Multivariate Data Research Articles

Investigating swimming technical skills by a double partition clustering of multivariate functional data allowing for dimension selection

Investigating swimming technical skills by a double partition clustering of multivariate functional data allowing for dimension selection

A convolution neural network integrating climate variables and spatial-temporal properties to predict influenza trends

The spread of influenza is contingent upon a multitude of outbreak-related factors, including viral mutation, climate conditions, acquisition of immunity, crowded environments, vaccine efficacy, social gatherings, and the health and age profiles of individuals in contact with infected individuals. An epidemic in the region impacted by spatial transmission risk from adjacent regions. A few influenzas epidemic models start highlighting the spatial correlations between influenza patients and geographically adjacent regions. The proposed model is based on the concept of climatic, immunization, and spatial correlations which are represented by a convolution neural network (CNN) for influenza epidemic forecasting. This study presents an integration of three determinants for predicting influenza outbreaks, multivariate climate data, spatial data on influenza vaccination, and spatial-temporal data of historical influenza patients. The performance of three comparison models, CNN, recurrent neural network (RNN), and long short-term memory (LSTM) was compared by the root mean squared error metric (RMSE). The findings revealed that the CNN model represents human interaction at intervals of 12, 16, 20, 24, and 28 weeks resulting in the best effectiveness of the lowest RMSE=0.00376 with learning rate=0.0001.

Unravelling Stock Market Patterns: Analysis and Predictive Modelling Using Time Series and Deep Learning

Abstract: Within the dynamic landscape of the financial markets, where fortunes can shift with the wind, the precision of future prediction holds immense value. This research embarks on a quest to enhance the accuracy of stock market forecasts, venturing into the domain of advanced time series models. We shall closely examine three prominent methodologies: the venerable ARIMA, the multifaceted VARMA, and the potent deep learning architecture known as LSTM. Through a rigorous investigation of their strengths and limitations, we shall subject these models to both univariate and multivariate data extracted from the Indian stock market. This comparative analysis aims to glean invaluable insights into their predictive capabilities, ultimately paving the path for the development of even more sophisticated forecasting tools. By wielding these instruments with confidence, investors can navigate the intricate dynamics of the market with a newfound certainty and optimize their decision-making for greater returns. Our endeavor transcends mere numerical analysis; it seeks to illuminate the complex dance of the market, unravel its hidden patterns, and ultimately empower investors to gain the upper hand in this challenging yet potentially rewarding financial sphere.

Analysis of Manifold and its Application

Manifold learning is a field of study in machine learning and statistics that is closely associated with dimensionality reduction algorithmic techniques is gaining popularity these days. There are two types of manifold learning approaches: linear and nonlinear. Principal component analysis (PCA) and multidimensional scaling (MDS) are two examples of linear techniques that have long been staples in the statistician's arsenal for evaluating multivariate data. Nonlinear manifold learning, which encompasses diffusion maps, Laplacian Eigenmaps, Hessian Eigenmaps, Isomap, and local linear embedding, has seen a surge in research effort recently. A few of these methods are nonlinear extensions of linear approaches. A nearest search, the definition of distances or affinities between points (a crucial component of these methods' effectiveness), and an Eigen problem for embedding high-dimensional points into a lower dimensional space make up the algorithmic process of the majority of these techniques. The strengths and weaknesses of the new method are briefly reviewed in this article. In the field of computer graphics, we utilize a particular manifold learning method was first presented in statistics and machine learning to create a global, Spectral-based shape descriptor.

Influence of regional and yearly weather patterns on multi-mycotoxin occurrence in Austrian wheat: a liquid chromatographic-tandem mass spectrometric and multivariate statistics approach.

Mycotoxin surveys play an essential role in our food safety system. The obtained occurrence data form the basis for the assessment of the exposure of humans and animals to these toxic fungal secondary metabolites. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has become the gold standard for mycotoxin determination because it enables selective and sensitive multi-toxin analysis. Simultaneous determination of several hundreds of secondary fungal metabolites is feasible using this technique. In this study, we combined a targeted dilute-and-shoot LC-MS/MS-based multi-analyte approach with multivariate statistics for the analysis of Austrian wheat from two different years and different geographical origins. We quantified 47 secondary fungal metabolites, including regulated emerging and masked mycotoxins. The resulting multi-mycotoxin occurrence data were further analyzed using both multivariate and univariate statistics. Principal component analysis (PCA) and analysis of variance (ANOVA) simultaneous component analysis (ASCA) were employed to identify regional and yearly trends within the dataset and to quantify the variance in metabolite occurrence attributed to the different effects. In addition, secondary fungal metabolites significantly impacted by these factors were selected via ANOVA. Of the 47 secondary metabolites identified, 39 were affected by the year, region or a combined effect. Moreover, our findings show that 43 of the secondary fungal metabolites were significantly influenced by the weather conditions. The results presented in this study underline the added value of combining targeted LC-MS/MS with multivariate statistics for monitoring a broad spectrum of secondary fungal metabolites in food crops. Through multivariate statistics, trends associated with the year or region can be readily studied. The approach presented could pave the way for a better understanding of the impact of climate change on plant pathogenic fungi and its implications for food safety. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Metabolome classification of olive by-products from different oil presses providing insights into its potential health benefits and valorization as analyzed via multiplex MS-based techniques coupled to chemometrics.

The Olive (Olea europaea L.) is one of the most popular edible oil-producing fruits, consumed worldwide for its myriad nutritional and health benefits. Olive oil production generates huge quantities of by-products from the fruit, which are considered environmental hazards. Recently, more and more efforts have been made to valorize olive by-products as a source of low-cost, value-added food applications. The main objective of this study was to globally assess the metabolome of olive fruit by-products, including olive mill wastewater, olive pomace, and olive seeds from fruits from two areas, Siwa and Anshas, Egypt. Gas chromatography-mass spectrometry (GC-MS) and ultra-high-performance liquid chromatography with mass spectrometry (UPLC-MS) were used for profiling primary and secondary metabolites in olive by-products. Also, multivariate data analyses were used to assess variations between olive by-product samples. A total of 103 primary metabolites and 105 secondary metabolites were identified by GC-MS and UPLC-MS, respectively. Fatty acids amounted to a major class in the olive by-products at 53-91%, with oleic acid dominating, especially in the pomace of Siwa. Mill wastewater was discriminated from other by-products by the presence of phenolics mainly tyrosol, hydroxyl tyrosol, and α-tocopherol as analyzed by UPLC-MS indicating their potential antioxidant activity. Pomace and seeds were rich in fatty acids/esters and hydroxy fatty acids and not readily distinguishable from each other. The current work discusses the metabolome profile of olive waste products for valorization purposes. Pomace and seeds were enriched in fatty acids/esters, though not readily distinguishable from each other.

Cross-validation to select the optimum rank for a reduced-rank approximation to multivariate data

ABSTRACT In this paper we consider the Gabriel form of cross-validation (CV) and we investigate how to estimate the optimum rank for lower rank approximations of any dataset that can be written in matrix form, with particular application in multivariate analysis and in the analysis of multienvironment trials. The literature related to the method suggests that it can produce overfitting and poor-quality predictions, characteristics that result in overestimation of the rank. Because of this, it is proposed to change the rank selection criterion, testing thirteen statistics both in the original method and in four proposed extensions that seek to solve the above problems. A comparison is made with two gold standard methods for CV through a simulation study and through the analysis of seventeen real datasets, two of which are general multivariate and fifteen are from experiments with genotype-by-environment interaction. It is concluded that from a predictive point of view, the highest accuracy in estimating the rank is obtained by using a regularized singular value decomposition.

A Spearman dependence matrix for multivariate functional data

We propose a nonparametric inferential framework for quantifying dependence among two families of multivariate functional data. We generalise the notion of Spearman correlation coefficient to situations where the observations are curves generated by a stochastic processes. In particular, several properties of the Spearman index are illustrated emphasising the importance of having a consistent estimator of the index. We use the notion of Spearman index to define the Spearman matrix, a mathematical object expressing the pattern of dependence among the components of a multivariate functional dataset. Finally, the notion of Spearman matrix is exploited to analyse two different populations of multivariate curves (specifically, Electrocardiographic signals of healthy and unhealthy people), in order to test if the pattern of dependence between the components is statistically different in the two cases. Abbreviations: ANA: anti-nuclear antibodies; APC: antigen-presenting cells; IRF:interferon regulatory factor

GAP-LSTM: Graph-Based Autocorrelation Preserving Networks for Geo-Distributed Forecasting.

Forecasting methods are important decision support tools in geo-distributed sensor networks. However, challenges such as the multivariate nature of data, the existence of multiple nodes, and the presence of spatio-temporal autocorrelation increase the complexity of the task. Existing forecasting methods are unable to address these challenges in a combined manner, resulting in a suboptimal model accuracy. In this article, we propose, a novel geo-distributed forecasting method that leverages the synergic interaction of graph convolution, attention-based long short-term memory (LSTM), 2-D-convolution, and latent memory states to effectively exploit spatio-temporal autocorrelation in multivariate data generated by multiple nodes, resulting in improved modeling capabilities. Our extensive evaluation, involving real-world datasets on traffic, energy, and pollution domains, showcases the ability of our method to outperform state-of-the-art forecasting methods. An ablation study confirms that all method components provide a positive contribution to the accuracy of the extracted forecasts. The method also provides an interpretable visualization that complements forecasts with additional insights for domain experts.

Weighted Robust Tensor Principal Component Analysis for the Recovery of Complex Corrupted Data in a 5G-Enabled Internet of Things

Technological developments coupled with socioeconomic changes are driving a rapid transformation of the fifth-generation (5G) cellular network landscape. This evolution has led to versatile applications with fast data-transfer capabilities. The integration of 5G with wireless sensor networks (WSNs) has rendered the Internet of Things (IoTs) crucial for measurement and sensing. Although 5G-enabled IoTs are vital, they face challenges in data integrity, such as mixed noise, outliers, and missing values, owing to various transmission issues. Traditional methods such as the tensor robust principal component analysis (TRPCA) have limitations in preserving essential data. This study introduces an enhanced approach, the weighted robust tensor principal component analysis (WRTPCA), combined with weighted tensor completion (WTC). The new method enhances data recovery using tensor singular value decomposition (t-SVD) to separate regular and abnormal data, preserve significant components, and robustly address complex data corruption issues, such as mixed noise, outliers, and missing data, with the globally optimal solution determined through the alternating direction method of multipliers (ADMM). Our study is the first to address complex corruption in multivariate data using the WTRPCA. The proposed approach outperforms current techniques. In all corrupted scenarios, the normalized mean absolute error (NMAE) of the proposed method is typically less than 0.2, demonstrating strong performance even in the most challenging conditions in which other models struggle. This highlights the effectiveness of the proposed approach in real-world 5G-enabled IoTs.

Water quality assessment of Bheemasandra Lake, South India: A blend of water quality indices, multivariate data mining techniques, and GIS.

An integrated approach combining water quality indices (WQIs), multivariate data mining, and geographic information system (GIS) was employed to examine the water quality of Bheemasandra Lake, located adjacent to a sewage treatment plant (STP) in Tumakuru city, India. The analysis of 22 lake water samples, examined before and after the monsoons, revealed that the physicochemical parameters namely - electrical conductivity, biochemical oxygen demand, turbidity, total dissolved solids, ammoniacal nitrogen, nitrates, phosphates, magnesium, total hardness, total alkalinity, and calcium - exceeded the acceptable limits stipulated by national and international standards. The Canadian Council of Ministers of the Environment WQI (pre-monsoon: 25.3; post-monsoon: 33.9) and weighted arithmetic WQI (pre-monsoon: 3398; post-monsoon: 2093) designated the water as unsafe for drinking. Irrigation WQIs (sodium adsorption ratio, sodium percentage, residual sodium carbonate, magnesium hazard, permeability index, and potential salinity) implied water's suitability for irrigation. However, electrical conductivity indicated otherwise. Industrial WQIs (Larson-Skold Index, Langelier Index, Aggressive Index, and Puckorius Scaling Index) illustrated scaling propensity and the chloride sulfate mass ratio alluded galvanic corrosion potential. Hierarchical cluster analysis gathered 22 sampling points into two clusters (cluster 1: relatively lower polluted regions; cluster 2: highly polluted regions) for each season based on similarities in water features. Principal component analysis extracted four (79.07% cumulative variance) and six (87.14% cumulative variance) principal components before and after the monsoons, respectively. These components identified the primary pollution sources as urban sewage and natural lithological processes. WQI maps, created using the inverse distance weighted interpolation technique, enhanced the visualization of spatial-temporal variations. This study highlights the dire consequences of urbanization, STP pollution, and sewage management failures, necessitating that concerned authorities should implement policies and measures to curb the negative impacts on the environment and public health.

Consistent Robust Analytical Approach for Outlier Detection in Multivariate Data using Isolation Forest and Local Outlier Factor

Outlier detection in real-time from multivariate streaming data is an important research subject in numerous areas. The new presentation of gradual Neighborhood Anomaly Variable (iLOF) and its variations has acquired consideration for their high recognition execution in information streams with evolving circulations. This paper presents a new intelligent exception location framework called include rich intelligent anomaly discovery, which integrates human interaction into the detection process to improve performance and simplify detection. It offers interactive mechanism that allows for instinctive client cooperation during every vital stage of the fundamental anomaly recognition calculation, for example, thick cell choice, area mindful distance thresholding, and last top exception approval. This approach helps resolve the challenge of specifying key parameters like density and distance thresholds in other outlier detection methods. Additionally, the system proposes an innovative optimization method to enhance grid-based space partitioning. The Local Outlier Factor (LOF) measures the level of outlierness of each occasion in light of the conveyance thickness in the dataset. Higher LOF values indicate a higher likelihood of an instance being an outlier. Instances with LOF values above a set threshold are identified as outliers. The calculation of LOF values involves several steps, which are detailed in original articles for further reference.

Children with Congenital Heart Diseases Exhibit Altered Deep Gray Matter Structures.

Children with congenital heart diseases (CHDs) have an increased risk of developing neurologic deficits, even in the absence of apparent brain pathology. The aim of this work was to compare quantitative macro- and microstructural properties of subcortical gray matter structures of pediatric CHD patients with normal appearing brain magnetic resonance imaging to healthy controls. We retrospectively reviewed children with coarctation of the aorta (COA) and hypoplastic left heart syndrome (HLHS) admitted to our hospital. We identified 24pediatric CHD patients (17COA, 7HLHS) with normal-appearing brain MRI. Using an atlas-based approach, the volume and apparent diffusion coefficient (ADC) were determined for the thalamus, caudate, putamen, pallidum, hippocampus, amygdala, nucleus accumbens, cerebral white matter, cerebral cortex, and brainstem. Multivariate statistics were used to compare the extracted values to reference values from 100 typically developing children without any known cardiac or neurological diseases. Multivariate analysis of covariance using the regional ADC and volume values as dependent variables and age and sex as co-variates revealed asignificant difference between pediatric CHD patients and healthy controls (p < 0.001). Post-hoc comparisons demonstrated significantly reduced brain volumes in most subcortical brain regions investigated and elevated ADC values in the thalamus for children with CHD. No significant differences were found comparing children with COA and HLHS. Despite normal appearing brain MRI, children with CHD exhibit wide-spread macro-structural and regional micro-structural differences of subcortical brain structures compared to healthy controls, which could negatively impact neurodevelopment, leading to neurological deficits in childhood and beyond.

Geochemical characteristics, mechanisms, and suitability of groundwater resource for sustainable water supply in Quetta valley

ABSTRACT Groundwater is a crucial water resource for various usages worldwide. The Quetta Valley of Pakistan was investigated regarding its groundwater quality sustainability based on integrated approaches of hydrochemistry, geographic information system, and multivariate statistics. A total of 29 groundwater samples were collected from monitoring wells to get insights into the hydrochemical suitability of groundwater for sustainable irrigation and drinking utilization. The results indicate groundwater is mainly featured by the hydrochemical facies of HCO3·Cl-Ca. Groundwater hydrochemical composition is dominantly governed by the dissolution of carbonates and silicate minerals in combination with positive cation exchange in the valley. Principal component analysis reveals a significant influence of geogenic factors on groundwater chemistry, further supported by PHREEQC simulation that detects a supersaturation of calcite, dolomite, and sulphate minerals in the aquifer. The irrigation water quality index divides groundwater in the study area into three zones, which signify low restriction and no restriction, except for a severe restriction in the southwestern part of the valley. Groundwater is generally suitable for irrigation across the valley. The entropy-weighted water quality index classifies groundwater as excellent and good quality for drinking. This study can provide crucial insights for authorities on groundwater suitability in Quetta Valley and similar regions worldwide.

Hubungan Antara Faktor Risiko Hendaya Kognitif Ringan Pada Pasien Lanjut Usia Di Jakarta Timur

The research objectives in detail are to determine the risk factors for mild cognitive impairment in the elderly and understanding the factors that affect the majority of mild cognitive impairment in the elderly. This research is an observational case-control study and the research method used is multi-variant data analysis and epidemiological information. The results between methods describe the effect of hypertension and physical activity have big influence on mild cognitive. Conclusion The factors that most influence mild cognitive impairment are hypertension and physical activity

Integrating CoDA and PCA for enhanced characterization of fluvial depositional processes: a case study of the Shendi formation, Sudan

Traditional approaches to fluvial sedimentary analysis often face challenges in deciphering complex, multivariate datasets. This study combines compositional data analysis (CoDA) with principal component analysis (PCA) to enhance the characterization of depositional processes and sub-environments within the Shendi Formation. The PCA applied to centered log ratio (clr)-transformed grain size distributions, reveals three principal components with ~ 91.86% of the data variance explained, representing distinct processes: bedload-dominated channel-bar dynamics, overbank deposition, and high-energy flood events. Specific lithofacies associations strongly correlate to each principal component. This integrated approach enables the identification of subtle yet significant patterns within the complex sedimentological record. The Shendi Formation exhibits characteristics of a dynamic fluvial setting with variations in flow energy, channel migration, and periodic flooding. Our findings demonstrate the power of CoDA-PCA in refining the understanding of fluvial depositional systems and highlight its potential for broader applications.

A Study of the Elemental Profiles of Wines from the North-Eastern Coast of the Black Sea

Due to the increasing consumer interest in wines with a controlled place of origin, PGI (Protected Geographical Indication) and PDO (Protected Designation of Origin), the most acute question is how to identify them. One of the most effective ways to confirm the place of origin of wine in global practice is a comprehensive study of the elemental profile using statistical analysis methods. In the period from 2020 to 2023, 152 grape samples of grapes were collected from various wineries in Crimea and Kuban. The grape must that was obtained from them was fermented in laboratory conditions. The elemental profile was determined in the prepared wines, which included 71 indicators. In the conducted work, it was revealed that wines from Crimea and Kuban differ statistically significantly in the concentration of the elements B, Ca, Cu, Mn, Na, Ni, Re, Si, Sn and U. At the same time, the contents of the elements U, Sn and Re prevail in wines from Crimea, and those of B, Ca, Cu, Mn, Na, Ni and Si prevail in wines from Kuban. At the same time, methods of univariate and multivariate statistics do not allow us to reliably classify wine samples from Crimea and Kuban by their place of origin. In order to reveal the non-linear dependence of the studied indicators in wines on the geographical place of grape growing, the method of a supervised learning Random Forest was used. After training the model on the dataset, the proportion of its correct predictions was 96%. The model used 61 parameters, among which the most important were Ni, Re, Ba, Rb, Na, U, Sb, Zn, Bi, Ag and Ti.

An efficient CNN-transformer hybrid approach for water turbine unit failure prediction

Water Turbine Unit is the core equipment of water power generation. It is the most important research object in hydroelectric power. The analysis of daily monitoring data of Water Turbines can evaluate its running state and avoid the loss caused by failure. In this paper, A hybrid neural network architecture which CNN combines Transformer is proposed to evaluate the health state of water turbines based on multivariate long time series data. It has two core components: (i) dilated convolution applies to capture low-level and local semantic information, then the output of convolutional layers is divided into subseries-level patches by time, these patches are regarded as input tokens of Transformer layers; (ii) utilizing self-attention of Transformer to extract high-level and global semantic information. Patching design naturally has benefit as follow: (i) local semantic information is retained in Transformer input tokens and high-level semantic confirm to common sense of human understanding through low-level construction; (ii) the length of Transformer input sequence is greatly shorted and attention can be more concentrated compared with point-wise form. Meanwhile, the computation and memory usage reduces at the same time. The experimental result indicates that the hybrid architecture can achieves excellent performance in time series understanding.

Untargeted Metabolomic to Access Chemical Differences Induced by Dual Endophyte Cultures Isolated from Euphorbia Umbellata.

Endophytic fungi live asymptomatically inside vegetal tissues, and such uncommon habitat contributes to their exceptional chemical diversity. Isolating natural products from endophytic fungi could fail due to silent biosynthetic gene clusters under ordinary in vitro culture conditions, and co-culturing has been assayed to trigger their metabolism. We carried out single and dual cultures with 13 endophyte strains isolated from Euphorbia umbellata leaves. Multivariate statistics applied to untargeted metabolomics compared the chemical profiles of all endophyte cultures. PCA analysis guided the selection of the Aspergillus pseudonomiae J1 - Porogramme brasiliensis J9 dual culture for its most significant chemical differentiation: Five compounds were putatively annotated in the J1-J9 culture according to UHPLC-HRMS data, kojic acid, haliclonol and its diastereoisomer, caffeic acid, and 2-(3,4-dihydroxyphenyl)acetaldehyde. Analysis by PLS-DA using VIP score showed that kojic acid displayed the most significative importance in discriminating single and dual J1-J9 cultures.

Deciphering complexity in Pd–catalyzed cross-couplings

Understanding complex reaction systems is critical in chemistry. While synthetic methods for selective formation of products are sought after, oftentimes it is the full reaction signature, i.e., complete profile of products/side-products, that informs mechanistic rationale and accelerates discovery chemistry. Here, we report a methodology using high-throughput experimentation and multivariate data analysis to examine the full signature of one of the most complicated chemical reactions catalyzed by palladium known in the chemical literature. A model Pd-catalyzed reaction was selected involving functionalization of 2-bromo-N-phenylbenzamide and multiple bond activation pathways. Principal component analysis, correspondence analysis and heatmaps with hierarchical clustering reveal the factors contributing to the variance in product distributions and show associations between solvents and reaction products. Using robust data from experiments performed with eight solvents, for four different reaction times at five different temperatures, we correlate side-products to a major dominant N-phenyl phenanthridinone product, and many other side products.