Linear Procedure Research Articles

A manifold learning perspective on surrogate modeling of nitrate concentration in the Kansas River

Abstract A non-linear surrogate model of nitrate concentration in the Kansas River (USA) is described. The model is an (almost) Piece-wise Linear response surface that provides a mean field approximation to the dynamics of the measured data for nitrate plus nitrite (target product) correlations to turbidity and chlorophyll-a concentrations (input variables). The method extends the United States Geological Survey’s linear procedures for surrogate data modeling allowing for better approximations for river systems exhibiting algal blooms due to nutrient-rich source waters. The model and visualization procedures illustrated in the Kansas River example should be generally applicable to many medium-size rivers in agricultural regions.

Abstract P488: Diet Significantly Alters Metabolic Gene Expression Associated With Probiotic Administration in Mice

The microbial flora in the gastrointestinal tract is known to be mediated by diet and is thus one potential mechanism by which diet influences health. Furthermore, probiotic supplementation also affects various biological markers, however it is unclear if a diet and probiotic interaction exists. Therefore, the goal of our study was to assess how diet and probiotic administration influenced metabolic health in mice. Weanling, male C57Bl/6 mice were randomly assigned to receive one of three diets: chow, American Institute of Nutrition 93 formulation (AIN93), or our novel Americanized diet (AD) formulated to match the 50 th percentile of nutrient intake in Americans. Mice were then administered a commercially-available probiotic for 2-weeks and then euthanized. Insulin sensitivity was determined by performing an insulin-tolerance test (ITT) in a separate cohort. Blood was collected for serum analysis of circulating metabolic markers. Liver and adipose tissue were collected and processed to quantify the mRNA expression of key metabolic genes by real-time RT-PCR. All data were analyzed using General Linear Model Procedures in SPSS with significance determined at P<0.05. Diet (P=0.001), but not probiotic supplementation (P=0.27), significantly affected ITT with the AD group having the lowest responsiveness to insulin ( P ≤0.001). Chow fed mice tended ( P =0.09) to have lower HDL than the AIN-93 and AD fed mice. Probiotic administration alone resulted in an upregulation of liver mRNA expression of SREBF2, particularly the AIN-93 diet group (P=0.024). Probiotic supplementation also modulated liver transcription of NF-κB1 with an increase in chow-fed mice, but decreased in those fed AD (P=0.016). Increased transcription of the key carbohydrate metabolism regulating gene, Mlxipl , was found to occur in liver of the chow and AIN-93 and also tended to differ in mice fed the AD (P=0.06). In the adipose tissue, diet (P<0.001), probiotic (P<0.001), and their interaction (P=0.01) influenced transcription of the androgen receptor ( Ar ) gene. The chow group had the most Ar transcription, while the AD had the lowest transcription in both the diet alone and probiotic treated group. Taken together, our data highlight the significance of diet in mediating the beneficial metabolic effects of probiotic administration. Diet appears to play a greater role in dictating the expression of key metabolic enzymes and transcription factors and future studies are needed to validate these findings and determine their physiological significance.

Abstract P331: Diet and Probiotic Administration Significantly Influence Behavior and Forebrain Inflammation in Male Mice

There is a high degree of comorbidity between psychological and gastrointestinal disease, which implies a functional relationship exists between the two systems. Collectively, research suggests that the gut-brain axis is bidirectional, and involves complex relationships between immune, neural, endocrine, and metabolic pathways. In this pilot study, we seek to further investigate the microbiome’s role in a bottom-up model by studying the short-term effects of different diets and probiotic supplementation on behavior and the expression of inflammatory biomarkers in the forebrain. Weanling, male C57Bl/6 mice were purchased from a commercial vendor and acclimated to a solid diet for one-week. Mice were randomly assigned to receive one of three diets for eight weeks: chow, the American Institute of Nutrition 1993 formulation (AIN), or our novel Americanized diet (AD). After six-weeks of diet, mice were then administered a commercially available probiotic, or water control, for the final two weeks of the study. Aggressive tendencies were measured during the probiotic administration period and dietary intake was recorded during the final week of the study. Animals were then euthanized and the forebrain was dissected and processed for the quantification of mRNA for Bdnf (BDNF), Nfkb1 (Nf-κb), Gfap (GFAP), Ptgs2 (PTGS2), and Nr3c1 (NR3C1) by real-time RT-PCR. Data were analyzed using General Linear Model Procedures in SPSS with significance determined at P<0.05. There was a significant decline (P<0.001) in aggressive behavior over the testing period, and mice administered the probiotic tended (P=0.15) to display less signs of aggression regardless of the diet. For dietary intake, there was a numerical increase in daily caloric intake in mice consuming the AD (~13 kcal/day), while mice consuming chow and AIN were similar (9 and 10 kcal/day, respectively). Additionally, there were numerical differences between probiotic and control groups for two of the diets. Quantification of gene expression in the forebrain revealed that diet significantly (P=0.002) affected GFAP expression, with mice consuming chow having significantly lower values than mice fed AIN or AD. Probiotic treatment significantly (P=0.02) affected NF-κB expression, with mice administered the probiotic having greater forebrain NF-kb as compared to control mice. This was most obvious in mice fed the chow and AIN diets, but not the AD. Collectively, our pilot study’s findings suggest that short term probiotic supplementation and dietary variation have potential effects on neuroinflammation, brain function, and satiety. Furthermore, our data indicates that the efficacy of probiotic administration is dependent upon the quality of diet consumed.

Training models using forces computed by stochastic electronic structure methods

Quantum Monte Carlo (QMC) can play a very important role in generating accurate data needed for constructing potential energy surfaces. We argue that QMC has advantages in terms of a smaller systematic bias and an ability to cover phase space more completely. The stochastic noise can ease the training of the machine learning model. We discuss how stochastic errors affect the generation of effective models by analyzing the errors within a linear least squares procedure, finding that there is an advantage to having many relatively imprecise data points for constructing models. We then analyze the effect of noise on a model of many-body silicon finding that noise in some situations improves the resulting model. We then study the effect of QMC noise on two machine learning models of dense hydrogen used in a recent study of its phase diagram. The noise enables us to estimate the errors in the model. We conclude with a discussion of future research problems.

Effect of R&D activity on level of innovation of EU countries in 2014 and 2020.

Research and development carried out by companies are pivotal for innovative economies of countries, especially in the unpredictable and changing social, economic and political environment. In this context, it is very important to answer, which factors identify the effectiveness of measures in relation to R&D activity and innovativeness in EU countries and how should the degree of development of a country be assessed in terms of R&D activity? The purpose of this article is to verify level of innovativeness and degree of research and development (R&D) activity in EU countries in the years 2014 and 2020 using Hellwig's measure of development. To achieve this, qualitative and quantitative analysis, synthesis, deduction and induction, comparative analysis, and reasoning by analogy of phenomena were employed. The research was conducted on the basis of the expertly selected variables for their relation to R&D activity from a number of sources, such as Eurostat, World Bank Data, etc.. The indicated variables were analysed using statistical methods and then subjected to a linear ordering procedure based on the Hellwig development pattern method. Thanks to the research results, it is possible to indicate areas in which the initiation of activities would have the greatest degree of influence on development of R&D activity, thus influencing the increase in the level of innovativeness of a country. Indicators relating to R&D activity were selected and then used as variables to study the effect of the degree of R&D activity in EU countries in the years 2014 and 2020 on the level of innovativeness of these countries. The conducted research coincides with the results presented in the European Innovation Scoreboard. There is a significant correlation between the development of R&D activities and innovation performance.

Electromagnetic–Computational Fluid Dynamics Couplings in Tungsten Inert Gas Welding Processes—Development of a New Linearization Procedure for the Joule Production Term

The finite volume method (FVM) was used to model a tungsten inert gas (TIG) arc welding process. A two-dimensional axisymmetric model of arc plasma integrating fluid–solid coupling was developed by solving electromagnetic and thermal equations in both the gas domain and the solid cathode. In addition, two additional coupling equations were considered in the gaseous domain where the arc is generated. This model also included the actual geometry of torch components such as the gas diffuser, the nozzle, and the electrode. The model was assessed using numerous numerical examples related to the prediction of the argon plasma mass fraction, temperature distribution, velocity fields, pressure, and electric potential in the plasma. A new linearization method was developed for the source term in the energy conservation equation, allowing for the prediction of Joule effects without artificial conductibility. This new method enhances the efficiency of the classical approach used in the literature.

Phenotypic Characterization and Husbandry Practice of Indigenous Chicken Ecotypes at West Guji, Ethiopia

This study was conducted with the objectives of characterization of indigenous chicken ecotypes and their husbandry practice at West Guji. A total of 204 households were randomly selected from two purposively selected woredas and 612 mature chickens were used for qualitative and quantitative traits data collection. The collected data were analyzed using the Statistical Package for Social Sciences (SPSS Version, 20) and the General Linear Model procedure (PROC GLM) of SAS. The qualitative data of feather distribution, plumage color, beak color, eye color, comb types, earlobe color, head shape, and shank color were collected. The result showed a significant difference between the districts (P< 0.05). Further, wing span, chest circumstance, body weight, and body length quantitative data were also recorded. The result revealed that there was a significant difference in quantitative traits in the study areas (P< 0.05). Egg production potentials, body weight, plumage color, hatchability, growth rate, and mothering ability were traits preferred by the household for female selection. The average age at first egg laying of indigenous chicken was 6.69±0.36 months and the number of eggs per hen per clutch was 12.16±0.26. The current finding revealed that about 93.5% of the observed chicken’s ecotypes were feathered and 6.45% were naked necks. The dominant chicken’s plumage colors identified were; brownish (29.28%), red (24.05%), and multicolor/ambesma (14.65%). The production systems practiced in the study area were extensive. Finally, phenotypic characterization of indigenous chicken ecotypes and performance evaluation should be supported by genetic characterization methods.

Nonlinear Optimal Control for a PMLSG-VSC Wave Energy Conversion Unit

This article aims to treat the nonlinear control problem for the complex dynamics of a wave energy unit (WEC) that consists of a Permanent Magnet Linear Synchronous Generator (PMLSG) and a Voltage Source Converter (VSC). The article has developed a globally stable nonlinear optimal control method for this wave power generation unit. The new method avoids complicated state-space model transformations and minimizes the energy dispersion by the control loop. A novel nonlinear optimal control method is proposed for the dynamic model of a wave energy conversion system, which includes a Permanent Magnet Linear Synchronous Generator (PMLSG) serially connected with an AC/DC three-phase voltage source converter (VSC). The dynamic model of this renewable energy system is formulated and differential flatness properties are proven about it. To apply the proposed nonlinear optimal control, the state-space model of the PMLSG-VSC wave energy conversion unit undergoes an approximate linearization process at each sampling instance. The linearization procedure relies on a first-order Taylor-series expansion and involves the computation of the system’s Jacobian matrices. It takes place at each sampling interval around a temporary operating point, which is defined by the present value of the wave energy conversion unit’s state vector and by the last sampled value of the control inputs vector. An H-infinity feedback controller is designed for the linearized model of the wave energy conversion unit. To compute the feedback gains of this controller, an algebraic Riccati equation is repetitively solved at each time step of the control algorithm. The global stability properties of the control scheme are proven through Lyapunov analysis.

Γ-convergence analysis of the nonlinear self-energy induced by edge dislocations in semi-discrete and discrete models in two dimensions

Abstract We propose nonlinear semi-discrete and discrete models for the elastic energy induced by a finite system of edge dislocations in two dimensions. Within the dilute regime, we analyze the asymptotic behavior of the nonlinear elastic energy, as the core-radius (in the semi-discrete model) and the lattice spacing (in the purely discrete one) vanish. Our analysis passes through a linearization procedure within the rigorous framework of Γ-convergence.

Activities of Organic Extracts of Cassia occidentalis Leaves on the Mortality and Fecundity of Callosobruchus maculatus (Main Insect Pest of Vigna unguiculata Seed Stocks)

Callosobruchus maculatus Fab (Coleoptera: Chrysomelidae) is the most formidable pest of cowpea (Vigna unguiculata) in the field. The damage continues even in stocks intended for food. The present work was carried out to evaluate the insecticidal effect of organic leaf extracts of Cassia occidentalis on mortality and emergence of F1 progeny in storage. The extracts were tested in contact in 90 mm diameter petri dishes with different doses. Phytochemical screening was carried out using coloring and precipitation tests. Mortality of C. maculatus was assessed between 1 and 17 days after infestation. The data were processed according to the General Linear Model procedure and using Minitab 19 software. The results of the statistical analysis showed that the methanolic extract gives a better mortality rate on Callosobruchus maculatus. These results are corroborated by phytochemical tests with the identification of molecules (alkaloids, flavonoids, tannins, polyphenols, etc.) likely to be responsible for this insecticidal activity.

Understanding tectonics from fluvial topography by using the stochastic‐threshold incision model: Theory and application to the Dadu River basin, eastern Tibetan Plateau

AbstractThe flood distribution and bedrock incision threshold exert important influences on the erosion efficiency, which can result into a nonlinear scaling between channel steepness (normalized channel gradients for the downstream increases in drainage area) and erosion rates. Here, we explore the incorporation of both river incision threshold and discharge probability distribution into erosion efficiency to construct a stochastic‐threshold incision model equation to simulate river power incision and the evolution of fluvial topography. Assuming time‐dependent tectonic uplift, we develop a closed‐form analytical solution to the model equation and derive a linear inverse procedure to retrieve relative uplift rate history from river longitudinal profiles. Application of the inverse scheme to the Dadu River basin in the eastern Tibetan Plateau produces a history with three‐phase increases in the uplift/incision rates during the Cenozoic. We use the inversion results to forward the time‐changed river long profile, which indicates the existence of paleo relief before the Oligocene and significant contributions of the late Miocene regional uplift and early Pleistocene river capture on the present topography.

SEMIPARAMETRIC ESTIMATION AND VARIABLE SELECTION FOR SPARSE SINGLE INDEX MODELS IN INCREASING DIMENSION

This paper considers semiparametric sieve estimation in high-dimensional single index models. The use of Hermite polynomials in approximating the unknown link function provides a convenient framework to conduct both estimation and variable selection. The estimation of the index parameter is formulated from solutions obtained by the routine penalized weighted linear regression procedure, where the weights are used in order to tackle the unbounded support of the regressors. The resulting index parameter estimator is shown to be consistent and sparse, and the asymptotic normality for the estimators of both the index parameter and the link function is established. To perform variable selection in the ultra-high dimension case, we further suggest a forward regression screening method, which is shown to enjoy the sure independence screening property. This screening procedure can be used before the penalized variable selection to reduce the burden of dimensionality. Numerical results show that both the variable selection procedures and the associated estimators perform well in finite samples.

Comparison of Linear and Nonlinear Procedures for the Analysis of the Seismic Performance of Straight Multi-Span RC Bridges

The assessment of the seismic performance of transportation infrastructures is of primary importance for the management of the aftermath of an earthquake. To perform such an assessment, various modeling approaches characterized by different levels of accuracy are available and modern seismic design codes provide recommendations about their use. Non-linear time-history analysis (NLTHA) is acknowledged as the most reliable method, but is difficult to implement and is computationally expensive. This paper aims to investigate the viability of less complex methods, but with low computational cost, for the assessment of straight, multi-span bridges and compare their performance against the results of NLTHA in order to quantify the expected accuracy. The study is developed considering three bridge archetypes with either simply-supported or continuous-deck layouts, representative of typical features of the Italian bridge stock. The bridges are analyzed first through nonlinear dynamic analyses, to define the benchmark solution; then linear dynamic analyses, such as Linear Time-History and Response Spectrum Analysis, nonlinear static analyses, such as MPA (Modal Pushover Analysis), and Equivalent Static Analysis are considered. A comparison among the examined procedures is eventually proposed, highlighting the strengths and weaknesses of each approach.

EVALUATION LITER SIZE OF DIFFERENT AGE MALES WITH DIFFERENT PARITY PARENTS ON SMALLHOLDER PIG FARMING IN BUAHAN VILLAGE-PAYANGAN


 
 
 This study aims to evaluate litter size of different ages males with different parent parity in smallholder pig far- ming, used 3 males of different ages (P1, P2, P3) and 18 sows of parity 1 – 4 (I1, I2, I3 and I4) with a 3 x 4 factorial RAL experimental design and unequal number of replications. The variables observed were litter size, weaning litter size, mortality and percentage of weaning litter size. Data were analyzed using a analyse of variant with the general linear model procedure. The results showed that the average litter size based on males P1, P2, P3 was 8.38; 9.13 and 10.38 (P>0.05) and based on parental parity I1, I2, I3 and I4 respectively 8.00; 8.83; 9.66 and 10.66 (P<0.05). For weaning litter size, respectively 5.75; 7.50 and 8.87 (P<0.05) and from parent parity was 6.17; 6.67; 8.17 and 8.50 (P<0.05), while the mortality respectively was 31.82%; 17.62% and 12.39% (P>0.05) and from parent parity respec- tively 23.54%; 23.21%; 15.74%; and 19.94% (P>0.05). Weaning litter size percentage of each male was 68.17; 82.50; 87.61 (P>0.05) and from parental parity 76.63; 76.78; 84.26 and 80.05 (P>0.05). Older males and fourth parity of parent produced better litter size, weaning litter size and weaning litter percentage with lower mortality. There was no interaction between the use of males of different ages and parents of different parity on the observed variables.
 
 

Comparing programming languages for data analytics: Accuracy of estimation in Python and R

AbstractSeveral open‐source programming languages, particularly R and Python, are utilized in industry and academia for statistical data analysis, data mining, and machine learning. While most commercial software programs and programming languages provide a single way to deliver a statistical procedure, open‐source programming languages have multiple libraries and packages offering many ways to complete the same analysis, often with varying results. Applying the same statistical method across these different libraries and packages can lead to entirely different solutions due to the differences in their implementations. Therefore, reliability and accuracy should be essential considerations when making library and package usage decisions while conducting statistical analysis using open source programming languages. Instead, most users take this for granted, assuming that their chosen libraries and packages produce accurate results for their statistical analysis. To this extent, this study assesses the estimation accuracy and reliability of Python and R's various libraries and packages by evaluating the univariate summary statistics, analysis of variance (ANOVA), and linear regression procedures using benchmarking data from the National Institutes of Standards and Technology (NIST). Further, experimental results are presented comparing machine learning methods for classification and regression. The libraries and packages assessed in this study include the stats package in R and Pandas, Statistics, NumPy, statsmodels, SciPy, statsmodels, scikit‐learn, and pingouin in Python. The results show that the stats package in R and statsmodels library in Python are reliable for univariate summary statistics. In contrast, Python's scikit‐learn library produces the most accurate results and is recommended for ANOVA. Among the libraries and packages assessed for linear regression, the results demonstrated that the stats package in R is more reliable, accurate, and flexible; thus, it is recommended for linear regression analysis. Further, we present results and recommendations for machine learning using R and Python.This article is categorized under: Algorithmic Development > Statistics Application Areas > Data Mining Software Tools

Application of holography method for the restoration of the Williams series near the crack tip

The article describes the processing of the results of a series of experiments performed by the interference-optical method of holographic interferometry (holographic photoelasticity) aimed at computing the amplitude coefficients of the M. Williams series constituting the stress and displacement fields at the crack tip for several cracked configurations. The main objective of this study is the experimental and computational determination of the coefficients of the M. Williams series for the stress, strain and displacement fields associated with the crack tip in an isotropic linearly elastic medium taking into account regular (non-singular) terms in the multiparameter Williams series. These coefficients are named generalized stress intensity factors. The method of holographic interferometry is shown to be a convenient and efficacious tool for reconstructing the stress field near the tip of the crack, because during the experiment it is possible to obtain two families of interference fringe patterns: absolute retardation fringes (isodromics) for vertical and horizontal polarizations. Experimental outcomes were thoroughly processed using the developed digital application allowing us to accumulate the isodromics orders and coordinates of points belonging to absolute retardations. In this work, absolute retardation fringes in a plate with a central horizontal crack and a crack inclined at different angles are obtained. For each type of experimental sample, the coefficients of the Williams series were calculated taking into account non-singular terms (in the representation of M. Williams, fifteen terms were preserved). A procedure for linearization of nonlinear algebraic equations following from the relations of Favre’s law is proposed. By solving the obtained overdeterministic system of linear algebraic equations, the generalized stress intensity factors (coefficients of the M. Williams series) are estimated. Conjointly, the finite element analysis of the specimens with same geometry was effectuated. The experimentally determined values of the Williams series are compared with the results of the finite element calculation of the stress-strain state performed in the SIMULIA Abaqus software.The results of the numerical and experimental studies were found to be quite consistent. It is lucidly shown that it is imperative to keep the higher order terms in the Williams series expansions for the fields associated with the crack tip.

The relationship between student engagement and dropout risk in early adolescence

One of the most reliable predictors of school effectiveness is student engagement, which is not only related to students’ current achievement but also long-term outcomes. Using linear regression and structural equation modeling procedures, the present study was aimed at examining the relationship between dropout risk, dimensions of student engagement, and family background. The survey study involved 3251 7th grade Hungarian students who completed the adapted Student Engagement Instrument and self-reported background questionnaire. According to the results, student performance is mainly related to parents’ educational attainment, but a moderate or weak correlation with behavioral, academic, and cognitive engagement is also detectable. While affective engagement shows a weak association with performance, it correlates with other engagement dimensions linked to achievement. This result suggests that support from parents, peers, and teachers may have an indirect effect on achievement; however, the present study could not confirm this hypothetical model. Based on our data, parental educational attainment and student engagement account for approximately 41 % of student performance which draws attention to the complexity of the relationship between student engagement and performance and highlights the importance of a comprehensive approach to dropout and student engagement in this context.

Developments in Quantifying the Response Factors Required for Linear Analytical and Seismic Design Procedures

Despite the recent initiatives and developments in building design provisions using performance-based design, practicing engineers frequently adopt force-based design approaches, irrespective of the structural system or building irregularity. Modern seismic building codes adopt the concept of simplifying the complex nonlinear response of a structure under seismic loading to an equivalent linear response through elastic analytical procedures using seismic design response factors. Nevertheless, code-recommended seismic design response factors may not result in a cost-effective design with a uniform margin of safety for different structural systems. Previous studies also adopted different methodologies for quantifying the seismic response factors. Hence, there is a pressing need for a comprehensive review covering the developments in quantifying these vital design factors. This paper presents a systematic review of the response factors used with linear analytical procedures recommended by modern building design provisions and the techniques employed in previous studies to evaluate these factors using SDOF and MDOF systems covering analytical, experimental and hybrid assessment approaches. Limitations and gaps identified from the previous studies indicated that most investigations focused on 2D analysis and regular low-rise buildings, while limited studies were directed to shear wall structures, considering mostly unidirectional seismic loading. This comparative review provides insights into previous studies’ methodologies and constraints and identifies the need for future research to calibrate seismic response factors to achieve more economical designs with consistent safety margins for different structural systems.

Little Owl Aggression and Territory in Urban and Rural Landscapes.

Urbanization is a major land use change across the globe with vast effects on wildlife. In this paper, we studied (1) the territorial displays of Little Owls in urban and rural landscapes, analyzing also (2) the size and habitat composition of the territories, and (3) the factors affecting territory size in both landscapes. To do that, we used t-tests, Principal Components Analysis, and General Linear mixed model procedures. The territory size was smaller in urban than in rural landscapes. Urban territories of Little Owls are characterized by a lower cover of grassland, tall crops, short crops, gardens, and orchards, as well as a higher cover of built-up areas than territories in rural landscapes. Territory size in rural landscapes was negatively correlated with seasonal progress and positively correlated with altitude. The rate of territorial displays was similar between urban and rural territories; however, birds differentially utilized various structures. In urban territories, birds mostly used buildings, whereas in rural territories, birds used electric pylons and trees. The compositional differences between territories in the two landscapes may have important consequences for other behavior types and possibly reproductive output in this species.

Impact of beyond-line-of-sight connectivity on the capacity and stability of mixed traffic flow: An analytical and numerical investigation

With the aid of wireless vehicle-to-vehicle communication, connected and automated vehicles (CAVs) are able to access the motion information of vehicles that are beyond-line-of-sight in mixed traffic environment. However, few attention has been paid to the influence of this feature on the mixed traffic flow characteristics. To fill this gap, this paper attempts to unveil the effect of beyond-line-of-sight connectivity on capacity and stability of mixed traffic flow. Specifically, we first formulate the distribution of various driving modes in mixed traffic based on the Markov model. Then, the analytical model of the capacity for mixed traffic is developed with proper time headway settings. Furthermore, the extensively implemented linear stability analysis procedure is extended to accommodate the beyond-line-of-sight connectivity feature. The analytical results show that (1) the proportion of CAVs in coordinated driving with beyond-line-of-sight reaches its peak at the half of penetration rate and lowest platoon clustering intensity for CAVs; (2) the capacity and stability performance are promoted with introducing the coordinated driving mode, and the increment becomes larger with the increase of communication range limit. (3) introducing coordinated driving can be an alternative to shifting the penetration rate of CAVs to improve the stability and capacity of mixed traffic in some cases.