PCA Method Research Articles

Measurement of Free Trade Zones’ Investment Attraction: Sustainable Corporate Development Perspective

This study explores the measurement of FTZs’ investment attraction from a sustainable corporate development perspective. Through a comprehensive evaluation framework, the study assesses FTZs’ investment attraction across five indicators: urban business environment, economic development strength, innovation and openness vitality, urban environmental friendliness, and high-quality enterprise development. The weights of each layer’s indicators are calculated using entropy weight and the PCA method. The evaluation results of the investment attractiveness of China’s FTZs show that the investment attraction of FTZs in different regions exhibits hierarchical characteristics and unbalanced development. East China and South China occupy the forefront. Disparities in provincial FTZs’ developmental balance are conspicuous. Guangdong, Shanghai, and Beijing top the charts in terms of congruence with their overall environment rankings, whereas Jiangsu, Zhejiang, and Fujian exhibit a mismatch between their sub-indices and their relatively strong overall investment attractiveness. Conversely, Henan, Anhui, Hebei, Heilongjiang, Hunan, and Tianjin, though balanced in sub-environmental development, lag in overall investment appeal, necessitating concerted efforts towards balanced development. On the whole, compared with other indicators, innovation and openness vitality and high-quality enterprise development seem relatively weak. Drawing from our insights, foreign investors can choose free trade zones according to their own development needs. Policymakers can devise targeted interventions to bolster FTZs’ investment attractiveness, tailored to scores and rankings.

PCA-Kriging-Based Oscillating Jet Actuator Optimization and Wing Separation Flow Control

In order to improve the separation control effect of an oscillating jet, the external flow field of the actuators and the wing wake are obtained via hot-wire measurements to optimize the actuator and achieve wing separation flow control. The optimization objectives are to improve the sweeping uniformity and range of the jet. In the present study, the PCA method is used for the modal decomposition of the velocity distribution. The modal-based actuator evaluation parameters are proposed, and the kriging surrogate models of the modal coefficients (principal components) on the actuator parameters are established. The multi-objective genetic algorithm was utilized to complete the optimization of the actuator, and the effect of flow separation control on the wing was verified. The results show that three patterns exist in the time-averaged velocity distribution of the external flow field: unimodal, broad and bimodal, from unimodal to bimodal, the degree of the jet sweeping uniformity gradually decreases, and the sweeping range gradually increases. The pattern of the velocity distribution modals affects the degree of jet sweeping uniformity, while the distance of the modal peaks affects the jet sweeping range. The two evaluation parameters are negatively correlated: insufficient sweeping range or poor sweeping uniformity of the jet are not conducive to wing separation flow control, and the two must be coordinated to achieve the optimal control effect.

What is a Feminist Quantitative Method? Opportunities for Feminist Econometrics

Though feminist economics encourages methodological plurality, quantitative methods and econometrics have overtaken the discipline in recent years. Many feminist economists have demonstrated reasons to be concerned about the increasing foothold of quantitative methods, and others have provided thoughtful criticisms of specific quantitative measurements. However, few have made distinctions between mainstream econometrics and feminist econometrics, and a succinct set of resources for those trying to do feminist quantitative research is difficult to find. Drawing upon insights from feminist economics, queer methods, and intersectional approaches, this article sets forth practical guidelines for feminists using econometric methods. Namely, it considers issues of data cleaning, replicability, survey weighting, comparison groups, non-binary measures of gender, intersectionality, causality claims, identification problems, atheoretical index composition, and measuring “difference.” It raises questions for contemporary feminist economists to consider as we grapple with the methodological identity of our field. HIGHLIGHTS Gender diagnostic estimation can be used to endogenously measure gender. Separate model approaches and CARTs are useful in intersectional work. Econometric models can be structured so they are “studying up.” Oaxaca–Blinder and PCA methods raise some feminist concerns. Cohen’s D and the Index of Similarity can be used to test gender differences.

Population Structure and Selection Signatures in Chinese Indigenous Zhaotong Pigs Revealed by Whole-Genome Resequencing.

Zhaotong pig (ZTP) is a Chinese indigenous pig breed in Yunnan Province, known for its unique body shape and appearance, good meat quality, strong foraging ability, and adaptability. However, there is still a lack of research on its genome. In order to investigate the genetic diversity, population structure, and selection signatures of the breed, we conducted a comprehensive analysis by resequencing on 30 ZTPs and comparing them with genomic data from 10 Asian wild boars (AWBs). A total of 45,514,452 autosomal SNPs were detected in the 40 pigs, and 23,649,650 SNPs were retained for further analysis after filtering. The HE, HO, PN, MAF, π, and Fis values were calculated to evaluate the genetic diversity, and the results showed that ZTPs had higher genetic diversity and lower inbreeding coefficient compared with AWBs. Population structure was analyzed using NJ tree, PCA, ADMIXTURE, and LD methods. It was found that ZTPs were population independent of AWBs and had a lower LD decay compared to AWBs. Moreover, the results of the IBS genetic distance and G matrix showed that most of the individuals had large genetic distances and distant genetic relationships in ZTPs. Selection signatures were detected between ZTPs and AWBs by using two methods, FST and π ratio. Totals of 1104 selected regions and 275 candidate genes were identified. Finally, functional enrichment analysis identified some annotated genes that might affect fat deposition (NPY1R, NPY5R, and NMU), reproduction (COL3A1, COL5A2, GLRB, TAC3, and MAP3K12), growth (STAT6 and SQOR), tooth development (AMBN, ENAM, and ODAM), and immune response (MBL2, IL1A, and DNAJA3). Our results will provide a valuable basis for the future effective protection, breeding, and utilization of ZTPs.

Reconstruction of a matrix of genotypic correlations between variants within a gene for joint analysis of imputed and sequenced data

When combining imputed and sequenced data in a single gene-based association analysis, the problem of reconstructing genetic correlation matrices arises. It is related to the fact that for a gene, we know the correlations between genotypes of all imputed variants and the correlations between genotypes of all sequenced variants, but we do not know the correlations between genotypes of variants, one of which is imputed and the other is sequenced. To recover these correlations, we propose an efficient method based on maximising the determinant of the matrix. This method has a number of useful properties and has an analytical solution for our task. Approbation of the proposed method was performed by comparing reconstructed and real correlation matrices constructed on individual genotypes from the UK biobank. Comparison of the results of gene-based association analysis performed by the SKAT, BT and PCA methods on reconstructed and real matrices, using modelled summary statistics and calculated summary statistics on real phenotypes, showed high quality of reconstruction and robustness of the method to different gene structures.

The ecological operation of ChiTan hydropower station based on hydrological alteration using the PCA method

ABSTRACT Water infrastructure affects the quantity, quality, and environment of water. During reservoir construction, only the social-economic benefits were considered and the health of the downstream river was neglected. Due to the severe downstream river system, the operators and managers faced the challenge of the downstream ecosystem. Many scholars are concerned with the reservoir's optimum functioning, which considers social and economic advantages. Numerous significant opinions and research findings are being presented on ecological scheduling at the moment, both in China and overseas, which surely deepen the connotations of ecological scheduling and serve as the research basis and critical reference of this work. The influence of hydropower development on hydrological conditions, the categorization and calculation of environmental runoff, and the building and solution of ecological scheduling models are all discussed and researched in this work. The study examined how the ChiTan hydropower project influenced the Jinxi River's flow. The total hydrologic alteration calculated using RVA is 50.53%. The component analysis is also utilized to eliminate the statistical redundancy in the hydrologic indicators. The indicators’ monthly flow for July and August's mean minimum flow for 7 days are relevant indicators for the Jinxi River basin's hydrologic modification regime.

Traces of Paleolithic expansion in the Nivkh gene pool based on data on autosomal SNP and Y chromosome polymorphism.

The Nivkhs are a small ethnic group indigenous of the Russian Far East, living in the Khabarovsk Territory and on Sakhalin Island, descending from the ancient inhabitants of these territories. In the Nivkhs, a specific Sakhalin-Amur anthropological type is prevalent. They are quite isolated, due to long isolation from contacts with other peoples. The gene pool of the Nivkhs and other Far Eastern and Siberian populations was characterized using a genome-wide panel of autosomal single-nucleotide polymorphic markers and Y chromosome haplogroups. Bioinformatic processing of frequencies of autosomal SNPs, Y chromosome haplogroups and YSTR haplotypes showed that the Nivkh gene pool is very different from the other populations'. Analysis of the SNP frequencies using the PCA method divided the Far Eastern populations in full accordance with the territories of their residence into the northern group of the Chukchi and Koryaks and the southern group, including the Nivkhs and Udege. The remoteness of the Nivkhs coincides with their geographic localization, with the Nivkhs and Udege demonstrating the greatest kinship. The Nivkhs have a specific component of their gene pool, which is present with much less frequency in the Udege and Transbaikal Evenks. According to the IBD blocks, the genotypes of the Nivkhs show a very small percentage of coincidence with the Udege, Koryaks, Evenks and Chukchi, the value of which is the lowest compared to the IBD blocks among all other Siberian populations. The Nivkh-specific composition of haplogroups and YSTR haplotypes was shown. In the Nivkhs, the C2a1 haplogroup is divided into three sublines, which have a fairly ancient origin and are associated with the ancestors of modern northern Mongoloids. The Nivkh haplogroup O2a1b1a2a-F238 is found among residents of China and Myanmar. The Q1a1a1-M120 line is represented among the Nivkhs, Koryaks, Evenks and Yukaghirs. Phylogenetic analysis of individual Y chromosomal haplogroups demonstrated the closeness of the Nivkh gene pool with the ancient population of the Amur and Okhotsk regions, the Koryaks, the Tungus peoples and the population of Southeast Asia. The Nivkh gene pool confirms the relative smallness of their ancestral groups without mixing with other populations.

Application of a novel local and automatic PCA algorithm for diffraction pattern denoising in TEM-ASTAR analysis in microelectronics

This paper introduces a novel denoising method for TEM-ASTAR™ Diffraction Pattern (DP) datasets, termed LAT–PCA (Local Automatic Thresholding – Principal Component Analysis). This approach enhances the established PCA algorithm by partitioning the 4D dataset (a 2D map of 2D DPs) into localized windows. Within these windows, PCA identifies a basis where the physical signal predominantly resides in the higher-order principal components. By thresholding lower-order components, the method effectively reduces noise while retaining the essential features of the DPs. The locality of the approach, focusing on small windows, enhances computational efficiency and aligns with the localized nature of the crystallographic grain signals in ASTAR. The automatic aspect of the method employs a theoretical pure noise distribution, i.e. a Marchenko-Pastur Distribution, to set a threshold, beyond which the components are mostly noise.The LAT–PCA method offers significant reductions in acquisition and post-processing times. With denoised data, selecting the correct parameters for accurate phase maps and grain orientations becomes more straightforward, facilitating robust quantitative grain analysis. Experiments performed on a silicon-germanium-carbon sample validate the method's efficacy. The sample was analyzed with varying acquisition times to produce a high signal-to-noise ratio reference dataset and a lower ratio test dataset. The LAT–PCA algorithm's denoising results on the test dataset were benchmarked against the reference, demonstrating considerable improvements and reliability.In summary, LAT–PCA is an effective, automatic solution for denoising TEM DP datasets. Its adaptability to different noise levels and local processing capability makes it a valuable tool for enhancing dataset quality and reducing the time required for data acquisition and analysis. This method can minimize acquisition time, conserve microscope usage, and reduce sample drift and deterioration, leading to more accurate characterizations with fewer deformation artifacts.

Hydrochemical Characteristics, Controlling Factors and Groundwater Sources of Zaozigou Gold Mine

The Zaozigou gold deposit is recognized as one of the largest and most significant independent gold deposits in northwest China, representing a colossal orogenic gold-antimony deposit. It is imperative to delve into the hydrochemical characteristics and controlling factors within the mining area to unveil the groundwater circulation evolution process and enhance water resource management. In 2018, a comprehensive collection of 50 groups of groundwater samples was conducted, alongside 17 groups of surface water and underground mine water samples in 2023. Compositional descriptive analysis, correlation analysis, Piper three-plot, Gibbs map, ion ratio method, hydrogeochemical simulation and PCA methods were employed to reveal the chemical characteristics and evolution process of groundwater in the Zaozigou gold mine. Furthermore, employing water isotopes theory allowed for the identification of recharge sources and circulation conditions within the Zaozigou Gold Mine. The findings indicated a transition from HCO3-Na·Ca type polluted water in 2018 to primarily the SO4-Ca·Mg type and HCO3·SO4-Na·Ca·Mg type groundwater hydrochemistry types by 2023. The hydrochemical characteristics of the study area are closely related to the mining depth and time. The chemical characteristics are influenced by various factors such as rock weathering, mineral dissolution, cation exchange processes, atmospheric precipitation as well as human activities related to pollution from industrial mining activities.

Chemometric Approach Application in Modern Wine Studies

The wine market is affected by the origin of wines, but the current wine traceability system has some limitations. The idea of geographical authenticity and quality has increased as one of the most important parameters influencing consumers’ preferences. Chemical parameters such as total polyphenolic content (TPC), alcohol and organic acid content (total acids, malic and tartaric acids), and antioxidant activity were observed in this work at 15 dry white wines of varieties Pinot blanc, Riesling, Sauvignon blanc, and Chardonnay, which originated from three different countries. FTIR and spectrophotometry methods (TPC and DPPH) were used and chemometric approaches such as ANOVA and PCA were selected as the most important for evaluation. In general, high levels of malic acid, TPC, and antioxidant activity were detected in Austrian wines. Pinot blanc, Chardonnay, and Sauvignon from the same producing region in Austria showed higher results. The higher overall acid concentration was a distinguishing feature of the Slovakian Sauvignon and Riesling varieties, particularly in the case of tartaric acid. Austrian samples showed significantly higher antioxidant activity compared to Hungarian Chardonnay (p = 0.002) and Sauvignon (p = 0.007), comparable to TPC. The application of statistical analysis was useful in demonstrating many discrepancies, particularly by application of the PCA method.

Analysis of Water Quality of Le'an River in Poyang Lake Basin Based on CCME-WQI Method

As the largest freshwater lake in China, Poyang Lake plays a key role in supporting the balance of aquatic ecosystems, and the water quality of its inlet rivers affects the lake's water quality. Le'an River, a typical inlet river of Poyang Lake, was selected as the research object. Based on the water quality data of six monitoring points in the upper, middle, and lower reaches of the mainstream of Le'an River from 2012 to 2020, the CCME-WQI method was used to evaluate the water quality of the river after systematically analyzing the spatiotemporal variation of the concentration of pollutants in the mainstream of the river. Finally, the main influencing factors of the water quality of the river were extracted and analyzed according to the PCA method. The results showed that： ① The water volume upstream and downstream of the river was more seriously polluted in the pre-study time period, which was due to the presence of Dexing Copper Mine in the upstream and small and medium-sized mines and farmland downstream. ② Before 2017, the water volume downstream of Le'an River had the worst water quality, with TP and NH4+-N exceeding the standard rate of 43.3% and 85.0%, respectively, and the lowest WQI mean value of 86.2. After 2017, due to the effective management of pollutant discharges in the watershed, the water volume downstream of the river improved significantly and continued to be in an excellent state, and the mean value of the WQI reached 100.0. ③ The factors influencing the water quality of the mainstem of the Le'an River could be divided into four categories： human activities, seasonal factors, atmospheric deposition of pollutants, and the physical and chemical properties of the water volume itself, with human activities being the dominant factor for water quality changes at Dawuhekou and Shizhenjie, whereas the seasonal factors had the greatest influence at the remaining locations. ④ Organic matter pollution was obvious in the upper and lower Le'an River water volume, and the water volume at Dawuhekou was mainly affected by nearby mining activities, whereas the water volume at Shizhenjie was mainly affected by agriculture. Le'an River had serious organic matter pollution downstream before 2017, and mining and agricultural activities in the watershed had a high degree of impact on water quality. The treatment of mineral processing wastewater should be upgraded, and the discharge of pollutants from agriculture in the downstream of the watershed should be regulated.

Total organic carbon content estimation for mixed shale using Xgboost method and implication for shale oil exploration

In this study, the Xgboost method is employed for TOC estimation in mixed carbonate and siliciclastic shale from the Hashan area, Junggar Basin. The results show that this approach is effective for TOC estimation in this area although the model performance is not very excellent with a correlation coefficient of 0.54 between measured TOC and predicted TOC values, likely due to a small samples dataset. Therefore, the PCA method is applied to debase dimension of well log data from five dimensional to two-dimensional data, which enhances the correlation coefficient between the predicted and measured TOC from 0.54 to 0.68. Based on the model, the isopleth maps of TOC distributions in Fengcheng Formation were redrawn showing two shale oil exploration targets, which likely correspond to two depositional centers of this strata. All the same, the model in this work provides reliable data for shale oil evaluation in the study area and a good example under similar geological setting.

A modified CNN-IDS model for enhancing the efficacy of intrusion detection system

As the security of computer networks in enterprises worldwide is dependent on the proper functioning of intrusion detection systems (IDSs) and intrusion prevention systems (IPSs), this effectiveness of both of them is of utmost priority. Leveraging diverse techniques, these network security systems are created to keep the reliability, the availability, and the integrity of the organizational networks safe. One plus point of using ML in intrusion detection system (IDS) is that it has successfully weeded out all the IDS attacks with a high degree of accuracy. In contrast, such systems may be believed to operate to their least competent levels when supersized data spaces have to be dealt with. In the process to solve this, application of feature selection techniques will play the crucial role to ignore non-relevant features which do not impact the issue of classification much. One more thing to keep in mind is that the ML-based IDSs often have problems with high false alarms and percentage accuracy because of the imbalanced training sets. The undertaking of this paper involves a through the analysis of the UNSW-NB15 intrusion detection data set as upon which our models will be tested and trained. We utilize two feature selection approaches: the PCA method, which is denoted as PCA, and the SVD method, called SVD. Furthermore, we categorize the datasets using these methods— Ridge Regression (RR), Stochastic Gradient Descent, and Convolutional Neural Network (CNN)-- on the transformed feature space. What is the most widely used for, is that it deals with both, binary and multiclass classification. The result measure that PCA and SVD are succeeded in getting better performance of IDS than others with enhancing the accuracy of classification models. More specifically, the RR classifier's precise was outstanding for the binary classification problem experiencing a rise in the accuracy from 98.13 % to 99.85 %. This shows the critical role of feature selection approaches and is also demonstrates the modeling capabilities of RR, SGD, and CNN classifiers and stands out as a solution to intrusion detection.

Impact of ambient conditions on the minor nutrients content in spirulina “Arthrospira platensis” (AP) extracted from Lake Chad revealed by fluorescence spectroscopy coupled with chemometric methods

In this study, the technique of fluorescence spectroscopy coupled with chemometric methods is used to analyse samples of Lake Chad Spirulina “Arthrospira platensis” (AP), either harvested and conditioned by using the traditional method at different seasons or industrially processed. The content of minor fluorescent nutrients is investigated. To this end, fluorescence excitation-emission matrices (EEMs) of 46 AP samples are recorded in aqueous solution. Synchronous fluorescence (SF) spectra are extracted from these EEMs and their important features are compared to those of PARAFAC methods. Synchronous fluorescence scanning allows different AP samples to be characterized in a single scan. The SF and PARAFAC methods yielded two groups of fluorescent compounds; the first group, consisting of vitamin-like molecules, shows excitation/emission (ex/em) peaks at 340/460, 390/462, 370/440 and 450/526 nm, attributed to caffeic acid, vitamin K, E and riboflavins respectively, while the second group, consisting of pigments, shows ex/em peaks at 610/654, 590/630 and 570/644 nm, attributed to phycocyanins, C-phycocyanin and allophycocyanin. Our fluorescence data showed that while both vitamins and pigments are present in AP during the rainy season, only fluorescent components of vitamin-like compounds are present during the dry season. PCA methods allowed classifying different AP samples according to their geographic origin and harvesting season. Fluorescence spectroscopy therefore appears to be a powerful technique for rapidly assessing the chemical composition of AP.

Enhanced Pansharpening Using Curvelet Transform Optimized by Multi Population Based Differential Evolution

In this study, a pansharpening process was conducted to merge the color information of low-resolution RGB images with the details of high-resolution panchromatic images to obtain higher quality images. During this process, weight optimization was performed using the Curvelet Transform method and the Multi Population Based Differential Evolution (MDE) algorithm. The proposed method was tested on Landsat ETM satellite image. For Landsat ETM data, the RGB images have a resolution of 30m, while the panchromatic images have a resolution of 15m. To evaluate the performance of the study, the proposed MDE-optimized Curvelet Transform-based pansharpening method was compared with classical IHS, Brovey, PCA, Gram-Schmidt and Simple Mean methods. The comparison process employed metrics such as RMSE, SAM, COC, RASE, QAVE, SID, and ERGAS. The results indicate that the proposed method outperforms classical methods in terms of both visual quality and numerical accuracy.

From Constrain to Capability: Factors Driving Tribal Women Empowerment through SHGs

In the current scenario, the empowerment of tribal women holds significant importance in confronting enduring inequalities and propelling the attainment of sustainable development objectives. Indigenous communities frequently encounter socio-economic marginalisation, with tribal women disproportionately affected by this disparity. Empowering tribal women is fundamental in fostering gender parity, given their pivotal contributions to their families, communities, and customary livelihoods. Moreover, amidst escalating globalisation and environmental crises, the imperative to empower tribal women intensifies, ensuring their inclusion in decision-making concerning land rights, natural resource management, and adaptation to climate change. Self-help groups are instrumental in empowering tribal women as they provide a platform for collective action, economic self-reliance, and community support. By participating in SHGs, tribal women gain access to financial resources, skill development initiatives, and valuable networks, enabling them to improve their socio-economic standing and advocate for their rights within their communities. Moreover, SHGs nurture leadership abilities and decision-making skills among tribal women, empowering them to confront challenges such as gender inequality and social exclusion. The paper aims to explore the perceptions of SHG members from five scheduled areas in Himachal Pradesh regarding 49 statements related to various aspects of tribal women's empowerment. It seeks to consolidate these numerous statements into groups based on their intercorrelation using the PCA method of factor analysis, to gain meaningful insights into the role of SHGs in the lives of these tribal women. The results of the systematic factor analysis using Principal Component Analysis and Varimax Rotation with Kaiser Normalization identify ten key factors. The most significant factor in empowering tribal women works out to be the improvement in skills, awareness, and knowledge. Other major factors include enhanced family conditions through better access to facilities, financial independence, and greater involvement in financial decisions. Additionally, community engagement, economic advancement, self-awareness, political awareness, knowledge of rights, and education and training are crucial components for analysing the impact of Self-Help Groups on tribal women's empowerment. By further analysing the ten driving factors identified in this study, a more systematic approach can be developed to assess the capability of these social groups to impact the lives of tribal women. This will allow for a precise evaluation of their overall empowerment through participation in SHGs.

Reconstruction of a Matrix of Genotypic Correlations between Variants within a Gene for Joint Analysis of Imputed and Sequenced Data

—When combining imputed and sequenced data in a single gene-based association analysis, the problem of reconstructing genetic correlation matrices arises. It is related to the fact that the correlations between genotypes of all imputed variants and the correlations between genotypes of all sequenced variants are known for a gene but we do not know the correlations between genotypes of variants, one of which is imputed, and the other is sequenced. To recover these correlations, we propose an efficient method based on maximising the determinant of the matrix. This method has a number of useful properties and an analytical solution for our task. Approbation of the proposed method was performed by comparing reconstructed and real correlation matrices constructed on individual genotypes from the UK Biobank. Comparison of the results of gene-based association analysis performed by the SKAT, BT, and PCA methods on reconstructed and real matrices using modelled summary statistics and calculated summary statistics on real phenotypes showed high quality of reconstruction and robustness of the method to different gene structures.

A pipeline for processing hyperspectral images, with a case of melanin-containing barley grains as an example.

Analysis of hyperspectral images is of great interest in plant studies. Nowadays, this analysis is used more and more widely, so the development of hyperspectral image processing methods is an urgent task. This paper presents a hyperspectral image processing pipeline that includes: preprocessing, basic statistical analysis, visualization of a multichannel hyperspectral image, and solving classification and clustering problems using machine learning methods. The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann-Whitney U-criterion and Pearson's criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. The source code is available at: https://github.com/igor2704/Hyperspectral_images. The pipeline was applied to identify melanin pigment in the shell of barley grains based on hyperspectral data. Visualization based on PCA, UMAP and ISOMAP methods, as well as the use of clustering algorithms, showed that a linear separation of grain samples with and without pigmentation could be performed with high accuracy based on hyperspectral data. The analysis revealed statistically significant differences in the distribution of median intensities for samples of images of grains with and without pigmentation. Thus, it was demonstrated that hyperspectral images can be used to determine the presence or absence of melanin in barley grains with great accuracy. The flexible and convenient tool created in this work will significantly increase the efficiency of hyperspectral image analysis.

Uyku Sağlığı ile Yaşam Tarzı Arasındaki İlişkinin PCA, Naive Bayes ve Rastgele Orman Ağaçları Yöntemleri ile İncelenmesi ve Karşılaştırılması

Uyku, günlük yaşamın temel bir unsuru olarak kabul edilir ve genel sağlık ile refahın sürdürülmesinde önemli bir rol oynar. Bu araştırma, Kaggle platformundan elde edilen "Uyku Sağlığı Yaşam Tarzı" veri setini kullanarak bir tahmin modeli oluşturmayı, bu modeli Principal Component Analysis (PCA) yöntemi, Naive Bayes yöntemi ve Rastgele Orman Ağaçları yöntemiyle değerlendirmeyi ve görselleştirmeler gerçekleştirmeyi amaçlamaktadır. İncelenen veri seti, KNIME platformunda PCA modülü ile boyut azaltma işlemine tabi tutulmuş ve elde edilen çıktılar sunulmuştur. Uyku kalitesini etkilediği düşünülen öznitelikler arasındaki ilişkiler, korelasyon hesaplamaları ile belirlenmiştir. Ayrıca, veri seti Naive Bayes ve Rastgele Orman Ağaçları yöntemleriyle analiz edilmiş, tahmin sonuçları KNIME ortamında değerlendirilmiştir. Elde edilen sonuçlar tablolar halinde sunulmuştur. Bu karşılaştırmaların dağılım matrisleri, KNIME platformundaki Scatter Plot modülü kullanılarak görselleştirilmiştir. Bu çalışmanın en önemli katkısı uyku verileri içeren veri setlerinde kullanılabilecek metodolojiler arasında en etkili olan yöntemi belirlemektir. Bulgular, tartışma ve sonuçlar bölümünde detaylı bir şekilde ele alınmıştır.

Foreign direct investment, Green Technological Innovation and Energy Poverty: Empirical evidences from Sub-Saharan African countries

This study investigates the unconditional and conditional effects of Foreign Direct Investment (FDI) and Green Technology Innovation (GTI) on Energy Poverty (EP) in 11 Sub-Saharan African (SSA) countries from 2000 to 2020. Given that EP captures the share of the population without access to energy, it includes electricity, clean fuel and technology for cooking (CFTP), renewable energy consumption (RECP), and the multidimensional energy poverty index (MEPI) which is computed using the PCA method. Furthermore, this study considere the overall, urban and rural supsamples, and the data from the World Bank and OECD databases. This study employs the FGLS and the Driscoll- Kraay approaches. The findings reveal that FDI and GTI negatively affect both electricity poverty and CFT poverty, whereas these effects are significantly positives for both REC poverty and MEPI. This means that, in the long term, FDI and GTI allow the alleviation of both electricity and CFT poverty; conversely, FDI and GTI strengthen renewable energy and MEPI irrespectively of the residential area. However, FDI-EP nexus is significanty affected through GTI development. Moreover, FDI and GTI contribute to strengthening the rural-urban poverty gap in access to both electricity and CFT. In light of these findings, policy implications are presented.