A simulation method on target strength and circular SAS imaging of Xrudder UUV including multiple acoustic scattering

Target strength(TS)and circular synthetic aperture sonar(CSAS)images provide essential information for active acoustic detection and recognition of non-cooperative unmanned undersea vehicles(UUVs),which pose a significant threat to underwater preset facilities.To access them,we propose an iterative physical acoustics(IPA)-based method to simulate the multiple acoustic scattered fields on rigid surfaces in high-frequency cases.It uses the Helmholtz integral equation with an appropriate Green's function in terms of the Neumann series,and then incorporates the ideas of triangulation and iteration into a numerical implementation.Then two approximate analytic formulae with precise physical meanings are derived to predict the TS and CSAS images of concave targets,respectively.There are no restrictions on the surface's curvature and the order of multiple scattering.The method is validated against the finite element method(FEM)for acoustic scattering from a sphere segment and against an experiment involving an X-rudder UUV's stern.On this basis,we simulate and analyze the TS and CSAS images of an X-rudder UUV.In addition,the influence of the angle of adjacent rudders on the multiple scattering characteristics is discussed.Results show that this method can potentially predict accurate UUV features,especially the multiple scattered features.

MII:A Novel Text Classification Model Combining Deep Active Learning with BERT

Active learning has been widely utilized to reduce the labeling cost of supervised learning.By selecting specific instances to train the model,the performance of the model was improved within limited steps.However,rare work paid attention to the effectiveness of active learning on it.In this paper,we proposed a deep active learning model with bidirectional encoder representations from transformers(BERT)for text classification.BERT takes advantage of the self-attention mechanism to integrate contextual information,which is beneficial to accelerate the convergence of training.As for the process of active learning,we design an instance selection strategy based on posterior probabilities Margin,Intra-correlation and Inter-correlation(MII).Selected instances are characterized by small margin,low intra-cohesion and high inter-cohesion.We conduct extensive experiments and analytics with our methods.The effect of learner is compared while the effect of sampling strategy and text classification is assessed from three real datasets.The results show that our method outperforms the baselines in terms of accuracy.

The oral microbiome of pregnant women facilitates gestational diabetes discrimination

The oral microbiota plays an important role in the development of various diseases,whereas its association with gestational diabetes mellitus (GDM) remains largely unclear.The aim of this study is to identify biomarkers from the oral microbiota of GDM patients by analyzing the microbiome of the saliva and dental plaque samples of 111 pregnant women.We find that the microbiota of both types of oral samples in GDM patients exhibits differences and significantly varies from that of patients with periodontitis or dental caries.Using bacterial biomarkers from the oral microbiota,GDM classification models based on support vector machine and random forest algorithms are constructed.The area under curve (AUC) value of the classification model constructed by combination of Lautropia and Neisseria in dental plaque and Streptococcus in saliva reaches 0.83,and the value achieves a maximum value of 0.89 by adding clinical features.These findings suggest that certain bacteria in either saliva or dental plaque can effectively distinguish women with GDM from healthy pregnant women,which provides evidence of oral microbiome as an informative source for developing noninvasive biomarkers of GDM.

Low temperature catalytic oxidation of NO over different-shaped CeO2

To investigate the effect of CeOnanomaterial morphology on its performance for NO catalytic oxidation.Three kinds of CeOnanomaterials including CeOnanorods,nanospheres and nanoparticles were prepared by hydrothermal method and used for catalytic oxidation of NO at low temperature. The experimental results show that CeOnanorods are of the best catalytic performance. Characterization techniques including TEM, XRD, H-TPR, NO-TPD and XPS were used to determine the relationship between the morphology of CeOnanomaterial and its catalytic performance. TEM images show that CeOnanorods predominantly exposed(110) and(10 0) planes, while CeOnanospheres and CeOnanoparticles predominantly exposed(111) plane. The excellent catalytic performance of CeOnanorods could be ascribed to the low crystallinity, high reducibility, strong NO adsorption ability and the presence of more surface chemisorbed oxygen.

In-chain functionalization through the combination of ring opening copolymerization and oxime "Click" reaction towards X-ray opaque polylactide copolymers

X-ray imaging functionalization of biodegradable polyesters is a great demand and challenge in biomedical applications.In this work,a strategy of in-chain functionalization through the combination of ring opening copolymerization and oxime "Click" postfunctionalization was developed towards X-ray opaque polylactide copolymers.A functionalized cyclic carbonate was first synthesized and used as comonomer of polylactide copolymers,which were subjected to postfunctionalization of oxime "Click" reaction towards iodinated polylactide copolymers.The chemical structure and physical properties of the target products were traced and confirmed.In vitro cytotoxicity evaluation with 3T3-Swiss albino by Alamar blue demonstrated a low cytotoxicity.The X-ray radiopacity was analyzed by Micro-CT and quantified by Hounsfield Units value,which could be tailorable by the feedstock.It is a promising X-ray visible implantable biomaterial in biomedical applications.

Inherently radiopaque polyurethane beads as potential multifunctional embolic agent in hepatocellular carcinoma therapy

The aim of the current study is to report an inherently radiopaque drug-eluted beads(DEBs)as promising embolic materials for TACE techniques.Firstly,the synthesized radiopaque iodinated polycaprolactonepolyurethanes(I-PCLUs)are synthesized by chain-extending method by using 4,4-′isopropylidinedi-(2,6-diiodophenol)(IBPA)as the radiopacifying agent.Then,doxorubicin(Dox)is introduced as a chemotherapeutic agent into I-PCLU beads via a double emulsification(W/O/W)method.The drug loading and controlled release behavior of two ratios of I-PCLU/Dox are found to be dependent upon the internal porous microstructure,and the radiopacity is well-retained after four weeks drug release.Besides,the I-PCLU/Dox beads exhibit positive in vitro anti-tumor effect.The in vivo intramuscular implantation and liver embolization results demonstrate that I-PCLU beads have good histocompatibility,occlusion effect and X-ray traceability.Furthermore,the drug-loaded I-PCLU beads are performed into a VX2 rabbit hepatocellular carcinoma(HCC)model using a micro-catheter,form embolization of hepatic arteries and inhibit the tumor growth after one week post-injection.Hence,this polymeric system provides a potential radiopaque chemoembolization candidate for HCC and other cancer therapies,which could bring opportunities to the next generation of multifunctional embolic agents.