On a general theory for compressing process and aeroacoustics:linear analysis

Of the three mutually coupled fundamental processes （shearing, compressing, and thermal） in a general fluid motion, only the general formulation for the compress- ing process and a subprocess of it, the subject of aeroacous- tics, as well as their physical coupling with shearing and thermal processes, have so far not reached a consensus. This situation has caused difficulties for various in-depth complex multiprocess flow diagnosis, optimal configuration design, and flow/noise control. As the first step toward the desired formulation in fully nonlinear regime, this paper employs the operator factorization method to revisit the analytic linear theories of the fundamental processes and their decomposi- tion, especially the further splitting of compressing process into acoustic and entropy modes, developed in 1940s-1980s. The flow treated here is small disturbances of a compressible, viscous, and heat-conducting polytropic gas in an unbounded domain with arbitrary source of mass, external body force, and heat addition. Previous results are thereby revised and extended to a complete and unified theory. The theory pro- vides a necessary basis and valuable guidance for developing corresponding nonlinear theory by clarifying certain basic issues, such as the proper choice of characteristic variables of compressing process and the feature of their governing equations.

Parameters Compressing in Deep Learning

With the popularity of deep learning tools in image decomposition and natural language processing,how to support and store a large number of parameters required by deep learning algorithms has become an urgent problem to be solved.These parameters are huge and can be as many as millions.At present,a feasible direction is to use the sparse representation technique to compress the parameter matrix to achieve the purpose of reducing parameters and reducing the storage pressure.These methods include matrix decomposition and tensor decomposition.To let vector take advance of the compressing performance of matrix decomposition and tensor decomposition,we use reshaping and unfolding to let vector be the input and output of Tensor-Factorized Neural Networks.We analyze how reshaping can get the best compress ratio.According to the relationship between the shape of tensor and the number of parameters,we get a lower bound of the number of parameters.We take some data sets to verify the lower bound.

Compressing Information of Target Tracking in Wireless Sensor Networks

Target tracking is a well studied topic in wireless sensor networks. It is a procedure that nodes in the network collaborate in detecting targets and transmitting their information to the base-station continuously, which leads to data implosion and redundancy. To reduce traffic load of the network, a data compressing based target tracking protocol is proposed in this work. It first incorporates a clustering based data gather method to group sensor nodes into clusters. Then a novel threshold technique with bounded error is proposed to exploit the spatial correlation of sensed data and compress the data in the same cluster. Finally, the compact data presentations are transmitted to the base-station for targets localization. We evaluate our approach with a comprehensive set of simulations. It can be concluded that the proposed method yields excellent performance in energy savings and tracking quality.

Superhard BC_2N:an Orthogonal Crystal Obtained by Transversely Compressing(3,0)-CNTs and(3,0)-BNNTs

By means of density functional theory calculations, an orthogonal boron-carbon-nitrogen compound called （3,0）- BC2N is predicted, which can be obtained by transversely compressing （3,03 carbon nanotubes （CNTs） and boron nitride nanotubes （BNNTs）. Its structural stability, elastic properties, mechanical properties and electronic structure are systematically investigated. The results show that （3,0）-BU2N is a superhard material with a direct bandgap. However, its similar structures, （3,0）-C and （3,0）-BN are indirect semiconductors. Strikingly, （3,0）-C is harder than diamond. We also simulate the x-ray diffraction of （3,0）-BC2N to support future experimental investigations. In addition, our study shows that the transition from （3,03 CNTS and BNNTs to （3,0）-BC2N is irreversible.

Protective effect of compressing arc extinguishing lightning protection device on superimposed lightning strikes

Traditional lightning protection measures can not solve the problem of superimposed lightning strikes.This paper presents a compressing arc extinguishing lightning protection device,which can solve the problem of superimposed lightning strikes.This device can extinguish the power frequency continuous current arc quickly in 1-2 ms.It is far less than the response time of relay protection,which can avoid lightning trips and improve the reliability of power supply.The computer simulation and engineering practice show that the compressing arc extinguishing device has good protection effect on superimposed lightning strikes.

Compressing ultrafast electron pulse by radio frequency cavity

An ultrafast electron diffraction technique with both high temporal and spatial resolution has been shown to be a powerful tool to observe the material transient structural change on an atomic scale.The space charge forces in a multi-electron bunch will greatly broaden the electron pulse width,and therefore limit the temporal resolution of the high brightness electron pulse.Here in this work,we design an ultrafast electron diffraction system,and utilize a radio frequency cavity to realize the ultrafast electron pulse compression.We experimentally demonstrate that the stretched electron pulse width of14.98 ps with an electron energy of 40 keV and the electron number of 1.0 ×10;can be maximally compressed to about0.61 ps for single-pulse measurement and 2.48 ps for multi-pulse measurement by using a 3.2-GHz radiofrequency cavity.We also theoretically and experimentally analyze the parameters influencing the electron pulse compression efficiency for single-and multi-pulse measurements by considering radiofrequency field time jitter,electron pulse time jitter and their relative time jitter.We suggest that increasing the electron energy or shortening the distance between the compression cavity and the streak cavity can further improve the electron pulse compression efficiency.These experimental and theoretical results are very helpful for designing the ultrafast electron diffraction experiment equipment and compressing the ultrafast electron pulse width in a future study.

Simultaneous laparoscopic and arthroscopic excision of a huge juxta-articular ganglionic cyst compressing the sciatic nerve:A case report

BACKGROUND A large ganglionic cyst extending from the hip joint to the intrapelvic cavity through the sciatic notch is a rare space-occupying lesion associated with compressive lower-extremity neuropathy.A cyst in the pelvic cavity compressing the intrapelvic-sciatic nerve is easily missed in the diagnostic process because it usually presents as atypical symptoms of an extraperitoneal-intrapelvic tumor.We present a case of a huge ganglionic cyst that was successfully excised laparoscopically and endoscopically by a gynecologist and an orthopedic surgeon.CASE SUMMARY A 52-year-old woman visited our hospital complaining of pain and numbness in her left buttock while sitting.The pain began 3 years ago and worsened,while the numbness in the left lower extremity lasted 1 mo.She was diagnosed and unsuccessfully treated at several tertiary referral centers many years ago.Magnetic resonance imaging revealed a suspected paralabral cyst(5 cm×5 cm×4.6 cm)in the left hip joint,extending to the pelvic cavity through the greater sciatic notch.The CA-125 and CA19-9 tumor marker levels were within normal limits.However,the cyst was compressing the sciatic nerve.Accordingly,endoscopic and laparoscopic neural decompression and mass excision were performed simultaneously.A laparoscopic examination revealed a tennis-ball-sized cyst filled with gelatinous liquid,stretching deep into the hip joint.An excisional biopsy performed in the pelvic cavity and deep gluteal space confirmed the accumulation of ganglionic cysts from the hip joint into the extrapelvic intraperitoneal cavity.CONCLUSION Intra-or extra-sciatic nerve-compressing lesion should be considered in cases of sitting pain radiating down the ipsilateral lower extremity.This large juxta-articular ganglionic cyst was successfully treated simultaneously using laparoscopy and arthroscopy.

Assessment of compressive strength of jet grouting by machine learning

Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.

Microstructure and texture evolution of 6016 aluminum alloy during hot compressing deformation

Microstructure and texture in 6016 aluminum alloy during hot compression were researched with a uni- axial compression experiment. Through the electron back- scattered diffraction （EBSD） and X-ray diffraction （XRD） analysis technology, it is shown that the subgrain nucle- ation and recrystallization occur in 6016 aluminum alloy during hot compressing, and strong rolling textures such as （110） fiber texture, Brass, S, and Goss form. With the deformation passes increasing, （110） fiber texture, Brass and S are enhanced. In the heat preservation stage after deformation, recrystallization continues until heat preser- vation for 60 s, and a duplex microstructure of deformation and recrystallization grains is built. At the beginning of heat preservation, recrystallization grains with the Goss texture and random orientation are formed in original grains with S or Brass texture, which makes the volume fraction of S and Brass texture decrease. Then, the complex grain growth process makes the volume fraction of Brass, S, and Goss texture increase, while that of random orien- tation decrease.

Color Image Compression and Encryption Algorithm Based on 2D Compressed Sensing and Hyperchaotic System

With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color images.It is predicated on 2D compressed sensing(CS)and the hyperchaotic system.First,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security.Then,the processed images are con-currently encrypted and compressed using 2D CS.Among them,chaotic sequences replace traditional random measurement matrices to increase the system’s security.Third,the processed images are re-encrypted using a combination of permutation and diffusion algorithms.In addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct images.Compared with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational complexity.Furthermore,it has better robustness.The experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.

Statistical analysis of physico-mechanical parameters of sandstones occurring in orogenic settings

In northeastern Sicily(Italy),sandstone rock masses widely crop out as cover deposits over crystalline terrains belonging to the orogenic belt.Despite being part of the same geological formation,these sandstones are characterized by highly different features in terms of texture and physico-mechanical properties.This poses a scientific question on the possibility of tracing these rocks to a single statistical model,which could be representative of their main engineering geological properties.Therefore,it is worth investigating on the possible reasons of such differences,that should be searched either in the current geographical sandstone distribution or in the rock texture.For this study,sandstone samples were collected from different sites and were analyzed at both the hand and thin section scales.Three sandstone types were recognized,characterized by a different texture.Then,the laboratory characterization allowed estimating their main physico-mechanical and ultrasonic properties,such as porosity,density,mechanical strength,deformability,and ultrasonic velocities.The rock mechanical strength proved linked to the rock compactness and to the presence of lithic fragments,while pores and a pseudo-matrix between grains represent weakening features.Rock data were also statistically analyzed by grouping the specimens according to a geographical criterion,with respect to their sampling area,but no link was found between location and rock properties.Finally,with the aim of achieving mathematical laws that could be used to predict some rock properties from others,useful for practical purposes when dealing with such a high property variability,single and multiple regression analyses were carried out.Results show that the Uniaxial Compressive Strength,porosity,and P-wave velocity are the best predictors for a quick,indirect estimation of the main physico-mechanical parameters.The methodological approach developed for this research can be taken as reference to study other worldwide cases,involving rocks characterized by a wide range of physico-mechanical properties and covering large regional territories.

Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block-GAN

In the context of high compression rates applied to Joint Photographic Experts Group(JPEG)images through lossy compression techniques,image-blocking artifacts may manifest.This necessitates the restoration of the image to its original quality.The challenge lies in regenerating significantly compressed images into a state in which these become identifiable.Therefore,this study focuses on the restoration of JPEG images subjected to substantial degradation caused by maximum lossy compression using Generative Adversarial Networks(GAN).The generator in this network is based on theU-Net architecture.It features a newhourglass structure that preserves the characteristics of the deep layers.In addition,the network incorporates two loss functions to generate natural and high-quality images:Low Frequency(LF)loss and High Frequency(HF)loss.HF loss uses a pretrained VGG-16 network and is configured using a specific layer that best represents features.This can enhance the performance in the high-frequency region.In contrast,LF loss is used to handle the low-frequency region.The two loss functions facilitate the generation of images by the generator,which can mislead the discriminator while accurately generating high-and low-frequency regions.Consequently,by removing the blocking effects frommaximum lossy compressed images,images inwhich identities could be recognized are generated.This study represents a significant improvement over previous research in terms of the image resolution performance.

Enhanced prediction of anisotropic deformation behavior using machine learning with data augmentation

Mg alloys possess an inherent plastic anisotropy owing to the selective activation of deformation mechanisms depending on the loading condition.This characteristic results in a diverse range of flow curves that vary with a deformation condition.This study proposes a novel approach for accurately predicting an anisotropic deformation behavior of wrought Mg alloys using machine learning(ML)with data augmentation.The developed model combines four key strategies from data science:learning the entire flow curves,generative adversarial networks(GAN),algorithm-driven hyperparameter tuning,and gated recurrent unit(GRU)architecture.The proposed model,namely GAN-aided GRU,was extensively evaluated for various predictive scenarios,such as interpolation,extrapolation,and a limited dataset size.The model exhibited significant predictability and improved generalizability for estimating the anisotropic compressive behavior of ZK60 Mg alloys under 11 annealing conditions and for three loading directions.The GAN-aided GRU results were superior to those of previous ML models and constitutive equations.The superior performance was attributed to hyperparameter optimization,GAN-based data augmentation,and the inherent predictivity of the GRU for extrapolation.As a first attempt to employ ML techniques other than artificial neural networks,this study proposes a novel perspective on predicting the anisotropic deformation behaviors of wrought Mg alloys.

Atomistic evaluation of tension–compression asymmetry in nanoscale body-centered-cubic AlCrFeCoNi high-entropy alloy

The tension and compression of face-centered-cubic high-entropy alloy(HEA) nanowires are significantly asymmetric, but the tension–compression asymmetry in nanoscale body-centered-cubic(BCC) HEAs is still unclear. In this study,the tension–compression asymmetry of the BCC Al Cr Fe Co Ni HEA nanowire is investigated using molecular dynamics simulations. The results show a significant asymmetry in both the yield and flow stresses, with BCC HEA nanowire stronger under compression than under tension. The strength asymmetry originates from the completely different deformation mechanisms in tension and compression. In compression, atomic amorphization dominates plastic deformation and contributes to the strengthening, while in tension, deformation twinning prevails and weakens the HEA nanowire.The tension–compression asymmetry exhibits a clear trend of increasing with the increasing nanowire cross-sectional edge length and decreasing temperature. In particular, the compressive strengths along the [001] and [111] crystallographic orientations are stronger than the tensile counterparts, while the [110] crystallographic orientation shows the exactly opposite trend. The dependences of tension–compression asymmetry on the cross-sectional edge length, crystallographic orientation,and temperature are explained in terms of the deformation behavior of HEA nanowire as well as its variations caused by the change in these influential factors. These findings may deepen our understanding of the tension–compression asymmetry of the BCC HEA nanowires.

High-Volume Mineral Admixtures Cement: The Effects of Particle Size Distribution

The effects of high-volume slag-fly ash cement with different particle sizes on hydration degree,microstructure and mechanical properties were systematically studied,by means of laser particle size(DLS),X-ray diffraction (XRD),comprehensive thermal analysis (TG-DTA),scanning electron microscopy(SEM) and mechanical properties tests.The results show that suitable particle size distribution of cementitious material has significantly promoting effects on hydration reaction rate and mechanical properties.Compared with slag without further grinding,the slag after ball milling for 4 h has an obvious improvement in reactivity,which also provides a faster hydration rate and higher compressive strength for the cementitious material.When the slag milled for 1 and 4 h is mixed at a mass ratio of 2:1 (i e,slag with D_(50) of 7.4μm and average size of 9.9μm,and slag with D_(50) value of 2.6μm and average size of 5.3μm),and a certain amount of fly ash is added in,the most obvious improvement of compressive strength of cement is achieved.

高马赫数球状气泡动力学理论研究

The compressibility of fluids has a profound influence on oscillating bubble dynamics,as characterized by the Mach number.However,current theoretical frameworks for bubbles,whether at the first or second order of the Mach number,are primarily confined to scenarios characterized by weak compressibility.Thus,a critical need to elucidate the precise range of applicability for both first-and second-order bubble theories arises.Herein,we investigate the suitability and constraints of bubble theories with different orders through a comparative analysis involving experimental data and numerical simulations.The focal point of our investigation encompasses theories such as the Rayleigh–Plesset,Keller,Herring,and second-order bubble equations.Furthermore,the impact of parameters inherent in the second-order equations is examined.For spherical oscillating bubble dynamics in a free field,our findings reveal that the first-and second-order bubble theories are applicable when Ma≤0.3 and 0.4,respectively.For a single sonoluminescence bubble,we define an instantaneous Mach number,Mai.The second-order theory shows abnormal sensibility when Mai is high,which is negligible when Mai≤0.4.The results of this study can serve as a valuable reference for studying compressible bubble dynamics.

Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this paper.Here,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its performance.Then,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation platforms.Subsequently,it is used toward secure communication application scenarios.Taking it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)model.Eventually,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

An Efficient Approach to Escalate the Speed of Training Convolution Neural Networks

Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation,instance segmentation, and many others. In image and video recognition applications, convolutional neural networks(CNNs) are widely employed. These networks provide better performance but at a higher cost of computation. With the advent of big data, the growing scale of datasets has made processing and model training a time-consuming operation, resulting in longer training times. Moreover, these large scale datasets contain redundant data points that have minimum impact on the final outcome of the model. To address these issues, an accelerated CNN system is proposed for speeding up training by eliminating the noncritical data points during training alongwith a model compression method. Furthermore, the identification of the critical input data is performed by aggregating the data points at two levels of granularity which are used for evaluating the impact on the model output.Extensive experiments are conducted using the proposed method on CIFAR-10 dataset on ResNet models giving a 40% reduction in number of FLOPs with a degradation of just 0.11% accuracy.

Rheological properties and concentration evolution of thickened tailings under the coupling effect of compression and shear

Cemented paste backfill(CPB)is a key technology for green mining in metal mines,in which tailings thickening comprises the primary link of CPB technology.However,difficult flocculation and substandard concentrations of thickened tailings often occur.The rheological properties and concentration evolution in the thickened tailings remain unclear.Moreover,traditional indoor thickening experiments have yet to quantitatively characterize their rheological properties.An experiment of flocculation condition optimization based on the Box-Behnken design(BBD)was performed in the study,and the two response values were investigated:concentration and the mean weighted chord length(MWCL)of flocs.Thus,optimal flocculation conditions were obtained.In addition,the rheological properties and concentration evolution of different flocculant dosages and ultrafine tailing contents under shear,compression,and compression-shear coupling experimental conditions were tested and compared.The results show that the shear yield stress under compression and compression-shear coupling increases with the growth of compressive yield stress,while the shear yield stress increases slightly under shear.The order of shear yield stress from low to high under different thickening conditions is shear,compression,and compression-shear coupling.Under compression and compression-shear coupling,the concentration first rapidly increases with the growth of compressive yield stress and then slowly increases,while concentration increases slightly under shear.The order of concentration from low to high under different thickening conditions is shear,compression,and compression-shear coupling.Finally,the evolution mechanism of the flocs and drainage channels during the thickening of the thickened tailings under different experimental conditions was revealed.

Effect of Partial Replacement of Fly Ash by Decoration Waste Powder on the Fresh and Mechanical Properties of Geopolymer Masonry Mortar

This study aims to investigate the feasibility of using decoration waste powder(DWP)as a partial replacement for fly ash(FA)in the preparation of geopolymer masonry mortar,and to examine the effect of different DWP replacement rates(0%-40%)on the fresh and mechanical properties of the mortar.The results showed that each group of geopolymer masonry mortar exhibited excellent water retention performance,with a water retention rate of 100%,which was due to the unique geopolymer mortar system and high viscosity of the alkaline activator solution.Compared to the control group,the flowability of the mortar containing lower contents of DWP(10%and 20%)was higher.However,as the DWP replacement rate further increased,the flowability gradually decreased.The DWP could absorb the free water in the reaction system of geopolymer mortar,thereby limiting the occurrence of geopolymerization reaction.The incorporation of DWP in the mortar resulted in a decrease in compressive strength compared to the mortar without DWP.However,even at a replacement rate of 40%,the compressive strength of the mortar still exceeded 15 MPa,which met the requirements of the masonry mortar.It was feasible to use DWP in the geopolymer masonry mortar.Although the addition of DWP caused some performance loss,it did not affect its usability.