<b>Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model

Training for policy analysis practice has evolved over forty years to a standardized core, including economics, statistics, management, politics/political science, and a practicum. The original model applied disciplinary methodology to the selection of better alternatives among possible policies for governments and nonprofit organizations. The most important mid-course correction in MPP history was the introduction of public management requirements in recognition that MPP alumni would (i) manage ‘policy shops’ and operating agencies as their careers advanced, and (ii) should advise on policy with awareness of implementability and manageability issues. Variations on this model include courses in law and public administration, concentrations in issue areas like health or environmental policy, and joint degrees with other professional schools. Current issues from which future evolution of the MPP enterprise is likely to flow include tensions between methodologies used by MPP faculty in research and inclusion of models like Bayesian inference and behavioral economics that may be more applicable in professional practice. Another source of variation is pedagogical: some courses offer the familiar ‘Theory T [for telling]’ model whereby content is presented didactically in lectures with discussion assigned to sections, while others move to ‘Theory C [for coaching]’ convention where content presentation is left to readings, and meetings are devoted to using the content to analyze policy questions.

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Association of lameness with milk yield and lactation curves in Chios dairy ewes

Journal of Dairy Research ◽

10.1017/s0022029915000059 ◽

2015 ◽

Vol 82 (2) ◽

pp. 193-199 ◽

Cited By ~ 3

Author(s):

Athanasios I Gelasakis ◽

Georgios Arsenos ◽

Georgios E Valergakis ◽

Georgios Banos

Keyword(s):

Linear Model ◽

Milk Yield ◽

General Linear Model ◽

General Linear ◽

Sheep Population ◽

Dairy Ewes ◽

Daily Milk Yield ◽

A Prospective Study ◽

On Farm ◽

Selection Of

The objective of the study was twofold: (i) to quantify the differences in daily milk yield (DMY) and total milk yield (TMY) between lame and non-lame dairy ewes and (ii) to determine the shape of lactation curves around the lameness incident. The overall study was a prospective study of lameness for the surveyed sheep population, with a nested study including the selection of matching controls for each lame ewe separately. Two intensively reared flocks of purebred Chios ewes and a total of 283 ewes were used. Data, including gait assessment and DMY records, were collected on a weekly basis during on-farm visits across the milking period. A general linear model was developed for the calculation of lactation curves of lame and non-lame ewes, whereas one-way ANOVA was used for the comparisons between lame ewes and their controls. Lameness incidence was 12·4 and 16·8% on Farms A and B, respectively. Average DMY in lame ewes was significantly lower (213·8 g, P < 0·001) compared with the rest of the flock, where DMY averaged 1·340 g. The highest DMY reduction in lame ewes was observed during the week 16 of the milking period (P < 0·001), whereas the reduction of DMY, for lame ewes, remained significant at P < 0·001 level from week 8 to week 28 of milking. Comparisons between lame and controls revealed that at the week of lameness diagnosis a significant DMY reduction (P ≤ 0·001) was observed in lame ewes (about 32·5%), which was maximised 1 week later (35·8%, P ≤ 0·001) and continued for several weeks after recovery, resulting in 19·3% lower TMY for lame ewes for the first 210 d of the milking period (P < 0·01). Moreover, at flock level, TMY for non-lame and lame ewes, as calculated by the general linear model, was 318·9 and 268·0 kg, respectively. The results of this study demonstrate evidence of significant financial losses in dairy sheep due to lameness which, however, need to be accurately estimated in further, more detailed, analyses.

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Bayesian Inference for Ratios of Coefficients in a Linear Model

Biometrics ◽

10.2307/2531898 ◽

1988 ◽

Vol 44 (1) ◽

pp. 87 ◽

Cited By ~ 15

Author(s):

John P. Bucephala ◽

Constantine A. Gatsonis

Keyword(s):

Bayesian Inference ◽

Linear Model

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Genetic Variability, Population Structure, and Relatedness Analysis of Meagre Stocks as an Informative Basis for New Breeding Schemes

Fishes ◽

10.3390/fishes6040078 ◽

2021 ◽

Vol 6 (4) ◽

pp. 78

Author(s):

Orestis Nousias ◽

Konstantinos Tzokas ◽

Leonidas Papaharisis ◽

Katerina Ekonomaki ◽

Dimitrios Chatziplis ◽

...

Keyword(s):

Genetic Data ◽

Population Parameters ◽

Genetic Population ◽

Data Set ◽

Overall Evaluation ◽

Relatedness Analysis ◽

Breeding Schemes ◽

Established Population ◽

Selection Of ◽

Microsatellite Analyses

This study evaluates the genetic diversity of different meagre broodstocks sampled in Greece. A multiplex of twelve microsatellite markers was used to genotype 946 fish from eleven stocks and batches used for broodstock selection, and the genetic data was used to calculate genetic population parameters as well as to investigate the genetic differentiation between stocks. The results from a relatedness analysis were used as the guiding lines for a fine-tuned and overall evaluation of the genetic distance between stocks, and the choice of candidate breeders from some of them. The approach implemented in this study uses well-established population genetics methods to evaluate the selection of breeder candidates in aquaculture commercial conditions utilizing a descriptive genetic data set based on microsatellite analyses, and to outline an efficient methodology for establishing the basis of new breeding schemes.

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The Influence of Machinery and Equipment Degradation on Their Depreciation

Экономическая наука современной России ◽

10.33293/1609-1442-2020-3(90)-34-50 ◽

2020 ◽

pp. 34-50

Author(s):

Sergey A. Smolyak

Keyword(s):

Mathematical Modeling ◽

Linear Model ◽

Financial Statements ◽

Fair Value ◽

Economic Benefits ◽

Suitable Method ◽

Operating Costs ◽

Operational Characteristics ◽

Numerous Data ◽

Selection Of

The preparation of financial statements in accordance with IFRS involves the choice of such methods of depreciation for assets that are supposed to reflect most accurately the expected pattern of consumption of future economic benefits from the use of assets. However, in relation to machinery and equipment items this requirement is difficult to implement, since it is not clear how to understand and measure the economic benefits associated with such assets and the pattern of their consumption. In our opinion, the consumption of future economic benefits from the use of an asset is reflected in its fair value, and the depreciation of an asset over a period of time expresses a decrease in the fair value of the asset in that period. Having regard to this position, it is necessary to be be choosing such a depreciation method which affords the best correspondence between the carrying amount of assets and their fair values. We show that the often used linear depreciation method does not satisfy this requirement even for an asset that generates equal annual benefits from year to year. The article is devoted to the selection of the most suitable method of depreciation for machinery and equipment items. In our opinion, it is necessary to take into account the dynamics of their degradation processes (deterioration of operational characteristics). In this regard, we provide numerous data on a decrease in productivity and an increase in operating costs with age for various machinery and equipment categories. The analysis of such data allows us to offer a simple linear model of machine impairment/degradation, as well as a depreciation method based on it. This method turns out to resemble the sum of the years’ digits method and can be considered as a generalization of such a method. Its applicability to measuring the depreciation of real machinery and equipment items is confirmed by the results of economic and mathematical modeling and experimental estimates.

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