Rare Isotope Production in peripheral heavy-ion collisions at beam energy of 15 MeV/nucleon

This paper presents our recent study on the production of neutron-rich rare isotopes with heavy-ion beams in the energy region of 15 MeV/nucleon. We present calculated production cross sections of neutron-rich nuclides from collisions of a 86Kr (15 MeV/nucleon) beam with 238U targets. Our calculations are based on a two-step approach: the dynamical stage of the collision is described with either the phenomenological Deep-Inelastic Transfer model (DIT), or with the microscopic Constrained Molecular Dynamics model (CoMD). The de-excitation of the hot heavy projectile fragments is performed with the Statistical Multifragmentation Model (SMM). We also performed calculations with a radioactive beam of 92Kr (15 MeV/nucleon) with a target of 238U and observed that the multinucleon transfer mechanism leads to very neutron-rich nuclides toward and beyond the astrophysical r-process path. In the future, we plan to experimentally investigate such reactions in the KOBRA spectrometer at the RISP facility in Korea. We conclude that the reaction mechanism at beam energies below the Fermi energy involving periph- eral nucleon exchange, constitutes a novel and effective route to access extremely neutron-rich isotopes toward the r-process path and the neutron drip-line

2D Modeling and Classification of Extended Objects in a Network of HRR Radars

In this thesis, the modeling of extended objects with low-dimensional representations of their 2D geometry is addressed. The ultimate objective is the classification of the objects using libraries of such compact 2D object models that are much smaller than in the state-of-the-art classification schemes based on (High Range Resolution) HRR data. The considered input information consists of HRR datasets measured at widely separated aspect angles of the object, thus being highly sparse in the angular dimension. Such input datasets are supposedly available from a network of scanning surveillance radars.IRCTR/MTSRadarElectrical Engineering, Mathematics and Computer Scienc

Nonstop Selection for High and Stable Crop Yield by Two Prognostic Equations to Reduce Yield Losses

Yield losses occurring at the field level, whether due to plant diseases or abiotic stresses, reveal reduced stability of the crop yield potential. The paper argues that the stability of crop yield potential is a trait with a clear genetic component, which can be successfully selected for at the single-plant level and incorporated into high-yielding cultivars. Two novel selection equations with prognostic power are presented, capable to objectively phenotype and evaluate individual plants in real field conditions in the absence of the masking effects of interplant competition and soil heterogeneity. The equations predict performance at the crop stand through the key concept of coefficient of homeostasis and are equally useful for early generation selection and for nonstop selection within finished cultivars in order to continuously incorporate the adaptive (genetic or epigenetic) responses of plants. Exploitation of adaptive responses acquires particular importance in view of the climate change effects on crop productivity and the changing biotic or abiotic micro-environments. Cotton is used as a case study to highlight the potential of nonstop selection for increasing crop yield and for the gradual build-up of disease resistance. In addition, the paper envisions and proposes the formation of international networks of researchers focusing on specific diseases as, for example, the cereal root-rot or the cotton Verticillium wilt that will concurrently use the proposed strategy in their respective environments to select for resistant genotypes, while gaining a deeper understanding of the nature of the genetic or epigenetic changes at the phenotypic and genomic levels

The Prognostic Breeding Application JMP Add-In Program

Prognostic breeding is a crop improvement methodology that utilizes prognostic equations to enable concurrent selection for plant yield potential and stability of performance. There is a necessity for plant breeders to accurately phenotype plants in the field and select effectively for high and stable crop yield in the absence of the confounding effects of competition. Prognostic breeding accomplishes this goal by evaluating plants for (i) plant yield potential and (ii) plant stability, in the same generation. The plant yield index, stability index and the plant prognostic equation are the main criteria used for the selection of the best plants and the best entries grown in honeycomb designs. The construction of honeycomb designs and analysis of experimental data in prognostic breeding necessitate the development of a computer program to ensure accurate measurement of the prognostic equations. The objective of this paper is to introduce the Prognostic Breeding Application JMP Add-In, a program for constructing honeycomb designs and analyzing data for the efficient selection of superior plants and lines. The program displays powerful controls, allowing the user to create maps of any honeycomb design and visualize the selected plants in the field. Multi-year soybean data are used to demonstrate key features and graphic views of the most important steps

Data from: SSR-marker analysis of the intracultivar phenotypic variation discovered within 3 soybean cultivars

Genetic variation within homogeneous gene pools in various crops is assumed to be very limited. One objective of this study was to use 144 simple sequence repeat (SSR) markers to determine if the single-plant lines selected at ultra-low plant density in honeycomb designs within the soybean cultivars Benning, Haskell, and Cook had unique SSR genetic fingerprints. Another objective was to investigate if the variation found was the result of residual genetic heterozygosity that could be detected in the original gene pool where selection initiated. Our results showed that the phenotypic variation for seed protein content and seed weight has a genotypic component identified by the SSR band variation. The seven lines from Haskell had a total of 63 variant alleles, the five lines from Benning had 34 variant alleles, and the seven lines from Cook had 34 variant alleles, therefore, possessing unique genetic fingerprints. Most of the intra-cultivar SSR band variation discovered was the result of residual heterozygosity in the initial plant selected to become the cultivar. More specifically, 82% of the SSR variant alleles were traced in the Benning Foundation seed source, 93% in the Haskell seed source, and 82% in the Cook seed source. The remaining variant bands (18% for Benning, 7% for Haskell, and 18% for Cook) could not be detected in the Foundation seed source and were likely the result of mutation or some other mechanism generating de novo variation. These results provide evidence that genetic variation among individual plants is present even in homogeneous gene pools and can be further utilized in breeding programs