Pairing and realistic shell-model interactions

This paper starts with a brief historical overview of pairing in nuclei, which fulfills the purpose of properly framing the main subject. This concerns the pairing properties of a realistic shell-model effective interaction which has proved very successful in describing nuclei around doubly magic 132Sn. We focus attention on the two nuclei 134Te and 134Sn with two valence protons and neutrons, respectively. Our study brings out the key role of one particle-one hole excitations in producing a significant difference between proton and neutron pairing in this region

Shell-model study of the N=82 isotonic chain with a realistic effective hamiltonian

We have performed shell-model calculations for the even- and odd-mass N=82 isotones, focusing attention on low-energy states. The single-particle energies and effective two-body interaction have been both determined within the framework of the time-dependent degenerate linked-diagram perturbation theory, starting from a low-momentum interaction derived from the CD-Bonn nucleon-nucleon potential. In this way, no phenomenological input enters our effective Hamiltonian, whose reliability is evidenced by the good agreement between theory and experiment.Comment: 7 pages, 11 figures, 3 tables, to be published in Physical Review

Privacy-preserving model learning on a blockchain network-of-networks.

ObjectiveTo facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks.Materials and methodsWe propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology.ResultsHierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level.DiscussionHierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns.ConclusionWe demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction

Remote monitoring of a thermal plume

A remote-sensing experiment conducted on May 17, 1977, over the Surry nuclear power station on the James River, Virginia is discussed. Isotherms of the thermal plume from the power station were derived from remotely sensed data and compared with in situ water temperature measurements provided by the Virginia Electric and Power Company, VEPCO. The results of this study were also qualitatively compared with those from other previous studies under comparable conditions of the power station's operation and the ambient flow. These studies included hydraulic model predictions carried out by Pritchard and Carpenter and a 5-year in situ monitoring program based on boat surveys

The Efficiency of Labor Input in the Tree Nut Growers Industry: A Stochastic Frontier Production Approach Study in Butte County, California

The U.S. government recruits immigrant workers through the H-2A program as a short-term solution to the agricultural sectors’ labor shortage problem. Although the sector insists hiring immigrant workers is essential for their survival, history has proven the socio-economic cost for doing so is enormous. This paper aims to investigate the contribution of labor to agricultural production efficiency. A discussion of marginal rate of technical substitution, economies of scale, and economies of scope will also be included. The stochastic production frontier regression approach was applied to input/output data collected from a survey of tree nut growers in Butte County, California. Results indicate the labor input is not significant in deciding farm production efficiency. Instead of attempting to increase short-term labor, producers’ and policy makers’ efforts should be directed toward improving the logistics of farm management and the quality of labor, thus more efficiently utilizing available resources.Stochastic Frontier Production Model, Labor Input Efficiency, Labor Economics, Labor and Human Capital, Production Economics, Productivity Analysis, Q120, J240, D240,