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Simple Summary: Growth traits have been widely studied as economically important traits in the beef cattle industry. However, traditional studies often miss how these traits change under different environmental conditions, only focusing on single genetic changes that affect traits directly. In our study, we analyzed how genetics and environment interact to affect growth in beef cattle, considering four growth traits and two environmental factors. This analysis uncovered several genetic markers for growth traits that are not usually evident in standard studies, showing that some genes have effects that can be obliterated by environmental conditions Further testing showed whether these genetic markers are grouped in specific genes or functional pathways, helping us understand how genetics can influence growth under different environmental conditions. By uncovering novel genetic loci, genes, and candidate biological mechanisms associated with growth traits, our study provides valuable information for selection prediction and breeding decisions in the beef cattle industry. Complex traits are widely considered to be the result of a compound regulation of genes, environmental factors, and genotype-by-environment interaction (G × E). The inclusion of G × E in genome-wide association analyses is essential to understand animal environmental adaptations and improve the efficiency of breeding decisions. Here, we systematically investigated the G × E of growth traits (including weaning weight, yearling weight, 18-month body weight, and 24-month body weight) with environmental factors (farm and temperature) using genome-wide genotype-by-environment interaction association studies (GWEIS) with a dataset of 1350 cattle. We validated the robust estimator's effectiveness in GWEIS and detected 29 independent interacting SNPs with a significance threshold of 1.67 × 10−6, indicating that these SNPs, which do not show main effects in traditional genome-wide association studies (GWAS), may have non-additive effects across genotypes but are obliterated by environmental means. The gene-based analysis using MAGMA identified three genes that overlapped with the GEWIS results exhibiting G × E, namely SMAD2, PALMD, and MECOM. Further, the results of functional exploration in gene-set analysis revealed the bio-mechanisms of how cattle growth responds to environmental changes, such as mitotic or cytokinesis, fatty acid β-oxidation, neurotransmitter activity, gap junction, and keratan sulfate degradation. This study not only reveals novel genetic loci and underlying mechanisms influencing growth traits but also transforms our understanding of environmental adaptation in beef cattle, thereby paving the way for more targeted and efficient breeding strategies. [ABSTRACT FROM AUTHOR]

This study investigates the effectiveness of robust estimators of location and dispersion, used in proficiency testing and listed in ISO 13528:2015, in outlier detection. The models utilize (a) kernel density plots, (b) Z-factors, (c) Monte Carlo simulations, and (d) distributions derived from at most two contaminating distributions and one main Gaussian. The simulation parameters cover a wide range of those commonly encountered in proficiency testing (PT) schemes, so the results presented are of fairly general application. We chose a functional sub-optimal solution by grouping and classifying the model settings, resulting in five matrices readily usable for selecting the best robust estimator. Whenever at most half of the distribution of each contaminating population is outside the central distribution, there is only one optimal estimator. For all other cases, the five matrices provide the appropriate robust statistic. The proposed method applies to 95.1% of 144 results for an existing PT for cement. These actual datasets indicate that the Hampel estimator for the mean and the Q-method for the standard deviation provide the most appropriate performance statistic in 86.1% of the cases. [ABSTRACT FROM AUTHOR]

Energy storage technologies are being used excessively in industrial applications and in automobiles. Battery state of charge (SOC) is an important metric to be monitored in these applications to ensure proper and safe functionality. Since SOC cannot be measured directly, this paper puts forth a novel machine learning architecture to improve on the existing methods of SOC estimation. This method consists of using combined stacked bi-directional LSTM and encoder–decoder bi-directional long short-term memory architecture. This architecture henceforth represented as SED is implemented to overcome the nonparallel functionality observed in traditional RNN algorithms. Estimations were made utilizing different open-source datasets such as urban dynamometer driving schedule (UDDS), highway fuel efficiency test (HWFET), LA92 and US06. The least Mean Absolute Error observed was 0.62% at 25 °C for the HWFET condition, which confirms the good functionality of the proposed architecture. [ABSTRACT FROM AUTHOR]

This study aims at developing models in analyzing the results of proficiency testing (PT) schemes for a limited number of participants. The models can determine the best estimators of location and dispersion using unsatisfactory results as a criterion by combining: (a) robust and classical estimators; (b) kernel density plots; (c) Z-factors; (d) Monte Carlo simulations; (e) distributions derived from the addition of one or two contaminating distributions and one main Gaussian. The standards ISO 13258:2015, ISO 5725:2:1994, and EN ISO/IEC 17043:2010 are the basis of the analysis. The study describes an algorithm solving the optimization problem for (a) Gaussian, bimodal or trimodal distributions; (b) participating labs from 10 to 30; (c) fraction of the contaminating population up to 0.10; (d) variation coefficient of the main distribution equal to 2; (e) equal standard deviations of all the distributions, and provide figures with the optimal estimators. We also developed a generalized algorithm using kernel density plots and the previous algorithm, which is not subject to restrictions (b)–(e) and implemented in the results of a PT for the 28-day strength of cement with 12–13 participants. Optimal estimators' figures and the generalized algorithm are helpful for a PT expert in choosing robust estimators. [ABSTRACT FROM AUTHOR]

For an industrial process, the estimation of feeding composition is important for analyzing production status and making control decisions. However, random errors or even gross ones inevitably contaminate the actual measurements. Feeding composition is conventionally obtained via discrete and low-rate artificial testing. To address these problems, a feeding composition estimation approach based on data reconciliation procedure is developed. To improve the variable accuracy, a novel robust M-estimator is first proposed. Then, an iterative robust hierarchical data reconciliation and estimation strategy is applied to estimate the feeding composition. The feasibility and effectiveness of the estimation approach are verified on a fluidized bed roaster. The proposed M-estimator showed better overall performance. [ABSTRACT FROM AUTHOR]

Although several data-driven soft sensors are available, online reliable prediction of the Mooney viscosity in industrial rubber mixing processes is still a challenging task. A robust semi-supervised soft sensor, called ensemble deep correntropy kernel regression (EDCKR), is proposed. It integrates the ensemble strategy, deep brief network (DBN), and correntropy kernel regression (CKR) into a unified soft sensing framework. The multilevel DBN-based unsupervised learning stage extracts useful information from all secondary variables. Sequentially, a supervised CKR model is built to explore the relationship between the extracted features and the Mooney viscosity values. Without cumbersome preprocessing steps, the negative effects of outliers are reduced using the CKR-based robust nonlinear estimator. With the help of ensemble strategy, more reliable prediction results are further obtained. An industrial case validates the practicality and reliability of EDCKR. [ABSTRACT FROM AUTHOR]

In an era of energy transition involving an increase in renewable energy and a reduction in coal-fired power generation and nuclear power generation, the role of combined heat and power (CHP) as a bridging energy is highly emphasized. This article attempts to look empirically into the impact of increasing the share of renewable energy in total electricity generation on CHP share in total electricity generation in a cross-country context. Data from 35 countries during the period 2009–2015 were used, and the least absolute deviations estimator was applied to obtain a more robust parameter estimate. The results showed that a 1%p increase in the share of renewable energy significantly increased the CHP share by 0.87%p. Therefore, the hypothesis that CHP serves as bridge energy in the process of energy transition was established. [ABSTRACT FROM AUTHOR]