PLANNING OF THE NUMBER OF TESTS WITH ZONAL ESTIMATES OF ARMAMENT AND MILITARY EQUIPMENT

Today, the problem of reducing the cost of resources and time during planning and conducting tests remains relevant. This problem is especially acute in qualified testing agencies, where testing prototypes at different stages of the life cycle is the main functional task. Automation of planning processes and optimization of testing processes will significantly reduce such costs and improve the overall quality of testing. The article considers the theoretical issues of forming quantitative plans for testing samples of armaments and military equipment (AME) in obtaining zonal estimates of stochastic parameters and characteristics. The need for zonal estimates arises to confirm the quality of the sample for a given stochastic characteristic in the range from the allowable probabilistic value to the maximum (minimum) possible. The authors propose an approach to obtaining zonal estimates of the parameters of the armament and military equipment test sample, which theoretically meet the requirements of a given accuracy and reliability. The approach is based on the use of a flexible test plan, which is terminated when statistical confirmation of the probabilistic value not worse than specified by the developer is obtained. Ensuring the quality of evaluation is carried out by virtue of the minimum required number of repetitions of the experiment and the balance of the number of successful to the total number of experiments. It is shown that a flexible test plan can significantly reduce the total number of experiments, which will to some extent reduce the resource and time costs of testing samples of armament and military equipment. For the practical application of the approach of obtaining zonal estimates of the AME sample quality, its formalization and algorithmics is required. It is assumed that the developed approach will be implemented in the form of a functional module as а part of the subsystem of automated planning and quality management of AME sample tests.

Genetic diversity and structure of native maize races from Northwestern Mexico

The objective of this work was to evaluate the genetic diversity of nine maize races (Zea mays ssp. mays) from Northwestern Mexico and one population of teosinte of the Balsas race (Zea mays ssp. parviglumis). A total of 649 alleles were identified, with an average of 20.9 alleles per locus using 31 microsatellite loci; 84.3% of them were polymorphic loci with a 0.49 expected heterozygosity. Graphic representation of principal coordinate analysis (PCoA) showed broad variation and population distribution. The highest probabilistic value obtained with the ΔK criterion confirmed the existence of five population groups clustered by the Bayesian model. This grouping coincided with the population distribution observed in the PCoA graph. Maize races examined retain broad genetic diversity among and within the evaluated populations.

ASPECTS OF EXCHANGEABILITY IN THE SHAPLEY VALUE

One of the important solution concepts in cooperative game theory is the Shapley value. The Shapley value is a probabilistic value in which each player subjectively assigns probabilities to the events which define their positions in a game. One of the most important concepts of subjective probability is the exchangeability. This paper characterizes the aspects of exchangeability in the Shapley value. We discuss exchangeability aspects in the Owen's multilinear characterization of the Shapley value; and, derive the Shapley value using exchangeability. We also link exchangeability to the Shapley's original derivation of the Shapley value. Lastly, we discuss exchangeability aspects in the semivalues. We show that, for a fixed finite set of players, the probability assignment in a semivalue cannot be a unique mixture of binomial distributions.

Probabilistic Value-Centric Optimization Design for Fractionated Spacecrafts Based on Unscented Transformation

Fractionated spacecrafts are of particular interest for pointing-intensive missions because of their ability to decouple physically the satellite bus and some imaging payloads, which possess a lesser lifecycle cost than a comparable monolithic spacecraft. Considering the probabilistic uncertainties during the mission lifecycle, the cost assessment or architecture optimization is essentially a stochastic problem. Thus, this research seeks to quantitatively assess different spacecraft architecture strategies for remote-sensing missions. A dynamical lifecycle simulation and parametric models are developed to evaluate the lifecycle costs, while the mass, propellant usage, and some other constraints on spacecraft are assessed using nonparametric, physics-based computer models. Compared with the traditional Monte Carlo simulation to produce uncertain distributions during the lifecycle, the unscented transformation is employed to reduce the computational overhead, just as it does in improving the extended Kalman filter. Furthermore, the genetic algorithm is applied to optimize the fractionated architecture based on the probabilistic value-centric assessments developed in this paper.

What is a nematode?

In their classic book “An Introduction to Nematology” Chitwood & Chitwood (1950) wrote (1): “Having briefly considered the history of general nematology, the question naturally arises, “What is a nematode?”. In answering this question it is necessary first to designate a nematode as a triploblastic, bilaterally symmetric, unsegmented, non-coelomate animal of the Series Scolecida Huxley, 1865”. Actually, definition of taxa is not an easy task, because monothetic characters are rare, as polythetic traits are more common and evident, albeit of probabilistic value. To highlight this problem, the following paradoxical but empirically correct diagnosis of the taxon Nematoda is given.