Principal Component Analysis Components Research Articles

The Multi-parameter Test of Gravitational Wave Dispersion with Principal Component Analysis

In this work, we consider a conventional test of gravitational wave (GW) propagation which is based on the phenomenological parameterized dispersion relation to describe potential departures from General Relativity (GR) along the propagation of GWs. But different from tests conventionally performed previously, we vary multiple deformation coefficients simultaneously and employ the principal component analysis (PCA) method to remedy the strong degeneracy among deformation coefficients and obtain informative posteriors. The dominant PCA components can be better measured and constrained, and thus are expected to be more sensitive to potential departures from the waveform model. Using this method we analyze ten selected events and get the result that the combined posteriors of the dominant PCA parameters are consistent with GR within 99.7% credible intervals. The standard deviation of the first dominant PCA parameter is three times smaller than that of the original dispersion parameter of the leading order. However, the multi-parameter test with PCA is more sensitive to not only potential deviations from GR but also systematic errors of waveform models. The difference in results obtained by using different waveform templates hints that the demands of waveform accuracy are higher to perform the multi-parameter test with PCA. Whereas, it cannot be strictly proven that the deviation is indeed and only induced by systematic errors. It requires more thorough research in the future to exclude other possible reasons in parameter estimation and data processing.

Fusion of diverse data sources for flood extent mapping and risk assessment in Sindh: A comparative study of inundation mapping approaches

Accurate and timely mapping of flood extent is crucial for effective disaster response and management. However, existing methods often face challenges in integrating diverse data sources and leveraging advanced techniques for precise inundation mapping. This research aimed to address this gap by proposing and evaluating four different methods for improving flood extent mapping in Sindh, Pakistan, through the integration of various data sources, including precipitation, Synthetic Aperture Radar (SAR), buildings, population, and optical data. The first strategy involves categorizing post-flood SAR data immediately using non-parametric (Support Vector Machine (SVM) and Random Forest (RF)), parametric (Maximum Likelihood Estimation (MLE)), and cluster (ISO) algorithms for Vertical transmit and Vertical received (VV) and Vertical transmit and Horizontal received (VH) polarisations. The second method relies on Otsu's method for automatic thresholding on post-flood event VH and VV data. The third approach utilizes both pre- and post-flood event data by creating a stack of Pre-event VH, Pre-event VV, Post-event VH, and Post-event VV data, which is then classified using the SVM, RF, MLE and ISO algorithms. Lastly, the fourth method employs the stack of Principal Component Analysis (PCA) bands, consisting of PCA components 1, 2, and 3, to categorize data for flooding using the SVM, RF, MLE and ISO algorithms. The rationale behind evaluating these diverse methods was to identify the most effective approach for accurately mapping flood extent by exploiting the strengths of different algorithms and data processing techniques. The accuracy and precision of these approaches were rigorously evaluated using Landsat-9 Normalized Difference Water Index (NDWI) as a reference dataset, along with error matrix and compound value (Cv) metrics. The findings show that a PCA-based technique with SVM is marginal superior (based on the overall accuracy and Kappa coefficient with value of 92.20 % and kappa coefficient 0.821) than the other approaches tested in the study. By reducing the dimensionality of the dataset while preserving relevant information, PCA offers a computationally efficient approach to flood extent mapping. Among all the approaches, the collective performance of the algorithms was computed using the Cv metric. The SVM algorithm with a Cv of 1.25 demonstrated the best performance, followed by the RF algorithm with Cv of 1.5, and the MLE algorithm with Cv of 2.5. The worst performing algorithm among all is found to be ISO with Cv of 4. It comes into view that first and second approach underperformed in VH polarisation compared to the VV polarisation and provide a more accurate representation of flooded area in VV polarisation. The flood extent with highest accuracy, together with the world's gridded population, latest Microsoft's Global ML Building Footprints, and OpenStreetMap building data are used in conjunction to estimate the number of people and buildings at risk within the study area.

Contribution of infectious diseases to the selection of ADH1B and ALDH2 gene variants in Asian populations.

The gene variants ADH1B*2 (Arg48His, rs1229984) and ALDH2*2 (Glu504Lys, rs671) are common in East Asian populations but rare in other populations. We propose that selective pressures from pathogen exposure and dietary changes during the neolithic transition favored these variants. Thus, their current association with differences in alcohol sensitivity likely results from phenotypic plasticity rather than direct natural selection. Samples sourced from the Allele Frequency Database (ALFRED) were utilized to compute the average frequency of ADH1B*2 and ALDH2*2 across 88 and 61 countries, respectively. Following computation of the average national allele frequencies, we tested the significance of their correlations with ecological variables. Subsequently, we subjected them to Principal Component Analysis (PCA) and Elastic Net regularization. For comprehensive evaluation, we collected individual-level phenotypic associations, compiling a Phenome-Wide Association Study (PheWAS) spanning multiple ethnicities. Following multiple testing correction, ADH1B*2 displayed significant correlations with Neolithic transition timing (r = 0.405, p.adj = 2.013e-03, n = 57) and historical trypanosome burden (r = -0.418, p.adj = 0.013, n = 57). The first two components of PCA explained 47.7% of the total variability across countries, with the top three contributors being the historical indices of population density and trypanosome and leprosy burdens. Historical burdens of the Mycobacteria tuberculosis and leprosy were the sole predictive variables with positive coefficients that survived Elastic Net regularization. Our analyses suggest that Mycobacteria may have played a role in the joint selection of ADH1B*2 and ALDH2*2, expanding the "toxic aldehyde hypothesis" to include Mycobacterium leprae. Additionally, our hypothesis, linked to dietary shifts from rice domestication, emphasizes nutritional deficiencies as a key element in the selective pressure exerted by Mycobacteria. This offers a plausible explanation for the high frequency of ADH1B*2 and ALDH2*2 in Asian populations.

Learning SAR-Optical Cross Modal Features for Land Cover Classification

Synthetic aperture radar (SAR) and optical images provide highly complementary ground information. The fusion of SAR and optical data can significantly enhance semantic segmentation inference results. However, the fusion methods for multimodal data remains a challenge for current research due to significant disparities in imaging mechanisms from diverse sources. Our goal was to bridge the significant gaps between optical and SAR images by developing a dual-input model that utilizes image-level fusion. To improve most existing state-of-the-art image fusion methods, which often assign equal weights to multiple modalities, we employed the principal component analysis (PCA) transform approach. Subsequently, we performed feature-level fusion on shallow feature maps, which retain rich geometric information. We also incorporated a channel attention module to highlight channels rich in features and suppress irrelevant information. This step is crucial due to the substantial similarity between SAR and optical images in shallow layers such as geometric features. In summary, we propose a generic multimodal fusion strategy that can be attached to most encoding–decoding structures for feature classification tasks, designed with two inputs. One input is the optical image, and the other is the three-band fusion data obtained by combining the PCA component of the optical image with the SAR. Our feature-level fusion method effectively integrates multimodal data. The efficiency of our approach was validated using various public datasets, and the results showed significant improvements when applied to several land cover classification models.

Influence of Orographic Factors on the Distribution of Lichens in the Franz Josef Land Archipelago.

During a geobotanical study of the Franz Josef Land archipelago, 111 lichen species were recorded on 130 sample plots. The significance of orographic factors in the distribution of lichens was assessed using principal component analysis (PCA) and multiple regression analysis. It was found that the absolute altitude and distance from the glacier are of the greatest importance for crustose lichens, while for fruticose lichens, the most critical factors were the slope exposure and steepness. Along the altitudinal gradient, the number of species decreased (from 88 to 25). The highest number of species (90) was recorded at distances of 0.1 to 1.0 km from the glacier edge, which is explained by the unstable species composition of areas recently released from under the glacier. The number of species in all groups generally decreased (from 81 to 52) with increasing slope steepness. With an increasing heat supply of slopes (on a gradient from northern to southern), the number of species steadily increased in all groups (from 39 to 75). The low sum of the explained variance values for the first two PCA components (21%) characterizes the specificity of the natural environment of polar deserts, where there is no leading environmental factor.

Variation in agronomic and grain nutritional traits of quinoa (Chenopodium quinoa Willd.) cultivars

Quinoa is a potential crop for its use as human food and animal feed due to the high nutritional grain and quality forage. However, quinoa’s growth, yield and quality are affected by many factors. The study was aimed to evaluate the variability of eight quinoa cultivars over two years for phenology, yield and attributing traits, and also for nutritional quality of grain. The experiments were arranged in Randomized Complete Block Design with three replications. Significant variations were exhibited among the cultivars for all studied traits. Three cultivars, 5-Red, 40-Red and 45-Red, had the highest potential growth performance, while Cahuil cultivar had the highest grain yield. Both Cahuil and Isluga cultivars exhibited more stable and higher contents of nutrient composition than other cultivars. Significant positive correlation were observed for grain yield with yield attributing traits, and for forage yield with most of the growth traits. No negative correlations among grain quality traits were observed, implying easier improvement for quinoa grain quality. The first two principal components in PCA (principal component analysis) explained 77.39% of the total variability. Based on forage yield, grain yield and nutritional quality over two years, 40-Red cultivar was selected for grain production and forage yield purposes, and Cahuil cultivar was selected for high grain yield and nutritional quality.

Evaluation of mine water quality based on the PCA–PSO–BP model

Abstract To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry.

Productive Performance and Fruit Quality of Short Days Italian Genetic-based Strawberry Cultivars and Genotypes in Southern Brazil

When grown under different climatic conditions, short days Italian genetic-based strawberry (IGBS) cultivars and genotypes present variations in productive performance and fruit quality. The objective of this study was to assess different productive performance and fruit quality of IGBS cultivars and genotypes grown in highland regions of southern Brazil. The experiment were conducted in randomized block design using three cultivars and 21 genotypes during the 2019, 2020 and 2021 growth seasons. The data were subjected to multivariate analysis using principal component analysis (PCA). IGBS cultivars and genotypes differed significantly in terms of productive and fruit quality parameters, according to PCA data. The first and second main components of PCA, respectively, were responsible for 61.9% and 72.4% of the productive performance and fruit quality parameters. The Pircinque cultivar and FRF PIR 256.04, ITA 13.097.05, ITA 12.103.22, ITA 10.107.07, and ITA 10.133.02 genotypes showed the highest indices of productive parameters of total production per plant, commercial production per plant, total fruit number per plant and commercial fruits number per plant. Among these, the Pircinque cultivar and the FRF PIR 256.04 genotypes demonstrated the best fruit quality parameters of fruit color (skin luminosity, chroma, and hue angle), flavor (soluble solids/ titratable acidity ratio), and fresh commercial fruit mass in highland regions of southern Brazil during the 2018, 2019 and 2020 growth seasons.

Trace-metal enrichment mechanisms in Bakken Formation black shales, Williston Basin, North Dakota, USA

The Bakken black shales represent a unique sedimentary system characterized by high total organic carbon contents (TOC; Lower Bakken shale = 10.8 and Upper Bakken shale = 11.8 avg. wt%) and enrichment factors (EF) of various trace metals (TMs) such as Cu (6.2–7.7), Mo (219.7–237.8), Ni (9.4–10.2), U (20.6–29.3), V (9.9–14.2), and Zn (10.4–12.2) that are considerably higher than many other Devonian-Mississippian (D–M) black shales. In this study, raw distributions of elemental concentrations combined with bivariate and principal component analysis (PCA) were used to clarify processes that contributed to high TM EF values in the Bakken shales. Total organic carbon shares heavier PCA component loadings (>0.445) and stronger Pearson correlation coefficients (r) with Cu, Mo, Ni, U, V, and Zn than with pyrite-associated (As, Co, Fe, and S) elements, suggesting that organic matter (OM) played a primary role in the scavenging and accumulation of TMs in the sediments. Reducing conditions within bottom waters or sediment pore waters may have accelerated the accumulation of redox-sensitive Cu, Mo, Ni, V, and Zn introduced into the sediments via an OM detritus host and most likely played a secondary role in their enrichment. Deep-water renewal times for the Bakken have been estimated to be ∼10–30 yrs. based on Mo/TOC results. The high TM EF values observed in the Bakken shales may reflect frequent resupply of TMs into basin waters, enhanced primary productivity that is necessary in scavenging TMs from the water column, the presence of H2S within sediment pore or bottom waters, or possibly secondary processes associated with basin-wide fluid and hydrocarbon migration. High TOC concentrations may be the result of a possible shallow water column (∼150 m), which would limit exposure time of OM to oxidation and degradation, coupled with intermittent fluctuations between dysoxic and anoxic/euxinic bottom-water redox conditions, the latter of which would reduce rates of OM degradation by benthic organisms.

INNV-24. MACHINE LEARNING-BASED SPECTROSCOPIC TISSUE DIFFERENTIATION IN FLUORESCENCE-GUIDED NEUROSURGERY

Abstract Maximal resection of malignant gliomas is hindered by difficulty in distinguishing tumor margins. Fluorescence-guided resection with 5-ALA assists in reaching this goal. Previously, we characterized five fluorophore emissions that accurately represent any spectrum measured from human brain tumor biopsies with a wide-field hyperspectral device. In this study, the effectiveness of these five spectra was explored for different tumor classification tasks in 891 hyperspectral widefield measurements of 184 patients harboring low- (n=30) and high-grade gliomas (n=115), non-glial primary brain tumors (n=19), radiation necrosis (n=2), miscellaneous (n=10) and metastases (n=8), which corresponds to up to 15000 spectra for a given test. The statistical differences in fluorophore abundances between classes were determined and visualized using dimensionality reduction techniques, including principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). Three machine-learning models were trained to classify tumor type (12 classes), grade (3 classes), and glioma margins (3 classes). Five algorithms were tested with varying hyperparameters for each classification task. We explored whether PCA projection onto five different axes than fluorophore abundances can provide more information for visualization and classification. The abundances of the five a priori fluorophore spectra matched or outperformed the five optimal PCA components for all classification tasks. These five axes capture 96-99% of the variance in the dataset. Using random forests and multilayer perceptrons, the classifiers achieved average test accuracies of 74-82%, 79%, and 81%, respectively. All five fluorophore abundances varied between tumor margin type as well as between grades (p < 0.01). For tissue type, at least four of five fluorophore abundances were found to be significantly different (p < 0.01) between all classes. These results demonstrate the differing contribution of fluorophores in different tissue classes tissue, as well as the five fluorophores value as potential optical biomarkers, opening new opportunities for intraoperative classification systems in fluorescence-guided neurosurgery.

Forecasting the Endemic/Epidemic Transition in COVID-19 in Some Countries: Influence of the Vaccination.

The objective of this article is to develop a robust method for forecasting the transition from endemic to epidemic phases in contagious diseases using COVID-19 as a case study. Seven indicators are proposed for detecting the endemic/epidemic transition: variation coefficient, entropy, dominant/subdominant spectral ratio, skewness, kurtosis, dispersion index and normality index. Then, principal component analysis (PCA) offers a score built from the seven proposed indicators as the first PCA component, and its forecasting performance is estimated from its ability to predict the entrance in the epidemic exponential growth phase. This score is applied to the retro-prediction of endemic/epidemic transitions of COVID-19 outbreak in seven various countries for which the first PCA component has a good predicting power. This research offers a valuable tool for early epidemic detection, aiding in effective public health responses.

How Worried Are You about Food Fraud? A Preliminary Multi-Country Study among Consumers in Selected Sub-Saharan African Countries.

Food fraud is an old, recurring, and global threat to public health. It poses a serious threat to food security in sub-Saharan Africa (SSA). Despite the prevalence of food fraud in SSA, little is known about how food fraud is viewed by consumers. This study aims to provide an overview of consumers' concerns about food fraud in SSA. A multi-country survey was conducted in October 2022-31 January 2023, and 838 valid responses were returned. To reduce the large and correlated dataset, Principal Component Analysis (PCA) was used. Five components were derived from PCA: (i) Staple foods; (ii) Premium food and drink products; (iii) Trust in reliable sources; (iv) Trust in less reliable sources; and (v) Trust in food vendors. The findings revealed Ghanaian (mean rank = 509.47) and Nigerian (mean rank = 454.82) consumers tended to score higher on the measure of food fraud concern suggesting that they were less confident in the safety and quality of the food they consume. Demographic characteristics including age, number of children, personal and family experience of food fraud and PCA components such as 'Staple foods', 'Trust in reliable sources', and 'Trust in food vendors' significantly predicted the model. This is the first preliminary study to provide empirical findings on consumers' concerns about food fraud in SSA. Practical and policy recommendations for the region are suggested. This includes (i) modelling the AfriFoodinTegrity in West Africa across other major regions such as Central, East, and Southern Africa; (ii) establish a regional sub-Saharan Africa Rapid Alert System for Food and Feed (SSA-RASFF) platform; and (iii) food safety and food fraud reports could be incorporated into SSA-RASFF portal for information sharing.

Diverse Interactions: Root-Nodule Formation and Herb-Layer Composition in Black Locust (Robinia pseudoacacia) Stands.

The black locust (Robinia pseudoacacia L.) is the second-most abundant deciduous tree in forest plantations, and one of the most important invasive woody species worldwide. The species has a strong transformer capacity, especially expressed by its nitrogen enrichment effect caused by nitrogen-fixing bacteria living in its root-nodules. The aim of this study was to explore the mutually interacting factors of nitrogen-fixing root-nodules, site characteristics, and herb-layer composition of 28 North Hungarian black locust stands. In the herb-layers of the study sites, a total of 121 plant species were identified, representing a relatively low species richness. The studied black locust stands showed high variability both in their herb-layer compositions and root-nodule formation, but no clear relationship could be demonstrated between these characteristics. The PCA component with which the species richness and Shannon-Wiener diversity index were strongly correlated was negatively associated with all root-nodule parameters (number, surface area, and weight), supporting the biodiversity-reducing effect of black locust by its nitrogen-fixing bacteria. All of the root-nodule parameters were negatively correlated with the PCA factor predominantly determined by stand age, confirming that the root-nodule biomass decreases as time progresses.

Improving Neutron-gamma discrimination in CLYC with Gaussian mixture model

Neutron and gamma discrimination is a common issue in neutron detection. Cs 2 LiYCl 6:Ce (CLYC) crystal, with its unique core-to-valence luminescence (CVL) mechanism for gamma rays, has excellent capability for neutron-gamma discrimination. This paper proposes a new preprocessing method called constant fraction alignment (CFA), based on the fast rise and slow decay characteristics of CLYC, to effectively improve discrimination performance. Unsupervised classification methods that do not require training data have a great advantage since pure neutron sources are difficult to obtain. As an unsupervised clustering method, Gaussian mixture model (GMM) is used to achieve neutron-gamma discrimination of data detected by CLYC, and the impact of different parameters on GMM results is studied. When the input dimension is high, GMM not only requires significantly more time but also reduces precision, so dimensionality reduction is necessary. Principal component analysis (PCA) is a commonly used dimensionality reduction method, and through analysis, it is found that using the first three components of PCA as inputs achieves the best performance. In the GMM solving process, selecting different covariance matrices based on data types can reduce running time while maintaining high accuracy. Diagonal mode is the most suitable according to comparison. Based on the same dataset, the accuracy of GMM is higher than that of charge comparison method and k-means++, with an accuracy of 99.96% for 252 Cf source and a false alarm rate as low as 0.01% for 137 Cs source.

RP-HPLC-Based Flavonoid Profiling Accompanied with Multivariate Analysis: An Efficient Approach for Quality Assessment of Houttuynia cordata Thunb Leaves and Their Commercial Products.

Chemical profiling for quality monitoring and evaluation of medicinal plants is gaining attention. This study aims to develop an HPLC method followed by multivariate analysis to obtain HPLC profiles of five specific flavonoids, including rutin (1), hyperin (2), isoquercitrin (3), quercitrin (4), and quercetin (5) from Houttuynia cordata leaves and powder products and assess the quality of H. cordata samples. Eventually, we successfully established HPLC-based flavonoid profiles and quantified the contents of 32 H. cordata fresh leave samples and four powder products. The study also quantified the contents of those five essential flavonoids using an optimized RP-HPLC method. Peak areas of samples were then investigated with principal component analysis (PCA) and hierarchical cluster analysis (HCA) to evaluate the similarity and variance. Principal components in PCA strongly influenced by hyperin and quercetin showed that the samples were clustered into subgroups, demonstrating H. cordata samples' quality. The results of HCA showed the similarity and divided the samples into seven subgroups. In conclusion, we have successfully developed a practical methodology that combined the HPLC-based flavonoid profiling and multivariate analysis for the quantification and quality control of H. cordata samples from fresh leaves and powder products. For further studies, we will consider various environmental factors, including climate and soil factors, to investigate their effects on the flavonoid contents of H. cordata.

Potential Toxic-Element Pollution in Surface Water and Its Implications for Aquatic and Human Health: Source–Pathway–Receptor Model

This study focused on assessing the extent of pollution in both flowing and non-flowing surface water within the Matjhabeng mining area of South Africa, with particular emphasis on the substantial impact of gold mine tailings. A comprehensive analysis of physical water-quality attributes, including potentially toxic elements (PTEs), and relevant pollution risk indices was undertaken. To comprehensively elucidate the potential risks to aquatic organisms and human health, a risk assessment framework predicated upon the source–pathway–receptor model was developed. Principal Component Analysis (PCA) was employed as a multivariate statistical tool to discern the potential origins of PTE contamination within the environment. The results substantiate pronounced pollution manifestations within the surface water milieu of the Matjhabeng mining area. Specifically, concentrations of critical PTEs, such as arsenic, cobalt, copper, iron, selenium, and zinc, exhibited transgressions of the regulatory thresholds stipulated by both the South African Department of Water Affairs and Forestry (DWAF) and the Canadian Council of the Ministers of the Environment (CCME). Additionally, concentrations of the aforementioned elements exceeded the stipulated DWAF guidelines for irrigation water usage. Pollution indices, encompassing the Single-Factor Pollution Index and the Nemerow Integrated Pollution Index, discerned moderate contamination stemming from As, while remarkably elevated pollution levels were identified for selenium. PCA elucidated 94.5% of the aggregate variance, revealing cobalt, copper, nickel, and zinc as coalescing within PC1, indicative of a common anthropogenic provenance that is conceivably linked to historical gold mine tailings. PC2 exhibited an aggregation of chromium, iron, and lead, reaffirming this shared anthropogenic etiology. The third PCA component was characterized by selenium, followed by arsenic and magnesium in the fourth. The resultant PTE contamination underscores a profound ecological and public health risk, impacting both the aquatic ecosystems and the local community within the precincts of the Matjhabeng Local Municipality (MLM) area, with consequential amplification of susceptibilities to deleterious health consequences. Urgent and concerted interventions are imperative to ameliorate the emergent decline in surface-water quality within the MLM locale. The adoption of nature-based remediation paradigms holds promise for efficaciously elevating water quality, ameliorating community health, and underpinning the long-term economic viability of the region.

Principal Component Analysis (PCA) unravels spectral components present in XPS spectra of complex oxide films on iron foil

Principal component analysis (PCA), as applied to the processing of the complex X-ray photoelectron spectroscopy (XPS) lineshapes, is discussed. PCA analysis example is provided of complex native iron oxide films on Fe foil XPS spectra further modified by argon and helium ion beams. Abstract PCA components are derived from Fe 2p, O 1 s and C 1 s regions leading to the assignments of surface (oxy)hydroxide, Fe2O3, FeO and Fe metal, as corroborated by the corresponding elemental quantification profiles. Detailed mathematical concepts behind PCA analysis of spectra data are provided.

On a Robust Descriptor of the Flue Organ Pipe Transient

The initial transient of an organ pipe is known to be of great influence to the perceived sound quality. At the same time, the unsteady process of the tone onset is essentially nonlinear and lacks exact repeatability, so the search for a robust descriptor is in place. Initial transients were recorded using an adjustable flue organ pipe. The blowing pressure and cut-up height were varied. Prony’s method was employed to analyze the results. Utilizing the Principal Component Analysis (PCA) on the standardized exponential model coefficients, it was shown that the transients are well described by just one scalar parameter. Its value is predominantly dependent on the number of important Prony’s components taking part in the transient process (i.e., the overall complexity of the transient signal). A strong correlation was found between the PCA component and the Strouhal number inverse.

A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning

In this work, we integrate the conventional unsupervised machine learning algorithm—the Principal Component Analysis (PCA) with the Random Matrix Theory to propose a novel global economic policy uncertainty (GPEU) index that accommodates global economic policy fluctuations. An application of the Random Matrix Analysis illustrates the majority of the PCA components of EPU’s mirror random patterns that lack substantial economic information, while the only exception—the dominant component—is non-random and serves as a fitting candidate for the GEPU index. Compared to the prevalent GEPU index, which amalgamates each economy’s EPU weighted by its GDP value, the new index works equally well in identifying typical global events. Most notably, the new index eliminates the requirement of extra economic data, thereby avoiding potential endogeneity in empirical studies. To demonstrate this, we study the correlation between gold future volatility and GEPU using the GARCH-MIDAS model, and show that the newly proposed GEPU index outperforms the previous version. Additionally, we employ complex network methodologies to present a topological characterization of the GEPU indices. This research not only contributes to the advancement of unsupervised machine learning algorithms in the economic field but also proposes a robust and effective GEPU index that outperforms existing models.

Dietary pattern and brain structure among young adults

AbstractBackgroundEnvironmental factors, such as nutrition, influence brain physiology and health throughout the life course. While research has focused on the extremes of the age spectrum, less is known about early adulthood, yet a critical period for the consolidation of brain maturation and the building of adult behaviors. Nutrition may impact late maturational changes in the post‐adolescent brain and contribute to the degree of brain reserve that minimizes the risk to develop dementia. We took advantage of a large sample of young adults to evaluate the association of dietary behavior with brain structure characteristics.MethodThis cross‐sectional study included 1721 university students (18‐35 years‐old) from the French i‐Share cohort, who underwent brain MRI. A 12‐item online Food Frequency Questionnaire was used to evaluate consumption in major food groups and to determine dietary patterns from Principal Component Analysis (PCA). Multivariable‐adjusted linear regressions were used to estimate the association of PCA scores with brain structure (cortical thickness and surface area, grey matter and white matter volumes, diffusion parameters).ResultThe first PCA component (explained variance, 17%) contrasted a healthy diet (higher fruit and vegetable intakes) to a poor diet (higher fast food, sugary drink and snack consumptions). In models adjusted for total intracranial volume, age, sex, physical activity, body mass index, alcohol and tobacco consumptions, a higher PCA score (reflecting healthier diet) was associated with lower total gray matter volume (β for 1 point score = –0.17 [95%CI, –0.31; –0.03] cm3), in various brain areas (Figure). Healthy diet was also associated with lower global white matter volume (β = –0.18 [–0.32; –0.03] cm3). No association was found with diffusion tensor imaging, and neurite orientation dispersion and density imaging metrics in the white matter.ConclusionIn this large sample of young adults, healthier diet was associated with lower brain volumes, independent of multiple potential confounders. Gray matter volume loss during post‐adolescence is a marker of brain maturation; thus, environmental factors emphasizing this loss may benefit maturation. Our results raise the hypothesis that a healthy diet may favor and a poor diet may hamper brain maturation; an assumption which deserves further investigation in other populations.