Search Results (28)

Global efforts to reduce climate change have increased, necessitating more comprehensive research. However, empirical evidence of the implication of synergizing foreign direct investment (FDI) and renewable energy consumption (REC) to reduce environmental pollution, specifically with nitrous oxide (N₂O) and methane (CH₄) emissions, is missing in the literature. This research investigates the impact of FDI, REC and their synergy in facilitating technological leapfrogging, analyzing their linear, non-linear and indirect effects on environmental pollution (CO₂, N₂O and CH₄ emissions). The analysis focuses on 81 developing countries, analyzing them at both the general level and by income groups—low-income countries (LICs), middle-income countries (MICs) and high-income countries (HICs), with government effectiveness and economic growth serving as mediating variables. Using Canonical Correlation Regression (CCR), Fully Modified Ordinary Least Squares (FMOLS) and clustered Pooled Least Square (PLS) techniques, the analysis covers data from 2003 to 2023. The results indicate that at the general level, FDI and REC increase N₂O and CH₄ emissions individually. However, their integration mitigates N₂O and CH₄ emissions. Additionally, the relationships remain consistent even when government effectiveness and economic growth are considered mediators. However, economic growth is more pronounced than government effectiveness in reducing environmental pollution. The non-linear analysis also reveals that FDI and REC have a significant U-shaped effect on CO₂ emissions. However, their synergy demonstrates an inverted U-shaped nexus with CO₂ emissions. At the income group levels, the interplay of FDI and REC reduces N₂O and CH₄ emissions in MICs; however, in LICs and HICs, it increases N₂O and CH₄ emissions. Full article

Modeling the brain dependence network is central to understanding underlying neural mechanisms such as perception, action, and memory. In this study, we present a broad range of statistical methods for analyzing dependence in a brain network. Leveraging a combination of classical and cutting-edge approaches, we analyze multivariate hippocampal local field potential (LFP) time series data concentrating on the encoding of nonspatial olfactory information in rats. We present the strengths and limitations of each method in capturing neural dynamics and connectivity. Our analysis begins with exploratory techniques, including correlation, partial correlation, spectral matrices, and coherence, to establish foundational connectivity insights. We then investigate advanced methods such as Granger causality (GC), robust canonical coherence analysis, spectral transfer entropy (STE), and wavelet coherence to capture dynamic and nonlinear interactions. Additionally, we investigate the utility of topological data analysis (TDA) to extract multi-scale topological features and explore deep learning-based canonical correlation frameworks for connectivity modeling. This comprehensive approach offers an introduction to the state-of-the-art techniques for the analysis of dependence networks, emphasizing the unique strengths of various methodologies, addressing computational challenges, and paving the way for future research. Full article

Smart manufacturing systems aim to enhance the efficiency, adaptability, and reliability of industrial operations through advanced data-driven approaches. Achieving these objectives hinges on accurate fault detection and timely maintenance, especially in highly dynamic industrial environments. However, capturing nonlinear and temporal dependencies in dynamic nonlinear industrial processes poses significant challenges for traditional data-driven fault detection methods. To address these limitations, this study presents an Incremental Pyraformer–Deep Canonical Correlation Analysis (DCCA) framework that integrates the Pyramidal Attention Mechanism of the Pyraformer with the Broad Learning System for incremental learning in a DCCA basis. The Pyraformer model effectively captures multi-scale temporal features, while the BLS-based incremental learning mechanism adapts to evolving data without full retraining. The proposed framework enhances both spatial and temporal representation, enabling robust fault detection in high-dimensional and continuously changing industrial environments. Experimental validation on the Tennessee Eastman (TE) process, Continuous Stirred-Tank Reactor (CSTR) system, and injection molding process demonstrated superior detection performance. In the TE scenario, our framework achieved a 100% Fault Detection Rate with a 4.35% False Alarm Rate, surpassing DCCA variants. Similarly, in the CSTR case, the approach reached a perfect 100% Fault Detection Rate (FDR) and 3.48% False Alarm Rate (FAR), while in the injection molding process, it delivered a 97.02% FDR with 0% FAR. The findings underline the framework’s effectiveness in handling complex and dynamic data streams, thereby providing a powerful approach for real-time monitoring and proactive maintenance. Full article

Modern industrial processes are characterized by increasing complexity, often exhibiting pronounced dynamic behaviors and significant nonlinearity. Addressing these dynamic and nonlinear characteristics is essential for effective process monitoring. However, many existing methods for process monitoring and fault diagnosis are insufficient in handling these challenges. In this article, we present a novel process monitoring approach, CVA-DisDAE, which integrates an improved Denoising Autoencoder (DAE) with Canonical Variate Analysis (CVA) to address the challenges posed by dynamic behaviors and nonlinear relationships in industrial processes. First, CVA is employed to reduce data dimensionality and minimize information redundancy by maximizing correlations between past and future observations, thereby effectively capturing process dynamics. Following this, we introduce a discriminative DAE model (DisDAE) designed to serve as a semi-supervised denoising autoencoder for precise feature extraction. This is achieved by incorporating both between-class separability and within-class variability into the traditional DAE framework. The key distinction between the proposed DisDAE and the conventional DAE lies in the integration of a linear discriminant analysis (LDA) penalty into the DAE’s loss function, resulting in extracted features that are more conducive to fault classification. Finally, we validate the effectiveness of the proposed semi-supervised dynamic process monitoring approach through its application to the Tennessee Eastman benchmark process, demonstrating its superior performance. Full article

Algae are highly sensitive to environmental factors, especially nutrient fluctuations; excessive nutrients can lead to the proliferation of specific algae species, resulting in dominance. In this study, we aimed to reevaluate changes in algal dominance from the perspective of resource utilization efficiency (RUE). We established 80 monitoring sites across different water systems, collecting water and phytoplankton samples. Using canonical correspondence analysis (CCA) and a generalized additive model (GAM), we analyzed the correlation between phytoplankton RUE and nutrient concentrations, quantifying the corresponding relationship between algal dominance and RUE. Our results indicate a significant negative correlation between the RUE of total phosphorus (TP) and total nitrogen (TN) concentration, but a positive correlation with N:P. The RUE of TN was negatively correlated with TN concentration and N:P. We constructed GAMs with interaction terms and confirmed a nonlinear relationship between algal dominance and RUE. When the RUE of TN was low, a positive correlation was observed, while a negative correlation was observed otherwise. These findings reveal the ecological adaptability of algal communities and provide valuable insights for predicting the risk of algal bloom outbreaks. Full article

The primary motivation of this study is to develop a compound intensity measure (IM) to evaluate ground motion damage potential based on principal component analysis (PCA) and canonical correlation analysis (CCA). To illustrate this, this study examines the correlation among intragroup IMs and intergroup IMs, as well as the correlation between various IMs and response variables. A compound IM, which can be obtained by a linear combination of ten IMs in the log-scale, is utilized to measure the ground motion damage potential. Elastoplastic, bilinear and hysteretic models are utilized to determine peak deformation and hysteretic energy as the response variables of Single-Degree-of-Freedom (SDOF) systems. On the basis of the SDOF systems, the overall structural damage index is obtained by a nonlinear time–history analysis for two reinforced concrete moment frame systems. It is clear that the developed compound IM shows significantly high-level correlation with structural response. The better the correlations, the more one can measure the earthquake damage potential. A single IM alone inadequately characterizes structural damage, highlighting the necessity of multiple IMs to estimate the possibility of structural damage. Full article

In intelligent process monitoring and fault detection of the modern process industry, conventional methods mostly consider singular characteristics of systems. To tackle the problem of suboptimal incipient fault detection in nonlinear dynamic systems with non-Gaussian distributed data, this paper proposes a methodology named Gap-Mixed Kernel-Dynamic Canonical Correlation Analysis. Initially, the Gap metric is employed for data preprocessing, followed by fault detection utilizing the Mixed Kernel-Dynamic Canonical Correlation Analysis. Ultimately, fault identification is conducted through a contribution method based on the $T^2$ statistic. Furthermore, a comparative analysis was conducted using Canonical Variate Analysis, Dynamic Canonical Correlation Analysis, and Mixed Kernel-Dynamic Canonical Correlation Analysis on the Tennessee Eastman process. Experimental results indicate varying degrees of improvements in the detection rate, false alarm rate, missed detection rate, and detection time compared to the comparative methods, demonstrating the industrial value and academic significance of the method. Full article

The purpose of this study was to analyze the influence of solar activity described by the sunspot number (SSN) on certain terrestrial variables that might impact the Southeastern European climate at different spatio-temporal scales (the North Atlantic Oscillation Index, NAOI, and the Greenland–Balkan Oscillation Index, GBOI—on a large scale; the Palmer Hydrological Drought Index, PHDI—on a regional scale; the Danube discharge at the Orsova (lower basin), Q, representative of the Southeastern European climate—on a local scale). The investigations were carried out for the 20th century using the annual and seasonal averages. To find the connections between terrestrial (atmospheric and hydrological) parameters and SSN, the wavelet coherence were used both globally and in the time–frequency domain. The analyses were carried out for the time series and considered simultaneously (in the same year or season), as well as with lags from 1 to 5 years between the analyzed variables. For the annual values, the type of correlation (linear/non-linear) was also tested using elements from information theory. The results clearly revealed non-linear links between the SSN and the terrestrial variables, even for the annual average values. By applying the wavelet transform to test the solar influence on the terrestrial variables, it was shown that the connections depend on both the terrestrial variable, as well as on the considered lags. Since, in the present study, they were analyzed using wavelet coherence, but only the cases in which the coherence was significant for almost the entire analyzed time interval (1901–2000) and the terrestrial variables were in phase or antiphase with the SSN were considered. Relatively few results had a high level of significance. The analysis of seasonal averages revealed significant information, in addition to the analysis of annual averages. Thus, for the climatic indices, the GBOI and NAOI, a significant coherence (>95%) with the solar activity, associated with the 22-year (Hale) solar cycle, was found for the autumn season for lag = 0 and 1 year. The Hale solar cycle, in the case of the PHDI, was present in the annual and summer season averages, more clearly at lag = 0. For the Danube discharge at Orsova, the most significant SSN signature (~95%) was observed at periods of 33 years (Brüuckner cycle) in the autumn season for lags from 0 to 3 years. An analysis of the redundancy–synergy index was also carried out on the combination of the terrestrial variables with the solar variable in order to find the best synergistic combination for estimating the Danube discharge in the lower basin. The results differed depending on the timescale and the solar activity. For the average annual values, the most significant synergistic index was obtained for the combination of the GBOI, PHDI, and SSN, considered 3 years before Q. Full article

Incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of mixed kernel principal components analysis and dynamic canonical correlation analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient faults in nonlinear and dynamic systems. Meanwhile, fault identification experiments were conducted, validating the high accuracy of the fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance. Full article

Incipient fault detection in a hydraulic system is a challenge in the condition monitoring community. Existing research mainly monitors abnormal working conditions in hydraulic systems by separately detecting the key working parameter, which often causes a high miss warning rate for incipient faults due to the oversight of parameter dependence. A principal component analysis provides an effective method for incipient fault detection by taking the correlation of multiple parameters into consideration, but this technique assumes the systems are Gaussian-distributed, making it invalid for a dynamic non-Gaussian system. In this paper, we combine a canonical variable analysis (CVA) and adaptive kernel density estimation (AKDE) for the early fault detection of nonlinear dynamic hydraulic systems. The collected hydraulic system data set was used to construct the typical variable space, and the state space and residual space are divided to represent the characteristics of different correlations between the two variables, which are quantitatively described using Hotelling’s T² and Q. In order to investigate the proper upper control limits, AKDE was utilised to estimate the underlying probability density functions of T² and Q by taking the nonlinearity of the hydraulic system variables into consideration. The advantages of the proposed approach for incipient fault detection are illustrated via a marine power plant lubrication system. Full article

The macro-invertebrate is an important part of the aquatic food web of urban rivers, and it is of great significance in understanding its ecological suitability for the stability of river ecosystems. Previous studies, such as those that have conducted suitability index and canonical correspondence analyses (CCAs), have widely used a macro-invertebrate suitability analysis; however, these studies can only confirm a few coupling relationships between the environment and macro-invertebrates. In our study, one-way ANOVA, HCA, PCA and GAM models were used to explain the differences in the spatial and temporal distribution of environmental factors, as well as to reduce data redundancy. A response curve of the critical environmental factors and macro-invertebrates was constructed, and the nonlinear relationship between these factors and benthic animals was quantified to analyze the ecological threshold of the macro-invertebrates. The study area was the Majiagou River, Harbin, China. The results show that COD had significant seasonal differences due to complex hydrological conditions, and most of the water quality factors had spatial differences. The GAM model explained 60% of the Margalef diversity index (MDI) variance. The relationship between chlorophyll-a and MDI was unimodal, and MDI and NH₄⁺-N essentially showed a negative correlation; when the total nitrogen (TN) value reached 5.8 mg/L, MDI reached its peak. When MDI was higher than the mean value, the chlorophyll-a range was 18.1 μg/L~83 μg/L. The NH₄⁺-N was less than 1.8 mg/L, and TN was 1.8~6.8 mg/L. This study provides a reference for the comprehensive management of urban river ecosystems. Full article

Canonical Correlation Analysis (CCA) infers a pairwise linear relationship between two groups of random variables, $\mathit{X}$ and $\mathit{Y}.$ In this paper, we present a new procedure based on Rényi’s pseudodistances (RP) aiming to detect linear and non-linear relationships between the two groups. RP canonical analysis (RPCCA) finds canonical coefficient vectors, $\mathit{a}$ and $\mathit{b}$, by maximizing an RP-based measure. This new family includes the Information Canonical Correlation Analysis (ICCA) as a particular case and extends the method for distances inherently robust against outliers. We provide estimating techniques for RPCCA and show the consistency of the proposed estimated canonical vectors. Further, a permutation test for determining the number of significant pairs of canonical variables is described. The robustness properties of the RPCCA are examined theoretically and empirically through a simulation study, concluding that the RPCCA presents a competitive alternative to ICCA with an added advantage in terms of robustness against outliers and data contamination. Full article

Traditional image genetics primarily uses linear models to investigate the relationship between brain image data and genetic data for Alzheimer’s disease (AD) and does not take into account the dynamic changes in brain phenotype and connectivity data across time between different brain areas. In this work, we proposed a novel method that combined Deep Subspace reconstruction with Hypergraph-Based Temporally-constrained Group Sparse Canonical Correlation Analysis (DS-HBTGSCCA) to discover the deep association between longitudinal phenotypes and genotypes. The proposed method made full use of dynamic high-order correlation between brain regions. In this method, the deep subspace reconstruction technique was applied to retrieve the nonlinear properties of the original data, and hypergraphs were used to mine the high-order correlation between two types of rebuilt data. The molecular biological analysis of the experimental findings demonstrated that our algorithm was capable of extracting more valuable time series correlation from the real data obtained by the AD neuroimaging program and finding AD biomarkers across multiple time points. Additionally, we used regression analysis to verify the close relationship between the extracted top brain areas and top genes and found the deep subspace reconstruction approach with a multi-layer neural network was helpful in enhancing clustering performance. Full article

A growing body of evidence indicates that living close to nature is associated with better health and well-being. However, the literature still lacks studies analyzing the benefits of this proximity for sleep and obesity, particularly in women. The purpose of this study was to explore how distance to natural spaces is reflected in women’s physical activity, sleep, and adiposity levels. The sample consisted of 111 adult women (37.78 ± 14.70). Accessibility to green and blue spaces was assessed using a geographic-information-system-based method. Physical activity and sleep parameters were measured using ActiGraph accelerometers (wGT3X-BT), and body composition was assessed using octopolar bioimpedance (InBody 720). Nonlinear canonical correlation analysis was used to analyze the data. Our findings reveal that women living in green spaces close to their homes had lower levels of obesity and intra-abdominal adiposity. We also demonstrated that a shorter distance to green spaces seemed to correlate with better sleep onset latency. However, no relationship was found between physical activity and sleep duration. In relation to blue spaces, the distance to these environments was not related to any health indicator analyzed in this study. Full article

The extra-long expressway tunnel has a high socio-economic effect on inter-regional development, with high traffic and strong traffic winds. Nevertheless, the impacts of the tunnel traffic volume on pollutant evolution are rarely considered. This study conducted a field measurement in a real-world extra-long highway tunnel for 578 days. For the first time, the nonlinear dynamics of traffic pollutants (CO, VOCs, NO₂, PM_2.5, PM₁₀) were analyzed using the Multifractal Detrended Fluctuation Analysis approach. Using the Random Forest model, the impacts of traffic and environmental parameters on air quality were quantified. The findings indicated that COVID-19 had a considerable impact on tunnel traffic, although the variance in pollutant concentration was not very noteworthy. The bidirectional effect of traffic was the main reason for this phenomenon. The Canonical Correlation Analysis was unable to quantify the correlation between pollutants and environmental parameters. The pollutant concentration evolution has a steady power-law distribution structure. Further, an inverse Random Forest model was proposed to predict air pollutants. Compared with other prediction models (baseline and machine learning), the proposed model provided higher goodness of fit and lower prediction error, and the prediction accuracy was higher under the semi-enclosed structure of the tunnel. The relative deviations between the predictions and measured data are less than 5%. These findings ascertain the nonlinear evolutionary mechanisms of pollutants inside the expressway tunnel, thus eventually improving tunnel environmental sustainability. The data in this paper can be used to clarify the changes in the traffic environment under the COVID-19 lockdown. Full article