Search Results (19)

The article investigates the accuracy of nonparametric univariate density estimation methods applied to various Gaussian mixture models. A comprehensive comparative analysis is performed for four popular estimation approaches: adaptive kernel density estimation, projection pursuit, log-spline estimation, and wavelet-based estimation. The study is extended with modified versions of these methods, where the sample is first clustered using the EM algorithm based on Gaussian mixture components prior to density estimation. Estimation accuracy is quantitatively evaluated using MAE and MAPE criteria, with simulation experiments conducted over 100,000 replications for various sample sizes. The results show that estimation accuracy strongly depends on the density structure, sample size, and degree of component overlap. Clustering before density estimation significantly improves accuracy for multimodal and asymmetric densities. Although no formal statistical tests are conducted, the performance improvement is validated through non-overlapping confidence intervals obtained from 100,000 simulation replications. In addition, several decision-making systems are compared for automatically selecting the most appropriate estimation method based on the sample’s statistical features. Among the tested systems, kernel discriminant analysis yielded the lowest error rates, while neural networks and hybrid methods showed competitive but more variable performance depending on the evaluation criterion. The findings highlight the importance of using structurally adaptive estimators and automation of method selection in nonparametric statistics. The article concludes with recommendations for method selection based on sample characteristics and outlines future research directions, including extensions to multivariate settings and real-time decision-making systems. Full article

The Chinese government’s rural rejuvenation program depends on improving the national Rural Emergency Management Capability (REMC). To increase the resilience of Chinese rural areas against external dangers, REMC and its driving elements must be effectively categorized and evaluated. This study examines the variations in REMC levels and driving factors across different cities and regions, revealing the spatial distribution patterns and underlying mechanisms. To improve REMC in Chinese rural areas, this research employs the Projection Pursuit Method to assess REMC in 280 cities from 2006 to 2020. Additionally, we identify 22 driving factors and use the Random Forest algorithm from machine learning to analyze their impact on REMC. The analysis is conducted at both national and city levels to compare the influence of various driving factors in different regions. The findings show that China’s REMC levels have improved over time, driven by economic growth and the formation of urban clusters. Notably, some underdeveloped regions demonstrate higher REMC levels than more developed areas. The four most significant driving factors identified are rural road density, rural Internet penetration, per capita investment in fixed assets, and the density of township health centers. At the city level, rural Internet penetration and the e-commerce turnover of agricultural products have particularly strong driving effects. Moreover, the importance of driving factors varies across regions due to local conditions. This study offers valuable insights for the Chinese government to enhance REMC through region-specific strategies tailored to local circumstances. Full article

The process of urbanization, which leads to increased population density, changes in land use patterns, and heightened demand for industrial and domestic water use, exacerbates the contradiction between the supply and demand of water resources. This study examines the discrepancies between the supply and demand of water resources amidst urbanization, utilizing data from 110 cities within the Yangtze River Economic Belt (YREB) spanning from 2012 to 2021. The research employs the projection pursuit clustering model and the Dagum Gini coefficient method to evaluate the developmental status of water resources. While the Yangtze River Delta (YRD) region maintains a leading position with a water resources development score of 9.827 in 2023, there is a 2.2% increase in intra-regional disparity. The water resources development score for the City Cluster in the Middle Reaches of the Yangtze River (CCRYR) has experienced a decline, from 8.263 in 2012 to 8.016 in 2021; however, a reduction in intra-regional disparities has been observed since the implementation of the 2016 Outline of the Yangtze River Economic Belt Development Plan (YREBP), which suggests the policy’s efficacy. The Chengdu-Chongqing Economic Zone (CCEZ), despite its initially lower level of development, has demonstrated significant growth, with scores rising from 7.036 in 2012 to 7.347 in 2021. Collectively, the water resources development in the YREB exhibits an upward trend, yet the development remains uneven. The CCRYR shows a catching-up effect because of the YREBP, and the differences in other regions are widening. The research results provide decision-making support for water resources planning and management, and are of great significance in promoting the sustainable use of water resources. Full article

Emerging technology like the Internet of Things (IoT) has great potential for use in real time in many areas, including healthcare, agriculture, logistics, manufacturing, and environmental surveillance. Many obstacles exist alongside the most popular IoT applications and services. The quality of representation, modeling, and resource projection is enhanced through interactive devices/interfaces when IoT is integrated with real-time applications. The architecture has become the most significant obstacle due to the absence of standards for IoT technology. Essential considerations while building IoT architecture include safety, capacity, privacy, data processing, variation, and resource management. High levels of complexity minimization necessitate active application pursuits with variable execution times and resource management demands. This article introduces the Organized Optimization Integration Validation Model (O2IVM) to address these issues. This model exploits k-means clustering to identify complexities over different IoT application integrations. The harmonized service levels are grouped as a single entity to prevent additional complexity demands. In this clustering, the centroids avoid lags of validation due to non-optimized classifications. Organized integration cases are managed using centroid deviation knowledge to reduce complexity lags. This clustering balances integration levels, non-complex processing, and time-lagging integrations from different real-time levels. Therefore, the cluster is dissolved and reformed for further integration-level improvements. The volatile (non-clustered/grouped) integrations are utilized in the consecutive centroid changes for learning. The proposed model’s performance is validated using the metrics of execution time, complexity, and time lag. Full article

This paper presents the Positive Energy District Database (PED DB), a pivotal web tool developed collaboratively by the COST Action ‘PED-EU-NET’, in alignment with international initiatives such as JPI Urban Europe and IEA EBC Annex 83. The PED DB represents a crucial step towards sharing knowledge, promoting collaboration, reinforcing decision-making, and advancing the understanding of Positive Energy Districts (PEDs) in the pursuit of sustainable urban environments. The PED DB aims to comprehensively map and disseminate information on PEDs across Europe, serving as a dynamic resource for sustainable urban development according to the objective of making the EU climate-neutral by 2050. Indeed, PEDs imply an integrated approach for designing urban areas—the districts—where a cluster of interconnected buildings and energy communities produce net zero greenhouse gas emissions, managing an annual local/regional overflow production of renewable energy. The paper describes the collaborative step-by-step process leading to the PED DB implementation, the current results and potentials of the online platform, and introduces its future developments towards a more user-friendly and stakeholders-tailored tool. The interactive web map offers a customizable visualizations and filters on multiple information related to PED case studies, PED-relevant cases, and PED Labs. Users can access detailed information through a table view, facilitating comparisons across different PED projects and their implementation phase. The paper offers insights and detailed analysis from the initial dataset that includes 23 PED cases and 7 PED-related projects from 13 European countries, highlighting the key characteristics of surveyed PEDs. Full article

According to the United Nations World Tourism Organization, tourism promotes sustainable economic development. Ensuring tourism safety is an essential prerequisite for its sustainable development. In this paper, based on the three evaluation index systems for tourism safety early warning and the collected sample data, we establish three projection pursuit dynamic cluster (PPDC) models by applying group search optimization, a type of swarm intelligence algorithm. Based on case studies, it is confirmed that the results derived from the PPDC models are consistent with the expert judgments. The importance of the evaluation indicators can be sorted and classified according to the obtained optimal projection pursuit vector coefficients, and the tourism risks of the destinations can be ranked according to the sample projection values. Among the three aspects influencing tourism safety in case one, the stability of the tourism destination has the most significant impact, followed by the frequency of disasters. Of the ten evaluation indicators, the frequency of epidemic disease affects tourism safety the most, and the unemployment ratio affects it the second most. Overall, the PPDC model can be adopted for tourism safety early warning with high-dimensional non-linear and non-normal distribution data modeling, as it overcomes the “curse of dimensionality” and the limitations associated with small sample sizes. Full article

Rapid urbanization has brought economic dividends to China, but it has been accompanied by inefficient land use. Meanwhile, the mechanism of forestry carbon sinks (FCSs) on land use efficiency (LUE) has not been sufficiently discussed in the context of the pursuit of “carbon neutrality” around the world. Based on the idea of the benefit–cost theory, this study investigated the impact of FCSs on LUE in 30 provincial-level regions (2006–2019) in China using the difference-in-difference model. The results showed that, first, via the mechanisms of public opinion (PO) and rewards and penalties (RP), FCSs could significantly improve the LUE in the regions, and that the former had a greater effect than the latter; second, the tests of the assumption of parallel trends showed that FCSs had a slower effect on the LUE under the PO mechanism than under the RP mechanism; third, the analysis of the LUE showed that the improvement in LUE mainly occurred in the eastern, central, and southwestern regions of China. The conclusions were as follows: (1) FCS is able to promote LUE via both the PO and RP mechanisms; (2) there is a lag in the promotion of LUE by FCS, and the lag is larger with the PO mechanism; and (3) there is spatial clustering in the promotion of LUE by FCS. In line with these conclusions, we propose policy recommendations to better exploit the policy effects of FCSs in three aspects, namely promoting the development of forestry carbon sink projects, improving the relevant mechanisms of FCSs, and improving the mechanisms of PO and RP. Full article

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. Full article

In the breeding of new sugarcane varieties, the survey data do not always conform with a normal or linear distribution. To apply non-normal or non-linear data to evaluate new material requires a suitable evaluation model or method. The projection pursuit clustering (PPC) model is a statistical method that does not require making normal assumptions or other model assumptions on sample data, and is suitable to analyze high-dimensional, non-linear, and non-normal data. However, this model has been applied infrequently to crop variety evaluation. In this study, 103 varieties that have been bred over the last 70 years in China were planted, and their main industrial and agronomic traits were collected. Through the exploratory analysis of the data structure characteristics, the PPC model was used to evaluate these sugarcane varieties. The model provided good projection directions of agronomic and industrial traits, with accurate projection values. PPC models could evaluate sugarcane resources well, and the results were objective and reliable. Thus, the PPC model could be used as a new method for crop variety evaluation. At the same time, 51 excellent industrial and agronomic variety resources were screened for application in breeding. Full article

Accurately evaluating the construction risk of deep foundation pit projects is crucial to formulate science-based risk response measures. Here, we propose a novel construction risk assessment method for deep foundation pit projects. A construction risk evaluation index system based on a work breakdown structure-risk breakdown structure matrix was established to deal with the complex risks of deep foundation pit construction. The projection pursuit method optimized by particle swarm optimization was used to extract the structural features from the evaluation data to obtain objective index weights. The calculation method of the five-element connection number in the set pair analysis was improved to evaluate the static construction risk. The partial derivatives of the five-element connection number were utilized to assess the dynamic construction risk. The Qi ‘an Fu deep foundation pit project in China was selected as a case study. The results show that the construction risk was acceptable and decreased during the construction period, which was consistent with actual conditions, demonstrating the effectiveness of this novel method. The proposed model showed better performance than classical methods (analytic hierarchy process, entropy weight method, classical set pair analysis, fuzzy comprehensive evaluation, gray clustering method, backpropagation neural network, and support vector machine). Full article

Road transport is an integral part of economic activity and is therefore essential for its development. On the downside, it accounts for 30% of the world’s GHG emissions, almost a third of which correspond to the transport of freight in heavy goods vehicles by road. Additionally, means of transport are still evolving technically and are subject to ever more demanding regulations, which aim to reduce their emissions. In order to analyse the sustainability of this activity, this study proposes the application of novel Artificial Intelligence techniques (more specifically, Machine Learning). In this research, the use of Hybrid Unsupervised Exploratory Plots is broadened with new Exploratory Projection Pursuit techniques. These, together with clustering techniques, form an intelligent visualisation tool that allows knowledge to be obtained from a previously unknown dataset. The proposal is tested with a large dataset from the official survey for road transport in Spain, which was conducted over a period of 7 years. The results obtained are interesting and provide encouraging evidence for the use of this tool as a means of intelligent analysis on the subject of developments in the sustainability of road transportation. Full article

Scientific water resource carrying capacity (WRCC) evaluations are necessary for providing guidance for the sustainable utilization of water resources. Based on the driving-pressure-state-impact-response feedback loop, this paper selects 21 indicators under five dimensions to construct a regional WRCC comprehensive evaluation framework. The projection pursuit clustering (PPC) method is implemented with the matter-element extension (MEE) model to overcome the limitations of subjective deviation and indicator attribute incompatibility in traditional comprehensive assessment methods affecting the accuracy of evaluations. The application of the integrated evaluation model is demonstrated in Baoding city in the Jing-Jin-Ji area from 2010 to 2017. The results indicate that the economic water consumption intensity is the most influential factor that impacts the WRCC change in Baoding, and the pressure subsystem and response subsystem are dominant in the entire system. The WRCC in Baoding significantly improved between 2010 and 2017 from a grade V extremely unsafe state to a grade III critical state. Natural water shortages and large population scales are the main negative factors during this period; however, the existing measures are still insufficient to achieve an optimal WRCC status. Considering the future population and industry inflow, additional actions must be proposed to maintain and promote harmonious conditions. Full article

In recent years, the digital transformation has been advancing in industrial companies, supported by the Key Enabling Technologies (Big Data, IoT, etc.) of Industry 4.0. As a consequence, companies have large volumes of data and information that must be analyzed to give them competitive advantages. This is of the utmost importance in fields such as Failure Detection (FD) and Predictive Maintenance (PdM). Finding patterns in such data is not easy, but cutting-edge technologies, such as Machine Learning (ML), can make great contributions. As a solution, this study extends Hybrid Unsupervised Exploratory Plots (HUEPs), as a visualization technique that combines Exploratory Projection Pursuit (EPP) and Clustering methods. An extended formulation of HUEPs is proposed, adding for the first time the following EPP methods: Classical Multidimensional Scaling, Sammon Mapping and Factor Analysis. Extended HUEPs are validated in a case study associated with a multinational company in the automotive industry sector. Two real-life datasets containing data gathered from a Waterjet Cutting tool are visualized in an intuitive and informative way. The obtained results show that HUEPs is a technique that supports the continuous monitoring of machines in order to anticipate failures. This contribution to visual data analytics can help companies in decision-making, regarding FD and PdM projects. Full article

Proper water use requires its monitoring and evaluation. An indexes system of overall water use efficiency is constructed here that covers water consumption per 10,000 yuan GDP, the coefficient of effective utilization of irrigation water, the water consumption per 10,000 yuan of industrial value added, domestic water consumption per capita of residents, and the proportion of water function zone in key rivers and lakes complying with water-quality standards and is applied to 31 provinces in China. Efficiency is first evaluated by a projection pursuit cluster model. Multidimensional efficiency data are transformed into a low-dimensional subspace, and the accelerating genetic algorithm then optimizes the projection direction, which determines the overall efficiency index. The index reveals great variety in regional water use, with Tianjin, Beijing, Hebei, and Shandong showing highest efficiency. Shanxi, Liaoning, Shanghai, Zhejiang, Henan, Shanxi, and Gansu also use water with high efficiency. Medium efficiency occurs in Inner Mongolia, Jilin, Heilongjiang, Jiangsu, Hainan, Qinghai, Ningxia, and Low efficiency is found for Anhui, Fujian, Jiangxi, Hubei, Hunan, Guangdong, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, and Xinjiang. Tibet is the least efficient. The optimal projection direction is a* = (0.3533, 0.7014, 0.4538, 0.3315, 0.1217), and the degree of influence of agricultural irrigation efficiency, water consumption per industrial profit, water used per gross domestic product (GDP), domestic water consumption per capita of residents, and environmental water quality on the result has decreased in turn. This may aid decision making to improve overall water use efficiency across China. Full article

The issue motivating the paper is the quantification of students’ academic performance and learning achievement regarding teaching quality, under interval number condition, in order to establish a novel model for identifying, evaluating, and monitoring the major factors of the overall teaching quality. We propose a projection pursuit cluster evaluation model, with entropy value method on the model weights. The weights of the model can then be obtained under the traditional real number conditions after a simulation process by Monte Carlo for transforming interval number to real number. This approach can not only simplify the evaluation of the interval number indicators but also give the weight of each index objectively. This model is applied to 5 teacher data collected from a China college with 4 primary indicators and 15 secondary sub-indicators. Results from the proposed approach are compared with the ones obtained by two alternative evaluating methods. The analysis carried out has contributed to having a better understanding of the education processes in order to promote performance in teaching. Full article