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 ℃ 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.

Synthesis and photoluminescence properties of single-crystal ZnO hexagonal pyramids by PEG400-assisted thermal decomposition route

Large-scale synthesis of ZnO hexagonal pyramids was achieved by a simple thermal decomposition route of precursor at 240 oC in the presence of PEG400. The precursor was obtained by room-temperature solid-state grinding reaction between Zn（CH3COO）2-2H2O and Na2CO3. Crystal structure and morphology of the products were analyzed and characterized by X-ray diffraction （XRD）, scanning electron microscopy （SEM）, transmission electron microscopy （TEM） and high-resolution transmission electron microscopy （HRTEM）. The results of further experiments show that PEG400 has an important role in the formation of ZnO hexagonal pyramids. Difference between the single and double hexagonal pyramid structure may come from the special thermal decomposition reaction. The photoluminescence （PL） spectra of ZnO hexagonal pyramids exhibit strong near-band-edge emission at about 386 nm and weak green emission at about 550 nm. The Raman-active vibration at about 435 cm-1 suggests that the ZnO hexagonal pyramids have high crystallinity.

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.

Applications of Inverted Pyramids Corroded by TMAH in PERL Silicon LED

In this paper, a novel and reliable structure of the side passivated emitter and the rear locallydiffused（PERL） silicon light emitting diodes （LEDs） is proposed. The inverted pyramids surface, the important interface in this structure, is given according to the experiment. The results show that the inverted pyramids surface has a low refection about 8%, in the anisotropic etching 70 ℃, 5% TMAH concentration, corrosion time of 90 min or 30 rain. Low refection means high light emitting rate. Most of the structure and manufacturing process can be compatible with planar CMOS technology, which makes the silicon LED greater potential for development in the future.

A Comparison of Population Age Pyramids

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Reasons Why the Great Pyramids of Giza Remain the Only Surviving Wonder of the Ancient World： Drawing Ideas from the Structure of the Giza Pyramids to Nuclear Power Plants

Selecting a site for a nuclear power plant requires extensive studies to ensure its safety and stability during its operation until its decommissioning. The 4,500-year old Egyptian pyramids at Giza are buildings to learn from. This paper tries to pin down the reasons for the survival of the Giza pyramids in order to reach a criterion for choosing sites for important buildings. It argues that the site selection and the geological properties of the area, being away from seismic effects,, floods and groundwater levels, the stability of the geometric form of the pyramid, the solidity of the structural engineering and precision of execution arguably are the reasons why the Great Pyramids of Giza are the only survivors of the seven wonders of the ancient world.

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.

IMTNet:Improved Multi-Task Copy-Move Forgery Detection Network with Feature Decoupling and Multi-Feature Pyramid

Copy-Move Forgery Detection(CMFD)is a technique that is designed to identify image tampering and locate suspicious areas.However,the practicality of the CMFD is impeded by the scarcity of datasets,inadequate quality and quantity,and a narrow range of applicable tasks.These limitations significantly restrict the capacity and applicability of CMFD.To overcome the limitations of existing methods,a novel solution called IMTNet is proposed for CMFD by employing a feature decoupling approach.Firstly,this study formulates the objective task and network relationship as an optimization problem using transfer learning.Furthermore,it thoroughly discusses and analyzes the relationship between CMFD and deep network architecture by employing ResNet-50 during the optimization solving phase.Secondly,a quantitative comparison between fine-tuning and feature decoupling is conducted to evaluate the degree of similarity between the image classification and CMFD domains by the enhanced ResNet-50.Finally,suspicious regions are localized using a feature pyramid network with bottom-up path augmentation.Experimental results demonstrate that IMTNet achieves faster convergence,shorter training times,and favorable generalization performance compared to existingmethods.Moreover,it is shown that IMTNet significantly outperforms fine-tuning based approaches in terms of accuracy and F_(1).

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

基于YOLOv8改进的脑癌检测算法

自动检测脑部肿瘤在磁共振成像中的位置是一个复杂、繁重的任务,需要耗费大量时间和资源。传统识别方案经常出现误解、遗漏和误导的情况,从而影响患者的治疗进度,对患者的生命安全产生影响。为了进一步提高鉴定的效果,引入了4项关键改进措施。首先,采用了高效的多尺度注意力EMA(Efficient Multi-scale Attention),这种方法既可以对全局信息进行编码,也可以对信息进行重新校准,同时通过并行的分支输出特征进行跨维度的交互,使信息进一步聚合。其次,引入了BiFPN(Bidirectional Feature Pyramid Network)模块,并对其结构进行改进,以便缩短每一次检测所需要的时间,同时提升图像识别效果。然后采用MDPIoU损失函数和Mish激活函数进行改进,进一步提高检测的准确度。最后进行仿真实验,实验结果表明,改进的YOLOv8算法在脑癌检测中的精确率、召回率、平均精度均值均有提升,其中Precision提高了4.48%,Recall提高了2.64%,mAP@0.5提高了2.6%,mAP@0.5:0.9提高了7.0%。

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.

An improved deep dilated convolutional neural network for seismic facies interpretation

With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.

Transformer-Based Cloud Detection Method for High-Resolution Remote Sensing Imagery

Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.

An Improved UNet Lightweight Network for Semantic Segmentation of Weed Images in Corn Fields

In cornfields,factors such as the similarity between corn seedlings and weeds and the blurring of plant edge details pose challenges to corn and weed segmentation.In addition,remote areas such as farmland are usually constrained by limited computational resources and limited collected data.Therefore,it becomes necessary to lighten the model to better adapt to complex cornfield scene,and make full use of the limited data information.In this paper,we propose an improved image segmentation algorithm based on unet.Firstly,the inverted residual structure is introduced into the contraction path to reduce the number of parameters in the training process and improve the feature extraction ability;secondly,the pyramid pooling module is introduced to enhance the network’s ability of acquiring contextual information as well as the ability of dealing with the small target loss problem;and lastly,Finally,to further enhance the segmentation capability of the model,the squeeze and excitation mechanism is introduced in the expansion path.We used images of corn seedlings collected in the field and publicly available corn weed datasets to evaluate the improved model.The improved model has a total parameter of 3.79 M and miou can achieve 87.9%.The fps on a single 3050 ti video card is about 58.9.The experimental results show that the network proposed in this paper can quickly segment corn weeds in a cornfield scenario with good segmentation accuracy.