Quantitative Comparative Study of the Performance of Lossless Compression Methods Based on a Text Data Model

Data compression plays a key role in optimizing the use of memory storage space and also reducing latency in data transmission. In this paper, we are interested in lossless compression techniques because their performance is exploited with lossy compression techniques for images and videos generally using a mixed approach. To achieve our intended objective, which is to study the performance of lossless compression methods, we first carried out a literature review, a summary of which enabled us to select the most relevant, namely the following: arithmetic coding, LZW, Tunstall’s algorithm, RLE, BWT, Huffman coding and Shannon-Fano. Secondly, we designed a purposive text dataset with a repeating pattern in order to test the behavior and effectiveness of the selected compression techniques. Thirdly, we designed the compression algorithms and developed the programs (scripts) in Matlab in order to test their performance. Finally, following the tests conducted on relevant data that we constructed according to a deliberate model, the results show that these methods presented in order of performance are very satisfactory:- LZW- Arithmetic coding- Tunstall algorithm- BWT + RLELikewise, it appears that on the one hand, the performance of certain techniques relative to others is strongly linked to the sequencing and/or recurrence of symbols that make up the message, and on the other hand, to the cumulative time of encoding and decoding.

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.

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.

Simultaneous Compression of the Celiac Trunk, Superior Mesenteric Artery, and Renal Arteries by the Median Arcuate Ligament: About One Case

Median arcuate ligament syndrome (MALS), is a rare abdominal vascular compression syndrome caused by the compression of the proximal celiac trunk by the median arcuate ligament. According to many authors, a low insertion of the diaphragmatic crura or an abnormally high origin of the celiac trunk from the aorta can cause compression of the celiac artery. Usually, patients with MALS are asymptomatic. Computed tomography (CT) angiography of the abdomen is the main imaging modality to confirm the diagnosis. The coexistence of celiac trunk and superior mesenteric artery compression by the median arcuate ligament is rarely described in the literature. To our knowledge, until now, a simultaneous combination of three abdominal vascular compressions by the median arcuate ligament has never been described. From this case, we report a simultaneous compression of the celiac trunk, superior mesenteric artery, and renal arteries by the median arcuate ligament.

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.

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.

Compressive performance of innovative reinforced pillars in closed/abandoned mines

Pillar is closely related to the stability and reliability of underground spaces in closed/abandoned mines.The present research introduced a new technique to strengthen square cement mortar columns via fiber-reinforced polymer(FRP)strips to verify the strengthening effect of FRP on pillars.Compared to a fully wrapped FRP jacket,the advantages of FRP strip are cost-effective and easy-to-construct.A series of compression tests as well as theoretical analysis were carried out to explore the mechanical behavior of square cement mortar specimens partially strengthened with FRP strips.The results verified the effectiveness of FRP strips in enhancing the stress and strain of cement mortar.Different from unconfined cement mortar specimens,these FRP-strengthened cement mortar specimens are featured with the double-peaked behaviors,mainly attributed to the stress state transformation from a one-dimensional to a three-dimensional stress state.It also indicated that the enhancement of stress increased with the FRP strip width.Moreover,the brittle-ductile transition ductile failure characteristics were also observed in FRP-confined cement mortar specimens.The ultimate ductility of the cement mortar specimen decreases gradually with the growth of the FRP strip width.The main contribution of this research is to enrich the strengthening techniques for residual pillars.

A whole process damage constitutive model for layered sandstone under uniaxial compression based on Logistic function

Bedding structural planes significantly influence the mechanical properties and stability of engineering rock masses.This study conducts uniaxial compression tests on layered sandstone with various bedding angles(0°,15°,30°,45°,60°,75°and 90°)to explore the impact of bedding angle on the deformational mechanical response,failure mode,and damage evolution processes of rocks.It develops a damage model based on the Logistic equation derived from the modulus’s degradation considering the combined effect of the sandstone bedding dip angle and load.This model is employed to study the damage accumulation state and its evolution within the layered rock mass.This research also introduces a piecewise constitutive model that considers the initial compaction characteristics to simulate the whole deformation process of layered sandstone under uniaxial compression.The results revealed that as the bedding angle increases from 0°to 90°,the uniaxial compressive strength and elastic modulus of layered sandstone significantly decrease,slightly increase,and then decline again.The corresponding failure modes transition from splitting tensile failure to slipping shear failure and back to splitting tensile failure.As indicated by the modulus’s degradation,the damage characteristics can be categorized into four stages:initial no damage,damage initiation,damage acceleration,and damage deceleration termination.The theoretical damage model based on the Logistic equation effectively simulates and predicts the entire damage evolution process.Moreover,the theoretical constitutive model curves closely align with the actual stress−strain curves of layered sandstone under uniaxial compression.The introduced constitutive model is concise,with fewer parameters,a straightforward parameter determination process,and a clear physical interpretation.This study offers valuable insights into the theory of layered rock mechanics and holds implications for ensuring the safety of rock engineering.

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.

Theoretical insights into thermal transport and structural stability mechanisms of triaxial compressed methane hydrate

The heat transfer and stability of methane hydrate in reservoirs have a direct impact on the drilling and production efficiency of hydrate resources,especially in complex stress environments caused by formation subsidence.In this study,we investigated the thermal transport and structural stability of methane hydrate under triaxial compression using molecular dynamics simulations.The results suggest that the thermal conductivity of methane hydrate increases with increasing compression strain.Two phonon transport mechanisms were identified as factors enhancing thermal conductivity.At low compressive strains,a low-frequency phonon transport channel was established due to the overlap of phonon vibration peaks between methane and water molecules.At high compressive strains,the filling of larger phonon bandgaps facilitated the opening of more phonon transport channels.Additionally,we found that a strain of0.04 is a watershed point,where methane hydrate transitions from stable to unstable.Furthermore,a strain of0.06 marks the threshold at which the diffusion capacities of methane and water molecules are at their peaks.At a higher strain of0.08,the increased volume compression reduces the available space,limiting the diffusion ability of water and methane molecules within the hydrate.The synergistic effect of the strong diffusion ability and high probability of collision between atoms increases the thermal conductivity of hydrates during the unstable period compared to the stable period.Our findings offer valuable theoretical insights into the thermal conductivity and stability of methane hydrates in reservoir stress environments.

Predicting uniaxial compressive strength of tuff after accelerated freeze-thaw testing: Comparative analysis of regression models and artificial neural networks

Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern construction as well. However, ignimbrites are particularly vulnerable to atmospheric conditions, such as freeze-thaw cycles, due to their high porosity, which is a result of their formation process. When water enters the pores of the ignimbrites, it can freeze during cold weather. As the water freezes and expands, it generates internal stress within the stone, causing micro-cracks to develop. Over time, repeated freeze-thaw (F-T) cycles lead to the growth of these micro-cracks into larger cracks, compromising the structural integrity of the ignimbrites and eventually making them unsuitable for use as building materials. The determination of the long-term F-T performance of ignimbrites can be established after long F-T experimental processes. Determining the long-term F-T performance of ignimbrites typically requires extensive experimental testing over prolonged freeze-thaw cycles. To streamline this process, developing accurate predictive equations becomes crucial. In this study, such equations were formulated using classical regression analyses and artificial neural networks (ANN) based on data obtained from these experiments, allowing for the prediction of the F-T performance of ignimbrites and other similar building stones without the need for lengthy testing. In this study, uniaxial compressive strength, ultrasonic propagation velocity, apparent porosity and mass loss of ignimbrites after long-term F-T were determined. Following the F-T cycles, the disintegration rate was evaluated using decay function approaches, while uniaxial compressive strength (UCS) values were predicted with minimal input parameters through both regression and ANN analyses. The ANN and regression models created for this purpose were first started with a single input value and then developed with two and three combinations. The predictive performance of the models was assessed by comparing them to regression models using the coefficient of determination (R2) as the evaluation criterion. As a result of the study, higher R2 values (0.87) were obtained in models built with artificial neural network. The results of the study indicate that ANN usage can produce results close to experimental outcomes in predicting the long-term F-T performance of ignimbrite samples.

The deteriorated degradation resistance of Mg alloy microtubes for vascular stent under the coupling effect of radial compressive stress and dynamic medium

The degradation of Mg alloys relates to the service performance of Mg alloy biodegradable implants.In order to investigate the degradation behavior of Mg alloys as vascular stent materials in the near service environment,the hot-extruded fine-grained Mg-Zn-Y-Nd alloy microtubes,which are employed to manufacture vascular stents,were tested under radial compressive stress in the dynamic Hanks'Balanced Salt Solution(HBSS).The results revealed that the high flow rate accelerates the degradation of Mg alloy microtubes and its degradation is sensitive to radial compressive stress.These results contribute to understanding the service performance of Mg alloys as vascular stent materials.

Bending Failure Mode and Prediction Method of the Compressive Strain Capacity of A Submarine Pipeline with Dent Defects

A dent is a common type of defects for submarine pipeline.For submarine pipelines,high hydrostatic pressure and internal pressure are the main loads.Once pipelines bend due to complex subsea conditions,the compression strain capacity may be exceeded.Research into the local buckling failure and accurate prediction of the compressive strain capacity are important.A finite element model of a pipeline with a dent is established.Local buckling failure under a bending moment is investigated,and the compressive strain capacity is calculated.The effects of different parameters on pipeline local buckling are analyzed.The results show that the dent depth,external pressure and internal pressure lead to different local buckling failure modes of the pipeline.A higher internal pressure indicates a larger compressive strain capacity,and the opposite is true for external pressure.When the ratio of external pressure to collapse pressure of intact pipeline is greater than 0.1,the deeper the dent,the greater the compressive strain capacity of the pipeline.And as the ratio is less than 0.1,the opposite is true.On the basis of these results,a regression equation for predicting the compressive strain capacity of a dented submarine pipeline is proposed,which can be referred to during the integrity assessment of a submarine pipeline.

STABILITY OF THE RAREFACTION WAVE IN THE SINGULAR LIMIT OF A SHARP INTERFACE PROBLEM FOR THE COMPRESSIBLE NAVIER-STOKES/ALLEN-CAHN SYSTEM

This paper is concerned with the global well-posedness of the solution to the compressible Navier-Stokes/Allen-Cahn system and its sharp interface limit in one-dimensional space.For the perturbations with small energy but possibly large oscillations of rarefaction wave solutions near phase separation,and where the strength of the initial phase field could be arbitrarily large,we prove that the solution of the Cauchy problem exists for all time,and converges to the centered rarefaction wave solution of the corresponding standard two-phase Euler equation as the viscosity and the thickness of the interface tend to zero.The proof is mainly based on a scaling argument and a basic energy method.

Nonlinear Study on the Mechanical Performance of Built-Up Cold-Formed Steel Concrete-Filled Columns under Compression

Given their numerous functional and architectural benefits,such as improved bearing capacity and increased resistance to elastic instability modes,cold-formed steel(CFS)built-up sections have become increasingly developed and used in recent years,particularly in the construction industry.This paper presents an analytical and numerical study of assembled CFS two single channel-shaped columns with different slenderness and configurations(backto-back,face-to-face,and box).These columns were joined by double-row rivets for the back-to-back and box configurations,whereas they were welded together for the face-to-face design.The built-up columns were filled with ordinary concrete of good strength.Finite element models were applied,using ABAQUS software,to assess mechanical performance and study the influence of assembly techniques on the behavior of cold-formed columns under axial compression.Analytical approaches based on Eurocode 3 and Eurocode 4 recommendations for un-filled and concrete-filled columns respectively were followed for the numerical analysis,and concrete confinement effects were also considered per American Concrete Institute(ACI)standards for face-to-face and box configurations.The obtained results indicated a good correlation between the numerical results and the proposed analytical methodology which did not exceed 8%.The failure modes showed that the columns failed due to instabilities such as local and global buckling.

Chaotic CS Encryption:An Efficient Image Encryption Algorithm Based on Chebyshev Chaotic System and Compressive Sensing

Images are the most important carrier of human information. Moreover, how to safely transmit digital imagesthrough public channels has become an urgent problem. In this paper, we propose a novel image encryptionalgorithm, called chaotic compressive sensing (CS) encryption (CCSE), which can not only improve the efficiencyof image transmission but also introduce the high security of the chaotic system. Specifically, the proposed CCSEcan fully leverage the advantages of the Chebyshev chaotic system and CS, enabling it to withstand various attacks,such as differential attacks, and exhibit robustness. First, we use a sparse trans-form to sparse the plaintext imageand then use theArnold transformto perturb the image pixels. After that,we elaborate aChebyshev Toeplitz chaoticsensing matrix for CCSE. By using this Toeplitz matrix, the perturbed image is compressed and sampled to reducethe transmission bandwidth and the amount of data. Finally, a bilateral diffusion operator and a chaotic encryptionoperator are used to perturb and expand the image pixels to change the pixel position and value of the compressedimage, and ultimately obtain an encrypted image. Experimental results show that our method can be resistant tovarious attacks, such as the statistical attack and noise attack, and can outperform its current competitors.

Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.

Fast compressed sensing spectral measurement with adaptive gradient multiscale resolution

We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed sensing.The method can apply variable measurement resolution over the entire spectral range to reduce the measurement time by over 75%compared to a global high-resolution measurement.Mimicking the characteristics of the human retina system,the resolution distribution follows the principle of gradually decreasing.The system allows the spectral peaks of interest to be captured dynamically or to be specified a priori by a user.The system was tested by measuring single and dual spectral peaks,and the results of spectral peaks are consistent with those of global high-resolution measurements.

Compression Properties and Energy Absorption of A Novel Double Curved Beam Negative Stiffness Honeycomb Structures

This paper presents the design of a novel honeycomb structure with a double curved beam.The purpose of this design is to achieve vibration isolation for the main engine of an offshore platform and reduce impact loads.An analytical formula for the force-displacement relationship of the honeycomb single-cell structure is presented based on the modal superposition method.This formula provides a theoretical basis for predicting the compression performance of honeycomb structures.The effects of structural geometric parameters,series and parallel connection methods on the mechanical and energy absorption properties are investigated through mathematical modeling and experimental methods.Furthermore,the study focuses on the vibration isolation and impact resistance performance of honeycomb panels.The results show that the designed honeycomb structure has good mechanical and energy absorption performance,and its energy absorption effect is related to the geometric parameters and series and parallel connection methods of the structure.The isolation efficiency of the honeycomb with 4 rows and 3 columns reaches 38%.The initial isolation frequency of the isolator is 11.7 Hz.

Compressive property and energy absorption characteristic of interconnected porous Mg-Zn-Y alloys with adjusting Y addition

In this study,interconnected porous Mg-2Zn-xY alloys with different phase compositions were prepared by various Y additions(x=0.4,3,and 6 wt.%)to adjust the compressive properties and energy absorption characteristics.Several characterization methods were then applied to identify the microstructure of the porous Mg-Zn-Y and describe the details of the second phase.Compressive tests were performed at room temperature(RT),200℃,and 300℃to study the impact of the Y addition and testing temperature on the compressive properties of the porous Mg-Zn-Y.The experimental results showed that a high Y content promotes a microstructure refinement and increases the volume fraction of the second phase.When the Y content increases,different Mg-Zn-Y ternary phases appear:I-phase(Mg_(3)Zn_(6)Y),W-phase(Mg_(3)Zn_(3)Y_(2)),and LPSO phase(Mg_(12)ZnY).When the Y content ranges between 0.4%and 6%,the compressive strength increases from 6.30MPa to 9.23 MPa,and the energy absorption capacity increases from 7.33 MJ/m^(3)to 10.97 MJ/m^(3)at RT,which is mainly attributed to the phase composition and volume fraction of the second phase.However,the average energy absorption efficiency is independent of the Y content.In addition,the compressive deformation behaviors of the porous Mg-Zn-Y are altered by the testing temperature.The compressive strength and energy absorption capacity of the porous Mg-Zn-Y decrease due to the softening effect of the high temperature on the struts.The deformation behaviors at different temperatures are finally observed to reflect the failure mechanisms of the struts.