Projection Pursuit Research Articles

Non-hypothetical projection pursuit regression for the prediction of hydration heat of Portland-cement-based cementitious system

In this study, the non-hypothetical projection pursuit regression (NH-PPR) is proposed. The proposed NH-PPR model can predict the hydration heat based on the four cement phases, FA, SL, cement fineness and hydration time. The NH-PPR model is proposed by using the multiple layer iteration method and the non-hypothetical and non-parametric ridge functions to enhance accuracy and solve the problems caused by the parameter selection and the subjective hypothesis. The modeling data set is applied to train model, the testing data set is regressed and fitted into the model, and then the obtained results are compared with the BP model. To further validate the proposed model, another published data set is used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtains the better accuracy, stability and versatility, and avoids the parameter selection and subjective hypothesis.

Multivariate nonparametric control charts based on projection pursuit

AbstractMultivariate 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 , 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.

Mechanical strength characteristics of saline soil stabilized by all-solid waste cementitious material based on projection pursuit regression modeling

Mechanical strength characteristics of saline soil stabilized by all-solid waste cementitious material based on projection pursuit regression modeling

Comparative Analysis and Empirical Study of Prefabrication Rate Calculation Methods for Prefabricated Buildings in Various Provinces and Cities in China

As an important part of the prefabricated building standard system, the evaluation standards for prefabricated buildings play an important guiding role in the construction and development of prefabricated buildings. However, at present, the national evaluation standards for prefabricated buildings are highly extensive and general, and some of their contents are not well considered, while local standards based on the national standard have been formulated in accordance with the actual situation in their respective regions, which makes the evaluation of prefabricated buildings in various regions more confusing, which also causes differences when calculating the prefabrication rate, restricting the development of prefabricated buildings. In order to promote the construction of a complete evaluation standard system for prefabricated buildings in China, this paper combs through the methods used for the calculation of the prefabrication rate of prefabricated buildings in 28 provincial-level administrative regions in China, conducts a comparative analysis and empirical research on the calculation of the prefabrication rate under different standards in four dimensions, and applies t-tests and simulated annealing algorithms to optimize the projection pursuit model to analyze the differences in the total scores of the prefabrication rate under different standards, as well as the key factors that affect them. The study shows that (1) the same building may have different prefabrication rates under different standards for prefabricated buildings, and the way of calculating the prefabrication rate and the scoring criteria of the evaluation items will result in significant differences in the calculated prefabrication rate. (2) Depending on the value of the optimal projection direction vector, it was found that among the evaluation criteria, the enclosure wall and the internal partition wall have the greatest influence on the total score value, while the main structure has the least influence on the total score value. (3) There are similarities as well as differences in the evaluation criteria of prefabricated buildings in each province. On this basis, by analyzing the reasons for these differences, corresponding suggestions are made for governments needing to formulate or revise local standards.

Watchdog malicious node detection and isolation using deep learning for secured communication in MANET

Mobile Ad-hoc Networks (MANETs) are wireless networks formed dynamically by connecting or leaving nodes to and from the network without any fixed infrastructure. These categories of wireless networks are susceptible to different attacks based on their dynamic topological structure. Due to this, security is a primary constraint in MANETs to preserve communication between mobile nodes. A Deep Neural Learned Projective Pursuit Regression-based Watchdog Malicious Node Detection and Isolation (DNLPPR-WMNDI) technique is proposed and modelled in this paper to improve the security feature of MANETs. The newly proposed DNLPPR-WMNDI technique initially selects the neighbouring nodes by applying the projection pursuit regression function. In multicasting, the route paths are established through the intermediate node with the help of control commands named RREQ and RREP. After then, Watchdog Malicious Node Detection and Isolation (WMNDI) technique is applied to detect malicious nodes based on the data packet forwarding time. Basically, a malicious node is affected by a node isolation attack. For better communication, a malicious node is isolated from the network and multicast routing is carried out by selecting the next neighbouring node and this improves the communication security. Simulation is done for the developed technique based on different performance metrics.

Future smart grids creation and dimensionality reduction with signal handling on smart grid using targeted projection

A smart grid is a developing electrical network of transmission lines, switches, transformers, protection equipment, sensors, and information technologies working together and are the result of a dynamic co-evolution process that leverages the integration of technological advances. These are facing several challenges with renewable energy resources, has high consumption, and the ever-increasing demand for electricity at commercial, residential, and industrial levels. In recent decades, the main grid is facing several challenges related to the integration of renewable energy resources, deployment of grid-level energy storage devices, deployment of new usages such as the electric vehicle, massive usage of power electronic devices at different electric grid stages, and the inter-connection with microgrids and prosumers. This paper aims to identify the challenges, control these perspectives, for attaining the energy infrastructures, to achieve the directions of future research and development, with respect to power generation, transmission, distribution, and consumption. In this study, future smart grids creation and dimensionality reduction with signal handling on the smart grid using, targeted projection pursuit for future smart grids signal analysis is proposed, in which the Signal handling on the smart grid uses Blind equalization and is imposed to transmit the signal which is inferred from the received signal. The smart grid is based on the Non-negative matrix factorization; the Sallen-key topology is implemented to reduce the noise in the signal. Moreover, in this paper, we perform the targeted projection pursuit, the Cognitive Dimensionality Reduction is used for the analysis of n-way arrays, nonlinear dimensionality reduction, and we use the Linear predictive coding, and Hilbert spectrum which represents the spectral envelope of a digital signal of speech in the compressed form. The experiments on the proposed method, show the effectiveness of our method is 92 % well organised with extensive results at Targeted projection pursuit for future smart grids. The signal analysis shows the execution between past research and the ongoing research has achieved an efficiency rate of 92.2 %, in which the current approach outperforms 94.87 % compared existing approach. The analysis of future smart grids creation, Signal Dimensionality Reduction is 75 % higher than the existing analysis. The proposed system flow is performed with more versatility, and energy efficiency is 75.65 % and 34.54 % efficiency than the existing system.

Projection Pursuit Random Forest for Mineral Prospectivity Mapping

Projection Pursuit Random Forest for Mineral Prospectivity Mapping

Estimation of projection pursuit regression via alternating linearization

The projection pursuit regression (PPR) has played an important role in statistical modeling. It can be used both as a data model for statistical interpretation and as an algorithmic model for approximating general non-parametric regressions. Existing estimation methods of PPR usually involve complicated minimization in order to achieve desired efficiency under general settings. This paper presents an algorithm by alternatively linearizing the estimation loss function, referred to as aPPR hereafter, which is easy to implement. The asymptotic theory is also established for both the PPR data model and the algorithmic model. Numerical performance of aPPR in model estimation and model interpretation is demonstrated through simulations and real data analysis.

Spatial distribution and suitability evaluation of nighttime tourism in Kunming utilizing multi-source data

In the post-pandemic era, nighttime tourism is vital in promoting the diversified development of tourism, enhancing the vitality of cities, and improving reemployment rates. Using Kunming City, China, as an example, this study used multi-theory and multi-source data to construct an evaluation model of the spatial distribution and suitability of nighttime tourism. The projection pursuit model and spatial analysis method were used to reveal spatial distribution and explore the suitability characteristics and spatial differences of nighttime tourism development. Our results revealed the following: (1) nighttime tourism resources showed a ‘large aggregation, small dispersion’ spatial distribution pattern, which is characterized by a distribution along the railway line; (2) at present, the spatial distribution of nighttime tourism in Kunming displays a pattern of ‘taking a high-density large gathering area as the center and extending around; ’ and (3) the most suitable nighttime tourism area accounts for 11.83% of the total land area of Kunming; highly suitable area accounts for 17.53%. The general suitable and unsuitable areas accounted for 43.29% and 27.35%, respectively. The results of this study assist in providing a scientific basis for the strategic planning and development of the nighttime tourism industry in Kunming.

Exploring the response of regional metabolism to population quality change in the late demographic dividend - Evidence from China

China's population and carbon emissions will reach two peaks in 2030. Especially in the late stage of demographic dividend, it is necessary to revisit the relationship between population and energy. Metabolism is a critical perspective to help understand the population-energy relationship. This paper establishes a new paradigm for assessing the impact of population quality change on regional metabolism based on panel data from 2003 to 2019 in China. We measure the population quality index (PQI) using the Projection Pursuit method from four aspects and regional metabolic efficiency (RME) using emergy analysis and Index-DEA. Then, the Tobit model is used to explore the impact of population quality change on regional metabolism from a multi-scale perspective. The results show that the influence has some variability at different scales. From the total sample, the response of regional metabolism to population quality is significantly positive. For different PQI regions, the RME has a positive response to PQI in both, but this response is more obvious in advanced PQI regions. For regions in different locations, RME has positive response to PQI in both East and West, but there is no obvious response in the Central. These findings are closely related to global sustainable development.

Toward ecological environmental risk for spoil ground group management in mega projects

PurposeThe numerous spoil grounds brought about by mega transportation infrastructure projects which can be influenced by the ecological environment. To achieve better management of spoil grounds, this paper aims to assess their comprehensive risk levels and categorize them into different categories based on ecological environmental risks.Design/methodology/approachBased on analysis of the environmental characteristics of spoil grounds, this paper first comprehensively identified the ecological environmental risk factors and developed a risk assessment index system to quantitatively describe the comprehensive risk levels. Second, this paper proposed a comprehensive model to determine the risk assessment and categorization of spoil ground group in mega projects integrating improved projection pursuit clustering (PPC) method and K-means clustering algorithm. Finally, a case study of a spoil ground group (includes 50 spoil grounds) in a mega infrastructure project in western China is presented to demonstrate and validate the proposed method.FindingsThe results show that our proposed comprehensive model can efficiently assess and categorize the spoil grounds in the group based on their comprehensive ecological environmental risk. In addition, during the process of risk assessment and categorization of spoil grounds, it is necessary to distinguish between sensitive factors and nonsensitive factors. The differences between different categories of spoil grounds can be recognized based on nonsensitive factors, and high-risk spoil grounds which need to be focused more on can be identified according to sensitive factors.Originality/valueThis paper develops a comprehensive model of risk assessment and categorization of a group of spoil grounds based on their ecological environmental risks, which can provide a reference for the management of spoil grounds in mega projects.

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.

Sustainability Analysis of the Water Environment Carrying Capacity of Harbin City Based on an Optimized Set Pair Analysis Posture-Deviation Coefficient Method Evaluation Model

To scientifically measure the water environment carrying capacity of Harbin City and its change trend, based on analysis of the implications of the sustainability of the urban water environment’s carrying capacity, an evaluation index system for the sustainability of the water environment carrying capacity of Harbin City was constructed. Most existing evaluation methods rely on static data to construct correlation functions between research objects and rank criteria, while the dynamic nature of the information is not considered enough. In this paper, we use hierarchical analysis (analytic hierarchy process, AHP) to determine the weights of each index of the system and then apply the projection tracing method (projection pursuit, PP) to optimize the determined weights. Combining the set pair analysis posture evaluation method and bias coefficients method to explore the dynamic balance mechanism between different index factor levels, a sustainability evaluation model for water environment carrying capacity integrating informational evolution is constructed. Finally, the applicability of the optimization model is tested by comparing the confidence criterion judging method. The model realizes quantitative evaluation of the carrying capacity of the urban water environment. It provides a new and effective means for accurate and reasonable determination of the coefficient of variance and the number of links and dynamic analysis of the water environment carrying capacity system and judgment of its sustainable development trend. The results show that the weight for water resource quality is 0.55, which is the subsystem with the greatest overall impact on the carrying capacity of the water environment in Harbin. The evaluation level of the set-to-potential eigenvalue for 2010–2017 is biased positive 2, and the rest of the years are quasi 2. The reduced value of the coefficient of oppositeness corresponding to the years 2010–2017 is more significant. The maximum value of the dynamic evolution of the load-carrying capacity level is nearly −0.35. From the vertical comparison of different levels, it is found that the water environment carrying capacity of Harbin City gradually recovered to the normal loadable level over time. This overall shows an improving trend.

Optimal modeling of anti breast cancer drug candidates

As breast cancer is one of the most common cancers with high mortality rate in the world, this paper studies the optimal screening of anti-breast cancer candidate drugs. First, the data of 729 compound molecular descriptors are preprocessed, rough cleaned and filtered to 253, and then the cluster feature tree and correlation analysis are used to further reduce the dimension of data redundancy information, and 29 representative molecular descriptors are screened. In order to determine the importance and significance variables affecting the activity of compounds, the preliminary results were obtained by Pearson correlation analysis, and the results were further verified by Spearman correlation analysis. The stability of molecular descriptors was determined by coefficient of variation analysis, and the most representative 20 variables were selected from 29 molecular descriptors by principal component analysis. Projection pursuit model (PP) can reduce the difficulty of storage and calculation of high-dimensional data. 253 variable information reflecting biological activity are extracted with this model, and then the projection direction is optimized by using accelerated genetic algorithm (RAGA). Finally, 20 projection function values in the optimal projection direction are calculated to complete the prediction of compound activity index.

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.

Geostatistics in the Presence of Multivariate Complexities: Comparison of Multi-Gaussian Transforms

One of the most challenging aspects of multivariate geostatistics is dealing with complex relationships between variables. Geostatistical co-simulation and spatial decorrelation methods, commonly used for modelling multiple variables, are ineffective in the presence of multivariate complexities. On the other hand, multi-Gaussian transforms are designed to deal with complex multivariate relationships, such as non-linearity, heteroscedasticity and geological constraints. These methods transform the variables into independent multi-Gaussian factors that can be individually simulated. This study compares the performance of the following multi-Gaussian transforms: rotation based iterative Gaussianisation, projection pursuit multivariate transform and flow transformation. Case studies with bivariate complexities are used to evaluate and compare the realisations of the transformed values. For this purpose, commonly used geostatistical validation metrics are applied, including multivariate normality tests, reproduction of bivariate relationships, and histogram and variogram validation. Based on most of the metrics, all three methods produced results of similar quality. The most obvious difference is the execution speed for forward and back transformation, for which flow transformation is much slower.

Comprehensive evaluation and spatial layout of high-quality logistics industry development based on accelerated genetic algorithm-projection pursuit model

Logistics industry is not only the link connecting economic activities, but also an important thrust to stimulate domestic demand. Under the development background of the new era, the high-quality development of logistics industry has become an indispensable force for high-quality economic development. Reasonable and effective evaluation is an important prerequisite to promote its high-quality development. Guided by the concept of high-quality development, it innovatively adds the dimension of logistics development resource cost. Based on the data of 31 provinces and cities in China from 2011 to 2019, in view of the limitations of projection pursuit, genetic algorithm and projection pursuit are organically combined, which were used to measure the high-quality development level of China’s regional logistics and analyze its spatial distribution characteristics. The reliability and accuracy of the evaluation results are improved, and the spatial information nature is analyzed by spatial exploration The results showed that the most important dimensions affecting the high-quality development of the logistics industry were logistics carrying capacity and technological innovation support. During the study period, there were significant differences in the high-quality development of regional logistics. The average annual logistics industry in the three provinces of Guangdong, Shandong and Jiangsu had relatively high level of high-quality development. By contrast, the development level of the three provinces of Tibet, Qinghai and Hainan was relatively low. In general, although the high-quality development of the logistics industry was increasing year by year, showing a trend of high level in the eastern region, followed by central region, low level in the western region, the gap had not been effectively shortened. In addition, the high-quality development of the regional logistics industry had a significant positive geographical correlation, showing a trickle drop effect in space.

Does industry-university-research cooperation promote the environmental efficiency of China’s high-tech manufacturing?

As one of the important strategic measures to increase the international competitiveness of high-tech manufacturing (HTM), industry-university-research cooperation (IURC) has received increasing attention in China. However, there is little literature to explore the links between IURC and the environmental efficiency (EE) of HTM. To incorporate a variety of environmental pollution indicators into the efficiency analysis framework and reduce the adverse effects of random errors on the estimation results, this article combined the projection pursuit model with the stochastic frontier analysis (SFA) method and proposed a translog stochastic frontier model considering undesirable outputs to analyze the multiple impacts of IURC on the EE of HTM. The results show that IURC has both a significant negative direct effect and a significant positive indirect effect on HTM’s EE. Although IURC cannot directly promote EE, it has a positive impact on EE of HTM through its complementary effect with research and development (R&D) investment. The results also confirm that the average EE of the whole country is only 0.346, while that of the eastern area is 0.595, and that of the central and western areas are 0.199 and 0.171, respectively. Therefore, it is particularly urgent to improve the EE of China’s HTM industry through a variety of measures, such as promoting IURC and increasing R&D investment in environmental technology. This study not only provides an improved SFA method for measuring EE, but also deepens research on the mechanism of the impact of IURC on HTM’s EE.

A Port Importance Evaluation Method Based on the Projection Pursuit Model in Shipping Networks

Shipping networks face natural or man-made port emergencies, and the failure of a port affects the network’s connectivity and efficiency. It is very necessary to give priority to the selection of ports that should be maintained or ensure service capacity in case of port failure. The importance evaluation of ports is of great significance to improve the efficiency of maritime transport. In view of this, this paper proposed a port importance evaluation method in shipping networks integrating the centrality index and vulnerability index. The indexes are, respectively, degree centrality, weighted degree centrality, betweenness centrality, closeness centrality, change rate of network efficiency, and connectivity. The weight of each index is calculated by the projection pursuit model. The results show that the proposed method integrates the different performances of each index. The importance of Singapore port, Colombo port, and Port Klang rank as the top three. They are the hub ports of the main lines of Asia, Europe, and Africa and occupy extremely important core positions in the network. Finally, the ports are classified based on importance value, and the shipping network after the failure of some ports is compared. This research can provide a scientific basis for ensuring the efficiency, connectivity, and stability of shipping networks.

WinterLab: Developing a Low-Cost, Portable Experiment Platform to Encourage Engagement in the Clectronics Lab

Encouraging student engagement is a key aim in any educational setting, and allowing students the freedom to pursue their own methods of solving problems through independent experimentation has been shown to markedly improve this. In many contexts, however, allowing students this flexibility in their learning is hampered by constraints of the material itself, such as in the electronics laboratory, where expensive and bulky equipment confines the learning environment to the laboratory room. Finding ourselves in the position of teaching one such laboratory course at the undergraduate level, we sought to encourage students to learn through independent investigation and the pursuit of personal projects, by providing a more flexible and inquiry-based learning environment and allowing them to take their measurement equipment—and their learning—beyond the laboratory itself. We present this project as a case of design both for and by students, with the lead designer undertaking the project after attending the course in question, and pursuing its development as a foundational step in their graduate career. We discuss the challenges and opportunities we encountered over the course of the design and development process, and the eventual key output of the project: a portable, low-cost, integrated electronics experimentation platform called the WinterLab board.