In plant breeding, the need to improve the prediction of future seasons or new locations and/or environments, also denoted as “leave one environment out,” is of paramount importance to increase the genetic gain in breeding programs and contribute to food and nutrition security worldwide. Genomic selection (GS) has the potential to increase the accuracy of future seasons or new locations because it is a predictive methodology. However, most statistical machine learning methods used for the task of predicting a new environment or season struggle to produce moderate or high prediction accuracies. For this reason, in this study we explore the use of the partial least squares (PLS) regression methodology for this specific task, and we benchmark its performance with the Bayesian Genomic Best Linear Unbiased Predictor (GBLUP) method. The benchmarking process was done with 14 real datasets. We found that in all datasets the PLS method outperformed the popular GBLUP method by margins between 0% (in the Indica data) and 228.28% (in the Disease data) across traits, environments, and types of predictors. Our results show great empirical evidence of the power of the PLS methodology for the prediction of future seasons or new environments.

Having health insurance is an important decision for enjoying the security of a safe future. Health insurance can protect people from a large amount of medical costs. More Indonesian have health insurance now a days than ever before since the government is committed to supporting the universal health care. This study aims to determine factors affecting the health insurance ownership and to understand their relations in Indonesia using a binary logistic regression model. The data used in this study came from the fifth wave of the Indonesian Family Life Survey (IFLS). There were 29,508 respondents where 14,653 among them have health insurance. The logistic regression model suggested that job, education, chronic condition, marital status, and inpatient care were statistically significant to the health insurance ownership (yes/no), while not significant for gender and health condition. Fitting the logistic regression model with age as the only explanatory variable yielded that the probability of having health insurance increased in line with age.

Genomic selection (GS) is revolutionizing plant breeding since the selection process is done with the help of statistical machine learning methods. A model is trained with a reference population and then it is used for predicting the candidate individuals available in the testing set. However, given that breeding phenotypic values are very noisy, new models must be able to integrate not only genotypic and environmental data but also high-resolution images that have been collected by breeders with advanced image technology. For this reason, this paper explores the use of generalized Poisson regression (GPR) for genome-enabled prediction of count phenotypes using genomic and hyperspectral images. The GPR model allows integrating input information of many sources like environments, genomic data, high resolution data, and interaction terms between these three sources. We found that the best prediction performance was obtained when the three sources of information were taken into account in the predictor, and those measures of high-resolution images close to the harvest day provided the best prediction performance.

Today, breeders perform genomic-assisted breeding to improve more than one trait. However, frequently there are several traits under study at one time, and the implementation of current genomic multiple-trait and multiple-environment models is challenging. Consequently, we propose a four-stage analysis for multiple-trait data in this paper. In the first stage, we perform singular value decomposition (SVD) on the resulting matrix of trait responses; in the second stage, we perform multiple trait analysis on transformed responses. In stages three and four, we collect and transform the traits back to their original state and obtain the parameter estimates and the predictions on these scale variables prior to transformation. The results of the proposed method are compared, in terms of parameter estimation and prediction accuracy, with the results of the Bayesian multiple-trait and multiple-environment model (BMTME) previously described in the literature. We found that the proposed method based on SVD produced similar results, in terms of parameter estimation and prediction accuracy, to those obtained with the BMTME model. Moreover, the proposed multiple-trait method is atractive because it can be implemented using current single-trait genomic prediction software, which yields a more efficient algorithm in terms of computation.

Health status of a population plays an important role in developing a country. A better health can promote economic growth and foster development of the country. The aim of this study was to investigate relationships among age, sex, weight, height, smoking behavior, and blood pressure on health status of adults in Indonesia. The path analysis was constructed using the secondary data of the fifth wave of the Indonesian Family Life Survey in 2014/2015. This survey was a large national survey with representing about 83% of the Indonesian population. The sample comprised 24,263 adults aged older than 17 years. The hypothesized model suggested that age, sex, weight, height, and smoking behavior had an effect on blood pressure and that all variables influenced health status. All path coefficients were statistically significant. The age, gender, and weight variables had positive relationships with blood pressure while in the opposite direction to the height and smoking behavior. The blood pressure, age, and smoking behavior had negative relationships with health status while in the opposite direction to the sex, weight, and height. Short male respondents who ever smoked and had high blood pressure were reported to have poor health status as age increased and weight decreased.

The Mexican Social Security Institute (IMSS) belongs to the Mexican health sector and provide health services to beneficiaries, employers, pensioners and retirees across Mexico. However, there are evidences that beneficiaries are not satisfied with the health services they receive. For this reason, with a sample of 417 out of 669 workers of the General Hospital number 1, located in the State of Colima, Mexico, was conducted this research to measure the level of satisfaction of IMSS workers, the factors underlying this satisfaction and to propose an instrument to measure job satisfaction in this Mexican institution with the purpose to generate efficient management models for workers that can help to improve the client satisfaction levels. Using Confirmatory Factor Analysis (CFA) was confirmed an instrument of 21 items and five factors (personal development, interpersonal relationship, recognition, work nature, and work environments) to measure job satisfaction in a Mexican hospital and also we shown evidence that the same construct is appropriate for both genders with the exception that female presented higher levels of satisfaction in their job for the personal development.

A farmer’s welfare classification can be performed to accommodate all significant issues that will assist policymakers, government, and scientists. This study aims to compare K-Nearest Neighbor (K-NN) and K-Means methods for clustering Indonesian farmers’ welfare using the fifth wave of Indonesia Family Life Survey (IFLS 5) data. The K-Means method is an unsupervised learning algorithm by classifying the data according to the closest distance between observed and centroids. The K-NN method is a supervised learning algorithm by classifying most of the nearest neighbour data. This study used fifteen factors affecting farmers’ welfare including land area, type of water, type of rice, income, expenditure, loan, mobile phone use, harvest frequency, crop failure, land ownership, gender, age, level of education, home ownership, and ownership of health insurance. The K-NN performed well to classify farmers’ welfare as the K-Means methods in the district data, with an accuracy of 89.8% compared to 53.7%. The K-NN classification results in provinces data showed that the provinces of Bali, East Java, South Kalimantan, Lampung, West Nusa Tenggara, South Sulawesi, and South Sumatra were included as prosperous provinces; while the provinces of Banten, DI Yogyakarta, West Java, Central Java, West Sumatra, and North Sumatra were included as non-prosperous provinces.

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