Projection Pursuit Research Articles

Dynamic evaluation of green innovation efficiency of patent-intensive industries: evidence from the new equipment manufacturing

ABSTRACT Patent-intensive industries can promote high-quality development of economy; however, they also generate the environmental pollution. Therefore, it is necessary to evaluate their green innovation efficiency (GIE), and it is beneficial to improve the GIE of pantent-intensive industries. We treat the patent-intensive industries as the research objects, in which the new equipment manufactruing is more representative. Based on the projection pursuit model and the stochastic frontier model, we measure the GIE of the new equipment manufacturing and explore the factors affecting GIE. Also, we analyze the dynamic evolution trend of GIE of the new equipment manufacturing based on the dynamic evaluation model with speed characteristics. The research results demonstrate that there is still space for improvement in GIE of the new equipment manufacturing to a certain extent. The GIE of different new equipment manufacturing has obvious differences, and the development is uneven. R&D investment intensity and industry market competition have a positive effect on GIE, while government R&D support and external R&D expenditures have negative influence on GIE. The dynamic GIE of the new equipment manufacturing shows the fluctuating trend over time. The research conclusions provide implications for patent-intensive industries to increase GIE and achieve sustainable development.

The Risk Assessment of Debris Flow Hazards in Zhouqu Based on the Projection Pursuit Classification Model

The Risk Assessment of Debris Flow Hazards in Zhouqu Based on the Projection Pursuit Classification Model

Dreams and constraints of an African publisher: Walter Bgoya, Tanzania Publishing House and Mkuki na Nyota, 1972–2020

AbstractThis article explores the history of two Tanzanian publishing houses and the remarkable life and career of Walter Bgoya, former general manager of Tanzania Publishing House (1972–90) and managing director of Mkuki na Nyota, which he founded in 1991. Using the lens of microhistory, and drawing from extensive interviews with Bgoya and conversations with two colleagues and three authors, the article first chronicles his early life and ideological formation and what influenced his career in book publishing. It then examines the key achievements and challenges faced by these publishing houses in different times of austerity (e.g. Structural Adjustment Programmes, foreign investment with conditionalities, declining state support and high printing costs), along with the complex ways in which Bgoya has navigated the shifting, often uncertain, political, financial and legislative landscapes, while retaining his intellectual freedom and core Pan-Africanist beliefs. Constraints have not hampered Bgoya's pursuit of ambitious projects or his commitment to publishing relevant and progressive books, either written by African authors or on African matters. I suggest that reducing the scale and identifying how specific conditions of austerity have affected the choices made by a publisher over time can yield insights into the ways in which cultural institutions have contributed to knowledge production and dissemination in postcolonial Africa.

Kurtosis-based projection pursuit analysis to evaluate South American rapadura

Rapadura is an artisanal candy obtained from concentrated sugarcane juice. In this study, a differentiation between South American rapadura producers has been tried using a Kurtosis-based projection pursuit analysis (kPPA) concerning essential minerals, acrylamide, moisture contents, pH, and color. These parameters revealed significant inter- and intra-country differences. Based on the employed measurements, a multivariate exploration with kPPA extracted information from rapadura even though it is a very artisanal product and was effective in separating classes, especially Brazilian and Ecuadorian rapadura, where principal component analysis failed. Moreover, ellipse confidence regions showed significant differences between non-organic and organic rapadura from Colombia and Peru in granulated form. From a chemometric point of view, the application of kPPA can be used in cases when other metrics (as based on the variance) fail and can be useful in the exploratory analysis of complex multivariate chemical data.

Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms

The curse of dimensionality problem occurs when the data are high-dimensional. It affects the learning process and reduces the accuracy. Feature selection is one of the dimensionality reduction approaches that mainly contribute to solving the curse of the dimensionality problem by selecting the relevant features. Irrelevant features are the dependent and redundant features that cause noise in the data and then reduce its quality. The main well-known feature-selection methods are wrapper and filter techniques. However, wrapper feature selection techniques are computationally expensive, whereas filter feature selection methods suffer from multicollinearity. In this research study, four new feature selection methods based on outlier detection using the Projection Pursuit method are proposed. Outlier detection involves identifying abnormal data (irrelevant features of the transpose matrix obtained from the original dataset matrix). The concept of outlier detection using projection pursuit has proved its efficiency in many applications but has not yet been used as a feature selection approach. To the author’s knowledge, this study is the first of its kind. Experimental results on nineteen real datasets using three classifiers (k-NN, SVM, and Random Forest) indicated that the suggested methods enhanced the classification accuracy rate by an average of 6.64% when compared to the classification accuracy without applying feature selection. It also outperformed the state-of-the-art methods on most of the used datasets with an improvement rate ranging between 0.76% and 30.64%. Statistical analysis showed that the results of the proposed methods are statistically significant.

Development evaluation of nature reserves under China’s forestry department: A spatiotemporal empirical study at the province level

It is important to evaluate the development level of nature reserves. In this study, we aimed to assess the development level of nature reserves under the administration of China’s forestry department in 31 provincial-level regions from 2005 to 2017 (excluding Hong Kong Special Administrative Region, Macao Special Administrative Region, and Taiwan Province). For this purpose, we analyzed the spatial and temporal evolution of nature reserve development in different regions using projection pursuit and spatial econometric methods. In terms of temporal distribution, the development level of nature reserves has been steadily improving, and the growth rate showed the trend of “strong fast” and “weak slow”. However, the development gap among different provinces is large. In terms of spatial distribution, the development of nature reserves presented the characteristics of “high in the west and low in the east” and “high in the north and south and low in the middle.” The endowment of natural resources, scientific research, and investment has a considerable effect on the development level of nature reserves. This study provides suggestions for the differential construction and sustainable development of nature reserves in China.

A Review on Outliers-Detection Methods for Multivariate Data

Data in practice are often of high dimension and multivariate in nature. Detection of outliers has been one of the problems in multivariate analysis. Detecting outliers in multivariate data is difficult and it is not sufficient by using only graphical inspection. In this paper, a nontechnical and brief outlier detection method for multivariate data which are projection pursuit method, methods based on robust distance and cluster analysis are reviewed. The strengths and weaknesses of each method are briefly discussed.

Projection Pursuit Regression (PPR) on Statistical Downscaling Modeling for Daily Rainfall Forecasting

Rainfall forecasting has an important role in people's lives. Rainfall forecasting in Indonesia has complex problems because it is located in a tropical climate. Rainfall prediction in Indonesia is difficult due to the complex topography and interactions between the oceans, land and atmosphere. With these conditions, an accurate rainfall forecasting model on a local scale is needed, of course taking into account the information about the global atmospheric circulation obtained from the General Circulation Model (GCM) output. GCM may still be used to provide local or regional scale information by adding Statistical Downscaling (SD) techniques. SD is a regression-based model in determining the functional relationship between the response variable and the predictor variable. Rainfall observations obtained from the Meteorology Climatology and Geophysics Council (BMKG) are a response variable in this study. The predictor variable used in this study is the global climate output from GCM. This research was conducted in a place, namely Kupang City, East Nusa Tenggara because it has low rainfall. The Projection Pursuit Regression (PPR) will be used in this SD method for this study. In PPR modeling, optimization needs to be done and model validation is carried out with the smallest Root Mean Square Error (RMSE) criteria. The expected results must have a pattern between the results of forecasts and observations showing or approaching the observational data. The PPR model is a good model for predicting rainfall because The results of the forecast and observation show that the results of the rainfall forecast are observational data.

A comprehensive evaluation framework for China’s oil import sustainability with a projection pursuit and matter-element extension model

ABSTRACT Given the serious imbalance between supply and demand for domestic oil resources, it is essential to accurately identify and measure the potential problems associated with oil imports to mitigate the negative influence of oil import uncertainty on China’s energy security. This paper designs a comprehensive assessment index framework based on the dimensions of the supply chain and establishes an integrated model to evaluate the sustainability of China’s oil imports from 2000 to 2017. The results show that since 2000, China’s oil import sustainability has experienced apparent dynamic changes from a critical state to strong unsustainability and then weak unsustainability. External dependence remains the most significant threat, and high dependence on oil imports is the root cause. External supply sustainability may become the main long-term risk source in the future. Recommendations for improvements in oil import sustainability are proposed considering the less optimistic future trend.

Uncertainty Quantification and Bayesian Inference of Cloud Parameterization in the NCAR Single Column Community Atmosphere Model (SCAM6)

Uncertainty quantification (UQ) in weather and climate models is required to assess the sensitivity of their outputs to various parameterization schemes and thereby improve their consistency with observations. Herein, we present an efficient UQ and Bayesian inference for the cloud parameters of the NCAR Single Column Atmosphere Model (SCAM6) using surrogate models based on a polynomial chaos expansion. The use of a surrogate model enables to efficiently propagate uncertainties in parameters into uncertainties in model outputs. We investigated eight uncertain parameters: the auto-conversion size threshold for ice to snow (dcs), the fall speed parameter for stratiform cloud ice (ai), the fall speed parameter for stratiform snow (as), the fall speed parameter for cloud water (ac), the collection efficiency of aggregation ice (eii), the efficiency factor of the Bergeron effect (berg_eff), the threshold maximum relative humidity for ice clouds (rhmaxi), and the threshold minimum relative humidity for ice clouds (rhmini). We built two surrogate models using two non-intrusive methods: spectral projection (SP) and basis pursuit denoising (BPDN). Our results suggest that BPDN performs better than SP as it enables to filter out internal noise during the process of fitting the surrogate model. Five out of the eight parameters (namely dcs, ai, rhmaxi, rhmini, and eii) account for most of the variance in predicted climate variables (e.g., total precipitation, cloud distribution, shortwave and longwave cloud radiative effect, ice, and liquid water path). A first-order sensitivity analysis reveals that dcs contributes ~40–80% of the total variance of the climate variables, ai around 15–30%, and rhmaxi, rhmini, and eii around 5–15%. The second- and higher-order effects contribute ~7 and 20%, respectively. The sensitivity of the model to these parameters was further explored using response curves. A Markov chain Monte Carlo (MCMC) sampling algorithm was also implemented for the Bayesian inference of dcs, ai, as, rhmini, and berg_eff using cloud distribution data collected at the Southern Great Plains (USA). The inferred parameters suggest improvements in the global Climate Earth System Model (CESM2) simulations of the tropics and sub-tropics.

Skewness-Based Projection Pursuit as an Eigenvector Problem in Scale Mixtures of Skew-Normal Distributions

This paper addresses the projection pursuit problem assuming that the distribution of the input vector belongs to the flexible and wide family of multivariate scale mixtures of skew normal distributions. Under this assumption, skewness-based projection pursuit is set out as an eigenvector problem, described in terms of the third order cumulant matrix, as well as an eigenvector problem that involves the simultaneous diagonalization of the scatter matrices of the model. Both approaches lead to dominant eigenvectors proportional to the shape parametric vector, which accounts for the multivariate asymmetry of the model; they also shed light on the parametric interpretability of the invariant coordinate selection method and point out some alternatives for estimating the projection pursuit direction. The theoretical findings are further investigated through a simulation study whose results provide insights about the usefulness of skewness model-based projection pursuit in the statistical practice.

Research on Outlier Detection for High-Dimensional Data Based on PPCLOF

Aiming at the “dimension disaster” problem encountered in the outlier detection of high-dimensional data, this paper uses the projection pursuit algorithm to perform non-linear dimensionality reduction on high-dimensional data by calculating the phase relationship between dimensions. According to the sample points obtained by dimensionality reduction, the LOF (Local Outlier Factor) algorithm is applied to calculate the outlier factor to obtain the relevant outlier data. In order to improve the calculation accuracy and efficiency of the LOF algorithm, clustering method is used to cut the outlier calculation data to reduce the amount of calculation. Experiments on real-world and artificial datasets, compared with the existing algorithms, demonstrated the effectiveness and efficiency of the proposed algorithm.

Effect of International Technology Transfer on the Technical Efficiency of High-Tech Manufacturing in China: A RAGA-PP-SFA Analysis

The sustainable development of China’s high-tech manufacturing (HTM) sector is restricted by dependence on technology introduction and foreign direct investment (FDI), low input-output efficiency, and environmental pollution. This study aimed to examine the roles of technology introduction and FDI in improving the technical efficiency of Chinese HTM from an environmental perspective. By integrating stochastic frontier analysis (SFA) and projection pursuit (PP) based on the real-coded accelerated genetic algorithm (RAGA), this study constructed a RAGA-PP-SFA model that considers undesirable outputs. This model includes various outputs, including environmental pollution, in the production function to improve estimation accuracy. Moreover, to verify the robustness of the estimation results, the results were provided when environmental pollutants were taken as input factors. The results showed that technology introduction could significantly promote HTM’s technical efficiency, while FDI had no significant positive effect. By comparing the estimated results with those that did not consider environmental pollution, this study not only reveals different roles of technology introduction and FDI in improving HTM’s technical efficiency but also confirms that ignoring environmental pollution will overestimate their roles (especially the role of FDI) in such improvement.

Urban Flood Analysis in Ungauged Drainage Basin Using Short-Term and High-Resolution Remotely Sensed Rainfall Records

Analyzing flooding in urban areas is a great challenge due to the lack of long-term rainfall records. This study hereby seeks to propose a modeling framework for urban flood analysis in ungauged drainage basins. A platform called “RainyDay” combined with a nine-year record of hourly, 0.1° remotely sensed rainfall data are used to generate extreme rainfall events. These events are used as inputs to a hydrological model. The comprehensive characteristics of urban flooding are reflected through the projection pursuit method. We simulate runoff for different return periods for a typical urban drainage basin. The combination of RainyDay and short-record remotely sensed rainfall can reproduce recent observed rainfall frequencies, which are relatively close to the design rainfall calculated by the intensity-duration-frequency formula. More specifically, the design rainfall is closer at high (higher than 20-yr) return period or long duration (longer than 6 h). Contrasting with the flood-simulated results under different return periods, RainyDay-based estimates may underestimate the flood characteristics under low return period or short duration scenarios, but they can reflect the characteristics with increasing duration or return period. The proposed modeling framework provides an alternative way to estimate the ensemble spread of rainfall and flood estimates rather than a single estimate value.

Combinatorial projection pursuit analysis for exploring multivariate chemical data

Kurtosis-based projection pursuit analysis (kPPA) has demonstrated the ability to visualize multivariate data in a way that complements other exploratory data analysis tools, such as principal components analysis (PCA). It is especially useful for partitioning binary data sets (2k classes) with a balanced design. Since kPPA is not a variance-based method, it can often provide unsupervised class separation where other methods fail. However, when multiple classifications are possible (e.g. by gender, age, disease state, etc.), the projection provided by kPPA (corresponding to the global minimum kurtosis) will not necessarily be the one of greatest interest to the researcher. Fortunately, the optimization algorithm for kPPA allows for interrogation of projections obtained from numerous local minima. This strategy provides the basis of a new method described here, referred to as combinatorial projection pursuit analysis (CombPPA) because it presents alternative combinations of class separation. The method is truly exploratory in that it allows the landscape of interesting projections to be more fully probed. The approach uses Procrustes rotation to map local minima among the kPPA solutions, whereupon the researcher can visualize different projections. To demonstrate the new method, the clustering of grape juice samples using visible spectroscopy is presented as a model problem. This problem is well-suited to this type of study because there are eight classes of samples symmetrically partitioned into two classes by type (organic/non-organic) or four classes by brand. Results presented show the different combinations of projections that can be obtained, including the desired partitions. In addition, this work describes new enhancements to the kPPA algorithm that improve the orthogonality of solutions obtained.

Research on Health Status Evaluation of Higher Education Based on Factor Analysis and Projection Pursuit Evaluation Model

Higher education system is an important factor for a country to provide its citizens with further education besides primary and secondary education, each country has different higher education systems, and each country has its own advantages and disadvantages. In order to cultivate higher quality talents, countries should begin to pay attention to the reform of higher education system in order to establish a healthier and sustainable higher education system. Firstly, we set up a national health evaluation model of higher education. The first step is to select 40 countries' data about higher education by consulting relevant literature and OECD database, and use factor analysis to select and process evaluation indicators, Finally, four first-level indicators, such as teachers' strength and learning environment, students' access to education, education financial investment, education level and achievements, and 12 second-level indicators, such as the ratio of institutional educators and international students, the proportion of students' expenditure on education to GDP, and the total expenditure of government educational institutions, are determined. In the second step, we first normalize the index data, and then nest the projection pursuit evaluation model with multiple indexes. We use the evaluation value of every three secondary indicators to represent the corresponding primary indicators, and then calculate the evaluation value of the primary indicators as the final result, so we can get the weight formula of all indicators. The analysis results show that the health status of higher education in Korea is poor, and the evaluation values of most indicators are lower than the average level, so we chose Korea as the follow-up research object and put forward an achievable and reasonable vision for its higher education system.

Identification of Brain Tumor Using Projection Pursuit Bivariate Multilayer Perceptred Classification

Identification of Brain Tumor Using Projection Pursuit Bivariate Multilayer Perceptred Classification

Analysis and comprehensive evaluation of sustainable land use in China: Based on sustainable development goals framework

Based on UN 2030 Sustainable Development Goals (SDGs), this study selects 43 indicators from three dimensions to construct an evaluation index system for sustainable land use (SLU). Thirty provincial administrative regions in China from 2002 to 2018 are chosen to comprise the study area. Real-coded accelerating genetic algorithm–projection pursuit techniques and an exploratory spatial data analysis model are used to study the spatiotemporal differences in the SLU levels of the provinces in China. Results show the following: (1) During the study period, the SLU level in China demonstrated an overall upward trend of fluctuations, along with obvious regional differences. (2) The spatial pattern of the SLU level in China was high in the eastern region and low in the western region in the east–west direction; for the north–south direction, an inverted U-shaped distribution was noted. (3) The main types of local spatial autocorrelation are H–H and L–L types, and both have evident spatial clustering characteristics. (4) The realization of Goal A is the main driving force for the improvement of the SLU level, whereas the failure to realize Goals B and C effectively is the main bottleneck in the improvement of the SLU level.

Application of Projection Pursuit Analysis Method Based on Kernel Function in Fault Diagnosis for Rolling Bearing

In view of nonlinear and non-Gaussian characteristics of fault feature for rolling bearing of wind turbine, the projection pursuit analysis method based on kernel function is put forward to make pattern recognition for all bearing running states. It elaborates the principle of projection pursuit method, and plan and steps for pattern recognition, then has a simulation and test by MATLAB software. The test results show that the projection pursuit analysis method based on kernel function is an effective method for fault diagnosis, and it can quickly and efficiently identify rolling bearing running states.

Evolution of spatial–temporal characteristics and financial development as an influencing factor of green ecology

This study aims to determine the impact of financial development on green ecology to promote cleaner production. Particularly, it estimates the evolution of characteristics of the green-ecology of 37 cities from the Yangtze River Delta and determines the impact of financial development on green-ecology using the projection pursuit model, Moran’s Index, and regression model. Results found that the green-ecology index showed an upward trend and the index in the East was higher than the West. The green ecology and its composition indexes were found with significant spatial positive agglomeration characteristics. The Eastern region was mainly distributed as high to high agglomeration while the Western region was distributed as low to low agglomeration. Regarding composition indexes of green ecology, financial development has promoted a positive effect on the green-growth and resource-environment indexes. The study results stress that the enterprises should strengthen the technological innovation for the improvement of resource use efficiency and reduction of pollution.