Regression Method Research Articles

Optimization of breeding program design through stochastic simulation with evolutionary algorithms.

The effective planning and allocation of resources in modern breeding programs is a complex task. Breeding program design and operational management have a major impact on the success of a breeding program and changing parameters such as the number of selected/phenotyped/genotyped individuals in the breeding program will impact genetic gain, genetic diversity, and costs. As a result, careful assessment and balancing of design parameters is crucial, taking into account the trade-offs between different breeding goals and associated costs. In a previous study, we optimized the resource allocation strategy in a dairy cattle breeding scheme via the combination of stochastic simulations and kernel regression, aiming to maximize a target function containing genetic gain and the inbreeding rate under a given budget. However, the high number of simulations required when using the proposed kernel regression method to optimize a breeding program with many parameters weakens the effectiveness of such a method. In this work, we are proposing an optimization framework that builds on the concepts of kernel regression but additionally makes use of an evolutionary algorithm to allow for a more effective and general optimization. The key idea is to consider a set of potential parameter settings of the breeding program, evaluate their performance based on stochastic simulations, and use these outputs to derive new parameter settings to test in an iterative procedure. The evolutionary algorithm was implemented in a Snakemake workflow management system to allow for efficient scaling on large distributed computing platforms. The algorithm achieved stabilization around the same optimum with a massively reduced number of simulations. Thereby, the incorporation of class variables and accounting for a higher number of parameters in the optimization framework leads to substantially reduced computing time and better scaling for the desired optimization of a breeding program.

Evolving Transparent Credit Risk Models: A Symbolic Regression Approach Using Genetic Programming

Credit scoring is a cornerstone of financial risk management, enabling financial institutions to assess the likelihood of loan default. However, widely recognized contemporary credit risk metrics, like FICO (Fair Isaac Corporation) or Vantage scores, remain proprietary and inaccessible to the public. This study aims to devise an alternative credit scoring metric that mirrors the FICO score, using an extensive dataset from Lending Club. The challenge lies in the limited available insights into both the precise analytical formula and the comprehensive suite of credit-specific attributes integral to the FICO score’s calculation. Our proposed metric leverages basic information provided by potential borrowers, eliminating the need for extensive historical credit data. We aim to articulate this credit risk metric in a closed analytical form with variable complexity. To achieve this, we employ a symbolic regression method anchored in genetic programming (GP). Here, the Occam’s razor principle guides evolutionary bias toward simpler, more interpretable models. To ascertain our method’s efficacy, we juxtapose the approximation capabilities of GP-based symbolic regression with established machine learning regression models, such as Gaussian Support Vector Machines (GSVMs), Multilayer Perceptrons (MLPs), Regression Trees, and Radial Basis Function Networks (RBFNs). Our experiments indicate that GP-based symbolic regression offers accuracy comparable to these benchmark methodologies. Moreover, the resultant analytical model offers invaluable insights into credit risk evaluation mechanisms, enabling stakeholders to make informed credit risk assessments. This study contributes to the growing demand for transparent machine learning models by demonstrating the value of interpretable, data-driven credit scoring models.

Dynamic Multiblock Regression for Process Modelling

ABSTRACTThe study introduces three novel strategies for incorporating capabilities for dynamic modelling into multiblock regression methods by integrating sequentially orthogonalised partial least squares (SO‐PLS) with different dynamic modelling techniques. The study evaluates these strategies using synthetic datasets and an industrial example, comparing their performance in predictive ability, identification of process dynamics, and quantification of block contributions. Results suggest that these approaches can effectively model the dynamics with performance comparable to state‐of‐the‐art methods, providing, at the same time, insight into the dynamic order and block contributions. One of the strategies, sequentially orthogonalised dynamic augmented (SODA)–PLS, shows promise by ensuring that redundant information in the time dimension is not included, resulting in simpler and more easily interpretable dynamic models. These multiblock dynamic regression strategies have potential applications for improved process understanding in industrial settings, especially where multiple data sources and inherent time dynamics are present.

Indicator dynamics of the “Combating Cardiovascular Diseases” regional program aimed at reducing mortality from acute coronary syndrome

Aim. To analyze the dynamics of the application of medical and organizational technologies for achieving the target reduction of mortality from acute coronary syndrome (ACS) in the region. Materials and methods. In 2018, the research covered 1 regional vascular center (RVC), 8 primary vascular departments (PVD), and 41 medical organizations (MO). In 2022, the structure of medical organizations changed to include 3 RSCs, 9 PSOs, and 39 MOs. The indicators characterizing the provision of medical care to ACS patients in the Irkutsk region were calculated and analyzed. The presence, direction, and strength of correlation between the number of deaths from ACS and the number of medical and organizational activities was studied using linear correlation analysis by the Pearson method. Results. Over time, the share of hospitalization of patients with ACS in the early stages up to 12 hours increased from 42.1% to 46.5%. The mortality rate from ACS decreased by 20.4%, primarily due to a decrease in mortality in the first 24 hours (by 28.7%). The key medical and organizational technologies that have the greatest impact on mortality rates were identified. A strong relationship (r = -0.729) was established between the number of deaths from ACS and the number of patients transferred to the regional vascular center for emergency coronoplasty; two medium-level relationships (r = -0.343) were found between the number of deaths from ACS and the number of hospitalized patients with ACS in the first 12 hours; a correlation (r = -0.415) was determined between the number of deaths from ACS and the number of hospitalized patients who underwent thrombolytic therapy. The linear regression method showed that the availability of such procedures as emergency coronoplasty and thrombolytic therapy has prognostic significance for further reduction of the expected number of deaths from ACS in the region. Conclusion. The creation of the RVC and PVD infrastructure in the region allowed the major indicators of the “Combating Cardiovascular Diseases” regional program to be achieved.

Rapid Prediction and Parameter Evaluation of Process-Induced Deformation in L-Shape Structures Based on Feature Selection and Artificial Neural Networks

The process-induced deformation (PID) during the manufacturing of thermosetting composite materials can significantly compromise manufacturing precision. This paper introduces an innovative method that combines a finite element analysis (FEA), feature classification algorithms, and an Artificial Neural Network (ANN) framework to rapidly predict the PID of a typical L-shaped structure. Initially, a comprehensive range of parameters that influence PID are compiled in this research, followed by the generation of a dataset through FEA considering viscoelastic constitutive models, validated by experimental results. Influential parameters are classified using Random Forest and LASSO regression methods, with each parameter rated according to its impact on PID, delineating their varying degrees of importance. Subsequently, through a hyperparameter analysis, an ANN framework is developed to rapidly predict the PID, while also refining the assessment of the parameters’ significance. This innovative approach achieves a computational time reduction of 98% with less than a 5% loss in accuracy, and highlights that under limited computational conditions, considering only a subset or all of the parameters—the peak temperature, corner angle, coefficient of chemical shrinkage, coefficient of thermal expansion, curing pressure, and E1—minimizes accuracy loss. The study demonstrates that machine learning algorithms can effectively address the challenge of predicting composite material PID, providing valuable insights for practical manufacturing applications.

Simultaneous Quantification of 66 Compounds in Two Tibetan Codonopsis Species Reveals Four Chemical Features by Database-Enabled UHPLC-Q-Orbitrap-MS/MS Analysis.

Codonopsis canescens Nannf. (CoC) and Codonopsis nervosa Nannf. (CoN) are two traditional Tibetan medicinal herbs (Zangdangshen), which have been widely used in the treatment of various diseases. In this study, their aerial and underground parts were systematically analyzed using database-enabled UHPLC-Q-Orbitrap-MS/MS technology. This technology introduced three adduct ions, [M - H]-, [M + H]+, and [M + NH4]+, to putatively identify a total of 66 compounds. During the putative identification, at least 16 isomers were successfully differentiated, such as isochlorogenic acid A vs. isochlorogenic acid B vs. isochlorogenic acid C. Thereafter, all these identified compounds were further quantified for their contents based on a linear regression method. Their contents were observed to vary from 0.00 to 39,127.03 µg/g. Through multiple comparisons of these quantification results, the study found the following four chemical features: (1) Four sesquiterpenes (especially atractylenolide III) enriched mainly in CoC and rarely in CoN; (2) four quinic acid derivatives were abundant in the aerial part of two species; (3) sixteen flavonoids (particularly diosmetin and chrysoeriol) showed higher content in CoC than in CoN; and (4) lobetyolin was ubiquitously distributed in four parts of both CoC and CoN. Based on these features and the relevant principles, four compounds (lobetyolin, atractylenolide III, diosmetin, and chrysoeriol) are recommended as the quality markers of two Tibetan Codonopsis species. All these findings can facilitate the sustainable development and quality control of the two traditional Tibetan medicinal herbs.

Environmental technology to stimulate life expectancy: A deep dive into the role of PM2.5 and carbon emission

As the Latin American economies contend with improving public health while simultaneously dealing with ecological issues, understanding the determinant of life expectancy becomes imperative. This study explores the interaction among environmental technology, economic development, cleaner energy transitions, industrial growth, particulate Matter 2.5 (PM2.5), carbon emission, and their collective impact on life expectancy across Latin American nations. The research incorporates advanced econometric approaches comprising Mean Group (MG), Pooled Mean Group (PMG), and Method of Moments Quantile Regression (MMQR). The study’s outcome established that cleaner energy transitions, Gross Domestic Product (GDP), industrialization and environmental technologies significantly improve life expectancy, especially at higher quantiles. However, the outcome highlighted that PM2.5 and carbon emissions reduce life expectancy. Additionally, this study supports the unidirectional connection between environmental technology, economic development, cleaner energy transitions, industrial growth, life expectancy, and the bidirectional connection flowing from carbon emission PM2.5 to life expectancy. The study offers a roadmap for Latin America and global economies to invest in technology, cleaner power supply and industrialization to provide a route toward sustainable development and better public health that will eventually improve their citizens’ life expectancy.

The role of financial inclusion and technological innovation in stimulating environmental sustainability in the European countries: A new perspective based on load capacity factor

Given the alarming level of climate change, policymakers across the globe are seeking strategies to mitigate environmental pollution to achieve sustainable development. In this context, renewable energy and technological advancements have emerged as an effective way to lower pollution and attain sustainable development. This study evaluates the effect of financial inclusion, technological innovation, and renewable energy on the load capacity factor (LCF) in European countries from 2004 to 2018. LCF is considered the most comprehensive indicator of ecological sustainability, combining both the biocapacity factor and ecological footprint. Hence, the present work fills the literature gap by exploring, for the first time, the effect of financial inclusion on the LCF. Applying the advanced Method of Moment Quantile Regression (MMQR), the study demonstrates that technological innovation and economic growth have adverse effects on LCF while renewable energy and financial inclusion promote LCF. The study indicates that technological innovation and economic growth undermine ecological excellence in European nations while green energy and financial inclusion enhance it. Moreover, the findings of the causality analysis reveal a causal association between financial inclusion, renewable energy, and LCF. Our study recommends prioritizing financial inclusion alongside investments in renewable energy to enhance ecological sustainability.

Towards Secure Energy Systems: Examining Asymmetric Impact of Energy Transition, Environmental Technology and Digitalization on Chinese City-level Energy Security

This study aims to investigate the asymmetric impacts of energy transition (ETI), environmental technology (ENT), and digitalization (DEI) on energy security (ESI) across Chinese cities, addressing a significant gap in empirical research at the city level. We employ the Method of Moments Quantile Regression (MMQR) to analyze data from 275 Chinese cities over the period 2007-2020, examining the differential effects of positive and negative shocks in ETI, ENT, and DEI on ESI across various quantiles. Utilizing MMQR, the analysis yields several key findings: (i) Positive shocks in ETI benefit cities with high pre-existing ESI, while negative shocks surprisingly provide a temporary boost to low-security cities but ultimately hinder progress at higher levels. (ii) Positive shocks in DEI correlate with higher ESI, especially in cities with moderate to high baseline security, whereas negative shocks in DEI exhibit minimal influence on ESI. (iii) Positive shocks in ENT generally enhance energy security across all quantiles except the lowest, indicating that advancements benefit cities at various security levels. Notably, negative shocks initially decrease ESI at the lowest quantile but subsequently increase it at moderate to high levels. These findings highlight the complex, non-linear relationships between ETI, ENT, DEI, and ESI in Chinese cities. The study provides quantitative evidence for policymakers to develop nuanced strategies that balance energy transition, digitalization, and environmental technology advancements with energy security goals. Future research should explore the mechanisms underlying these asymmetric effects and their implications for sustainable urban development in China.

Strategy towards sustainable energy transition: The effect of policy uncertainty, environmental technology and natural resources rent in the OECD nations

The stupendous increase in environmental pollutants alongside climate alterations are calling for integrated tasks at the global level to meet the Paris Agreements. Energy conversion from fossil fuel to renewable energy sources is an important step to address the adverse effects of extreme climatic conditions. This study explores the key contributors of energy transition in the context of the OECD countries during the period 1991 to 2019. To this end the major determinants chosen include natural resources rents, policy uncertainty, environmental technologies, environmental policies, income and globalization. The quantile regression technique is used to explore heterogeneous role of the contributory factors of energy transition across its different levels. This study applies method of moments quantile regression (hereafter MM-QR) by Machado and Silva (2019) that provides evidence on how the covariates affect the integral conditional distributions of energy transition. The study documents interesting findings. (i) Natural resources rents reduce the pace of the progress of sustainable energy transition in OECD economies. (ii) An escalation in the world uncertainty index leads to an improvement in energy transition index. (iii) Environmental technologies support the transition concerning cleaner and more efficient energy sources. (iv) Finally, as natural resources, uncertainty, environmental technologies, and globalization impacts energy transition differently, their effects need to be rationalized with varying environmental policy instruments. The study concludes that energy transition process requires the task of upgrading the quality of implementation of policies related to energy technology.

Gender empowerment and environmental impact: A quantile regression analysis in BRICS economies

This study, with its significant findings, delves into the impact of women's status on environmental quality in BRICS economies (Brazil, Russia, India, China, South Africa) from 1960 to 2022. Using a novel method, the Method of Moments Quantile Regression, this study has been able to analyse the relationship. The results, which are of utmost importance, show that women's political empowerment and leadership positions (government chief executives) significantly reduce carbon emissions, while the impact of women's civil liberties along with population growth increases emissions across the analysed quantiles. Economic growth is insignificantly negatively associated with environmental quality. The paper's findings reveal a unidirectional causal relationship from women's political empowerment to emissions, carbon emissions to women's civil liberties and emissions to economic growth. Additionally, a bi-directional causality connection is evident between population growth and environmental quality. These insights, which are crucial for policymaking, suggest that promoting improved women's status is a crucial policy strategy for mitigating climate change in BRICS economies. Thus, this paper suggests that empowerment of women is an effective strategy for reducing carbon emissions. It emphasizes the need for climate policy to promote gender equality, prioritise women's leadership in the clean energy industry, and enhance their access to resources and opportunities.

The joint effect of changes in urbanization and climate on trends in floods: A comparison of panel and single-station quantile regression approaches

Estimates of annual maximum (peak) flow quantiles are needed for basins undergoing changes in both urbanization and climate. Most previous work on the effect of urbanization on peak flows has considered urbanization alone and only the spatial variation in flood quantiles or its mean temporal effect, and most work on the effect of nonstationarity in climate has focused on single-station analyses, which give uncertain results for extreme quantiles. To address these gaps, three approaches to the statistical estimation of the joint effects of changes in impervious cover and climate on the estimation of peak-flow quantiles were compared: single-station quantile regression; a fixed effect panel-quantile regression (pQR) method using a location (mean) shift to homogenize the panel; and a location-scale panel regression model (pQRmom), which accounts for both scale (variance) and location effects. The different approaches were applied to a dataset consisting of instantaneous annual peak flows from 127 minimally nested basins in the midwestern United States with at least 4 % change in imperviousness. The annual maximum daily discharge from a water-balance model was selected as the primary climate predictor; in addition, to provide a comparison of climate predictors, precipitation was also considered. The coefficients from single-station regressions were usually sufficiently certain to determine the effects of climate variation but usually too uncertain to estimate the effects of urbanization. The panel-quantile regression approaches give much more certain results, but their estimates of quantile dependence differ: although both indicate urbanization effects decreasing with decreasing annual exceedance probability (AEP), the pQRmom urbanization coefficients are insignificantly different from zero for AEPs less than 0.10, whereas the pQR coefficients remain positive and are significant except for AEP = 0.01, the smallest AEP value considered. Although the location-scale structure of the pQRmom approach has less flexible quantile dependence than the pQR approach, the pQRmom approach has somewhat lower overall error, and it is found that by subsetting the dataset to homogenize the scale effects, the pQR and pQRmom results become similar, indicating the insignificant urbanization coefficients for small AEPs of the pQRmom results are likely correct for the study dataset.

Modelling of the trade-off between the deep soil moisture and vegetation restoration in the hilly area of the Loess Plateau, China

Excessive depletion of soil moisture by artificial forests in the vegetation restoration areas of the Loess Plateau has attracted widespread attention. To assess potential risks of soil moisture deficit, we needed on-site vegetation and soil sampling data, as well as UAV images from the Chaigou Watershed for three-dimensional analysis, combining both sampling and raster data. Three-dimensional surfaces for assessment of trade-off were established innovatively by the local regression and interpolation methods. The results indicated that soil moisture benefits at 20–40 cm depth are lower than at 0–20 cm due to infiltration and surface disturbance. In some areas of the Chaigou Watershed, grass and shrub-grass vegetation are facing risks of soil moisture deficit based on trade-off values (RSMD) and multiparameter evaluations. Analysis of deep soil water content variability revealed the moisture decreases significantly with the deepening of the soil layer in some plots. R (Richness), H (Shannon’s Diversity), Margalef, COHESION, and CONTAG were applicable in interpolation and fitted with the local regression model (R2 > 0.6) corresponding to the trade-off, but SPLIT was proven to be inapplicable in this study area. The zero trade-off inflection points were 6 %–8 %, while the trend inflection points were 7.5 %–9 % for soil moisture and vegetation indices of vegetation and landscape in typical sampling sites of the Chaigou Watershed. Three-dimensional fitting model is more comprehensive and effective in assessing deep soil moisture conditions and grass plots on shady slopes generally had the best trade-off status in this region.

Analysis of Canopy Interception Characteristics and Influencing Factors in Typical Artificial Forest in the Loess Plateau Semi-Arid Region

Interception loss (IL) is an important process in the hydrological cycle within semi-arid forest ecosystems, directly affecting the amount of effective rainfall. However, the factors influencing IL during individual rainfall events remain to be quantified. This study collected rainfall, vegetation, and interception data during the 2022 and 2023 growing seasons in a typical black locust forest within the Zhifanggou watershed. It employed the Random Forest Regression (RFR) and back-propagation neural network (BPNN) methods to quantitatively evaluate the contribution rates of various factors to the IL and interception loss percentage (ILP). The IL among the 48 effective rainfall events was 172.05 mm, accounting for 19.54% of the rainfall amount. IL and ILP increased as the distance from the trunk decreased. During all rainfall events, both IL and ILP were significantly negatively correlated with the leaf area index (LAI) and canopy cover (CC); IL is significantly positively correlated with total rainfall (TR) and rainfall intensity (RI), while ILP is significantly negatively correlated with TR, RI, and rainfall duration (RD). The BPNN and RFR results indicated that rainfall, canopy, and tree characteristics contributed 43.06%, 44.79%, and 12.15% to IL, respectively, and 57.27%, 34.09%, and 8.63% to ILP, respectively. TR, CC, and LAI represented the primary influencing factors. Rainfall and canopy characteristics were the main factors affecting IL (ILP). As rainfall event magnitude increases, canopy contributions to IL and ILP decrease. In semi-arid areas, managing forest canopies to control IL helps address water imbalances in ecosystems.

Establishment and Validation of a Dynamic Nomogram for Persistent Organ Failure in Acute Biliary Pancreatitis: A Retrospective Study.

The objective of this study was to create a predictive model for the onset of persistent organ failure (POF) in individuals suffering from acute biliary pancreatitis (ABP) by utilizing indicators observed within 24hours of hospital admission. Early detection of high-risk POF patients is crucial for clinical decision-making. Clinical data and laboratory indicators within 24hours of admission from ABP patients diagnosed at The First Affiliated Hospital of Wenzhou Medical University between January 1, 2016, and January 1, 2024 were collected and retrospectively analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression (stepwise regression) methods were employed to identify variables for constructing the prediction model. The prediction model's performance was evaluated using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). It was compared with other scoring systems such as SIRS, BISAP, APACHE II, CTSI, and MCTSI. Additionally, a web-based calculator was created to simplify the calculation process. Out of 324 ABP patients, 25 developed POF. Initial screening identified 18 variables; through LASSO regression and multivariable logistic regression analysis, five variables including BMI, Hb, ALB, Ca, and LIP were determined as independent predictors of POF. According to these factors to build prediction model, draw the nomogram. The AUC's receiver operating characteristic curve analysis demonstrated a significantly higher value in comparison to other scoring systems. Calibration curve and DCA show that the established model to predict the accuracy of POF is higher, clinical decision of net benefit is also higher. A network calculator utilizing this predictive model was developed. A predictive model incorporating five risk indicators has been established exhibiting high discriminatory power and accuracy which aids in early identification of ABP patients at risk for developing POF. This holds significant value in guiding clinical decision-making.

Effective stress intensity factor range for fatigue cracks propagating in mixed mode I-II loading

The actual service axles are often subjected to mixed-mode loading, and predicting the mixed-mode I-II crack propagation behaviour using the mode I effective stress intensity factor (ΔKI) differs from the real service conditions. To effectively predict the fatigue crack propagation behaviour of actual service structures, the I-II stress intensity factor range (ΔKP-R) considering two closure effects was adopted to describe the fatigue crack propagation under mixed mode loading. A test database was established based on monitoring data of mode I and mixed-mode I-II (30/45/60°) crack propagation tests under different stress ratios. Combining domain knowledge and symbolic regression (SR) methods, an angle factor was proposed for constructing correlation functions between ΔKI and ΔKP-R. The results showed that the loading angle (α) only affects the initial projection of the load parallel and perpendicular to the fatigue crack growth (FCG) direction. Compared with the geometric correction factor, the correlation function acquired by the angle factor constructed by the SR method has higher accuracy, and the balance parameters (SCORE) obtained by the former are significantly higher than those obtained by the latter under the same function complexity. The SR verification results demonstrated that constructing mode I and I-II correlation functions with angle factors has a good predictive effect.

Identification of candidate genes and genomic prediction for early heifer pregnancy in Nelore beef cattle

This study aimed to assess the influence of differential weighting in genomic regions harboring candidate causal loci on the prediction accuracy and inflation for early heifer pregnancy (heifers that calved up to 30 months of age) in Nellore (Bos indicus) heifers using the single step genomic BLUP model (ssGBLUP). Phenotypic records of 102,294 Nellore heifers born between 2010 and 2017 were used in this study. The pedigree dataset harbored information from 176,107 animals born between 1998 and 2017, including 5,145 sires and 35,705 dams. Seven different models for genomic prediction were defined by combining the SNP weights obtained in the iterations (1st and 2nd) of the weighted single step GWAS (ssw1GBLUP and ssw2GBLUP) or candidate QTLs reported in the literature. Hence, the lambda (λ) values estimated in the WssGWAS were used to weight the SNPs adjacent to the candidate regions or QTL previously reported in the literature. To estimate the genetic parameters and perform the WssGWAS and WssGBLUP for early heifer pregnancy, a single-trait Bayesian analysis considering a threshold animal model was used. Accuracy, bias, and inflation parameters were evaluated in the validation subset based on the linear regression (LR) method. Genomic windows of ten consecutive SNPs that explained more than 0.5% of the additive genetic variance were selected to explore and determine possible candidate genes. Among the identified genes, we can highlight the PGRMC2, TENM3, GRIP1, TMEM45A, and KLF3, given their roles in endocrine fertility, expression of contractile proteins, average daily gain, dry matter intake, and fat deposition. Several genomic regions associated with QTL related to early heifer pregnancy were identified. The identification of such regions and the respective candidate genes associated with sexual precocity and fertility would contribute to improve the genetic knowledge regarding early sexual precocity of Nellore cattle. The prediction accuracy increased roughly 25.6% using the ssGBLUP compared to BLUP models. The prediction accuracy with the WssGBLUP when incorporating weighted SNPs with the λ values obtained in the 1st (ssw1GBLUP) and 2nd iteration (ssw2GBLUP) of the WssGWAS was higher (∼18%) than that described for the ssGBLUP model. The inflation also increased the weighting of the most relevant SNPs obtained with the GWAS, most likely overestimating the GEBV. The models that weighted SNPs close to QTLs reported in the literature yielded to less biased and deflated predictions compared to ssw1GBLUP and ssw2GBLUP models. Genomic selection is a feasible alternative for genomic evaluation of early heifer pregnancy in Nellore beef cattle by increasing the prediction accuracy of young animals. In addition, the use of information obtained from the WssGWAS is an alternative to increase reliability and reduce genomic prediction bias. Therefore, the results obtained herein indicate that it is possible to improve the prediction accuracy and reduce the bias of genomic prediction by using genomic information and differentially weighted genomic regions harboring candidate QTLs previously reported in the literature for early heifer pregnancy.

Quantifying social media predictors of violence during the 6 January US Capitol insurrection using Granger causality.

Protests involving brute force are growing in number and are viewed as a likely source of increased collective violence in industrialized nations. Yet, our scientific understanding of how violent protests are related to a leader's social media communications during protests remains nascent. Here, we analyse new data from the 6 January 'march on the US Capitol' to quantify the links between leadership, social media and levels of violence. Using data on thousands of live footage videos, Trump's tweets and rally speech, other rally speeches and #StopTheSteal tweets, we apply Granger regression methods to analyse the links between former President Trump's tweets, #StopTheSteal tweets, rally speeches and the severity and duration of outbreaks of violence and weapons use during the riot. We find that Trump's tweets predict bursts in rioters' levels and duration of violence and weapons use. Trump's tweets also predict changes in the volume and sentiments of #StopTheSteal tweets, which in turn explain additional variance in levels of violence and weapons use over the course of the riot. Our findings reveal new patterns of behaviour that link an authority figure's online behaviour during a protest and the shift from peaceful protesting to violence.

Force model of robot bone grinding based on finite element analysis

Bone grinding represents a formidable challenge during open epiphyseal surgery, as grinding force may result in potential damage to bone and surrounding tissues. To address this issue, this study establishes a correlation between grinding force and bone density, as well as feed rate and rotational speed. Given the limited availability of experimental data on bone grinding, a finite element analysis (FEA) model is developed. Subsequently, the impact of bone density, feed rate and rotational speed on grinding force is determined through linear regression method. The accuracy and sensitivity of the grinding force model are then assessed, revealing a maximum prediction deviation of 4.87%. Finally, based on the established model, safe operating parameters and boundary conditions for robot bone grinding are identified.

Predicting insurance charges using linear regression models

Abstract. Linear regression method can be performed to predict the outcome from one or many input values. Its versatility allows it to be applied on many datasets that contain correlated values. However, researches on the application of linear regression on medical insurance costs, a highly important part of peoples life, are few. This paper studies an insurance dataset from Kaggle by applying linear regression on it. The author validates the dataset at first and explores the correlation between each individual factor and their corresponding charges to better show how insurance costs differ from person to person with different background. Many figures are also included to help visualize the correlation between factors. In the end, the author creates a multilinear regression model to predict the insurance charges. The R-Squared score of the model and a result table including regression coeffects are also provided to show the accuracy and details of the model.