Numerical Investigation of the Multiphase Flow Originating from the Muzzle of Submerged Parallel Guns

A two-dimensional model,employing a dynamic mesh technology,is used to simulate numerically the transient multiphaseflowfield produced by two submerged parallel guns.After a grid refinement study ensuring grid inde-pendence,five different conditions are considered to assess the evolution of cavitation occurring in proximity to the gun muzzle.The simulation results show thatflow interference is enabled when the distance between the par-allel barrels is relatively small;accordingly,the generation and evolution of the vapor cavity becomes more com-plex.By means of the Q criterion for vorticity detection,it is shown that cavitation causes the generation of vorticity and the evolution of the vapor cavity can result in an asymmetric distribution of vorticity for a certain distance of the barrels.In particular,the evolution of the vapor cavity can hinder the expansion of the gas and force it toflow outward,while an asymmetric distribution of vorticity can lead to a gas jetflowing outward and rotating simultaneously.

Physics-constrained indirect supervised learning

This study proposes a supervised learning method that does not rely on labels.We use variables associated with the label as indirect labels,and construct an indirect physics-constrained loss based on the physical mechanism to train the model.In the training process,the model prediction is mapped to the space of value that conforms to the physical mechanism through the projection matrix,and then the model is trained based on the indirect labels.The final prediction result of the model conforms to the physical mechanism between indirect label and label,and also meets the constraints of the indirect label.The present study also develops projection matrix normalization and prediction covariance analysis to ensure that the model can be fully trained.Finally,the effect of the physics-constrained indirect supervised learning is verified based on a well log generation problem.

Clinical Observation on Treatment of Moderate and Severe Periodical Mastalgia with TCM Hot-wet Compress Therapy

[Objectives] To observe the clinical therapeutic efficacy of local hot-wet compress therapy of digestion ointment prescription on the patients with moderate and severe mastalgia. [Methods] Firstly,60 patients with mastalgia were selected,and divided into the experimental group and control group( 30 cases for each group). The control group was treated with acupuncture at distal point,and the experimental group was treated with TCM local hot-wet compress therapy combined with acupuncture at distal point. Then,the clinical therapeutic efficacy of two groups was compared. [Results]The experimental group was superior to the control group in relieving the breast pain,improving emotional integral,reducing breast lumps,improving lump texture,and improving TCM syndrome integral( P < 0. 05). [Conclusions]The digestion ointment prescription local hot-wet compress therapy has a significant therapeutic effect on treatment of mastalgia and can significantly improve the breast pain symptom of patients.

Discovery of Partial Differential Equations from Highly Noisy and Sparse Data with Physics-Informed Information Criterion

Data-driven discovery of partial differential equations(PDEs)has recently made tremendous progress,and many canonical PDEs have been discovered successfully for proof of concept.However,determining the most proper PDE without prior references remains challenging in terms of practical applications.In this work,a physics-informed information criterion(PIC)is proposed to measure the parsimony and precision of the discovered PDE synthetically.The proposed PIC achieves satisfactory robustness to highly noisy and sparse data on 7 canonical PDEs from different physical scenes,which confirms its ability to handle difficult situations.The PIC is also employed to discover unrevealed macroscale governing equations from microscopic simulation data in an actual physical scene.The results show that the discovered macroscale PDE is precise and parsimonious and satisfies underlying symmetries,which facilitates understanding and simulation of the physical process.The proposition of the PIC enables practical applications of PDE discovery in discovering unrevealed governing equations in broader physical scenes.

Two kinds of average approximation accuracy

Rough set theory places great importance on approximation accuracy,which is used to gauge how well a rough set model describes a target concept.However,traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set model.To overcome this,two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is proposed.The first is the relative average approximation accuracy,which is based on all sets in the universe and has several basic properties.The second is the absolute average approximation accuracy,which is based on undefinable sets and has yielded significant conclusions.We also explore the relationship between these two types of average approximation accuracy.Finally,the average approximation accuracy has practical applications in addressing missing attribute values in incomplete information tables.

Effectiveness of external Sanjierupi Gao on mastalgia caused by mammary gland hyperplasia: a placebo controlled trial

OBJECTIVE:To evaluate the curative effect of external application of the Chinese drug,Sanjierupi Gao,on mastalgia caused by mammary gland hyperplasia.METHODS:This randomized,double-blinded,and placebo controlled study enrolled 260 patients with mammary gland hyperplasia from five hospitals.Patients were randomly and equally divided in-to a Sanjierupi Gao treatment group and a placebo control group.An adhesive plaster was applied to the most painful area on either breast for 7 h a day.Treatment lasted for two menstrual cycles without application during menstruation.Mastalgia was used as the main index of curative effect.The change before and after treatment in days of mastalgia,the time to alleviate pain,pain extent,and severe pain scores were observed.RESULTS:Compared to the control group,the treatment group had significantly fewer days of mastalgia(P<0.01),a significantly lower severe pain score(P<0.01),and significantly less subjective pain and tenderness(P<0.05 and P<0.01,respectively).Three days before the follow-up visit,the pain score in the treatment group was significantly lower than that in the control group(P<0.05).A non-parametric test was used to compare the time to alleviate mastalgia between the two groups and found no statistical difference(Z=0.313,P=0.754).CONCLUSION:Application of Sanjierupi Gao can decrease mastalgia duration in patients with mammary gland hyperplasia during menstruation and alleviate the extent of mastalgia.The time to alleviate pain is psychologically influenced.

Composite Sinusoidal Nanograting With Long-Range SERS Effect for Label-Free TNT Detection

A composite one-dimensional （1D） Ag sinusoidal nanograting aiming at label-free surface enhanced Raman scattering （SERS） detection of TNT with robust and reproducible enhancements is discussed. 1D periodic sinusoidal SiO2 grating followed by Ag evaporation is proposed for the creation of reproducible and effective SERS substrate based on surface plasmon polaritons （SPPs）. The optimal structure of 1D sinusoidal nanograting and its long-range SERS effect are analyzed by using the finite difference time domain （FDTD）. Simulation SERS enhancement factor （EF） can be 5 orders of magnitude as possible. This SERS substrate is prepared by the interference photolithography technology, its SERS performance is tested by Rh6G detection experiments, and the actual test EF is about 10. The label-free SERS detection capacity of TNT is demonstrated in the experiment.

A Comparative Study of Stochastic Collocation Methods for Flow in Spatially Correlated Random Fields

Stochastic collocation methods as a promising approach for solving stochastic partial differential equations have been developed rapidly in recent years.Similar to Monte Carlo methods,the stochastic collocation methods are non-intrusive in that they can be implemented via repetitive execution of an existing deterministic solver without modifying it.The choice of collocation points leads to a variety of stochastic collocation methods including tensor product method,Smolyak method,Stroud 2 or 3 cubature method,and adaptive Stroud method.Another type of collocation method,the probabilistic collocation method(PCM),has also been proposed and applied to flow in porous media.In this paper,we discuss these methods in terms of their accuracy,efficiency,and applicable range for flow in spatially correlated random fields.These methods are compared in details under different conditions of spatial variability and correlation length.This study reveals that the Smolyak method and the PCM outperform other stochastic collocation methods in terms of accuracy and efficiency.The random dimensionality in approximating input random fields plays a crucial role in the performance of the stochastic collocation methods.Our numerical experiments indicate that the required random dimensionality increases slightly with the decrease of correlation scale and moderately from one to multiple physical dimensions.

Adaptive Stroud Stochastic Collocation Method for Flow in Random Porous Media via Karhunen-Loeve Expansion

In this paper we develop a Stochastic Collocation Method(SCM)for flow in randomly heterogeneous porous media.At first,the Karhunen-Lo`eve expansion is taken to decompose the log transformed hydraulic conductivity field,which leads to a stochastic PDE that only depends on a finite number of i.i.d.Gaussian random variables.Based on the eigenvalue decay property and a rough error estimate of Stroud cubature in SCM,we propose to subdivide the leading dimensions in the integration space for random variables to increase the accuracy.We refer to this approach as adaptive Stroud SCM.One-and two-dimensional steady-state single phase flow examples are simulated with the new method,and comparisons are made with other stochastic methods,namely,the Monte Carlo method,the tensor product SCM,and the quasiMonte Carlo SCM.The results indicate that the adaptive Stroud SCM is more efficient and the statistical moments of the hydraulic head can be more accurately estimated.