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Accurate analysis of the influencing factors of traffic accidents helps identify safety hazards and bottlenecks in the transportation system, and proposes corresponding improvement measures and suggestions based on the analysis results. To address the challenges of limited mining and analysis accuracy, coupled with prolonged analysis durations, inherent in conventional approaches to dissecting the factors influencing urban road traffic accidents, this study introduces a novel analysis methodology grounded in weighted association rules. This innovative approach not only enhances our understanding of accident causation but also contributes to safer road networks and improved traffic management strategies. By leveraging weighted association rules to mine urban road traffic accident data, we initially extract valuable insights. Subsequently, principal component analysis is employed to streamline the data's dimensionality, facilitating a more focused and efficient analysis. Within the framework of Grey correlation analysis, we meticulously establish comparison and reference sequences, calculating both the weighted Grey correlation degree and weight coefficient of these sequences to rigorously screen factors. This meticulous process culminates in a comprehensive analysis of the factors influencing urban road traffic accidents. Experimental results underscore the efficacy of this new method with a peak data mining accuracy of 97.4%, an unparalleled analysis accuracy of 97.8%, and a streamlined analysis time of merely 1.36 seconds. [ABSTRACT FROM AUTHOR]

In order to study the influence of impeller structural parameters on the hydraulic performance and casting moulding of spiral centrifugal pumps, this paper selects a double vane spiral centrifugal pump with a specific rotation number of 170 as the research object. The Plackett–Burman experimental design is used to screen the influencing factors, and the results show that the vane thickness and the impeller outlet width are the significant influencing factors. Based on this result, five different scenarios were set for these two key parameters, numerical calculations were carried out using numerical simulation software for each of the five flow ratio cases, and casting simulations were carried out for the model of each scenario using AnyCasting6.0 to analyze the influence of these two factors on the hydraulic performance and casting forming of the spiral centrifugal pump. It was found that in terms of vane thickness, a moderate increase in vane thickness improved the hydraulic performance at small flow rates, but an excessive increase at large flow rates led to a decrease in efficiency and an increase in the probability of casting defects. In terms of impeller outlet width, increasing the outlet width caused the design point to be shifted, leading to a decrease in efficiency at small flow rates, but an increase in efficiency when the design flow rate was higher. At the same time, increasing the outlet width makes casting defects more likely to occur at the blade and back cover joint than on the blade surface. The study in this paper clarifies the significant effects of these two parameters on the performance and casting quality of spiral centrifugal pumps, and provides guidance for the optimal design of spiral centrifugal pumps. [ABSTRACT FROM AUTHOR]

Deformation monitoring for mass concrete structures such as high-arch dams is crucial to their safe operation. However, structure deformations are influenced by many complex factors, and deformations at different positions tend to have spatiotemporal correlation and variability, increasing the difficulty of deformation monitoring. A novel deep learning-based monitoring model for high-arch dams considering multifactor influences and spatiotemporal data correlations is proposed in this paper. First, the measurement points are clustered to capture the spatial relationship. Successive multivariate mode decomposition is applied to extract the common mode components among the correlated points as spatial influencing factors. Second, the relationship between various factors and deformation components is extracted using factor screening. Finally, a deep learning prediction model is constructed with stacked components to obtain the final prediction. The model is validated based on practical engineering. In nearly one year of high-arch dam deformation prediction, the root mean square error is 0.344 and the R2 is 0.998, showing that the modules within the framework positively contribute to enhancing prediction performance. The prediction results of different measurement points as well as the comparison results with benchmark models show its superiority and generality, providing an advancing and practical approach for engineering structural health monitoring, particularly for high-arch dams. [ABSTRACT FROM AUTHOR]

Small steel bars (SSBs) are common steel products, and their surface defects pose a significant quality issue. Despite numerous studies conducted to detect and predict such defects, identifying the underlying causes, or 'quality factors,' remains a challenge. Most of the existing methods focused on validation of known factors based on the theories of physical properties and material composition; thus, they lack exploring new quality factors. This study proposes a two-step method for identifying these quality factors, considering two common issues that often arise when analyzing operational data in SSB manufacturing processes: 'merged measurement' and 'variation due to operation dates.' These issues are resulted because the rolling process is lack of measurement capability and exposed to external environment. They make it difficult to use various analytical methods such as statistical analysis and data mining methods. The proposed method sequentially employs statistical difference testing and k-means clustering, thereby providing a practical heuristic for overcoming the aforementioned difficulties. The effectiveness of the proposed method was demonstrated through a case study that highlighted its ability to effectively identify the quality factors critical to surface defects on SSBs. [ABSTRACT FROM AUTHOR]

In order to study the influence of impeller structural parameters on the hydraulic performance and casting moulding of spiral centrifugal pumps, this paper selects a double vane spiral centrifugal pump with a specific rotation number of 170 as the research object. The Plackett–Burman experimental design is used to screen the influencing factors, and the results show that the vane thickness and the impeller outlet width are the significant influencing factors. Based on this result, five different scenarios were set for these two key parameters, numerical calculations were carried out using numerical simulation software for each of the five flow ratio cases, and casting simulations were carried out for the model of each scenario using AnyCasting6.0 to analyze the influence of these two factors on the hydraulic performance and casting forming of the spiral centrifugal pump. It was found that in terms of vane thickness, a moderate increase in vane thickness improved the hydraulic performance at small flow rates, but an excessive increase at large flow rates led to a decrease in efficiency and an increase in the probability of casting defects. In terms of impeller outlet width, increasing the outlet width caused the design point to be shifted, leading to a decrease in efficiency at small flow rates, but an increase in efficiency when the design flow rate was higher. At the same time, increasing the outlet width makes casting defects more likely to occur at the blade and back cover joint than on the blade surface. The study in this paper clarifies the significant effects of these two parameters on the performance and casting quality of spiral centrifugal pumps, and provides guidance for the optimal design of spiral centrifugal pumps.

Deformation monitoring for mass concrete structures such as high-arch dams is crucial to their safe operation. However, structure deformations are influenced by many complex factors, and deformations at different positions tend to have spatiotemporal correlation and variability, increasing the difficulty of deformation monitoring. A novel deep learning-based monitoring model for high-arch dams considering multifactor influences and spatiotemporal data correlations is proposed in this paper. First, the measurement points are clustered to capture the spatial relationship. Successive multivariate mode decomposition is applied to extract the common mode components among the correlated points as spatial influencing factors. Second, the relationship between various factors and deformation components is extracted using factor screening. Finally, a deep learning prediction model is constructed with stacked components to obtain the final prediction. The model is validated based on practical engineering. In nearly one year of high-arch dam deformation prediction, the root mean square error is 0.344 and the R2 is 0.998, showing that the modules within the framework positively contribute to enhancing prediction performance. The prediction results of different measurement points as well as the comparison results with benchmark models show its superiority and generality, providing an advancing and practical approach for engineering structural health monitoring, particularly for high-arch dams.

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Quantitative investment strategies have been increasingly used in the capital market. In order to help enterprises manage risks better and enhance the reliability of quantitative investment strategies, this paper designed a quantitative investment data model for Enterprise A. The constituent stocks of the Shanghai and Shenzhen 300 index were regarded as the stock pool. Then, factor screening was performed in the Uqer quantitative factor library. Two data models were established: a scoring model and a regression model. The two models were tested through backtesting. The return and risk were compared between the two models by taking the return rate, net value, Alpha, Beta, Sharpe ratio, maximum retracement and information ratio as the evaluation indicators. The backtest results showed that the data model established by the regression method had a higher return rate, annualized return rate, net value, larger α value, smaller β value, a Sharpe ratio of 0.76, a maximum retracement of 25.34%, and an information ratio of 2.42, which had better balance in return and risk compared with the scoring method. In addition, the larger the number of positions was, the smaller the frequency of position transfer was, and the less effective the model was in quantitative investment. The experimental results verify the reliability of the regression model in the formulation of the quantitative investment strategy of Enterprise A. The investment strategy of Enterprise A can be adjusted and determined by the regression model to promote the balance between enterprise benefits and risks. The research results provide some references for the theoretical research of quantitative investment.

Levees are linear structures that are continuously reconstructed throughout the years and whose construction and behavior depends on local soil conditions, as well as requirements regarding impermeability and mechanical resistance. This results in various levee cross sections, even within the same levee. In situations of extreme water events, when timely actions are required, this variability poses a problem for decision-making based on observed behavior, which is highly dependent on the specific section parameters. Creating models for each problematic section becomes impractical, and because of that, in this study, 91 different cross sections from 16 levees are considered to identify the key levee parameters with the largest effects on three observed mechanisms: deformations, exit hydraulic gradients, and factors of safety. The implemented factor screening methodology is based on the sequential bifurcation method (SB) and numerical analyses. The SB method successively investigates groups of factors and uses their cumulative effects to identify the important groups and to discard the unimportant based on a previously selected parameter Δ , until the groups are reduced to single factors that may be deemed important. It is found that approximately 30% of all the factors used to describe the most complex sections are considered important by at least one of the investigated mechanisms. [ABSTRACT FROM AUTHOR]

Landslides are geological disasters affected by a variety of factors that have the characteristics of a strong destructive nature and rapid development and cause major harm to the safety of people's lives and property within the scope of the disaster. Excessive landslide susceptibility mapping (LSM) factors can reduce the accuracy of LSM results and are not conducive to researchers finding the key LSM factors. In this study, with the Three Gorges Reservoir area to the Padang section as an example, the frequency ratio (FR), index of entropy (IOE), Relief-F algorithm, and weights-of-evidence (WOE) Bayesian model were used to sort and screen the importance of 20 LSM factors; then, the LSMs generated based on different factor sets modeled are evaluated and further scored. The results showed that the IOE screening factor was better than the FR, Relief-F, and WOE Bayesian models in the case of retaining no fewer than eight factors; the score for 20 factors without screening was 45 points, and the score for 12 factors screened based on the IOE was 44.8 points, indicating that there was an optimal retention number that had little effect on the LSM results when IOE screening was used. The core factor set obtained by the method for comparing the increase in scores and the increase in corresponding factors effectively improved the accuracy of the LSM results, thus verifying the effectiveness of the proposed method for ranking the importance of LSM factors. The method proposed in this study can effectively screen the key LSM factors and improve the accuracy and scientific soundness of LSM results. [ABSTRACT FROM AUTHOR]

For large classes of group testing problems, we derive lower bounds for the probability that all significant items are uniquely identified using specially constructed random designs. These bounds allow us to optimize parameters of the randomization schemes. We also suggest and numerically justify a procedure of constructing designs with better separability properties than pure random designs. We illustrate theoretical considerations with a large simulation-based study. This study indicates, in particular, that in the case of the common binary group testing, the suggested families of designs have better separability than the popular designs constructed from disjunct matrices. We also derive several asymptotic expansions and discuss the situations when the resulting approximations achieve high accuracy. • Bounds for the probability that random designs detect all defective items. • Construction of nearly doubly regular designs with superior separability. • Extension for the noisy group testing when several lies are allowed. • Asymptotic expansions for the existential bounds for general group testing. [ABSTRACT FROM AUTHOR]

Nonregular two‐level designs are attractive screening designs due to their good projection properties and flexible run sizes. In particular, the 12‐run Plackett–Burman (PB) design has become quite popular. However, existing methods struggle with the identification of active factors when the number of active factors exceeds the projectivity of the designs. This is especially the case when interactions are present, the variance is high and the number of runs is small. In this paper, we propose a method for analysing nonregular two‐level designs that particularly addresses the issues above. It exploits the projection properties of designs and is here applied on the 12‐run PB design and the 16‐run no‐confounding (NC) designs. In the construction of the method, the use of test‐ and penalty‐based procedures are avoided. Instead, the number of allowed terms in a model is restricted. The effectiveness of the method and comparison between designs are evaluated by simulations for different scenarios. Ways to evaluate the reliability of the screening procedure are pointed out. An example with real data is given to demonstrate how one might perform the analysis in practice. [ABSTRACT FROM AUTHOR]

In this paper we provide a thorough investigation of the cluster sampling scheme for Morris' elementary effects method (MM), a popular model‐free factor screening method originated in the setting of design and analysis of computational experiments. We first study the sampling mechanism underpinning the two sampling schemes of MM (i.e., cluster sampling and noncluster sampling) and unveil its nature as a two‐level nested sampling process. This in‐depth understanding sets up a foundation for tackling two important aspects of cluster sampling: budget allocation and sampling plan. On the one hand, we study the budget allocation problem for cluster sampling under the analysis of variance framework and derive optimal budget allocations for efficient estimation of the importance measures. On the other hand, we devise an efficient cluster sampling algorithm with two variants to achieve enhanced statistical properties. The numerical evaluations demonstrate the superiority of the proposed cluster sampling algorithm and the budget allocations derived (when used both separately and in conjunction) to existing cluster and noncluster sampling schemes. [ABSTRACT FROM AUTHOR]

In this paper, we explore the challenges associated with biomarker identification for diagnosis purpose in biomedical experiments, and propose a novel approach to handle the above challenging scenario via the generalization of the Dantzig selector. To improve the efficiency of the regularization method, we introduce a transformation from an inherent nonlinear programming due to its nonlinear link function into a linear programming framework under a reasonable assumption on the logistic probability range. We illustrate the use of our method on an experiment with binary response, showing superior performance on biomarker identification studies when compared to their conventional analysis. Our proposed method does not merely serve as a variable/biomarker selection tool, its ranking of variable importance provides valuable reference information for practitioners to reach informed decisions regarding the prioritization of factors for further investigations. [ABSTRACT FROM AUTHOR]

‘Resilience’ is a new concept in the research and application of urban construction. From the perspective of building adaptability in a mountainous environment and maintaining safety performance over time, this paper innovatively proposes machine learning methods for evaluating the resilience of buildings in a mountainous area. Firstly, after considering the comprehensive effects of geographical and geological conditions, meteorological and hydrological factors, environmental factors and building factors, the database of building resilience evaluation models in a mountainous area is constructed. Then, machine learning methods such as random forest and support vector machine are used to complete model training and optimization. Finally, the test data are substituted into models, and the models’ effects are verified by the confusion matrix. The results show the following: (1) Twelve dominant impact factors are screened. (2) Through the screening of dominant factors, the models are comprehensively optimized. (3) The accuracy of the optimization models based on random forest and support vector machine are both 97.4%, and the F1 scores are greater than 94.4%. Resilience has important implications for risk prevention and the control of buildings in a mountainous environment.

Nonregular fractional factorial designs such as Plackett-Burman designs and other orthogonal arrays are widely used in various screening experiments for their run size economy and flexibility. The traditional analysis focuses on main e�ffects only. Hamada and Wu (1992) went beyond the traditional approach and proposed an analysis strategy to demonstrate that some interactions could be entertained and estimated beyond a few significant main effects. Their groundbreaking work stimulated much of the recent developments in design criterion creation, construction and analysis of nonregular designs. This paper reviews important developments in optimality criteria and comparison, including projection properties, generalized resolution, various generalized minimum aberration criteria, optimality results, construction methods and analysis strategies for nonregular designs.

In this paper, we study the methodological underpinnings of the Morris elementary effects method, a model-free factor-screening technique originally proposed for deterministic simulation experiments, and develop an efficient Morris method–based framework (EMM) for simulation factor screening. Equipped with an efficient cluster-sampling procedure, EMM can simultaneously screen the main and interaction (or nonlinear) effects of all factors and control the overall false discovery rate at a prescribed level. Despite focusing on deterministic simulation experiments, we reveal the connections between EMM (also the Morris method) and other factor-screening methods, such as sequential bifurcation, and examine the resulting implications in the stochastic simulation setting under some commonly stipulated assumptions in design of experiments. Numerical experiments are presented to demonstrate the efficiency and efficacy of EMM. [ABSTRACT FROM AUTHOR]

• An elementary effects-based method is proposed for identifying important factors. • The method integrates a distribution-free sequential multiple testing procedure. • It uses online kernel density estimation and two stopping rules for error control. • Type I and Type II familywise error rates can be controlled at prescribed levels. • The method can achieve a high computational efficiency. The Morris method (MM) is a well known model-free factor screening approach that is considered particularly effective when the number of factors is large or when the computer model is computationally expensive to run. In this paper, we propose the controlled Morris method (CMM) for simulation-based factor screening, which integrates a distribution-free sequential multiple testing procedure with MM to control the Type I and Type II familywise error rates at the prescribed levels while achieving a high computational efficiency. Numerical experiments are provided to demonstrate the efficacy and efficiency of CMM. [ABSTRACT FROM AUTHOR]

Complex environmental models typically require global sensitivity analysis (GSA) to account for non-linearities and parametric interactions. However, variance-based GSA is highly computationally expensive. While different screening methods can estimate GSA results, these techniques typically impose restrictions on sampling methods and input types. As an alternative, this work evaluates two decision tree-based methods to approximate GSA results: random forests, and Extra-Trees. These techniques are applicable with common sampling methods, and continuous or categorical inputs. The tree-based methods are compared to reference Sobol GSA and Morris screening techniques, for three cases: an Ishigami-Homma function, a H1N1 pandemic model, and the CDICE integrated assessment model. The Extra-Trees algorithm performs favorably compared to Morris elementary effects, accurately approximating the relative importance of Sobol total effect indices. Furthermore, Extra-Trees can estimate variable interaction importances using a pairwise permutation measure. As such, this approach could offer a user-friendly option for screening in models with inputs of mixed types. [ABSTRACT FROM AUTHOR]

This paper focuses on extending the Morris' elementary effects method (MM) for sensitivity analysis/factor screening originated in the context of deterministic computer experiments to the stochastic simulation setting. Given a fixed simulation budget to expend, the main objective is to provide efficient and accurate estimates of main and interaction (or nonlinear) effects coined by the standard MM for characterizing the importance of each factor, despite the impact of simulation errors. Taking into account both the factor/input sampling uncertainty rooted in MM and the random errors inherent in a stochastic simulation, we develop efficient budget allocation strategies for implementing MM in this new context. Under each strategy proposed, we derive its corresponding optimal budget partition and optimal budget allocation rules. Numerical results corroborate the practical effectiveness of the proposed budget allocation strategies. [ABSTRACT FROM AUTHOR]

Aiming at the situation that many factors are involved in most simulation experiments, such as supply chain system or other computer simulation experiment, sequential bifurcation (SB) is widely used due to its high efficiency in screening. However, SB usually ignores the factors which are significant to dispersion effects. According to the theory of quality design used in products or supply chain, dispersion effects play an equally important part as location effects, and hence a new sequential bifurcation method considering both location effects and dispersion effects is proposed to screen the significant factors out and classify them simultaneously. Firstly, the significant factors are classified as adjusted factors, important factors and robust factors, according to their significance to the location effects and dispersion effects. Secondly, combined sequential probability ratio test (SPRT) to control the two types of error during the screening procedure, the significant factors are screened out and classified into three types. Finally, the efficiency and robustness of the proposed method which considers location effects and dispersion effects simultaneously is verified through a simulation model. [ABSTRACT FROM AUTHOR]

A screening design is an experimental plan used for identifying the expectedly few active factors from potentially many. In this paper, we compare the performances of 3 experimental plans, a Plackett-Burman design, a minimum run resolution IV design, and a definitive screening design, all with 12 and 13 runs, when they are used for screening and 3 out of 6 factors are active. The functional relationship between the response and the factors was allowed to be of 2 types, a second-order model and a model with all main effects and interactions included. D-efficiencies for the designs ability to estimate parameters in such models were computed, but it turned out that these are not very informative for comparing the screening performances of the 2-level designs to the definitive screening design. The overall screening performance of the 2-level designs was quite good, but there exist situations where the definitive screening design, allowing both screening and estimation of second-order models in the same operation, has a reasonable high probability of being successful. [ABSTRACT FROM AUTHOR]

The development of the new economic normal is influencing the total economy, the industrial structure and the layout in China to a great extent. The pillar industry faces a transformation from factor-driven to innovation-driven. The traditional electricity demand prediction model is no longer applicable due to these new factors. Thus, this paper makes a quantitative analysis of the new influencing factors, and establishes the quantitative relationship based on econometrics. According to industry division, this paper proposes a long term electricity demand forecasting model that is suitable for the new economic normal by using system dynamics method. Finally, the paper combines the actual situation of Tianjin and the forecasting model to predict the electricity demand of Tianjin. Simultaneously, the paper makes some suggestions about the development of the grid company. [ABSTRACT FROM AUTHOR]

With the rapid development of the vehicle industry, crashworthiness has become a crucial aspect in vehicle body design. In fact, crashworthiness is a multivariable optimization design problem for a vehicle body, regardless of structure or material. However, when crashworthiness involves a large number of design variables, including both material and structure variables, it is more difficult to deal with. In this paper, an integrated design technique for materials and structures of vehicle body under crash safety consideration is suggested. First, a finite element model of the vehicle body is established according to relevant vehicle safety standards. Then, the material parameters of the vehicle body are set as analytical factors for factor screening. Next, significant factors are obtained using a three-level saturated design integrated with multi-index comprehensive balance analysis and the Max Ur method, with an improved evaluation method. These screened material parameters along with the corresponding continuous variables of the structure, are considered as the design variables of the integrated design of the vehicle body. Both the weight and the crashworthiness properties are set as the design objectives. Optimal Latin hypercube sampling and radius basis functions are utilized to construct highly accurate surrogate models. Furthermore, the non-dominated sorting genetic algorithm II is implemented to seek the optimal solutions. Finally, two cases considering the roof module and the frontal module of a vehicle body are analyzed to verify the proposed method. [ABSTRACT FROM AUTHOR]

Supersaturated designs (SSDs) are factorial designs in which the number of experimental runs is smaller than the number of parameters to be estimated in the model. While most of the literature on SSDs has focused on balanced designs, the construction and analysis of unbalanced designs has not been developed to a great extent. Recent studies discuss the possible advantages of relaxing the balance requirement in construction or data analysis of SSDs, and that unbalanced designs compare favorably to balanced designs for several optimality criteria and for the way in which the data are analyzed. Moreover, the effect analysis framework of unbalanced SSDs until now is restricted to the central assumption that experimental data come from a linear model. In this article, we consider unbalanced SSDs for data analysis under the assumption of generalized linear models (GLMs), revealing that unbalanced SSDs perform well despite the unbalance property. The examination of Type I and Type II error rates through an extensive simulation study indicates that the proposed method works satisfactorily. [ABSTRACT FROM AUTHOR]

Heavy metals (HMs) discharged from smelting production may pose a major threat to human health and soil ecosystems. In this study, the spatial distribution characteristics of HMs in the soil of a non-ferrous metal smelting site were assessed. This study employed the geodetector (GD) by optimizing the classification condition and supplementing the correlation analysis (CA). The contribution of driving factors, such as production workshop distributions, hydrogeological conditions, and soil physicochemical properties, to the distribution of HMs in soil in the horizontal and vertical dimensions was assessed. The results showed that the main factors underlying the spatial distribution of As, Cd, Hg, Pb, Sb, and Zn in the horizontal direction were the distance from the sintering workshop (the maximum q value of that factor, q=0.28), raw material yard (q=0.14), and electrolyzer (q=0.29), while those in the vertical direction were the soil moisture content (q=0.17), formation lithology (q=0.12), and soil pH (q=0.06). The findings revealed that the CA is a simple and effective method to supplement the GD analysis underlying the spatial distribution characteristics of HMs at site scale. This study provides useful suggestions for environmental management to prevent HMs pollution and control HMs in the soil of non-ferrous metal smelting sites. [Display omitted] • Exploring the optimal grading method and number of GD in site-scale. • Combining other methods to improve the deficiencies of GD in factor screening. • From comprehensive perspectives to identify the main factors underlying the site-scale. [ABSTRACT FROM AUTHOR]

Economic development, population growth, industrialization and urbanization have led to large increases in anthropogenic carbon emission that has caused a variety of negative impacts on climate change and eco-environment systems. This study develops an input-output-based Bayesian neural network (IO-BNN) method for simulating the carbon emission of various economic sectors and generating the desired schemes of emission reduction. IO-BNN is applied to Guangdong province to identify its carbon emission path, carbon peak, and carbon reduction potential over a long-term planning horizon (2021–2050), in which multiple scenarios are designed to examine the effects of different environmental policies on economic and energy activities. Major findings are: (i) the key sectors and factors affecting Guangdong's carbon emission and economic development are equipment (Equ), construction (Con), transport and storage (Tra), other service (Oth), per capita energy consumption (CEC), and primary energy consumption (EC); (ii) under different environmental policies, Guangdong's carbon emission would reach the peak during 2025–2035 and then continuously decrease during 2036–2050; (iii) Guangdong's carbon emission would peak in 2025 under the optimal scenario, associated with adjustment of industrial structure (i.e. part of secondary industry would be shifted to tertiary industry) as well as reduction of primary energy consumption. [Display omitted] • An input-output-based bayesian neural network method (IO-BNN) is developed. • Six key factors influencing carbon emission are identified. • The impact of key sectoral structure and energy use on carbon emission is analyzed. • Optimal scenario for sustainable development is presented. [ABSTRACT FROM AUTHOR]

This paper applied a method of screening active factors to material parameters selection under crashworthiness design criterion. The three-level unreplicated saturated factorial design technique was used to deal with factors screening of material parameters in the nonlinear dynamic system. Three types of experiment statistic methods were utilised to analyse the accuracy of all the analytical factors, named H statistic, P statistic and B statistic. Multiobjective crashworthiness optimisation platform was established with discrete variables of material parameters. Radial basis functions regression model was employed to get fitting curves of analytical objectives so as to reduce design sampling points and improve the design efficiency. NSGA-II served as the optimisation algorithm. Material parameters selection based on three-level unreplicated saturated factorial design extends the range of factor screening in the crashworthiness optimisation design of vehicle body. It not only breaks through the traditional design method of just selecting several experienced variables, but also shortens the design cycles of vehicle body. [ABSTRACT FROM AUTHOR]

Supersaturated designs are factorial designs in which the number of potential effects is greater than the run size. They are commonly used in screening experiments, with the aim of identifying the dominant active factors with low cost. However, an important research field, which is poorly developed, is the analysis of such designs with non-normal response. In this article, we develop a variable selection strategy, through the modification of the PageRank algorithm, which is commonly used in the Google search engine for ranking Webpages. The proposed method incorporates an appropriate information theoretical measure into this algorithm and as a result, it can be efficiently used for factor screening. A noteworthy advantage of this procedure is that it allows the use of supersaturated designs for analyzing discrete data and therefore a generalized linear model is assumed. As it is depicted via a thorough simulation study, in which the Type I and Type II error rates are computed for a wide range of underlying models and designs, the presented approach can be considered quite advantageous and effective. [ABSTRACT FROM AUTHOR]

Supersaturated designs (SSDs) are defined as fractional factorial designs whose experimental run size is smaller than the number of main effects to be estimated. While most of the literature on SSDs has focused only on main effects designs, the construction and analysis of such designs involving interactions has not been developed to a great extent. In this paper, we propose a backward elimination design-driven optimization (BEDDO) method, with one main goal in mind, to eliminate the factors which are identified to be fully aliased or highly partially aliased with each other in the design. Under the proposed BEDDO method, we implement and combine correlation-based statistical measures taken from classical test theory and design of experiments field, and we also present an optimality criterion which is a modified form of Cronbach's alpha coefficient. In this way, we provide a new class of computer-aided unbalanced SSDs involving interactions, that derive directly from BEDDO optimization. [ABSTRACT FROM PUBLISHER]

Supersaturated designs (SSDs) are defined as fractional factorial designs whose experimental run size is smaller than the number of main effects to be estimated. The main goal using the class of SSDs is to identify the important effects efficiently, that is, at a minimal computational cost and time. Several methods for analyzing SSDs have been proposed in recent literature. While most of the literature on SSDs has focused on main effects models, the analysis of such designs involving models with interactions has not been developed to a great extent. In this paper, we attempt to relate several penalty and loss functions with support vector machines,with one main goal in mind, screening active effects in SSDs. In this spirit, we propose a penalized wrapper screening method for identifying in one stage the important main effects and two-factor interactions of two-level SSDs, by assuming generalized linear models. We also carry out simulation studies and a real data analysis to assess the performance of the proposed screening procedure, showing that the proposed method works satisfactorily. [ABSTRACT FROM AUTHOR]