Nanoindentation Models of Monolayer Graphene and Graphyne under Point Load Pattern Studied by Molecular Dynamics

Molecular dynamics simulations are performed to study the nanoindentation models of monolayer suspended graphene and graphyne. Fullerenes are selected as indenters. Our results show that Young＇s modulus of monolayer-thick graphyne is almost half of that of graphene, which is estimated to be 0.50 TPa. The mechanical properties of graphene and graphyne are different in the presence of strain. A pre-tension has an important effect on the mechanical properties of a membrane. Both the pre-tension and Young＇s modulus plots demonstrate index behavior. The toughness of graphyne is stronger than that of graphene due to Young＇s modulus magnitude. Young＇s moduli of graphene and graphyne are almost independent of the size ratio of indenter to membrane.

Linking a farmer crop selection model(FCS) with an agronomic model(EPIC) to simulate cropping pattern in Northeast China

In this paper, authors established a farmer crop selection model（FCS） for the three provinces of Liaoning, Jilin and Heilongjiang of the Northeast China. With linking to the environmental policy integrated climate model（EPIC）, the simulated results of FCS model for maize, rice and soybean were spatialized with 1 km×1 km grids to obtain cropping pattern. The reference map of spatial distribution for the three staple crops acquired by remote sensing imageries was applied to validate the simulated cropping pattern. The results showed that（1） the total simulation accuracy for the study area was 78.62%, which proved simulation method was applicable and feasible;（2） simulation accuracy for Jilin Province was the highest among the three provinces with a rate of 82.45% since its simple cropping system and not complex topography;（3） simulation accuracy for maize was the best among the three staple crops with a ratio of 81.14% because the study area is very suitable for maize growth. We hope this study could provide the reference for cropping pattern forecasting and decision-making.

Modeling study on the flow patterns of gas–liquid flow for fast decarburization during the RH process

A water model and a high-speed video camera were utilized in the 300-t RH equipment to study the effect of steel flow patterns in a vacuum chamber on fast decarburization and a superior flow-pattern map was obtained during the practical RH process. There are three flow patterns with different bubbling characteristics and steel surface states in the vacuum chamber： boiling pattern（BP）, transition pattern（TP）, and wave pattern（WP）. The effect of the liquid-steel level and the residence time of the steel in the chamber on flow patterns and decarburization reaction were investigated, respectively. The liquid-steel level significantly affected the flow-pattern transition from BP to WP, and the residence time and reaction area were crucial to evaluate the whole decarburization process rather than the circulation flow rate and mixing time. A superior flow-pattern map during the practical RH process showed that the steel flow pattern changed from BP to TP quickly, and then remained as TP until the end of decarburization.

Transformation from computation independent model to platform independent model with pattern

Model driven architecture(MDA) is an evolutionary step in software development.Model transformation forms a key part of MDA.The transformation from computation independent model(CIM) to platform independent model(PIM) is the first step of the transformation.This paper proposes an approach for this transformation with pattern.In this approach, we take advantage of"reuse"from various standpoints.Feature model is used to describe the requirement of the application.This can help us bring"reuse"into effect at requirement level.Moreover we use pattern to transform CIM to PIM.This can help us bring"reuse"into effect at development level.Meanwhile, pattern was divided into four hierarchies.Different hierarchies of pattern are used to help us utilize reuse at different phase of development.From another standpoint, feature model describes the problem of a domain while pattern describe the problem across domains.This can help us reuse the element in and across domains.Finally, the detailed process of the transformation is given.

A New Computer Aided Volleyball Training System Based on Pattern Recognition Model

This paper proposes the novel computer aided volleyball training system based on pattern recognition model. In general the quality of volleyball training at the same time, we should strengthen coordination, mobile power, quality training, strengthen mobile exercises, moving fast and flexible to overcome the sudden fall of soft volleyball on the defensive and pilling adverse, such practice can be used for mobile.Strengthen the strength training, enhance the strength of the upper and lower limbs, improve the team＇ s bounce and batting speed is conducive to compete for the advantage of the Internet, better undermine the defense. With the help of the proposed system, the training process will be more effective, and the performance will be verified in the later discussions.

Rapid determination of tannin in Danshen and Guanxinning injections using UV spectrophotometry for quality control

A technique for the determination of tannin content in traditional Chinese medicine injections(TCMI)was developed based on ultraviolet(UV)spectroscopy.Chemometrics were used to construct a mathematical model of absorption spectrum and tannin reference content of Danshen and Guanxinning injections,and the model was veried and applied.The results showed that the established UV-based spectral partial least squares regression(PLS)tannin content model performed well with a correlation coefficient(r)of 0.952,root mean square error of calibration(RMSEC)of 0.476
g/ml,root mean square error of validation(RMSEV)of 1.171
g/ml,and root mean square error of prediction(RMSEP)of 0.465
g/ml.Pattern recognition models using linear discriminant analysis(LDA)and k nearest neighbor(k-NN)classiers based on UV spectrum could successfully classify different types of injections and different manufacturers.The established method to measure tannin content based on UV spectroscopy is simple,rapid and reliable and provides technical support for quality control of tannin in Chinese medicine injections.

A method for detecting miners based on helmets detection in underground coal mine videos

In order to monitor dangerous areas in coal mines automatically,we propose to detect helmets from underground coal mine videos for detecting miners.This method can overcome the impact of similarity between the targets and their background.We constructed standard images of helmets,extracted four directional features,modeled the distribution of these features using a Gaussian function and separated local images of frames into helmet and non-helmet classes.Out experimental results show that this method can detect helmets effectively.The detection rate was 83.7%.

User-Level Sentiment Evolution Analysis in Microblog

People's attitudes towards public events or products may change overtime,rather than staying on the same state.Understanding how sentiments change overtime is an interesting and important problem with many applications.Given a certain public event or product,a user's sentiments expressed in microblog stream can be regarded as a vector.In this paper,we define a novel problem of sentiment evolution analysis,and develop a simple yet effective method to detect sentiment evolution in user-level for public events.We firstly propose a multidimensional sentiment model with hierarchical structure to model user's complicate sentiments.Based on this model,we use FP-growth tree algorithm to mine frequent sentiment patterns and perform sentiment evolution analysis by Kullback-Leibler divergence.Moreover,we develop an improve Affinity Propagation algorithm to detect why people change their sentiments.Experimental evaluations on real data sets show that sentiment evolution could be implemented effectively using our method proposed in this article.

Local Discontinuous Galerkin Methods with Decoupled Implicit-Explicit Time Marching for the Growth-Mediated Autochemotactic Pattern Formation Model

In this paper,two fully-discrete local discontinuous Galerkin(LDG)methods are applied to the growth-mediated autochemotactic pattern formation model in self-propelling bacteria.The numerical methods are linear and decoupled,which greatly improve the computational efficiency.In order to resolve the time level mismatch of the discretization process,a special time marching method with high-order accuracy is constructed.Under the condition of slight time step constraints,the optimal error estimates of this method are given.Moreover,the theoretical results are verified by numerical experiments.Real simulations show the patterns of spots,rings,stripes as well as inverted spots because of the interplay of chemotactic drift and growth rate of the cells.

Method to generate training samples for neural network used in target recognition

Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.

Anomaly detection of hot components in gas turbine based on frequent pattern extraction

Hot components operate in a high-temperature and high-pressure environment. The occurrence of a fault in hot components leads to high economic losses. In general, exhaust gas temperature(EGT) is used to monitor the performance of hot components.However, during the early stages of a failure, the fault information is weak, and is simultaneously affected by various types of interference, such as the complex working conditions, ambient conditions, gradual performance degradation of the compressors and turbines, and noise. Additionally, inadequate effective information of the gas turbine also restricts the establishment of the detection model. To solve the above problems, this paper proposes an anomaly detection method based on frequent pattern extraction. A frequent pattern model(FPM) is applied to indicate the inherent regularity of change in EGT occurring from different types of interference. In this study, based on a genetic algorithm and support vector machine regression, the relationship model between the EGT and interference was tentatively built. The modeling accuracy was then further improved through the selection of the kernel function and training data. Experiments indicate that the optimal kernel function is linear and that the optimal training data should be balanced in addition to covering the appropriate range of operating conditions and ambient temperature. Furthermore, the thresholds based on the Pauta criterion that is automatically obtained during the modeling process, are used to determine whether hot components are operating abnormally. Moreover, the FPM is compared with the similarity theory, which demonstrates that the FPM can better suppress the effect of the component performance degradation and fuel heat value fluctuation. Finally, the effectiveness of the proposed method is validated on seven months of actual data obtained from a Titan130 gas turbine on an offshore oil platform. The results indicate that the proposed method can sensitively detect malfunctions in hot components during the early stages of a fault, and is robust to various types of interference.

Layered Software Patterns for Data Analysis in Big Data Environment

The proliferation of textual data in society currently is overwhelming, in particular, unstructured textual data is being constantly generated via call centre logs, emails, documents on the web, blogs, tweets, customer comments, customer reviews, etc.While the amount of textual data is increasing rapidly, users ability to summarise, understand, and make sense of such data for making better business/living decisions remains challenging. This paper studies how to analyse textual data, based on layered software patterns, for extracting insightful user intelligence from a large collection of documents and for using such information to improve user operations and performance.

Numerical simulation of nanoparticle pattern fabricated by electrostatic spray deposition

Electrospray deposition （ESD） as a patterning method of nanoparticles deposited on a substrate has attracted much attention due to several advantages over other methods. However, obtaining an optimum ESD processing condition for nanoparticle pattern relies much on trial experiments because of the lack of reliable numerical simulation. In this study, the deposition characteristics of nanoparticle generated by electrospray were investigated by using a three-dimensional Lagrangian model. Three important process parameters, including solution dielectric constant, applied voltage and surface charge density on mask were considered by fixing the geometrical parameters of the ESD device. Simulation result showed that under the condition of without a mask, the spray diameter increases with increasing solvent dielectric constant, and higher applied voltage makes the spray area wider. Controllability of focusing by changing surface charge density on the mask was confirmed： higher surface charge density on the mask results in more focused deposition. Validity of the numerical simulation developed in this study was verified by comnarison with exoerimental data.

POSITIVITY-PRESERVING LOCAL DISCONTINUOUS GALERKIN METHOD FOR PATTERN FORMATION DYNAMICAL MODEL IN POLYMERIZING ACTIN FLOCKS

In this paper,we apply local discontinuous Galerkin(LDG)methods for pattern formation dynamical model in polymerizing actin focks.There are two main dificulties in designing effective numerical solvers.First of all,the density function is non-negative,and zero is an unstable equilibrium solution.Therefore,negative density values may yield blow-up solutions.To obtain positive numerical approximations,we apply the positivitypreserving(PP)techniques.Secondly,the model may contain stif source.The most commonly used time integration for the PP technique is the strong-stability-preserving Runge-Kutta method.However,for problems with stiff source,such time discretizations may require strictly limited time step sizes,leading to large computational cost.Moreover,the stiff source any trigger spurious filament polarization,leading to wrong numerical approximations on coarse meshes.In this paper,we combine the PP LDG methods with the semi-implicit Runge-Kutta methods.Numerical experiments demonstrate that the proposed method can yield accurate numerical approximations with relatively large time steps.

Modeling the control mechanism for generating the rise-fall patterns in FO contours

The strategy of modeling the control mechanism for generating F0 contour of speech signal is studied in this paper. Based on some dynamic characteristics of vocal cord strain, the complex laryngeal mechanism relative to local F0 regulation is simplified to be a feasible physical model. Furthermore, a model function is deduced as the control mechanism for the generation process of local rise-fall patterns, and two kinds of basic feature patterns result with so called rise-fall commands defined by model parameters. on the logarithmic scale of F0 versus time the local characteristics of an F0 contour are approximated by the sum of these patterns generated by appropriate commands. The experimenial results in analyzing and synthesizing the F0 contours of spoken Chinese utterances indicate that the observed F0 contours can be always approximated well by the model, and a good correlation exists between some model parameters and the transition duration of local F0 rising or falling. The model lays a foundation for Chinese F0 contour synthesis by rule.

The Neurocomputational Mechanism Underlying Decision-Making on Unfairness to Self and Others

Fairness is a fundamental value in human societies,with individuals concerned about unfairness both to themselves and to others.Nevertheless,an enduring debate focuses on whether self-unfairness and other-unfairness elicit shared or distinct neuropsychological processes.To address this,we combined a three-person ultimatum game with computational modeling and advanced neuroimaging analysis techniques to unravel the behavioral,cognitive,and neural patterns underlying unfairness to self and others.Our behavioral and computational results reveal a heightened concern among participants for self-unfairness over other-unfairness.Moreover,self-unfairness consistently activates brain regions such as the anterior insula,dorsal anterior cingulate cortex,and dorsolateral prefrontal cortex,spanning various spatial scales that encompass univariate activation,local multivariate patterns,and whole-brain multivariate patterns.These regions are well-established in their association with emotional and cognitive processes Lanxin Luo and Han Xu contributed equally to this work.Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s12264-024-01245-8.Yue-JiaLuo luoyj@bnu.edu.cn Chunliang Feng chunliang.feng@m.scnu.edu.cn 1 Key Laboratory of Brain,Cognition and Education Sciences(South China Normal University),Ministry of Education,Guangzhou 510631,China 2 School of Psychology,South China Normal University,Guangzhou 510631,China 3 Center for Studies of Psychological Application,South China Normal University,Guangzhou 510631,China relevant to fairness-based decision-making.Conversely,other-unfairness primarily engages the middle occipital gyrus.Collectively,our findings robustly support distinct neurocomputational signatures between self-unfairness and other-unfairness.

An Experimental Study of The Rayleigh-Taylor Instability Critical Wave Length

A physical model has been constructed to represent the condensate film pattern on a horizontal downward- facing surface with fins,which is based on visual observation in experiment.The results of analysis using this model confirms the validity of the critical wave length formula obtained from Rayleigh-Taylor stability analysis. This formula may be used as a criterion to design horizontal downward-facing surfaces with fins that can best destabilize the condensate film,thus enhancing condensation heat transfer.

A New Fast Edge Detection Algorithm Based on NAM-structured Plane Decomposition

The nonsymmetry and antipacking pattern representation model （NAM）, inspired by the concept of the packing problem, uses a set of subpatterns to represent an original pattern. The NAM is a promising method for image representation because of its ability to focus on the interesting subsets of an image. In this paper, we develop a new method for gray-scale image representation based on NAM, called NAM-structured plane decomposition （NAMPD）, in which each subpattern is associated with a rectangular region in the image. The luminance function of pixels in this region is approximated by an oblique plane model. Then, we propose a new and fast edge detection algorithm based on NAMPD. The theoretical analyses and experimental results presented in this paper show that the edge detection algorithm using NAMPD performs faster than the classical ones because it permits the execution of operations on subpatterns instead of pixels.

Modeling of strain and filtration properties of a semi-finished leather product

A study on deformation and filtration properties of a leather semi-finished product after chrome tanning are presented.The analytical dependences of compressive load on compressive(recovery)strain and moisture content of a leather semi-finished product are obtained.The empirical dependences of hydraulic gradient on filtration rates are determined for various compression ratios of the leather semi-finished product.It was revealed that with an increase in compressive load and moisture content of the leather semi-finished product,the compressive deformation increases.The shoulder section is subject to the greatest deformation,then the belly section and the least deformation is observed in the butt section.A linear relationship has been established between the hydraulic gradient and the rate of moisture filtration through the leather semi-finished product in the directions perpendicular and parallel to its surface.