Two-step Principal Component Analysis Research Articles

Comparisons of Different Representative Species Selection Schemes for Reduced-Order Modeling and Chemistry Acceleration of Complex Hydrocarbon Fuels

The simulation of engine combustion processes, such as autoignition, an important process in the co-optimization of fuel-engine design, can be computationally expensive due to the large number of thermo-chemical scalars needed to describe the full chemical system. Yet, the inherent correlations between the different chemical species during oxidation can significantly reduce the complexity of representing this system. One strategy is to select a subset of representative species that accurately captures the combustion process at a fraction of the computational cost of the full system. In this study, we compare the performance of four different techniques to select these species. They include the two-step principal component analysis (PCA) approach, directed relation graphs (DRGs), the global pathway selection (GPS) approach, and the manifold-informed species selection method. A parametric study of the representative species selection is carried out on data from the simulation of homogeneous and perfectly stirred reactors by investigating seven cumulative variances and 47 different cut-off percentages for the two-step PCA, and 65 and 51 thresholds for the DRGs and GPS, respectively. Results show that these selection methods capture key important species that can accurately describe the chemical system and track each stage of oxidation. The two-step PCA is sensitive to the cumulative variance, and DRGs and GPS are sensitive to the choice of target variables. By selecting key representative species and reducing the number of thermo-chemical scalars, these three methods can be used to develop computationally efficient hybrid chemistry schemes.

Financial Technologies for All MENA citizens: Tackling barriers and promoting inclusion

The digital divide within the financial sector, closely intertwined with the rapid evolution of financial technologies, commonly known as "FinTech," has fundamentally reshaped the landscape of financial services. These innovations have ushered in novel avenues for accessibility, enhancing the security of financial transactions and transcending traditional spatiotemporal constraints. This paper embarks on a journey to address key inquiries within the MENA region. It explores digital financial inclusion levels across MENA countries, identifies segments facing digital financial exclusion, scrutinizes the role of the digital divide, often compounded by a lack of financial literacy, as a barrier to financial inclusion, and investigates how FinTech can serve as a catalyst for inclusive development, with a specific focus on marginalized groups like women and the elderly. Our research employs a comprehensive mixed-methodological approach. We draw from micro-level data sourced from 9053 individuals, extracted from the World Bank's latest Global Findex 2021 database. Our comparative analysis, rooted in a two-step principal component analysis method to create the MENA Digital Financial Inclusion Index (DFII), shedding light on the current state of digital financial inclusion in the MENA region. Despite recent initiatives, our findings underscore that digital financial inclusion in the region remains relatively low. Furthermore, our estimations, based on the Heckman selection methodology, emphasize the pivotal role played by factors such as educational attainment, labor force participation, information and communication technologies, and internet access as primary drivers of digital financial inclusion in the MENA region. Our research contributes a substantial body of empirical evidence regarding digital financial inclusion levels across MENA countries, charting a path toward bridging the digital divide within the financial sector. These strategies encompass enhancing digital financial literacy, catalyzing broader participation in the labor market, and facilitating expanded access to mobile phones and the Internet. In light of our comprehensive dataset, the policy implications drawn from our study are of paramount interest to financial sector regulators in the MENA region, grounded in the real-life experiences of the individuals constituting our database, with the overarching goal of elevating digital financial inclusion in the region to new heights.

A Data-Based Hybrid Chemistry Acceleration Framework for the Low-Temperature Oxidation of Complex Fuels

The oxidation of complex hydrocarbons is a computationally expensive process involving detailed mechanisms with hundreds of chemical species and thousands of reactions. For low-temperature oxidation, an accurate account of the fuel-specific species is required to correctly describe the pyrolysis stage of oxidation. In this study, we develop a hybrid chemistry framework to model and accelerate the low-temperature oxidation of complex hydrocarbon fuels. The framework is based on a selection of representative species that capture the different stages of ignition, heat release, and final products. These species are selected using a two-step principal component analysis of the reaction rates of simulation data. Artificial neural networks (ANNs) are used to model the source terms of the representative species during the pyrolysis stage up to the transition time. This ANN-based model is coupled with C0–C4 foundational chemistry, which is used to model the remaining species up to the transition time and all species beyond the transition time. Coupled with the USC II mechanism as foundational chemistry, this framework is demonstrated using simple reactor homogeneous chemistry and perfectly stirred reactor (PSR) calculations for n-heptane oxidation over a range of composition and thermodynamic conditions. The hybrid chemistry framework accurately captures correct physical behavior and reproduces the results obtained using detailed chemistry at a fraction of the computational cost.

Three-dimensional visualization and detection of early bruise in apple based on near-infrared hyperspectral imaging coupled with geometrical influence correction

Visible-near-infrared (Vis-NIR) spectral imaging holds great promise for the automatic detection of fruit defects. However, uneven brightness resulting from fruit geometry and the limitations of one-directional imaging significantly restrict the current detection to a limited area. This study presents a rotation measurement system that combines a line-scan NIR-hyperspectral imaging (HSI) camera with a laser profile. A total of 72 apple samples with bruises in the central and edge regions were prepared. A 360° scanning approach was employed to collect HSI and shape data from the entire surface of the samples over a 6-h post-bruising period. Height and angle corrections were applied to eliminate the surface geometric influences on the HSI data, resulting in improved reflectance spectrum uniformity. A two-step principal component analysis method was employed for image enhancement, followed by a straightforward bruise detection technique using global segmentation and connected-domain selection. The results demonstrated an overall improvement in bruise detection over time. Moreover, the correction significantly enhanced the detection accuracy. After 6 h of bruising, the corrected data achieved a classification accuracy of 90.3% and an identification rate of 83.3% for central bruises and 61.1% for edge bruises, whereas the uncorrected data yielded 70.8%, 58.3%, and 31.9%, respectively. Thus, this study successfully detected early bruising across the entire surface of apples and improved the detection in low-intensity edge areas. The proposed method has the potential to contribute to the comprehensive evaluation of agricultural products with irregular geometries.

The relationship between climate risk, climate policy uncertainty, and CO2 emissions: Empirical evidence from the US

This paper examines the relationship between climate risk and climate policy uncertainty, and CO2 emissions in the US over the 2000–2022 period using a structural Factor- Augmented Vector AutoRegression (FAVAR) model with a two-step principal component analysis based on monthly observations. We employ a very recent measure to proxy for uncertainty regarding climate policy based on the Climate Policy Uncertainty Index (CPU) of Gavriilidis (2021), while Climate Risk is proxied by financial cost of natural disasters and number of deaths. We use different variables for CO2 emissions, based on total and sectoral emission (commercial, electric power, residential sector, transportation, and industrial sector). The results indicate that a significant percentage of the variance of CO2 emissions in the US, is explained by Natural Disasters Cost, which also seem to account for a significant percentage of the US Total Renewable Energy Consumption variance. Shocks to disaster costs seem to decrease all type of emissions significantly and also increase renewable energy use significantly. Natural disasters increase political disagreement among U.S. politicians, as well as, the climate policy uncertainty, highlighting the need for efficient policymaking and regulations. In further results, we find that an increase in Partisan Conflict decreases emissions and explains a significant amount of renewable energy variance.

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.

Improved scalability and risk factor proxying with a two-step principal component analysis for multi-curve modelling

We consider the practice-relevant problem of modelling multiple price curves to support activities such as price curve simulation and risk management. In this multi-curve setting, the challenge is to jointly capture the risk-factor relationships within each curve and the risk-factor relationships between the curves. Contributing to the existing literature, we develop a novel two-step Principal Component Analysis (PCA) method, which we label PCA2, that addresses this challenge. The concept of PCA2 first derives components describing the dynamics of each curve, and then, second, combines these to describe the dynamics across all the curves. The benefits of PCA2 over PCA are: (i) improved scalability allowing for greater computational efficiency and smaller data structures rendering multi-threading more feasible; (ii) components that remain identifiable at the curve level; and (iii) leveraging the last property, PCA2, unlike PCA, offers the capability of proxying new curves for which limited historical data exists, using the first-step components from a related curve and estimating second-level correlations empirically. PCA2 is a novel multi-curve modelling approach that will appeal, for these reasons, to many practitioners, especially those working in risk management.

A TWO-STEP FEATURE EXTRACTION ALGORITHM: APPLICATION TO DEEP LEARNING FOR POINT CLOUD CLASSIFICATION

Abstract. Most deep learning (DL) methods that are not end-to-end use several multi-scale and multi-type hand-crafted features that make the network challenging, more computationally intensive and vulnerable to overfitting. Furthermore, reliance on empirically-based feature dimensionality reduction may lead to misclassification. In contrast, efficient feature management can reduce storage and computational complexities, builds better classifiers, and improves overall performance. Principal Component Analysis (PCA) is a well-known dimension reduction technique that has been used for feature extraction. This paper presents a two-step PCA based feature extraction algorithm that employs a variant of feature-based PointNet (Qi et al., 2017a) for point cloud classification. This paper extends the PointNet framework for use on large-scale aerial LiDAR data, and contributes by (i) developing a new feature extraction algorithm, (ii) exploring the impact of dimensionality reduction in feature extraction, and (iii) introducing a non-end-to-end PointNet variant for per point classification in point clouds. This is demonstrated on aerial laser scanning (ALS) point clouds. The algorithm successfully reduces the dimension of the feature space without sacrificing performance, as benchmarked against the original PointNet algorithm. When tested on the well-known Vaihingen data set, the proposed algorithm achieves an Overall Accuracy (OA) of 74.64% by using 9 input vectors and 14 shape features, whereas with the same 9 input vectors and only 5PCs (principal components built by the 14 shape features) it actually achieves a higher OA of 75.36% which demonstrates the effect of efficient dimensionality reduction.

Coordinated Cyber-Attack Detection Model of Cyber-Physical Power System Based on the Operating State Data Link

Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets. This paper proposes a model for cyber and physical data fusion using a data link for detecting attacks on a Cyber–Physical Power System (CPPS). The two-step principal component analysis (PCA) is used for classifying the system’s operating status. An adaptive synthetic sampling algorithm is used to reduce the imbalance in the categories’ samples. The loss function is improved according to the feature intensity difference of the attack event, and an integrated classifier is established using a classification algorithm based on the cost-sensitive gradient boosting decision tree (CS-GBDT). The simulation results show that the proposed method provides higher accuracy, recall, and F-Score than comparable algorithms.

Geospatial indicators of exposure, sensitivity, and adaptive capacity to assess neighbourhood variation in vulnerability to climate change-related health hazards

BackgroundAlthough the frequency and magnitude of climate change-related health hazards (CCRHHs) are likely to increase, the population vulnerabilities and corresponding health impacts are dependent on a community’s exposures, pre-existing sensitivities, and adaptive capacities in response to a hazard’s impact. To evaluate spatial variability in relative vulnerability, we: 1) identified climate change-related risk factors at the dissemination area level; 2) created actionable health vulnerability index scores to map community risks to extreme heat, flooding, wildfire smoke, and ground-level ozone; and 3) spatially evaluated vulnerability patterns and priority areas of action to address inequity.MethodsA systematic literature review was conducted to identify the determinants of health hazards among populations impacted by CCRHHs. Identified determinants were then grouped into categories of exposure, sensitivity, and adaptive capacity and aligned with available data. Data were aggregated to 4188 Census dissemination areas within two health authorities in British Columbia, Canada. A two-step principal component analysis (PCA) was then used to select and weight variables for each relative vulnerability score. In addition to an overall vulnerability score, exposure, adaptive capacity, and sensitivity sub-scores were computed for each hazard. Scores were then categorised into quintiles and mapped.ResultsTwo hundred eighty-one epidemiological papers met the study criteria and were used to identify 36 determinant indicators that were operationalized across all hazards. For each hazard, 3 to 5 principal components explaining 72 to 94% of the total variance were retained. Sensitivity was weighted much higher for extreme heat, wildfire smoke and ground-level ozone, and adaptive capacity was highly weighted for flooding vulnerability. There was overall varied contribution of adaptive capacity (16–49%) across all hazards. Distinct spatial patterns were observed – for example, although patterns varied by hazard, vulnerability was generally higher in more deprived and more outlying neighbourhoods of the study region.ConclusionsThe creation of hazard and category-specific vulnerability indices (exposure, adaptive capacity and sensitivity sub-scores) supports evidence-based approaches to prioritize public health responses to climate-related hazards and to reduce inequity by assessing relative differences in vulnerability along with absolute impacts. Future studies can build upon this methodology to further understand the spatial variation in vulnerability and to identify and prioritise actionable areas for adaptation.

A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from China.

Investor sentiment is a research focus in behavior finance. This paper chooses five proxy variables according to China’s reality and uses a two-step principal component analysis to construct an investor sentiment index. The five proxy variables are the number of new stock accounts, turnover ratio, margin balance, net active purchasing amount, and investor attention. In the final part of this study, using the price data from the Shanghai and Shenzhen Security Exchange, this paper investigates the dynamic relationship between investor sentiment and stock market realized volatility based on the thermal optimal path. The empirical results show that when the market fluctuates severely, investor sentiment leads stock market realized volatility over one or two steps. The prediction power is also checked. The results indicate that investor sentiment indeed forecasts the realized volatility. This research supports regulators and financial institutions in taking advanced measures.

Microhotplate gas sensors incorporated with Al electrodes and 3D hierarchical structured PdO/PdO2-SnO2:Sb materials for sensitive VOC detection

High-performance, low-power microhotplate (MHP) gas sensors loaded with 3D hierarchical structured PdO/PdO2-SnO2:Sb materials on a pair of Al gas sensing electrodes were fabricated through advanced high-accuracy electrohydrodynamic printing method. The PdO/PdO2-SnO2:Sb materials with various PdO/PdO2 concentrations were initially synthesized via a mild and simple hydrothermal reaction. Their micromorphology and chemical composition were characterized by SEM, XRD, TEM, and XPS, which proved the successful synthesis. The gas sensing performance of the sensors to benzene, ethanol, acetone, formaldehyde, and ethanol-acetone mixed gas (volume ratio 1:1) was tested. The sensing property of the materials is regulated with different content of PdO/PdO2. The identification of the VOCs is realized by two-step principal component analysis (PCA). Simulation calculations on the I–V characteristic of the MHP gas sensors reveal that carriers have to get through Al2O3 barriers by Poole-Frenkel emission, which significantly affects the carrier conduction as well as the gas sensing properties. The gas sensing mechanism is analyzed from the aspects of gas sensing material and electrodes.

Two‐step support vector data description for dynamic, non‐linear, and non‐Gaussian processes monitoring

AbstractIn this study, a the two‐step support vector data description (TS‐SVDD) method is proposed to handle the problem of fault detection for dynamic, non‐linear, and non‐Gaussian processes. First, the dynamic structure of the data is identified and the data is divided into two components: innovation component and dynamic component. Then, the innovation component is used to make the SVDD model for fault detection. Moreover, in order to overcome the issue with two‐step principal component analysis (TS‐PCA) that the choice of parameters q and D affects the fault detection effect of the methods, a genetic algorithm (GA) is used to optimize the parameters. The proposed method combines the advantages of TS‐PCA in processing dynamic process data and SVDD in dealing with non‐linear and non‐Gaussian process data. In order to evaluate the effectiveness and superiority of the proposed method, TS‐SVDD is applied to the Tennessee Eastman (TE) process and the intelligent industrial processes control test facility (I2PC‐TF), and the fault detection performance is compared with TS‐PCA and SVDD in terms of dault detection rate (FDR) and false alarm rate (FAR). The results show that TS‐SVDD has a better monitoring performance.

Long-latency event-related responses to vowels: N1-P2 decomposition by two-step principal component analysis

The N1-P2 complex of the auditory event-related potential (ERP) has been used to examine neural activity associated with speech sound perception. Since it is thought to reflect multiple generator processes, its functional significance is difficult to infer. In the present study, a temporospatial principal component analysis (PCA) was used to decompose the N1-P2 response into latent factors underlying covariance patterns in ERP data recorded during passive listening to pairs of successive vowels. In each trial, one of six sounds drawn from an /i/−/e/ vowel continuum was followed either by an identical sound, a different token of the same vowel category, or a token from the other category. Responses were examined as to how they were modulated by within- and across-category vowel differences and by adaptation (repetition suppression) effects. Five PCA factors were identified as corresponding to three well-known N1 subcomponents and two P2 subcomponents. Results added evidence that the N1 peak reflects both generators that are sensitive to spectral information and generators that are not. For later latency ranges, different patterns of sensitivity to vowel quality were found, including category-related effects. Particularly, a subcomponent identified as the Tb wave showed release from adaptation in response to an /i/ followed by an /e/ sound. A P2 subcomponent varied linearly with spectral shape along the vowel continuum, while the other was stronger the closer the vowel was to the category boundary, suggesting separate processing of continuous and category-related information. Thus, the PCA-based decomposition of the N1-P2 complex was functionally meaningful, revealing distinct underlying processes at work during speech sound perception.

Two‐step principal component analysis for dynamic processes monitoring

AbstractIn this study, a two‐step principal component analysis (TS‐PCA) is proposed to handle the dynamic characteristics of chemical industrial processes in both steady state and unsteady state. Differently from the traditional dynamic PCA (DPCA) dealing with the static cross‐correlation structure and dynamic auto‐correlation structure in process data simultaneously, TS‐PCA handles them in two steps: it first identifies the dynamic structure by using the least squares algorithm, and then monitors the innovation component by using PCA. The innovation component is time uncorrelated and independent of the initial state of the process. As a result, TS‐PCA can monitor the process in both steady state and unsteady state, whereas all other reported dynamic approaches are limited to only processes in steady state. Even tested in steady state, TS‐PCA still can achieve better performance than the existing dynamic approaches.

Genetic parameter estimates and principal component analysis on performance and carcass traits of a terminal pig sire line

This study aimed to estimate genetic parameters for five traits related to performance and carcass in a terminal pig sire line using multitrait (MUV) and reduced rank animal models, and to analyze the relationship between the breeding values (EBVs) of the traits using genetic principal component analysis (PCA) applied directly on the mixed model equations (one-step PCA) and on the EBVs obtained using the MUV (two-step PCA). The heritabilities were very similar using both procedures ranged from 0.032 ± 0.014 for feed conversion to 0.102 ± 0.029 for lean meat. Except for some differences in the heritability for feed conversion ratio, the heritability estimates were remarkably similar using One-step PCA or MUV. The genetic correlations obtained using One-Step PCA were more reliable due to their lower standard errors compared to those obtained using MUV. The one-step PCA had better fit than MUV and could reduce the time computing due to the reduction of the additive genetic matrix.

Strategy to Find Molecular Signatures in a Small Series of Rare Cancers: Validation for Radiation-Induced Breast and Thyroid Tumors

Methods of classification using transcriptome analysis for case-by-case tumor diagnosis could be limited by tumor heterogeneity and masked information in the gene expression profiles, especially as the number of tumors is small. We propose a new strategy, EMts_2PCA, based on: 1) The identification of a gene expression signature with a great potential for discriminating subgroups of tumors (EMts stage), which includes: a) a learning step, based on an expectation-maximization (EM) algorithm, to select sets of candidate genes whose expressions discriminate two subgroups, b) a training step to select from the sets of candidate genes those with the highest potential to classify training tumors, c) the compilation of genes selected during the training step, and standardization of their levels of expression to finalize the signature. 2) The predictive classification of independent prospective tumors, according to the two subgroups of interest, by the definition of a validation space based on a two-step principal component analysis (2PCA). The present method was evaluated by classifying three series of tumors and its robustness, in terms of tumor clustering and prediction, was further compared with that of three classification methods (Gene expression bar code, Top-scoring pair(s) and a PCA-based method). Results showed that EMts_2PCA was very efficient in tumor classification and prediction, with scores always better that those obtained by the most common methods of tumor clustering. Specifically, EMts_2PCA permitted identification of highly discriminating molecular signatures to differentiate post-Chernobyl thyroid or post-radiotherapy breast tumors from their sporadic counterparts that were previously unsuccessfully classified or classified with errors.

Global term structure modelling using principal component analysis

Principal component analysis (PCA) is a technique commonly applied to the interest rate markets to describe yield curve dynamics in a parsimonious manner. Despite an increase in global investing and the growing interconnectedness of the international markets, PCA has not been widely applied to decomposing joint structure of global yield curves. Our objective is to describe the joint structure with a model that can potentially be used for scenario analysis and for estimating the risk of interest rate-sensitive portfolios. In this study, we examine three variations of the PCA technique to decompose global yield curve and interest rate implied volatility structure. We conclude that global yield curve structure can be described with 15–20 factors, whereas implied volatility structure requires at least 20 global factors. The procedure that we identify as preferable is a two-step PCA, with local curves decomposed in the first step and combined local PCs decomposed into a joint structure (PCA of PCs) in the second step. This procedure has a key advantage in that it makes any scenario analysis more meaningful by keeping local PCA factors, which have important economic interpretations as shift, twist and butterfly moves of the yield curve.

Time Series Segmentation Using a Novel Adaptive Eigendecomposition Algorithm

In this paper we present a new technique for time series segmentation built around a fast principal component analysis (PCA) algorithm that is on-line and stable. The traditional Generalized Likelihood Ratio Test (GLRT) has been used to solve the segmentation problem, but this has enormous limitations in terms of complexity and speed. Newer methods use gated experts and mixture models to detect transitions in time series. These techniques perform better than GLRT, but most of them require extensive training of relatively large neural networks. The segmentation method discussed in this paper is based on a novel idea that involves solving the generalized eigendecomposition of two consecutive windowed time series and can be formulated as a two-step PCA. Thus, the performance of our segmentation technique mainly depends on the efficiency of the PCA algorithm. Most of the existing techniques for PCA are based on gradient search procedures that are slow and they also suffer from convergence problems. The PCA algorithm presented in this paper is both online, and is proven to converge faster than the current methods.