Principal Component Analysis Regression Research Articles

Comparing vegetative growth patterns of cultivated (Daucus carota L. subsp. sativus) and wild carrots (Daucus carota L. subsp. carota) to eliminate genetic contamination from weed to crop

Wild carrot is a problematic weed that can threaten the genetic purity of cultivated carrots by hybridization. Wild carrots must be controlled before flowering to avoid the undesirable crossing with cultivated carrots. Understanding wild carrot's vegetative growth pattern helps formulate sustainable weed management practices. However, little is known about the vegetative growth patterns of wild and cultivated carrots. A pot experiment was carried out to compare and model the vegetative growth pattern of different morphological traits in both wild and cultivated carrots. This study was executed in a glasshouse located in Palmerston North, New Zealand. A factorial randomized complete block design (RCBD) with two factors and four replications was used. The first factor was assigned to the carrot genotype (cultivated and wild) and the second factor to length of juvenile stages (12-weeks, 8-weeks, and 4-weeks). Plant height, leaf number, shoot fresh and dry weight, root fresh and dry weight, root diameter and root length were measured. Data were analysed using analysis of variance (ANOVA), principal component analysis (PCA), correlation, and regression analysis. At the 8-week juvenile stage (9-11 leaves stage), wild carrot’s shoot and root characteristics exhibited rapid growth. Correlation analysis indicated positive and significant (p<0.05) correlations between above and below-ground morphological traits. PCA showed that morphological characteristics, except plant height, can be used to distinguish wild and cultivated carrots. To predict the vegetative growth pattern of most of the morphological traits of wild and cultivated carrots, power regression models were selected based on higher R2 and adj-R2 values and lower values of RMSE, AIC and BIC. The study showed wild carrots grew more quickly than cultivated carrots during the vegetative phase. It is recommended that appropriate weed management practices, such as hoeing, tilling, hand pulling, or herbicide spraying, be implemented before wild carrot leaf stages 9–11.

Combining principal component analysis and logistic regression for multifactorial fall risk prediction among community-dwelling older adults

Falls require comprehensive assessment in older adults due to their diverse risk factors. This study aimed to develop an effective fall risk prediction model for community-dwelling older adults by integrating principal component analysis (PCA) with machine learning. Data were collected for 45 fall-related variables from 1630 older adults in Taiwan, and models were developed using PCA and logistic regression. The optimal model, PCA with stepwise logistic regression, had an area under the receiver operating characteristic curve of 0.78, sensitivity of 74 %, specificity of 70 %, and accuracy of 71 %. While dimensionality reduction via PCA is not essential, it aids practicality. Our framework combines PCA and logistic regression, providing a reliable method for fall risk prediction to support consistent screening and targeted health promotion. The key innovation is using PCA prior to logistic regression, overcoming conventional limitations. This offers an effective community-based fall screening tool for older adults.

Trends in violent offending among people with schizophrenia: A 10-year ecological study

Violent offending committed by people with schizophrenia has been a public concern. The present study aims to examine the incidence of violent offending among people with schizophrenia and its correlations with mental health resources and economic factors. In this study, an examination of violent offending by people with schizophrenia and those identified as not criminally responsible on account of mental disorder (NCRMD) between 2010 and 2019 in China's Hunan province was undertaken. Principal component analysis (PCA) and regression analyses were used to explore the association of violent offending in people with schizophrenia and those identified as NCRMD with violent offending in the general population, mental health medical resources, and provincial GDP. Between 2010 and 2019, a total of 2,093 people with schizophrenia committed violent offending in Hunan province, including 1,374 (65.6%) cases identified as NCRMD. Over the period, the incidence of violent offending in people with schizophrenia and those identified as NCRMD has been decreasing. The incidences were positively correlated with the incidence of violent offending in the general population and negatively associated with mental health resources and provincial GDP. These findings may be valuable in helping to develop strategies for violence prevention and risk management for people with schizophrenia.

Morbidity pattern among tribal farmers in a selected district of Tripura, India

Morbidity pattern among tribal farmers in a selected district of Tripura, India

The Role of Strategic Planning and Productivity in Predicting Competitive Advantage

Studies have been conducted to determine the nature of the relationship between strategic planning and competitive advantage, with some studies presenting that strategic planning has an impact on the organisation's competitive advantage, while other studies present that strategic planning has no impact on competitive advantage. Therefore, this research was conducted to settle this existing inconclusive debate by including a contingency variable as a mediator of this relationship using data collected from management employees of TAZARA. The research study contributes to the literature by determining the mediating effect of productivity on the relationship between strategic planning (SP) and competitive advantage. Reliability, model fit and validity were tested using factor analysis, principal component analysis and regression analysis using Jamovi software. The results of the study show that there is a positive significant relationship between strategic planning (SP) and competitive advantage, and also between productivity and competitive advantage. Productivity was found to partially mediate the relationship between strategic planning and competitive advantage. The results of this study would help decision makers to focus more on productivity when implementing strategic planning in order to survive this tense, fierce competition of the 21st century. Replication of this study in other industries is recommended. Future studies should also include other moderating and/or mediating variables.

A STATISTICAL ANALYSIS OF GEOLOGICAL AND ENGINEERING PREDICTORS OF OILFIELD PERFORMANCE RESPONSE: A CASE STUDY OF OILFIELDS ON THE UK CONTINENTAL SHELF

Oilfield production is controlled by a wide range of geological and engineering parameters, many of which are at least partially interrelated. This paper uses multivariate statistical methods (principal component analysis, regression analysis and analysis of variance) to determine how these parameters are related, and which of them are most significant in controlling and predicting oilfield performance. The analysis is based on a database of publicly available oilfield data from the United Kingdom Continental Shelf (UKCS), from which a series of geological, engineering and fluid‐related control variables from 136 fields were pre‐processed and analyzed. This dataset is a subset of a much wider project database for UKCS oil, gas and condensate fields. For this study, the project database was divided into two datasets: a first dataset with 10 parameters from 136 fields, and a second, more detailed dataset with 27 parameters from 38 fields. Both datasets were analysed using principal component analysis in order to investigate possible correlations between numerically/statistically interrogable predictor variables such as porosity, permeability, number of production wells, gas‐oil ratio and reservoir temperature. A regression analysis was then carried out on the predictor variables in order to obtain a ranking of predictability (i.e. how indicative a predictor is of a particular outcome) and sensitivity (how sensitive an outcome is to slight changes in a predictor) in relation to recovery factor based on R‐squared and regression coefficient values. The results showed that key variables from the principal component analysis included field size, number of production wells, PVT, gross depositional environment and reservoir quality. High‐ranking parameters of predictability and sensitivity from the regression analysis were found to include API, net‐to‐gross, porosity and reservoir depth. These results are consistent with previous studies and suggest that it should be possible to forecast oilfield recovery based on only a few selected input variables. As a preliminary test of forecasting ability of the variable permutations put forward, a best‐subsets multiple regression was carried out using a statistical software package and yielded results which corroborated the main findings.

Low-carbon-oriented commercial district urban form optimization and impact assessment analysis

Commercial buildings have become one of the main sources of carbon emissions. However, the current research on urban form optimization has lacked analyses of the low-carbon indicators of buildings. With the Beijing commercial district as an example, a low-carbon-oriented urban form optimization path is established in this study, and a new method of urban form optimization is developed from a low-carbon perspective. With the help of the parametric platform and multiobjective optimization algorithm, a dynamic optimization model of urban morphology based on parametric modeling, numerical simulation and algorithm optimization is developed, and the morphological characteristics of low-carbon commercial blocks in Beijing are assessed with a Pareto-optimal solution set. Through principal component regression analysis, the correlations between urban form factors and low-carbon evaluation indicators are discussed. The study shows that the multiobjective optimization algorithm effectively optimizes the low-carbon indicators of buildings at the block scale. The urban forms obtained from the Pareto solution set display several commonalities reflected from three perspectives: building type, vertical dimension and building orientation. It is found that the optimal block layout form reduces the building carbon emission intensity and the building outer skin radiation difference, and the renewable energy-based carbon reduction is large. The urban form factors that have a significant impact on the carbon emission intensity of buildings include the maintenance coefficients, the average area to perimeter ratio, and the sky view factor, with respective impact coefficients of 0.301, −0.309, and −0.319.

TV뉴스 정보특성이 관광동기, 여행만족도, 행동의도에 미치는 영향 연구

Purpose: TV news contents spread out via various media instantly and simultaneously. Focusing on those who are exposed to TV news contents on Gangneung, the city of cultural tourism, this study aims to identify the impact of TV news contents on travel motivation (i.e., intrinsic (PUSH) and the destination attraction factors (PULL)), travel satisfaction, and behavioral intention&#x0D; Methods: Using quota sampling, 656 data were collected from Gangneung visitors. Several statistical analyses were conducted using SPSS 28.0, including frequency analysis, reliability analysis, principal component analysis, correlation analysis, and regression analysis.&#x0D; Results: The results revealed the following key findings. Firstly, among the factors of timeliness, reliability, and usefulness, the usefulness factor showed the most significant influence in both PUSH and PULL factors, indicating that positive or negative news coverage about the region alters travelers' destination selection and decision-making. Secondly, among the PUSH and PULL factors, sociability, pleasure-seeking, natural scenery, and convenience had a significant impact on travel satisfaction, while escapism and entertainment did not affect travel satisfaction. Thirdly, travel satisfaction significantly influenced the intention to revisit and TV news viewing intention. Lastly, timeliness and usefulness of TV news had a significant impact on viewing intention, but reliability did not show such influence.&#x0D; Conclusion: Positive or negative news coverage about the region in TV news affects the development of the local tourism industry. In case of small local cities with a weak financial independency, the utilization of news media is an effective avenue to tourism promotion.

Optimizing Levothyroxine Replacement: A Precision Dosage Model for Post-Thyroidectomy Patients.

Thyroidectomy is commonly performed for benign or malignant thyroid tumors, often resulting in hypothyroidism. Levothyroxine (LT4) supplementation is crucial to maintain hormone levels within the normal range and suppress TSH for cancer control. However, determining the optimal dosage remains challenging, leading to uncertain outcomes and potential side effects. We analyzed clinical examination data from 510 total thyroidectomy patients, including demographic information, blood tests, and thyroid function. Using R, we applied data preprocessing techniques and identified 274 samples with 98 variables. Principal Component Analysis, correlation analysis, and regression analysis were conducted to identify factors associated with optimal LT4 dosage. The analysis revealed that only eight variables significantly influenced the final satisfactory dosage of LT4 in tablets: Benign0/Malignant1 (benign or malignant), BQB (electrophoretic albumin ratio), TP (total protein), FDP (fibrin degradation products), TRAB_1 (thyroid-stimulating hormone receptor antibody), PT (prothrombin time), MONO# (monocyte count), and HCV0C (hepatitis C antibody). The resulting predictive model was: . Parameters such as benign/malignant status, TRAB_1, and BQB ratio during medication can serve as observational indicators for postoperative LT4 dosage. The calculated linear model can predict the LT4 dosage for patients after thyroidectomy, leading to improved treatment effectiveness and conserving medical resources.

Interpreting Image Patterns for Agricultural Sprays Using Statistics and Machine Learning Techniques

The atomization of liquid spray solutions through nozzles is a mechanism for delivering many pesticides to the target. The smallest drop sizes (&lt;150 μm) are known as driftable fines and have a propensity for wind-induced convection. Many agricultural applications include oil-in-water formulations. The experimental metrics obtained from spray images of these formulations include the distance from the nozzle origin to the drop centroid once a drop has formed; the hole location and surface area for holes that form in the liquid sheet (all hole areas approximated as polygons); the angles formed between polygon segments (whose vertices are represented as boundary points); and the ligament dimensions that form from intersecting holes, such as the ligament aspect ratio (R/L), ligament length (L), and ligament radius (width), along with the number of drops a ligament breaks up into. These metrics were used in a principal component regression (PCR) analysis, and the results illustrated that 99% of the variability in the response variable (DT10) was addressed by 10 principal components. Angles formed by the colliding holes, hole distance from the nozzle, drop distance, hole number, ligament number, and drop number were negatively correlated to the atomization driftable fine fraction, while hole area, ligament distance, ligament area, and boundary area were positively correlated. Thus, to decrease/minimize driftable fines, one needs to increase the negatively correlated metrics.

The Determinant of Demand and Supply to Increase Tourism Visit Sustainably by Using Principal Component Regression Analysis

Objectives: This research is entitled Determinants of Demand and Supply to Increase Visits in a Sustainable Way Using Principal Component Regression Analysis. Tourism is important where Malang Regency has attractive natural resource potential, and a very strategic geographical location surrounding two cities, namely Malang City and Batu City, surrounded by western and southern routes, and has an interesting culture so that tourism development is different. from the other. Visits of tourists, especially foreign tourists who have been unstable in the last two years have decreased. Limited use of technology in management and development.&#x0D; &#x0D; Method: Research data collection technique with a quantitative descriptive method with the Principal Component Regression Analysis tool. Meanwhile, supply tourism has a very high appeal, such as an attractive tourist attraction.&#x0D; &#x0D; Results: Based on the aspect of accessibility, they tend to have important infrastructure and access but have not been managed optimally, besides that there are still many traditional promotions and management that have caused tourist visits to be not optimal and the quality of human resources still needs to be improved.&#x0D; &#x0D; Conclusions: Based on the research results, it is known that in terms of tourism supply, the biggest factors influencing tourist attractions are attractions and amenities. Meanwhile, from the tourism demand side, these are preference and motivation factors which are influenced by changes in consumer behavior. The results of this study are expected to be input in developing tourism in Malang Regency which can increase tourist visits thereby increasing economic growth and sustainable community welfare.

The Sailing Boat Price Study Based on Principal Component Regression Analysis

This paper conducts research on the pricing of second-hand sailboats under certain assumptions. Initially, a substantial amount of potentially relevant indicators and data related to sailboat pricing were collected from the internet. The Pearson correlation coefficient was used to investigate the correlation between these indicators and to filter them. Due to the distinctive characteristics of sailboats, they were categorized into monohull sailboats and catamarans for separate analysis. Subsequently, the processed data were used for price prediction using a BP neural network, yielding preliminary results. To obtain an explicit method for calculating sailboat prices, this paper employed principal component analysis to calculate the pricing objective function for both types of sailboats. This led to the derivation of two multivariate linear regression equations, which were subjected to significance testing. The results indicate that the objective functions obtained in this paper can serve as references for pricing second-hand sailboats.

Neural signatures of emotion regulation

Emotional experience is central to a fulfilling life. Although exposure to negative experiences is inevitable, an individual’s emotion regulation response may buffer against psychopathology. Identification of neural activation patterns associated with emotion regulation via an fMRI task is a promising and non-invasive means of furthering our understanding of the how the brain engages with negative experiences. Prior work has applied multivariate pattern analysis to identify signatures of response to negative emotion-inducing images; we adapt these techniques to establish novel neural signatures associated with conscious efforts to modulate emotional response. We model voxel-level activation via LASSO principal components regression and linear discriminant analysis to predict if a subject was engaged in emotion regulation and to identify brain regions which define this emotion regulation signature. We train our models using 82 participants and evaluate them on a holdout sample of 40 participants, demonstrating an accuracy up to 82.5% across three classes. Our results suggest that emotion regulation produces a unique signature that is differentiable from passive viewing of negative and neutral imagery.

Soft sensor for in-line quality control of turning processes based on non-destructive testing techniques and advanced data fusion

AbstractThis study describes the systematic process of training, testing, and validating a soft sensor designed for quality control of a turning process on components made of AISI 4140 steel. The soft sensor allows product quality to be predicted and unfavorable surface conditions to be identified, in particular the appearance of a phenomenon known as “White Layer”, often characterized in the case of AISI 4140 steel by an ultra-fine-grained microstructure (UFG). Basis of the soft sensor is a data fusion supported by non-destructive testing techniques (NDT), particularly micromagnetic methods (3MA). A critical part of this work is to address challenges such as lift-off compensation and in-process detection using 3MA. The application of machine-learning techniques, including Principal Component Analysis (PCA) and regression analysis, is detailed. These techniques result in robust models capable of detecting the occurrence of the White Layer phenomenon while minimizing the influence of measurement setup variations and process disturbances. In addition, the study demonstrates the integration of NDT into the machining process which drives the soft sensor and allows suitable adjustments of the process parameters. The data-driven soft sensor approach demonstrates a possible In-Line control system and discusses different control theories and their respective advantages and disadvantages. This system can effectively set targeted surface conditions in real time during the turning process.

PCA and logistic regression in 2-[18F]FDG PET neuroimaging as an interpretable and diagnostic tool for Alzheimer’s disease

Objective. to develop an optimization and training pipeline for a classification model based on principal component analysis and logistic regression using neuroimages from PET with 2-[18F]fluoro-2-deoxy-D-glucose (FDG PET) for the diagnosis of Alzheimer’s disease (AD). Approach. as training data, 200 FDG PET neuroimages were used, 100 from the group of patients with AD and 100 from the group of cognitively normal subjects (CN), downloaded from the repository of the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Regularization methods L1 and L2 were tested and their respective strength varied by the hyperparameter C. Once the best combination of hyperparameters was determined, it was used to train the final classification model, which was then applied to test data, consisting of 192 FDG PET neuroimages, 100 from subjects with no evidence of AD (nAD) and 92 from the AD group, obtained at the Centro de Diagnóstico por Imagem (CDI). Main results. the best combination of hyperparameters was L1 regularization and C ≈ 0.316. The final results on test data were accuracy = 88.54%, recall = 90.22%, precision = 86.46% and AUC = 94.75%, indicating that there was a good generalization to neuroimages outside the training set. Adjusting each principal component by its respective weight, an interpretable image was obtained that represents the regions of greater or lesser probability for AD given high voxel intensities. The resulting image matches what is expected by the pathophysiology of AD. Significance. our classification model was trained on publicly available and robust data and tested, with good results, on clinical routine data. Our study shows that it serves as a powerful and interpretable tool capable of assisting in the diagnosis of AD in the possession of FDG PET neuroimages. The relationship between classification model output scores and AD progression can and should be explored in future studies.

THE MODERATING EFFECT OF IMPORTANT INNOVATIONS ON THE RELATIONSHIP BETWEEN QUALITY RESULTS AND PRODUCTIVITY

There are some empirical studies that have revealed that quality has a positive significant influence on productivity. However, very few considered including contingency variables to bring more insights on the nature of this relationship. This study therefore attempted to investigate further the impact of quality results on productivity by including important innovations as the moderating variable to bring more insights on the nature of this relationship by collecting sample data from management employees of Tanzania Zambia Railway Authority. The model fit, validity and reliability were verified by Principal Component Analysis, Factor Analysis and Regression Analysis with the help of the Jamovi software. The results of the study present a significant positive relationship between quality results and productivity. The results also reveal that important innovations moderate the association between quality results and productivity. This study presents a theoretical contribution to literature by being the first study to empirically test the relationship between quality results and productivity with important innovations as a moderating variable. The study recommends decision makers in the organizations to always ensure that, they encourage important innovations in their organizations while paying attention to quality results. It is also hoped that this study will be replicated to other sectors and that the future studies will include other contingency variables as either moderating or mediating variables to further bring more insights on this relationship.

Efficient and Accurate Indoor Positioning System: A Hybrid Approach Integrating PCA, WKNN, and Linear Regression

Efficient and Accurate Indoor Positioning System: A Hybrid Approach Integrating PCA, WKNN, and Linear Regression

Analysis of translesion polymerases in colorectal cancer cells following cetuximab treatment: A network perspective

AbstractIntroductionAdaptive mutagenesis observed in colorectal cancer (CRC) cells upon exposure to EGFR inhibitors contributes to the development of resistance and recurrence. Multiple investigations have indicated a parallel between cancer cells and bacteria in terms of exhibiting adaptive mutagenesis. This phenomenon entails a transient and coordinated escalation of error‐prone translesion synthesis polymerases (TLS polymerases), resulting in mutagenesis of a magnitude sufficient to drive the selection of resistant phenotypes.MethodsIn this study, we conducted a comprehensive pan‐transcriptome analysis of the regulatory framework within CRC cells, with the objective of identifying potential transcriptome modules encompassing certain translesion polymerases and the associated transcription factors (TFs) that govern them. Our sampling strategy involved the collection of transcriptomic data from tumors treated with cetuximab, an EGFR inhibitor, untreated CRC tumors, and colorectal‐derived cell lines, resulting in a diverse dataset. Subsequently, we identified co‐regulated modules using weighted correlation network analysis with a minKMEtostay threshold set at 0.5 to minimize false‐positive module identifications and mapped the modules to STRING annotations. Furthermore, we explored the putative TFs influencing these modules using KBoost, a kernel PCA regression model.ResultsOur analysis did not reveal a distinct transcriptional profile specific to cetuximab treatment. Moreover, we elucidated co‐expression modules housing genes, for example, POLK, POLI, POLQ, REV1, POLN, and POLM. Specifically, POLK, POLI, and POLQ were assigned to the “blue” module, which also encompassed critical DNA damage response enzymes, for example. BRCA1, BRCA2, MSH6, and MSH2. To delineate the transcriptional control of this module, we investigated associated TFs, highlighting the roles of prominent cancer‐associated TFs, such as CENPA, HNF1A, and E2F7.ConclusionWe found that translesion polymerases are co‐regulated with DNA mismatch repair and cell cycle‐associated factors. We did not, however, identified any networks specific to cetuximab treatment indicating that the response to EGFR inhibitors relates to a general stress response mechanism.

Linking Customer Focus and Competitive Advantage: The Mediation of Transformational Leadership

Purpose: The empirical studies that have been conducted to determine the nature of the relationship between customer focus and competitive advantage have revealed that customer focus fosters competitive advantage; however, none of the studies attempted to include a contingency variable to gain more insight into the nature of this relationship. To fill this gap, this study was carried out in order to further investigate this link by introducing 'transformational leadership' as a contingency variable. &#x0D; Study design/methodology/approach: The study adopted a quantitative research approach with sample data collected from management employees from TAZARA. Validity, reliability and model fit were tested using factor analysis, principal component analysis and regression analysis in Jamovi software.&#x0D; Findings: The results show that there is a positive and significant relationship between customer focus and competitive advantage, and between transformational leadership and competitive advantage. Transformational leadership was found to partially mediate the relationship between customer focus and competitive advantage.&#x0D; Originality/value: This was the first study to empirically test the mediating effect of transformational leadership on customer focus and competitive advantage. As organisations relentlessly seek to gain and maintain competitive advantage through customer focus, their leaders require a transformational leadership style to ensure success. Replication of this study in other sectors is strongly recommended, with future studies also recommended to include other moderating and/or mediating variables.

Changing determinant driver and oil volatility forecasting: A comprehensive analysis

Academic research relies on exogenous drivers to enhance the accuracy of forecasting oil volatility. Following the relevant literature, this study collects 62 exogenous drivers that reflect the movements of oil demand, oil supply, oil inventory, macroeconomic fundamentals, financial indicators, and measures of uncertainty. Our empirical results indicate that dimension reduction regressions, especially principal component analysis regression (PCA), successfully predict both WTI and Brent oil volatility at the one-month ahead forecast horizon. Shrinkage methods, on the other hand, outperform their counterparts for medium- and long-term forecast horizons. Furthermore, the unsupervised learning method (PCA) achieves superior forecasting performance during periods of oil price decrease, whereas supervised learning methods (i.e., shrinkage methods) significantly improve volatility accuracy. Additionally, the empirical results reveal that movements in the Kilian index, World industrial production index, global economic conditions index, U.S. steel production, Chicago Fed national activity index, capacity utilization for manufacturing, U.S. default yield spread, and MSCI emerging market index have a significant impact on oil volatility.