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The objective of this work is to identify which carcass and cut characteristics have the best discriminatory power, between sexes and slaughter weights, through discriminant analysis. Were used 32 goats, with initial average weights of 3.11 kg for males and 3.06 kg, for females, for animals slaughtered at 70 days; 3.65 kg for males and 3.25 kg for females for animals slaughtered at 100 days of weight. Objective assessments consisted of morphometric measurements: external carcass length (ECL); internal carcass length (ICL); leg length (LEL); chest width (CHW); croup width (CRW); thigh perimeter (THP); croup perimeter (CRP); chest perimeter (CHP); chest depth (CHD); internal chest depth (ICD) using the hypsometer and flexible tape (Truper®). In the total of 18 primary variables evaluated, the following variables were included in the discriminant model, using the stepwise method: empty body weight, chest depth, chest width, thigh circumference, neck, loin, leg length, and rump width. The discriminant analysis was efficient to discriminate and identify the carcass and cut characteristics with better discriminatory power between the sex and slaughter weight of the animals.

A typical approach to the joint analysis of two high-dimensional datasets is to decompose each data matrix into three parts: a low-rank common matrix that captures the shared information across datasets, a low-rank distinctive matrix that characterizes the individual information within a single dataset, and an additive noise matrix. Existing decomposition methods often focus on the orthogonality between the common and distinctive matrices, but inadequately consider the more necessary orthogonal relationship between the two distinctive matrices. The latter guarantees that no more shared information is extractable from the distinctive matrices. We propose decomposition-based canonical correlation analysis (D-CCA), a novel decomposition method that defines the common and distinctive matrices from the space of random variables rather than the conventionally used Euclidean space, with a careful construction of the orthogonal relationship between distinctive matrices. D-CCA represents a natural generalization of the traditional canonical correlation analysis. The proposed estimators of common and distinctive matrices are shown to be consistent and have reasonably better performance than some state-of-the-art methods in both simulated data and the real data analysis of breast cancer data obtained from The Cancer Genome Atlas. Supplementary materials for this article are available online.

Incipient fault detection in a hydraulic system is a challenge in the condition monitoring community. Existing research mainly monitors abnormal working conditions in hydraulic systems by separately detecting the key working parameter, which often causes a high miss warning rate for incipient faults due to the oversight of parameter dependence. A principal component analysis provides an effective method for incipient fault detection by taking the correlation of multiple parameters into consideration, but this technique assumes the systems are Gaussian-distributed, making it invalid for a dynamic non-Gaussian system. In this paper, we combine a canonical variable analysis (CVA) and adaptive kernel density estimation (AKDE) for the early fault detection of nonlinear dynamic hydraulic systems. The collected hydraulic system data set was used to construct the typical variable space, and the state space and residual space are divided to represent the characteristics of different correlations between the two variables, which are quantitatively described using Hotelling’s T2 and Q. In order to investigate the proper upper control limits, AKDE was utilised to estimate the underlying probability density functions of T2 and Q by taking the nonlinearity of the hydraulic system variables into consideration. The advantages of the proposed approach for incipient fault detection are illustrated via a marine power plant lubrication system.

The boiler is an essential energy conversion facility in a thermal power plant. One small malfunction or abnormal event will bring huge economic loss and casualties. Accurate and timely detection of abnormal events in boilers is crucial for the safe and economical operation of complex thermal power plants. Data-driven fault diagnosis methods based on statistical process monitoring technology have prevailed in thermal power plants, whereas the false alarm rates of those methods are relatively high. To work around this, this paper proposes a novel fault detection and identification method for furnace negative pressure system based on canonical variable analysis (CVA) and eXtreme Gradient Boosting improved by genetic algorithms (GA-XGBoost). First, CVA is used to reduce the data redundancy and construct the canonical residuals to measure the prediction ability of the state variables. Then, the fault detection model based on GA-XGBoost is schemed using the constructed canonical residual variables. Specially, GA is introduced to determine the optimal hyperparameters of XGBoost and speed up the convergence. Next, this paper presents a novel fault identification method based on the reconstructed contribution statistics, considering the contribution of state space, residual space and canonical residual space. Besides, the proposed statistics renders different weights to the state vectors, the residual vectors and the canonical residual vectors to improve the sensitivity of faulty variables. Finally, the real industrial data from a boiler furnace negative pressure system of a certain thermal power plant is used to demonstrate the ability of the proposed method. The result demonstrates that this method is accurate and efficient to detect and identify the faults of a true boiler.

ABSTRACT Genetic diversity is important for conservation and genetic improvement of common beans. This study aimed to estimate the genetic diversity among common bean genotypes from the Embrapa germplasm collection using univariate and multivariate analyses. The experiment was conducted in the region of Aquidauana-MS, at the State University of Mato Grosso do Sul, in a randomized block design with three replications and twenty-three genotypes, in 2021. The agronomic traits considered in the study were plant height, height to first pod, number of branches, number of pods per plant, number of grains per pod, hundred-grain mass, and grain yield. Descriptive analysis, univariate and multivariate variance analyses, mean clustering, phenotypic correlation network, UPGMA analysis (Unweighted Pair Group Method with Arithmetic Mean), and canonical variables were used to examine the data. The genotypes showed significant differences for plant height, height to first pod, number of branches per plant, number of pods per plant and yield, with potential for the selection of these traits. CNFC17278, CNFC17305, CNFC19133 and CNFC19198 showed superior yield potential compared to the other lines. The combined use of statistical methodologies can provide more information about the genotypes studied.

Explicit methods to generate new canonical sets of elements are presented, together with an analysis on the structure of existing canonical elements that are used in the study of the perturbed two-body problem. New non-singular sets of canonical elements are introduced. Among these sets, the resonant canonical variables are non-singular for circular orbits and display an action-angle-like behaviour for the unperturbed Kepler problem, if their associated generalized frequency is tuned to match the conserved energy. They also prove to be an efficient tool for both qualitative and quantitative studies of perturbations.

Pineapple cultivation worldwide depends on a limited number of cultivars. In Brazil, the cultivar ‘‘Pérola’’ accounts for 85% of the commercial planted area but is susceptible to fusariosis, the most significant disease affecting pineapple crops. This study introduces the cultivar ‘Unemat Rubi’, emphasizing its superior fruit quality and resistance to fusariosis through multivariate analyses, correlation networks, and genetic parameters. Eighteen genotypes were evaluated for qualitative and quantitative traits and resistance to fusariosis, using a randomized block design with five replications and 20 plants per plot. The genotypes were grouped into two main clusters based on their resistance or susceptibility to fusariosis. ‘Unemat Rubi’ was classified in Cluster I, along with its female parent (‘BRS Imperial’), sharing resistance to fusariosis, cylindrical fruits with yellow pulp, and no leaf spines. However, ‘Unemat Rubi’ was superior to ‘BRS Imperial’ in terms of fruit weight and diameter, presenting a mass above 1.5 kg and an average diameter above 10 cm. No correlations were observed between the groups of chemical and physical traits of fruit and resistance to fusariosis, only between the groups of physical and chemical traits. There was a high and positive correlation for FMWOC and FMWC (0.99) and a strong and significant correlation between DLL with FMWOC and FMWC, both with 0.74. Heritability estimates exceeded 90% for most traits, except for fruit diameter. The cultivar ‘Unemat Rubi’, registered at the Brazilian Ministry of Agriculture and Livestock under number 56,622, represents a significant advancement in pineapple breeding by integrating superior fruit quality with fusariosis resistance, making it a promising candidate for commercial expansion.

The production of specialty coffee has several factors and parameters that are added up in the course of production, so that the quality is expressed in the act of consumption. Based on this scenario, this study included the analysis of ten genotypes of arabica coffee, the materials being subjected to irrigated and rainfed water regimes, in a low altitude region, to identify responses for sensory and physical–chemical quality. The genotypes were evaluated in a split-plot scheme with a randomized block design, with three replications. Arabica coffee fruits were harvested with 80% cherry seeds and processed by the wet method. Subsequently, the characteristics related to physical–chemical and sensory analyses were evaluated. The genotypes of the Paraíso group showed great variability for the physical–chemical and sensory variables for rainfed and irrigated regimes. The genotypes of the Catuaí group, however, showed less variability for sensory characteristics in both cultivation environments and for physical–chemical characteristics in the irrigated regime. In the sensorial data set, the genotypes Catuaí 144 CCF and Catuaí 144 SFC (when irrigated) and Paraíso H 419-3-3-7-16-2, Paraíso H 419-3-3-7-16-11 and Catucaí 24-137 (rainfed cultivation), are more favourable to the production of specialty coffee at low altitude.

Examined in this paper is the Gray and Wyner source coding for a simple network of correlated multivariate Gaussian random variables, Y1:Ω→Rp1 and Y2:Ω→Rp2. The network consists of an encoder that produces two private rates R1 and R2, and a common rate R0, and two decoders, where decoder 1 receives rates (R1,R0) and reproduces Y1 by Y^1, and decoder 2 receives rates (R2,R0) and reproduces Y2 by Y^2, with mean-square error distortions E||Yi−Y^i||Rpi2≤Δi∈[0,∞],i=1,2. Use is made of the weak stochastic realization and the geometric approach of such random variables to derive test channel distributions, which characterize the rates that lie on the Gray and Wyner rate region. Specific new results include: (1) A proof that, among all continuous or finite-valued random variables, W:Ω→W, Wyner’s common information, C(Y1,Y2)=infPY1,Y2,W:PY1,Y2|W=PY1|WPY2|WI(Y1,Y2;W), is achieved by a Gaussian random variable, W:Ω→Rn of minimum dimension n, which makes the two components of the tuple (Y1,Y2) conditionally independent according to the weak stochastic realization of (Y1,Y2), and a the formula C(Y1,Y2)=12∑j=1nln1+dj1−dj, where di∈(0,1),i=1,…,n are the canonical correlation coefficients of the correlated parts of Y1 and Y2, and a realization of (Y1,Y2,W) which achieves this. (2) The parameterization of rates that lie on the Gray and Wyner rate region, and several of its subsets. The discussion is largely self-contained and proceeds from first principles, while connections to prior literature is discussed.

In this work, the secular evolution of exoplanetary systems is investigated, when the variability of the masses of celestial bodies is the leading factor of dynamical evolution. The masses of the parent star and the planets change due to the particles leaving the bodies and falling on them. At the same time, bodies masses are assumed to change isotropically at different rates. The law of mass change is considered to be known and given function of time. The relative motions of the planets are investigated by the methods of the canonical perturbation theory in the absence of resonances. It is assumed that the orbits of the planets do not intersect. Evolutionary equations in analogues of Poincaré variables (Λi, λi, ξi, ηi, pi, qi) are obtained and used to study the K2-3 exoplanetary system. All analytical and numerical calculations are performed with the aid of the Wolfram Mathematica.