Bayesian Analysis of State Voter Registration Database Integrity

How do we ensure a statewide voter registration database’s accuracy and integrity, especially when the database depends on aggregating decentralized, sub-state data with different list maintenance practices? We develop a Bayesian multivariate multilevel model to account for correlated patterns of change over time in multiple response variables, and label statewide anomalies using deviations from model predictions. We apply our model to California’s 22 million registered voters, using 25 snapshots from the 2020 presidential election. We estimate countywide change rates for multiple response variables such as changes in voter’s partisan affiliation and jointly model these changes. The model outperforms a simple interquartile range (IQR) detection when tested with synthetic data. This is a proof-of-concept that demonstrates the utility of the Bayesian methodology, as despite the heterogeneity in list maintenance practices, a principled, statistical approach is useful. At the county level, the total numbers of anomalies are positively correlated with the average election cost per registered voter between 2017 and 2019. Given the recent efforts to modernize and secure voter list maintenance procedures in the For the People Act of 2021, we argue that checking whether counties or municipalities are behaving similarly at the state level is also an essential step in ensuring electoral integrity.

Random Forest for Genomic Prediction

We give a detailed description of random forest and exemplify its use with data from plant breeding and genomic selection. The motivations for using random forest in genomic-enabled prediction are explained. Then we describe the process of building decision trees, which are a key component for building random forest models. We give (1) the random forest algorithm, (2) the main hyperparameters that need to be tuned, and (3) different splitting rules that are key for implementing random forest models for continuous, binary, categorical, and count response variables. In addition, many examples are provided for training random forest models with different types of response variables with plant breeding data. The random forest algorithm for multivariate outcomes is provided and its most popular splitting rules are also explained. In this case, some examples are provided for illustrating its implementation even with mixed outcomes (continuous, binary, and categorical). Final comments about the pros and cons of random forest are provided.

Growth regulators and their reflection on different hop genotypes cultivated under in vitro conditions

Hops is a new culture in Brazil. Tissue culture can be an important technique for rapid hop propagation. This paper aims to characterize responses from different genotypes under different growth regulators through the interrelationship of response variables important to hop in vitro growth. Three genotypes were cultivated in six culture media with different combinations of growth regulators, BAP (6-benzylaminopurine), IAA (3-indolacetic acid) and GA3 (gibberellic acid). The means were compared by orthogonal contrasts and the interrelationship of the response variables was performed by path analysis. American genotypes showed favorable root development under the BAP + IAA combination, while the use of IAA improved shoot development. The origin of genotypes was important for defining the best protocol for in vitro cultivation. The path coefficient showed that the variable number of shoots has stronger direct effect on the number of nodal segments. Additionally, in tissue culture assays, the use of a covariable and proper error distribution significantly increased experimental accuracy.

Support Vector Machines and Support Vector Regression

In this chapter, the support vector machines (svm) methods are studied. We first point out the origin and popularity of these methods and then we define the hyperplane concept which is the key for building these methods. We derive methods related to svm: the maximum margin classifier and the support vector classifier. We describe the derivation of the svm along with some kernel functions that are fundamental for building the different kernels methods that are allowed in svm. We explain how the svm for binary response variables can be expanded for categorical response variables and give examples of svm for binary and categorical response variables with plant breeding data for genomic selection. Finally, general issues for adopting the svm methodology for continuous response variables are provided, and some examples of svm for continuous response variables for genomic prediction are described.

Reproducing Kernel Hilbert Spaces Regression and Classification Methods

The fundamentals for Reproducing Kernel Hilbert Spaces (RKHS) regression methods are described in this chapter. We first point out the virtues of RKHS regression methods and why these methods are gaining a lot of acceptance in statistical machine learning. Key elements for the construction of RKHS regression methods are provided, the kernel trick is explained in some detail, and the main kernel functions for building kernels are provided. This chapter explains some loss functions under a fixed model framework with examples of Gaussian, binary, and categorical response variables. We illustrate the use of mixed models with kernels by providing examples for continuous response variables. Practical issues for tuning the kernels are illustrated. We expand the RKHS regression methods under a Bayesian framework with practical examples applied to continuous and categorical response variables and by including in the predictor the main effects of environments, genotypes, and the genotype ×environment interaction. We show examples of multi-trait RKHS regression methods for continuous response variables. Finally, some practical issues of kernel compression methods are provided which are important for reducing the computation cost of implementing conventional RKHS methods.

Effect of Nutritional Factors and Copper on the Regulation of Laccase Enzyme Production in Pleurotus ostreatus

This research aimed to establish the relationship between carbon–nitrogen nutritional factors and copper sulfate on laccase activity (LA) by Pleurotus ostreatus. Culture media composition was tested to choose the nitrogen source. Yeast extract (YE) was selected as a better nitrogen source than ammonium sulfate. Then, the effect of glucose and YE concentrations on biomass production and LA as response variables was evaluated using central composite experimental designs with and without copper. The results showed that the best culture medium composition was glucose 45 gL−1 and YE 15 gL−1, simultaneously optimizing these two response variables. The fungal transcriptome was obtained in this medium with or without copper, and the differentially expressed genes were found. The main upregulated transcripts included three laccase genes (lacc2, lacc6, and lacc10) regulated by copper, whereas the principal downregulated transcripts included a copper transporter (ctr1) and a regulator of nitrogen metabolism (nmr1). These results suggest that Ctr1, which facilitates the entry of copper into the cell, is regulated by nutrient-sufficiency conditions. Once inside, copper induces transcription of laccase genes. This finding could explain why a 10–20-fold increase in LA occurs with copper compared to cultures without copper when using the optimal concentration of YE as nitrogen sources.

Modelling and Multi-objective Optimization of Elastic Abrasive Cutting of C45 and 42Cr4 Steels

Elastic abrasive cutting is a new high-performance method to produce workpieces made of materials of different hardness, which ensures lower wear of cut-off wheels and higher quality machined surfaces. However, the literature referring to elastic abrasive cutting is scarce; additional studies are thus needed. This paper proposes a new approach for modelling and optimizing the elastic abrasive cutting process, reflecting the specifics of its particular implementation. A generalized utility function has been chosen as an optimization parameter. It appears as a complex indicator characterizing the response variables of the elastic abrasive cutting process. The proposed approach has been applied to determine the optimum conditions of elastic abrasive cutting of С45 and 42Cr4 steels. To solve the optimization problem, a model of the generalized utility function reflecting the complex influence of the elastic abrasive cutting conditions has been developed. It is based on the findings of the complex study and modelling of the response variables of the elastic abrasive cutting process (cut-off wheel wear, time per cut, cut piece temperature, cut off wheel temperature and workpiece temperature) depending on the conditions of its implementation (compression force F exerted by the cut-off wheel on the workpiece, workpiece rotational frequency nw, cut off wheel diameter ds). By applying a genetic algorithm, the optimal conditions of elastic abrasive cutting of С45 and 42Cr4 steels: ds = 120 mm; F = 1 daN; nw = 63.7 min–1 and nw = 49.9 min–1, respectively for С45 and 42Cr4 steels, have been determined. They provide the best match between the response variables of the elastic abrasive cutting process.

Hierarchical Bayesian Small Area Estimation Using Weakly Informative Priors in Ecologically Homogeneous Areas of the Interior Western Forests

The U.S. Forest Inventory and Analysis Program (FIA) collects inventory data on and computes estimates for many forest attributes to monitor the status and trends of the nation's forests. Increasingly, FIA needs to produce estimates in small geographic and temporal regions. In this application, we implement area level hierarchical Bayesian (HB) small area estimators of several forest attributes for ecosubsections in the Interior West of the US. We use a remotely-sensed auxiliary variable, percent tree canopy cover, to predict response variables derived from ground-collected data such as basal area, biomass, tree count, and volume. We implement four area level HB estimators that borrow strength across ecological provinces and sections and consider prior information on the between-area variation of the response variables. We compare the performance of these HB estimators to the area level empirical best linear unbiased prediction (EBLUP) estimator and to the industry-standard post-stratified (PS) direct estimator. Results suggest that when borrowing strength to areas which are believed to be homogeneous (such as the ecosection level) and a weakly informative prior distribution is placed on the between-area variation parameter, we can reduce variance substantially compared the analogous EBLUP estimator and the PS estimator. Explorations of bias introduced with the HB estimators through comparison with the PS estimator indicates little to no addition of bias. These results illustrate the applicability and benefit of performing small area estimation of forest attributes in a HB framework, as they allow for more precise inference at the ecosubsection level.