The aim of this paper is the imputation of missing data of COVID-19 hospitalized and intensive care curves in several Spanish regions. Taking into account that the curves of cases, deceases and recovered people are completely observed, a function-on-...
We examine the significance of fourty-one potential covariates of bitcoin returns for the period 2010–2018 (2872 daily observations). The recently introduced principal component-guided sparse regression is employed. We reveal that economic poli...
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...
This study investigates and compares various multivariate regression methods, including principal component regression (PCR) and partial least squares regression (PLSR), for flight load analysis and demonstrates their high learning efficiency and str...
Underwater source localization is an important task, especially for real-time operation. Recently, machine learning methods have been combined with supervised learning schemes. This opens new possibilities for underwater source localization. However,...
The specific geographic location of Sarajevo, which is located in a valley surrounded by mountains, provides the opportunity to analyze the relation between the concentration of
Handwritten digit recognition has been used in many consumer electronic devices for a long time. However, we found that the recognition system used in current consumer electronics is sensitive to image or character rotations. To address this problem,...
Predicting sediment yield is necessary for good land and water management in any river basin. However, sometimes, the sediment data is either not available or is sparse, which renders estimating sediment yield a daunting task. The present study inves...
Milling force is a parameter affecting wood processing quality, tool life, and energy consumption, and its variation is influenced by the multi-factor coupling of cutting parameters and tool geometric factors. This study systematically investigates m...
Accurately predicting machining-induced deformation is crucial for high-precision CNC turning, particularly when working with dissimilar metallic alloys. This study presents a novel, data-driven framework that integrates empirical deformation analysi...
Color is one of the key characteristics used to evaluate the sensory quality of red wine, and anthocyanins are the main contributors to color. Monomeric anthocyanins and CIELAB color values were investigated by HPLC-MS and spectrophotometry during fe...
Amid rising global energy demand and Taiwan’s transition toward a non-nuclear and low-carbon future, identifying an optimal renewable energy (RE) mix has become essential. This study analyzes eight RE sources using a three-model framework&mdash...
Crop yield forecasting is becoming more essential in the current scenario when food security must be assured, despite the problems posed by an increasingly globalized community and other environmental challenges such as climate change and natural dis...
Smart growth is widely adopted by urban planners as an innovative approach, which can guide a city to develop into an environmentally friendly modern city. Therefore, determining the degree of smart growth is quite significant. In this paper, sustain...
Domestic energy modelling is complex, in terms of user input and the approach used to define the model; therefore, there is an increase in the sources of uncertainties. Previous efforts to perform sensitivity and uncertainty analyses have focused on...
Accurate wind data are crucial for successful search and rescue (SAR) operations on the sea surface in maritime accidents, as survivors or debris tend to drift with the wind. As maritime accidents frequently occur outside the range of wind stations,...
We propose a generalized regression neural network (GRNN) approach based on grey relational analysis (GRA) and principal component analysis (PCA) (GP-GRNN) to improve the accuracy of density functional theory (DFT) calculation for homolysis bond diss...
We address the estimation of regression parameters for the ill-conditioned predictive linear model in this study. Traditional least squares methods often encounter challenges in yielding reliable results when there is multicollinearity. Therefore, we...
Many methods have been developed to allow for consideration of measurement errors during multivariate data analyses. The incorporation of the error structure into the analytical framework, usually described in terms of the covariance matrix of measur...
This paper applied the fuzzy function approach, combined with the ridge regression model, to produce daily rainfall projections from large-scale climate variables. This study developed a statistical downscaling model based on principal components, c-...
Functional data are generally curves indexed over a time domain, and land-use regression (LUR) is a promising spatial technique for generating high-resolution spatial estimation of retrospective long-term air pollutants. We developed a methodology fo...
Flash floods in the Sinai often cause significant damage to infrastructure and even loss of life. In this study, the susceptibility to flash flooding is determined using hydro-morphometric characteristics of the catchments. Basins and their hydro-mor...
The advancement of artificial intelligence has brought visible-light positioning (VLP) to the forefront of indoor positioning research, enabling precise localization without additional infrastructure. However, the complex interplay between light prop...
Accurate prediction of the mechanical performance of fiber-reinforced cement mortars (FRCM) is challenging because fiber geometry and properties vary widely and interact with the cement matrix in a non-trivial way. In this study, we propose an interp...
Susurluk Basin is among the basins that may be most affected by drought risk due to its agricultural, economic, and natural resources. In this study, regional hydrological drought risk models were developed for water supply systems in the Susurluk Ba...
Groundwater nitrate contamination in the Central Valley (CV) aquifer of California is a ubiquitous groundwater problem found in various parts of the valley. Heavy irrigation and application of fertilizer over the last several decades have caused grou...
This paper presents a new and novel hybrid modeling method for the segmentation of high dimensional time-series data using the mixture of the sparse principal components regression (MIX-SPCR) model with information complexity (
In light of the nonlinearity, high dimensionality, and time-varying nature of the operational conditions of the pulverizer in power plants, as well as the challenge of the real-time monitoring of quality variables in the process, a data-driven KPCA&n...
A first principal component combines several indicators so as to maximize their internal consistency for measuring a construct. First principal components are extracted here from Swiss Economic Institute and World Bank datasets containing yearly soci...
For mixtures of compounds with very similar spectral features, common for larger organic molecules, multivariate analysis (MVA) methods can be applied to determine the concentration of the individual components. We analyzed photoacoustic spectra of m...
Previous studies have shown that volatile organic compounds (VOCs) are potential biomarkers of breast cancer. An unanswered question is how urinary VOCs change over time as tumors progress. To explore this, BALB/c mice were injected with 4T1.2 triple...
The double-stranded B-form and A-form have long been considered the two most important native forms of DNA, each with its own distinct biological roles and hence the focus of many areas of study, from cellular functions to cancer diagnostics and drug...
Using a large database of U.S. seasoned equity offering (SEO) announcements from 2010 to 2015, we examine the effects of several explanatory variables—firm specific, macroeconomic, fixed income, and stock market variables—on the announcem...
Evaluating the impact of different meteorological conditions on apple yield and predicting the future yield in Yantai City is essential for production. Furthermore, it provides a scientific basis for the increase in apple yield. In this study, first,...
Wheat is the main grain crop in our country, and the traditional wheat yield estimation method is time-consuming and laborious. By estimating wheat yield efficiently, quickly and non-destructively, agricultural producers can quickly obtain informatio...
25 September 2022
This study aims to examine people’s perception of well-being during the COVID-19 pandemic in Japan and quantitatively clarify key factors towards realizing evidence-based policymaking. In March 2022, 400 participants responded to a survey condu...
Previous studies have mostly examined how sustainable cities try to promote non-motorized travel by creating a walking-friendly environment. Such existing studies provide little data that identifies how the built environment affects pedestrian volume...
The modal frequencies of a structure are affected by continuous changes in ambient factors, such as temperature, wind speed etc. This study incorporates nonlinear principal component analysis (NLPCA) with support vector regression (SVR) to build a ma...
The grain moisture content of maize inbred lines at maturity is one of the most important indicators for mechanical harvesting of kernels. In this study, 116 maize inbred lines from a wide range of sources were used as research materials and 30 trait...
In the moist environment of soil-water-air, there is a problem of low accuracy in monitoring volatile organic compounds (VOCs) using a photoionization detector (PID). This study is based on the PID water-soil-gas VOC online monitor developed by this...
In arid and semi-arid lands like Iran water is scarce, and not all the wastewater can be treated. Hence, groundwater remains the primary and the principal source of water supply for human consumption. Therefore, this study attempted to spatially asse...
The development of new point-of-care diagnostic testing tools for the detection of infectious diseases such as COVID-19 are a key aspect of clinical care and research. Accurate predictive classification methods are required to correctly identify and...
The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviole...
Specific monosaccharide residue, β-D-galactofuranose (Galf) featuring a five-membered ring structure, is found in the glycans of fungi and bacteria, but is normally absent in healthy mammals and humans. In this study, synthetic oligosaccharides...
The paper presents an application of an electronic nose prototype comprised of six TGS-type sensors and one PID-type sensor to identify odour interaction phenomena in odorous three-component mixtures. The investigation encompassed eight odorous mixtu...
Hyperspectral imaging (HSI) in the spectral range of 400–1000 nm was tested to differentiate three different particle size fractions of milk powder. Partial least squares discriminant analysis (PLS-DA) was performed to observe the relationship...
The increased incidence of opportunistic fungal infections, associated with greater resistance to the antifungal drugs currently in use has highlighted the need for new solutions. In this study twenty four coumarin derivatives were screened in vitro ...
Functional data such as curves, shapes, and manifolds have become more and more common with modern technological advancements. The multiplicative regression model is well suited for analyzing data with positive responses. In this study, we study the...
This paper presents a novel mapping prediction method for surface solar radiation with linear regression models. The dataset for surface solar radiation prediction is the daily surface incoming shortwave radiation (SIS) product from CM SAF SARAH-E. T...
The information value method is widely used in predicting the susceptibility of geological disasters. However, most susceptibility evaluation models assume that the weight of each influencing factor is equal, which is inconsistent with the actual sit...
of 42