattern arrangement perpendicular to the substrate primary flat.

GaN hexagonal pyramids formed by a photo-assisted chemical etching method

A series of experiments were conducted to systematically study the effects of etching conditions on GaN by a con-venient photo-assisted chemical （PAC） etching method. The solution concentration has an evident influence on the surface morphology of GaN and the optimal solution concentrations for GaN hexagonal pyramids have been identified. GaN with hexagonal pyramids have higher crystal quality and tensile strain relaxation compared with as-grown GaN. A detailed anal- ysis about evolution of the size, density and optical property of GaN hexagonal pyramids is described as a function of light intensity. The intensity of photoluminescence spectra of GaN etched with hexagonal pyramids significantly increases compared to that of as-grown GaN due to multiple scattering events, high quality GaN with pyramids and the Bragg effect.

Controlled growth of Mo2C pyramids on liquid Cu surface

Precise spatial control of 2D materials is the key capability of engineering their optical,electronic,and mechanical properties.However,growth of novel 2D Mo2C on Cu surface by chemical vapor deposition method was revealed to be seed-induced 2D growth,limiting further synthesis of complex Mo2C spatial structures.In this research,we demonstrate the controlled growth of Mo2C pyramids with numerous morphologies,which are characterized with clear terraces within the structures.The whole evolution for Mo2C pyramids in the coursed of CVD process has been detected,posing significant potential in probing growth mechanism.The formation of the Mo2C pyramids arises from the supersaturation-induced nucleation and concentration-gradient driven diffused growth of a new Mo2C layer on the edged areas of intrinsic ones,as supported by STEM imaging.This work provides a novel Mo2C-based pyramid structure and further reveals a sliding growth mechanism,which could offer impetus for the design of new 3D spatial structures of Mo2C and other 2D materials.

A Comparison of Population Age Pyramids

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The Non-Equivalence of Pyramids and Their Pseudo-Cones: Important New Insights

The simulation of indentations with so called “equivalent” pseudo-cones for decreasing computer time is challenged. The mimicry of pseudo-cones having equal basal surface and depth with pyramidal indenters is excluded by basic arithmetic and trigonometric calculations. The commonly accepted angles of so called “equivalent” pseudo-cones must not also claim equal depth. Such bias (answers put into the questions to be solved) in the historical values of the generally used half-opening angles of pseudo-cones is revealed. It falsifies all simulations or conclusions on that basis. The enormous errors in the resulting hardness HISO and elastic modulus Er-ISO values are disastrous not only for the artificial intelligence. The straightforward deduction for possibly ψ-cones (ψ for pseudo) without biased depths’ errors for equal basal surface and equal volume is reported. These ψ-cones would of course penetrate much more deeply than the three-sided Berkovich and cube corner pyramids (r a/2), and their half-opening angles would be smaller than those of the respective pyramids (reverse with r > a/2 for four-sided Vickers). Also the unlike forces’ direction angles are reported for the more sideward and the resulting downward directions. They are reflected by the diameter of the parallelograms with length and off-angle from the vertical axis. Experimental loading curves before and after the phase-transition onsets are indispensable. Mimicry of ψ-cones and pyramids is also quantitatively excluded. All simulations on their bases would also be dangerously invalid for industrial and solid pharmaceutical materials.

A Study of the Pit-Aided Construction of Egyptian Pyramids

Pyramids,symbols of the Ancient Egyptian civilization,are visited by tourists and studied by researchers from all around the world.However,the techniques used by Ancient Egyptians to construct the pyramid,specifically,how such a tall structure could have been constructed from huge blocks of stone with the limited productive forces at the time,remains a mystery to the world.Though numerous theories,such as the use of ramps,levers,pulleys,fluid buoyancy,and cast-in-place concrete,have been proposed in academia,no consensus has been reached to date.Based on mechanical principles and the productive forces available at the time,the famous Pyramid of Khufu is used as a case study in this paper to propose a theory of pit-aided construction.The main steps include the digging of the pit,the transportation of stone blocks into the pit,the layer-by-layer construction,and the layer-by-layer filling of soil until the top of the pyramid is completed.The main idea of the pit-aided construction was to use the self-weight of the stone material to achieve the transportation of stone blocks by converting potential energy to kinetic energy,thereby avoiding the large amounts of work that must be done to elevate the huge blocks of stone.The proposed theory of pit-aided construction is consistent with the cultural custom of burial that is associated with tomb construction,namely laying the deceased to rest through burial,and is also consistent with the productive forces available in Ancient Egypt at the time.

The Formation and Characterization of GaN Hexagonal Pyramids

GaN with hexagonal pyramids is fabricated using the photo-assisted electroless chemical etching method.Defective areas of the GaN substrate are selectively etched in a mixed solution of KOH and K2S2O8 under ultraviolet illumination,producing submicron-sized pyramids.Hexagonal pyramids on the etched GaN with well-defined{1011}facets and very sharp tips are formed.High-resolution x-ray diffraction shows that etched GaN with pyramids has a higher crystal quality,and micro-Raman spectra reveal a tensile stress relaxation in GaN with pyramids compared with normal GaN.The cathodoluminescence intensity of GaN after etching is significantly increased by three times,which is attributed to the reduction in the internal reflection,high-quality GaN with pyramids and the Bragg effect.

Hyperbolic Coxeter Pyramids

Hyperbolic Coxeter polytopes are defined precisely by combinatorial type. Polytopes in hyperbolic n-space with n + p faces that have the combinatorial type of a pyramid over a product of simplices were classified by Tumarkin for small p. In this article we generalise Tumarkin’s methods and find the remaining hyperbolic Coxeter pyramids.

Body-area sensor network featuring micropyramids for sports healthcare

Monitoring physiological signals of the human body can provide extremely important information for sports healthcare,preventing injuries and providing efficient guidance for individual sports.However,the signals related to human healthcare involve both subtle and vigorous signals,making it difficult for a sensor to satisfy the full-scale monitoring at the same time.Here,a novel conductive elastomer featuring homogeneously micropyramid-structured PDMS/CNT composite is used to fabricate highperformance piezoresistive sensors by a drop-casting method.Benefiting from the significant increase in the contact area of microstructure during deformation,the flexible sensor presents a broad detection range(up to 185.5 kPa),fast response/recovery time(44/13 ms),ultrahigh sensitivity(242.4 kPa–1)and excellent durability over 8,000 cycles.As a proof of concept,the as-fabricated pressure sensor can be used for body-area sports healthcare,and enable the detection of full-scale pressure distribution.Considering the fabulous sensing performance,the sensor may potentially become promising in personal sports healthcare and telemedicine monitoring.

Regulation of specific abnormal calcium signals in the hippocampal CA1 and primary cortex M1 alleviates the progression of temporal lobe epilepsy

Temporal lobe epilepsy is a multifactorial neurological dysfunction syndrome that is refractory,resistant to antiepileptic drugs,and has a high recurrence rate.The pathogenesis of temporal lobe epilepsy is complex and is not fully understood.Intracellular calcium dynamics have been implicated in temporal lobe epilepsy.However,the effect of fluctuating calcium activity in CA1 pyramidal neurons on temporal lobe epilepsy is unknown,and no longitudinal studies have investigated calcium activity in pyramidal neurons in the hippocampal CA1 and primary motor cortex M1 of freely moving mice.In this study,we used a multichannel fiber photometry system to continuously record calcium signals in CA1 and M1 during the temporal lobe epilepsy process.We found that calcium signals varied according to the grade of temporal lobe epilepsy episodes.In particular,cortical spreading depression,which has recently been frequently used to represent the continuously and substantially increased calcium signals,was found to correspond to complex and severe behavioral characteristics of temporal lobe epilepsy ranging from gradeⅡto gradeⅤ.However,vigorous calcium oscillations and highly synchronized calcium signals in CA1 and M1 were strongly related to convulsive motor seizures.Chemogenetic inhibition of pyramidal neurons in CA1 significantly attenuated the amplitudes of the calcium signals corresponding to gradeⅠepisodes.In addition,the latency of cortical spreading depression was prolonged,and the above-mentioned abnormal calcium signals in CA1 and M1 were also significantly reduced.Intriguingly,it was possible to rescue the altered intracellular calcium dynamics.Via simultaneous analysis of calcium signals and epileptic behaviors,we found that the progression of temporal lobe epilepsy was alleviated when specific calcium signals were reduced,and that the end-point behaviors of temporal lobe epilepsy were improved.Our results indicate that the calcium dynamic between CA1 and M1 may reflect specific epileptic behaviors corresponding to different grades.Furthermore,the selective regulation of abnormal calcium signals in CA1 pyramidal neurons appears to effectively alleviate temporal lobe epilepsy,thereby providing a potential molecular mechanism for a new temporal lobe epilepsy diagnosis and treatment strategy.

An anti-aliasing filtering of quantum images in spatial domain using a pyramid structure

As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum computing.Low-pass filtering can effectively achieve anti-aliasing effects on images.Currently,most quantum image filterings are based on classical domains and grayscale images,and there are relatively fewer studies on anti-aliasing in the quantum domain.This paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial domain.It achieves the effect of anti-aliasing filtering on quantum images during the scaling process.First,we use the novel enhanced quantum representation(NEQR)and the improved quantum representation of color images(INCQI)to represent classical images.Since aliasing phenomena are more pronounced when images are scaled down,this paper focuses only on the anti-aliasing effects in the case of reduction.Subsequently,we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image,achieving the anti-aliasing effect.The constructed pyramid model is then used to select an appropriate image for upscaling to the original image size.Finally,the complexity of the circuit is analyzed.Compared to the images experiencing aliasing effects solely due to scaling,applying anti-aliasing filtering to the images results in smoother and clearer outputs.Additionally,the anti-aliasing filtering allows for manual intervention to select the desired level of image smoothness.

NFHP-RN:AMethod of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-ResNet

Due to the rapid evolution of Advanced Persistent Threats(APTs)attacks,the emergence of new and rare attack samples,and even those never seen before,make it challenging for traditional rule-based detection methods to extract universal rules for effective detection.With the progress in techniques such as transfer learning and meta-learning,few-shot network attack detection has progressed.However,challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning,difficulties in capturing rich information from original flow in the case of insufficient samples,and the challenge of high-level abstract representation.To address these challenges,a few-shot network attack detection based on NFHP(Network Flow Holographic Picture)-RN(ResNet)is proposed.Specifically,leveraging inherent properties of images such as translation invariance,rotation invariance,scale invariance,and illumination invariance,network attack traffic features and contextual relationships are intuitively represented in NFHP.In addition,an improved RN network model is employed for high-level abstract feature extraction,ensuring that the extracted high-level abstract features maintain the detailed characteristics of the original traffic behavior,regardless of changes in background traffic.Finally,a meta-learning model based on the self-attention mechanism is constructed,achieving the detection of novel APT few-shot network attacks through the empirical generalization of high-level abstract feature representations of known-class network attack behaviors.Experimental results demonstrate that the proposed method can learn high-level abstract features of network attacks across different traffic detail granularities.Comparedwith state-of-the-artmethods,it achieves favorable accuracy,precision,recall,and F1 scores for the identification of unknown-class network attacks through cross-validation onmultiple datasets.

Approximation of H(div)with High-Order Optimal Finite Elements for Pyramids,Prisms and Hexahedra

Classical facet elements do not provide an optimal rate of convergence of the numerical solution toward the solution of the exact problem in H(div)-norm for general unstructured meshes containing hexahedra and prisms.We propose two new families of high-order elements for hexahedra,triangular prisms and pyramids that recover the optimal convergence.These elements have compatible restrictions with each other,such that they can be used directly on general hybrid meshes.Moreover the H(div)proposed spaces are completing the De Rham diagram with optimal elements previously constructed for H1 and H(curl)approximation.The obtained pyramidal elements are compared theoretically and numerically with other elements of the literature.Eventually,numerical results demonstrate the efficiency of the finite elements constructed.

A fusion algorithm for remote sensing images based on nonsubsampled pyramids and bidimensional empirical decomposition

In order to improve the quality of remote sensing image fusion,a new method combining nonsubsampled Laplacian pyramid (NLP)and bidimensional empirical mode decomposition(BEMD)is proposed.First,the high resolution panchromatic image (PAN)is decomposed using NLP until the approximate component and the low resolution multispectral image(MS)contain features with a similar scale.Then,the approximation component and the MS are decomposed by BEMD,resulting in a number of bidimensional intrinsic mode functions(BIMF)and a residue respectively.The instantaneous frequency is computed in 4 directions of the BIMFs.Considering the positive or negative coefficients in the corresponding position,a weighted algorithm is designed for fusing the high frequency details using the instantaneous frequency and the coefficient absolute value of the BIMFs as fusion feature.The fused image is then obtained through inverse BEMD and NLP.Experimental results have illustrated the advantage of this method over the IHS,DWT andà-Trous wavelet in both spectral and spatial detail qualities.

Self-Assembled Plasmonic Pyramids from Anisotropic Nanoparticles for High-Efficient SERS

Surface-enhanced Raman scattering(SERS)substrates play important roles for the enhancement of inelastic scattering signals.Traditional substrates such as roughened electrodes and colloidal aggregates suffer from well-known signal reproducibility issues,whereas for current dominant two-dimensional planar systems,the hot spot distributions are limited by the zero-,one-or two-dimensional plane.The introduction of a three-dimensional(3D)system such as a pyramid geometry breaks the limitation of a single Cartesian SERS-active area and extends it into the z-direction,with the tip potentially offering additional benefits of strong field enhancement and high sensitivity.However,current 3D pyramidal designs are restricted to film deposition on prepared pyramid templates or self-assembly in pyramidal molds with spherical building blocks,hence limiting their SERS effectiveness.Here,we report on the fabrication of a new class of low cost and well-defined plasmonic nanoparticle pyramid arrays from different anisotropic shaped nanoparticles using combined top-down lithography and bottom-up self-assembly approach.These pyramids exhibit novel optical scattering properties that can be exploited for the design of reproducible and sensitive SERS substrate.The SERS intensity was found to decrease drastically in accordance with a power law function as the focal planes move from the apex of the pyramid structure towards the base.In comparison to sphere-based building blocks,pyramids assembled from anisotropic rhombic dodecahedral gold nanocrystals with numerous sharp tips exhibited the strongest SERS performance.Graphical Abstract Macroscale pyramidal array films with plasmonic tunability as a new class of SERS substrate for sensitive detection of chemicals.

BOUNDING PYRAMIDS AND BOUNDING CONES FOR TRIANGULAR BEZIER SURFACES

This paper describes practical approaches on how to construct bounding pyramids and bounding cones for triangular Bezier surfaces. Examples are provided to illustrate the process of construction and comparison is made between various surface bounding volumes. Furthermore, as a starting point for the construction, we provide a way to compute hodographs of triangular Bezier surfaces and improve the algorithm for computing the bounding cone of a set of vectors. [ABSTRACT FROM AUTHOR]

Numerical Integration over Pyramids

Pyramidal elements are often used to connect tetrahedral and hexahedral elements in the finite element method.In this paper we derive three new higher order numerical cubature formulae for pyramidal elements.

Semantic segmentation of pyramidal neuron skeletons using geometric deep learning

Neurons can be abstractly represented as skeletons due to the filament nature of neurites.With the rapid development of imaging and image analysis techniques,an increasing amount of neuron skeleton data is being produced.In some scienti fic studies,it is necessary to dissect the axons and dendrites,which is typically done manually and is both tedious and time-consuming.To automate this process,we have developed a method that relies solely on neuronal skeletons using Geometric Deep Learning(GDL).We demonstrate the effectiveness of this method using pyramidal neurons in mammalian brains,and the results are promising for its application in neuroscience studies.

Shear behavior of intact granite under thermo-mechanical coupling and three-dimensional morphology of shear-formed fractures

The shear failure of intact rock under thermo-mechanical(TM)coupling conditions is common,such as in enhanced geothermal mining and deep mine construction.Under the effect of a continuous engineering disturbance,shear-formed fractures are prone to secondary instability,posing a severe threat to deep engineering.Although numerous studies regarding three-dimensional(3D)morphologies of fracture surfaces have been conducted,the understanding of shear-formed fractures under TM coupling conditions is limited.In this study,direct shear tests of intact granite under various TM coupling conditions were conducted,followed by 3D laser scanning tests of shear-formed fractures.Test results demonstrated that the peak shear strength of intact granite is positively correlated with the normal stress,whereas it is negatively correlated with the temperature.The internal friction angle and cohesion of intact granite significantly decrease with an increase in the temperature.The anisotropy,roughness value,and height of the asperities on the fracture surfaces are reduced as the normal stress increases,whereas their variation trends are the opposite as the temperature increases.The macroscopic failure mode of intact granite under TM coupling conditions is dominated by mixed tensileeshear and shear failures.As the normal stress increases,intragranular fractures are developed ranging from a local to a global distribution,and the macroscopic failure mode of intact granite changes from mixed tensileeshear to shear failure.Finally,3D morphological characteristics of the asperities on the shear-formed fracture surfaces were analyzed,and a quadrangular pyramid conceptual model representing these asperities was proposed and sufficiently verified.

基于特征增强的轻量级无人机目标检测算法

针对无人机航拍图像特征少,小尺寸目标多以及检测任务实时性要求高等问题,以YOLOX算法为基础提出基于特征增强的轻量级无人机目标检测算法。首先,设计更加轻量的密集残差网络结构ResNet_G优化模型的主干网络,提升模型对图像特征的利用率,同时降低模型复杂度;其次,提出基于注意力机制的Atrous Spatial Pyramid Pooling(ASPP)模块作为特征增强模块,加强上下文信息关联度以减少丢失小目标特征;最后,使用Focal Loss函数与CDIoU Loss函数,改善负样本对模型权重的影响以提高对密集目标的识别能力。实验结果表明,与原网络相比,改进后算法在VisDrone2021数据集上平均检测精度提升5.08%,参数量减少0.25 M,推理时间降低2.21 ms。