BLOWUP CRITERION FOR THE COMPRESSIBLE FLUID-PARTICLE INTERACTION MODEL IN 3D WITH VACUUM

In this article, we consider the blowup criterion for the local strong solution to the compressible fluid-particle interaction model in dimension three with vacuum. We establish a BKM type criterion for possible breakdown of such solutions at critical time in terms of both the L^∞ （0, T; L^6）-norm of the density of particles and the ^L1（0, T; L^∞）-norm of the deformation tensor of velocity gradient.

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.

Optimized Binary Neural Networks for Road Anomaly Detection:A TinyML Approach on Edge Devices

Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.

Low rank optimization for efficient deep learning:making a balance between compact architecture and fast training

Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning techniques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network compression.The other is a spatial and temporal balance for tensorized neural networks.For accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.

Effect of warm acupuncture on nitric oxide synthase and calcitonin gene-related peptide in a rat model of lumbar nerve root compression

BACKGROUND： Varying degrees of inflammatory responses occur during lumbar nerve root compression. Studies have shown that nitric oxide synthase （NOS） and calcitonin gene-related peptide （CGRP） are involved in secondary disc inflammation. OBJECTIVE： To observe the effects of warm acupuncture on the ultrastructure of inflammatory mediators in a rat model of lumbar nerve root compression, including NOS and CGRP contents. DESIGN, TIME AND SETTING： Randomized, controlled study, with molecular biological analysis, was performed at the Experimental Center, Sixth People＇s Hospital Affiliated to Shanghai Jiao Tong University, between September 2006 and April 2007. MATERIALS： Acupuncture needles and refined Moxa grains were purchased from Shanghai Taicheng Technology Development Co., Ltd., China; Mobic tablets were purchased from Shanghai Boehringer Ingelheim Pharmaceuticals Co., Ltd., China; enzyme linked immunosorbent assay （ELISA） kits for NOS and CGRP were purchased from ADL Biotechnology, Inc., USA. METHODS： A total of 50, healthy, adult Sprague-Dawley rats, were randomly divided into five groups normal, model, warm acupuncture, acupuncture, and drug, with 10 rats in each group. Rats in the four groups, excluding the normal group, were used to establish models of lumbar nerve root compression. After 3 days, Jiaji points were set using reinforcing-reducing manipulation in the warm acupuncture group. Moxa grains were burned on each needle, with 2 grains each daily. The acupuncture group was the same as the warm acupuncture group, with the exception of non-moxibustion. Mobic suspension （3.75 mg/kg） was used in the oral drug group, once a day. Treatment of each group lasted for 14 consecutive days. Modeling and medication were not performed in the normal group. MAIN OUTCOME MEASURES： The ultrastructure of damaged nerve roots was observed with transmission electron microscopy; NOS and CGRP contents were measured using ELISA. RESULTS： The changes of the radicular ultramicrostructure were characterized by Wallerian degeneration; nerve fibers were clearly demyelinated; axons collapsed or degenerated; outer Schwann cell cytoplasm was swollen and its nucleus was compacted. Compared with the normal group, NOS and CGRP contents in the nerve root compression zone in the model group were significantly increased （P 〈 0.01）. Nerve root edema was improved in the drug, acupuncture and the warm acupuncture groups over the model group. NOS and CGRP expressions were also decreased with the warm acupuncture group having the lowest concentration （P 〈 0.01）. CONCLUSION： In comparison to the known effects of Mobic drug and acupuncture treatments, the warm acupuncture significantly decreased NOS and CGRP expression which helped improve the ultrastructure of the compressed nerve root.

REVIEW ON MATHEMATICAL ANALYSIS OF SOME TWO-PHASE FLOW MODELS

The two-phase flow models are commonly used in industrial applications, such as nuclear, power, chemical-process, oil-and-gas, cryogenics, bio-medical, micro-technology and so on. This is a survey paper on the study of compressible nonconservative two-fluid model, drift-flux model and viscous liquid-gas two-phase flow model. We give the research developments of these three two-phase flow models, respectively. In the last part, we give some open problems about the above models.

A BLOW-UP CRITERION OF STRONG SOLUTIONS TO THE QUANTUM HYDRODYNAMIC MODEL

In this article,we focus on the short time strong solution to a compressible quantum hydrodynamic model.We establish a blow-up criterion about the solutions of the compressible quantum hydrodynamic model in terms of the gradient of the velocity,the second spacial derivative of the square root of the density,and the first order time derivative and first order spacial derivative of the square root of the density.

Selection of regression models for predicting strength and deformability properties of rocks using GA

Recently,many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties.Although statistical analysis is a common method for developing regression models,but still selection of suitable transformation of the independent variables in a regression model is diffcult.In this paper,a genetic algorithm(GA)has been employed as a heuristic search method for selection of best transformation of the independent variables(some index properties of rocks)in regression models for prediction of uniaxial compressive strength(UCS)and modulus of elasticity(E).Firstly,multiple linear regression(MLR)analysis was performed on a data set to establish predictive models.Then,two GA models were developed in which root mean squared error(RMSE)was defned as ftness function.Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.

MoTransFrame:Model Transfer Framework for CNNs on Low-Resource Edge Computing Node

Deep learning technology has been widely used in computer vision,speech recognition,natural language processing,and other related fields.The deep learning algorithm has high precision and high reliability.However,the lack of resources in the edge terminal equipment makes it difficult to run deep learning algorithms that require more memory and computing power.In this paper,we propose MoTransFrame,a general model processing framework for deep learning models.Instead of designing a model compression algorithm with a high compression ratio,MoTransFrame can transplant popular convolutional neural networks models to resources-starved edge devices promptly and accurately.By the integration method,Deep learning models can be converted into portable projects for Arduino,a typical edge device with limited resources.Our experiments show that MoTransFrame has good adaptability in edge devices with limited memories.It is more flexible than other model transplantation methods.It can keep a small loss of model accuracy when the number of parameters is compressed by tens of times.At the same time,the computational resources needed in the reasoning process are less than what the edge node could handle.

Comparative Study on Constitutive Models to Predict Flow Stress of Fe-Cr-Ni Preform Reinforced Al-Si-Cu-Ni-Mg Composite

The isothermal compression tests were carried out on Gleeble-3500 thermal-mechanical simulation machine in a temperature range of 298-473 K and strain rate range of 0.001-10 s^-1. The experimental results show that the flow stress data are negatively correlated with temperature for temperature softening, and the strain rates sensitivity of this composite increases with elevating temperature. Based on the experimental data, Johnson-Cook, modified Johnson-Cook and Arrhenius constitutive models were established. The accuracy of these three constitutive models was analyzed and compared. The results show that the values predicted by Johnson-Cook model could not agree well with the experimental values. The prediction accuracy of Arrhenius model is higher than that of Johnson-Cook model but lower than that of the Modified Johnson-Cook model.

A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning

Asynchronous federated learning(AsynFL)can effectivelymitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security.However,the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning(FL)algorithm uses synchronous or asynchronous aggregation.Therefore,there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction.This paper proposes a novel Fixed-point Asynchronous Federated Learning(FixedAsynFL)algorithm,which could mitigate the resource consumption caused by frequent data communication while alleviating the effect of device heterogeneity.FixedAsynFL uses fixed-point quantization to compress the local and global models in AsynFL.In order to balance energy consumption and learning accuracy,this paper proposed a quantization scale selection mechanism.This paper examines the mathematical relationship between the quantization scale and energy consumption of the computation/communication process in the FixedAsynFL.Based on considering the upper bound of quantization noise,this paper optimizes the quantization scale by minimizing communication and computation consumption.This paper performs pertinent experiments on the MNIST dataset with several edge nodes of different computing efficiency.The results show that the FixedAsynFL algorithm with an 8-bit quantization can significantly reduce the communication data size by 81.3%and save the computation energy in the training phase by 74.9%without significant loss of accuracy.According to the experimental results,we can see that the proposed AsynFixedFL algorithm can effectively solve the problem of device heterogeneity and energy consumption limitation of edge nodes.

Experimental Study on the Repair Effect of Xianlinggubao Capsule on Osteoporotic Vertebral Compression Fracture in Rabbits

[Objectives]To observe the effect of Xianlinggubao Capsule on osteoporotic vertebral compression fracture(OVCF)in rabbits and the influence mechanism of the repair of fractures.[Methods]Female June age 30 rabbits were randomly divided into control group,model control group and Xianlinggubao group.After bilateral ovariectomy,the model control group and Xianlinggubao group were injected with dexamethasone continuously for 4 weeks,and then the OVCF compound model was established by surgery.The Xianlinggubao group was treated with Xianlinggubao at a dose of 300 mg/(kg·d)for 60 d,while the blank control group and the model control group were treated with the same amount of normal saline for 60 d.The number of blood vessels and the expression of bone morphogenetic protein-2(BMP-2)were detected by immunohistochemical staining and the bone mineral density(BMD)in the callus of the third lumbar fracture area of rabbits was measured.The content of serum phosphorus(P),alkaline phosphatase(ALP)and total calcium(TCa)in rabbit venous blood were measured by automatic biochemical analyzer.The content of vascular endothelial growth factor(VEGF)and platelet-derived growth factor(PDGF)in rabbit venous blood were measured by ELISA kit.[Results]The number of blood vessels and the expression of BMP-2 in the callus of the third lumbar fracture area of rabbits was high in Xianlinggubao group,the content of serum P,ALP,TCa,VEGF and PDGF was obviously increased,BMD was obviously increased,the bone microstructure of the third lumbar vertebrae fracture area of rabbits was basically restored.Compared with the model control group(P<0.05),the difference was statistically significant.[Conclusions]Xianlinggubao Capsule can increase calcium and phosphorus deposition,promote the formation of blood vessels in the fracture area of OVCF in rabbits,and have a strong repair effect on OVCF in rabbits.

Two-dimensional simulation on the rolling process of semi-solid 60Si2Mn by finite element method

The effect of various process variables on the law of metal flow for semi-solid rolling 60Si2Mn was studied by finite element method. Semi-solid 60Si2Mn can be described as compressible rigid visco-plastic porous material saturated with liquid. In terms of ther-mo-mechanical coupling condition, the distributions of stress, velocity and temperature were studied using software MARC. The simulation results show that the rigid visco-plastic model can accurately describe the semi-solid 60Si2Mn rolling process. The great deformation can achieve completely in view of low flow stress of semi-solid slurry.

A THREE-DIMENSIONAL CLOUD-SCALE MODEL SUITABLE FOR COMPRESSIBLE ATMOSPHERE

A three-dimensional cloud-scale model has been designed.The governing equations of the model are composed of two groups of equations:one group includes compressible motion equations,continuity equation, pressure equation and thermodynamic equation,which are of Eulerian type,and the other consists of cloud- precipitation microphysics equations which are of Lagrangian type.Since the degree of influence of sound wave on the air motion is quite different from that on the temperature or hydrometeors,the time splitting procedure is used in solving governing equations.Both unstaggered and staggered meshes have been utilized.Integra- tion schemes adopted are the Eulerian backward difference method for the unstaggered mesh and semi-implicit method for staggered mesh.Several experiments of modelling have been conducted and a reasonable three- dimensional image of deep convection is obtained.With this model the horizontal and vertical vortex circula- tions are simulated.Furthermore,the effects of horizontal vortex on the formation and development of downdraft within cloud have also been studied.

Optimization Study on a Single-cylinder Compressed Air Engine

The current research of compressed air engine（CAE） mainly focused on simulations and system integrations. However, energy efficiency and output torque of the CAE is limited, which restricts its application and popularization. In this paper, the working principles of CAE are briefly introduced. To set a foundation for the study on the optimization of the CAE, the basic mathematical model of working processes is set up. A pressure-compensated valve which can reduce the inertia force of the valve is proposed. To verify the mathematical model, the prototype with the newly designed pressure-compensated intake valve is built and the experiment is carried out, simulation and experimental results of the CAE are conducted, and pressures inside the cylinder and output torque of the CAE are obtained. Orthogonal design and grey relation analysis are utilized to optimize structural parameters. The experimental and optimized results show that, first of all, pressure inside the cylinder has the same changing tendency in both simulation curve and experimental curve. Secondly, the highest average output torque is obtained at the highest intake pressure and the lowest rotate speed. Thirdly, the optimization of the single-cylinder CAE can improve the working efficiency from an original 21.95% to 50.1%, an overall increase of 28.15%, and the average output torque increases also increases from 22.047 5 N · m to 22.439 N · m. This research designs a single-cylinder CAE with pressure-compensated intake valve, and proposes a structural parameters design method which improves the single-cylinder CAE performance.

Global Existence of Large DataWeak Solutions for a Simplified Compressible Oldroyd–B Model Without Stress Diffusion

We start with the compressible Oldroyd–B model derived in[2](J.W.Barrett,Y.Lu,and E.Suli,Existence of large-data finite-energy global weak solutions to a compressible Oldroyd–B model,Commun.Math.Sci.,15(2017),1265–1323),where the existence of global-in-time finite-energy weak solutions was shown in two dimensional setting with stress diffusion.In the paper,we investigate the case without stress diffusion.We first restrict ourselves to the corotational setting as in[28](P.L.Lions,and N.Masmoudi,Global solutions for some Oldroyd models of non-Newtonian flows,Chin.Ann.Math.,Ser.B,21(2)(2000),131–146)We further assume the extra stress tensor is a scalar matrix and we derive a simplified model which takes a similar form as the multi-component compressible Navier–Stokes equations,where,however,the pressure term related to the scalar extra stress tensor has the opposite sign.By employing the techniques developed in[30,35],we can still prove the global-in-time existence of finite energy weak solutions in two or three dimensions,without the presence of stress diffusion.

Enhancing the robustness of object detection via 6G vehicular edge computing

Academic and industrial communities have been paying significant attention to the 6th Generation (6G) wireless communication systems after the commercial deployment of 5G cellular communications. Among the emerging technologies, Vehicular Edge Computing (VEC) can provide essential assurance for the robustness of Artificial Intelligence (AI) algorithms to be used in the 6G systems. Therefore, in this paper, a strategy for enhancing the robustness of AI model deployment using 6G-VEC is proposed, taking the object detection task as an example. This strategy includes two stages: model stabilization and model adaptation. In the former, the state-of-the-art methods are appended to the model to improve its robustness. In the latter, two targeted compression methods are implemented, namely model parameter pruning and knowledge distillation, which result in a trade-off between model performance and runtime resources. Numerical results indicate that the proposed strategy can be smoothly deployed in the onboard edge terminals, where the introduced trade-off outperforms the other strategies available.

Real-time modular dynamic modeling for compression system of altitude ground test facilities

Modeling of a centrifugal compressor is of great significance to surge characteristics and fluid dynamics in the Altitude Ground Test Facilities(AGTF).Real-time Modular Dynamic System Greitzer(MDSG)modeling for dynamic response and simulation of the compression system is introduced.The centrifugal compressor,pipeline network,and valve are divided into pressure output type and mass flow output type for module modeling,and the two types of components alternate when the system is established.The pressure loss and thermodynamics of the system are considered.An air supply compression system of AGTF is modeled and simulated by the MDSG model.The simulation results of mass flow,pressure,and temperature are compared with the experimental results,and the error is less than 5%,which demonstrates the reliability,practicability,and universality of the MDSG model.

Effective Model Compression via Stage-wise Pruning

Automated machine learning(AutoML)pruning methods aim at searching for a pruning strategy automatically to reduce the computational complexity of deep convolutional neural networks(deep CNNs).However,some previous work found that the results of many Auto-ML pruning methods cannot even surpass the results of the uniformly pruning method.In this paper,the ineffectiveness of Auto-ML pruning,which is caused by unfull and unfair training of the supernet,is shown.A deep supernet suffers from unfull training because it contains too many candidates.To overcome the unfull training,a stage-wise pruning(SWP)method is proposed,which splits a deep supernet into several stage-wise supernets to reduce the candidate number and utilize inplace distillation to supervise the stage training.Besides,a wide supernet is hit by unfair training since the sampling probability of each channel is unequal.Therefore,the fullnet and the tinynet are sampled in each training iteration to ensure that each channel can be overtrained.Remarkably,the proxy performance of the subnets trained with SWP is closer to the actual performance than that of most of the previous AutoML pruning work.Furthermore,experiments show that SWP achieves the state-of-the-art in both CIFAR-10 and ImageNet under the mobile setting.

A pruning-then-quantization model compression framework for facial emotion recognition

Facial emotion recognition achieves great success with the help of large neural models but also fails to be applied in practical situations due to the large model size of neural methods.To bridge this gap,in this paper,we combine two mainstream model compression methods(pruning and quantization)together,and propose a pruningthen-quantization framework to compress the neural models for facial emotion recognition tasks.Experiments on three datasets show that our model could achieve a high model compression ratio and maintain the model’s high performance well.Besides,We analyze the layer-wise compression performance of our proposed framework to explore its effect and adaptability in fine-grained modules.