Principal Component Analysis Regression Research Articles

Spatial analysis of oil palm growth and soil properties in a plantation in Nigeria

Relationships between spatially correlated soil properties and crop performance at field scale are vital when planning specific site management for sustainable crop production. This study investigated the correlations and relationships between some soil properties and oil palm trunk diameter at breast height at the Oil Palm Plantation, Teaching and Research Farm, Ekiti State University, Ado Ekiti, southwest Nigeria. Soil samples were collected from 0 - 10 cm surface layer at eighty-one georeferenced points within the plantation to determine soil properties and oil palm trunk diameter at breast height. The soil properties and trunk diameter at breast height varied widely with particle density, soil pH, bulk density and field capacity showing least variability (CV < 12%), oil palm trunk diameter at breast height, soil organic matter, air capacity, soil texture, soil water content, permanent wilting point, total porosity, and available water showed moderate variability (12.0% < CV < 60.0%), while saturated hydraulic conductivity was highly variable (CV > 60%). Classical linear multiple regression showed that the sand, soil pH and bulk density could only explain 16% of the variability in DBH whereas the principal component regression analysis had explained about 72% of the variability in DBH. The minimum suitable sampling interval for spatially independent variables was 10 m (1 lag), while it was the sampling point (0 lag) for spatially dependent variables. The results could be used as baseline data for delineating soil and nutrient management zones (MZs) for the oil palm plantation and other crops in the study area.

Assessing User Contentment in Long-Distance Public Transport: An Analytical Approach Utilizing Principal Component and Regression Analysis

This study assesses the performance of long-route public transport services, considering passenger contentment as a key metric through the application of a passenger satisfaction approach. A self-rating questionnaire with a 5-point Likert scale was used to rate 16 different variables. The variables were selected after a literature review and consulting with relevant stakeholders. Data collected from com passengers were analyzed to determine the passengers’ comfort and satisfaction using Principal Components Analysis (PCA) and Regression Analysis. Variables with higher-than-average scores indicate that passengers are satisfied with that variable.16 variables with strong correlation among each other were collapsed into different component using PCA. Furthermore, the components extracted from PCA along with two extra variables that are age and sex were regressed to find the mean passenger satisfaction score. This research fills the gap by specifically targeting long-distance public transport in Nepal, an area that has not been extensively studied. It explores the perceptions of passengers regarding the facilities provided by long-route public transport services and identifies the major factors contributing to passenger satisfaction. Previous studies have often emphasized factors such as service quality, comfort, and safety in urban bus services, but there is a notable lack of research addressing the unique challenges and passenger experiences associated with long-distance bus travel. This study can offer theoretical assistance in devising relevant strategies to enhance the satisfaction level of public commuters.

NMR coupled with multivariate data analysis for monitoring the degradation of a formulated therapeutic monoclonal antibody.

The function of a protein is directly coupled to its higher-order structure (HOS). Deviations in this critical quality attribute (CQA) may be linked to a decrease in the efficacy and/or safety of the final therapeutic product. High-resolution nuclear magnetic resonance (NMR) spectroscopy has been recently highlighted for protein HOS characterization, thanks to its ability to capture small changes at the molecular and structural levels (primary, secondary, tertiary and quaternary structures). The present study was carried out to demonstrate the ability of NMR (1D 1H and 2D 1H-13C experiments) coupled with multivariate data analysis (PCA and PLS regression) to monitor the degradation of a formulated mAb-like protein and for the simultaneous quantification of related CQAs, i.e., potency, purity and impurities (aggregates and fragments). The results indicate that this approach could be applied to mitigate product quality risks during development, manufacturing and stability studies of mAb therapeutics.

Rural Transportation and its Impact on the Output and Income Generated from Cashew Production in Selected Rural Settlements in Kwara State, Nigeria

Rural transportation plays a crucial role in agricultural production and livelihood of farmers in developing countries. Information on its impact on cashew’s outputs and income is not clearly documented in the literature. Consequently, this study identified the types of roads and transport modes of cashew farmers, assessed the transport services patronized by cashew farmers, examined the annual outputs and income from cashew; and assessed the impact of transport on both in Kwara South Senatorial District Kwara State, Nigeria. Data used were obtained from 1,373 farmers systematically selected from thirty-six rural settlements in the study area. Responses were analysed using principal component analysis, regression analysis and correlation analysis. Results showed that tarred roads connected only about 44.4% of the settlements in the area. About 56.4% of respondents rated the transport services as good. Significant relationships were found between cashew output and; road condition (b = −0.151, p = 0.000) and; transport services (b = −0.097, p = 0.000), while, income from cashew was directly and significantly correlated with transport mode (r = 0.059, p < 0.05), road condition (r = −0.153, p < 0.05) and, transport services (r = −0.096, p < 0.05). The study concluded that variations in transport facilities only accounted for some level of spatial variations in both cashew’s output and income in the study area and, recommended improvements of transport facilities to enhance increase in farmers’ crop output and income to achieve improved rural livelihood.

Revealing Tourists’ Perceptions and Increasing the Likelihood of a Visit

Using the theory of planned behavior, this study looks at the factors that influence people's intentions to visit Croatia in the next five years, focusing on the perceptions of potential tourists from Germany, Austria, and Slovenia, the three most important tourism markets for Croatia (46,7 % of overnight stays in 2022). The study uses a diverse sample of 1200 respondents and employs principal component analysis, ANOVA, and regression analysis. The main results show that the perception of safety, nature and quality, facilities offered, level of education, country of origin, previous visits to Croatia, and frequency of trips abroad play an important role in the intention to travel to Croatia in the next five years. The respondents' attitudes towards sustainability aspects during the trip show nuanced relationships, with pro-environmental behavior having no significant influence, while pro-socio-economic behavior during the trip shows a negative correlation to visit. The research offers valuable perspectives for marketers and policymakers seeking to increase Croatia's attractiveness and competitiveness. It highlights the complex interplay of demographic, perceptual, and experiential factors that influence travel intentions.

Construction and evaluation of a model for efficient identification of photothermal sensitivity of tobacco cultivars based on agronomic traits

The photothermal sensitivity of tobacco refers to the degree to which tobacco responds to changes in light and temperature conditions in its growth environment, which is crucial for determining the planting area of cultivars and improving tobacco yield and quality. In order to accurately and effectively evaluate the photothermal sensitivity of tobacco cultivars, this study selected five cultivars and their hybrid combinations with significant differences planted under different ecological conditions from 2021 to 2022 as materials. The experiment was conducted in two locations with significant differences in temperature and light. We measured the agronomic traits and biomass of the experimental materials, and constructed an effective tobacco photothermal sensitivity evaluation model using principal component analysis, membership function, and regression analysis. The reliability of the model was evaluated by utilizing the photosynthetic characteristics, chlorophyll content, and antioxidant enzyme system activity of the experimental materials. The results showed that tobacco biomass is the most important principal component in agricultural traits, and the comprehensive evaluation model for tobacco photothermal sensitivity is: y = 0.4571y1 + 0.2406y2 + 0.1725y3, where the fitting coefficients R2 of y1, y2, and y3 are 0.945, 0.851, and 0.977, respectively; The photothermal sensitivity of the experimental materials was calculated using this model, and the comprehensive ranking of the 11 experimental materials is: G3 < G5 < G10 < G9 < G11 < G6 < G7 < G2 < G4 < G8 < G1. Conventional identification methods have found that G2, G4, G6, G7, G8, and G11 are sensitive materials, G3, G5, and G10 are insensitive materials, and G1 and G9 are intermediate materials. The consistency rate of the evaluation results of the two methods reached 90.91%. And there is a significant correlation between the agronomic traits selected in the model and the physiological indicators selected by conventional evaluation methods, providing a scientific basis for evaluating the light temperature sensitivity of tobacco cultivars using agronomic traits in this study. The results indicate that the photothermal sensitivity evaluation model established in this study provides an efficient, convenient, and reliable method for evaluating the photothermal sensitivity of tobacco.

The influence of natural factors on poverty in West Halmahera

Poverty remains one of the persistent social issues faced by governments in many regions, including coastal and small island communities. The poverty experienced in these areas is driven by a combination of natural, social, and economic factors. Dynamic environmental conditions, such as seasonal changes, extreme weather, wide geographic dispersion between sea and land, isolation due to limited accessibility, and vulnerability to natural disasters, often trap coastal populations in a cycle of poverty. Data from West Halmahera Regency indicates that poverty levels have shown little improvement up until 2023. This research focuses on a sample of 390 poor residents from the region, utilizing Confirmatory Factor Analysis (CFA) and Principal Component Regression Analysis (PCRA) to examine the factors affecting poverty. The findings reveal that within the Natural Capital variable (X1), the most significant indicators are the distance from the house to a clean water source (X4), with a coefficient of 0.894, the condition of the water source near the residence (X5), with a coefficient of 0.884, and accessibility to clean water sources (X3), with a coefficient of 0.89. West Halmahera, being a coastal and mountainous region, suffers from an unequal distribution of essential infrastructure, particularly in areas far from the district capital. Clean water infrastructure remains absent in many areas, and even in mountainous regions where water piping exists, geographic challenges hinder functionality. Addressing these issues requires collaboration between local governments, the private sector, universities, and the community. Solutions may include programs that incorporate local wisdom and focus on infrastructure development, sustainable water management, and environmentally friendly practices. Such initiatives could enhance access to clean water, improve the local economy, and ultimately reduce poverty in West Halmahera.

Rapid Method for Simultaneous Determination of γ-Oryzanol Compounds in Rice (Oryza sativa) Grains using UV-Vis Spectroscopy and Chemometrics

Rice (Oryza sativa) contains γ-oryzanol, which consists of 4 main compounds: Cycloartenol ferulate, 2,4-methylenecycloartanyl ferulate, campesterol ferulate, and β-sitosterol ferulate, that contribute to the health benefits of rice. This research aimed to develop a rapid method to determine the 4 major γ-oryzanol compounds in 45 varieties covering pigmented (black and red) and non-pigmented (white) rice grain. The method was developed by integrating UV-Vis spectroscopy with chemometrics, specifically principal component analysis (PCA) and partial least square (PLS) regression. Sampling was performed across the Indonesian archipelago collecting 180 samples, which comprises 60 samples for each type of rice in form of rice husk, bran, whole grain, and polished rice. The results of PCA on the spectroscopic data successfully identified distinction for the 3 types of rice attributable by different type and levels of chemical compound in the grain. White rice exhibited a characteristic absorption at 325 nm while in pigmented (red and black) rice showed a maximum absorbance at 280 nm, indicating the presence of different composition of chemical compounds. A reliable model to predict the 4 major γ-oryzanol compounds was established with R2 calibration, R2 cross-validation, and R2 prediction higher than 0.9 was obtained using the spectroscopic data in the range from 200 to 400 nm. However, the PLS modeling was unsuccessful for red and black rice samples most likely due to the interfering high absorption of red colored compounds. Compared to the existing techniques for analyzing individual compounds of the γ-oryzanol by high-performance liquid chromatography, the newly developed approach using UV-Vis spectroscopy combined with chemometrics is more practical, faster, and cost-efficient and mainly, solvent and residues free. HIGHLIGHTS A rapid method was developed to determine 4 major γ-oryzanol compounds in rice. This method integrates UV-Vis spectroscopy with PCA and PLS regression chemometrics. PCA successfully identified the distinction among white, red, and black rice types. Reliable PLS models were established for white rice with R2 values above 0.9. The developed approach is more practical, faster, and residue-free than HPLC. GRAPHICAL ABSTRACT

Revolutionizing Early Detection: The Influence of Machine Learning in Esophageal Adenocarcinoma Diagnosis

Machine learning is an analysis technique of big data that uses computers and mathematical models to develop algorithms. These algorithms are applied in many situations and one of the most common uses is in developing prediction models. Machine learning is the activity of making prognosis of other data points which are related to the self using key factors from a dataset. It is designed to effectively predict the dysplasia within a given setting using machine learning algorithms on a dataset with primary focus on Barrett’s Esophagus. The study investigates the use of five classification methods: As per the classification, the techniques identified include Naïve Bayes, Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors, and Logistic Regression. Barrett’s Esophagus Below are the methods applied using and without Principal Component Analysis (PCA) on the special dataset of Barrett’s esophagus. The primary objective is the identification of the effectiveness of each algorithm by comparing their functional characteristics such as F1 score, recall value, accuracy percentage, precision ratio, and specificity coefficient. From the results obtained from the experiments, two algorithms that yield good accuracy for predicting Barrett’s esophagus in this dataset are the SVM algorithm with PCA and Logistic Regression algorithm with PCA and both algorithms returned review ratings of 1. undefined This career has earned a maximum Score of 0.1000, Naïve Bayes perform well in the assessment with 0.964 of accuracy, it followed by K-Nearest Neighbors with 0.9349 and Logistic Regression with 0.923.

Factors affecting community pharmacists' capacity to deliver pharmacy services in Zhengzhou, China: a cross-sectional study.

To assess the factors that affect the capacity of community pharmacists to deliver pharmacy services (PSs) in Zhengzhou, China. An online, cross-sectional survey was conducted. A stratified sampling method was used to select community pharmacists for questionnaires. A total of 124 community pharmacists from various districts of Zhengzhou were included in this study. Principal component regression analysis showed that cumulative hours of continuing education (CE), job satisfaction, career prospects, awareness of the need for CE and income had a positive impact on pharmacists' capacity to deliver PS, with a gradually decreasing degree of influence. Most pharmacists felt that their capacity to provide PS met the basic public requirements. Cumulative CE hours had the most significant effect on pharmacists' capacity to provide PS, while income had the least effect. The government, grassroots community pharmacies, pharmacists and the public should formulate timely and targeted measures to enhance the PS capacity of community pharmacists.

Principal component regression analysis to predict lifetime milk yield of Jaffarabadi buffaloes

The study aims to devise most appropriate prediction model for lifetime milk production of Jaffarabadi Buffalo, based on principal components formulated on initially expressed lactation records as predictors. Lactation milk yield, lactation period and peak milk yield records of first, second and third lactations of animals under study were used of 24 years (1987 to 2010). Principal components (PCs) were derived from data set using principal component regression analysis (PCRA), the principal components were used as predictors for predicting lifetime milk yield (LTMY). Multiple linear regression models were fitted to identify the best fitted model for prediction of lifetime milk yield with the first principal component to all principal component as a predictor. The equation LTMY = 7825.8768+2.8118 (PC1) - 13.7098 (PC2) - 599.0908 (PC3) + 3.0266 (PC4) - 8.8196 (PC5) - 257.9315 (PC6) + 2.6042 (PC7) explained 98.9% variation in the estimated values with adjusted R2= 59.09% variation in the estimated values. The curve estimation analysis showing the appropriateness of first seven principal components as predictor was the most appropriate model for lifetime milk yield. These prediction equations may be helpful in selection at an early stage of Jaffarabadi Buffalo based on early part lactation records.

Regional analysis of specific suspended sediment loads in northern Iran using multivariate statistical techniques

ABSTRACT Predicting suspended sediment loads in areas without detailed measurements, or with only short-term records, is crucial for the sustainable management of water resources. This study aimed to establish the relationships between specific suspended sediment loads and the characteristics of 23 sub-basins within the Haraz-Neka River basin, in Iran, to create regional models to estimate the sediment loads. To achieve this, several analytical methods were used, including cluster analysis, principal component analysis, principal component and classification analysis, and general linear modelling. Among these, the principal component analysis regression model was the most effective for estimating suspended sediment loads in the clusters. The principal component and classification analysis revealed that the best predictor was the first principal component, which strongly correlated with the minimum and mean elevation of the sub-basins. The general linear model regression showed the best overall performance for estimating regional suspended sediment loads in the study area.

Effect of biotic and abiotic factors on the population dynamics of Nilaparvata lugens in the middle Gangetic Plains of India

Aim: To investigate the effect of biotic and abiotic factors on the population dynamics of brown planthopper Nilaparvata lugens (Stal) (Hemiptera: Delphacidae) in the middle Gangetic plains of India. Methodology: Field trials were conducted in the middle Gangetic plains of India at Agricultural Research Farm, B.H.U., Varanasi, Uttar Pradesh, India during Kharif season of 2018 and 2019 on the variety Swarna sub-1 to study the population dynamics and certain biotic and abiotic interaction with brown planthopper. Results: During Kharif 2018 and 2019, the highest mean number of brown planthopper population (45.00 ± 2.31 and 39.33 ±1.82 nos., respectively) were recorded during September in both seasons. Abiotic factors such as rainfall (r = - 0.556), relative humidity in the morning (r = 0.476), maximum temperature (r = 0.511), and sunshine hour (r = - 0.546) influenced planthopper population dynamics, and predictions were made with reasonable accuracy (R2 = 0.89) using the principal component regression analysis technique. Crop phenology was the most influential biotic factor on pest population density. The crop's booting stage had the highest incidence of planthoppers. Three different predatory populations (wolf spider, damselfly and ladybird beetle) were observed to feed on planthoppers in Kharif 2018 and four different predatory populations (wolf spider, damselfly, ladybird beetle and green mirid bug) were observed to feed on planthoppers in Kharif 2019, resulting in a reduction in crop pest density. Interpretation: These findings can be used to develop the precise management strategies for brown planthopper in the rice ecosystem. Key words: Abiotic, Biotic factors, Brown planthopper, Crop phenology, Middle Gangetic Plains, Natural enemies, Rice

'JSDNE': A novel R package for estimating age quantitatively with the auricular surface by Dirichlet normal energy

Age estimation plays a significant role in forensic anthropology and bioarchaeology. However, widely-used traditional methods involving macroscopic observation suffer from subjectivity and statistical bias. The present research aims to minimize both issues by applying computational and mathematical approaches. A laser scanner was used to reconstruct 890 auricular surfaces of adult individuals from three known-age European skeletal collections. Dirichlet Normal Energy (DNE) was applied to assess the curvature of the auricular surface and its relationship with known age-at-death. Ten variables had high correlations, including total DNE per Total polygon faces, Mean value of DNE on apex, proportion of polygon faces with DNE of less than 0.0001 and proportion of polygon faces with DNE of over 0.6. The variables were used to develop age prediction models which are freely available in a novel R package, JSDNE. The package predicts age mathematically, objectively, and user-independently. It includes three functions: principal component quadratic discriminant analysis (PCQDA), principal component regression analysis (PCR), and principal component logistic regression analysis (PCLR), which produce age estimates with 91%, 76%, and 92.9% levels of accuracy, respectively. JSDNE package (https://cran.r-project.org/package=JSDNE) can be downloaded automatically using install.packages("JSDNE"). The detailed code and the raw data of this study are openly available at https://github.com/jisunjang19/cran-JSDNE, doi: 10.5281/zenodo.12708779 or ‘JSDNE’ package.

Beyond Traditional Banking: Exploring the Influence of Technology on Depositors' Decision-Making in Bank Selection

Purpose: The banking sector is crucial for the economy, and with growing competition, banks must pinpoint factors influencing depositors' choices. This study aims to uncover how technology adoption by commercial banks plays a role in depositors' decisions. Methodology: A hybrid approached is attempted in this study to explore the phenomenon of interest. In the qualitative phase, focus group discussion was conducted on two heterogeneous groups, each with ten participants. The recorded audio was further transcribed using speech-to-text modules. A natural language processing tool has been used to identify words or phrases that are semantically related or have similar meanings to depositors’ sentiment analysis. The quantitative phase involved descriptive statistics, principal component analysis and regression analysis to explore insight numerically. Findings: Respondents expressed a generally positive sentiment toward technology in banking, influencing their selection decisions, in the qualitative phase. In the quantitative phase, significant technological aspects affecting depositors' post-selection evaluations are identified. Findings from both approaches converge on the same line. Practical implications: Findings of this study may aid decision-makers in banking industry to articulate distinct technological aspect implied in banking service delivery and better design service offerings to attract new depositors and gain competitive advantages. Originality/Value: This is the first study to focus solely on technology to explore how technology influences depositors' bank choices in the context of Bangladesh. Research Limitations: Significant parts of several conversations were sorted out for sentiment analysis. If it was possible to consider all relevant statements of the focus group discussion for sentiment analysis, it might convey a more pertinent sentiment score.

Sodium Bicarbonate Tolerance During Seedling Stages of Maize (Zea mays L.) Lines

ABSTRACT(1) Soil alkalinization and salinization represent a growing global challenge. Maize (Zea mays L.), with its relatively low tolerance to salt and alkali, is increasingly vulnerable to saline‐alkali stress. Identifying maize genotypes that can withstand salinity and alkalinity is crucial to broaden the base of salt‐alkali‐tolerant maize germplasm. (2) In this study, we screened 65 maize germplasm resources for alkali stress using a 60 mM NaHCO3 solution. We measured fifteen morphological and physiological indices, including seedling height, stem thickness, and leaf area. Various analytical methods—correlation analysis, principal component analysis, subordinate function analysis, cluster analysis, stepwise discriminant analysis, and ridge regression analysis—were used to assess the seedling alkali tolerance of these maize germplasm resources. The physiological indices of six tested maize varieties were analyzed in greater detail. (3) The findings revealed complex correlations among traits, particularly strong negative associations between conductivity and root traits such as length, volume, surface area, diameter, and number of branches. The 15 evaluation indices were reduced to 7 principal components, explaining 77.89% of the variance. By applying affiliation functions and weights, we derived a comprehensive evaluation of maize seedling alkali tolerance. Notably, three germplasms—Liang Yu 99, Bi Xiang 638, and Gan Xin 2818—demonstrated significant comprehensive seedling alkali tolerance. Cluster analysis grouped the 65 maize germplasm resources into four distinct categories (I, II, III, and IV). The results of the cluster analysis were confirmed by multiclass stepwise discriminant analysis, which achieved a correct classification rate of 92.3% for 60 maize genotypes regarding alkalinity tolerance. Using principal component and ridge regression analyses, we formulated a regression equation for alkali tolerance: D‐value = −1.369 + 0.002 * relative root volume + 0.003 * relative number of root forks + 0.006 * relative chlorophyll SPAD + 0.005 * relative stem thickness + 0.005 * relative plant height + 0.001 * relative conductivity + 0.002 * relative dry weight of underground parts. Under sodium bicarbonate stress, morphological indices and germination rates were significantly reduced, germination was inhibited, photosynthetic pigment levels in maize leaves decreased to varying degrees, and the activities of peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT) significantly increased. Alkali stress markedly enhanced the antioxidant enzyme activities in maize varieties, with alkali‐resistant varieties exhibiting a greater increase in antioxidant enzyme activities than alkali‐sensitive varieties under such stress. (4) By screening for alkali tolerance in maize seedlings, the identified alkali‐tolerant genotypes can be further utilized as suitable donor parents, thereby enhancing the use of alkali‐tolerant germplasm resources and providing theoretical guidance for maize cultivation in saline‐alkaline environments.

Assessment of the Addition of Cricket (Acheta domesticus) Powder to Chickpea (Cicer arietinum) and Flaxseed (Linum usitatissimum) Flours: A Chemometric Evaluation of Their Pasting Properties

Edible insects have been evaluated as an alternative and sustainable source of protein because of their nutritive and functional properties for humans and domestic animals. The objective of this study was to assess the use of chemometric [principal component analysis (PCA) and partial least squares (PLS)] combined with Rapid Visco Analyser (RVA) profiles to evaluate the addition of cricket powder (CKP) to chickpea (CPF) and flaxseed (FxF) flours. The results of this study showed that the addition of CKP powder to both CPF and FxF flours affects the pasting properties of the samples; in particular, a reduction in the peak (PV) and final viscosity (FV) was observed. The use of chemometric data techniques such as PCA and PLS regression allowed for a better interpretation of the RVA profiles. Both PCA and PLS regression allowed to qualitative and quantitatively identify the addition level of CKP powder to CPF and FxF flour samples. Differences in the PLS loadings associated with the RVA profile due to the addition of cricket powder were observed. The development of these methodologies will provide researchers and the food industry with better tools to both improve and monitor the quality of ingredients with functional properties as well as to further understand the use of insects as alternative sources of protein.

Estimation of Regional Background Ozone in Henan Province from 2019 to 2021

Clarifying the regional transmission and local generation contributions of ozone （O3） is important for controlling O3 pollution. To quantify the regional background and spatial-temporal variations of O3, a comprehensive study was conducted using multiple methods including principal component analysis （PCA） and TCEQ, with Henan Province as a case study. Based on monitoring data from 59 national sites in Henan Province during 2019-2021, four methods were employed to estimate the regional background of O3. Method 1 was the traditional method, performing O3 univariate-multisite PCA analysis. Method 2 was a multivariate-single-site PCA analysis considering nitrogen dioxide and meteorological conditions as constraints. Method 3 combined PCA and multiple linear regression （MLR） to determine regional background contributions, drawing on the idea of source apportionment. Method 4 was the TCEQ method that used the lowest measured O3-8h concentration as the regional background. The estimation results showed that Methods 1 and 2 were basically equal, and Methods 3 and 4 were approximately 37-60 μg·m-3 lower than Method 1. From 2019 to 2021, the changes in regional background ρ（O3-8h） estimated by Methods 1-4 were 1.6, -13.4, 5.9, and -3.5 μg·m-3, respectively. The average estimations derived from multiple methods showed that the regional background ρ（O3-8h） in Henan Province from 2019 to 2021 concentrations were 82.0, 79.0, and 79.7 μg·m-3, accounting for 75.9%, 76.4%, and 78.7% of the total regional O3-8h, respectively. The regional background O3-8h estimated by the four methods showed obvious seasonal changes, characterized by summer &gt; spring &gt; fall &gt; winter.

Exploring Spatial Variability and Yield-Determining Factors in Cotton Production: A Multivariate Analysis

ABSTRACT The yield of cotton crops is severely affected by the soil’s chemical characteristics. This research probes the influence of soil properties on cotton yield, with a focus on assessing within-field spatial variability for advanced site-specific management in cotton production. Geostatistical analysis and semivariograms were conducted to examine the spatial variability of soil chemical properties. While multivariate analysis was employed to identify key yield-determining factors. Principal Component Analysis (PCA) was employed to explore leading soil indicators, as opposed to using a pre-defined set of indicators, and to analyze the spatial variability of soil properties. The soil characteristics encompassed saturation, soil pH, electrical conductivity (EC), organic matter (OM), phosphorous (P), potassium (K), calcium carbonate (CC), and micronutrients such as zinc (Zn), copper (Cu), iron (Fe), manganese (Mn), and boron (B). For this study, one thousand soil samples were meticulously collected from the Multan district, facilitated by Global Positioning System (GPS) devices. The PCA revealed that the first four PCs accounted for 57% of the total variation. The first PC, Cu, B, Mn, OM, and Fe emerged as the most influential variables, while the second PC exhibited a high correlation with CC, pH, and saturation. The third PC highlighted the dominant role of K and P in its construction, whereas the fourth PC was primarily shaped by Zn. The Principal Component Regression (PCR) analysis further revealed that EC, K, P, and Zn had positive impacts on cotton yield, while pH, Cu, Fe, Mn, and B had negative impacts.

Perbandingan Analisis Komponen Utama Robust Minimum Covarian Determinant dengan Least Trimmed Square pada Data Produk Domestik Regional Bruto

Regression analysis is a method to examine the relationship between variables and determine their influence. However, the problem of multicollinearity often arises in linear regression analysis and can cause interpretation problems. To handle multicollinearity, Principal Component Analysis (PCA) is used. However, this method has a weakness when the data contains outliers. Therefore, it was developed into robust PCA using the Minimum Covariance Determinant (MCD) method and the Least Trimmed Square (LTS) estimation method. This study uses Gross Regional Domestic Product data in Indonesia in 2020, which has problems with multicollinearity and outliers. This data is modeled using two robust PCA methods, namely MCD and LTS. The robust PCA model with MCD has an adjusted value of 87.87% and an MSE value of 0.0700. However, in the robust PCA regression model with LTS, the adjusted value is 98.93% and the MSE value is 0.0325. Thus, the effective method in handling multicollinearity and outliers is the LTS method because it shows better results.