Projection Pursuit Regression Model Research Articles

Study of stress-strain relationship of hydraulic asphalt concrete at low to intermediate temperatures based on experimental data regression

The conventional principal structural theory modeling is constrained by the underlying assumptions that make the established constitutional model incapable of accurately describing the stress–strain relationship of asphalt concrete. Subsequently, in this study, the traditional modeling idea was changed and the projection pursuit regression (PPR) method was used to analyze triaxial test data obtained from asphalt concrete. The PPR models utilized to calculate deviatoric stress and volumetric strain were developed and the validity and applicability of the PPR model and this method were verified. The results show that the modeling method avoids the problem of inconsistency between a priori model assumptions and objective reality. Asphalt concrete has complex strain softening, strain hardening, shear shrinkage, and shear expansion characteristics. The model employed to calculate deviatoric stress and volumetric strain based on PPR method is superior to the E–B and “Nanshui” models in its capacity to reflect the characteristics of strain softening and shear deformation. The results calculated using the PPR model can accurately describe the stress–strain relationship of asphalt concrete. Studying the stress–strain relationship of asphalt concrete from the perspective of the examination of data breaks through the constraints of traditional modeling ideas, which has certain theoretical significance. The research results can provide a new solution for non-hypothesis modeling of nonlinear data and accurately describing the stress–strain relationship of asphalt concrete.

Unraveling the Role of Microalgae in Mitigating Antibiotics and Antibiotic Resistance Genes in Photogranules Treating Antibiotic Wastewater.

Harnessing the potential of specific antibiotic-degrading microalgal strains to optimize microalgal-bacterial granular sludge (MBGS) technology for sustainable antibiotic wastewater treatment and antibiotic resistance genes (ARGs) mitigation is currently limited. This article examined the performance of bacterial granular sludge (BGS) and MBGS (of Haematococcus pluvialis, an antibiotic-degrading microalga) systems in terms of stability, nutrient and antibiotic removal, and fate of ARGs and mobile genetic elements (MGEs) under multiclass antibiotic loads. The systems exhibited excellent performance under none and 50 μg/L mixed antibiotics and a decrease in performance at a higher concentration. The MBGS showed superior potential, higher nutrient removal, 53.9 mg/L/day higher chemical oxygen demand (COD) removal, and 5.2-8.2% improved antibiotic removal, notably for refractory antibiotics, and the system removal capacity was predicted. Metagenomic analysis revealed lower levels of ARGs and MGEs in effluent and biomass of MBGS compared to the BGS bioreactor. Particle association niche and projection pursuit regression models indicated that microalgae in MBGS may limit gene transfers among biomass and effluent, impeding ARG dissemination. Moreover, a discrepancy was found in the bacterial antibiotic-degrading biomarkers of BGS and MBGS systems due to the microalgal effect on the microcommunity. Altogether, these findings deepened our understanding of the microalgae's value in the MBGS system for antibiotic remediation and ARG propagation control.

Exploring carbon dioxide emissions forecasting in China: A policy-oriented perspective using projection pursuit regression and machine learning models

Achieving a balance between future greenhouse gas reduction and sustained economic growth is of utmost importance. This study leverages machine learning (ML), specifically projection pursuit regression (PPR), to evaluate the key factors that influence CO2 emission predictions in China. The analysis notably identifies the escalating electricity consumption as a primary influencing factor. Based on empirical findings, it is evident that building electricity consumption will continue to rise steadily until 2050 unless new restrictions or technological advancements are implemented. Relying solely on the reduced carbon intensity of electricity will not enable China to achieve carbon neutrality. Therefore, there is a pressing need for more energy-efficient building retrofits and technologies to reduce power consumption in both residential and commercial properties. This policy-oriented study underscores its practical implications, offering valuable insights to policymakers for developing targeted CO2 reduction strategies that align with sustainable development and climate goals.

Metagenomic insights into the potential risks of representative bio/non-degradable plastic and non-plastic debris in the upper and lower reaches of Haihe Estuary, China

As vectors for microorganisms and genetic elements, vast amounts of solid wastes, including plastics and non-plastics, enter oceans through estuaries globally. The heterogeneity of microbiomes developed on different types of plastic and non-plastic matrices and their potential environmental risks in field estuarine regions have not been fully explored. Here, microbial communities, antibiotic resistance genes (ARGs), virulence factors (VFs), and mobile genetic elements (MGEs) on substrate debris (SD) covering non-biodegradable plastics, biodegradable plastics, and non-plastics were first comprehensively characterized based on metagenomic analyzes (substrate identity). These selected substrates were field-exposed at both ends of the Haihe Estuary, China (geographic location). For substrate identity: conspicuously diverse functional gene profiles on different substrates were obtained; the relative gene copies of ARGs, VFs, and MGEs on non-biodegradable plastics were highest at both locations; non-biodegradable plastic matrices recruited the most abundant unique ARGs from ambient waters; the relative abundance of potential bacterial hosts carrying multiple ARGs and VFs (BH-AV) was much higher on non-biodegradable plastic surfaces than on the other two substrates, especially in the coastal water environment. For geographic locations: more abundant specific ARGs, VFs, and MGEs were significantly enriched on SD from the upper estuary; the average relative abundance of identified BH-AV on SD from the upper estuary was 1.99–7.14 folds from the lower estuary. Finally, the results of the Projection Pursuit Regression model verified the higher comprehensive potential risks arising from non-biodegradable plastics (substrate identity) and SD from the upstream of the estuary (geographic location). Based on comparative analysis, our results alert us to pay particular attention to ecological risks triggered by conventional non-biodegradable plastics in rivers and coastal environments and highlight the microbiological risk from terrestrial solid waste to the downstream marine environment.

The potential evaluation of groundwater by integrating rank sum ratio (RSR) and machine learning algorithms in the Qaidam Basin.

Groundwater is a vital resource in arid areas that sustains local industrial development and environmental preservation. Mapping groundwater potential zones and determining high-potential regions are essential for the responsible use of the local groundwater resource. When utilizing machine learning or deep learning algorithms to forecast groundwater potential in arid areas, difficulties such as inaccurate and overfitting predictions might occur due to a shortage of borehole samples. In this study, a database of groundwater conditioning factors with a size of 275,157 × 9 was created in the Qaidam Basin, and 85 known borehole samples were collected. The groundwater potential was evaluated using a combination of rank sum ratio (RSR), projection pursuit regression (PPR) and random forest (RF) algorithms, resulting in four models: PPR, RSR-PPR, RSR-RF, and RF. Results indicated that the groundwater potential was higher in mountainous regions surrounding the Qaidam Basin and decreased progressively towards the central and northwestern regions where most industries and facilities are located. The two primary factors, according to the PPR and RF models, were evapotranspiration (0.246, 0.225) and landform (0.176, 0.294). In terms of their ability to accurately forecast the borehole samples, the four models ranked as follows: RF > RSR-RF > RSR-PPR > PPR. The accuracy of the four models in the low-potential area was 0.73 (PPR), 0.60 (RSR-PPR), 0.87 (RSR-RF), and 0.80 (RF), respectively. However, the RF model showed overfitting due to a lack of samples, especially in high-potential regions, which limits its applicability. The RSR-RF method was applied directly to evaluate the entire factor database, avoiding the risk of overfitting caused by a limited number of training samples. The results demonstrate that the RSR-RF model is effective for classifying groundwater potential types in samples and mapping groundwater potential of the study area. This research presents a novel approach for groundwater potential predictions in areas with insufficient sample sizes, providing a reference for policymakers and researchers.

Water Eutrophication Evaluation Based on the Improved Projection Pursuit Regression Model Under the Hesitant Fuzzy Environment

The water eutrophication restricts the development of economy and society in China, which attracts increasing attention. It also affects the health and ecological environment. The evaluation of water eutrophication is very complicated due to the dynamic variability of the water quality data. This paper adopts the hesitant fuzzy set (HFS) to depict the massive data of samples and uncertain preference information of experts, which reduces the complexity of calculation and avoids the loss of information. After that, we construct the projection index function based on the main factors of water eutrophication. The particle swarm optimization (PSO) algorithm is applied to determine the global optimal projection direction by optimizing the projection index function. Therefore, we construct an improved projection pursuit regression (PPR) model. Finally, the water eutrophication evaluation of several lakes in China is used to demonstrate the improved PPR model. Also, the comparative analysis and contribution rate analysis are conducted to validate its rationality and advantages.

Metagenomic insights into environmental risk of field microplastics in an urban river

Microplastics (MPs) are emerging as anthropogenic vectors for the colonization and transportation of microbial communities in aquatic ecosystems. However, the composition of the microbiome and its environmental risk on field MPs at watershed scale has rarely been explored. Here, geographical distributions of microbiome, antibiotic resistance genes (ARGs) and virulence factors (VFs) on field MPs at watershed scale were characterized and their potential environmental risks were evaluated based on the data from metagenomic analyzes. The succession of microbial communities on MPs was observed along the watershed, and some ARGs and VFs were significantly enriched on MPs in urban region in comparison with rural region. Potential environmental risk of MPs conducted by Projection Pursuit Regression model in midstream (peri-urban region) and downstream (urban region) were significantly higher than that in upstream (rural region), and exhibit close relationships with MPs concentration and water velocity. Furthermore, our source tracking results demonstrated that the microbiome, ARGs and VFs in urban region MPs were largely derived from rural region MPs. Our results caution us that special attention should be paid to the risks posed by MPs in urban water bodies, and highlight the threat of MPs from rural upstream areas.

Urbanization drives the succession of antibiotic resistome and microbiome in a river watershed.

Urbanization is a process of ecosystem evolution mediated by human activities. One of the main consequences is the alteration of antibiotic resistome and microbiome in aquatic environment, which may transfer from water to sediments and exert a long-term health concern to aquatic animals and even humans. However, the role of urbanization in shaping resistome and microbiome in water and sediments is largely unknown. Here, a typical watershed with a significant gradient of urbanization was selected, and the resistome and microbiome in both water and sediments were profiled. In water, the relative abundance of ARGs and bacterial diversity increased gradually along the urbanization, but were relatively stable in sediments. The abundance of potential multidrug-resistant bacteria in peri-urban and urban water were significantly higher than rural water, while in sediments, unique potential multidrug-resistant bacteria were detected at different urbanization levels. Population size was identified as the key factor shaping the ARGs profiles. Finally, environmental risk assessment based on the projection pursuit regression model suggested that the water in urban region had higher potential environmental risk of antibiotic resistance, in contrary to the sediments in rural and peri-urban regions. Our findings revealed distinct responses of water and sediment to urbanization in terms of antibiotic resistome and microbiome. This work provides important guide for hierarchically controlling ARGs dissemination in watershed.

Prediction of thermal conductivity ofconcrete under variable temperatures in cold regions using projection pursuit regression

A projection pursuit regression (PPR) model was proposed in this paper to predict the thermal conductivity of concrete (TCC) under variable temperatures in cold regions. The PPR model was established based on experimental data obtained from a single–factor experiment. Unlike the conventional PPR model that selected a random form from an empirical distribution function to describe the ridge functions in advance, the PPR model in this paper can directly apply numerical functions to describe the ridge functions obtained by projection. The comparison study indicated that, for the TCC prediction, the PPR model (R = 0.985) outperformed trained model based on Back–propagation (BP) neural network (R = 0.974). The PPR model simulation results demonstrated that the aggregate volume fraction, sand rate, saturation, water–cement ratio, fly ash content, and slag content all had impacts on the TCC under variable temperatures in cold regions. Especially, in temperature-sensitive zone (0 °C to −10 °C), it was found that the TCC of specimens with high water–cement ratio and high saturation degree increase abruptly but other factors did not cause the similar abrupt change of the TCC during the same temperature range. The correlation between the water-cement ratio and temperature–sensitive zone was explained after an in–depth study of the concrete pore structure.

Brand Preference Prediction Method of Cross-Border E-Commerce Consumers Based on Potential Tag Mining

There are many brands in cross-border e-commerce platforms. Obtaining consumers’ preference for brands will help promote the development of cross-border e-commerce industry. A brand preference prediction method of cross-border e-commerce consumers based on potential tag mining is proposed. Preprocess the cross-border e-commerce brand comment information obtained, build a HowNet emotion dictionary, and calculate consumers’ emotional tendency towards the brand on this basis. The projection pursuit regression model is optimized by differential evolution algorithm to reduce the dimension of the obtained consumer brand emotion information. Mining the potential labels of the information after dimensionality reduction, combined with Bayesian personalized sorting method and paired interaction tensor decomposition method, this paper constructs a brand preference’s prediction model to predict the brand preference of cross-border e-commerce consumers. The experimental results show that the proposed method has high accuracy of brand tendency calculation results, small average absolute error of prediction results, and high model accuracy.

Average relative flow of single-wing labyrinth drip irrigation tape based on projection pursuit regression

The hydraulic performance of single-wing labyrinth drip irrigation tapes under the coupling effect of water and fertilizer affects the operating efficiency of the entire drip irrigation system. In this study, three types of single-wing labyrinth drip irrigation tapes were studied. We evaluated the average relative flow of each type and conducted indoor uniform orthogonal tests of three factors, namely, fertilizer concentration, sediment content, and operating pressure. The results showed that the order of the factors affecting the average relative flow of single-wing labyrinth drip irrigation tape was sediment content > fertilizer concentration > operating pressure. The projection pursuit regression (PPR) models of the average relative flow of three types of single-wing labyrinth drip irrigation tapes (H1, H2, and H3) were established. The root mean square errors (nRMSE) of these three models were 0.66%, 0.74%, 0.34%, respectively, indicating their excellent prediction performance. The optimal performance of the three types of tapes were obtained when the fertilizer concentration was 0.6 g/L, the sediment content was 1 g/L, and the operating pressure was 40 kPa. Under the optimal condition, the average relative flows of H1-type, H2-type, and H3-type were 0.792, 0.764, and 0.700, respectively.

Estimation of graphical models for skew continuous data

AbstractWe 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.

Evaluation and prediction on abrasion resistance of hydraulic concrete after exposure to different freeze-thaw cycles

Considering that the hydraulic concrete structures in cold regions will suffer from the combined action of freeze-thaw (FT) damage and hydraulic abrasion, the morphology indexes such as the abrasion depth, abrasion volume and fractal dimension of hydraulic concrete subjected to different FT cycles (0, 100, 200, 300 and 400 FT cycles) are determined by the underwater method. On this basis, a prediction model for the abrasion resistance of concrete subjected to different FT damage is established by using the projection pursuit regression (PPR) theory. The results show that with the aggravation of FT damage, the number of isolated matrices and abrasion depth on the concrete surface continue to increase, and the surface roughness parameters increase exponentially, which significantly affects the evolution of abrasion depth, abrasion volume and fractal dimension of hydraulic concrete with abrasion time, showing a continuous decline in abrasion resistance. And a power function is used to establish the prediction models of the morphology indexes of hydraulic concrete subject to different FT cycles, which have high prediction accuracy. In addition, the relationship functions between abrasion depth, abrasion volume and fractal dimension are proposed, and both of them have a good linear correlation. Eventually, the PPR model based on morphology indexes is proved to be a promising method to predict the abrasion performance of hydraulic concrete structures in cold regions.

Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons.

System identification techniques—projection pursuit regression models (PPRs) and convolutional neural networks (CNNs)—provide state-of-the-art performance in predicting visual cortical neurons’ responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron’s receptive field. In this paper, we show that using a dictionary of diverse kernels with complex shapes learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction as well as algorithmic data efficiency and convergence speed. Extensive experimental results also indicate that these sparse-code kernels provide important information on the component features of a neuron’s receptive field. In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity. We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 neurons.

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.

Application of projection pursuit regression model for blasting vibration velocity peak prediction

Application of projection pursuit regression model for blasting vibration velocity peak prediction

Evaluation and Prediction on the Hydraulic Abrasion Performance of High Belite Cement-Based Concrete

High belite cement (HBC) concrete is increasingly applied to the hydraulic projects, where much attention should be paid to the abrasion performance. In this paper, the mass loss, abrasion rate and morphology indexes such as depth of abrasion, volume loss and fractal dimension of HBC-based concrete at different wear periods were determined through underwater method. Moreover, a new method by transforming the survey area of worn surface was put forward to simulating practical erosion conditions, based on which, the abrasion performance prediction model was built by Projection Pursuit Regression (PPR) theory. Results show that the compounding addition of 30% mineral admixtures had little contribution to the abrasion resistance of HBC concrete after being long-term cured, while reducing water-binder (w/b) ratios was still an effective way for improving that. The worn surface of concrete specimens was divided into three regions for abrade zone, transitional zone and sound zone, and the abrasion evolution was found to be related to the wear duration and space position. Furthermore, there were good correlations between mass loss rate, depth of abrasion, volume loss and fractal dimension, and the fitting formulas were also proposed with good coefficients. Eventually, the PPR model based on the data of transformed survey area was proved to be a promising approach for predicting the abrasion performance.

Regional Flood Frequency Analysis Through Some Machine Learning Models in Semi-arid Regions

The machine learning models (MLMs), including support vector regression (SVR), multivariate adaptive regression spline (MARS), boosted regression trees (BRT), and projection pursuit regression (PPR) are compared to traditional method i.e. nonlinear regression (NLR) in regional flood frequency analysis (RFFA). In this study, the Karun and Karkheh watersheds, which is located in the southwestern of Iran, with the same climatic and physiographic conditions are considered. Fifty-four hydrometric stations with a period of 21 years (1993–2013) are selected based on the instructions of U.S. Federal Agencies Bulletin 17 B were applied for RFFA. The generalized normal (GNO) probability distribution function (PDF) is selected by the L-moment method among five PDFs, including the GNO, generalized Pareto (GP), generalized logistic (GL), generalized extreme value (GEV) and Pearson type 3 (P ІІІ) to estimate flood discharge for the expected return periods. Twenty-five predictor variables, such as physiographic, climatologic, geologic, soil and land use variables are extracted. Follow land, maximum 24-h rainfall, mean watershed slope, compactness coefficient, mean and maximum watershed elevation variables are recognized as the appropriate combination of input using gamma test (GT). The overall results indicate that the SVR, PPR, and MARS models in comparison to the NLR and BRT models have a better performance to estimate flood discharge with the expected return periods. Future, the SVR model based on radial basis function (RBF) kernel is chosen as the best model in terms of the mean of the Nash-Sutcliff coefficient (M-Ef) and the mean of relative root mean squared error (M-RMSEr) (i.e. 0.94 and 63.93, respectively) for different return periods.

Experimental Design for Optimization of Orthogonal Projection Pursuit Models

Bayesian optimization and kernelized bandit algorithms are widely used techniques for sequential black box function optimization with applications in parameter tuning, control, robotics among many others. To be effective in high dimensional settings, previous approaches make additional assumptions, for example on low-dimensional subspaces or an additive structure. In this work, we go beyond the additivity assumption and use an orthogonal projection pursuit regression model, which strictly generalizes additive models. We present a two-stage algorithm motivated by experimental design to first decorrelate the additive components. Subsequently, the bandit optimization benefits from the statistically efficient additive model. Our method provably decorrelates the fully additive model and achieves optimal sublinear simple regret in terms of the number of function evaluations. To prove the rotation recovery, we derive novel concentration inequalities for linear regression on subspaces. In addition, we specifically address the issue of acquisition function optimization and present two domain dependent efficient algorithms. We validate the algorithm numerically on synthetic as well as real-world optimization problems.

Optimization of mixture proportions in ternary low-heat Portland cement-based cementitious systems with mortar blends based on projection pursuit regression

The low hydration heat of low-heat Portland (LHP) cement-based cementitious systems affects their mechanical properties at an early age. Models for predicting mechanical and thermal performances and optimizing the mixture proportion in ternary LHP cement-based cementitious systems with mortar blends will be useful. In this study, the projection pursuit regression (PPR) model, which is a nonassuming modeling technique, is applied to predict the relationship between the mixture proportion and compressive strength and hydration heat of LHP cement-based cementitious systems with mortar. The optimum mixture proportion was determined via analysis of the contour plots compressive strength and hydration heat as functions of fly ash and ground-granulated blast-furnace slag content in accordance with the demand of actual mass concrete engineering. The PPR model was compared with other methods and found to present higher calculation accuracy. The stability and modeling efficiency of the PPR model were determined and verified by proposing the “accuracy consistency test” criterion and modeling sample selection criteria. The multiobjective optimization problem was converted into two single-objective optimization problems by the PPR model and the optimization method. The problems of artificial parameter assignment and assumptions, which exist in traditional multiobjective optimization, were thus avoided. The PPR model is a valuable tool for predicting the properties of cementitious systems and optimizing the mixture proportion in cementitious systems.