Abstract: To adequately meet the nutritional needs of broilers, it is necessary to know the values of apparent metabolizable energy corrected by the nitrogen balance (AMEn) of the feedstuffs. To determine AMEn values, biological assays, feedstuff composition tables, or prediction equations are used as a function of the chemical composition of feedstuffs, the latter using statistical methodologies such as multiple linear regression, neural networks, and Bayesian networks (BN). BN is a statistical and computational methodology that consists of graphical (graph) and probabilistic models of quantitative and/or qualitative variables. Ensembles of BN in the area of broiler nutrition are expected, as there is no research showing their AMEn prediction performance. The purpose of this article is to propose and use ensembles of hybrid Bayesian networks (EHBNs) and obtain prediction equations for the AMEn of feedstuffs used in broiler nutrition from their chemical compositions. We trained 100, 1,000, and 10,000 EHBN, and in this way, empirical distributions were found for the coefficients of the covariates (crude protein, ether extract, mineral matter, and crude fiber). Thus, the mean, median, and mode of these distributions were calculated to build prediction equations for AMEn. It is observed that the method for obtaining the coefficients of the covariates discussed in this article is an unprecedented proposal in the field of broiler nutrition. The data used to obtain the equations were obtained by meta-analysis, and the data for the validation of the equations were obtained from metabolic tests. The proposed equations were evaluated by precision measures such as the mean square error (MSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE). The best equations for predicting AMEn were derived from the mean or mode coefficients for the 10,000 EHBN results. In conclusion, the methodology used is a good tool to obtain prediction equations for AMEn as a function of the chemical composition of their feedstuffs. The coefficients were found to differ from those found by other methodologies, such as the usual neural network or multiple linear regressions. The field of broiler nutrition contributed with new equations and with a never-applied methodology and differentiated in obtaining its coefficients by empirical distributions.

Abstract: One of the most widely used electro-mechanical systems in large-scale mining is the electric motor. This device is employed in practically every phase of production. For this reason, it needs to be inspected regularly to maintain maximum operability, thus avoiding unplanned stoppages. In order to identify potential faults, regular check-ups are performed to measure the internal parameters of the components, especially the brushes and brush-holders. Both components must be properly aligned and calibrated to avoid electric arcs to the internal insulation of the motor. Although there is an increasing effort to improve inspection tasks, most inspection procedures are manual, leading to unnecessary costs in inspection time, errors in data entry, and, in extreme cases, measurement errors. This research presents the design, development, and assessment of an integrated measurement prototype for measuring spring tension and other key parameters in brush-holders used in electric motors. It aims to provide the mining industry with a new, fully automatic inspection system that will facilitate maintenance and checking. Our development research was carried out specifically on the brush system of a SAG grinding mill motor. These machines commonly use SIEMENS motors; however, the instrument can be easily adapted to any motor by simply changing the physical dimensions of the prototype.

Abstract: The novel multiferroic flexible films of Dy2NiMnO6 (DNMO) were prepared by incorporating DNMO particles in Polyvinylidene fluoride-based co-polymeric P(VDF-HFP) system. The structural studies validated the crystalline structure of DNMO remains unaffected by the presence of polymer. The scanning electron microscopic image of the composite sample confirms the uniform distribution of DNMO particles in the polymeric matrix. The enhancement in the ferroelectric parameters was observed for the composite sample in comparison with P(VDF-HFP) polymeric film. The composite sample exhibits a large polarization and coercive field at room temperature with enhanced dielectric behavior with reduced dielectric loss. M-H curves of composite show ferromagnetic nature with small increase in coercivity at 5 K. The field- and temperature-dependent magnetic characteristics of DNMO have remained unaltered even though the engagement of P(VDF-HFP) for achieving flexibility. The simultaneous existence of ferroelectric and magnetic order in the flexible DNMO may discover its application in the field of flexible magnetoelectronics.

Abstract: For decades, Out-of-Stock (OOS) events have been a problem for retailers and manufacturers. In grocery retailing, an OOS event is used to characterize the condition in which customers do not find a certain commodity while attempting to buy it. This paper focuses on addressing this problem from a manufacturer’s perspective, conducting a case study in a retail packaged foods manufacturing company located in Latin America. We developed two machine learning based systems to detect OOS events automatically. The first is based on a single Random Forest classifier with balanced data, and the second is an ensemble of six different classification algorithms. We used transactional data from the manufacturer information system and physical audits. The novelty of this work is our use of new predictor variables of OOS events. The system was successfully implemented and tested in a retail packaged foods manufacturer company. By incorporating the new predictive variables in our Random Forest and Ensemble classifier, we were able to improve their system’s predictive power. In particular, the Random Forest classifier presented the best performance in a real-world setting, achieving a detection precision of 72% and identifying 68% of the total OOS events. Finally, the incorporation of our new predictor variables allowed us to improve the performance of the Random Forest by 0.24 points in the F-measure.

Abstract: Due to the development of integrated circuits and the lack of responsiveness to existing technology, researchers are looking for an alternative technology. Quantum-dot cellular automata (QCA) technology is one of the promising alternatives due to its higher switch speed, lower power dissipation, and higher device density. One of the most important and widely used circuits in digital logic calculations is the full adder (FA) circuit, which actually creates the problem of finding its optimal design and increasing performance. In this paper, we designed and implemented two new FA circuits in QCA technology and then implemented ripple carry adder (RCA) circuits. The proposed FAs and RCAs showed excellent performance in terms of QCA evaluation parameters, especially in cost and cost function, compared to the other reported designs. The proposed adders' approach was 46.43% more efficient than the best-known design, and the reason for this superiority was due to the coplanar form, without crossovers and inverter gates in the designs.