Principal Component Analysis Methodology Research Articles

Hydrothermal carbonization of primary sewage sludge in a prototype reactor: Sensitivity analysis of key parameters for hydrochar and liquid phase upscaling and reuse

Hydrothermal carbonization (HTC) of untreated sewage sludge with high water content was evaluated as an alternative to usual wastewater treatment. This bold approach allowed bioproducts useful as biofuel and nutrient sources to be obtained directly from raw sludge. HTC experiments were performed in a scalable arrangement (technology readiness level >4) composed of a 10-liter high-pressure reactor. The influence of the experimental conditions, including temperature (180-220°C), reaction time (60-120min), and pH (6.5-12.0), was evaluated on nutrients and heavy metal distribution through the liquid phase by a 23 central composite design with a triple central point. The bioproducts were characterized by a set of six analytical techniques, and the results were refined by statistical procedures (Principal Component Analysis and Response Surface Methodology). pH is a variable that impacts bioproducts’ characteristics, making it a guiding factor for application. Basic pH promotes transfer of components from solid to liquid phase, while natural pH favors the heating value of hydrochars. Liquid phase is a carbon- and nitrogen-rich source that can be useful for plant nutrition, mainly due to the increase of ammonia nitrogen from 20.6 to 51.7wt.% under pH 6.5, and an expressive decrease of chemical oxygen demand from 32 to 6-16 mgO2L-1. The hydrochar can be considered a renewable solid fuel, reaching up to 12.43 MJ kg-1 under pH 6.5; the basic pH favored an increase in the O/C ratio and lower HHV values. HTC of primary sewage sludge can replace steps of a conventional treatment station and promotes treated wastewater reuse.

Analyzing the Impact of Scientific and Technological Innovations on Financial Performance: A PCA-Based Study of an AI Healthcare Startup

This study explores the relationship between scientific and technological innovations and the financial performance of startups, focusing on a representative Artificial Intelligence (AI) healthcare startup. Utilizing Principal Component Analysis (PCA), the research aims to dissect the complex interplay between innovation-related metrics—such as R&D spending, patent counts, and technology adoption rates—and financial outcomes like revenue growth, profitability, and market share. The PCA methodology enabled the reduction of high-dimensional data into PCA, which clearly illustrates how several dimensions of innovation impact financial metrics. The approach used in the investigation helps people comprehend more thoroughly how development benefits business viability in several different manners and provides startups with practical advice to use when preparing their revolutionary investments. This paper aims to assist the ecosystem of startup consumers (including investors, business owners, and policymakers) in making better decisions that balance technological progress with economic objectives.

Production of Fucoxanthin from Microalgae Isochrysis galbana of Djibouti: Optimization, Correlation with Antioxidant Potential, and Bioinformatics Approaches.

Fucoxanthin, a carotenoid with remarkable antioxidant properties, has considerable potential for high-value biotechnological applications in the pharmaceutical, nutraceutical, and cosmeceutical fields. However, conventional extraction methods of this molecule from microalgae are limited in terms of cost-effectiveness. This study focused on optimizing biomass and fucoxanthin production from Isochrysis galbana, isolated from the coast of Tadjoura (Djibouti), by testing various culture media. The antioxidant potential of the cultures was evaluated based on the concentrations of fucoxanthin, carotenoids, and total phenols. Different nutrient formulations were tested to determine the optimal combination for a maximum biomass yield. Using the statistical methodology of principal component analysis, Walne and Guillard F/2 media were identified as the most promising, reaching a maximum fucoxanthin yield of 7.8 mg/g. Multiple regression models showed a strong correlation between antioxidant activity and the concentration of fucoxanthin produced. A thorough study of the optimization of I. galbana growth conditions, using a design of experiments, revealed that air flow rate and CO2 flow rate were the most influential factors on fucoxanthin production, reaching a value of 13.4 mg/g. Finally, to validate the antioxidant potential of fucoxanthin, an in silico analysis based on molecular docking was performed, showing that fucoxanthin interacts with antioxidant proteins (3FS1, 3L2C, and 8BBK). This research not only confirmed the positive results of I. galbana cultivation in terms of antioxidant activity, but also provided essential information for the optimization of fucoxanthin production, opening up promising prospects for industrial applications and future research.

A data-driven approach for rapid detection of aeroelastic modes from flutter flight test based on limited sensor measurements

Flutter flight test involves the evaluation of the airframe’s aeroelastic stability by applying artificial excitation on the aircraft lifting surfaces. The subsequent responses are captured and analyzed to extract the frequencies and damping characteristics of the system. However, noise contamination, turbulence, non-optimal excitation of modes, and sensor malfunction in one or more sensors make it time-consuming and corrupt the extraction process. In order to expedite the process of identifying and analyzing aeroelastic modes, this study implements a time-delay embedded Dynamic Mode Decomposition technique. This approach is complemented by Robust Principal Component Analysis methodology, and a sparsity promoting criterion which enables the automatic and optimal selection of sparse modes. The anonymized flutter flight test data, provided by the fifth author of this research paper, is utilized in this implementation. The methodology assumes no knowledge of the input excitation, only deals with the responses captured by accelerometer channels, and rapidly identifies the aeroelastic modes. By incorporating a compressed sensing algorithm, the methodology gains the ability to identify aeroelastic modes, even when the number of available sensors is limited. This augmentation greatly enhances the methodology’s robustness and effectiveness, making it an excellent choice for real-time implementation during flutter test campaigns.

Correlation Between Scientific and Technological Innovation and Financial Performance of Seed Enterprises: Application of Principal Component Analysis Algorithm in Performance Evaluation

This study explores the relationship between scientific and technological innovations and the financial performance of startups, focusing on a representative Artificial Intelligence (AI) healthcare startup. Utilizing Principal Component Analysis (PCA), the research aims to dissect the complex interplay between innovation-related metrics—such as R&D spending, patent counts, and technology adoption rates—and financial outcomes like revenue growth, profitability, and market share. The PCA methodology enabled the reduction of high-dimensional data into PCA, which clearly illustrates how various dimensions of innovation impact financial metrics. The approach used in the investigation helps people comprehend more thoroughly how development benefits business viability in several different manners and provides startups with practical advice to use when preparing their revolutionary investments. The objective of the research is to assist the ecosystem of startup consumers (including investors, business owners, and policymakers) in making better decisions that balance technological progress with economic objectives.

The Impact of the Great Recession on Well-Being across Europe Ten Years On: A Cluster Analysis

To evaluate variations in the well-being dimensions of European citizens, we rely upon Principal Component Analysis methodology, whereby a large set of interrelated indicators are reduced to a small number of aggregate synthetic variables. We find that the 2008 crisis impinged differently on the various dimensions of well-being. The evolution of the indicators has affected different clusters of countries in various ways. Most importantly, we observe that there has been a shift of the principal component from the poor in terms of material deprivation to the risk of poverty for the worsening conditions in the labor market.

The Impact of the Iraqi Government's Financial Policy on Reduction of Unemployment Rates During the COVID-19 Pandemic

This research paper aimed at analyzing the role of the financial policy taken up by the Iraqi government in confronting the outbreak of the COVID-19 pandemic and mitigating the repercussions of the pandemic on unemployment. It relied on quantitative and qualitative methods to analyze and evaluate the effects of financial policy on unemployment rates. Several international indicators were used, such as the Oxford University Government Response Tracker (OXCGRT) to know the strength of the stimulus package adopted by Iraq. The Principal Component Analysis (P) methodology was also used to analyze the composition of the stimulus financial policy taken. The results of the analysis generally came up with the fact that the the role of financial policy adopted by Iraq in promoting economic growth and accelerating recovery from the repercussions of the pandemic was significant on unemployment rates, depending on the size of that fiscal policy in Iraq.

Adulteration Detection and Quantification in Olive Oil Using Excitation-Emission Matrix Fluorescence Spectroscopy and Chemometrics.

This research investigates the use of excitation-emission matrix fluorescence (EEMF) in conjunction with chemometric models to rapidly identify and quantify adulteration in olive oil, a critical concern where sample availability is limited. Adulteration is simulated by blending soybean, peanut, and linseed oils into olive oil, creating diverse adulterated samples. Principal component analysis (PCA) was applied to the EEMF spectral data as an initial exploratory measure to cluster and differentiate adulterated samples. Spatial clustering enabled vivid visualization of the variations and trends in the spectra. The novel application of parallel factor analysis (PARAFAC) for data decomposition in this paper focuses on unraveling correlations between the decomposed components and the actual adulterated components, which offers a novel perspective for accurately quantifying adulteration levels. Additionally, a comparative analysis was conducted between the PCA and PARAFAC methodologies. Our study not only unveils a new avenue for the quantitative analysis of adulterants in olive oil through spectral detection but also highlights the potential for applying these insights in practical, real-world scenarios, thereby enhancing detection capabilities for various edible oil samples. This promises to improve the detection of adulteration across a range of edible oil samples, offering significant contributions to food safety and quality assurance.

Factors of regional financial inclusion in Morocco

The purpose of this paper is twofold: first, provide a comparative analysis of interregional and intraregional inequalities in terms of financial inclusion across Moroccan regions; second, explore the determinants of regional financial inclusion in a panel of 12 Moroccan regions. We used the principal component analysis methodology to construct our regional financial inclusion indexes, and the panel data estimation methodology to identify the determinants of regional financial inclusion. Our regional financial inclusion index shows that the majority of regions have a low level of financial inclusion. This is symptomatic of a very asymmetric distribution. For all regions, we found that intraregional inequalities in access to bank branches by locality/province or prefecture increase as we move away from the region’s main city. Empirical results indicated that a region’s human development level, as proxied by the Human Development Index (HDI), the size of its population, and the industrialization level of its economic fabric are the main determinants of a region’s financial inclusion level. The Moroccan financial system authorities have implemented several reforms (the banking law no. 2‐91‐967 of July 21, 1993) and strategies (National Financial Inclusion Strategy) aimed at promoting financial inclusion in Morocco; however, the increase in the level of financial inclusion at the macrolevel has not benefited all segments of the Moroccan population. This paper is original as, to our knowledge, it is the first to mobilize interregional and intraregional data, as opposed to previous studies that are national‐level oriented. Given the mapping of interregional and intraregional inequalities in access to, and use of, formal financial services, policymakers can rely on our index to formulate useful financial inclusion policies and to target rural localities, provinces or prefectures, and priority regions in Morocco’s National Financial Inclusion Strategy. The financial system should provide financial services tailored to regional financing needs. Moroccan policymakers should focus on filling gaps in levels of regional human development, as the latter is a key factor in regional financial inclusion.

Machine learning class numbers of real quadratic fields

We implement and interpret various supervised learning experiments involving real quadratic fields with class numbers 1, 2 and 3. We quantify the relative difficulties in separating class numbers of matching/different parity from a data-scientific perspective, apply the methodology of feature analysis and principal component analysis, and use symbolic classification to develop machine-learned formulas for class numbers 1, 2 and 3 that apply to our dataset.

Deep Kernel Principal Component Analysis for multi-level feature learning

Principal Component Analysis (PCA) and its nonlinear extension Kernel PCA (KPCA) are widely used across science and industry for data analysis and dimensionality reduction. Modern deep learning tools have achieved great empirical success, but a framework for deep principal component analysis is still lacking. Here we develop a deep kernel PCA methodology (DKPCA) to extract multiple levels of the most informative components of the data. Our scheme can effectively identify new hierarchical variables, called deep principal components, capturing the main characteristics of high-dimensional data through a simple and interpretable numerical optimization. We couple the principal components of multiple KPCA levels, theoretically showing that DKPCA creates both forward and backward dependency across levels, which has not been explored in kernel methods and yet is crucial to extract more informative features. Various experimental evaluations on multiple data types show that DKPCA finds more efficient and disentangled representations with higher explained variance in fewer principal components, compared to the shallow KPCA. We demonstrate that our method allows for effective hierarchical data exploration, with the ability to separate the key generative factors of the input data both for large datasets and when few training samples are available. Overall, DKPCA can facilitate the extraction of useful patterns from high-dimensional data by learning more informative features organized in different levels, giving diversified aspects to explore the variation factors in the data, while maintaining a simple mathematical formulation.

An attention-PCA based forecast combination approach to crude oil price

Crude oil price forecasting has garnered considerable attention due to its pivotal role in both market dynamics and economic stability. In this study, we present an attention-based principal component analysis (attention-PCA) methodology designed to improve the performance of oil price forecasting models. The attention-PCA approach enables greater focus on predictor variables with superior forecasting capabilities. Furthermore, we develop a diversity enhancement mechanism for forecast combination by incorporating multiple attention mechanisms, varying numbers of principal components, and a range of forecasting models. The empirical results demonstrates that attention-PCA-based individual forecasting models significantly outperform benchmark models, reducing the Mean Absolute Percentage Error (MAPE) by up to 43.2%. The proposed forecast combination strategy yields the most accurate and diverse forecasts among those evaluated, with the MAPE of the optimal combination model standing at 4.40%.

Genomic signals of local adaptation in Picea crassifolia

BackgroundGlobal climate change poses a grave threat to biodiversity and underscores the importance of identifying the genes and corresponding environmental factors involved in the adaptation of tree species for the purposes of conservation and forestry. This holds particularly true for spruce species, given their pivotal role as key constituents of the montane, boreal, and sub-alpine forests in the Northern Hemisphere.ResultsHere, we used transcriptomes, species occurrence records, and environmental data to investigate the spatial genetic distribution of and the climate-associated genetic variation in Picea crassifolia. Our comprehensive analysis employing ADMIXTURE, principal component analysis (PCA) and phylogenetic methodologies showed that the species has a complex population structure with obvious differentiation among populations in different regions. Concurrently, our investigations into isolation by distance (IBD), isolation by environment (IBE), and niche differentiation among populations collectively suggests that local adaptations are driven by environmental heterogeneity. By integrating population genomics and environmental data using redundancy analysis (RDA), we identified a set of climate-associated single-nucleotide polymorphisms (SNPs) and showed that environmental isolation had a more significant impact than geographic isolation in promoting genetic differentiation. We also found that the candidate genes associated with altitude, temperature seasonality (Bio4) and precipitation in the wettest month (Bio13) may be useful for forest tree breeding.ConclusionsOur findings deepen our understanding of how species respond to climate change and highlight the importance of integrating genomic and environmental data in untangling local adaptations.

Screening Optimal Oat Varieties for Cultivation in Arid Areas in China: A Comprehensive Evaluation of Agronomic Traits

This study was undertaken to identify oat (Avena sativa L.) varieties optimal for cultivation in the Jiuquan region, China, in 2021. A selection of 27 domestic and international oat varieties were analyzed, considering ten key agronomic traits, including plant height, stem diameter, spike length, leaf width, and yield. Employing methods such as cluster analysis, principal component analysis, and grey correlation degree, a comprehensive evaluation was conducted. The principal component analysis distilled the ten indicators to three core components. The most influential factors in the first principal component were plant height, ear length, and hay yield, while leaf length and leaf area index were the highest contributors to the second component. The stem-to-leaf ratio emerged as the principal indicator in the third component. The cluster analysis resulted in the classification of the 27 oat varieties into 3 categories. Following a comprehensive evaluation through the grey correlation degree and principal component analysis methodologies, we found that the oat varieties Sweety 1, Fuyan 1, Dingyan 2, Baler, Quebec, and Longyan 2 received the highest scores. These varieties, hence, appear to be the most suitable for cultivation and promotion in the Jiuquan region. This study thus provides invaluable insights into oat cultivation practices, offering guidance for farmers, agricultural policymakers, and future research in the field.

Toroidal PCA via density ridges

Principal Component Analysis (PCA) is a well-known linear dimension-reduction technique designed for Euclidean data. In a wide spectrum of applied fields, however, it is common to observe multivariate circular data (also known as toroidal data), rendering spurious the use of PCA on it due to the periodicity of its support. This paper introduces Toroidal Ridge PCA (TR-PCA), a novel construction of PCA for bivariate circular data that leverages the concept of density ridges as a flexible first principal component analog. Two reference bivariate circular distributions, the bivariate sine von Mises and the bivariate wrapped Cauchy, are employed as the parametric distributional basis of TR-PCA. Efficient algorithms are presented to compute density ridges for these two distribution models. A complete PCA methodology adapted to toroidal data (including scores, variance decomposition, and resolution of edge cases) is introduced and implemented in the companion R package ridgetorus. The usefulness of TR-PCA is showcased with a novel case study involving the analysis of ocean currents on the coast of Santa Barbara.

Contagion Patterns Classification in Stock Indices: A Functional Clustering Analysis Using Decision Trees

This paper aims to identify the main determinants of the countries that present contagion during the period 2000–2021, based on the determination of the behavior patterns of 18 stock market indices of 15 of the main economies. To do that, first, the B-spline method and Bezier curves are used to smooth observations by minimizing the noise. Subsequently, the Functional Principal Component Analysis (FPCA) methodology is applied. Then, the K-means clustering algorithm is used to determine the main groups using the silhouette method and cross-validation, considering the sum of squares of the distances as the function to minimize. Finally, classification trees and macroeconomic and financial analyses are used to determine the rules of variables that give a direct explanation of the contagion (clustering) between the stock indices. The main empirical results obtained suggest that the most significant macroeconomic variables are the Gross Domestic Product, the Consumer Price Index, and Foreign Direct Investment, while in the financial aspect and the most representative are Domestic Credit and number of companies listed on the stock market. It is worth noticing that government spending does not have a significant effect at any time as a determinant of contagion. Finally, it is important to mention, and surprising, that Mexico’s IPC was not clustered in the same group of US stock market indices anytime, despite the strong commercial relationship and the geographical closeness.

Corporate governance, external financing, and earnings management: new evidence from an emerging market

This study investigates the relationship between corporate governance, external financing, and earnings management in an emerging market. Using a sample of Vietnamese listed companies in the period of 2010–2020, the results indicate that corporate governance, which is measured by a principal component analysis (PCA) methodology, is a useful mechanism to control earnings management. However, when firms engage in external financing activities, corporate governance is not significantly associated with both accrual-based and real earnings management. In addition, the study also examines the role of corporate governance in moderating the effects of earnings management on firm value, and how it is encouraged by external financing needs. The study shows that while good corporate governance lessens the influence of earnings management on firm value, external financing needs only prompts earnings manipulation and have no effect on firm value, directly or indirectly. Therefore, the findings could provide implications for managers and regulators to enhance governance practice to alleviate firm devaluation caused by earnings management practice.

Polarity induced vapochromism and vapoluminescence of polythiophene derivatives for volatile organic compounds classification

Polarity induced vapochromic and vapoluminescent properties of cationic poly-3-alkoxythiophene derivatives (PT) casted on polyvinylidene fluoride (PVDF) membranes are reported. PT with six different pendant groups are designed to differentially interact with volatile organic compounds (VOC) of varying polarities, thereby enabling their classification. PT exhibit a rapid vapochromic response with a concurrent modulation of vapoluminescence due to the non-covalent cation-π interactions between the pendant groups and the PT backbone. Adsorption of VOC on pendant groups alters the conformation of PT backbone, thus resulting in an increase in intensity and blue shifting of fluorescence emission within the visible spectrum. The vapoluminescent responses are found to be more sensitive with a limit of detection (LOD) of ∼7 ppm and a wider dynamic range as compared to the vapochromic responses with a LOD of ∼60 ppm for the detection of a model VOC: chloroform. Notably, all the PT illustrate an instantaneous recovery of colour and luminescence upon desorption of VOC. PT interaction with VOC of varying polarities was ascertained using density functional theory (DFT) and principal component analysis (PCA) methodologies. In summary, the polarity induced vapochromic and vapoluminescent properties of PT could yield a selective and sensitive vapochromic and fluorometric dual-mode VOC detection platform.

An enhanced temporal algorithm- coupled optimized adaptive sparse principal component analysis methodology for fault diagnosis of chemical processes

Principal component analysis (PCA) is a classic fault diagnosis method widely used in chemical process data modeling. However, the limitation of PCA to handle dynamic and time-variant data has been progressively exposed with the ever-increasing development of artificial intelligence technologies and applications of automation systems in the chemical industry. In this paper, an enhanced temporal algorithm-coupled optimized adaptive sparse PCA (ETA-OASPCA) methodology is proposed with the aim to improve the conventional PCA method by introducing temporal state computation and dynamic adaptive sparsity. The ETA is constructed to extract the state transitions of the time-variant process data by introducing the “Transition” and “Hold”, acquire the region of interest by employing a Maximum Fuzzy c-means (MFCM) method, and then use the region time prediction automaton to label the possible abnormal states. The introduction of dynamic adaptive sparsity in PCA, achieved by presenting an iterative interior point optimization method to update the dynamic sparse penalty term, can impose sparsity constraints on dynamic data calculations, thus enhancing the interpretability of the PCA model. The effectiveness of the proposed method was validated through its application in the Eastman Tennessee process, showing that an average fault detection rate, false alarm rate, and latency of 99.4%, 0.37%, and 4.11 mins can be achieved, respectively.

A Principal Component Analysis Methodology of Oil Spill Detection and Monitoring Using Satellite Remote Sensing Sensors

Monitoring, assessing, and measuring oil spills is essential in protecting the marine environment and in efforts to clean oil spills. One of the most recent oil spills happened near Port Fourchon, Louisiana, caused by Hurricane Ida (Category 4), that had a wind speed of 240 km/h. In this regard, Earth Observation (EO) Satellite Remote Sensing (SRS) images can effectively highlight oil spills in marine areas as a “fast and no-cost” technique. However, clouds and the sea surface spectral signature complicate the interpretation of oil spill areas in the optical images. In this study, Principal Component Analysis (PCA) has been applied of Landsat-8 and Sentinel-2 SRS images to improve information from the optical sensor bands. The PCA produces an output unrelated to the main bands, making it easier to distinguish oil spills from clouds and seawater due to the spectral diversity between oil, clouds, and the seawater surface. Then, an additional step has been applied to highlight the oil spill area using PCAs with different band combinations. Furthermore, Sentinel-1 (SAR), Sentinel-2 (optical), and Landsat-8 (optical) SRS images have been analyzed with cross-sections to suppress the “look-alike” effect of marine oil spill areas. Finally, mean and high-pass filters were used for Land Surface Temperature (LST) SRS images estimated from the Landsat thermal band. The results show that the seawater value is about −17.5 db and the oil spill area shows a value between −22.5 db and −25 db; the Landsat 8 satellites thermal band 10, depicting contrast at some areas for oil spill, can be determined by the 3 × 3 and 5 × 5 Kernel High pass and the 3 × 3 Mean filter. The results demonstrate that the SRS images should be used together to improve oil spill detection studies results.