Strategic Planning for Equitable RWIS Implementation:A Comprehensive Study Incorporating a Multi-variable Semivariogram Model

This paper extends the previously developed method of optimizing Road Weather Information Systems(RWIS)station placement by unveiling a sophisticated multi-variable semivariogram model that concurrently considers multiple vital road weather variables.Previous research primarily centered on single-variable analysis focusing on road surface temperature(RST).The study bridges this oversight by introducing a framework that integrates multiple critical weather variables into the RWIS location allocation framework.This novel approach ensures balanced and equitable RWIS distribution across zones and aligns the network with areas both prone to traffic accidents and areas of high uncertainty.To demonstrate the effectiveness of this refinement,the authors applied the framework to Maine’s existing RWIS network,conducted a gap analysis through varying planning scenarios and generated optimal solutions using a heuristic optimization algorithm.The analysis identified areas that would benefit most from additional RWIS stations and guided optimal resource utilization across different road types and priority locations.A sensitivity analysis was also performed to evaluate the effect of different weightings for weather and traffic factors on the selection of optimal locations.The location solutions generated have been adopted by MaineDOT for future implementations,attesting to the model’s practicality and signifying an important advancement for more effective management of road weather conditions.

The Research and Application of Prediction Control in Multi-variable Control Process

The modern industrial control objects become more and more complicated,and higher control quality is required, so a series of new control strategies appear,applied,modified and develop quickly.This paper researches a new control strategy- prediction control-and its application in Multi-Variable Control Process.The research result is worthy for automatic control in pro- cess industry.

Non-Probabilistic Reliability Research on Multi-Variable Hydraulic Turbine Blade Model

Based on the interval mathematics and possibility theory, the variables existing in hydraulic turbine blade are described. Considering the multi-failure mode in turbine blade, multi-variable model is established to meet the actual situation. Thus, non-probabilistic reliability index is presented by comparing with the output range and the given range.

Multi-variable special polynomials using an operator ordering method

Using an operator ordering method for some commutative superposition operators,we introduce two new multi-variable special polynomials and their generating functions,and present some new operator identities and integral formulas involving the two special polynomials.Instead of calculating compli-cated partial differential,we use the special polynomials and their generating functions to concsely address the normalzation,photoount distributions and Wigner distributions of several quantum states that can be realized physically,the rsults of which provide real convenience for further investigating the properties and applications of these states.

ENHANCING GROUND RESOLUTION OF TM6 BASED ON MULTI-VARIATE REGRESSION MODEL AND SEMI-VARIOGRAM FUNCTION

It is well known that Landsat TM images are the most widely used remote sensing data in various fields.Usually,it has 7 different electromagnetic spectrum bands,among which the sixth one has much lower ground resolution compared with the other six bands.Nevertheless,it is useful in the study of rock spectrum reflection,geothermal resources exploration,etc.To improve the ground resolution of TM6 to the level as that of the other six bands is a problem .This paper presents an algorithm based on the combination of multivariate regression model with semivariogram function which can improve the ground resolution of TM6 by "fusing" the data of other six bands.It includes the following main steps: （1） testing the correlation between TM6 and one of TM15,7.If the correlation coefficient between TM6 and another one is greater than a given threshold value,then select the band to the regression analysis as an argument.（2） calculating the size of the template window within which some parameters needed by the regression model will be calculated; （3） replacing the original pixel values of TM6 by those obtained by regression analysis; （4） using image entropy as a measurement to evaluate the quality of the fused image of TM6.The basic mechanism of the algorithm is discussed and the V C ++ program for implementing this algorithm is also presented.A simple application example is given in the last part of this paper,showing the effectiveness of the algorithm.

SecMVX:Analysis on the Vulnerability of Multi-Variant Execution

As an active defenses technique,multivariant execution(MVX)can detect attacks by monitoring the consistency of heterogeneous variants with parallel execution.Compared with patch-style passive defense,MVX can defend against known and even unknown vulnerability-based attacks without relying on attack feature information.However,variants generated with software diversity technologies will introduce new vulnerabilities when they execute in parallel.First,we analyze the security of MVX theory from the perspective of formal description.Then we summarize the general forms and techniques for attacks against MVX,and analyze the new vulnerabilities arising from the combination of variant generation technologies.We propose SecMVX,a secure MVX architecture and variant generation technology.Experimental evaluations based on CVEs and SPEC 2006 benchmark show that SecMVX introduces 11.29%of the average time overhead,and avoids vulnerabilities caused by the improper combination of variant generation technologies while keeping the defensive ability of MVX.

An Efficient Maple Program for Calculating Adomian Polynomials

The immense quest for proficient numerical schemes for the solution of mathematical models featuring nonlinear differential equations led to the realization of the Adomian decomposition method (ADM) in the 80th. Undoubtedly, the solution of nonlinear differential equations using ADM is presided over by the acquisition of Adomian polynomials, which are not always easy to find. Thus, the present study proposes easy-to-implement Maple programs for the computation of Adomian polynomials. In fact, the proposed algorithms performed remarkably on several test functions, consisting of one- and multi-variable nonlinearities. Moreover, the introduced programs are advantageous in terms of simplicity;coupled with the requirement of less computational time in comparison with what is known in the literature.

农户响应农业面源污染治理教育引导机制的行为研究——以测土配方施肥项目为例

由于农业面源污染的特殊性质以及政府监管治理机制的无效率,农业面源污染的治理机制从侧重于政府监管发展为以教育培训手段为主。文章利用四川省实地调研数据,将农户的行为响应分为对农业面源污染教育培训项目的认知、参与意愿及最终参与行为响应三个阶段,并构建Multi-variate Probit模型分析不同阶段的影响因素。结果显示:农户个人禀赋、种植特征、社会资本及治理机制反应等变量在不同行为响应阶段具有显著影响;培训班的可获得性在每个行为响应阶段都具有正向的显著影响。

A PRECISE SINGULAR FINITE ELEMENT FOR CRACK ANALYSIS

The multi-variable finite element algorithm based on the generalized Galerkin’smethod is more flexible to establish a finite element model in the continuum mechanies.Byusing this algorithm and numerical tests a new singular finite element for elasto-plasticfracture analysis has been formulated.The results of numerical tests show that the newelement possesses high accuracy and good performance.Some rules for formulating amulti-variable singular finite element are also discussed in this paper.

On-Line Real Time Realization and Application of Adaptive Fuzzy Inference Neural Network

In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.

State Space Model Predictive Control of an Aerothermic Process with Actuators Constraints

This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected through a data acquisition system for real time control. The interaction between the process variables is shown to be challenging for single variable controllers, therefore multi-variable control is worth considering. A multi-variable state space model is obtained from on-line experimental data. The controller design is translated into a Quadratic Programming (QP) problem, in which a cost function subject to actuators linear inequality constraints is minimized. The outcome of the experimental results is that the main control objectives, such as set-point tracking and perturbations rejection under actuators constraints, are well achieved for both controlled variables simultaneously.

Detection of Parkinson’s Disease with Multiple Feature Extraction Models and Darknet CNN Classification

Parkinson’s disease(PD)is a neurodegenerative disease in the central nervous system.Recently,more researches have been conducted in the determination of PD prediction which is really a challenging task.Due to the disorders in the central nervous system,the syndromes like off sleep,speech disorders,olfactory and autonomic dysfunction,sensory disorder symptoms will occur.The earliest diagnosing of PD is very challenging among the doctors community.There are techniques that are available in order to predict PD using symptoms and disorder measurement.It helps to save a million lives of future by early prediction.In this article,the early diagnosing of PD using machine learning techniques with feature selection is carried out.In the first stage,the data preprocessing is used for the preparation of Parkinson’s disease data.In the second stage,MFEA is used for extracting features.In the third stage,the feature selection is performed using multiple feature input with a principal component analysis(PCA)algorithm.Finally,a Darknet Convolutional Neural Network(DNetCNN)is used to classify the PD patients.The main advantage of using PCA-DNetCNN is that,it provides the best classification in the image dataset using YOLO.In addition to that,the results of various existing methods are compared and the proposed DNetCNN proves better accuracy,performance in detecting the PD at the initial stages.DNetCNN achieves 97.5%of accuracy in detecting PD as early.Besides,the other performance metrics are compared in the result evaluation and it is proved that the proposed model outperforms all the other existing models.

Application Study on Multi-Vary Analysis

In this article, we studied the bearings made by one company in Shanghai. Through statistical process controlling the quality characteristic of bearings’ diameters and multi-vary analysis is applied to find the key variation factors which have an influence on the quality characteristic of the bearings, the quality level of the bearings of this company is improved.

Machine learning based techno-economic process optimisation for CO_(2) capture via enhanced weathering

This work evaluated the practicability and economy of the enhanced weathering(EW)-based CO_(2) capture in series packed bubble column(S-PBC)contactors operated with different process configurations and conditions.The S-PBC contactors are designed to fully use the advantages of abundant seawater and highly efficient freshwater through a holistic M4 model,including multi-physics,machine learning,multi-variable and multiobjective optimisation.An economic analysis is then performed to investigate the cost of different S-PBC configurations.A data-driven surrogate model based on a novel machine learning algorithm,extended adaptive hybrid functions(E-AHF),is implemented and trained by the data generated by the physics-based models.GA and NSGA-II are applied to perform single-and multi-objective optimisation to achieve maximum CO_(2) capture rate(CR)and minimum energy consumption(EC)with the optimal values of eight design variables.The R2 for the prediction of CR and EC is higher than 0.96 and the relative errors are lower than 5%.The M4 model has proven to be an efficient way to perform multi-variable and multi-objective optimisation,that significantly reduces computational time and resources while maintaining high prediction accuracy.The trade-off of the maximum CR and minimum EC is presented by the Pareto front,with the optimal values of 0.1014 kg h−1 for CR and 6.1855 MJ kg−1 CO_(2) for EC.The calculated net cost of the most promising S-PBC configuration is around 400$t−1 CO_(2),which is about 100$t−1 CO_(2) lower than the net cost of current direct air capture(DAC),but compromised by slower CO_(2) capture rate.

A GEO Satellite Position and Beam Features Estimation Method Based on Beam Edge Positions

Constrained by orbital configuration and spectrum sharing,non-geostationary orbit(NGEO)satellites may be interfered when they are in the beam range of geostationary orbit(GEO)satellite.However,it is difficult for NGEO operators to determine the signal source.Herein,we propose a method to locate the GEO signal source and estimate beam features,including beam pointing azimuth,elevation,and beamwidth,by the beam edge positions.We transform this estimation problem into two optimization problems by minimizing the estimation error,and solve both of them through a multi-variable joint iteration method with acceptable computation complexity.Numerical results show that when NGEO satellites pass through the beam twice,the longitude estimation error is about 0.01 degree,and the estimation results will be more and more accurate as the number of passing times increases.Besides,the proposed method is also effective when there are kilometer-level errors in beam edge positions.

Analysis of an Adulterated Herbal Medicinal Product Using Ultra-Performance Liquid Chromatography Coupled with QTOF Mass Spectrometry

The reports of severe adverse effects and fatalities associated with herbal medicinal products adulterated with synthetic compounds have raised global concerns.The objective of this study is to analyze one commercial herbal medicinal product suspected to be adulterated with synthetic drugs in order to identify potential adulterants,to verify if the product contained the herbs listed as ingredients in label claim and to determine quality consistency among different batches of the product.Analyses of suspected product obtained from seven different batches were performed using ultra performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF-MS) with multiple data processing tools and multivariate analyses.In addition,23 individual powdered herbs(12 as per label claim and 11 suspected herbs),11 marker compounds of the labeled herbs and five suspected synthetic drugs as adulterants were also concurrently analyzed to have clear understanding of product composition.Based on our analysis,the major ingredients of studied product were found to be 5 synthetic compounds:caffeine,chlorphenamine,piroxicam,betamethasone and oxethazaine.Three of them have been found to exceed their recommended doses.From the herbal composition analysis,Gan Cao(Glycyrrhizae radix et rhizoma) was found to be the main ingredient,which is not among the claimed 12 herbs that were supposed to be in the product.Other herbs detected as minor ingredients were Mu Gua(Chaenomelis fructus),Dang Gui(Angelicae sinensis radix),and Huang Qi(Astragali radix),which are among the 12 herbs that were supposed to be in the product.Based on our results we demonstrated that UPLC-QTOF MS is an effective and versatile tool for the analysis of herbal medicinal products.It is highly desirable to have a streamlined process with automatic workflow and fit-forpurpose database to increase efficiency and productivity of sample analysis.Results of this work also highlight the need for the better quality control and regulatory measures to protect consumers from the potentially harmful effects of such adulterated products.

The Novel Triangle MGM(1,m,N)Model and Its Applications

The MGM(1,m,N)model is an effective grey multi-variate forecasting model that considers multiple system characteristic sequences affected by multiplefactors.Nevertheless,it isregularly inaccurate in the application This is because the model requires a strong correlation between the system characteristic sequences That reduces the applicability of the model.To solve this problem,this paper proposes a novel multi-variate grey model.This model does not require a certain correlation between system characteristic sequences and has higher applicability Through numerical integration,a two-point trapezoidal formula,and a recursive method,thetime-response expressions ofthetwo model forms are obtained Some properties of the proposed model are further discussed Finally,the validity of the proposed model is evaluated by using two real cases related to China's invention patent development.Theresultsshow that the novel models outperformother models inbothsimulation and prediction applications.

Red X工具在解决钣金焊合件尺寸问题中的探究

Red X工具是分析系统和统计工程的方法,通过分析极端样件差异找到复杂问题产生的根本原因。本文以N400车型前轮罩为例,基于其焊合总成焊接后无法取出焊具的问题,通过运用问题定义树、项目定义树、测量系统验证、配对对比、工位查找等几个Red X策略步骤的研究分析,找到了问题的根源。不仅成功解决了此问题,还为后续车型开发提供了重要的设计参考依据。