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In the semiconductor industry, virtual metrology (VM) is a cost-effective and efficient technique for monitoring the processes from one wafer to another. This technique is implemented by generating a predictive model that uses real-time data from equipment sensors in conjunction with measured wafer quality characteristics. Before establishing a prediction model for the VM system, appropriate selection of relevant input variables should be performed to maintain the efficiency of subsequent analyses considering the large dimensionality of the sensor data inputs. However, wafer production processes usually employ multiple sensors, which leads to cost escalations. Herein, we propose a variant of the sparse principal component analysis (PCA) called true sparse PCA (TSPCA). The proposed method uses a small number of input variables in the first few principal components. The main contribution of the proposed TSPCA is reducing the number of essential sensors. Our experimental results demonstrate that compared to the existing sparse PCA methods, the proposed approach can reduce the number of sensors required while explaining an approximately equivalent amount of variance. [ABSTRACT FROM AUTHOR]

Purpose: Hostile aortic neck characteristics, including short length, severe suprarenal and infrarenal angulation, conicity, and large diameter, have been associated with increased risk for type Ia endoleak (T1aEL) after endovascular aneurysm repair (EVAR). This study investigates the mid-term discriminative ability of a statistical shape model (SSM) of the infrarenal aortic neck morphology compared with or in combination with conventional measurements in patients who developed T1aEL post-EVAR. Materials and Methods: The dataset composed of EVAR patients who developed a T1aEL during follow-up and a control group without T1aEL. Principal component (PC) analysis was performed using a parametrization to create an SSM. Three logistic regression models were created. To discriminate between patients with and without T1aEL, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were calculated. Results: In total, 126 patients (84% male) were included. Median follow-up time in T1aEl group and control group was 52 (31, 78.5) and 51 (40, 62.5) months, respectively. Median follow-up time was not statistically different between the groups (p=0.72). A statistically significant difference between the median PC scores of the T1aEL and control groups was found for the first, eighth, and ninth PC. Sensitivity, specificity, and AUC values for the SSM-based versus the conventional measurements–based logistic regression models were 79%, 70%, and 0.82 versus 74%, 73%, and 0.85, respectively. The model of the SSM and conventional measurements combined resulted in sensitivity, specificity, and AUC of 81%, 81%, and 0.92. Conclusion: An SSM of the infrarenal aortic neck determines its 3-dimensional geometry. The SSM is a potential valuable tool for risk stratification and T1aEL prediction in EVAR. The SSM complements the conventional measurements of the individual preoperative infrarenal aortic neck geometry by increasing the predictive value for late type Ia endoleak after standard EVAR. Clinical Impact: A statistical shape model (SSM) determines the 3-dimensional geometry of the infrarenal aortic neck. The SSM complements the conventional measurements of the individual pre-operative infrarenal aortic neck geometry by increasing the predictive value for late type Ia endoleaks post-EVAR. The SSM is a potential valuable tool for risk stratification and late T1aEL prediction in EVAR and it is a first step toward implementation of a treatment planning support tool in daily clinical practice. [ABSTRACT FROM AUTHOR]

We explored the aging effects on insulating materials used in power transformers through dielectric spectroscopy. Frequency domain spectroscopy (FDS) was conducted for both aged and new Kraft paper and pressboard samples to determine their dielectric properties across a temperature range of 40-100 °C for pressboard and 40-70 °C for Kraft paper. Principal component analysis (PCA) was employed to classify the data, identify key factors contributing to variance, and elucidate the relationships between different parameters. The analysis revealed positive correlations between conductivity and frequency, as well as between permittivity and tan delta, with distinct differences in behavior observed between aged and new samples at lower frequencies. Additionally, the impact of temperature was evident, with increased temperature leading to an upward shift in dielectric behavior. At lower temperatures, aging had a reduced effect on the materials' properties. These findings provide significant insights into the dielectric behavior and aging mechanisms of insulation materials in power transformers. [ABSTRACT FROM AUTHOR]

This study aimed to optimize the formula of plant-based almond yogurt to improve its quality and storage stability. Using sweet almonds as the main ingredient and adding conventional yogurt stabilizers such as emulsifiers and thickeners, plant-based almond yogurt was produced through lactic acid fermentation. The optimal stabilizer formula for yogurt was determined using principal component synthesis evaluation analysis and response surface optimization methodology, and the physical properties were characterized. The results showed that the optimal formula for plant-based almond yogurt was 0.4% tartaric acid mono-diglyceride, 2.0% hydroxypropyl distarch phosphate, and 0.1% glutamine transaminase, with a comprehensive score of 0.990. The predicted and verified comprehensive scores of plant-based yogurt were close. The quality and acceptability of the plant-based yogurt significantly improved after adjustment, and the micro-particles of the mixed plant-based almond yogurt (MPBAY) were fuller, the distribution of milk fat was more uniform, and the texture was richer. This study provides a certain research idea and scientific theoretical value reference for the development of plant-based yogurt and the study of plant protein gel aggregation systems.

The multicollinearity of measurement data leads to the low accuracy of the data-driven methods for estimating voltage-power sensitivity in distribution networks. In this paper, a data-driven method embedded with topological information is proposed to address the problem. First, the voltage-power sensitivity matrix is decomposed into principal and secondary components, where the principal component is closely related to the distribution network topology and the secondary component is the error between the principal component and the actual value. Then, the principal and secondary components are estimated sequentially in two stages, and their data-driven estimation models based on quadratic programming are established, respectively. The key of the model in the first stage is the constraint based on the distribution network topology information, and the key of the model in the second stage is the constraint that the ratio of the secondary component to the principal component is tiny. Finally, the accuracy and efficiency of the proposed method is validated in the IEEE 33-bus system with a set of measurement data, and comparisons are made with ordinary least square regression, ridge regression, and LASSO regression. The simulation results show that the accuracy of the proposed method is significantly improved by orders of magnitude.

It is well known that numerous operational, material and design variables act upon the performance of a plant-based microbial fuel cell which is an emerging sustainable and versatile energy device like hydrogen fuel cells. However, due to the high complexity of these bioelectrochemical systems, new solutions are required to optimize performance and uncover hidden relationships between dominant fuel cell variables. For this purpose, a database of 229 observations was created for plant-based microbial fuel cells (PMFCs) with 159 descriptor variables and a target variable (maximum power density) based on experimental results from 51 recent publications. Then, some machine learning solutions like principal component analysis (PCA), classification trees and SHapley Additive exPlanations (SHAP) analysis were applied. The PCA indicated mainly two routes involving low and high chemical oxygen demand (COD) towards high maximum power density which consists of the plant family, wastewater type, support media, construction design, separator type, anode and cathode electrodes and light source. SHAP analysis revealed that the most important factors for high performance are operating temperature, natural light, soil support medium, and constructed wetland design. Finally, the classification tree successfully demonstrated nine routes towards high maximum power density which exclude the use of graphite plate cathode electrodes. [Display omitted] • Machine learning was applied to determine high power density routes in PMFC. • Principal component analysis indicated two routes to high maximum power density. • Shapley analysis revealed the most important factors for high-performance. • Most influential variables were found as temperature, natural light, soil support. [ABSTRACT FROM AUTHOR]

The simplest approach to convey the results of scientific analysis, which can include complex comparisons, is typically through the use of visual items, including figures and plots. These statistical plots play a critical role in scientific studies, making data more accessible, engaging, and informative. A growing number of visual representations have been utilized recently to graphically display the results of oncologic imaging, including radiomic and radiogenomic studies. Here, we review the applications, distinct properties, benefits, and drawbacks of various statistical plots. Furthermore, we provide neuroradiologists with a comprehensive understanding of how to use these plots to effectively communicate analytical results based on imaging data. [ABSTRACT FROM AUTHOR]

The water quality index (WQI) is a unified measure that summarizes many water test characteristics to provide a single value indication of water quality. The Tigris River is a vital water source in southeastern Iraq. Monthly water samples were collected from August 2022 to July 2023. Eighteen parameters were analyzed. The principal component analysis (PCA) was applied to analyze the environmental variables. First, it helped us choose significant water quality limits and determine their comparative weights. Second, we constructed excellence curves for the selected parameters to estimate the WQI conclusion based on water quality values. Six variables were selected to be included in the guide based on their significant influence on water quality which involved total dissolved solids (TDS), total hardness (TH), dissolved oxygen (DO), total coliform (TC), chemical oxygen demand (COD) and chlorides (Cl- ), aiming to capture the specific features of the Iraqi waters. In this analysis, an eigenvalue of 8.325 was observed, signifying a substantial value given by the initial fifteen factors examined. The cumulative effect of the three components, namely F1, F2, and F3, amounted to 46.25, 69.46, and 79.878%, respectively. The results of the Iraq water quality index (WQI) for the Tigris River in southeast Iraq revealed acceptability, with scores of 65.32, 53.03, and 57.15 during winter, spring, and autumn, while the summer season exhibited poor water quality with a score of 23.45. On an annual average basis, the water quality of the Tigris River was found to be poor, with a mean of 49.74± 18.256. These results suggest that the water in the entire river is unsuitable for potable water. [ABSTRACT FROM AUTHOR]

This study aimed to optimize the formula of plant-based almond yogurt to improve its quality and storage stability. Using sweet almonds as the main ingredient and adding conventional yogurt stabilizers such as emulsifiers and thickeners, plant-based almond yogurt was produced through lactic acid fermentation. The optimal stabilizer formula for yogurt was determined using principal component synthesis evaluation analysis and response surface optimization methodology, and the physical properties were characterized. The results showed that the optimal formula for plant-based almond yogurt was 0.4% tartaric acid mono-diglyceride, 2.0% hydroxypropyl distarch phosphate, and 0.1% glutamine transaminase, with a comprehensive score of 0.990. The predicted and verified comprehensive scores of plant-based yogurt were close. The quality and acceptability of the plant-based yogurt significantly improved after adjustment, and the micro-particles of the mixed plant-based almond yogurt (MPBAY) were fuller, the distribution of milk fat was more uniform, and the texture was richer. This study provides a certain research idea and scientific theoretical value reference for the development of plant-based yogurt and the study of plant protein gel aggregation systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Oat holds significant importance in global agriculture and nutrition due to their adaptability and versatility. In the present study, Principal Component Analysis (PCA) and Regression analysis were carried out to identify the cause and effect relationship among various traits. PCA on 13 yield attributes revealed five main components contributing to 80.75% cumulative variance. PC1, associated with green fodder yield, dry matter yield, tillers per plant and seed yield was a prominent contributor. PC2 was influenced largely by days to 50% flowering and days to maturity. Biplot analysis identified two distinct trait groups. Multiple regression analysis revealed tillers per plant, test weight and number of spikelets as significant predictors of seed yield. The findings offer insights into genetic association among traits in oat by uncovering the quantitative relationships among them and to identify patterns of genetic variation among different oat genotypes. The analysis of individual trait regression graphs enhances understanding of trait contributions to seed yield. This study advances oat improvement strategies for enhanced crop productivity and resilience.

Background and objectives: Grass pea (Lathyrus sativus L.), an annual pulse crop belonging to the family of Fabaceae is under grown for food and feed purposes. Lathyrus genus involve in 187 species and subspecies, are cultivated for grain and forage purposes. The world demand for legume proteins is increasing for animal feeding. Grass pea would be a nice alternative for cropping systems in marginal lands and environments. This crop is relatively tolerant to several abiotic stresses; making it a reliable candidate for expansion in the semiarid areas of the world which are predicted to become more drought-prone due to climate change. Materials and methods: This research was carried out in Plant Production and Breeding Department, Faculty of Agriculture, University of Maragheh, Maragheh, East Azerbaijan Province, Iran. The twenty-five grass pea genotypes were provided by ICARDA. In order to evaluate genotypes in terms of salinity tolerance, genotypes were studied at factorial experiments in completely random design with two replications in 2017. Salinity treatments were applied at four levels (0, 40, 80 and 120 mM of NaCl). Different seedlings traits in the laboratory and agronomic trait in pots in the field of were evaluated. Results: The genotypes had significant differences in most of the studied traits. With increasing salinity levels plant height, shoot weight, number of branches, shoot dry weight, leaf width, leaf number, first branch location and seedling time, root length, length of shoots, seedling length, root dry weight and stem dry weight were decreased. The cluster analysis of high-yielding genotypes based on the standardized mean with Euclidean distance and Ward algorithm divided the studied genotypes into three clusters. The first cluster included genotypes 19 and 20. The genotypes 24, 25, 8, 16, 10, 2, and 5 were located into the second cluster, and finally, the third cluster included genotypes 3, 6, 1, 7, 23, 15, 4, 22, 18, 14, 13, 12, 11, 21, 9 and 17. Principal component analysis (PCA) reduced the traits in three main components with 70.25 % of variation, which according to the results the first component was named the grain yield component and the second component as the number of leaves. These components can be used in breeding programs for the selection of genotypes and breeding goals in resistance to salt stress. Conclusion: With increasing in salinity levels, the yield significantly decreased compared to the control. Genotypes 21 and 18 had the highest and lowest germination rates, respectively, and genotypes 8 and 15 had the highest and lowest germination percentages, respectively. In terms of the durability of genotypes to salinity, genotypes 8, 9, 10, 12, 13, 14, 23, and 24 were the best genotypes and the most sensitive genotypes to salinity with low performance were genotypes 19 and 20. Genotypes 5, 14, 10, 16, 21, 24 and 25 (control) had the highest yield. [ABSTRACT FROM AUTHOR]

We address the estimation of regression parameters for the ill-conditioned predictive linear model in this study. Traditional least squares methods often encounter challenges in yielding reliable results when there is multicollinearity. Therefore, we employ a better shrinkage method, average least squares-centered penalized regression (ALPR), as it offers a more efficient approach for handling multicollinearity than ridge regression. Additionally, we integrate ALPR with the principal component (PC) dimension reduction method for enhanced performance. We compared the proposed PCALPR estimation technique with existing ones for ill-conditioned problems through comprehensive simulations and real-life data analyses using the mean squared error. This integration results in superior model performance compared to other methods, highlighting the potential of combining dimensionality reduction techniques with penalized regression for enhanced model predictions. [ABSTRACT FROM AUTHOR]

This study was conducted to investigate the variability of Ethiopian black cumin genotypes by using morpho-agronomic traits. Sixty-four genotypes were tested at Debre Zeit and Kulumsa Agricultural Research center in 2021 using an 8 × 8 simple lattice design with two replications. Analysis of variance revealed significant (p ≤ 0.001 or p ≤ 0.01) differences among the genotypes for all traits studied, except the number of primary branches per plant. The effect of location was significant (p ≤ 0.001 or p ≤ 0.05) for all traits except the number of primary branches per plant. It is expected to improve all phenological traits as well as seed yield and yield-related qualitative traits by 4–41% over improved varieties by the selection of the top 5% landraces. Thus, through selection, it would also be possible to shorten the flowering and maturity periods of the genotypes. High broad sense heritability values coupled with high to moderate genetic advance as a percentage of mean values were shown by the number of capsules per plant and plant height which indicates possibilities for improvement of these traits through selection. Plant height, number of primary branches per plant, number of seeds per capsule and thousand seeds weight had a positive and significant phenotypic and genotypic association with seed yield per hectare. Plant height and seed yield per plant had a positive direct effect on seed yield per hectare phenotypically and genotypically. This would be a direct selection criterion for further improvement of the genotypes. The principal component analysis of 12 quantitative traits exhibited 81.5% of the total variance captured by the first four principal components (PCs). Days to 50% flowering, days to full blooming, days to maturity, number of seeds per plant, and seed yield per plant were the main contributor traits for the variation in the first and second PCs. The genotypes were grouped into three different clusters (C-I = 35.93%, C-II = 9.38%, and C-III = 54.69%) based on 12 quantitative traits with significant inter-cluster distances. This clearly showed that there was sufficient diversity among the genotypes which can be exploited for the future black cumin improvement program. [ABSTRACT FROM AUTHOR]

Genetic variability in a crop population is essential for successful plant breeding. Fifteen cucumber genotypes (Cucumis sativus L.) were evaluated in the greenhouse under warm conditions to estimate the magnitude of their genetic variability, trait heritability, morphological features, and pomological characteristics. The genotypes appeared in five categories based on their performance and determination of the most reliable discriminative features that accounted for more significant variability. Cluster Analysis and Principal Component Analysis (PCA) assisted in the classification. The measured characteristics were cucurbit chlorotic yellows virus, fungal severities of infestation, off-type plant, stem, internode, leaf, petiole, fruit and fruit trail length, leaf width, male flower percentage, fruit color and groove, fruit diameter, fruit fresh weight, fruit dry matter and ash percentage, fruit pH, EC and TSS, fruit yield, and total fruit count. The IR4 and IR5 genotypes showed the highest fruit yield (460.85 and 425.86 kg/plot) and number (108.72 and 84.22 fruit/plant). IR11 had the highest value of fruit length (16.60 cm). High broad-sense heritability was associated with all the traits except for cucurbit chlorotic yellows virus and fungal severities of infestation, fruit pH, and total fruit yield. Cluster analysis and its comparison of means showed that IR4 and IR5 from the fifth cluster expressed the best agronomic traits and yield potentials in warm conditions (40 °C/32 °C day/night). Hence, selection for any characteristic would favor genotypes in these clusters. The PCA involved fruit dry matter and TSS as the most discriminating trait that accounted for more significant cucumber variability, which can become crucial information in cucumber improvement programs. [ABSTRACT FROM AUTHOR]

ABSTRACT: Owing to rich in vitamins, proteins, mineral elements, and active substances, the highly medicinal and economic value of mung bean seeds have been proven to be beneficial to human. In order to fully identificate and innovative utilization of excellent germplasm resources for mung bean yield and quality breeding, the genetic diversity level of eight seed phenotypic traits of 302 mung bean germplasm resources were comprehensively evaluated by diversity analysis, correlation analysis, principal component analysis (PCA), and cluster analysis. The results showed the genetic diversity of eight seed phenotypic traits of 302 mung bean germplasm resources with a Shannon diversity Index ranging from 0.25 to 2.09. For most traits, their coefficient of variation (CV) were more than 10% with the highest being seed shape (41.51%). Correlation analysis showed that the 100-seed weight had extremely positive correlations with seed size, seed length, seed width, and seed thickness. PCA of seed phenotypic traits showed that the cumulative contribution rate of the first three principal components was 78.551%. These principal components included seed width, seed coat gloss and seed color. The 302 mung bean germplasm resources were divided into eight groups by cluster analysis. A total of 140 germplasm resources with large-seed were selected from group I and group II, of which 9 germplasm resources in group II were likely to be high-yield varieties. Overall, this study not only indicated sufficient variation among the studied germplasm resources, but also provided a theoretical basis for the selection, utilization and hybrid breeding of mung bean germplasm resources.

PurposeTo investigate spatiotemporal correlations among ganglion cell complex (GCC) superpixel thickness measurements and explore underlying patterns of longitudinal change across the macular region.DesignLongitudinal cohort study.SubjectsOne hundred eleven eyes from 111 subjects from the Advanced Glaucoma Progression Study with ≥ 4 visits and ≥ 2 years of follow-up.MethodsWe further developed our proposed Bayesian hierarchical model for studying longitudinal GCC thickness changes across macular superpixels in a cohort of glaucoma patients. Global priors were introduced for macular superpixel parameters to combine data across superpixels and better estimate population slopes and intercepts.Main outcome measuresBayesian residual analysis to inspect cross-superpixel correlations for subject random effects and residuals. Principal component analysis (PCA) to explore underlying patterns of longitudinal macular change.ResultsAverage (standard deviation [SD]) follow-up and baseline 10-2 visual field mean deviation were 3.6 (0.4) years and -8.9 (5.9) dB, respectively. Superpixel-level random effects and residuals had the greatest correlations with nearest neighbors; correlations were higher in the superior than in the inferior region and strongest among random intercepts, followed by random slopes, residuals, and residual SDs. PCA of random intercepts showed a first large principal component (PC) across superpixels that approximated a global intercept, a second PC that contrasted the superior and inferior macula, and a third PC, contrasting inner and nasal superpixels with temporal and peripheral superpixels. PCs for slopes, residual SDs, and residuals were remarkably similar to those of random intercepts.ConclusionsIntroduction of cross-superpixel random intercepts and slopes is expected to improve estimation of population and subject parameters. Further model enhancement may be possible by including cross-superpixel random effects and correlations to address spatiotemporal relationships in longitudinal data sets.

Copyright of Xinjiang Agricultural Sciences is the property of Xinjiang Agricultural Sciences Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

If the sample Mahalanobis distance (SMD) is atypically large, it is essential to statistically estimate or test the contribution of each studentized principal component (SPC) decomposed from the SMD to consider its cause. However, there are no appropriate probability models for the SPCs of small samples. This study proposes a precise probability model for the SPCs of small samples without estimating the population eigenvalues or eigenvectors. The proposed model for an SPC comprises an elementary formula of sample size and the SPC's index multiplied by one random variable following the t-distribution, which is simpler and requires no further computing compared with previous models. Numerical experiments demonstrated that the proposed model performs well under the weak condition that population eigenvalues are closely distinct with various dimensions and sample size. For practical implementation, the proposed model was applied for correcting the SMD to the population Mahalanobis distance, demonstrating better performance than other models. Additionally, the proposed model enables precise statistical testing of SPCs for discriminant analysis, cluster analysis, and projection pursuit, and the model improves the expectation–maximization algorithm for Gaussian mixture models. [ABSTRACT FROM AUTHOR]

This study determined fatty acid compositions of two cultivated Ganoderma. The canopy and stalk lipids of the Ganoderma were analyzed for fatty acid composition, β-carotene content and antioxidant activities. The results showed that Ganoderma samples contained 51 fatty acids. Their lipid extraction yields ranged between 0.45% and 1.09%. The β-carotene content of the red Ganoderma canopy extract was higher than its stalk extract. The canopy extract also had the highest DPPH hydroxyl radical scavenging activity. The data of the overall heat map showed that over 30 fatty acids were positively and highly correlated to the lipid in the black Ganoderma stalk. The main types of fatty acids in these Ganoderma samples were palmitic, oleic, and linoleic acids. The concentration of linoleic acid in the red Ganoderma canopy was as high as 164.02 mg/g lipid. These results suggested that these Ganoderma lipids are lipid-based antioxidants and potential sources of dietary supplements. [ABSTRACT FROM AUTHOR]

This study mainly aims to compare the temporal evolution from 2009 to 2019 of the monthly average net wages received by workers in their various categories of qualifications, within the various economic activities in the legal, public and private sectors in Algeria. This comparison, which the study aims at, is based on a multidimensional statistical analysis of the dispersion of wages in Algeria, using two integrated methods represented in the analysis by the principal components and the hierarchical ascending clustering according to the Ward algorithm. After the two processes of analyzing the data, using the statistical software R, and interpreting the results of the two methods, it was concluded that 18 dimensions representing the variables of the study were summarized into two dimensions that represent two principal components, which we were able to name as the factor of average activity wages for the study period and the factor of the extent of wage dispersion between executives and enforcement agents within the activity. According to these two factors, the clustering process produced five different groups of activities. [ABSTRACT FROM AUTHOR]

Yang and Huang have introduced a generalized Mahalanobis loss function which can be applied to the estimator for regression coefficients whether its covariance matrix is singular or non singular. In this paper, we give the detailed comparisons among those estimators that can be derived from the modified ridge principal component estimator under the generalized Mahalanobis loss function by the average loss criterion. Also, we obtain conditions for the superiority of one estimator over the others. Furthermore, two numerical examples are given to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Body length in dairy cattle is an essential linear trait to thoroughly investigate because cattle's bodies are stretched horizontally, unlike humans. As a result, the current study aimed to identify dairy cattle's most prominent body length characteristics primarily related to milk delivery as a selection basis. One hundred twenty-one heads of Holstein cows were employed as a research sample. The samples were utterly lactating, with ages between 2 and 6 years. Principal component analysis (PCA), correlation, and regression were used consecutively to examine the data. The R type 4.2.1 with RStudio software was applied as an instrument for statistical analysis. A stipulated output of the PCA has unmasked four body length traits as major components, comprising absolute frame lengthiness (AFL), relative frame lengthiness (RFL), rump lengthiness (RML), and fore udder lengthiness (FUL). The correlation and regression analysis designated the FUL as the priority in dealing with the milk delivery characteristic, followed by the long distance between teats side-view (TSL). However, with PCA excluding TSL as a substantial component of body length, the second priority was given to the RFL. The ultimate recommendation is to prioritize the FUL trait for the initial cow selection program. Presumedly, the RFL could be used as the first initiative trait for the calf-heifer selection plan. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic significantly disrupted international economic activities. This study examines its impact on the world economy, analyzing different regions and income groups separately. The empirical findings reveal that COVID-19 had a negative and substantial impact on global trade performance. Surprisingly, high-income countries experienced a positive impact on trade due to COVID-19, showing greater adaptability and, in some cases, emerging as exporters of medicines and COVID-19-related products. In contrast, middle-income and low-income countries did not observe any significant impact on trade performance from COVID-19. This finding implies that trade deterioration during the pandemic might be attributed to mediating factors rather than the direct influence of COVID-19 itself. Furthermore, when examining the effects by region, the results are mixed, reflecting the diverse characteristics of different regions. Consequently, it becomes evident that the COVID-19 pandemic has exacerbated existing inequalities, with the trade performance of affluent and advanced countries being the least negatively affected. In conclusion, the COVID-19 pandemic had far-reaching consequences on global trade, impacting various regions and income groups differently. The study highlights the need for targeted policies to address the disparities in trade performance and foster a more resilient and equitable global economy. [ABSTRACT FROM AUTHOR]

In most of the cases, it is observed that the energy is supplied to the system by p1 sources and it is consumed by p2 sources and the two sets of sources are linearly dependent. In this article, we propose a procedure to compute and estimate the stress-strength reliability R = Pr(a'x > b'y), when both x and y are distributed dependently multivariate normal distribution, with a and b two known vectors. In this paper, we consider the principal component analysis to estimate the a and b. The Bayesian estimator of R and the corresponding credible intervals are obtained using the Gibbs sampling technique under the Conjugate and Jeffreys Prior distribution. Actually, we want to find out overall strength is larger than overall stress. In order to do that we take a'x and b'y as their representatives e.g., principal components of the respective vectors do the job approximately. [ABSTRACT FROM AUTHOR]

Copyright of China Condiment is the property of China Condiment and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Isometric feature mapping (ISOMAP) is a nonlinear dimensionality reduction method and closely reflects the actual nonlinear distance by the view of tracing along the local linearity in the original nonlinear structure. Thus, the first leading 20 principal components (PCs) of low-dimensional space can reveal the characteristics of real structures and be utilized for clustering. In this study, a k-means algorithm was used to diagnose SST clustering based on ISOMAP. Warm and cold El Niño–Southern Oscillation events were subdivided into Central Pacific and Eastern Pacific types, and a two-dimensional cluster map was used to depict the relationship. The leading low-dimensional PCs of ISOMAP were considered as the orthogonal basis, and their trajectories demonstrated meaningful patterns that could be learned by machine learning algorithms. Predictions of SST in the Pacific Ocean were performed using support vector regression (SVR) and feedforward neural network (NN) models based on the low-dimensional PCs of ISOMAP. The forecast skills, the root-mean-square error (RMSE) and anomaly correlation coefficient (ACC), were comparable to those of current numerical models.

With the continuous increase in the resolution of high-resolution remote sensing satellite images, shadows have increasingly impacted feature target recognition and classification in images, urban reconstruction, and image interpretation. Although various shadow detection methods exist for different features, undetected small shadowed areas, misclassified dark areas, and highlighted non-shadowed areas remain. Moreover, it is still difficult to distinguish waters from shadowed regions owing to their similar characteristics. To further solve these problems, we propose a novel mixed shadow detection index (MSDI) for extracting urban feature shadows, using the original information of the first principal component that contains the differences between most of the different objects in the image, as well as the difference features in the shadowed areas and water bodies. We evaluated the effectiveness and robustness of our method by conducting comparison experiments using Gaofen-1, Gaofen-2, and WorldView-3 images from different scenes, times, and acquisition locations. Through visual analysis and data analysis of our method, we found that the method achieved excellent shadow detection results, with an average total accuracy 94% for shadow detection. The proposed shadow detection algorithm could accurately recognize waters, and it could also accurately recognize easily missed small shadowed areas and easily confused non-shadowed areas.

Abstract The use of biomass for energy production constitutes a promising strategy that warrants the search for new sources of biomass. Elephant grass has been gaining notoriety due to its high dry matter yield and rapid growth. The present study was carried out to quantify the genetic divergence of nine elephant grass half-sib families in order to identify genotypes with greater genetic divergence and productive potential for hybridization, using the hierarchical clustering methodology based on principal components. Half-sib families were generated using genotypes from the Active Germplasm Bank of Elephant Grass. The experiment was laid out in a randomized-block design with nine half-sib families, three replicates, and eight plants per plot. A total of 216 genotypes of elephant grass were evaluated. Principal component (PC), biplot, and hierarchical clustering analyses for diversity estimation were conducted using R software. The first two PCs of biplot analysis accounted for 64% of the cumulative variation. Dry matter yield was the most important trait for genotype discrimination (0.89), followed by plant height (0.67) and stem diameter (0.61) in PC1. In this analysis, the distances between accessions were considered and there were no family links, which indicates the existence of wide variability within the evaluated families, since genotypes belonging to the same family were not grouped together, but rather distributed into different groups. Crosses between genotypes of group three and genotypes of groups one and two are recommended for the development of high-yielding genotypes when aiming at energy production.

Background. Identification of typical violators for categorized objects is carried out at the early stages of designing protection systems, since errors in solving this problem can lead not only to an increase in material and time resources, but also to the violator’s omission and the emergence of an emergency. The object of the study is the effectiveness of physical protection systems of categorized objects. The subject of the study is the identification of typical violators of categorized objects. The purpose of the study is to develop a methodological apparatus for the reasonable identification of typical violators for categorized objects. Materials and methods. Identification of typical violators is solved on the basis of the ratios of their characteristics and parameters of categorized objects using the principal component method and the information-probabilistic method. Categories of objects and typical violators are described by a set of heterogeneous characteristics, which are reduced based on Pearson’s chi-square test into a common information field. Using the principal component method for the general information field, a matrix of factor loadings was formed, because of which the analysis of the relationship and significance of indicators characterizing typical violators and categorized objects was carried out. Using the cluster analysis method for the generated components, we obtained the base type of the intruder for each category of objects. Results. A methodical apparatus has been developed for the reasonable identification of typical violators for categorized objects, which can be used to justify the requirements for the effectiveness of a physical protection system at the stage of its design. Conclusions. The preparedness potential of a typical violator is consistent with the potential consequences of their impact on the categorized object and with their ability to overcome the physical protection system of objects. Thus, the required value of the probability of a safe state of an object depends on the hazard potential of the base violator for each category of the object.

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Soybean is an important economic crop in the world, which provides plant protein, oil and a variety of active substances beneficial to human health. In order to comprehensively identify the quality characters of soybean resources abundant in China, 16 kinds of seed quality traits of 520 soybean germplasms in Huang-Huai-Hai ecoregion were evaluated in 6 environments across 2 years with reference to the national standard method. The results showed that the genetic variations of the tested seed quality traits were abundant in the soybean germplasms, and the variation coefficients of isoflavone and tocopherol contents were relative high, while the variation coefficients of protein, oil and fatty acid components were relatively low. Meanwhile, there were significant correlations among the 16 seed quality traits. Principal component analysis showed that the 16 quality traits could be attributed to 5 principal components. Based on the results of principal component analysis, 520 germplasms could be divided into 3 categories by cluster analysis with significant differences. Based on the above results and high stability coefficient analysis, 35 germplasms with multiple or single elite seed quality traits were screened out, which provided important resources for genetic improvement of seed quality traits in soybean. [ABSTRACT FROM AUTHOR]

The interannual variation of the Indian summer monsoon (ISM) affects millions of people in India and the global weather and climate. The teleconnections that affect this variation are not stable. The recent four decades of the second dominant mode of ISM rainfall show a unique north-south tripole pattern, with above-normal rainfall in the north and peninsular India sandwiching suppressed rainfall in central-east India. The pattern relates to extending the Indo-Pacific warm-pool's warmer sea-surface temperature (SST) towards the south of the equatorial eastern Indian Ocean. Most of the time, this warming and the extension of the warm-pool's warmer SST are associated with La Niña events, which activate more in situ vigorous convection. The Rossby-gyres generated west of the equatorial heating increase the tropospheric height over north India, shifting and strengthening the Tibetan High northwards, facilitating heavy rainfall in the north. Meanwhile, the more vigorous convection south of the equatorial eastern Indian Ocean produces compensatory subsidence over central-east India, suppressing rainfall. The northern hemispheric Rossby-gyres brings anomalous cyclonic circulation over peninsular India, producing excess rainfall. Also, the dipole pressure anomaly between the northwest Pacific and south tropical Indian Ocean generates anomalous lower-level easterly winds over the Bay of Bengal. It supplies excess moisture to the north India convections. The co-occurrence of the active Atlantic intertropical convergence zone supports this tripole rainfall pattern. This teleconnection could further be examined in climate models. [ABSTRACT FROM AUTHOR]

In most of the urban areas of Tanzania, poor and inadequate solid waste management is a growing environmental and public health concern. Despite an array of solid waste management options, choosing an appropriate one remains a challenge. This study applied a novel two-stage-based approach to assess the environmental impacts and sustainability performance of the 27 scenarios for municipal solid wastes (MSW) management of Dar es Salaam City, Tanzania. In the first stage, the study utilized a life cycle analysis (LCA) to quantify the environmental impacts of MSW scenarios. In the second stage, the study used the principal component analysis (PCA) to assess the sustainability performance of the same. The LCA results indicated that no scenario performed better in all environmental impacts. The current SWM option, which involves landfilling most of the wastes at the Pugu landfill, leads to adverse environmental impacts and therefore poses an environmental and public health risks potential. The PCA results indicated that the scenario that involves the composting of organic wastes, recycling of the recyclable materials and landfilling the rest of the waste is the more sustainable option with a score of -1.7105. Based on the sensitivity analysis, the transportation process is responsible for the higher environmental burdens in most scenarios. Thus, promoting resource recovery at the premises of the waste generation would be crucial for minimizing the environmental impacts. This study demonstrated that the decision-makers could potentially use a combined LCA-PCA methodology to select the optimal MSW scenario. [ABSTRACT FROM AUTHOR]

This study seeks to enrich the existing literature on academic performance in Nigerian universities and recommend a culture of evidence-driven decision-making in the higher education sector. It investigates educational and environmental factors affecting Nigerian university students' academic performances. Data was collected from eight different Schools of the Federal University of Technology, Owerri (FUTO) using the stratified random sampling technique. The educational factors comprise of human resources, physical facilities and instructional materials, while the environmental factors are internet accessibility, transportation, security, students' housing, and electricity. A structured questionnaire was used to collect the data. Principal Component Analysis (PCA) and Mixed Linear Model (MLM) Analysis were used to analyze the collected data. Findings show that among the eight variables studied, only "instructional materials" significantly explained the "Cumulative Grade Point Average" (CGPA) of the students. Though there was a non-significant "Schools" effect, the intra-class correlation Coefficient (ICC) was 22%. This study, therefore, contributes to the development of strategies that can effectively address the poor performance of most Nigerian university students. However, to inform policy initiatives to improve educational quality in Nigerian universities, more studies are required to identify additional factors that may affect students' academic achievements using more universities. [ABSTRACT FROM AUTHOR]

The classical regression model is very sensitive to econometrics problems, one this econometrics problem is Multicollinearity, to overcome this problem,we will use two solutions: Firstly via using principal component regression and second solution via using quantile regression. When mix between these methods together give as robust model against the Multicollinearity problem. The simulation scenario and real data using in this study. [ABSTRACT FROM AUTHOR]

One-class classification is a special multi-class approach where data from only a single class are available for classifier training. It is an approach with several applications in real-world scenarios, for instance, for outlier or novelty detection. This paper presents a new one-class classifier based on principal curves. The method exploits the good capacity of data representation of the principal curves to build a compact data representation of the known class. The use of principal curves gives the proposed method a good capacity for dealing with different shapes of the feature space, leading to better performance rates. The results showed high performances of the proposed method for synthetic and real data sets, outperforming other known one-class learning algorithms. Moreover, it builds decision boundaries more uniform around the known class than other models and is a fast method during the operating stage since classification is performed by simply mapping the Euclidean distances from data to the principal curve. [ABSTRACT FROM AUTHOR]

Isometric feature mapping (ISOMAP) is a nonlinear dimensionality reduction method and closely reflects the actual nonlinear distance by the view of tracing along the local linearity in the original nonlinear structure. Thus, the first leading 20 principal components (PCs) of low-dimensional space can reveal the characteristics of real structures and be utilized for clustering. In this study, a k-means algorithm was used to diagnose SST clustering based on ISOMAP. Warm and cold El Niño–Southern Oscillation events were subdivided into Central Pacific and Eastern Pacific types, and a two-dimensional cluster map was used to depict the relationship. The leading low-dimensional PCs of ISOMAP were considered as the orthogonal basis, and their trajectories demonstrated meaningful patterns that could be learned by machine learning algorithms. Predictions of SST in the Pacific Ocean were performed using support vector regression (SVR) and feedforward neural network (NN) models based on the low-dimensional PCs of ISOMAP. The forecast skills, the root-mean-square error (RMSE) and anomaly correlation coefficient (ACC), were comparable to those of current numerical models. [ABSTRACT FROM AUTHOR]

Background and Objectives: Camel breeding is one of the sources of income for people on the edge of arid and desert regions across various parts of the world. Traits associated with camel growth, including birth weight, weaning weight, daily weight gain, and oneyear-old weight, are considered as the principal economic attributes for camel owners. To manage and enhance the genetic and phenotypic values of these traits, animal weight recording is imperative. Recording the weight of camels, particularly due to their restless temperament and substantial size, presents numerous challenges. The utilization of effective mathematical methodologies can substantially mitigate this issue. Various approved efficient mathematical methods have been proposed to predict the weight of camels based on their body dimension, and their efficacy has been proven. Therefore, the present study was conducted to compare the effectiveness of principal component analysis and multiple regression in estimating the weight of fattening camels based on their body dimensions. Materials and Methods: In order to compare the efficiency of principal components analysis and multiple regression in estimating the weight of fattening camels based on body dimensions, the records of 220 fattening camels at the Bafgh station in Yazd were used. For this purpose, newborn camels were fed for a 9-month period using standard diets. During the period, each of the fattening camels was weighed and different body sizes including body length (BL), whither height (WH), breast girth (BG), abdomen width (AW), hump height to the ground (HH), muzzle girth (MG), neck length (NEL), whither to pin length (WPL), tail length (TL), pelvic width (PW), abdomen to hump height (ABH) and the head length (HL) were measured. The body dimensions of the camels were recorded using a tape measure, and their body weight was measured using a scale. Subsequently, the data were analyzed using the principal component analysis and multiple regression methods. In order to fit the predictive models, the body weight of camels was introduced as the dependent variable, and the body size of camels was introduced as the independent variable. The analysis of regression models was performed using both one-variable and multivariable linear models, and the best models were selected to estimate the weight of camels based on their body dimensions through the stepwise method. The performance of the above models was evaluated by comparing their coefficients of determination (R2). Results: According to the results, the correlations between the body weight of camels and their different body dimensions, including BL, WH, BG, AW, HH, MG, NEL, WPL, TL, PW,ABH, and HL, were 0.93, 0.89, 0.89, 0.89, 0.94, 0.73, 0.89, 0.90, 0.80, 0.85, 0.89, and 0.79, respectively. The results showed that, among the six multiple regression models fitted to estimate the weight of fattening camels, model No. 6 - utilizing head length, body length, breast girth, neck length, muzzle girth, and whither height as predictive variables - had the lowest error (12.06) and the highest accuracy (0.92) compared to the other models. Additionally, the results showed that utilizing the first and second principal components, along with both of them in the model, could explain 82.1%, 3.73% and 85% of the variance in body weight, respectively. The weight of fattening camels was determined using principal component analysis with an accuracy of 0.93 and an error of 11.54. Conclusion: The results of the present study showed that in order to estimate the weight of fattening camels, the use of principal component analysis, in addition to simplifying predictive models, has higher efficiency and accuracy as well as less error compared to multiple regression, and this method can be a suitable alternative to the multiple regression method in predicting the weight of camels. [ABSTRACT FROM AUTHOR]

Tourism expenditures and receipts are an important component of many countries' balance of payments. Indeed, the determination of whether shocks to tourism expenditures and receipts are permanent or transitory in nature is relevant to understanding the appropriate policy response. This study introduces new panel stationarity tests that simultaneously model structural breaks and a common factor structure. The results from several panel stationarity tests including the new tests reject the null hypothesis of stationarity for the panel of 63 countries. Based on tests of stationarity with a common factor structure without structural breaks at the individual country level, the null hypothesis of stationarity is rejected in 24 and 26 countries with respect to per capita international tourism expenditures and tourism receipts, respectively. The inclusion of the Fourier approximation for smooth structural breaks alongside the common factor structure increases the number of rejections of the null hypothesis of stationarity to 48 and 54 countries for per capita international tourism expenditures and receipts, respectively. Hence, shocks to either tourism expenditures or receipts will likely be permanent in nature requiring a policy response in order to return tourism expenditures and receipts to their original trend path. JEL Codes: Z32, C33. [ABSTRACT FROM AUTHOR]