A novel high-Cr CoNi-based superalloy with superior high-temperature microstructural stability, oxidation resistance and mechanical properties

A novel multicomponent high-Cr CoNi-based superalloy with superior comprehensive performance was prepared,and the evaluation of its high-temperature microstructural stability,oxidation resistance,and mechanical properties was conducted mainly using its cast polycrystalline alloy.The results disclosed that the morphology of theγ′phase remained stable,and the coarsening rate was slow during the long-term aging at 900–1000℃.The activation energy forγ′precipitate coarsening of alloy 9CoNi-Cr was(402±51)kJ/mol,which is higher compared with those of CMSX-4 and some other Ni-based and Co-based superalloys.Importantly,there was no indica-tion of the formation of topologically close-packed phases during this process.All these factors demonstrated the superior microstructural stability of the alloy.The mass gain of alloy 9CoNi-Cr was 0.6 mg/cm^(2) after oxidation at 1000℃ for 100 h,and the oxidation resistance was comparable to advanced Ni-based superalloys CMSX-4,which can be attributed to the formation of a continuous Al_(2)O_(3) protective layer.Moreover,the compressive yield strength of this cast polycrystalline alloy at high temperatures is clearly higher than that of the conventional Ni-based cast superalloy and the compressive minimum creep rate at 950℃ is comparable to that of the conventional Ni-based cast superalloy,demonstrating the alloy’s good mechanical properties at high temperature.This is partially because high Cr is bene-ficial in improving theγandγ′phase strengths of alloy 9CoNi-Cr.

Plant-based dietary patterns and risk of esophageal cancer:A prospective cohort study spanning 17 years

Objective: Plant-based diets have multiple health benefits for cancers;however, little is known about the association between plant-based dietary patterns and esophageal cancer(EC).This study presents an investigation of the prospective associations among three predefined indices of plant-based dietary patterns and the risk of EC.Methods: We performed endoscopic screening for 15,709 participants aged 40-69 years from two high-risk areas of China from January 2005 to December 2009 and followed the cohort until December 31, 2022. The overall plant-based diet index(PDI), healthful plant-based diet index(h PDI), and unhealthful plant-based diet index(u PDI), were calculated using survey responses to assess dietary patterns. We applied Cox proportional hazard regression to estimate the multivariable hazard ratios(HRs) and 95% confidence intervals(95% CIs) of EC across 3plant-based diet indices and further stratified the analysis by subgroups.Results: The final study sample included 15,184 participants in the cohort. During a follow-up of 219,365person-years, 176 patients with EC were identified. When the highest quartile was compared with the lowest quartile, the pooled multivariable-adjusted HR of EC was 0.50(95% CI, 0.32-0.77) for h PDI. In addition, the HR per 10-point increase in the h PDI score was 0.42(95% CI, 0.27-0.66) for ECs. Conversely, u PDI was positively associated with the risk of EC, and the HR was 1.80(95% CI, 1.16-2.82). The HR per 10-point increase in the u PDI score was 1.90(95% CI, 1.26-2.88) for ECs. The associations between these scores and the risk of EC were consistent in most subgroups. These results remained robust in sensitivity analyses.Conclusions: A healthy plant-based dietary pattern was associated with a reduced risk of EC. Emphasizing the healthiness and quality of plant-based diets may be important for preventing the development of EC.

端氨基液体丁腈橡胶/环氧树脂绝缘胶黏剂的制备与性能

介绍了一种端氨基液体丁腈橡胶(ATBN)增韧改性环氧树脂(EP)的绝缘胶黏剂;借助红外光谱表征胶黏剂固化前后的分子结构变化;通过拉伸与T剥离强度测试表征胶黏剂的黏接性能,借助弯曲与冲击强度测试及冲击断面扫描电镜照片进一步解释胶黏剂的黏接特性;通过击穿场强、体积电阻率以及介电性能测试考核胶黏剂的绝缘性能。研究发现,ATBN分子结构中的活性基团参与了胶黏剂的固化反应;当ATBN添加量为EP的20%时,胶黏剂的拉伸和T剥离强度达到4.92 MPa和9.87 N/mm,分别较纯EP体系提高77%和98%;在此添加量下弯曲强度为113 MPa,冲击强度为18.73kJ/m^2,体积电阻率为1.54×10^(14)Ω·m,击穿场强为25 kV/mm,工频下介电常数为4.53,介电损耗为0.0148。

Robust Reversible Audio Watermarking Scheme for Telemedicine and Privacy Protection

The leakage of medical audio data in telemedicine seriously violates the privacy of patients.In order to avoid the leakage of patient information in telemedicine,a two-stage reversible robust audio watermarking algorithm is proposed to protect medical audio data.The scheme decomposes the medical audio into two independent embedding domains,embeds the robust watermark and the reversible watermark into the two domains respectively.In order to ensure the audio quality,the Hurst exponent is used to find a suitable position for watermark embedding.Due to the independence of the two embedding domains,the embedding of the second-stage reversible watermark will not affect the first-stage watermark,so the robustness of the first-stage watermark can be well maintained.In the second stage,the correlation between the sampling points in the medical audio is used to modify the hidden bits of the histogram to reduce the modification of the medical audio and reduce the distortion caused by reversible embedding.Simulation experiments show that this scheme has strong robustness against signal processing operations such as MP3 compression of 48 db,additive white Gaussian noise(AWGN)of 20 db,low-pass filtering,resampling,re-quantization and other attacks,and has good imperceptibility.

A Robust 3-D Medical Watermarking Based on Wavelet Transform for Data Protection

In a telemedicine diagnosis system,the emergence of 3D imaging enables doctors to make clearer judgments,and its accuracy also directly affects doctors’diagnosis of the disease.In order to ensure the safe transmission and storage of medical data,a 3D medical watermarking algorithm based on wavelet transform is proposed in this paper.The proposed algorithm employs the principal component analysis(PCA)transform to reduce the data dimension,which can minimize the error between the extracted components and the original data in the mean square sense.Especially,this algorithm helps to create a bacterial foraging model based on particle swarm optimization(BF-PSO),by which the optimal wavelet coefficient is found for embedding and is used as the absolute feature of watermark embedding,thereby achieving the optimal balance between embedding capacity and imperceptibility.A series of experimental results from MATLAB software based on the standard MRI brain volume dataset demonstrate that the proposed algorithm has strong robustness and make the 3D model have small deformation after embedding the watermark.

A Multi-Feature Learning Model with Enhanced Local Attention for Vehicle Re-Identification

Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario.It has gradually become a core technology of intelligent transportation system.Most existing vehicle re-identification models adopt the joint learning of global and local features.However,they directly use the extracted global features,resulting in insufficient feature expression.Moreover,local features are primarily obtained through advanced annotation and complex attention mechanisms,which require additional costs.To solve this issue,a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA)is proposed in this paper.The model consists of global and local branches.The global branch utilizes both middle and highlevel semantic features of ResNet50 to enhance the global representation capability.In addition,multi-scale pooling operations are used to obtain multiscale information.While the local branch utilizes the proposed Region Batch Dropblock(RBD),which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local regions.Then features from both branches are combined to provide a more comprehensive and distinctive feature representation.Extensive experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.

Vehicle Re-Identication Model Based on Optimized DenseNet121 with Joint Loss

With the increasing application of surveillance cameras,vehicle re-identication(Re-ID)has attracted more attention in the eld of public security.Vehicle Re-ID meets challenge attributable to the large intra-class differences caused by different views of vehicles in the traveling process and obvious inter-class similarities caused by similar appearances.Plentiful existing methods focus on local attributes by marking local locations.However,these methods require additional annotations,resulting in complex algorithms and insufferable computation time.To cope with these challenges,this paper proposes a vehicle Re-ID model based on optimized DenseNet121 with joint loss.This model applies the SE block to automatically obtain the importance of each channel feature and assign the corresponding weight to it,then features are transferred to the deep layer by adjusting the corresponding weights,which reduces the transmission of redundant information in the process of feature reuse in DenseNet121.At the same time,the proposed model leverages the complementary expression advantages of middle features of the CNN to enhance the feature expression ability.Additionally,a joint loss with focal loss and triplet loss is proposed in vehicle Re-ID to enhance the model’s ability to discriminate difcult-to-separate samples by enlarging the weight of the difcult-to-separate samples during the training process.Experimental results on the VeRi-776 dataset show that mAP and Rank-1 reach 75.5%and 94.8%,respectively.Besides,Rank-1 on small,medium and large sub-datasets of Vehicle ID dataset reach 81.3%,78.9%,and 76.5%,respectively,which surpasses most existing vehicle Re-ID methods.

A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis

The key to preventing the COVID-19 is to diagnose patients quickly and accurately.Studies have shown that using Convolutional Neural Networks(CNN)to analyze chest Computed Tomography(CT)images is helpful for timely COVID-19 diagnosis.However,personal privacy issues,public chest CT data sets are relatively few,which has limited CNN’s application to COVID-19 diagnosis.Also,many CNNs have complex structures and massive parameters.Even if equipped with the dedicated Graphics Processing Unit(GPU)for acceleration,it still takes a long time,which is not conductive to widespread application.To solve above problems,this paper proposes a lightweight CNN classification model based on transfer learning.Use the lightweight CNN MobileNetV2 as the backbone of the model to solve the shortage of hardware resources and computing power.In order to alleviate the problem of model overfitting caused by insufficient data set,transfer learning is used to train the model.The study first exploits the weight parameters trained on the ImageNet database to initialize the MobileNetV2 network,and then retrain the model based on the CT image data set provided by Kaggle.Experimental results on a computer equipped only with the Central Processing Unit(CPU)show that it consumes only 1.06 s on average to diagnose a chest CT image.Compared to other lightweight models,the proposed model has a higher classification accuracy and reliability while having a lightweight architecture and few parameters,which can be easily applied to computers without GPU acceleration.Code:github.com/ZhouJie-520/paper-codes.

A Two-Stage Vehicle Type Recognition Method Combining the Most Effective Gabor Features

Vehicle type recognition(VTR)is an important research topic due to its significance in intelligent transportation systems.However,recognizing vehicle type on the real-world images is challenging due to the illumination change,partial occlusion under real traffic environment.These difficulties limit the performance of current state-of-art methods,which are typically based on single-stage classification without considering feature availability.To address such difficulties,this paper proposes a two-stage vehicle type recognition method combining the most effective Gabor features.The first stage leverages edge features to classify vehicles by size into big or small via a similarity k-nearest neighbor classifier(SKNNC).Further the more specific vehicle type such as bus,truck,sedan or van is recognized by the second stage classification,which leverages the most effective Gabor features extracted by a set of Gabor wavelet kernels on the partitioned key patches via a kernel sparse representation-based classifier(KSRC).A verification and correction step based on minimum residual analysis is proposed to enhance the reliability of the VTR.To improve VTR efficiency,the most effective Gabor features are selected through gray relational analysis that leverages the correlation between Gabor feature image and the original image.Experimental results demonstrate that the proposed method not only improves the accuracy of VTR but also enhances the recognition robustness to illumination change and partial occlusion.

A Robust Watermarking Scheme Based on ROI and IWT for Remote Consultation of COVID-19

In the current dire situation of the corona virus COVID-19,remote consultations were proposed to avoid cross-infection and regional differences in medical resources.However,the safety of digital medical imaging in remote consultations has also attracted more and more attention from the medical industry.To ensure the integrity and security of medical images,this paper proposes a robust watermarking algorithm to authenticate and recover from the distorted medical images based on regions of interest(ROI)and integer wavelet transform(IWT).First,the medical image is divided into two different parts,regions of interest and non-interest regions.Then the integrity of ROI is verified using the hash algorithm,and the recovery data of the ROI region is calculated at the same time.Also,binary images with the basic information of patients are processed by logistic chaotic map encryption,and then the synthetic watermark is embedded in the medical carrier image using IWT transform.The performance of the proposed algorithm is tested by the simulation experiments based on the MATLAB program in CT images of the lungs.Experimental results show that the algorithm can precisely locate the distorted areas of an image and recover the original ROI on the basis of verifying image reliability.The maximum peak signal to noise ratio(PSNR)value of 51.24 has been achieved,which proves that the watermark is invisible and has strong robustness against noise,compression,and filtering attacks.

Radiation-hardened property of single-walled carbon nanotube film-based field-effect transistors under low-energy proton irradiation

Strong C-C bonds,nanoscale cross-section and low atomic number make single-walled carbon nanotubes(SWCNTs)a potential candidate material for integrated circuits(ICs)applied in outer space.However,very little work combines the simulation calculations with the electrical measurements of SWCNT field-effect transistors(FETs),which limits further understanding on the mechanisms of radiation effects.Here,SWCNT film-based FETs were fabricated to explore the total ionizing dose(TID)and displacement damage effect on the electrical performance under low-energy proton irradiation with different fluences up to 1×1015 p/cm2.Large negative shift of the threshold voltage and obvious decrease of the on-state current verified the TID effect caused in the oxide layer.The stability of the subthreshold swing and the off-state current reveals that the displacement damage caused in the CNT layer is not serious,which proves that the CNT film is radiation-hardened.Specially,according to the simulation,we found the displacement damage caused by protons is different in the source/drain contact area and channel area,leading to varying degrees of change for the contact resistance and sheet resistance.Having analyzed the simulation results and electrical measurements,we explained the low-energy proton irradiation mechanism of the CNT FETs,which is essential for the construction of radiation-hardened CNT film-based ICs for aircrafts.

A Real-time Cutting Model Based on Finite Element and Order Reduction

Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in real-time.Therefore,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time performance.The proposed model uses the finite element model to construct a deformation model of the virtual lung.Meanwhile,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation computation.In addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual lung.Then,the Bezier curve is adopted to draw the incision outline after the virtual lung has been cut.Finally,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual lung.Experimental results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.

Soft Tissue Deformation Model Based on Marquardt Algorithm and Enrichment Function

In order to solve the problem of high computing cost and low simulation accuracy caused by discontinuity of incision in traditional meshless model,this paper proposes a soft tissue deformation model based on the Marquardt algorithm and enrichment function.The model is based on the element-free Galerkin method,in which Kelvin viscoelastic model and adjustment function are integrated.Marquardt algorithm is applied to fit the relation between force and displacement caused by surface deformation,and the enrichment function is applied to deal with the discontinuity in the meshless method.To verify the validity of the model,the Sensable Phantom Omni force tactile interactive device is used to simulate the deformations of stomach and heart.Experimental results show that the proposed model improves the real-time performance and accuracy of soft tissue deformation simulation,which provides a new perspective for the application of the meshless method in virtual surgery.

Effects of 5-Aminolevulinic Acid on the Content of Total Flavonoids and Expression of CHS and CHI Genes in Young Apples

[ Objective] This study aimed to investigate the effects of 5-aminolevulinic acid （ALA） on the content of total flavonoids and relative expression levels of chalcoue sythase （CHS） and chalcone isomerase （CHI） genes in young apples to determine the appropriate ALA concentration and processing time. [Method] Before thinning, young apples were treated with 0 （ CK）, 100,200,300 and 400 mg/L ALA. At 12 d after ALA treatment, the content of total flavonoids in young apples was determined by ultraviolet spectrophotometry. The relative expression levels of CHS and CHI genes in young apples were determined by qPCR. [ Result ] When ALA concentration was 0 - 300 mg/L, the content of total flavonoids and relative expression levels of CHS and CHI genes in young apples were improved with the increase of ALA concentration. As ALA concentration rose to 400 rag/L, various indicators showed a downward trend. Moreover, the content of total flavouoids and relative expression levels of CHS and CHI genes in young apples treated with different concentrations of ALA were improved significantly, which reached the maximum at 9 d and declined since 12 d. [ Conclusion] Compared with CK, spraying young apples with 300 mg/L ALA at 6 -9 d before thinning was conducive to improving the content of flavanoids in thinned young apples.

Cooperative effect of polyvinylpyrrolidone and HPMC E5 on dissolution and bioavailability of nimodipine solid dispersions and tablets

Solid dispersion(SD)systems have been extensively used to increase the dissolution and bioavailability of poorly water-soluble drugs.To circumvent the limitations of polyvinylpyrrolidone(PVP)dispersions,HPMC E5 was applied in the formulation process and scaling-up techniques,simultaneously.In this study,SD of nimodipine(NMP)and corresponding tablets were prepared through solvent method and fluid bed granulating one step technique,respectively.Discriminatory dissolution media were used to obtain reliable dissolution results.Meanwhile,the stability study of SDs was investigated with storage under high temperature and humidity conditions.Moreover,the solubility of SDs was measured to explore the effect of carriers.The preparations were characterized by DSC,PXRD,and FTIR.Dramatical improvements in the dissolution rate of NMP were achieved by the ingenious combination of the two polymers.Binary NMP/PVP/HPMC-SDs released steadily,while the dissolution of single NMP/PVP-SDs decreased rapidly in water.The fluid-bed tablets(FB-T)possessed a similar dissolution behavior to the commercial Nimotop TM tablets.The characterization patterns implied that NMP existed in an amorphous state in our SDs.Furthermore,the results of stability tests suggested a better stability of the binary SDs.A special cooperative effect of PVP and HPMC was discovered on dissolution characteristics of NMP SDs and tablets,which could be extended to other drugs henceforth.Finally,the bioavailability of FB-T was evaluated in beagle dogs with Nimotop TM as the reference,and the results showed a higher AUC 0–12h value for FB-T.

A Virtual Puncture Surgery System Based on Multi-Layer Soft Tissue and Force Mesh

Puncture is a common operation in surgery,which involves all kinds of tissue materials with different geometry and mechanical properties.As a new cross-disciplinary research area,Virtual Surgery(VS)makes simulation of soft tissue in puncture operation possible in virtual environment.In this paper,we introduce a VS-based puncture system composed by three-layer soft tissue,simulated with spherical harmonic function(SHF),which is covered with a force mesh,constructed by mass spring model(MSM).The two models are combined together with a parameter of SHF named surface radius,which provides MSM with real-time deformation data needed in force calculation.Meanwhile,force calculation,divided into the surface spring force and the puncture damping force,makes the force presentation better accord to the corresponding tissue characteristics.Moreover,a deformation resumption algorithm is leveraged to simulate the resumption phenomenon of the broken tissue surface.In evaluation experiment,several residents are invited to grades our model along with other four mainstream soft tissue models in terms of 7 different indicators.After the evaluation,the scores are analyzed by a comprehensive weighted grading method.Experiment results show that the proposed model has better performance during puncture operation than other models,and can well simulate surface resumption phenomenon when tissue surface is broken.

A Novel Twist Deformation Model of Soft Tissue in Surgery Simulation

Real-time performance and accuracy are two most challenging requirements in virtual surgery training.These difficulties limit the promotion of advanced models in virtual surgery,including many geometric and physical models.This paper proposes a physical model of virtual soft tissue,which is a twist model based on the Kriging interpolation and membrane analogy.The proposed model can quickly locate spatial position through Kriging interpolation method and accurately compute the force change on the soft tissue through membrane analogy method.The virtual surgery simulation system is built with a PHANTOM OMNI haptic interaction device to simulate the torsion of virtual stomach and arm,and further verifies the real-time performance and simulation accuracy of the proposed model.The experimental results show that the proposed soft tissue model has high speed and accuracy,realistic deformation,and reliable haptic feedback.

Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one perspective.When existing methods reconstruct the mesh surface of complex objects,the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework;the 3D topology is easily limited by predefined templates and inflexible,and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology,thus destroying the surface details;the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices,and the training time of the reconstructed network is too long.In this paper,we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network(GCN)and topology modification.We use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the topology.Meanwhile,a feature fusion method is proposed to make full use of the features of each stage of the image hierarchically.We use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training process.Extensive experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces,but also has better qualitative and quantitative results.

An Optimization Scheme for Task Offloading and Resource Allocation in Vehicle Edge Networks

The vehicle edge network(VEN)has become a new research hotspot in the Internet of Things(IOT).However,many new delays are generated during the vehicle offloading the task to the edge server,which will greatly reduce the quality of service(QOS)provided by the vehicle edge network.To solve this problem,this paper proposes an evolutionary algorithm-based(EA)task offloading and resource allocation scheme.First,the delay of offloading task to the edge server is generally defined,then the mathematical model of problem is given.Finally,the objective function is optimized by evolutionary algorithm,and the optimal solution is obtained by iteration and averaging.To verify the performance of this method,contrast experiments are conducted.The experimental results show that our purposed method reduces delay and improves QOS,which is superior to other schemes.

A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout(Salmo Letnica)

Ohrid trout(Salamo letnica)is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia(FYROM).The growth of Ohrid trout was examined in a controlled environment for a certain period,thereafter released into the lake to grow their natural population.The external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their growth.The data mining methods-based computational model can be used for fast,accurate,reliable,automatic,and improved growth monitoring procedures and classification of Ohrid trout.With this motivation,a combined approach of principal component analysis(PCA)and support vectormachine(SVM)has been implemented for the visual discrimination and quantitative classification of Ohrid trout of the experimental and natural breeding and their growth stages.The PCA results in better discrimination of breeding categories of Ohrid trout at different development phases while the maximum classification accuracy of 98.33% was achieved using the combination of PCA and SVM.The classification performance of the combination of PCA and SVM has been compared to combinations of PCA and other classification methods(multilayer perceptron,naive Bayes,randomcommittee,decision stump,random forest,and random tree).Besides,the classification accuracy of multilayer perceptron using the original features has been studied.