Principal Component Analysis Methodology Research Articles

Financial inclusion through FinTech and women's financial empowerment

PurposeWomen's financial empowerment (WFE) is one of the sustainable development goals. This research examines the effect of financial inclusion through FinTech (FIF) on WFE at the international level. The purpose of this paper is to address this issue.Design/methodology/approachThe authors analyze the dataset of 113 countries from the Global Findex (2017) and World Bank databases using principal component analysis (PCA) and cross-sectional data methodology. Initially, they calculate the overall index for FIF with PCA. Then, they investigate the effect of FIF on WFE in two groups of countries classified by low and high levels of gender discrimination.FindingsThe results show that the relationship between FIF and WFE is positive and significant in countries classified by low levels of gender discrimination. However, this effect is insignificant in countries with high gender discrimination. Hence, gender inequality is a barrier and denies financial independence to women.Research limitations/implicationsGiven these results, it seems unlikely that FinTech will be able to eliminate the gender gap in financial inclusion on its own for women's empowerment. To directly address the gender gap and alter attitudes and social norms across demographics, FinTech development may need to be supplemented with focused policy initiatives.Originality/valueThis study provides evidence of FIF's impact on WFE at the international level.

Human retinal biometric recognition system based on multiple feature extraction

Retinal biometric trait is one of the safest and most secure biometric traits that can be used for personal identification and authentication. The poor contrast in captured retinal images initially requires the adoption of a preprocessing technique, especially when the vessel segmentation is required. As a result, an effective preprocessing technique is employed before the feature extraction process. The reliability of the segmentation depends on the consistency of the contrast across the images. Due to the complexity of the retinal vascular patterns, segmenting the vessels manually is a time-consuming task that also requires extensive training and abilities to distinguish the thinner blood vessel pixels from the background of the retinal images. We present a unique blood vessel segmentation technique based on Radial Chebyshev Moments (RCMs) shape descriptors. Moment-based shape descriptors are typically strong, concise, and simple to compute. They are also insensitive to the object’s scale, rotation, and translation. Furthermore, to eliminate the eye movement and to overcome the various lighting problems, the following multiple features are extracted in the feature extraction stage to accurately authenticate a person: statistical (gray-level) features, shape-based (RCM) features, and structural features, such as minutiae, bifurcation points, optical disc, and enclosed regions. The principal components analysis methodology is used to optimize the feature vector, and the target image is identified using the support vector machine classifier. This step helps to determine whether the test image belongs to the authorized person or not. RIDB and VARIA are the two standard retinal authentication databases and DRIVE and STARE are the two general retinal databases that are used for evaluating the performance of the system. The system yielded a high identification rate of 98%, 97.5%, 97.2% and 94.4% with average processing time of 0.987 s, 0.40 s, 1.262 s and 0.65 s, respectively. From the results, it is evident that the proposed method is more effective and efficient for authenticating individuals.

Identification and characterization of nucleotide metabolism and neuroendocrine regulation-associated modification patterns in stomach adenocarcinoma with auxiliary prognostic assessment and immunotherapy response prediction.

The significance of nucleotide metabolism and neuroendocrine in cellular immune response and cancer is becoming more well-established. However, the mechanisms underlying nucleotide metabolism and neuroendocrine involvement in stomach adenocarcinoma (STAD) remain unclear. First, a pan-cancer overview of nucleotide metabolism and neuroendocrine-related genes (NMNGs) was explored through the integration of expression profiles, prognostic values, mutation information, methylation levels, and pathway-regulation relationships. We next extensively assessed variations in prognosis and tumor microenvironment (TME) features across the various modification patterns, based on an extensive analysis of the NMNG modification patterns of 808 STAD samples based on 46 NMNGs. Utilizing principal component analysis methodologies, the NMNGscore was developed to measure NMNG alteration patterns of individual tumors. Pan-cancer analysis shows that NMNGs mostly act as risk genes in multiple cancer types, especially in STAD. Based on the NMNGs we detected two different NMNG modification patterns in STAD. Both patterns showed distinct immune cell infiltration features and biological behavior, with NMNGcluster A exhibiting a worse prognosis and a larger amount of immune infiltration. Differentially expressed genes with prognostic relevance were used to classify the STAD samples into three genomic subgroups. Analysis of survival rates revealed that cluster B genes were associated with longer life expectancy than clusters A and C. Individual STAD patients' NMNG alteration patterns were analyzed by analyzing their NMNGscore signatures. NMNGscore and immune cells showed a statistically significant adverse correlation with each other. Increased longevity, a higher incidence of mutations, and a better response to immunotherapy were associated with patients' NMNG scores. Our findings provide a personalized prediction tool for prognosis and immunotherapy sensitivity in patients, as well as a promising knowledge of nucleotide metabolism and neuroendocrine in STAD.

Using statistical analysis to create a new database of Nanofluids’ specific heat capacity

Nowadays, heat transfer fluids (HTFs) with high thermal properties are needed to develop more efficient and compact energy systems to achieve sustainable development goals. Nanofluids (NFs), through the incorporation of nanoparticles in conventional HTFs, become one of the most suitable techniques to improve their thermophysical properties. However, despite its potential industrial applications, there is not only a lack of a theoretical framework but also a clear trend about its behavior. Therefore, this work aims to perform a critical review and statistical analysis to understand the NFs heat capacity (Cp). To this end, a wide variety of NFs from the literature was processed using Principal Component Analysis (PCA) and Response Surface Methodology (RSM). Finally, a database with Ansys Granta Constructor 2021 software was created and an analysis with Ansys Granta Selector 2021 was performed. As a result, the key parameters that impact the Cp of several nanofluids are obtained as well as: their high-temperature dependence, the nature of the liquid medium, and the type of nanoparticles. In addition, the results allow to identify and design nanofluids with specific properties for specific working conditions.

Comparative Research on the Relationship between Investor Sentiment and Stock Price during COVID-19 Pandemic and Russian-Ukrainian War

The current world is significantly influenced by the Russia-Ukraine war and the Covid-19 epidemic. This paper explores the relationship between investor sentiment and stock price during the Russian-Ukrainian war and the COVID-19 pandemic, which is currently a research hotspot in the academic field. First, we choose the data of Shanghai and Shenzhen 300 index and use the principal component analysis (PCA) methodology to determine the investor sentiment index; Second, this paper uses the commonly time series model, i.e., VAR model to conduct the empirical research to make a comprehensively investigation regarding the lead-lag relationship between investor sentiment and related stock returns. The research results show that the stock price has a significant positive effect on investor sentiment in these two periods. The rise of stock price will make investor sentiment rise in several time period lags. However, investor sentiment’s impact on stock price is insignificant, which is contrary with the previous case. Compared with the Russian Ukrainian war period, the estimation of the epidemic period has a larger impact on investor sentiment, and the long-term impact is more significant. The results in this paper may benefit certain investors in the financial management.

Examination of morphological traits of children's faces related to perceptions of cuteness using Gaussian process ordinal regression.

Konrad Lorenz, an ethologist, proposed that certain physical elements are perceived as cute and induce caretaking behavior in other individuals, with the evolutionary function of enhancing offspring survival. He called these features Kindchenschema, baby schema. According to his introspection, these include a large forehead, chubby round features, and chubby cheeks. Previous studies are limited to examining the effects of these facial features on perceived cuteness. However, other morphological factors may be related to perceived cuteness. This study uses Bayesian optimization, one of the global sequential optimization methods for estimating unknown functions, to search for facial morphological features that enhance the perceptions of facial cuteness. We applied Bayesian optimization incorporating Gaussian process ordinal regression (GPOR), which allows an estimation of the latent cuteness function based on evaluations using the Likert scale. A total of 96 preschool children provided the facial images used in this study. We summarized the facial shape variations using methodologies of geometric morphometrics and principal component analysis (PCA) up to the third principal component (PC), which we refer to as the face space. A total of 40 participants evaluated the images created by warping the average facial texture of the children's faces with randomly generated parameters in the face space. Facial traits related to perceived cuteness were estimated based on the averaged cuteness function. Perceived cuteness was linked to the relative lower position of facial components and narrower jawline but not to the forehead height.

Association between fibromyalgia syndrome clinical severity and body composition. A principal component analysis

IntroductionThe type of body composition modulates the severity of some musculoskeletal conditions, in fibromyalgia syndrome (FMS), this type of association remains relatively unexplored. ObjectiveTo analyze the association between the type of body composition and FMS using Principal Component Analysis (PCA). The FMS clinical outcome measures were: Symptom Severity Scale (SSS), Widespread Pain Index (WPI; and Fibromyalgia Impact Questionnaire (FIQ). MethodsForty-three women with FMS (ACR 2010 criteria) were clinically and anthropometrically evaluated. The anthropometric data were integrated into two indicators using a PCA methodology (PCA-Fat and PCA-muscle). Additionally, the patients were classified into high and low categories for each clinical indicator, which were used as dependent variables in binomial logistic regression (BLR) models. ResultsWe found a positive correlation between PCA-Fat with WPI (r=0.326, P=.043) and FIQ (r=0.325, P=.044), and negative correlation (r=−0.384, P=.013) between PCA-muscle and SSS. In the BLR analysis, PCA-Fat was a significant predictor for high WPI (OR=2.477, P=.038); while for high SSS, PCA-muscle (OR=0.303, P=.009) was an inversely significant predictor. ConclusionsThe results suggest that the volume of fat mass can negatively modulate the severity of FMS. We propose that the evaluation of body composition should be a basic element for the clinical approach of patients with FMS.

Modeling and optimisation of magnetic field assisted electrochemical spark drilling using hybrid technique

ABSTRACT The present paper focuses on the application of a hybrid methodology for multi-objective optimisation (MOO) of an in-house deigned and fabricated magnetic field-assisted electrochemical spark drilling (MF-ECSD) process where an electromagnetic unit is added in the setup to create magnetic field of different intensities. The process combines Taguchi methodology (TM) with response surface methodology (RSM) for modelling and grey relational analysis (GRA) with principal component analysis (PCA) methodology for MOO. TM is utilised as the core values in RSM to create the second-order response model, and it is used to find the optimum level of input parameters, namely, voltage (V), electrolyte concentration (EC), tool rotational speed (TRS) and magnetic field intensity (MFI). Material removal rate (MRR), machining depth (MD) and overcut (OC) are the responses. PCA is used to calculate the weight associated with each quality feature.

A Comparative Study of Explanatory and Predictive Models in Air Cargo Throughput

Air cargo throughput has played an important role in the contribution of total national commerce volume and regional GDP, by the expansion of the global economy. Previous studies have identified the critical significance of air freight in national or international economic growth. Various models, including linear regression and time-series models, have been applied to analyze and predict the factors influencing air cargo volumes and the trends for future development. However, many models are implemented as a single methodology, but a few studies reviewed and mixed explanatory and predictive models at the same time. Therefore, three mainly used methodologies of MLR (multiple linear regression), PCA (principal component analysis), and ARIMA (autoregressive integrated moving average model) are chosen for this paper to compare and analyze the air cargo throughput of one Asia’s representative airport, Shanghai Pudong Airport (PVG). The results of the explanatory model shows that GDP, population, oil consumption, and exchange rate are regarded as the four most important variables influencing PVG air cargo volumes. The outcomes of regressive predictive model presents that MLR-PCA has a better performance than MLR, while in ARIMA predictors, the ARIMA (p:12, d:0, q:0) is shown to have a superior predictive fit than the ARIMA (p:1, d:0, q:0). By comparing different methodologies, this paper contributes to the industry’s study of the variables affecting air cargo and the future improvement of air cargo throughput forecasting capabilities.

Phenotypic variation from waterlogging in multiple perennial ryegrass varieties under climate change conditions.

Identifying how various components of climate change will influence ecosystems and vegetation subsistence will be fundamental to mitigate negative effects. Climate change-induced waterlogging is understudied in comparison to temperature and CO2. Grasslands are especially vulnerable through the connection with global food security, with perennial ryegrass dominating many flood-prone pasturelands in North-western Europe. We investigated the effect of long-term waterlogging on phenotypic responses of perennial ryegrass using four common varieties (one diploid and three tetraploid) grown in atmospherically controlled growth chambers during two months of peak growth. The climate treatments compare ambient climatological conditions in North-western Europe to the RCP8.5 climate change scenario in 2050 (+2°C and 550 ppm CO2). At the end of each month multiple phenotypic plant measurements were made, the plants were harvested and then allowed to grow back. Using image analysis and principal component analysis (PCA) methodologies, we assessed how multiple predictors (phenotypic, environmental, genotypic, and temporal) influenced overall plant performance, productivity and phenotypic responses. Long-term waterlogging was found to reduce leaf-color intensity, with younger plants having purple hues indicative of anthocyanins. Plant performance and yield was lower in waterlogged plants, with tetraploid varieties coping better than the diploid one. The climate change treatment was found to reduce color intensities further. Flooding was found to reduce plant productivity via reductions in color pigments and root proliferation. These effects will have negative consequences for global food security brought on by increased frequency of extreme weather events and flooding. Our imaging analysis approach to estimate effects of waterlogging can be incorporated into plant health diagnostics tools via remote sensing and drone-technology.

Principal component analysis and response surface methodology: optimization for H2 evolution from water catalyzed adopting V–Bi under visible light

Energy generation by photocatalytic water splitting with semiconductors under visible light is an effective method for generating pollution-free energy. In this work, a highly efficient vanadium pentoxide (V2O5)-based composite photocatalyst was prepared by hydrothermal calcination. The preparation conditions were optimized using single factor experiments, factor analysis (FA), principal component analysis (PCA), and response surface method (Design Expert). The results showed that the optimal preparation conditions were 6% Bi2S3 loading, 10 h hydrothermal time, 175.0°C hydrothermal temperature, 4.0 h calcination time, and a calcination temperature of 400.0°C. The results of FA and PCA analysis showed that the hydrothermal temperature, calcination time, and calcination temperature were the three main influencing factors. According to the optimization analysis using the response surface method, the highest hydrogen production rate (590.151 μmol/(g۰h)) was obtained at a hydrothermal temperature of 168.74°C, a calcination time of 3.97 h, and a calcination temperature of 390.15°C. The quantum yield of the catalyst was 27.71%.

Comparing SRI funds to conventional funds using a PCA methodology

In this paper, we investigate characteristic differences between Socially Responsible Investment (SRI) funds and conventional funds across 35 different categories, including previously unexplored areas, such as fund manager skills and investment strategies. Further, we examine SRI and conventional funds globally rather than from just one country (e.g., US) or one region (e.g., Europe), covering funds listed in 22 different countries. We also adopt a new Principal Component Analysis (PCA) methodology for matching SRI funds against their conventional counterparts that significantly increases the sample size from previous studies, reducing selection bias and possibly explaining contradictory findings in the prior literature. Contributing to the literature, our findings show that: (i) SRI funds have more diversified portfolios than conventional funds; (ii) SRI funds have lower cash holdings while investing more in US equities; and (iii) SRI fund managers charge a smaller fee and are more successful in managing their portfolios. This is reassuring for investors who invest in SRI funds and for the future health and sustainability of the planet.

Evaluation of outward foreign direct investment environment in China

This paper explores the importance of outward foreign direct investment (OFDI) of Chinese firms for the Chinese economy. Using the Chinese Bureau of Statistics dataset from 2008 to 2020 and applying the Principal Component Analysis (PCA) and Data Envelopment Analysis (DEA) methodology, we evaluate China’s OFDI environment. Firstly, we determine the main inputs of Chinese OFDI and run a PCA to reduce their dimension. Secondly, when employing the DEA method, we take the year as the decision-making unit which allows us to evaluate the trend of China’s OFDI efficiency from 2008 to 2020. Finally, some policy implications are raised pointing to the fact that to efficiently promote OFDI, China needs to adjust its policies and resource allocations. This can be achieved by strengthening its ability to transform scientific research results towards the enterprises’ competitive advantages. Furthermore, the Chinese government should enhance its policies to assist and guide Chinese companies to invest abroad.

Modelling Deprivation Level and Multimorbidity in a Health District

Deprivation is associated with an increased risk of developing chronic health conditions and with worse outcomes in multimorbidity. The goal of our study was to develop an integrated population index of deprivation (IPID) to observe the influence of deprivation on morbidity and the subsequent use of healthcare resources in one health district, using the socioeconomic, clinical and geographical data from its administrative health records. Eight socioeconomic indicators were identified and weighted using the methodology of two-phase principal component analysis, providing an index that allowed each census section to be classified into seven deprivation groups. Secondly, the possible relation between the IPID and the variables for multimorbidity and healthcare resources was analysed using the theory of multiple comparisons. It was observed that places with a greater proportion of healthy people presented lower values of deprivation and that, at lower levels of deprivation, there were fewer hospital admissions. The results show that living in an area with a higher deprivation index is associated with greater consumption of healthcare resources and disease burden. Identifying areas of sociosanitary vulnerability can help to identify health inequalities and allow intervention by clinical practices and healthcare management to reduce them.

Robust functional principal component analysis for non-Gaussian longitudinal data

Functional principal component analysis is essential in functional data analysis, but the inference will become unconvincing when non-Gaussian characteristics occur (e.g., heavy tail and skewness). The focus of this manuscript is to develop a robust functional principal component analysis methodology to deal with non-Gaussian longitudinal data, where sparsity and irregularity along with non-negligible measurement errors must be considered. We introduce a Kendall’s τ function to handle the non-Gaussian issues. Moreover, the estimation algorithm is studied and the asymptotic theory is discussed. Our method is validated by a simulation study and it is applied to analyze a real world dataset.

On accounting for speckle extinction via DIC and PCA

Digital Image Correlation (DIC) is a full-field measurement technique that generally relies on brightness conservation principles. This work aims to analyze cases in which the speckle pattern vanished after heating, thereby severely altering the brightness. Sets of images acquired during 6 experiments were registered and then used to obtain optimal sets of reference images for each time step via Principal Component Analysis (PCA). The proposed methodology significantly reduced the gray level residuals and provided insight into brightness variations that occurred in the analyzed cases. It was possible to study cases with severe speckle pattern changes by updating the reference image when standard DIC did not converge and using the PCA methodology to obtain optimal reference states.

An Effective Approach Based on Deep Residual Google Net Convolutional Neural Network Classifier for the Detection of Glaucoma

Glaucoma is a disease caused by fluid pressure build-up in the inner eye. Early detection of glaucoma is critical as it is expected that 111.8 million people worldwide shall suffer from glaucoma in 2040. In the diagnosis of glaucoma, the use of machine learning method is hoped to be highly promising. This paper provides an important method to master learning to diagnose glaucoma. Initially, human retinal fundus images are preprocessed by means of histogram equalization in order to enhance them. The segmentation is performed by semantic segmentation method, mainly the features are extracted using density with correlation based feature extraction approach. PCA (principal component analysis) methodology is used to choose the most optimal features. Ultimately, through the usage of the Deep residual Google Net CNN Classification method, the retinal image is classified/predicted as regular and abnormal. The Deep residual Google Net CNN classifier is designed to distinguish view patterns with minimal pre-processing from pixel pictures. ORIGA and STARE datasets are used in this work. The findings are then analyzed and contrasted to illustrate the efficacy of the new technique with alternate current techniques. Test accuracy of 99%, Specificity of 98.9% and 100% Sensitivity were achieved. The quantitative results are analyzed for specifications like sensitivity, specificity, accuracy, positive predictive rate, false predictive rate and assured to provide excellent outcomes when compared with traditional methods.

Assessing the Knowledge, Attitude, and Practices of Cigarette Smokers and Use of Alternative Nicotine Delivery Systems in Pakistan: A Cross-Sectional Study

Background. This research has been conducted to assess smokers’ knowledge and behavior vis-à-vis combustible smoking cessation, prevalence, and risk, and the use of alternative nicotine delivery systems to quit smoking. Methods. A mixed-method approach utilizing cross section primary survey data and comprising descriptive and s-KAP index analysis has been adopted to ascertain the relationship between dependent and independent s-KAP variables; the principal component analysis methodology has been used to determine the use of alternative nicotine delivery systems. Results. Most of the smokers were aged between 15 and 35 years. A predominant 69.8% of the smokers came from middle-class background. Moreover, 71.3% were unaware of any alternative Tobacco Harm Reduction product. A majority of the respondents (68.2%) were keen to quit smoking. However, when asked why they had not succeeded, 52.9% reported addiction to nicotine as the main impediment. In Pakistan, lack of smoking cessation services is the weak link in the fight against the tobacco epidemic. Smokers are generally unaware of the Tobacco Harm Reduction products available in Pakistan; moreover, only 10.9% of the respondents were willing to spend more than Rs. 4000 per month on Tobacco Harm Reduction products. The average s-KAP score for young adults below the age of 20 was much lower than the national average but improved with the level of education. Interestingly, the score of smokers who had ever tried to quit smoking was slightly higher than that of those who had never tried to quit. Conclusion. There is intent to quit combustible smoking but the policy and infrastructure necessary for successful quitting are missing. Pakistan needs to concentrate on two fronts: a large scale awareness campaign against the use and harms of combustible smoking and simultaneously providing affordable and accessible smoking services across the country. Pakistan should look at the use and regulation of safer nicotine products in the UK. The country should carefully weigh the options of ensuring how to incorporate the use of safer nicotine delivery systems in its tobacco control efforts.

Financial Inclusion, Financial Frictions, and Economic Growth: Evidence from Africa

ABSTRACT This paper aims to first, develop an analytical framework that examines the role of financial inclusion in promoting investment and economic growth, through the reduction of financial imperfections and considering the heterogeneity of access to the financial system. When performing a comparative analysis of the levels of financial inclusion in Africa, we mobilize the principal component analysis methodology to build the African Financial Inclusion Index (AFIndex). To test the empirical validity of the analytical conclusions, we use a panel data regression technique on a database constituted of 33 African countries over the period 2004–2019. Based on model simulations, financial inclusion stimulates investment and economic growth. Empirical results provide evidence attesting to the fact that financial inclusion has a significant and positive impact on investment and economic growth in Africa. The AFIndex shows that most African economies have a low level of financial inclusion. Our model is original as it provides analytical evidence on the negative impact of financial exclusion on economic growth, emphasizing the vital role of financial inclusion for economic growth. The AFIndex is a useful tool for designing and evaluating policies and/or programs intended for promoting financial inclusion in Africa.

Interaction between environmental and socioeconomic determinants for cutaneous leishmaniasis risk in Latin America.

ABSTRACTObjective.Determine and characterize potential risk areas for the occurrence of cutaneous leishmaniasis (CL) in Latin America (LA).Method.Ecological observational study with observation units defined by municipalities with CL transmission between 2014-2018. Environmental and socioeconomic variables available for at least 85% of the municipalities were used, combined in a single database, utilizing the R software. The principal component analysis methodology was combined with a hierarchical cluster analysis to group clusters of municipalities based on their similarity. The V-test was estimated to define the positive or negative association of the variables with the clusters and separation by natural breaks was used to determine which ones contributed the most to each cluster. Information on cases was also incorporated in the analyses to attribute CL risk for each cluster.Results.This study included 4,951 municipalities with CL transmission (36.5% of the total in LA) and seven clusters were defined by their association with 18 environmental and socioeconomic variables. The historical risk of CL is positively associated with the Amazonian, Andean and Savannah clusters in a decreasingly manner; and negatively associated with the Forest evergreen, Forest/crop and Forest/populated clusters. The Agricultural cluster did not reveal any association with the CL cases.Conclusions.The study made it possible to identify and characterize the CL risk by clusters of municipalities and to recognize the epidemiological distribution pattern of transmission, which provides managers with better information for intersectoral interventions to control CL.