Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer

BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

Prognostic model for prostate cancer based on glycolysis-related genes and non-negative matrix factorization analysis

Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential.Methods:First,gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO),and glycolysis-related genes were obtained from the Molecular Signatures Database(MSigDB).Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained,which were used in nonnegative matrix factorization(NMF)to identify clusters.The correlation between clusters and clinical features was discussed,and the differentially expressed genes(DEGs)between the two clusters were investigated.Based on the DEGs,we investigated the biological differences between clusters,including immune cell infiltration,mutation,tumor immune dysfunction and exclusion,immune function,and checkpoint genes.To establish the prognostic model,the genes were filtered based on univariable Cox regression,LASSO,and multivariable Cox regression.Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model.A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting.Result:The genes obtained from MSigDB were enriched in glycolysis functions.Two clusters were identified by NMF analysis based on 272 glycolysis-related genes,and a prognostic model based on DEGs between the two clusters was finally established.The prognostic model consisted of LAMPS,SPRN,ATOH1,TANC1,ETV1,TDRD1,KLK14,MESP2,POSTN,CRIP2,NAT1,AKR7A3,PODXL,CARTPT,and PCDHGB2.All sample,training,and test cohorts from The Cancer Genome Atlas(TCGA)and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups.The area under the ROC curve showed great performance of this prognostic model.Conclusion:A prognostic model based on glycolysis-related genes was established,with great performance and potential significance to the clinical application.

基于SWOT-CLPV矩阵模型的体育非物质文化遗产数字化传播研究

随着国家一系列有关体育非物质文化遗产保护政策的出台,体育非物质文化遗产的重要地位逐渐彰显。在现如今的数字信息大爆炸时代,有必要利用数字媒体积极传播富含深刻意义的体育非物质文化遗产的相关信息,以便让更多人了解。本文主要采用文献资料法和逻辑分析法,利用SWOT-CLPV矩阵模型对体育非物质文化遗产数字化传播的优势、劣势、机遇和挑战进行分析,并在深入研究其杠杆效应、抑制性、脆弱性和问题性的基础上提出促进体育非物质文化遗产数字化有效传播的优化路径,即:推进体育非遗+数字化传播新模式,提升数字化传播效率;大力培养体育科技复合型人才,以文旅为依托吸引更多受众参与;建立标准化的数字影像信息库,传递体育非遗的正向能量;转变传播观念及采用多元数字化形式,凝聚文化认同感。

Facilitated Prediction of Micropollutant Degradation via UV-AOPs in Various Waters by Combining Model Simulation and Portable Measurement

The degradation of micropollutants in water via ultraviolet(UV)-based advanced oxidation processes(AOPs)is strongly dependent on the water matrix.Various reactive radicals(RRs)formed in UV-AOPs have different reaction selectivities toward water matrices and degradation efficiencies for target micropollutants.Hence,process selection and optimization are crucial.This study developed a facilitated prediction method for the photon fluence-based rate constant for micropollutant degradation(K′_(p,MP))in various UV-AOPs by combining model simulation with portable measurement.Portable methods for measuring the scavenging capacities of the principal RRs(RRSCs)involved in UV-AOPs(i.e.,HO^(·),SO_(4)^(·-),and Cl^(·))using a mini-fluidic photoreaction system were proposed.The simulation models consisted of photochemical,quantitative structure–activity relationship,and radical concentration steady-state approximation models.The RRSCs were determined in eight test waters,and a higher RRSC was found to be associated with a more complex water matrix.Then,by taking sulfamethazine,caffeine,and carbamazepine as model micropollutants,the k′_(p,MP) values in various UV-AOPs were predicted and further verified experimentally.A lower k′_(p,MP) was found to be associated with a higher RRSC for a stronger RR competition;for example,k′_(p,MP) values of 130.9 and 332.5 m^(2) einstein^(–1),respectively,were obtained for carbamazepine degradation by UV/H_(2)O_(2) in the raw water(RRSC=9.47×10^(4) s^(-1))and sand-filtered effluent(RRSC=2.87×10^(4) s^(-1))of a drinking water treatment plant.The developed method facilitates process selection and optimization for UV-AOPs,which is essential for increasing the efficiency and cost-effectiveness of water treatment.

FLOCKING OF A THERMODYNAMIC CUCKER-SMALE MODEL WITH LOCAL VELOCITY INTERACTIONS

In this paper, we study the flocking behavior of a thermodynamic Cucker–Smale model with local velocity interactions. Using the spectral gap of a connected stochastic matrix, together with an elaborate estimate on perturbations of a linearized system, we provide a sufficient framework in terms of initial data and model parameters to guarantee flocking. Moreover, it is shown that the system achieves a consensus at an exponential rate.

Creep mechanism and creep constitutive model of aluminum silicate short-fiber-reinforced magnesium matrix composite

By the constant stress tensile creep test method, creep tests were performed on aluminum silicate short fiber-reinforced AZ91D magnesium matrix composite with volume fraction of 30% and its matrix alloy AZ91D under different temperatures and stresses. The results indicate that the composite and the matrix have the same true stress exponent and true activation energy for creep, which are 3 and 144.63 kJ/mol, respectively. The creep of the composite is controlled by the creep of its matrix, which is mainly the controlling of viscous slip of dislocation, and the controlling of grain boundary slippage as a supplement. The creep constitutive model obtained from the experiment data can well describe the creep deformation pattern of the composite.

Error modeling of 3-RSR parallel robot based on D-H transformation matrix

By selecting any one limb of 3-RSR parallel robot as a research object, the paper establishes a position and orienta- tion relationship matrix between the moving platform and the base by means of Denavit-Hartenberg （D-H） transformation matrix. The error mapping model is derived from original error to the error of the platform by using matrix differential method. This model contains all geometric original errors of the robot. The nonlinear implicit function relation between po- sition and orientation error of the platform and the original geometric errors is simplified as a linear explicit function rela- tion. The results provide a basis for further studying error analysis and error compensation.

AVO forwarding modeling in two-phase media: multiconstrained matrix mineral modulus inversion

AVO forward modeling is based on two-phase medium theory and is considered an effective method for describing reservoir rocks and fluids. However, the method depends on the input matrix mineral bulk modulus and the rationality of the two-phase medium model. We used the matrix mineral bulk modulus inversion method and multiple constraints to obtain a two-phase medium model with physical meaning. The proposed method guarantees the reliability of the obtained AVO characteristicsin two-phase media. By the comparative analysis of different lithology of the core sample, the advantages and accuracy of the inversion method can be illustrated. Also, the inversion method can be applied in LH area, and the AVO characteristics can be obtained when the porosity, fluid saturation, and other important lithology parameters are changed. In particular, the reflection coefficient amplitude difference between the fast P wave and S wave as a function of porosity at the same incidence angle, and the difference in the incidence angle threshold can be used to decipher porosity.

Modeling mechanism of a novel fractional grey mode based on matrix analysis

To fully display the modeling mechanism of the novelfractional order grey model （FGM （q,1））, this paper decomposesthe data matrix of the model into the mean generation matrix, theaccumulative generation matrix and the raw data matrix, whichare consistent with the fractional order accumulative grey model（FAGM （1,1））. Following this, this paper decomposes the accumulativedata difference matrix into the accumulative generationmatrix, the q-order reductive accumulative matrix and the rawdata matrix, and then combines the least square method, findingthat the differential order affects the model parameters only byaffecting the formation of differential sequences. This paper thensummarizes matrix decomposition of some special sequences,such as the sequence generated by the strengthening and weakeningoperators, the jumping sequence, and the non-equidistancesequence. Finally, this paper expresses the influences of the rawdata transformation, the accumulation sequence transformation,and the differential matrix transformation on the model parametersas matrices, and takes the non-equidistance sequence as an exampleto show the modeling mechanism.

Advances in graphene reinforced metal matrix nanocomposites:Mechanisms,processing,modelling,properties and applications

Graphene has been extensively explored to enhance functional and mechanical properties of metalmatrix nanocomposites for wide-range applications due to their superior mechanical,electrical and thermal properties.This article discusses recent advances of key mechanisms,synthesis,manufacture,modelling and applications of graphene metal matrix nanocomposites.The main strengthening mechanisms include load transfer,Orowan cycle,thermal mismatch,and refinement strengthening.Synthesis technologies are discussed including some conventional methods(such as liquid metallurgy,powdermetallurgy,thermal spraying and deposition technology)and some advanced processing methods(such as molecular-level mixing and friction stir processing).Analytical modelling(including phenomenological models,semi-empirical models,homogenization models,and self-consistent model)and numerical simulations(including finite elements method,finite difference method,and boundary element method)have been discussed for understanding the interface bonding and performance characteristics between graphene and different metal matrices(Al,Cu,Mg,Ni).Key challenges in applying graphene as a reinforcing component for the metal matrix composites and the potential solutions as well as prospectives of future development and opportunities are highlighted.

Model Predictive Control-Based Direct Torque Control for Matrix Converter-Fed Induction Motor with Reduced Torque Ripple

To reduce the torque ripple in motors resulting from the use of conventional direct torque control(DTC),a model predictive control(MPC)-based DTC strategy for a direct matrix converter-fed induction motor is proposed in this paper.Two new look-up tables are proposed,these are derived on the basis of the control of the electromagnetic torque and stator flux using all the feasible voltage vectors and their associated switching states.Finite control set model predictive control(FCS-MPC)has then been adopted to select the optimal switching state that minimizes the cost function related to the electromagnetic torque.Finally,the experimental results are shown to verify the reduced torque ripple performance of the proposed MPC-based DTC method.

Modified molecular matrix model for predicting molecular composition of naphtha＇

To improve the naphtha composition prediction model based on molecular type homologous series matrix （MTHS）, this paper puts forward a novel molecular matrix to characterize the naphtha composition and the norreal distribution hypothesis to better describe the molecular composition distribution within each homologous series of the molecular matrix. Through prediction calculation of eight groups of naphtha samples and eight groups of gasoline samples, it is verified that the normal distribution hypothesis is more applicable than gamma distribution hypothesis for the prediction model. According to the prediction results of the samples, the restrain range of normal distribution parameters during model computing process is summarized. With the bulk properties of naphtha samples and the value range of distribution parameters as input conditions, this study utilizes the improved novel molecular matrix to predict the composition of naphtha samples. As the results show, the novel molecular matrix can predict more detailed composition information of naphtha and improve prediction accuracy with less unknown parameters.

A Study Regarding Solution of a Knowledge Model Based on the Containing-Type Error Matrix Equation

An error matrix equation based on error matrix theory was presented in previous research of the error-eliminating theory. The purpose of solving the error matrix equation is to create a decision support on how to switch from bad to good status. A matrix based on error logic is used to express current status u, expectant status u1 and transformation matrix T. It is u, u1, and T that are used to build error matrix equation T (u)= u1. This allows us to find a method whereby bad status “u” changes to good status “u1” by solving the equation. The conversion method that transform from current to expectant status can be obtained from the transformation matrix T. On this basis, this paper proposes a new kind of error matrix equation named “containing-type error matrix equation”. This equation is more suitable for analyzing the realistic question. The method of solving, existence and form of solution for this type of equation have been presented in this paper. This research provides a potential useful new technique for decision analysis.

Model predictive torque control of permanent magnet synchronous motor system driven by matrix converter

Employing matrix converter （MC） as driving mode, the strategy of model predictive torque control （MPTC） is proposed for three phase permanent magnet synchronous motor （PMSM） system. MC is applied instead of conventional AC DC AC converter to increase the power factor （PF） of the system input side. MPTC is used to select optimal voltage space vector to enable the system to have satisfactory torque and flux control effect. The resultant MPTC strategy not only makes the MC fed PMSM system operate reliably and have perfect control performance, but also makes the PF of the system input side be 1. Compared with direct torque control （DTC）, the proposed MPTC strategy guarantees that MC fed PMSM has better command following characteristics in the presence of variation of load torque and tracking reference speed. Simulation results verify the feasibility and effectiveness of the proposed strategy.

A New Quantum Number Triangular Array That Defines the Internal Organization of Valence Quarks, the Hadron Quark Model, and the CKM Matrix

Purpose: The Harmonic Neutron Hypothesis, HNH, has demonstrated that many of the fundamental physical constants, including the quarks, are associated with partial harmonic fractional exponents, , of a fundamental frequency, vF. The model has shown that the properties of the quarks are based on a progression of prime number composites. They also fall on three separate power law lines related to integer factors of the Y-intercept, , of a fundamental electromagnetic line which is scaled by the Rydberg constant, R and Planck’s constant. The quark lines are scaled by the quantum number factors {1, 2, 3}, and their Y-intercepts are referred to as nbem. The goal is to present a new proto-quark model in a six-quark inverted triangular array that defines the global organization of the valence quarks, which determines the hadronic quantum numbers, the standard hadron quark model, and the Cabibbo-Kobayashi-Maskawa (CKM) matrix. Methods: The charm, bottom, top quarks are associated with power law line Y-intercept, nbem equal to 1;the strange and down quarks with nbem equal to 2;and the up quark with nbem equal to 3. An inverted equilateral triangular array with three rows arranged from upper row (triangle base) to bottom row (triangle vertex), is associated respectively with nbem numbers 1, 2, and 3. The novelty of our perspective thus defines a new global valence quark organization which supersedes the Standard hadron composite quark model. The quarks are ordered via relative mass, partial fractions, and nbem quantum number. The top row of our inverted triangle includes the c, b, and t quarks from left to right;the middle row depicts the d and s quarks;and the bottom row, the up quark. Results: Our array depicts a quantum generator of the global organization of the valence quarks defining the composite quark model. The vertices of the triangular array are the up quarks, the midpoints are the down quarks. All weak transitions are from a corner to a midpoint or vice versa. The standard 3 by 3 CKM matrix is generated from the new quark triangle with each up type quark (u, c, and t) transforming to each down type (d, s, and b), with their experimental flavor transition magnitudes given. Conclusion: A new quark quantum number, nbem, is an important discovery that generates a new proto-valence quark triangle that secondarily generates the composite quark model and the CKM matrix.

An Application of the Modified Shear Lag Model to Study the Influence of Thermal Residual Stresses on the Stiffness and Yield Strength of Short Fiber Reinforced Metal Matrix Composites

The modified shear lag model proposed recently was applied to calculate thermal residual stresses and subsequent stress distributions under tensile and compressive loadings. The expressions for the elastic moduli and the yield strengths under tensile and compressive loadings were derived which take account of thermal residual stresses. The asymmetries in the elastic modulus and the yield strength were interpreted using the derived expressions and the obtained results of the stress calculations. The model predictions have exhibited good agreements with the experimental results and also with the other theoretical predictions

Ceramic particles reinforced copper matrix composites manufactured by advanced powder metallurgy:preparation, performance, and mechanisms

Copper matrix composites doped with ceramic particles are known to effectively enhance the mechanical properties,thermal expansion behavior and high-temperature stability of copper while maintaining high thermal and electrical conductivity.This greatly expands the applications of copper as a functional material in thermal and conductive components,including electronic packaging materials and heat sinks,brushes,integrated circuit lead frames.So far,endeavors have been focusing on how to choose suitable ceramic components and fully exert strengthening effect of ceramic particles in the copper matrix.This article reviews and analyzes the effects of preparation techniques and the characteristics of ceramic particles,including ceramic particle content,size,morphology and interfacial bonding,on the diathermancy,electrical conductivity and mechanical behavior of copper matrix composites.The corresponding models and influencing mechanisms are also elaborated in depth.This review contributes to a deep understanding of the strengthening mechanisms and microstructural regulation of ceramic particle reinforced copper matrix composites.By more precise design and manipulation of composite microstructure,the comprehensive properties could be further improved to meet the growing demands of copper matrix composites in a wide range of application fields.

PROPER APPLICATION OF A KIND OF MATRIX CON-STRUCTION METHOD IN PHYSICAL PARAMETER IDENTIFICATION OF DYNAMIC MODEL

The expressions of matrix construction by using the singular value decomposition (SVD) are applied to the physics parameter identification of dynamic model. Then, based upon to the characteristics of a kind of matrix construction method, the orders of the parameter identification model can be reduced. After reducing, the mathematics and physics correspondence relations between the subsystem and the original system are distinct. the condensation errors can be avoided. The numerical example shows the benefit of the presented methodology.

IMPROVED ESTIMATES OF THE COVARIANCE MATRIX IN GENERAL LINEAR MIXED MODELS

In this article, the problem of estimating the covariance matrix in general linear mixed models is considered. Two new classes of estimators obtained by shrinking the eigenvalues towards the origin and the arithmetic mean, respectively, are proposed. It is shown that these new estimators dominate the unbiased estimator under the squared error loss function. Finally, some simulation results to compare the performance of the proposed estimators with that of the unbiased estimator are reported. The simulation results indicate that these new shrinkage estimators provide a substantial improvement in risk under most situations.