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In view of the different views of academia on the weight allocation of vulnerability assessment indicators, this study creatively proposed a data-based objective evaluation framework of water resource vulnerability, and applied it to the evaluation of water resource vulnerability in Hubei Province. According to the conceptual model of DPSIR proposed by the United Nations, five vulnerability factors are proposed: driving force, pressure, state, influence and response. In this study, 15 indicators were selected and the projection tracing model was used to identify vulnerability. Aiming at the complex problem of optimization calculation of projection index function in the projection tracing model, the accelerated genetic algorithm is used to speed up the optimization speed, solves the optimization problem in the process of projection tracing, and determines the objective weight of all indicators. Example calculation shows that the model can deal with complex multi-index optimization problems, and is an effective way to solve the comprehensive evaluation of complex vulnerability, and the weighting method is important for the evaluation of water resources vulnerability. The results of this paper show that the combination of projection tracing method and machine learning algorithm can improve the efficiency, objectivity and accuracy of high-dimensional data analysis, and can provide scientific basis for policy makers. [ABSTRACT FROM AUTHOR]

As a carrier of multi-industrial technology integration and the key to industrial competition, the intelligent connected vehicle (ICV) has been taken seriously around the world. However, as a fast-growing emerging industry, its development process varies greatly from place to place. Hence, the merits and demerits are analyzed for the development of the ICV industry in different cities scientifically and to clarify the development of different links in each city, this paper suggests an extensive assessment framework integrating rough set theory and projection pursuit-based computation to systematically assess and thoroughly evaluate the level of competitiveness of the ICV industry. First, through big data text analysis technology, we constructed a "5 + 24" two-tier evaluation index system composed of 24 level-II evaluation indexes as well as five level-I evaluation indexes and selected 19 typical cities as input data for the comprehensive evaluation system. Further, the Adaptive Random Forest based Crossover Tactical Unit (ARF-CTU) algorithm is proposed for evaluating the performance of the industrial vehicle industry. However, the ARF algorithm is employed to estimate the lowering of overfitting issues and handling of high dimensional data. Moreover, the continuously varying conditions are analyzed by CTU. Then, we constructed a comprehensive evaluation system in the rough set theory and projection pursuit: (I) Quoting the rough set non-decision-making algorithm for attribute reduction, that is, under the premise of unchanged classification ability, derive a new evaluation system, and calculate the index weight and score based on the new system. (II) Based on the projection pursuit technology, the index score is mapped by a genetic algorithm to a linear structure, and a one-dimensional projection vector is an output. [ABSTRACT FROM AUTHOR]

The development of healthy building technology has become a major trend in the global construction industry, especially in China, owing to accelerating urbanization and increasing health awareness among residents. However, an effective evaluation framework to quantify and evaluate the maturity of healthy building technology is lacking. This paper proposes a novel maturity evaluation model for healthy building technology. After analyzing the Driver–Pressure–State–Impact–Response (DPSIR) framework for asserting the maturity of healthy building in China, it constructs an evaluation indicator system, comprising five and twenty-seven first- and second-class indicators, respectively. Subsequently, this paper constructs an improved projection pursuit model based on border collie optimization. The model obtains evaluation results by mining evaluation data, thus overcoming the limitations of traditional evaluation models in dealing with complex data. The empirical research results demonstrate that China is in the optimization stage in terms of the level of maturity of healthy building technology. The weight of impact is as high as 0.2743, which is the most important first-level indicator. Strict green energy utilization policy requirements are the most important secondary indicator, with a weight of 0.0513. Notably, the model is more advanced than other algorithms. In addition, this paper offers some countermeasures and suggestions to promote healthy building in China. Developing and applying this model can promote and popularize healthy building technology in China and even the globe and contribute to a healthier and more sustainable living environment. [ABSTRACT FROM AUTHOR]

Abstract In view of the different views of academia on the weight allocation of vulnerability assessment indicators, this study creatively proposed a data-based objective evaluation framework of water resource vulnerability, and applied it to the evaluation of water resource vulnerability in Hubei Province. According to the conceptual model of DPSIR proposed by the United Nations, five vulnerability factors are proposed: driving force, pressure, state, influence and response. In this study, 15 indicators were selected and the projection tracing model was used to identify vulnerability. Aiming at the complex problem of optimization calculation of projection index function in the projection tracing model, the accelerated genetic algorithm is used to speed up the optimization speed, solves the optimization problem in the process of projection tracing, and determines the objective weight of all indicators. Example calculation shows that the model can deal with complex multi-index optimization problems, and is an effective way to solve the comprehensive evaluation of complex vulnerability, and the weighting method is important for the evaluation of water resources vulnerability. The results of this paper show that the combination of projection tracing method and machine learning algorithm can improve the efficiency, objectivity and accuracy of high-dimensional data analysis, and can provide scientific basis for policy makers.

Nonparametric tests for equality of multivariate distributions are frequently desired in research. It is commonly required that test-procedures based on relatively small samples of vectors accurately control the corresponding Type I Error (TIE) rates. Often, in the multivariate testing, extensions of null-distribution-free univariate methods, e.g., Kolmogorov-Smirnov and Cramér-von Mises type schemes, are not exact, since their null distributions depend on underlying data distributions. The present paper extends the density-based empirical likelihood technique in order to nonparametrically approximate the most powerful test for the multivariate two-sample (MTS) problem, yielding an exact finite-sample test statistic. We rigorously apply one-to-one-mapping between the equality of vectors' distributions and the equality of distributions of relevant univariate linear projections. We establish a general algorithm that simplifies the use of projection pursuit, employing only a few of the infinitely many linear combinations of observed vectors' components. The displayed distribution-free strategy is employed in retrospective and group sequential manners. A novel MTS nonparametric procedure in the group sequential manner is proposed. The asymptotic consistency of the proposed technique is shown. Monte Carlo studies demonstrate that the proposed procedures exhibit extremely high and stable power characteristics across a variety of settings. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Copyright of Resources Development & Market is the property of Sichuan Resource Development & Market Magazine Co., Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper aims to analyse a statistical procedure for the definition of territorial indicators, such as the biometric function of life expectancy of citizens of a territory e0, applying a methodology of Top–Down spatial disaggregation, using census data from 2011 in Italy. Through spatial regressions with the methodology proposed in 1971 by Chow and Lin with the use of ISTAT elaborations of annual mortality tables, which are structured from the national level to the regional level, up to the smallest details of the main level, as a dependent variable and predictors a number k of census variables plus accidents in regression models, life expectancy can also be defined at municipal levels (not elaborated by ISTAT) and even at sub-municipal levels (Census Area). The structure of the 2011 census was characterized by 152 variables, collected with CAPI and universal CAWI survey on all the survey units, throughout the national territory, divided into administrative areas of competence and 402,677 more granular areas in census sections. This structure represents a very relevant and useful information asset for applying a spatial disaggregation of indicators, based on three fundamental assumptions: Structural similarity, whereby the aggregate model and the disaggregate model are structurally similar, i.e., the relationships between the variables are valid both at the aggregate (Top) and at the disaggregate (Down) level, with the consequence that the regression parameters are the same in the two models. Error similarity: for spatially correlated errors they present the same structure at both aggregate (Top) and disaggregate (Down) levels, significantly testing for zero spatial correlation; Reliable indicators: the variables in the regression models are efficient predictors at both aggregates (Top) and disaggregate (Down) levels, estimable from model efficiency tests. As we see in the following, compared to others, the best predictors of the census and income variables show us a good interaction in terms of active regressors on the estimation variable. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract As a carrier of multi-industrial technology integration and the key to industrial competition, the intelligent connected vehicle (ICV) has been taken seriously around the world. However, as a fast-growing emerging industry, its development process varies greatly from place to place. Hence, the merits and demerits are analyzed for the development of the ICV industry in different cities scientifically and to clarify the development of different links in each city, this paper suggests an extensive assessment framework integrating rough set theory and projection pursuit-based computation to systematically assess and thoroughly evaluate the level of competitiveness of the ICV industry. First, through big data text analysis technology, we constructed a "5 + 24" two-tier evaluation index system composed of 24 level-II evaluation indexes as well as five level-I evaluation indexes and selected 19 typical cities as input data for the comprehensive evaluation system. Further, the Adaptive Random Forest based Crossover Tactical Unit (ARF-CTU) algorithm is proposed for evaluating the performance of the industrial vehicle industry. However, the ARF algorithm is employed to estimate the lowering of overfitting issues and handling of high dimensional data. Moreover, the continuously varying conditions are analyzed by CTU. Then, we constructed a comprehensive evaluation system in the rough set theory and projection pursuit: (I) Quoting the rough set non-decision-making algorithm for attribute reduction, that is, under the premise of unchanged classification ability, derive a new evaluation system, and calculate the index weight and score based on the new system. (II) Based on the projection pursuit technology, the index score is mapped by a genetic algorithm to a linear structure, and a one-dimensional projection vector is an output.

Mining will destroy and occupy a large amount of land resources and cause lasting negative impact on ecological environment, so it is urgent to evaluate the change of ecological environment quality in mining area. In order to accurately monitor the ecological environment quality of the mining area, this study took 24 annual Landsat images of the Zhangjiamao Mining area from 2000 to 2023 as the basic data, and calculated four ecological indicators of NDVI (Normalized Difference Vegetation Index, NDVI), WET (Humidity Index, WET), LST (Land Surface Temperature, LST) and NDBSI (Normalized Differential Build-up and bare Soil Index, NDBSI). In addition, four population intelligent optimization algorithms including the Fruit Fly Optimization Algorithm-Projection Pursuit Clustering (FOA–PPC), the Particle Swarm Optimization-Projection Pursuit Clustering (PSO–PPC), the Grey Wolf Optimizer-Projection Pursuit Clustering (GWO–PPC) and the Dung Beetle Optimizer-Projection Pursuit Clustering (DBO–PPC) were used to derive the ecological environment quality evaluation method in the mining area, and the average correlation was used to verify the accuracy. The results showed that: ① The average correlation and intra class aggregation of the DBO–PPC model are higher than those of the PSO–PPC model, FOA–PPC model, and GWO–PPC model, and are closer to the EI index, indicating that the DBO–PPC model can better evaluate the ecological environment of the study area; ② Based on the DBO–PPC model, the average ecological and environmental quality of the Zhangjiamao mining area from 2000 to 2023 is about 0.4, and the ecological and environmental quality is mainly poor or relatively poor, accounting for about 55.94% of the total area. In terms of space, the ecological environment of the Changjiagou Reservoir is superior during the study period. The ecological environment in the northeastern and central areas of the mining area is better, with more vegetation coverage. ③ The proportion of subsidence area in the mining area is 81.28%, and the maximum subsidence is –0.15 m. Subsidence in the coal extraction area is significantly higher than that in the whole mining area, accounting for 89.56% of the coal extraction area, and the ecological environment quality decreases at a rate of –0.000 4, indicating that mining activities cause surface subsidence in the study area, which further affects the ecological environment. To sum up, the DBO–PPC model has a strong rationality in monitoring and evaluating the ecological environment quality in the mining areas, so as to provide technical means for the sustainable development of ecological environment in the mining areas.

Heavy metal pollution in farmland soil threatens soil environmental quality. It is an important task to quickly grasp the status of heavy metal pollution in farmland soil in a region. Hyperspectral remote sensing technology has been widely used in soil heavy metal concentration monitoring. How to improve the accuracy and reliability of its estimation model is a hot topic. This study analyzed 440 soil samples from Sihe Town and the surrounding agricultural areas in Yushu City, Jilin Province. Considering the differences between different types of soils, a local regression model of heavy metal concentrations (As and Cu) was established based on projection pursuit (PP) and light gradient boosting machine (LightGBM) algorithms. Based on the estimations, a spatial distribution map of soil heavy metals in the region was drawn. The findings of this study showed that considering the differences between different soils to construct a local regression estimation model of soil heavy metal concentration improved the estimation accuracy. Specifically, the relative percent difference (RPD) of As and Cu element estimations in black soil increased the most, by 0.30 and 0.26, respectively. The regional spatial distribution map of heavy metal concentration derived from local regression showed high spatial variability. The number of characteristic bands screened by the PP method accounted for 10–13% of the total spectral bands, effectively reducing the model complexity. Compared with the traditional machine model, the LightGBM model showed better estimation ability, and the highest determination coefficients (R2) of different soil validation sets reached 0.73 (As) and 0.75 (Cu), respectively. In this study, the constructed PP–LightGBM estimation model takes into account the differences in soil types, which effectively improves the accuracy and reliability of hyperspectral image estimation of soil heavy metal concentration and provides a reference for drawing large-scale spatial distributions of heavy metals from hyperspectral images and mastering soil environmental quality. [ABSTRACT FROM AUTHOR]

In order to solve the issues of high-dimensional and nonlinear in the classification and evaluation of water resources carrying capacity, and to improve the accuracy of the water resources carrying capacity classification and evaluation model, an improved projection pursuit method based on the maximum information entropy principle was proposed. The density window width R in the model had been improved, and a reasonable value for the density window width had been derived. The model had been applied to the evaluation of water resources carrying capacity in Jining City from 2010 to 2018. The grey GM (1,1) model was established to predict the state of water resources carrying capacity in Jining City from 2019 to 2025, and the prediction results were consistent with the actual situation. The results show that the evaluation level of water resources carrying capacity in Jining City from 2010 to 2017 is level Ⅲ, and the evaluation level of water resources carrying capacity in 2018 is level Ⅱ. It is predicted that the water resources carrying capacity of Jining City will be level Ⅱ from 2019 to 2025, and the overall water resources carrying capacity status will remain good. By analyzing the impact of various evaluation indicators on water resources carrying capacity, relevant suggestions are proposed to improve the city’s water resources carrying capacity. [ABSTRACT FROM AUTHOR]

Heilongjiang reclamation area serves as a crucial hub for commodity grain production and strategic reserves in China, playing a vital role in maintaining national food security. Investigating the assessment of agricultural drought risk in this region can yield valuable insights into spatial and temporal variations in drought risk. Such insights can aid in formulating effective strategies for disaster prevention and mitigation, thereby minimizing food losses caused by drought disasters. This study employs a comprehensive indicator system comprising 17 indicators categorized into hazard, exposure, vulnerability, and resistance capacity. The projection pursuit model is applied to evaluate regional drought risk, while the PSO algorithm, optimized by the SSA algorithm, addresses the limitations of low local search ability and search accuracy during the large-scale search process of the PSO optimization algorithm. This study examines and compares the optimization and convergence capabilities of three algorithms: real number encoding-based genetic algorithm (RAGA), particle swarm optimization algorithm (PSO), and sparrow algorithm-based improved particle swarm optimization algorithm (SSAPSO). The analysis demonstrates that SSAPSO exhibits superior optimization performance and convergence properties, establishing it as a highly effective algorithm for optimization tasks. The findings reveal the following trends: over time, agricultural drought risk in Heilongjiang reclamation area has generally declined, with fluctuations observed in hazard and vulnerability, an increase in exposure, and a continuous enhancement of resistance capacity. Spatially, the western region exhibits significantly higher agricultural drought risk compared to the eastern region, primarily due to elevated hazard and vulnerability, coupled with lower resistance capacity. As the agricultural economy grows and agricultural expertise accumulates, the risk of agricultural drought decreases. However, variations in economic growth among different regions lead to diverse spatial distributions of risk. [ABSTRACT FROM AUTHOR]

To adapt to the rapid development of power transmission and transformation projects, improve their emergency response capability level, and reduce the losses caused by accidents, the projection pursuit method was introduced into the emergency response capability evaluation of power transmission and transformation projects. The emergency response capability evaluation system of power transmission and transformation projects has been established mostly from each composition and structure of power transmission and transformation engineering systems, and highly subjective evaluation methods have been adopted to assess the models established. In this study, a total of 19 concrete indexes were selected from 4 aspects--monitoring and early warning capability, emergency control capability, emergency rescue and disposal capability, and emergency support capability--to establish an emergency response capability evaluation index system of power transmission and transformation projects. Then, an emergency response capability evaluation model for power transmission and transformation projects was constructed based on the projection pursuit model, followed by optimization using real code accelerated genetic algorithm (RAGA); for high-dimensional data, this model could directly find the structure and features of data itself, avoiding the limitations of subjective judgment and contributing to more truthful and reliable evaluation results; finally, this model was used to evaluate and analyze the emergency response capability of six power transmission and transformation projects: GZXS SOOkV, JXXYSOOkV, QHXN 750kV, YNZT SOOkV, JSNJ SOOkV, and SXXA 750kV. The results show that the six power transmission and transformation projects are different in the emergency response capability level; the emergency response capability level of power transmission and transformation projects is greatly affected by the early warning personnel deployment capability, daily emergency drill capability, emergency technology implementation capability, emergency training and education capability, and risk response capability. [ABSTRACT FROM AUTHOR]

The factors affecting the remaining oil recoverable potential in ultra-high water-cut reservoirs are extremely complex， exhibiting substantial variations in their impact. Conventional methods often rely on singular indicators such as remaining oil saturation or remaining oil abundance to assess remaining oil recoverable potential， which fails to guide tapping remaining oil in ultra-high water-cut reservoirs effectively. By considering the factors affecting the remaining oil recoverable potential in these reservoirs and comprehensively characterizing the heterogeneity of reservoirs， the scale of remaining oil reserves， watered conditions， and oil/water flow capacity， we constructed a quantitative assessment indicator system to tap the remaining oil recoverable potential in ultra-high water-cut reservoirs. In addition， according to the control degree difference of the indicators on remaining oil recoverable potential， we determined the objective weights of each evaluation indicator by combining the accelerated genetic algorithm and the projection pursuit model. We constructed the remaining oil recoverable potential index to form a new quantitative assessment method of the remaining oil recoverable potential in ultra-high water-cut reservoirs. Taking the NmIL sand body of the main production layer in the south area of Bohai Q Oilfield as an example， we quantitatively assessed the remaining oil recoverable potential in ultra-high water-cut reservoirs. The results reveal that the new method can comprehensively characterize the effects of reservoir physical properties， remaining oil reserves， and dynamic characteristics of oil/water flow on the remaining oil recoverable potential in different locations and realize the differential quantitative assessment of the distribution of remaining oil recoverable potential in NmIL sand body of the main production layer. In addition， the dominant recoverable potential area is clearly characterized， and it is taken as the potential area for the subsequent adjustment of infill wells in the NmIL sand body to accurately guide the deployment of infill horizontal wells， thereby offering a novel perspective for tapping remaining oil resources in such reservoirs.

Multivariate nonparametric control charts are highly sought‐after due to their flexibility to adapt to different distribution assumptions. However, many of the existing multivariate nonparametric control charts are only distribution‐free for certain distribution families. Although those distribution families may contain different distributions, it is still difficult to verify whether the underlying multivariate distribution from a particular application belongs to those distribution families in practice. A few existing multivariate nonparametric control charts are fully nonparametric. As shown in the literature and in our simulation studies, most of them are not efficient in detecting location shifts. In this paper, we propose a new multivariate nonparametric control chart based on the idea of projection pursuit. The proposed control chart is fully nonparametric and can be applied to any multivariate distribution as long as its covariance matrix exists. The control limit of the proposed control chart only depends on the nominal ARL0$\text{ARL}_0$, which makes its implementation much easier. Our simulation study and real data analysis show that the proposed control chart performs well across a variety of settings, and compares favorably with existing multivariate nonparametric control charts. [ABSTRACT FROM AUTHOR]

With the rapid development of urban and social economies, the safety accidents in the construction process of the new chemical plant have caused huge losses to the city. The purpose of this study is to evaluate the risks in the construction process of chemical projects and propose preventive measures. A novel risk assessment model based on multi-intelligence algorithm optimization projection pursuit was developed to assess the construction safety risk and determine the risk level. In this model, the best-worst method and the entropy weight method were used as subjective and objective evaluation methods, respectively. The theory based on the idea of the distance function was applied to the model to calculate the combined weight value. The results showed that the three evaluation objects with the highest risk value were the air compression station plant, regional control room, and hazardous and solid waste temporary repository. The risk values of these three buildings were 2.2557, 2.2160, and 2.1654, respectively, and the corresponding risk level was high. On-site safety managers should take immediate measures in these high-risk buildings to reduce the possibility of accidents. This study is a new attempt to consider the construction safety risk of the new chemical project. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To ensure the water quality of rivers, it is crucial to scientifically evaluate their water quality status. This study takes a river in Jiangsu, China, as an example to establish six targeted main indicators for river water quality evaluation and uses a projection pursuit model optimized by the genetic algorithm to determine weights. Applying the improved fuzzy evaluation model to the final evaluation of water quality, the results indicate that this article adopts a weight calculation model that reduces dimensionality without losing data features, and the comprehensive evaluation model is also more complete, resulting in more accurate evaluation results. According to model analysis, the summer water quality is good and peaks from June to July. This article proposes corresponding measures and suggestions in response to the reasons behind this seasonal change. The evaluation model used in this article is superior to other models in terms of accuracy and portability, making it an excellent choice for river water quality evaluation. It can provide valuable technical guidance for similar river water quality evaluations. HIGHLIGHTS In the evaluation of river water quality, the high-dimensional data dimensionality reduction operation optimized by the genetic algorithm projection pursuit (GA-PP) is not easy to lose the original features of the data and is quite suitable for calculating weights.; The improved fuzzy evaluation model is also superior to the traditional fuzzy comprehensive model and can more comprehensively evaluate water quality data.;

The development of healthy building technology has become a major trend in the global construction industry, especially in China, owing to accelerating urbanization and increasing health awareness among residents. However, an effective evaluation framework to quantify and evaluate the maturity of healthy building technology is lacking. This paper proposes a novel maturity evaluation model for healthy building technology. After analyzing the Driver–Pressure–State–Impact–Response (DPSIR) framework for asserting the maturity of healthy building in China, it constructs an evaluation indicator system, comprising five and twenty-seven first- and second-class indicators, respectively. Subsequently, this paper constructs an improved projection pursuit model based on border collie optimization. The model obtains evaluation results by mining evaluation data, thus overcoming the limitations of traditional evaluation models in dealing with complex data. The empirical research results demonstrate that China is in the optimization stage in terms of the level of maturity of healthy building technology. The weight of impact is as high as 0.2743, which is the most important first-level indicator. Strict green energy utilization policy requirements are the most important secondary indicator, with a weight of 0.0513. Notably, the model is more advanced than other algorithms. In addition, this paper offers some countermeasures and suggestions to promote healthy building in China. Developing and applying this model can promote and popularize healthy building technology in China and even the globe and contribute to a healthier and more sustainable living environment.

Mineral prospectivity mapping (MPM) is a fundamental task in mineral exploration. In recent years, the random forest (RF), which is recognized as a significant model for ensemble machine learning algorithms, has been widely used in MPM due to its advantages of high performance and robustness. Nevertheless, the RF method does not fully consider the correlation between variables. In this regard, a hybrid model, namely, the projection pursuit RF (PPRF), has been proposed. In this study, for validation and comparison purposes, the PPRF and ordinary RF methods were employed for the MPM of porphyry Cu–Mo deposits in the Eastern Tianshan orogenic belt, northwestern China, under the same conditions. In the case study, the performance of these two methods was critically investigated. The overall prediction accuracies of the PPRF and RF methods were evaluated by the area under the receiver operating characteristic curve and prediction–area plot approaches. To analyze the sensitivity of the PPRF and RF methods to unbalanced data caused by the limited number of known deposits in the study area, the synthetic minority oversampling technique was used to generate a balanced dataset. The results demonstrated that the PPRF method not only outperforms the ordinary RF method in the overall accuracy of MPM but is also less sensitive to unbalanced data than the RF method. Therefore, the PPRF method provides a novel and useful data-driven method with excellent performance for MPM. It utilizes the combination of predictor variables in the projection feature space rather than the variables themselves to identify the inherent characteristics of a given dataset and may thus discover deep hidden features that the ordinary RF method cannot find. [ABSTRACT FROM AUTHOR]

This article proposes a nonparametric projection-based adaptive-to-model specification test for regressions with discrete and continuous predictors. The test statistic is asymptotically normal under the null hypothesis and omnibus against alternative hypotheses. The test behaves like a locally smoothing test as if the number of continuous predictors was one and can detect the local alternative hypotheses distinct from the null hypothesis at the rate that can be achieved by existing locally smoothing tests for regressions with only one continuous predictor. Because of the model adaptation property, the test can fully use the model structure under the null hypothesis so that the dimensionality problem can be significantly alleviated. A discretization-expectation ordinary least squares estimation approach for partial central subspace in sufficient dimension reduction is developed as a by-product in the test construction. We suggest a residual-based wild bootstrap method to give an approximation by fully using the null model and thus closer to the limiting null distribution than existing bootstrap approximations. We conduct simulation studies to compare it with existing tests and two real data examples for illustration. [ABSTRACT FROM AUTHOR]

Heavy metal pollution in farmland soil threatens soil environmental quality. It is an important task to quickly grasp the status of heavy metal pollution in farmland soil in a region. Hyperspectral remote sensing technology has been widely used in soil heavy metal concentration monitoring. How to improve the accuracy and reliability of its estimation model is a hot topic. This study analyzed 440 soil samples from Sihe Town and the surrounding agricultural areas in Yushu City, Jilin Province. Considering the differences between different types of soils, a local regression model of heavy metal concentrations (As and Cu) was established based on projection pursuit (PP) and light gradient boosting machine (LightGBM) algorithms. Based on the estimations, a spatial distribution map of soil heavy metals in the region was drawn. The findings of this study showed that considering the differences between different soils to construct a local regression estimation model of soil heavy metal concentration improved the estimation accuracy. Specifically, the relative percent difference (RPD) of As and Cu element estimations in black soil increased the most, by 0.30 and 0.26, respectively. The regional spatial distribution map of heavy metal concentration derived from local regression showed high spatial variability. The number of characteristic bands screened by the PP method accounted for 10–13% of the total spectral bands, effectively reducing the model complexity. Compared with the traditional machine model, the LightGBM model showed better estimation ability, and the highest determination coefficients (R2) of different soil validation sets reached 0.73 (As) and 0.75 (Cu), respectively. In this study, the constructed PP–LightGBM estimation model takes into account the differences in soil types, which effectively improves the accuracy and reliability of hyperspectral image estimation of soil heavy metal concentration and provides a reference for drawing large-scale spatial distributions of heavy metals from hyperspectral images and mastering soil environmental quality.

It is of great practical significance to optimise the decision-making of cloud manufacturing service providers, which can ensure the efficient operation of the cloud manufacturing services. In order to effectively optimise, this paper constructs the evaluation index system, sorts out 7 important evaluation indexes. A method is proposed on the basis of the TOPSIS method and Grey Correlation Analysis (GRA), which is a decision-making method combining static distance and dynamic trend, and make the evaluation results more reasonable. In addition, in order to reflect the true weight of the high dimensional index, the evaluation model of projection pursuit index is established. The improved Particle Swarm Optimisation (PSO) algorithm is used to optimise the projection index function and model parameters, and to obtain the objective weights of evaluation indexes. Then the objective weights combine with the subjective weights obtained by the Analytic hierarchy process (AHP), which makes the weights of the evaluation indexes achieve the unity of subjective and objective. Finally, 20 sets of simulation examples are given to illustrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

By applying principal component analysis on a data set one creates an orthogonal set of vectors which point in the directions of maximal variance. This paper develops several forms of what will be called skew component analysis in which an orthogonal set of vectors is formed which include the direction of maximal skew. This process will involve numerical approximation of maxima as well as solving multi-polynomial systems of equations, both of which are computer intensive and for which a tractable solution was found. Skew component analysis is then applied to a novel classification algorithm to illustrate its effectiveness. [ABSTRACT FROM AUTHOR]

Aiming at the problems of weak evaluation methods and unclear driving mechanism of regional flood disaster resilience, an evaluation index system of flood disaster resilience from natural, economic, social and technical dimensions was constructed, and Projection Pursuit Model Based on Bald Eagle Search Optimization Algorithm(BES-PP)was established. Based on this, the BES-PP model was used to evaluate the flood disaster resilience of Jiamusi City from 2003 to 2020. The results show that the resilience level of Jiamusi city can be divided into three stages: the first stage (2003-2006) resilience rose steadily but at a low level; the second stage (2007-2013) resilience fluctuated obviously; the third stage (2014-2020) resilience rose rapidly and finally reached a high level. In order to analyze the driving mechanism, the BES-PP model was used to analyze the weights of each index. The results showed that the natural rights-protecting weights of economic peacekeeping reached 1.211 68 and 0.932 42, respectively, and their average weights reached 0.302 92 and 0.310 81, which were much higher than the technical dimensions of social peacekeeping. It shows that the index of natural and economic dimensions has a high degree of influence on the resilience of flood disaster and the important role of natural and economic factors in avoiding flood disaster should be paid attention to. In order to verify the performance of the BES-PP model, Projection Pursuit Model Based on Slime Mould Algorithm (SMA-PP)and Projection Pursuit Model Based on Whale Optimization Algorithm (WOA-PP) are introduced for comparative analysis. The results show that the BES-PP model has obvious advantages in performance. In addition, the evaluation results of BES-PP, SMA-PP and WOA-PP were analyzed by using the serial number sum theory, and it was found that the evaluation results of the BES-PP model were more reasonable. The research results provide a scientific basis for the improvement of disaster prevention and reduction capacity of Jiamusi city, and also provide some valuable information for the assessment of disaster resilience. [ABSTRACT FROM AUTHOR]

Aiming at the problem that manufacturing enterprises that rely more on asset projects currently lack effective means of divestiture of non performing assets, starting from incomplete information theory, this paper derives an optimal decision-making model for the divestiture path of non performing assets caused by the quality and safety crisis of manufacturing enterprises in an incomplete information environment, and uses the comprehensive method of SUMDII to establish a comprehensive integrated optimization model that reflects subjective evaluation and objective information. In addition, this study provides specific decision-making methods and implementation steps for optimizing the stripping path of non-performing assets. The empirical analysis results verify and demonstrate the feasibility, operability, accuracy, and applicability of the established model. The results show that the model designed in the study exhibits strong stability in sensitivity testing. When the parameter vectors are taken as (1,0,0,0), (1,1,0,0), (1,1,0,0), and (1,1,1,1), respectively, the ranking results corresponding to the first three parameter vectors are stable, all of which are A2 > A4 > A1 > A3. At the same time, the applied decision-making result of the model is A4 > A1 > A2 > A3, which is consistent with the best scheme evaluated by experts and superior to most comparative models. At the same time, in the analysis of decision-making characteristics, the research and design model has the most comprehensive review of decision-making elements, which is superior to other comparative models. It can be seen that the model designed by the research can lead to higher quality NPL divestiture schemes, which can help manufacturing enterprises improve asset quality and weaken the negative impact of the quality and safety crisis in manufacturing enterprises. [ABSTRACT FROM AUTHOR]

The research on urban flood resilience will contribute to building a more resilient city and provide valuable reference for municipal decision-makers. There are many frameworks and approaches for empirical studies on what constitutes urban flood resilience and how to evaluate it. In this study, a typical region suffering from flood disaster in China-Jiangsu Province was selected as the study area, including 13 prefecture-level cities. The pressure-state-response (PSR) framework, the projection pursuit based on real-coded accelerated genetic algorithm (RAGA-PP) and the technique for order preference by similarity to an ideal solution based on the Kullback-Leibler distance (KL-TOPSIS) were combined to develop a hybrid multi-criteria approach for assessing urban flood resilience. Then the grey relational analysis obtained the important factors. The results illustrate that (1) the development of each subsystem in the city is uncoordinated, that is, the pressure-subsystem has little influence on urban flood resilience, while the state-subsystem and the response-subsystem have great influence. (2) The urban flood resilience in Jiangsu Province presents obvious polarization trend, that is, Southern Jiangsu is more resilient than Northern Jiangsu. The underlying factors are closely related to the level of economic development. Furthermore, the proposed method provides a practical evaluation approach for other fields. HIGHLIGHTS The PSR framework, RAGA-PP and KL-TOPSIS were combined to develop hybrid multi-criteria indices approach for assessing urban flood resilience.; The development of each subsystem in the city is uncoordinated.; Southern Jiangsu is more resilient than Northern Jiangsu.; The underlying factors affecting the urban flood resilience are closely related to the level of economic development.;

Regional innovation capability is an important indicator of both regional innovative and long‐term development. The purpose of this study is to build an evaluation index system for regional innovation capability in order to identify regional differences and support innovation more effectively. After establishing a reasonable evaluation value for regional innovation capability, a combination of simulated annealing optimized projection pursuit (SA–PP) and N‐layer nested fuzzy comprehensive evaluation models is used to assess China's regional innovation capabilities. The results show that the SA–PP model effectively mitigates the risk of erroneous evaluation results caused by index weight uncertainty, resulting in a more reasonable, robust, and intelligent assessment of regional innovation capability. Furthermore, the nested fuzzy comprehensive evaluation model is capable of easily resolving the evaluation factor set's heterogeneity and multilayer problems. The most significant influences on China's regional innovation capability are knowledge acquisition and enterprise innovation. The comprehensive score of the proposed combinational evaluation model manifests that provinces with strong regional innovation capabilities are mainly concentrated in the southeast coastal regions. The research results allow for precise weight determination and object ranking. [ABSTRACT FROM AUTHOR]

Water, energy, food, and ecology are essential for human survival and development. The Yangtze River Delta is one of the most important regions for China's sustainable development. It is of great significance to study the coupling coordinated development level of water-energy-food-ecology in the Yangtze River Delta for sustainable development. In this study, we establish the water-energy-food-ecology (WEFE) coupling and coordination development index system. Then, we analyze the degree of coupling coordination (DCC) of WEFE based on the coupling coordination evaluation model and projection pursuit model. The results show that the DCC of WEFE in the Yangtze River Delta shows obvious spatial-temporal characteristics. From the temporal view, the DCC in the Yangtze River Delta has shown an upward trend; from the spatial view, the DCC of Jiangsu, Anhui, and Zhejiang is higher than that of Shanghai. HIGHLIGHTS Water, energy, food, and ecology are essential for human survival and development.; It is of great significance to study the coordinated development level of WEFE in the Yangtze River Delta.; The coupling coordination degree of WEFE in the Yangtze River Delta shows obvious spatial-temporal characteristics.; The coupling coordination degrees in the Yangtze River Delta are gradually increasing in recent years.;

【Objective】 The purpose of this paper is to investigate the evolution of water resource vulnerability in the southern wet mountain regions in China. 【Method】 We took Xiangxi Tujiazu & Miaozu Autonomous Prefecture as an example, and developed a VSD model to calculate their water resource vulnerability evaluation index by considering exposure, sensitivity and adaptation. It included three hierarchies and 15 indicators. The evaluation model was developed based on the projection pursuit-cloud model. 【Result】 The trend of water resources vulnerability in Xiangxi Autonomous Prefecture can be divided into four categories including good category, category in continuous improvement, continuously worsening category, and category in fluctuation. The good category includes water consumptions by agriculture, industry, domestic use and water quality compliance rate; the continuous improvement category includes eco-environmental water demand, forest coverage rate, water storage of large and medium-sized reservoirs, sewage recovery rate, and length of embankments; continuously worsening category includes tourism revenue, effective irrigation area; the fluctuation category includes water yield coefficient, water resources per capita, annual rainfall, and groundwater water exploitation. The comprehensive evaluation changed from moderate vulnerability to weak vulnerability since the work for aquatic ecological civilization, the system for the river-leader and model river have been carried out. 【Conclusion】 The enhancement of the water resources system in Xiangxi Prefecture is related to its eco-civilization of water, river chief system, but it should also develop ecological tourism and water infrastructure construction. The method we developed in this paper can be used to evaluate and analyze water resource vulnerability in the other wet mountainous regions.

Environmental benefits, economic benefits and the crop production and quality benefits should be considered when installing the ubsurface drainage system in a farmland. However, few researches have considered the comprehensive benefit of the drainage system with the multiple indicators. In order to find out an appropriate subsurface drainage scheme with best comprehensive effects including the soil salts removing, crop quality improvement and yield enhancement, a field experiment was carried out in south of China, and the principle component analysis and projection pursuit model was introduced to evaluate the stand or fall of different subsurface drainage schemes. Four schemes including S1 (depth of 60 cm, spacing of 4 m), S2 (depth of 60 cm, spacing of 6 m), S3 (depth of 80 cm, spacing of 4 m) and S4 (depth of 80 cm, spacing of 6 m) were included in this study and the flue-cured tobacco variety 'K326' was used as plant material. The results showed that S1 was conductive to improve the flue-cured tobacco quality and remove the soil salts. Although tobacco yield of S1 was suboptimal, it was still in a considerably high level of 2187 kg/ha. However, S3 was proved to be the worse scheme due to the lower tobacco yield, smaller desalination rate and poorer tobacco quality. The calculation results of projection pursuit model have shown that S1 was the recommended scheme with the highest projection value of 1.921, indicating that subsurface drainage scheme with 60 cm depth and 4 m space was appropriate to apply in tobacco cultivated area of south China. [ABSTRACT FROM AUTHOR]

In this study, the research aims to use some methods that deal with several independent variables with a dependent variable, where two methods were used to deal with, which is the method of slice inverse regression (SIR), which is considered a non-classical method, and two methods of machine learning, which is (TLBO, PSO), which is most popular of the teaching methods machine learning, the work of (SIR), (TLBO, PSO) is based on making reduced linear combinations of a partial set of the original explanatory variables, which may suffer from the problem of heterogeneity and the problem of multicollinearity between most of the explanatory variables. These new combinations of linear compounds resulting from the two methods will reduce the largest number of explanatory variables to reach one or more new dimensions called the effective dimension. The root mean square error criterion will be used to compare the two methods to indicate the preference of the methods. [ABSTRACT FROM AUTHOR]

The projection pursuit model is an important tool for processing high-dimensional nonnormal and nonlinear data, and it has a wide range of applications. In this paper, clustering analysis and information entropy theory are simultaneously introduced into the projection pursuit model. The K-means clustering method is used to cluster high-dimensional data, and information entropy is used to measure the overall dispersion and local aggregation of projection data. A projection pursuit model based on clustering and information entropy is proposed. The new model has both the classification advantages of clustering analysis and the evaluation advantages of the projection pursuit model. In the case test, different cases are tested by using the Shapiro-Wilk test, Kolmogorov-Smirnov test, Epps-Pulley test, etc. The results show that in most cases, the new model is better than the original model, and the advantage is clearer when the data dimension is higher. Finally, the new model is applied to the agricultural drought risk assessment of Qiqihar, a major grain-producing area in China that is prone to drought. The regional agricultural drought risk shows a downward trend over time. Spatially, the risk of the central and southern regions is low, while the risk of the northern and western regions is high. Regional ability to resist disasters is the main reason for the spatial and temporal differences. This paper extends projection pursuit model theory, analyzes the characteristics of the spatiotemporal variation in regional agricultural drought risk, and provides a reference for identifying the hidden dangers of drought disasters and disaster reduction. [ABSTRACT FROM AUTHOR]

This paper introduces the R package FKSUM, which offers fast and exact evaluation of univariate kernel smoothers. The main kernel computations are implemented in C++, and are wrapped in simple, intuitive and versatile R functions. The fast kernel computations are based on recursive expressions involving the order statistics, which allows for exact evaluation of kernel smoothers at all sample points in log-linear time. In addition to general purpose kernel smoothing functions, the package offers purpose built and readyto-use implementations of popular kernel-type estimators. On top of these basic smoothing problems, this paper focuses on projection pursuit problems in which the projection index is based on kernel-type estimators of functionals of the projected density.

As the first of the six elements of tourism, food is an important part of tourism activities. As an important driving force for the economic development of tourist destinations, food tourism plays an increasingly important role in tourism and has gradually become an important hub connecting tourists, local residents, and tourist destinations. This study takes Yunnan wild mushrooms as a case, obtained data through a questionnaire survey and applied a projection pursuit model (PPM) to explore the driving factors affecting food tourism consumption. Research shows that the quality and abundance of gourmet resources are the most important factors affecting tourist consumption of gourmet foods, and the needs and experiences of tourists also promote their consumption, to a certain extent. This provides a guide for Yunnan Province to expand the source market. Second, we compared PPM with structural equation modeling (SEM) and demonstrated that the former requires less data than the latter and is immune to nonlinearities, interaction effects, and the researchers' excessive prior knowledge. This makes PPM more robust, anti-interference, accurate, and objective than SEM when dealing with problems. Finally, we put forward reasonable suggestions from four aspects of government, enterprises, local residents, and tourists, which provide an effective way for Yunnan Province to develop food tourism. In addition, PPM's advantages in data selection, data processing, and result stability fill the gaps in data processing in the tourism field.

The direpack package establishes a set of modern statistical dimensionality reduction techniques into the Python universe as a single, consistent package. Several of the methods included are only available as open source through direpack, whereas the package also offers competitive Python implementations of methods previously only available in other programming languages. In its present version, the package is structured in three subpackages for different approaches to dimensionality reduction: projection pursuit, sufficient dimension reduction and robust M estimators. As a corollary, the package also provides access to regularized regression estimators based on these reduced dimension spaces, as well as a set of classical and robust preprocessing utilities, including very recent developments such as generalized spatial signs. Finally, direpack has been written to be consistent with the scikit-learn API, such that the estimators can flawlessly be included into (statistical and/or machine) learning pipelines in that framework.

We consider a new approach for estimating non‐Gaussian undirected graphical models. Specifically, we model continuous data from a class of multivariate skewed distributions, whose conditional dependence structure depends on both a precision matrix and a shape vector. To estimate the graph, we propose a novel estimation method based on nodewise regression: we first fit a linear model, and then fit a one component projection pursuit regression model to the residuals obtained from the linear model, and finally threshold appropriate quantities. Theoretically, we establish error bounds for each nodewise regression and prove the consistency of the estimated graph when the number of variables diverges with the sample size. Simulation results demonstrate the strong finite sample performance of our new method over existing methods for estimating Gaussian and non‐Gaussian graphical models. Finally, we demonstrate an application of the proposed method on observations of physicochemical properties of wine. [ABSTRACT FROM AUTHOR]

The water environment carrying status (WECS) is the embodiment of the ability of an aquatic ecosystem to maintain healthy and stable development; as such, it is an essential factor in the sustainable social and economic development of a region. To evaluate the water environment carrying status of a region, a new Pressure-State-Response-Support (PSRS) framework is proposed firstly. Secondly, a water carrying status index system containing 21 indicators is constructed based on this new model, and a water environment carrying status index (WECSI) is introduced to describe the water environment carrying status. Then, projection pursuit and entropy methods are used to calculate the water environment carrying status index of eight cities along the Yangtze River in Jiangsu province as examples. The results reveal that although the government has made substantial effort, the pressure subsystem is still dominated the changes of water environment carrying status, the eight cities still face significant pressure. The analysis indicated that government departments should commit to alleviating pressure and monitoring and supervising surface water quality while continuing to maintain a supporting force. The proposed framework and methodology is sound and effective for measuring the carrying status of regional water environment, and the methods can provide guidance for the Ecological Environment Department in ensuring the carrying capabilities of water environment and achieving sustainable development mission at regional level.

Previously conducted studies have established that as a disaster-bearing body, a coal mine is vulnerable to flood disasters and their consequent impacts. The purpose of this study is to put forward a quantitative evaluation method of the risk of coal mine flood disaster. Based on the scientific theory of disaster risk, a risk assessment model and index system for coal mine flood disaster was constructed, and a risk assessment method was proposed based on the projection pursuit and fuzzy cluster analysis. The results show that the risk of coal mine flood disaster was mainly determined by the hazard of disaster-causing factors, the stability of the disaster-prone environment, and the vulnerability of disaster-bearing bodies. Further research shows that the maximum daily rainfall had the greatest impact the risk of coal mine flood disaster. Therefore, the early warning mechanism should be established between the coal mine and the meteorological department to improve the fortification level. A risk assessment method of coal mine flood disaster was proposed in this study, which is of great significance for energy sustainability. [ABSTRACT FROM AUTHOR]

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Multivariate data is often visualized using linear projections, produced by techniques such as principal component analysis, linear discriminant analysis, and projection pursuit. A problem with projections is that they obscure low and high density regions near the center of the distribution. Sections, or slices, can help to reveal them. This article develops a section pursuit method, building on the extensive work in projection pursuit, to search for interesting slices of the data. Linear projections are used to define sections of the parameter space, and to calculate interestingness by comparing the distribution of observations, inside and outside a section. By optimizing this index, it is possible to reveal features such as holes (low density) or grains (high density). The optimization is incorporated into a guided tour so that the search for structure can be dynamic. The approach can be useful for problems when data distributions depart from uniform or normal, as in visually exploring nonlinear manifolds, and functions in multivariate space. Two applications of section pursuit are shown: exploring decision boundaries from classification models, and exploring subspaces induced by complex inequality conditions from a multiple parameter model. The new methods are available in R, in the tourr package. for this article are available online. [ABSTRACT FROM AUTHOR]