14,139 Results Found

Essential trace elements are required at very low quantities in the human body but are essential for various physiological functions. Each trace element has a specific role and a lack of these elements can easily cause a threat to health and can be p...

According to the behavior of its neuronal connections, it is possible to determine if the brain suffers from abnormalities such as epilepsy. This disease produces seizures and alters the patient’s behavior and lifestyle. Neurologists employ the...

Moisture is one of the major causes of building decay, compromising the indoor air quality and the durability of building components. Infrared thermography is a non-destructive technique that can be used to prevent damage caused by the presence of wa...

Kinematics data are primary biomechanical parameters. A principal component analysis (PCA) of waveforms is a statistical approach used to explore patterns of variability in biomechanical curve datasets. Differences in experienced and recreational run...

A manufacturer’s fabric first undergoes an abrasion test and manual visual inspection to grade the fabric prior to shipment to ensure that there are no defects present. Manual visual classification consumes a considerable amount of human resour...

The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as...

This paper proposes a comprehensive method for the compaction uniformity evaluation of subgrade in highways based on the principle components analysis and BP neural network. A field test on resilient and Young’s moduli of subgrade during compac...

The electronic voltage transformer (EVT) has received much attention with the recent global trend to establish smart grids and digital substations. One of the main issues of the EVT is the deterioration of its performance with long-term operation whi...

Groundwater drinking water supply surveillance data were accessed to summarize water quality delivered as public and private water supplies in southern Saskatchewan as part of an exposure assessment for epidemiologic analyses of associations between...

For two or more dimensions, the two main approaches to estimating legislators’ ideal points from roll-call data entail arbitrary, yet consequential, identification and modeling assumptions that bring about both indeterminateness and undue constraints...

Communities in urban space are the most basic living units. Community visual features directly reflect the local living quality and influence the perception of residents and visitors. The evaluation of the community visual features is of great signif...

In this article, multilevel principal components analysis (mPCA) is used to treat dynamical changes in shape. Results of standard (single-level) PCA are also presented here as a comparison. Monte Carlo (MC) simulation is used to create univariate dat...

An extended version of Principal Components Analysis (PCA) of monument stone decay phenomena occurring at “Basilica da Estrela” church, Lisbon, Portugal, is now presented. The PCA rationale and general methodological procedure is presented, as a firs...

Integrating the representation of the territory, through airborne remote sensing activities with hyperspectral and visible sensors, and managing complex data through dimensionality reduction for the identification of cannabis plantations, in Albania,...

Rapid data growth in large systems has introduced significant challenges in real-time monitoring and analysis. One of these challenges is detecting anomalies in time series data with high-dimensional inputs that contain complex inter-correlations bet...

The full-spectrum Solar-Induced chlorophyll Fluorescence (SIF) within the 650-800 nm spectral region can provide important information regarding physiological and biochemical activities in vegetation. This paper proposes a new Full-spectrum Spectral...

Mapping species composition is a focus of the wetland science community as this information will substantially enhance assessment and monitoring abilities. Hyperspectral remote sensing has been utilized as a cost-efficient approach. While hyperspectr...

The process of domestication, despite its short duration as it compared with the time scale of the natural evolutionary process, has caused rapid and substantial changes in the phenotype of domestic animal species. Nonetheless, the genetic mechanisms...

This study aimed to characterize the chemical composition and spatial distribution of groundwater in the Kızılırmak Delta of Turkey and to evaluate the suitability of groundwater in the Kızılırmak Delta for drinking wate...

3D facial surface imaging is a useful tool in dentistry and in terms of diagnostics and treatment planning. Between-group PCA (bgPCA) is a method that has been used to analyse shapes in biological morphometrics, although various “pathologies&rd...

Nonlinearity may cause a model deviation problem, and hence, it is a challenging problem for process monitoring. To handle this issue, local kernel principal component analysis was proposed, and it achieved a satisfactory performance in static proces...

This study was aimed at assessing the spatial and temporal distribution of surface water quality variables of the Xin’anjiang River (Huangshan). For this purpose, 960 water samples were collected monthly along the Xin’anjiang River from 2...

Recently, with the popularization of intelligent terminals, research on intelligent big data has been paid more attention. Among these data, a kind of intelligent big data with functional characteristics, which is called functional data, has attracte...

The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nos...

This research investigated a mathematical model of earphone design with principal component analysis. Along with simplifying the design problem, a predictive model for the sound quality indicators, namely, total harmonic distortion, power of output,...

The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like compression, event detection, and event recognition. This technique is based on...

This article first selects the “Urban Statistical Yearbook” data of 264 prefecture-level cities in China from 2004 to 2018 as the raw data, and uses principal component analysis and the Wilson model to calculate the spatial information di...

Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications. In this paper, a superpixelwise kernel principal component analysis (SuperKPCA) method for DR that performs kernel principal component analysis (KPC...

In recent years, bioenergy has become a promising renewable energy source that can potentially reduce the greenhouse emissions and generate economic growth in rural areas. Gaining understanding and controlling biomass chemical composition contributes...

High-plasticity clay soils pose significant challenges in geotechnical engineering due to their poor mechanical properties, such as low strength and high compressibility. Lime–cement stabilization offers a sustainable solution, but optimizing a...

Machine learning algorithms play an important role in fault detection and fault diagnosis of gas sensor arrays. Because the gas sensor array will see stability degradation and a shift in output signal amplitude under long-term operation, it is very i...

In this paper, two-dimensional quantitative structure–activity relationship (2D-QSAR) and principal component analysis (PCA) methods were employed to screen the main parameters affecting the genotoxicity of fluoroquinolones (FQs), and the rules...

This study proposes a fault diagnosis method (FD) for multistage centrifugal pumps (MCP) using informative ratio principal component analysis (Ir-PCA). To overcome the interference and background noise in the vibration signatures (VS) of the centrifu...

Objectives: Munich Functional Developmental Diagnosis (MFDD) is a scale for assessing the psychomotor development of children in the first months or years of life. The tool is based on standardized tables of physical development and is used to detect...

In response to the high demand of the operation reliability and predictive maintenance, health monitoring and fault diagnosis and classification have been paramount for complex industrial systems (e.g., wind turbine energy systems). In this study, da...

The method of measuring temperature using luminescence by analyzing the emission spectra of Pr³⁺-doped YF₃ using principal component analysis is presented. The Pr³⁺-doped YF₃ is synthesized using a solid-state technique, and its single-phase orthorho...

Glyphosate is an herbicide used worldwide with harmful health effects, and efforts are currently being made to develop sensors capable of detecting its presence. In this work, an array of four interdigitated microwave sensors was used together with t...

Background and Objectives: This study aimed to group diseases classified by the International Classification of Diseases using principal component analysis, and discuss a systematic approach to reducing the preventable death rate from a perspective o...

The presented paper introduces principal component analysis application for dimensionality reduction of variables describing speech signal and applicability of obtained results for the disturbed and fluent speech recognition process. A set of fluent...

The present contribution is devoted to the theory of fuzzy sets, especially Atanassov Intuitionistic Fuzzy sets (IF sets) and their use in practice. We define the correlation between IF sets and the correlation coefficient, and we bring a new perspec...

The accurate prediction of road traffic crash (RTC) severity contributes to generating crucial information, which can be used to adopt appropriate measures to reduce the aftermath of crashes. This study aims to develop a hybrid system using principal...

Principal component analysis (PCA) is used to reduce the dimensionalities of high-dimensional datasets in a variety of research areas. For example, biological macromolecules, such as proteins, exhibit many degrees of freedom, allowing them to adopt i...

Principal Component Analysis (PCA) is a method based on statistics and linear algebra techniques, used in hyperspectral satellite imagery for data dimensionality reduction required in order to speed up and increase the performance of subsequent hyper...

The detection of drowsiness while driving plays a vital role in ensuring road safety. Existing detection methods need to reduce external interference and sensor intrusiveness, and their algorithms must be modified to improve accuracy, stability, and...

This work focuses on functional data presenting spatial dependence. The spatial autocorrelation of stock exchange returns for 71 stock exchanges from 69 countries was investigated using the functional Moran’s I statistic, classical principal co...

Quartz crystal nanobalance (QCN) sensors are considered as powerful mass-sensitive sensors to determine materials in the sub-nanogram level. In this study, a singlepiezoelectric quartz crystal nanobalance modified with polystyrene was employed to det...

In this paper, a combined approach of Principal Component Analysis (PCA)-based Extreme Learning Machine (ELM) for boiler output forecasting in a thermal power plant is presented. The input used for this prediction model is taken from the boiler unit...

Pattern recognition in agricultural soils using interdigitated microwave sensors combined with principal component analysis offers a novel approach to soil characterization. In this study, soil samples were collected at the “El Potrillo”...

The use of principal component analysis (PCA) provides information about the main characteristics of teams, based on a set of indicators, instead of displaying individualized information for each of these indicators. In this work we have considered r...

Many factors influence the evaluation process of thief zones. The evaluation index contains very complex information. How to quickly obtain effective information is the key to improve the evaluation quality for thief zones. Considering that the corre...