Principal Component Analysis Procedure Research Articles

Institutional framework, macroeconomic instability and financial markets: perspective from emerging economies

Purpose The study evaluates the role of institutional framework and macroeconomic instability on financial market development among emerging economies.Design/methodology/approach The study uses panel data compiled from 32 countries from the sub-region of Sub-Sahara Africa (SSA), covering the period starting from 1996 to 2019. Empirical analyses were carried out using the two-step system generalized method of moments (TS-GMM) statistical framework.Findings Reviewed results suggest that institutional quality, effective governance and corruption control have a significant positive impact on financial market development among economies in the sub-region. Further empirical estimates show that macroeconomic risk and macroeconomic uncertainty have significant adverse effects on financial market development. Additionally, reported empirical estimates suggest that an improved institutional framework has the potential to lessen the adverse effect of macroeconomic instability on financial market development among economies in the sub-region.Originality/value The uniqueness of this empirical inquiry compared to related studies in the present literature stems from the fact that studies employing similar empirical approaches on the subject matter for economies in the sub-region are rare. Additionally, the analysis pursued in this study employs critical variables whose impact on financial market performance in the sub-region has not been examined per our review. These variables include indexes such as macroeconomic risk and institutional quality, which are unique to this study based on their construction; these indexes are generated using a principal component analysis procedure with different underlying variables compared to what may be found in the literature.

Fast Fractional Fourier Transform-Aided Novel Graphical Approach for EEG Alcoholism Detection.

Given its detrimental effect on the brain, alcoholism is a severe disorder that can produce a variety of cognitive, emotional, and behavioral issues. Alcoholism is typically diagnosed using the CAGE assessment approach, which has drawbacks such as being lengthy, prone to mistakes, and biased. To overcome these issues, this paper introduces a novel paradigm for identifying alcoholism by employing electroencephalogram (EEG) signals. The proposed framework is divided into various steps. To begin, interference and artifacts in the EEG data are removed using a multiscale principal component analysis procedure. This cleaning procedure contributes to information quality improvement. Second, an innovative graphical technique based on fast fractional Fourier transform coefficients is devised to visualize the chaotic character and complexities of the EEG signals. This elucidates the properties of regular and alcoholic EEG signals. Third, thirty-four graphical features are extracted to interpret the EEG signals' haphazard behavior and differentiate between regular and alcoholic trends. Fourth, we propose an ensembled feature selection method for obtaining an effective and reliable feature group. Following that, we study many neural network classifiers to choose the optimal classifier for building an efficient framework. The experimental findings show that the suggested method obtains the best classification performance by employing a recurrent neural network (RNN), with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the sixteen selected features. The proposed framework can aid physicians, businesses, and product designers to develop a real-time system.

Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach.

Dynamically predicting patient survival probabilities using longitudinal measurements has become of great importance with routine data collection becoming more common. Many existing models utilize a multi-step landmarking approach for this problem, mostly due to its ease of use and versatility but unfortunately most fail to do so appropriately. In this article we make use of multivariate functional principal component analysis to summarize the available longitudinal information, and employ a Cox proportional hazards model for prediction. Additionally, we consider a centred functional principal component analysis procedure in an attempt to remove the natural variation incurred by the difference in age of the considered subjects. We formalize the difference between a 'relaxed' landmarking approach where only validation data is landmarked and a 'strict' landmarking approach where both the training and validation data are landmarked. We show that a relaxed landmarking approach fails to effectively use the information contained in the longitudinal outcomes, thereby producing substantially worse prediction accuracy than a strict landmarking approach.

Robust Principal Component Analysis: A Median of Means Approach.

Principal component analysis (PCA) is a fundamental tool for data visualization, denoising, and dimensionality reduction. It is widely popular in statistics, machine learning, computer vision, and related fields. However, PCA is well-known to fall prey to outliers and often fails to detect the true underlying low-dimensional structure within the dataset. Following the Median of Means (MoM) philosophy, recent supervised learning methods have shown great success in dealing with outlying observations without much compromise to their large sample theoretical properties. This article proposes a PCA procedure based on the MoM principle. Called the MoMPCA, the proposed method is not only computationally appealing but also achieves optimal convergence rates under minimal assumptions. In particular, we explore the nonasymptotic error bounds of the obtained solution via the aid of the Rademacher complexities while granting absolutely no assumption on the outlying observations. The derived concentration results are not dependent on the dimension because the analysis is conducted in a separable Hilbert space, and the results only depend on the fourth moment of the underlying distribution in the corresponding norm. The proposal's efficacy is also thoroughly showcased through simulations and real data applications.

Development of Korean Representative Headforms for the Total Inward Leakage Testing on Filtering Facepiece Respirators

BackgroundThe lack of headforms that accurately reflect the head characteristics of Koreans and the demographic composition of the Korean population can lead to inadequate FFR testing and reduced effectiveness of FFRs. MethodDirect measurements of 5,110 individuals and 3D measurements of 2,044 individuals, aged between 9 and 69 years, were sampled from the data pool of Size Korea surveys based on the age and gender ratios of the Korean resident demographics. Seven head dimensions were selected based on the ISO 16976-2, availability of Size Korea measurements, and their relevance to the fit performance of FFRs. A principal component analysis (PCA) was performed using the direct measurements to extract the main factors explaining the head characteristics and then the main factors were standardized and remapped to 3D measurements, creating five size categories representing Korean head shapes. Lastly, representative 3D headforms were constructed by averaging five head shapes for each size category. ResultsThe study identified two main factors explaining Korean head characteristics by the PCA procedure specified in ISO 16976-2 and developed five representative headforms reflecting the anthropometric features of Korean heads: medium, small, large, short & wide, and long & narrow. ConclusionThis study developed representative headforms tailored to the Korean population for conducting total inward leakage (TIL) tests on filtering facepiece respirators (FFRs). The representative headforms can be used for TIL testing by employing robotic headforms to enhance the performance of FFRs for the Korean target population.

Regulatory policy uncertainty, banking industry innovations and financial development among emerging markets

PurposeThis study examines the extent to which regulatory policy uncertainty, macroeconomic risk, banking industry innovations, etc. influence variability in financial sector development among emerging economies in sub-Sahara Africa (SSA).Design/methodology/approachData for the empirical inquiry were compiled from a sample of 25 economies from the subregion from 2010 to 2020. Empirical estimates examining the relationships noted above were carried out using the two-step system generalized method of moments estimation technique.FindingsResults the empirical estimates suggest that regulatory policy uncertainty and macroeconomic risk adversely influence or constrain financial sector development among the economies examined in the study. Banking industry innovations on the other hand is found to positively influence the development of the financial sector in these economies. Furthermore, moderating empirical analysis suggests that effective governance positively moderates the relationship between banking industry innovations and financial development among economies in the subregion.Originality/valueThis study’s approach to the mechanics of financial development among economies in SSA is designed to offer different perspectives to those found in the existing literature on financial development in three fundamental ways. First, although the verification of the role of banking industry innovations in financial development may not be new, it is important to point out that the approach used in this study is based on an index for innovations with different constituents or principal components in its construction; making the variable significantly different from what has been examined in the literature. In addition, the review of regulatory policy uncertainty and macroeconomic risk (both variables are multifaceted constructs using the principal component analysis procedure) further brings into this study’s analysis, a different approach to examining conditions influencing variability in financial development among developing economies.

Restoring soil nutrient stocks using local inputs, tillage and sorghum-green gram intercropping strategies for drylands in Eastern Kenya

Soil macronutrient and micronutrient availability is particularly critical in semi-arid agro-ecological zones that are characterized by poor soil fertility and low rainfall regimes. An experiment was initiated in Siakago, Embu County to investigate the effects of tied-ridges, conventional tillage and input applications on soil nutrient fertility using a randomized complete block design with a split-split plot arrangement for 4 seasons (2018–2021). The treatments comprised of two main plot tillage systems, three cropping systems allocated to the sub-plots and four soil input management treatments assigned to sub-sub plots. ANOVA was used to test the effects of different treatments including tillage, crop system and soil fertility management using Genstat software. The data was also subjected to Principal Component Analysis procedures using R ("FactoMineR" and "factoextra") to examine the inter-relationship patterns between different soil fertility parameters and to reduce the data into independent soil fertility components. There were significant main effects due to crop system (Soil Mn), tillage and crop system interaction (SOC and TSN) and soil fertility management (TOC, TSN, Ca, Zn). Soil inputs significantly influenced soil carbon concentrations (p = 0.002), with the lowest values observed in the control (0.2 %), followed by sole fertilizer (0.35 %), manure + fertilizer (0.41 %) and the fully decomposed manure treatment (0.61 %). The soil-extracted manganese values recorded significant effects due to crop system, while soil-extracted Zn values were significant due to soil fertility management. Multivariate analysis results revealed the structure of soil nutrient distribution. Tied ridging can improve soil micronutrient availability through reduced soil erosion, conservation of soil organic matter, which can improve soil micronutrient availability. Soil conservation practices such as tied-ridging integrated with organic input applications can enhance multiple nutrient availability for improved crop performance and human nutrition in dryland farming systems where farmers lack soil moisture, technologies and resources to enhance crop nutrient availability.

A novel two-way functional linear model with applications in human mortality data analysis

Recently, two-way or longitudinal functional data analysis has attracted much attention in many fields. However, little is known on how to appropriately characterize the association between two-way functional predictor and scalar response. Motivated by a mortality study, in this paper, we propose a novel two-way functional linear model, where the response is a scalar and functional predictor is two-way trajectory. The model is intuitive, interpretable and naturally captures relationship between each way of two-way functional predictor and scalar-type response. Further, we develop a new estimation method to estimate the regression functions in the framework of weak separability. The main technical tools for the construction of the regression functions are product functional principal component analysis and iterative least square procedure. The solid performance of our method is demonstrated in extensive simulation studies. We also analyze the mortality dataset to illustrate the usefulness of the proposed procedure.

Secondary ion mass spectrometry, a powerful tool for revealing ink formulations and animal skins in medieval manuscripts

Book production by medieval scriptoria have gained growing interest in recent studies. In this context, identifying ink compositions and parchment animal species from illuminated manuscripts is of great importance. Here, we introduce time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a non-invasive tool to identify both inks and animal skins in manuscripts, at the same time. For this purpose, both positive and negative ion spectra in inked and non-inked areas were recorded. Chemical compositions of pigments (decoration) or black inks (text) were determined by searching for characteristic ion mass peaks. Animal skins were identified by data processing of raw ToF-SIMS spectra using principal component analysis (PCA). In illuminated manuscripts from the fifteenth to sixteenth century, malachite (green), azurite (blue), cinnabar (red) inorganic pigments, as well as iron-gall black ink, were identified. Carbon black and indigo (blue) organic pigments were also identified. Animal skins were identified in modern parchments of known animal species by a two-step PCA procedure. We believe the proposed method will find extensive application in material studies of medieval manuscripts, as it is non-invasive, highly sensitive and able to identify both inks and animal skins at the same time, even from traces of pigments and tiny scanned areas.

Effect of Education on Ease of Doing Business in Conditions of Innovation Development: Factor Analysis and Multiple Regression

Education accelerates changes and transformations in social life, as well as one of the main factors of progress and development in general and business. The article substantiates and formalises the relationships between indicators of the level of education (taking into account its innovative development) and the ease of doing business based on evidence from 28 world countries and the data from the World Bank, the United Nations, Tufts University and Standard & Poor’s Ratings Services. The article aims to find indicators of innovative education that most significantly contribute to the ease of doing business. For this, cognitive analysis of statistical data is carried out, and with the help of descriptive analysis tools, a statistically significant characteristic space of indicators is formed. Checking the density and direction of the relationship is carried out by calculating the values of the Pearson correlation coefficients. The multidimensionality of input feature space is reduced to the four most significant indicators from nine investigated ones (digital development indicator; human development index; digital trust, financial literacy index) using the procedure of principal component analysis and orthogonal transformation using the Varimax method in the Statgraphics Centurion 19 software. The quality of the factorisation is confirmed by Kaiser-Meier-Olkin testing and Bartlett’s sphericity testing. As a result of the developed multiple econometric models, which describe the dependence of ease of doing business and the above indicators, and the Backward Stepwise Selection hard screening procedure in Statgraphics 19, a statistically significant model of the effect of digital development on ease of doing business is built. It shows that with the value increase of digital development by 1%, ease of doing business will also increase by 0.79%. The obtained results can be useful to scientists for further research, as well as to change-makers in education and business and all stakeholders in the direction of «business–education» competition.

TO DRAW OR NOT TO DRAW: UNDERSTANDING THE TEMPORAL ORGANIZATION OF DRAWING BEHAVIOR USING FRACTAL ANALYSES

Studies on drawing often focused on spatial aspects of the finished products. Here, the drawing behavior was studied by analyzing its intermittent process, between drawing (i.e. marking a surface) and interruption (i.e. a pause in the marking gesture). To assess how this intermittence develops with age, we collected finger-drawings on a touchscreen by 185 individuals (children and adults). We measured the temporal structure of each drawing sequence to determine its complexity. To do this, we applied temporal fractal estimators to each drawing time series before combining them in a Principal Component Analysis procedure. The youngest children (3-year-old) drew in a more stereotypical way with long-range dependence detected in their alternations between states. Among older children and adults, the complexity of drawing sequences increased showing a less predictable behavior as their drawings become more detailed and figurative. This study improves our understanding of the temporal aspects of drawing behavior, and contributes to an objective understanding of its ontogeny.

An Efficiency–Accuracy Balanced Power Leakage Evaluation Framework Utilizing Principal Component Analysis and Test Vector Leakage Assessment

The test vector leakage assessment (TVLA) is a widely used side-channel power leakage detection technology which requires evaluators to collect as many power traces as possible to ensure accuracy. However, as the total sample size of the power traces increases, the amount of redundant information will also increase, thus limiting the detection efficiency. To address this issue, we propose a principal component analysis (PCA)-TVLA-based leakage detection framework which realizes a more advanced balance of accuracy and efficiency. Before implementing TVLA to detect leakage, we project the original power data onto their most significant feature dimensions extracted by the PCA procedure and screen power traces according to the magnitude of their corresponding components in the variance of the projection vector. We verified the overall performance of the proposed framework by measuring the detection capability and efficiency with t-values and the required time, respectively. The results show that compared with similar existing schemes, under the best circumstances, the proposed framework decreases the t-value by 4.3% while saving time by 25.2% on the MCU platform and decreases the t-value by 2.4% while saving time by 38.0% on the FPGA platform.

Environmental niches and seasonal succession of phytoplankton assemblages in a subarctic coastal bay: Applications to remote sensing estimates

The seasonal and spatial variability of surface phytoplankton assemblages and associated environmental niches regarding major nutrients, physical (temperature and salinity), and optical characteristics (inherent and apparent optical properties) were investigated in an anthropized subarctic coastal bay, in the Gulf of St. Lawrence: the Bay of Sept-Îles (BSI), Québec, Canada. Seven major phytoplankton assemblages were identified by applying a combined Principal Component Analysis and Hierarchical Cluster Analysis procedures, using pigment concentrations and <20 µm autotrophic cell abundances as inputs. The resulting phytoplankton groups from BSI (n = 7) were more diverse than at a station monitored in a central portion of the St. Lawrence Estuary (n = 2). The temporal distribution of the phytoplankton assemblages of BSI reflected the major seasonal (spring to fall) signal of a nearshore subarctic environment. Before the freshet, spring bloom was dominated by large (microphytoplankton) cells (diatoms), and the succession followed a shift towards nanophytoplankton and picophytoplankton cells throughout summer and fall. Most of the phytoplankton assemblages occupied significantly different environmental niches. Taking temperature and the bio‐optical properties (ultimately, the remote sensing reflectance) as inputs, a framework to classify five major groups of phytoplankton in the BSI area is validated. The demonstrated possibility to retrieve major phytoplankton assemblages has implications for applying remote sensing imagery to monitoring programs.

Do determinants of eco-innovations vary? An investigation of innovative SMEs through a quantile regression approach

Ecological innovations (EIs) are believed to play a crucial role both for the future competitiveness of companies and environmental protection. Thus, many researchers have investigated the determinants stimulating the adoption of EIs, but they usually consider determinants as a static concept. In this paper, these determinants are hypothesized to vary with companies’ propensity to adopt them, that is, the degree to which companies consider relevant the choice to invest in EIs.In this view, the paper investigated a sample of 222 innovative SMEs following a multi-step procedure. By means of a composite indicator, firstly the SMEs’ propensity to adopt EIs was calculated. Secondly, in order to identify the determinants that stimulate SMEs to invest in eco-innovation, the procedure of Principal Component Analysis based on the ScotLass algorithm was applied. Thirdly, the determinants individuated as the principal components extracted were investigated through OLS and quantile regression.The results suggest that among the SMEs with a higher propensity to invest in EIs, the more evident determinants are the ambition to reach better economic performance, the capacity to establish networking, and the endogenous competencies of SMEs. Also, the variable structural nature concerning the firm's size shows a high relevance.

Application of ASTER images for identification and localization of iron minerals in red mud dumps of the Ural aluminum plant

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was used to identify and mapping of industrially and environmentally important iron oxides and hydroxides in red mud dumps. In paper we were focused on specified region of interests - one of the largest boxite processing manufactory in Russia Ural Aluminum Plan (UAP). Initially we use ASTER L1T images. The graph of processing includes band relationship, principal component analysis and spectral angle mapper procedures for identifying spatial distribution of targeted minerals. Several minerals were mapped within red mud dumps No 2 and No 3: Hematite, Goetite, Jarosite and Magnetite. At the next stage the Scanning Electronic Microscopy (SEM) of red mud mineral samples was carried out. The results show a good correlation between remote sensing techniques and standard mineralogical analysis (SEM) method. In both cases the principal iron oxides and hydroxides were observed. Combination of remote sensing and traditional mineralogical analysis methods allows to increase the reliability of assessing ore potential and efficiency of planning utilization technologies for wastes in technogenic deposits. In addition, processing of several ASTER images from different time gap (2004 and 2015) allows to trace in dynamics the transformation of mineral composition in red muds.

Detection Thresholds in Very Sparse Matrix Completion

We study the matrix completion problem: an underlying \(m \times n\) matrix P is low rank, with incoherent singular vectors, and a random \(m \times n\) matrix A is equal to P on a (uniformly) random subset of entries of size dn. All other entries of A are equal to zero. The goal is to retrieve information on P from the observation of A. Let \(A_1\) be the random matrix where each entry of A is multiplied by an independent \(\{0,1\}\)-Bernoulli random variable with parameter 1/2. This paper is about when, how and why the non-Hermitian eigen-spectra of the matrices \(A_1 (A - A_1)^*\) and \((A-A_1)^*A_1\) captures more of the relevant information about the principal component structure of A than the eigen-spectra of \(A A^*\) and \(A^* A\). We show that the eigenvalues of the asymmetric matrices \(A_{1} (A - A_{1})^{*}\) and \((A-A_{1})^{*} A_{1}\) with modulus greater than a detection threshold are asymptotically equal to the eigenvalues of \(PP^*\) and \(P^*P\) and that the associated eigenvectors are aligned as well. The central surprise is that by intentionally inducing asymmetry and additional randomness via the \(A_1\) matrix, we can extract more information than if we had worked with the singular value decomposition (SVD) of A. The associated detection threshold is asymptotically exact and is non-universal since it explicitly depends on the element-wise distribution of the underlying matrix P. We show that reliable, statistically optimal but not perfect matrix recovery, via a universal data-driven algorithm, is possible above this detection threshold using the information extracted from the asymmetric eigen-decompositions. Averaging the left and right eigenvectors provably improves estimation accuracy but not the detection threshold. Our results encompass the very sparse regime where d is of order 1 where matrix completion via the SVD of A fails or produces unreliable recovery. We define another variant of this asymmetric principal component analysis procedure that bypasses the randomization step and has a detection threshold that is smaller by a constant factor but with a computational cost that is larger by a polynomial factor of the number of observed entries. Both detection thresholds allow to go beyond the barrier due to the well-known information theoretical limit \(d \asymp \log n\) for exact matrix completion found in the literature.

Volatiles Distinguishing the European ‘Conference’ and the Asian ‘Yali’ Pears Stored at Different Post-Harvest Temperatures

A total of 124 identical volatile aromatic compounds were identified during storage of the European ‘Conference’ and the Asian ‘Yali’ pear cultivars in different temperature conditions. Only 5 volatiles were statistically differentiated in both cultivars by means of successive multinomial logistic regression: 3-methylbutan-1-al, 2-methylpropyl acetate, 2-methoxy-4-vinylphenol, ethanol, and eugenol. Significant statistical data obtained by sequential multinomial logistic regression developed by the principal component analysis (PCA) procedure and distinguishing the different ‘Conference’ and ‘Yali’ pears storage regimes were dimensionless in themselves. The PCA components were expressed as linear combinations of selected variables necessary to distinguish the cultivars. The eigenvalues of the first three PCA components differentiated the storage regimes. For each principal components were selected volatiles with a probability higher than 0.4. Combinations created from PCA components were shown using clusters distinguishing the pear storage conditions used. Analytical data from SPME-GC/MS such as concentration (ng kg−1) demonstrated multiple and order-of-magnitude differences between the ‘Conference’ and ‘Yali’ pears. The ‘Yali’ cultivar exhibited significantly higher concentrations of eugenol.

Application of data smoothing and principal component analysis to develop a parameter ranking system for the anaerobic digestion process

A parameter ranking system to enhance data interpretation of the anaerobic digestion process was developed using principal component analysis (PCA) and data smoothing. The experimental data collected from the start-up operation of a pilot-scale, two-stage anaerobic digester for food wastewater treatment was used to demonstrate the enhanced PCA procedures. The correlation of multiple parameters in the anaerobic digestion process could be identified by the ranked parameters based on statistically scored outcomes. According to the parameters ranked for their impact on biogas production and methane yield, the biochemical oxygen demand (BOD) in the food wastewater was shown to have the highest correlated ranks with a score of 0.246, and the pH and ammonium in the food wastewater were shown to have lower ranked scores of 0.834 and 1.019, respectively. Therefore, ammonia toxicity and pH shock might not have had significant influence, and the operating priority should be focused on the organic loads. This application of PCA to anaerobic digestion can be helpful for addressing stabilization issues by identifying the core parameters that primarily contribute to early detection of operating problems.

Impact damage characterization in CFRP samples with self-organizing maps applied to lock-in thermography and square-pulse shearography images

This paper proposes an approach based on self-organizing maps (SOMs) to characterize defects in optical lock-in thermography (LT) and square-pulse shearography (SPS) images of forty samples made of carbon fiber reinforced epoxy. Low-energy impact damages from 1 J to 12 J were inflicted on the samples in a controlled way. The intersection over union metric was used to evaluate the segmentation results, determining which of the configurations were the best ones for characterizing damages in the LT and SPS images. The outcomes were compared to techniques from the literature, such as principal component analysis and absolute thermal contrast-based segmentation procedures. The best configuration of the proposed approach is at least equivalent to the ones of optimal state-of-the-art tools, and provided clean and reliable segmentation results. Besides, since SOM is a single tool and has potentially a higher capability of adaptation to the image due to the training process, this shows that such technique can be useful for damage detection and description in other industrial scenarios.

Using Weather and Schedule Based Pattern Matching and Feature Based Principal Component Analysis for Whole Building Fault Detection—Part II Field Evaluation

Abstract In a heating, ventilation, and air conditioning (HVAC) system, a whole building fault (WBF) refers to a fault that occurs in one component but may trigger additional faults/abnormalities on different components or subsystems resulting in significant impacts on the energy consumption or indoor air quality in buildings. At the whole building level, interval data collected from various components/subsystems can be used to detect WBFs. In the Part I of this study, a novel data-driven method which includes weather and schedule-based pattern matching (WPM) procedure and a feature based principal component analysis (FPCA) procedure was developed to detect the WBF. This article is the second of a two-part study of the development of the whole building fault detection method. In the Part II of the study (this paper), various WBFs were designed and imposed in the HVAC system of a campus building. Data from both imposed fault and naturally occurred faults were collected through the building automation system (BAS) to evaluate the developed fault detection method. Evaluation results show that the developed WPM-FPCA method reaches a satisfactory detection rate (85% and 100% under two principal component retention rates) and a 0% false alarm rate (under two principal component retention rates).