Number Of New Variables Research Articles

The investigation of a complex productivity indicator based on the main components method for evaluating sheep of North Caucasian Meat and Wool breed

Improving the productive traits of animals through the use of genetic potential is impossible without a systematic evaluation of productivity characteristics and conformation traits. Special attention has always been paid to the study of the physique of animals in association with their productivity. The priorities in the selection of farm animals today are the parameters of meat productivity. For breeding work in order to increase productivity indicators in sheep, it is necessary to use the most informative parameters. The above dictates the need to apply new approaches to the evaluation of phenotypic parameters. Each trait has certain significance in the description of the phenotype, which can be evaluated on the basis of mathematical methods of analysis. Mathematical analysis, as well as mathematical modeling methods, is successfully used in animal husbandry. A new approach to the lifetime evaluation of sheep meat productivity using a multidimensional analysis method as the analysis of the main components has been presented in the article. The main purpose of the applied analysis method is to reduce the dimensionality for a multidimensional matrix of initial data. As a result of this analysis, a certain number of new variables are obtained which cannot be isolated during the direct study of the object, but has a high degree of correlation with the actual traits. We have described one of the possible ways to solve the problem of forming a complex numerical indicator using the principal component method. This indicator is called a complex indicator of productivity. At the same time, it is expected that the indicator under consideration does not impose restrictions on the number of parameters used in its formation. It is assumed that the high value of the complex productivity indicator obtained on the basis of lifetime measurements of the physique of animals will correspond to the same high value of the complex indicator obtained on the basis of slaughter parameters. As the result of the analysis, the mathematical and statistical method for calculating the correlation between lifetime traits and slaughter indicators of animal productivity has been carried out. On the base of conducted method of mathematical analysis, it can be assumed that the complex indicator can be used as the criterion for evaluating the productivity of sheep.

Principal component and multivariate factor analysis of detailed sheep milk fatty acid profile

Fatty acid (FA) profile is one of the most important aspects of the nutritional properties of milk. The FA content in milk is affected by several factors such as diet, physiology, environment, and genetics. Recently, principal component analysis (PCA) and multivariate factor analysis (MFA) have been used to summarize the complex correlation pattern of the milk FA profile by extracting a reduced number of new variables. In this work, the milk FA profile of a sample of 993 Sarda breed ewes was analyzed with PCA and MFA to compare the ability of these 2 multivariate statistical techniques in investigating the possible existence of latent substructures, and in studying the influence of physiological and environmental effects on the new extracted variables. Individual scores of PCA and MFA were analyzed with a mixed model that included the fixed effects of parity, days in milking, lambing month, number of lambs born, altitude of flock location, and the random effect of flock nested within altitude. Both techniques detected the same number of latent variables (9) explaining 80% of the total variance. In general, PCA structures were difficult to interpret, with only 4 principal components being associated with a clear meaning. Principal component 1 in particular was the easiest to interpret and agreed with the interpretation of the first factor, with both being associated with the FA of mammary origin. On the other hand, MFA was able to identify a clear structure for all the extracted latent variables, confirming the ability of this technique to group FA according to their function or metabolic origin. Key pathways of the milk FA metabolism were identified as mammary gland de novo synthesis, ruminal biohydrogenation, desaturation performed by stearoyl-coenzyme A desaturase enzyme, and rumen microbial activity, confirming previous findings in sheep and in other species. In general, the new extracted variables were mainly affected by physiological factors as days in milk, parity, and lambing month; the number of lambs born had no effect on the new variables, and altitude influenced only one principal component and factor. Both techniques were able to summarize a larger amount of the original variance into a reduced number of variables. Moreover, factor analysis confirmed its ability to identify latent common factors clearly related to FA metabolic pathways.

Informed Clustering of Donation Service Areas for Improved Benchmarking.

The donation community continuously strives to collaborate and share effective practices to further the mission of saving and healing lives. Donation service areas in which the Organ Procurement Organizations (OPOs) work are multifaceted in their demographics, inciting the Organ Procurement and Transplantation Network to consider a more holistic and objective measure of similarity rather than the size of population alone or locational proximity alone. This would allow OPOs, as a part of their quality improvement efforts, to learn from and mentor other organizations that are dealing with similar challenges. By incorporating multiple informative characteristics together, we can distinguish those likenesses only revealed by taking into account multiple factors simultaneously. We used statistical approaches that take many characteristics of interest describing a donation service area and purposely excluded performance measures that an OPO may be able to influence by their own practices. Unsupervised learning methods combined the original characteristics into a smaller number of new variables, eliminating correlation and overlap in information from the original characteristics, and clustered donation service areas based on the general characteristics and population of the area. This analysis is a first step in providing a different perspective for OPOs to learn from other organizations that may face similar challenges, as well as to share best practices and open new lines of communication.

Regional Diversity of Attractiveness and Entrepreneurship of Tourism – Challenges for Local Governments on the Example of the Świętokrzyskie Voivodeship

Purpose. The study aims to diagnose the tourist attractiveness of the Świętokrzyskie voivodeship poviats based on features of the geographical and socio-economic environment. The recognition of local development problems regarding organisation in local action groups that affect the creation of the tourist function of a village and the creation of new tourist products are also assessed by residents. Method. Principal Component Analysis was used as the research method, in which the process of reducing multidimensionality, i.e. reducing the number of features, occurs. A smaller number of new variables explains and simplifies the interpretation of the phenomenon with little information loss. The components are the result of data reduction and a way to obtain a simplified image of the relationship based on the correlation structure between the observed features. Fourteen Świętokrzyskie poviats were analysed due to 10 features that express the multi-faceted nature of the local economy in the aspect of tourist attractiveness and entrepreneurship for 2018. Then, using the hierarchical Ward method, types of poviats with a similar structure of phenomena related to the tourist development of the area were presented. The obtained components can be treated as the main meta-features of the structural dimension of the attractiveness and entrepreneurship space of the Świętokrzyskie region. Findings. It has been shown is studies that tourism plays one of the key roles in the development of a region. It is an important element of activating the local economy. The 3 calculated main components explain 78.1% of the variation of the original features. The following names for the given factors may be suggested: "poviat entrepreneurship" (explained by the first component - containing 43% variability of the original features), "accommodation and transport availability" (second component - 19% variation of the original features), "EU funding" (third component -16% variation of original features). The classification indicates the similarity of poviats within the distinguished classes from the point of view of more than one factor measuring the level of urbanisation of the area. Four classes of poviats can be distinguished: the first class is the city of Kielce, the second – the buski and kielecki poviats, the third, which is very numerous – 10 poviats and the fourth – the kazimiersk poviat Research and conclusions limitations. The survey studies were conducted in 2018 on a small sample of 60 people, which does not allow to draw general conclusions, but their results may facilitate the diagnosis of tourism development. Gestors and local government officials involved in local action groups, who agreed to assess the strengths and weaknesses of tourism at their place of residence, were selected for the survey. Practical implications. The work may be of significance to various decision-making entities interested in the structure and position of their poviat in comparison to other poviats. The study is an assessment and diagnosis of the state of local development related to tourism. The actions implemented for the development of tourism infrastructure, the use of EU subsidies in poviats and the degree of implementation of the activities adopted in the tourism development strategy were identified. Originality.Detection of unknown dependencies and regularities in tourism economy among spatial systems using multidimensional methods. Comprehensive development using a quantitative and qualitative approach. Type of paper. A case study - counties of the Swiętokrzyskie voivodeship

New variable stars towards the Galactic Bulge. I. The bright regime★†

We report the detection of 1143 variable stars towards the Galactic bulge, including 320 previously uncatalogued variables, using time-series photometry extracted from data obtained with the VIMOS imager at the Very Large Telescope. Observations of the Sagittarius Window Eclipsing Extrasolar Planet Search (SWEEPS) field in the Galactic Bulge were taken over 2 years between March and October at a cadence of $\sim$ 4 days, enabling the detection of variables with periods up to $\sim$100 days. Many of these were already known, but we detected a significant number of new variables, including 26 Cepheids, a further 18 Cepheid candidates, and many contact binaries. Here we publish the catalog of the new variables, containing coordinates, mean magnitudes as well as periods and classification; full light curves for these variables are also made available electronically.

Determinantes de la apertura de datos… y rendición de cuentas en los gobiernos regionales españoles

In order to improve governments’ transparency and accountability a large volume of literature has been developed searching for the key factors that determine greater dissemination of public information. However, most of this research was focused on municipalities but almost none on the regional level. This paper aims to fill the gap in the literature related to the regional governments and to highlight the differences between key factors of dissemination and transparency in local and regional governments. For this comparison, the field of study for those key factors are the Spanish Autonomous Communities, what allows taking advantage on the rich literature focused on Spanish municipalities. To this end, a transparency index for governmental open data portals and another one for both transparency portals and institutional web sites are constructed as a weighted sum of 20 information items related to governments’ transparency and accountability. Weightings are based on the Tim Tim Berners-Lee open data scale; therefore, both indexes value not only the dissemination but also the openness and reutilization levels of the information. In order to figure out what key factors are associated with different levels in transparency, an analysis on the existing literature about the determinants of accountability and selection of the most common variables was conducted. In addition, a number of new variables were included summing up a total of 26 which are divided into six categories related to quality of governance, economic performance, social and demographic characteristics, fiscal and budgetary policies and regional specific identity features. Results show that absolute majority and political strength are associated with lower levels of transparency while the greater levels are related to existing regulation on transparency, economic competitiveness and budget expenditure on education and health. Nevertheless, the most outstanding finding is the positive link to regional specific identity features, which leads to conclude that the idiosyncrasy of each type of government are key to improve transparency and accountability.

On deflation and multiplicity structure

This paper presents two new constructions related to singular solutions of polynomial systems. The first is a new deflation method for an isolated singular root. This construction uses a single linear differential form defined from the Jacobian matrix of the input, and defines the deflated system by applying this differential form to the original system. The advantages of this new deflation is that it does not introduce new variables and the increase in the number of equations is linear in each iteration instead of the quadratic increase of previous methods. The second construction gives the coefficients of the so-called inverse system or dual basis, which defines the multiplicity structure at the singular root. We present a system of equations in the original variables plus a relatively small number of new variables that completely deflates the root in one step. We show that the isolated simple solutions of this new system correspond to roots of the original system with given multiplicity structure up to a given order. Both constructions are “exact” in that they permit one to treat all conjugate roots simultaneously and can be used in certification procedures for singular roots and their multiplicity structure with respect to an exact rational polynomial system.

Valuations on rational function fields that are invariant under permutation of the variables

We study and characterize the class of valuations on rational functions fields that are invariant under permutation of the variables and can be extended to valuations with the same property whenever a finite number of new variables is adjoined. The Gauß valuation is in this class, which constitutes a natural generalization of the concept of Gauß valuation. Further, we apply our characterization to show that the most common ad hoc generalization of the Gauß valuation is also in this class.

A multivariate data analysis approach towards vibration analysis and vibration-based damage assessment:: Application for delamination detection in a composite beam

This paper introduces a novel methodology for structural vibration analysis and vibration-based monitoring which utilises a special type of Principal Components Analysis (PCA), known as Singular Spectrum Analysis (SSA). In this study the methodology is introduced and demonstrated for the purposes of damage assessment in structures using their free decay response. The method׳s damage assessment properties are first demonstrated on a numerical example for a two degree-of-freedom (2DoF) spring–mass and damper system with nonlinear stiffness. The method is then applied to an experimental case study of a composite laminate beam. The method is based on the decomposition of the frequency domain structural variation response using new variables, the Principal Components (PCs). Only a certain number of new variables are used to approximate the original vibration signal with very good accuracy. The presented results demonstrate the potential of the method for vibration based signal reconstruction and damage diagnosis. The healthy and the different damaged scenarios are clearly distinguishable in the new space of only two reconstructed components where a strong clustering effect is observed.

The Dawn of a New Age? Democracies and Military Victory

Democracies are perceived often by the public as relatively docile and not suited best for wars. This paper challenges this perception by analyzing the relationship between regime type and war outcomes. It builds upon David Lake’s 1992 model in “Powerful Pacifists: Democratic States and War” by updating it to the present period. It examines the empirical evidence and scholarly debate in order to test and elaborate on the argument that democratic states in the modern era are more prone to fighting and winning wars. This paper, furthermore, adds a number of new variables to the Lake model to analyze the possibility that more factors are needed in the equation to better understand regime type and war outcomes. This research finds that regime type and alliances are significant variables in winning wars and that democracies win the large majority (84%) of wars that they are involved in. The paper concludes with recommendations for further research.

ANS-based preprocessing of company performance indicators

This work examines an artificial neural system (ANS) capable of dimensionality-reduction and its fitness to a business data analysis problem. While ANSs are often used in the later stages of explorative data analysis to place similar cases in clusters or to identify known patterns, the role of the ANS here is in the early pre-processing stage, to produce a smaller number of new variables which maintain important structures in the data. The ANS examined is a specially configured variation of the back-propagation multilayer perceptron (BP-MLP). The method is tested against others, on a supervised classification problem where the goal is to separate groups of similarly performing companies in the food and beverage sector of the Greek industry from their economic and non-economic characteristics. A typical pattern recognition algorithm is applied to classify the companies in performance groups, using a minimal pre-processing of the original company data b dimensionality-reduction through principal component analysis (PCA) c variables resulting from the examined ANS-based pre-processing method. The method’s ability to produce useful variables is evaluated by comparing the various groups of companies produced by the classifier algorithm using the three procedures.

Logical Function Decomposition Method for Synthesis of Digital Logical System Implemented with Programmable Logic Devices (PLD)

The paper consists in the use of some logical functions decomposition algorithms with application in the implementation of classical circuits like SSI, MSI and PLD. The decomposition methods use the Boolean matrix calculation. It is calculated the implementation costs emphasizing the most economical solutions. One important aspect of serial decomposition is the task of selecting “best candidate” variables for the G function. Decomposition is essentially a process of substituting two or more input variables with a lesser number of new variables. This substitutes results in the reduction of the number of rows in the truth table. Hence, we look for variables which are most likely to reduce the number of rows in the truth table as a result of decomposition. Let us consider an input variable purposely avoiding all inter-relationships among the input variables. The only available parameter to evaluate its activity is the number of “l”s or “O”s that it has in the truth table. If the variable has only “1” s or “0” s, it is the “best candidate” for decomposition, as it is practically redundant.

A Whole New Ballgame: The Rise of Canada's Fifth Party System

This article asserts that Canadian politics underwent a major transition beginning in late 2003 and represents the rise of a new era in Canadian politics: the fifth-party system. This era is drastically different from the previous “fourth party system.” The fifth party system is characterized by the introduction of a number of new variables, most notably new patterns of partisan competition, amendments to Canada's system of campaign finance, the growing importance of policies appealing to the family, and growing importance of Web 2.0.

Solution of the Train Platforming Problem

In this paper we study a general formulation of the train platforming problem, which contains as special cases all the versions previously considered in the literature as well as a case study from Rete Ferroviaria Italiana, the Italian Infrastructure Manager. In particular, motivated by our case study, we consider a general quadratic objective function, and propose a new way to linearize it by using a small number of new variables along with a set of constraints that can be separated efficiently by solving an appropriate linear program. The resulting integer linear programming formulation has a continuous relaxation that leads to strong bounds on the optimal value. For the instances in our case study, the solution approach based on this relaxation produces solutions that are much better than those produced by a simple heuristic method currently in use, and that often turn out to be (nearly) optimal.

Estimation of Genetic Variance Components of Body Size Measurements in Hanwoo (Korean Cattle) Using a Multivariate Linear Model

The objectives of this study were to quantify the combination values of the principal components and factors calculated using body measurements of Hanwoo (Korean Cattle) and estimate their heritabilities. The technique of multivariate analysis was used to reduce a large number of variables to a smaller number of new variables and characterize cattle according to body shape. The analyses were performed using 1,979 cattle at 12 months of age and 936 cattle at 24 months of age. The data for the analyses was obtained from progeny tests performed on Korean Cattle for 6 years from 2003 to 2008. The phenotypic correlations among these traits were estimated to range from 0.32 to 0.90 at 12 months of age and from 0.21 to 0.82 at 24 months of age. The first principal components (PC1s) indicated a weighed average of overall body measurements, accounting for 99.91% of the total variation for both periods of test. The two first PCs had positive coefficients for all body measurements. The major sources of PC, such as chest girth (CG), body length (BL), rump height (RH), and wither height (WH) were similar for both test periods. The heritabilities for PC1, the first factor score (FS1), and the second factor score (FS2) were estimated by multivariate REML method. The estimated heritabilities for PC1, FS1, and FS2 were 0.33, 0.38, and 0.40, respectively, at 12 months of age and 0.26, 0.76, and 0.58 at 24 months of age. Further studies are needed to determine whether the heritabilities of FS1 and FS2 at 24 months of age were overestimated.

Locally tensor product functions in algebraic grid optimization

This paper deals with an a posteriori local improvement of numerical grids generated trough a new algebraic mixed scheme. The improvement is achieved by an optimization strategy where only a small number of new variables is used to provide an “optimal” algebraic grid. The proposed new mixed scheme is based on a special family of functions that is tensor product functions in sub-regions, and it controls the position of the grid points by moving a subset of control points of the scheme.

A tale of two methods: putting biology before statistics in the study of phenotypic evolution

A tale of two methods: putting biology before statistics in the study of phenotypic evolution

A Functional Analysis of Mouse Models of Cardiac Disease through Metabolic Profiling

Since the completion of the human and mouse genomes, the focus in mammalian biology has been on assessing gene function. Tools are needed for assessing the phenotypes of the many mouse models that are now being generated, where genes have been "knocked out," "knocked in," or mutated, so that gene expression can be understood in its biological context. Metabolic profiling of cardiac tissue through high resolution NMR spectroscopy in conjunction with multivariate statistics has been used to classify mouse models of cardiac disease. The data sets included metabolic profiles from mouse models of Duchenne muscular dystrophy, two models of cardiac arrhythmia, and one of cardiac hypertrophy. The metabolic profiles demonstrate that the strain background is an important component of the global metabolic phenotype of a mouse, providing insight into how a given gene deletion may result in very different responses in diverse populations. Despite these differences associated with strain, multivariate statistics were capable of separating each mouse model from its control strain, demonstrating that metabolic profiles could be generated for each disease. Thus, this approach is a rapid method of phenotyping mouse models of disease.

Trilinear Monomials with Mixed Sign Domains: Facets of the Convex and Concave Envelopes

Convex underestimators of nonconvex functions, frequently used in deterministic global optimization algorithms, strongly influence their rate of convergence and computational efficiency. A good convex underestimator is as tight as possible and introduces a minimal number of new variables and constraints. Multilinear monomials over a coordinate aligned hyper-rectangular domain are known to have polyhedral convex envelopes which may be represented by a finite number of facet inducing inequalities. This paper describes explicit expressions defining the facets of the convex and concave envelopes of trilinear monomials over a box domain with bounds of opposite signs for at least one variable. It is shown that the previously used approximations based on the recursive use of the bilinear construction rarely yield the convex envelope itself.

The effects of p-menthane monoterpenes and related compounds on the percutaneous absorption of propranolol hydrochloride across newborn pig skin: III. Correlation between penetration enhancing activity and physicochemical properties of terpenes determined by principal components analysis

Principal components analysis (PCA) and multivariate regression analysis have been employed to study the relationship between a series of calculated physicochemical properties of terpenes (solubility parameter, calculated logKoct, molecular mass, molecular volume, calculated surface tension, scaled H-bonding donor and acceptor values, non-carbonyl carbons per molecule) to the permeation enhancing activity of each terpene for propranolol HCl across newborn pig skin. With PCA, a smaller number of new variables (five principal components) were used as predictors in regression analysis in order to predict the enhancing activity of terpenes on the permeation of the model drug propranolol HCl. A fairly high coefficient of determination (R2 = 0.87) suggested that the resultant regression equation was able to adequately predict trends in the enhancing activity of the terpenes. PCA results suggested the importance of a combination of the physicochemical properties on the enhancing activities of the terpenes. A suitable balance between these terpene properties is required to lead to a considerable enhancement of propranolol HCl penetration.