Correlation Analysis Algorithm Research Articles

Prediction of cement-stabilized recycled concrete aggregate properties by CNN-LSTM incorporating attention mechanism

Cement-stabilized recycled concrete aggregate (CSRCA), as an eco-friendly and cost-effective building material, has its mechanical properties and fatigue resistance influenced by multiple factors. Machine learning-based prediction methods offer a new solution for the rapid assessment of CSRCA performance. To efficiently and accurately predict the mechanical properties and fatigue resistance of CSRCA, this study proposes a machine learning-based CSRCA performance prediction model. By collecting data on various influencing factors such as mix ratios, aggregate characteristics, and compaction, and combining convolutional neural networks (CNN) with long short-term memory networks (LSTM), the study introduces an attention mechanism (ATT) to optimize feature weighting, thus constructing a CNN-LSTM-ATT prediction model. Additionally, Pearson correlation analysis and random forest algorithms are used to investigate the key influencing factors and feature importance of CSRCA's mechanical and fatigue performance. The Shapley Additive Explanation (SHAP) is employed to enhance the model's interpretability. The results indicated that each input parameter exhibited a significant nonlinear relationship with both compressive strength and fatigue life. Specifically, increasing cement dosage, degree of compaction, and 19mm sieve passing rate contributed positively to the mechanical properties and fatigue life of CSRCA. Conversely, the recycled aggregate crushing value had a significant negative impact on compressive strength but minimal effect on fatigue life. Notably, recycled aggregate dosage demonstrated opposing effects on compressive strength and fatigue life. The prediction accuracy of the CNN-LSTM-ATT model, incorporating the attention mechanism, surpassed that of the conventional CNN-LSTM model, enhancing the accuracy, robustness, and generalization ability of the prediction framework. The prediction metrics for compressive strength were R² = 0.993, MAE = 0.101, and RMSE = 0.144, while those for fatigue life were R² = 0.994, MAE = 55.05, and RMSE = 73.43, the prediction ability is significantly higher than other prediction models of the same type. Consequently, the CNN-LSTM-ATT model provides a scientific foundation for the performance prediction and proportioning design of CSRCA, holding significant implications for engineering practice.

Research on Multi-Scale Signal Processing and Efficient Model Construction Strategies in Lithium-Ion Battery State Prediction

Abstract To tackle the question of limited generalization and inefficiency in predicting state of health (SOH) and state of charge (SOC) in lithium-ion batteries across diverse sequence lengths, a novel hybrid model is developed. This model integrates multivariate variational mode decomposition (MVMD), informer, and long short-term Memory (LSTM) networks. Initially, battery health features are extracted from the charge and discharge curves, which are then validated for their relevance to SOH and SOC via correlation analysis and random forest algorithms. These features undergo multi-scale decomposition using MVMD, thereby encapsulating the intricate dynamics of battery state changes across various time scales. This decomposition enhances the model's adaptability to different sequence lengths, bolstering its generalization capability. Subsequently, the informer model is utilized to identify temporal patterns within the decomposed features. Finally, LSTM exploits its capacity to capture temporal dependencies for further refinement of the predictions. This hybrid strategy yields substantial enhancements in both efficiency and accuracy. Compared to the transformer model, the proposed hybrid model demonstrates a 30% reduction in SOH prediction error and a 22% decrease in SOC prediction error, concurrently slashing training time significantly. Spanning diverse sequence lengths and battery types, demonstrates the model's strong generalization capabilities.

Machine learning–based predictive model for post-stroke dementia

BackgroundPost-stroke dementia (PSD), a common complication, diminishes rehabilitation efficacy and affects disease prognosis in stroke patients. Many factors may be related to PSD, including demographic, comorbidities, and examination characteristics. However, most existing methods are qualitative evaluations of independent factors, which ignore the interaction amongst various factors. Therefore, the purpose of this study is to explore the applicability of machine learning (ML) methods for predicting PSD.Methods9 acceptable features were screened out by the Spearman correlation analysis and Boruta algorithm. We developed and evaluated 8 ML models: logistic regression, elastic net, k-nearest neighbors, decision tree, extreme gradient boosting, support vector machine, random forest, and multilayer perceptron.ResultsA total of 539 stroke patients were included in this study. Among the 8 models used to predict PSD, extreme gradient boosting and random forest showed the highest area under the curve (AUC) of the receiver operating characteristic curve (ROC), with values of 0.7287 and 0.7285, respectively. The most important features for predicting PSD included age, high sensitivity C-reactive protein, stroke side and location, and the occurrence of cerebral hemorrhage.ConclusionOur findings suggest that ML models, especially extreme gradient boosting, can best predict the risk of PSD.

Prediction and clustering of Alzheimer’s disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data

Early detection of Alzheimer’s disease (AD) is crucial to maximize clinical outcomes. Most disease progression analyses include people with diagnoses of cognitive impairment, limiting understanding of AD risk among those with normal cognition. The objective was to establish AD progression models through a deep learning approach to analyze heterogeneous, multi-modal datasets, including clustering analyses of population subsets. A multi-head deep-learning architecture was built to process and learn from biomedical and imaging data from the National Alzheimer’s Coordinating Center. Shapley additive explanation algorithms for feature importance ranking and pairwise correlation analysis were used to identify predictors of disease progression. Four primary disease progression clusters (slow, moderate and rapid converters or non-converters) were subdivided into groups by race and sex, yielding 16 sub-clusters of participants with distinct progression patterns. A multi-head and early-fusion convolutional neural network achieved the most competitive performance and demonstrated superiority over a single-head deep learning architecture and conventional tree-based machine-learning methods, with 97% test accuracy, 96% F1 score and 0.19 root mean square error. From 447 features, 2 sets of 100 predictors of disease progression were extracted. Feature importance ranking, correlation analysis and descriptive statistics further enriched cluster analysis and validation of the heterogeneity of risk factors.

Investigation of age-hardening behaviour of Al alloys via feature screening-assisted machine learning

Age hardening stands as a crucial strengthening process for aluminium (Al) alloys. However, determining the optimal ageing conditions has traditionally relied on resource-intensive trial-and-error methods. To streamline the design of Al alloys, this study introduces a novel feature screening-assisted machine learning (ML) approach to explore age-hardening behaviour across varying compositions and heat treatment parameters, focusing on yield tensile strength (YTS), ultimate tensile strength (UTS), and elongation (EL). A comprehensive pool of features, incorporating alloy composition and fundamental elemental properties, was generated to provide metallurgical insights for ML predictions. Subsequently, feature screening methodologies, including correlation analysis, feature elimination, and multi-objective genetic algorithm (MOGA), were employed to identify key features from the vast feature pool, balancing model complexity and prediction accuracy. Employing support vector regression models trained on these optimized feature sets, we demonstrate enhanced prediction accuracy for strength properties while maintaining model simplicity for EL, with minimal impact on accuracy. In addition, experimental results further validate the method's ability to reproduce ageing curves across all three mechanical properties for both commercialized and newly designed Al alloys.

Estimating water quality parameters of freshwater aquaculture ponds using UAV-based multispectral images

UAV imaging technology has become one of the means to quickly monitor water quality parameters in freshwater aquaculture ponds. The change of sunlight during a long flight affects the quality of UAV images, which will reduce the accuracy of monitoring water quality. This study aims to propose a method to correct spectral variation during UAV imaging and apply it to detect dissolved organic matter (DOM) concentration and dissolved oxygen (DO) content in freshwater aquaculture ponds. Firstly, a spectral correction method was used to transform UAV-based multispectral images. The spectral data before and after correction was extracted. Secondly, 18 spectral indices before and after correction were constructed. The optimal combination of indices was identified using correlation analysis algorithm. The estimation models of water quality parameters were then constructed and compared using the Random Forest (RF), Support Vector Regression (SVR), and BP neural network (BP) methods. The results showed that the accuracy of estimating DOM concentration using corrected spectral indices was significantly improved compared to pre-correction models, with the highest improvement of 38 % (SVR), the lowest of 23 % (BP), and an average improvement of 31 %. The RF model performed best, achieving R² = 0.81, RMSE = 3.34 mg/L, and MAE = 2.17 mg/L. For DO content estimation, the accuracy of models using corrected spectral indices was also improved significantly, with the highest improvement of 97 % (RF), the lowest of 39 % (SVR), and an average improvement rate of 67 %. The Random Forest model was again optimal, with R² = 0.69, RMSE = 1.97 mg/L, and MAE = 1.47 mg/L. This study indicates that the proposed spectral correction method helps to map the concentration of DOM and DO in freshwater aquaculture ponds with high accuracy using UAV-based multispectral images.

Multimodal Emotion Recognition Based on Facial Expressions, Speech, and Body Gestures

The research of multimodal emotion recognition based on facial expressions, speech, and body gestures is crucial for oncoming intelligent human–computer interfaces. However, it is a very difficult task and has seldom been researched in this combination in the past years. Based on the GEMEP and Polish databases, this contribution focuses on trimodal emotion recognition from facial expressions, speech, and body gestures, including feature extraction, feature fusion, and multimodal classification of the three modalities. In particular, for feature fusion, two novel algorithms including supervised least squares multiset kernel canonical correlation analysis (SLSMKCCA) and sparse supervised least squares multiset kernel canonical correlation analysis (SSLSMKCCA) are presented, respectively, to carry out efficient facial expression, speech, and body gesture feature fusion. Different from the traditional multiset kernel canonical correlation analysis (MKCCA) algorithms, our SLSKMCCA algorithm is a supervised version and is based on the least squares form. The SSLSKMCCA algorithm is implemented by the combination of SLSMKCCA and a sparse item (L1 Norm). Moreover, two effective solving algorithms for SLSMKCCA and SSLSMKCCA are presented in addition, which use the alternated least squares and augmented Lagrangian multiplier methods, respectively. The extensive experimental results on the popular public GEMEP and Polish databases show that the recognition rate of multimodal emotion recognition is superior to bimodal and monomodal emotion recognition on average, and our presented SLSMKCCA and SSLSMKCCA fusion methods both obtain very high recognition rates, especially for the SSLSMKCCA fusion method.

METHODOLOGICAL APPROACH TO ASSESSING THE IMPACT OF ENERGY EFFICIENCY ON THE FINANCIAL PERFORMANCE OF A GAS COMPANY

Currently, the use of energy-saving technologies that reduce costs and improve the production process is the most important factor in the development of the gas industry. The relevance of the research topic is due to the fact that in order to resolve issues related to reducing energy costs in the gas sector of the Russian economy, optimal use of non-renewable energy resources, and reducing technological losses, an integrated approach to assessing energy efficiency is required, as well as a methodology for assessing the impact of energy efficiency indicators on the financial performance of a company. The purpose of this article is to develop a methodological approach to assessing the impact of the level of energy efficiency on the financial performance of a gas company. The research methodology is based on correlation and regression analysis tools. As part of the study, a system of energy efficiency indicators for a gas company's activities has been formed and, using correlation and regression analysis tools, a methodology for assessing the impact of the level of energy efficiency on the financial performance of a gas company is proposed. An algorithm for correlation and regression analysis is presented, which makes it possible to formalize a step-by-step assessment of the impact of the level of energy efficiency on the financial performance of a gas company. The proposed methodological approach allows us to identify correlation-regression dependencies between the company’s key performance indicators (sales revenue and net profit) and the proposed energy efficiency indicators, determine the most significant indicators that affect the company’s performance indicators and determine the equation for the relationship between the company’s performance and energy efficiency indicators. Testing of the proposed approach using the example of the gas company PJSC Gazprom made it possible to quantify how changes in the energy intensity of gas production and the energy intensity of gas transportation will affect changes in sales revenue and net profit of the company. It has been established that the energy efficiency indicator such as the specific energy intensity of gas transportation has the greatest impact on the financial performance of the company. Regression equations have been established to describe the nature of the relationship between energy efficiency indicators and sales revenue and net profit of a gas company.

Monitoring and zoning soybean maturity using UAV remote sensing

Timely and accurate determination of the maturity status during the soybean harvest period is crucial for devising strategic harvesting plans, significantly enhancing the intelligent management of soybean production and minimizing losses. To explore the maturation dynamics of soybeans and pinpoint the precise maturity stage for optimal harvesting, we collected Unmanned Aerial Vehicle (UAV) remote sensing data, along with physical and chemical parameters, during the Beginning maturity(R7)-Full maturity(R8) stages from 2021 to 2022. We analyzed the variations in these parameters throughout the maturation period. Employing UAV multispectral technology and correlation analysis algorithms, we established a relationship between the spectral and physicochemical parameters during the maturation period, which led to the formulation of a soybean maturity evaluation index. Furthermore, we incorporated the concept of precision management zoning. In line with actual field production requirements, this index, combined with real-time moisture content, established a standard for grading soybean maturity. Additionally, our study examined the impact of spatial scale on maturity assessments by conducting a theoretical analysis of regionalized variables and analyzing the spatial correlations and variability of soybean maturity across different field plots. We determined the optimal theoretical model for various sampling distances, thereby establishing feasible zoning ranges. Comparative analysis of zoning algorithms, including Hierarchical Clustering (HC), K-Means, and its optimized versions K-Means++ and Mini Batch K-Means, was conducted using the partition performance index as the criterion. This analysis identified the most suitable algorithm for assessing soybean field maturity, which was subsequently verified through field tests. Results indicated that soybean leaf chlorophyll content and photosynthesis decreased sharply initially, then stabilized at a low rate. Water content in both soybean plants and seeds decreased rapidly before slowing, with plant dehydration rates exceeding those of the seeds. Green Normalized Difference Vegetation Index(GNDVI) showed a positive correlation with various physiological indicators, and soybean maturity was classified into three levels using the unit gridding management method, considering actual moisture content. Spatial correlation analysis of maturity variations in small and large test fields revealed Moran indices ranging from 0.78 to 0.95 and 0.70–0.90, respectively. Optimal detection ranges were determined to be 2.00–30.00 m for small fields and 2.00–67.00 m for large fields. The zoning categories for small and large fields were three and four, respectively. Comparative studies showed that the Mini Batch K-Means algorithm, an optimization of K-Means, achieved Calinski-Harabasz Index(CH) scores and silhouette coefficients of 10521.97, 109508.27, and 0.57, 0.54, respectively, indicating comparable zoning effectiveness to K-Means and K-Means++ and superiority over the HC algorithm. The zoning speeds were 12.50 s and 191.68 s, respectively, underscoring its efficiency in maturity zoning. Furthermore, zoning results corresponded well with actual soybean maturity. Terrain had little impact on soybean maturity, which was more susceptible to slope aspect. This study provides a theoretical foundation and practical guidelines for applying UAV remote sensing technology in determining soybean maturity and optimal harvesting periods, thereby reducing potential harvest losses.

The metabolites mainly composed of lipids in tongue coating are non-invasive potential biomarkers for chronic gastritis

The changes in tongue coating metabolites in patients with chronic gastritis (CG) under different gastroscopy indicators were analyzed, and these metabolites were screened for potential non-invasive biomarkers to assist in the diagnosis of chronic gastritis. The technology of gas chromatography and liquid chromatography combined with mass spectrometry has been used to more comprehensively detect tongue coating metabolites of 350 CG patients. Spearman correlation analysis and random forest algorithm were used to screen metabolites that can serve as potential biomarkers. Compared with healthy individuals, CG group showed significant changes in the content of 101 metabolites, with an increase in the content of 54 metabolites and a decrease in the content of 47 metabolites. These differential metabolites are mainly composed of 47 lipids and lipid like substances. 1 metabolite was associated with bile reflux, 1 metabolite was associated with gastric mucosal erosion, 10 metabolites were associated with atrophy, 10 metabolites were associated with intestinal metaplasia, and 3 metabolites were associated with Helicobacter pylori infection. The ROC model composed of 5 metabolites can distinguish between CG group and healthy individuals, with an accuracy of 95.4%. The ROC model composed of 5,6-Dihydroxyindole can distinguish between chronic superficial gastritis group and chronic atrophic gastritis group, with an accuracy of 75.3%. The lipids and lipid like metabolites were the main abnormal metabolites in patients with chronic gastritis. It was worth noting that the content of Sphinganine 1-phase, 4-Ipomenol, and Nervonic acid in tongue coating increased, and the content of 1-Methyladenosine and 3-Hydroxycapric acid decreased, which helped to identify CG patients. The decrease in the content of 5,6-dihydroxyindole reminded patients that the development trend of CG was shifting from superficial to atrophic or even intestinal metaplasia. The detection of these metabolic markers of tongue coating was expected to be developed as a non-invasive and convenient technology in the future to assist us in monitoring and diagnosing the occurrence and development of CG.

ClusterMatch aligns single-cell RNA-sequencing data at the multi-scale cluster level via stable matching.

Unsupervised clustering of single-cell RNA sequencing (scRNA-seq) data holds the promise of characterizing known and novel cell type in various biological and clinical contexts. However, intrinsic multi-scale clustering resolutions poses challenges to deal with multiple sources of variability in the high-dimensional and noisy data. We present ClusterMatch, a stable match optimization model to align scRNA-seq data at the cluster level. In one hand, ClusterMatch leverages the mutual correspondence by canonical correlation analysis and multi-scale Louvain clustering algorithms to identify cluster with optimized resolutions. In the other hand, it utilizes stable matching framework to align scRNA-seq data in the latent space while maintaining interpretability with overlapped marker gene set. Through extensive experiments, we demonstrate the efficacy of ClusterMatch in data integration, cell type annotation, and cross-species/timepoint alignment scenarios. Our results show ClusterMatch's ability to utilize both global and local information of scRNA-seq data, sets the appropriate resolution of multi-scale clustering, and offers interpretability by utilizing marker genes. The code of ClusterMatch software is freely available at https://github.com/AMSSwanglab/ClusterMatch.

Power fault analysis and classification based on correlation analysis and random forest algorithm

The electric power system is one of the indispensable infrastructures in modern society, and its stable operation is crucial for guaranteeing economic development and social security. By calculating the correlation coefficient and drawing the correlation heat map, we find that there is a correlation between the voltage at each location and the occurrence of electrical faults, and at the same time, there is also a certain correlation between various types of electrical faults and the voltage at each location. These results indicate that there are complex and close interrelationships between different locations and between different types in the power system. In order to better identify and classify different types of electrical faults, we used the random forest model for prediction. The model is used to classify whether an electrical fault occurs or not and to determine its category. The prediction results show that the Random Forest achieved 99.63% in classifying whether an electrical fault occurred or not, and 91.26% in classifying the category of electrical faults. This shows that the Random Forest algorithm model is able to detect electrical faults well, as well as judge the category of electrical faults. In summary, this paper provides insight into the operation of the power system by studying the correlation between the voltage at various locations in the power system and the different types of electrical faults and using the random forest model to make predictions. These findings are of great significance in ensuring the stable operation of power systems.

Hybrid Brain-Computer Interface Controlled Soft Robotic Glove for Stroke Rehabilitation.

Soft robotic glove controlled by a brain-computer interface (BCI) have demonstrated effectiveness in hand rehabilitation for stroke patients. Current systems rely on static visual representations for patients to perform motor imagination (MI) tasks, resulting in lower BCI performance. Therefore, this study innovatively used MI and high-frequency steady-state visual evoked potential (SSVEP) to construct a friendly and natural hybrid BCI paradigm. Specifically, the stimulation interface sequentially presented decomposed action pictures of the left and right hands gripping a ball, with the pictures flashing at specific stimulation frequencies (left: 34 Hz, right: 35 Hz). Integrating soft robotic glove as feedback, we established a comprehensive "peripheral - central - peripheral" hand rehabilitation system to facilitate the hand rehabilitation of patients. Filter bank common spatial pattern (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms were used to identify MI and SSVEP signals, respectively. Additionally, we proposed a novel fusion algorithm to decide the final output of the system. The feasibility of the proposed system was validated through online experiments involving 12 healthy subjects and 9 stroke patients, achieving accuracy rates of 95.83 ± 6.83% and 63.33 ± 10.38, respectively. The accuracy of MI and SSVEP in 12 healthy subjects reached 81.67 ± 15.63% and 95.14 ± 7.47%, both lower than the accuracy after fusion, these results confirmed the effectiveness of the proposed algorithm. The accuracy rate was more than 50% in both healthy subjects and patients, confirming the effectiveness of the proposed system.

Abnormality in Peripheral and Brain Iron Contents and the Relationship with Grey Matter Volumes in Major Depressive Disorder.

Major depressive disorder (MDD) is a prevalent mental illness globally, yet its etiology remains largely elusive. Recent interest in the scientific community has focused on the correlation between the disruption of iron homeostasis and MDD. Prior studies have revealed anomalous levels of iron in both peripheral blood and the brain of MDD patients; however, these findings are not consistent. This study involved 95 MDD patients aged 18-35 and 66 sex- and age-matched healthy controls (HCs) who underwent 3D-T1 and quantitative susceptibility mapping (QSM) sequence scans to assess grey matter volume (GMV) and brain iron concentration, respectively. Plasma ferritin (pF) levels were measured in a subset of 49 MDD individuals and 41 HCs using the Enzyme-linked immunosorbent assay (ELISA), whose blood data were simultaneously collected. We hypothesize that morphological brain changes in MDD patients are related to abnormal regulation of iron levels in the brain and periphery. Multimodal canonical correlation analysis plus joint independent component analysis (MCCA+jICA) algorithm was mainly used to investigate the covariation patterns between the brain iron concentration and GMV. The results of "MCCA+jICA" showed that the QSM values in bilateral globus pallidus and caudate nucleus of MDD patients were lower than HCs. While in the bilateral thalamus and putamen, the QSM values in MDD patients were higher than in HCs. The GMV values of these brain regions showed a significant positive correlation with QSM. The GMV values of bilateral putamen were found to be increased in MDD patients compared with HCs. A small portion of the thalamus showed reduced GMV values in MDD patients compared to HCs. Furthermore, the region of interest (ROI)-based comparison results in the basal ganglia structures align with the outcomes obtained from the "MCCA+jICA" analysis. The ELISA results indicated that the levels of pF in MDD patients were higher than those in HCs. Correlation analysis revealed that the increase in pF was positively correlated with the iron content in the left thalamus. Finally, the covariation patterns obtained from "MCCA+jICA" analysis as classification features effectively differentiated MDD patients from HCs in the support vector machine (SVM) model. Our findings indicate that elevated peripheral ferritin in MDD patients may disrupt the normal metabolism of iron in the brain, leading to abnormal changes in brain iron levels and GMV.

A Study on Insurance Rate Decision Making in Disaster Frequent Areas Based on Pearson Correlation Analysis and Random Forest Algorithm

A Study on Insurance Rate Decision Making in Disaster Frequent Areas Based on Pearson Correlation Analysis and Random Forest Algorithm

Research on the Correlation Analysis Algorithm between School Teachers' Equipment and Teaching Quality

In this research, we delve into the correlation between school teachers' equipment and teaching quality, aiming to uncover insights that contribute to educational advancements. The study employs a specialized Correlation Analysis Algorithm to systematically analyse the relationship between the equipment utilized by school teachers and the resulting impact on the quality of teaching. By examining various factors such as technological tools, teaching aids, and instructional devices, we seek to discern patterns and correlations that influence the effectiveness of educational practices. The research is driven by the overarching goal of enhancing teaching quality through informed decisions regarding the selection and integration of educational tools. The abstract provides a glimpse into the methodology and objectives of the study, emphasizing its potential contributions to the optimization of teaching practices in educational institutions.

Research on the Application of Decision Tree and Correlation Analysis Algorithm in College Students’ Physical Fitness Analysis

With the advent of the big data era, data-driven decision-making and analysis are increasingly valued in various fields. Especially in the field of education, how to use big data technology to better understand student needs, optimize the education process, and improve education quality has become an important research topic. This paper will explore the application of decision trees and related analysis algorithms in the analysis of college students’ physical fitness, in order to provide scientific basis for improving the physical health level of college students. This paper studies the application of DT (decision tree) and correlation analysis algorithm in the analysis of college students’ physical fitness. In this paper, the method of big data and DM (data mining) is proposed to extract the rules contained in the data information, so as to directly provide auxiliary decision-making for physical fitness test and analysis. The research results show that through training the training set, a good classification accuracy rate is achieved, and through optimizing the depth, the accuracy rate can reach more than 85.033%. Using DM technology as a carrier, this paper digs into the rules behind the new knowledge of college students’ physical fitness, and digs out the previously unknown, implied and potentially useful information and knowledge.

Unveiling the drivers of atmospheric methane variability in Iran: A 20-year exploration using spatiotemporal modeling and machine learning

Understanding the factors controlling spatial and temporal variability of atmospheric methane concentration (XCH4) is crucial for mitigating its impacts and implementing emission reduction strategies. This study comprehensively investigates XCH4 and its driving factors (environmental, meteorological, and anthropogenic activity) across Iran over 20 years, from 2003 to 2022. It combines multi-source satellite observations, advanced spatiotemporal modeling techniques, correlation analysis, and machine learning algorithms. The spatiotemporal analysis showed notable spatial variation, with high XCH4 levels in central, southern, and eastern Iran and lower levels in the northwest and north. Moreover, distinct seasonal cycles emerged, with maximum XCH4 occurring during summer (August-September) and minimum levels in spring (April-May). Correlation analysis and variable importance assessment were developed to elucidate the key drivers governing XCH4 dynamics. Correlation analysis revealed that vegetation cover, precipitation, and soil moisture were negatively correlated with XCH4, while temperature indices showed a positive correlation, exhibiting the highest correlation in time dispersion and quantity among the studied variables. The Permutation Importance technique, used with a Random Forest classifier, a machine learning-based approach that considers the role of all variables together, showed that land surface temperature, wind speed, soil moisture, and vegetation cover are the dominant controls, with their importance ranked respectively. Surprisingly, anthropogenic emissions played a relatively minor role in shaping XCH4 distributions at the regional scale. These findings highlight the significant influence of meteorological variables and ecosystem processes on XCH4 modulation, revealing intricate Earth system feedbacks that inform targeted mitigation strategies and predictive models for curbing greenhouse gas emissions and mitigating climate change impacts.

Enhancing Incipient Fault Detection for Interface Converter Sensors through Signal Correlation Analysis

Incipient faults in interface converters can potentially lead to catastrophic failures. Detection of incipient faults contributes to proactive fault management and predictive maintenance, which effectively improves system reliability. In this paper, a detection method in correlation space is proposed to address this problem, which is based on the inherent feature of correlation changes when a fault occurs. First, a convolution process is used to weaken noise and highlight the correlation feature. Second, the current signals are transmitted to correlation space by using the Pearson correlation coefficient. Third, an accumulation and a reference compensation method are designed for enhancing the features and equalizing influence of reference adjustment. Finally, a fault detection rule is designed based on correlation features and fault excitation. Experiments on a hardware‐in‐loop(HIL) semi‐physical platform indicate that the proposed method outperforms three commonly used correlation analysis algorithms. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Computerized diagnosis of knee osteoarthritis from x‐ray images using combined texture features: Data from the osteoarthritis initiative

AbstractThe prevalence of knee osteoarthritis (KOA) cases has witnessed a significant increase on a global scale in recent years, emphasizing the need for automated diagnostic computer systems to aid in early‐stage osteoarthritis (OA) diagnosis. The accurate characterization of knee KOA stages through feature extraction poses significant research challenges due to the complexity of identifying relevant attributes. In this study, the development of a KOA diagnostic system is presented, leveraging a combination of Gabor, and Tamura parameters using the Canonical Correlation Analysis algorithm. Two feature selection algorithms, namely Principal Component Analysis and Relief, were employed for KOA classification. Furthermore, various classifiers, including K‐Nearest Neighbors, AdaBoost, Bagging, and Random Forest, were used to assess the proposed feature extraction approach. The diagnostic system was assessed using a dataset comprising 688 x‐ray images sourced from the OA initiative (OAI) dataset, consisting of 344 images from healthy subjects (Grade 0) and 344 images from pathological patients (Grade 2). To mitigate overfitting, a 10‐fold cross‐validation method was utilized. The experimental results indicate that the combination of Tamura and Gabor parameters with the Random Forest classifier achieved remarkable performance in KOA diagnosis, yielding an accuracy of 94.59%, and an area under the curve of 98.3%. Notably, the combined Gabor and Tamura models exhibited superior performance compared to individual models, as well as existing techniques reported in the literature.