Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions

Bias in dairy genetic evaluations, when it exists, has to be understood and properly addressed. The origin of biases is not always clear. We analyzed 40 yr of records from the Lacaune dairy sheep breeding program to evaluate the extent of bias, assess possible corrections, and emit hypotheses on its origin. The data set included 7 traits (milk yield, fat and protein contents, somatic cell score, teat angle, udder cleft, and udder depth) with records from 600,000 to 5 million depending on the trait, ～1,900,000 animals, and ～5,900 genotyped elite artificial insemination rams. For the ～8% animals with missing sire, we fit 25 unknown parent groups. We used the linear regression method to compare “partial” and “whole” predictions of young rams before and after progeny testing, with 7 cut-off points, and we obtained estimates of their bias, (over)dispersion, and accuracy in early proofs. We tried (1) several scenarios as follows: multiple or single trait, the “official” (routine) evaluation, which is a mixture of both single and multiple trait, and “deletion” of data before 1990; and (2) several models as follows: BLUP and single-step genomic (SSG)BLUP with fixed unknown parent groups or metafounders, where, for metafounders, their relationship matrix gamma was estimated using either a model for inbreeding trend, or base allele frequencies estimated by peeling. The estimate of gamma obtained by modeling the inbreeding trend resulted in an estimated increase of inbreeding, based on markers, faster than the pedigree-based one. The estimated genetic trends were similar for most models and scenarios across all traits, but were shrunken when gamma was estimated by peeling. This was due to shrinking of the estimates of metafounders in the latter case. Across scenarios, all traits showed bias, generally as an overestimate of genetic trend for milk yield and an underestimate for the other traits. As for the slope, it showed overdispersion of estimated breeding values for all traits. Using multiple-trait models slightly reduced the overestimate of genetic trend and the overdispersion, as did including genomic information (i.e., SSGBLUP) when the gamma matrix was estimated by the model for inbreeding trend. However, only deletion of historical data before 1990 resulted in elimination of both kind of biases. The SSGBLUP resulted in more accurate early proofs than BLUP for all traits. We considered that a snowball effect of small errors in each genetic evaluation, combined with selection, may have resulted in biased evaluations. Improving statistical methods reduced some bias but not all, and a simple solution for this data set was to remove historical records.     

Efficiency evaluation of healthcare services in China based on stochastic multicriteria acceptability analysis and directional distance function

Evaluating the efficiency of healthcare services accurately can help in analyzing the rationality of inputs and outputs in such services. Considering the consistency and equity of assessment criteria, this study conducts the stochastic multicriteria acceptability analysis (SMAA-2) with a directional distance function to evaluate the efficiency of healthcare services in 31 provincial administrative regions of mainland China, as observed in 2018. We use SMAA-DDF to explore all the projection directions to the efficient frontier instead of a certain projection direction. We measure the maximum and average efficiencies for each of the 31 provincial healthcare services. Our empirical findings show that only seven provinces achieve optimal healthcare service efficiency; the eastern area performed the best, followed by the central, western, and northeast areas. Furthermore, the path along the projection directions is provided to help inefficient provinces improve their efficiency and obtain the best possible positions.     

Acidification of the electrolyte during the galvanic corrosion of AA7075: A numerical and experimental study

The electrochemical interactions between aluminum alloy 7075 and low-carbon steels under gelled electrolytes were studied. Such electrolytes provided the opportunity to investigate both thick and thin electrolyte systems. The electrolyte was chemically modified to visually track the acidic fronts during the anodic reaction and the subsequent hydrolysis process. Two mathematical models were validated for both thick and ultrathin electrolytes. The acidification of thick electrolytes was extended some millimeters beyond the aluminum alloy surface, whereas the acidic front was localized next to the metallic joint using ultrathin electrolytes. The combination of both numerical and experimental results allows proving (and explaining why) that the acidification process is more aggressive under dilute than under concentrated electrolytes.     

Form selection of concomitant polymorphs: A case study informed by crystallization kinetics modeling

Molecular mechanisms and process kinetics of crystallizing concomitant polymorphs remain poorly understood. Solvent-mediated phase transformation and concomitant crystallization are difficult to be distinguished in practice, as multiple forms can be detected at the same time. Herein, we developed a population balance model to simulate a concomitant crystallization process of two polymorphs of tolfenamic acid. Our kinetic modeling aims to understand concomitant crystallization and help guide form selection of such a molecular system. Crystallization kinetics of ethanolic solutions were uncovered from induction time measurements, as well as seeded and unseeded crystallization experiments. Experimental and simulation results demonstrate that the stable form I crystallizes concomitantly with the metastable form II. The faster growing form II results in an intermediate decline in the composition of form I in crystallized samples, a characteristic feature of the concomitantly crystallized system. A four-quadrant scheme of attainable polymorph outcome was simulated under various crystallization conditions.     

A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

Mathematical Modeling of Drying Processes of Selected Fruits and Vegetables

This study investigates the behavior of fruit and vegetable samples during drying. The experimental data are fitted to several different thin-layer drying models. Regression analysis is used to determine model parameters, while statistical indicators serve to evaluate the goodness of fit. The power function model gives the best fit for all examined samples. Based on this model, different drying and heat storage technologies can be combined to ensure that the required residual moisture content of an agricultural product is reached. It is demonstrated on the case of a specific Togolese processing plant that under favorable conditions, fossil fuel consumption can be decreased by 33 %.     

Comparing open data benchmarks: Which metrics and methodologies determine countries’ positions in the ranking lists?

An understanding of the similar and divergent metrics and methodologies underlying open government data benchmarks can reduce the risks of the potential misinterpretation and misuse of benchmarking outcomes by policymakers, politicians, and researchers. Hence, this study aims to compare the metrics and methodologies used to measure, benchmark, and rank governments' progress in open government data initiatives. Using a critical meta-analysis approach, we compare nine benchmarks with reference to meta-data, meta-methods, and meta-theories. This study finds that both existing open government data benchmarks and academic open data progress models use a great variety of metrics and methodologies, although open data impact is not usually measured. While several benchmarks’ methods have changed over time, and variables measured have been adjusted, we did not identify a similar pattern for academic open data progress models. This study contributes to open data research in three ways: 1) it reveals the strengths and weaknesses of existing open government data benchmarks and academic open data progress models; 2) it reveals that the selected open data benchmarks employ relatively similar measures as the theoretical open data progress models; and 3) it provides an updated overview of the different approaches used to measure open government data initiatives’ progress. Finally, this study offers two practical contributions: 1) it provides the basis for combining the strengths of benchmarks to create more comprehensive approaches for measuring governments’ progress in open data initiatives; and 2) it explains why particular countries are ranked in a certain way. This information is essential for governments and researchers to identify and propose effective measures to improve their open data initiatives.