A distributed programming environment for Ada

Despite considerable commercial exploitation of fault tolerance systems, significant and difficult research problems remain in such areas as fault detection and correction. A research project is described which constructs a distributed computing test bed for loosely coupled computers. The project is constructing a tool kit to support research into distributed control algorithms, including a distributed Ada compiler, distributed debugger, test harnesses, and environment monitors. The Ada compiler is being written in Ada and will implement distributed computing at the subsystem level. The design goal is to provide a variety of control mechanics for distributed programming while retaining total transparency at the code level

Paradigmatic Approaches to Studying Environment and Human Health: (Forgotten) Implications for Interdisciplinary Research

Copyright © 2013 ElsevierInterdisciplinary research is increasingly promoted in a wide range of fields, especially so in the study of relationships between the environment and human health. However, many projects and research teams struggle to address exactly how researchers from a multitude of disciplinary and methodological backgrounds can best work together to maximize the value of this approach to research. In this paper, we briefly review the role of interdisciplinary research, and emphasize that it is not only our discipline and methods, but our research paradigms, that shape the way that we work. We summarize three key research paradigms - positivism, postpositivism and interpretivism - with an example of how each might approach a given environment-health research issue. In turn, we argue that understanding the paradigm from which each researcher operates is fundamental to enabling and optimizing the integration of research disciplines, now argued by many to be necessary for our understanding of the complexities of the interconnections between human health and our environment as well as their impacts in the policy arena. We recognize that a comprehensive interrogation of research approaches and philosophies would require far greater length than is available in a journal paper. However, our intention is to instigate debate, recognition, and appreciation of the different worlds inhabited by the multitude of researchers involved in this rapidly expanding field

Design and Simulated Performance of Calorimetry Systems for the ECCE Detector at the Electron Ion Collider

We describe the design and performance the calorimeter systems used in the ECCE detector design to achieve the overall performance specifications cost-effectively with careful consideration of appropriate technical and schedule risks. The calorimeter systems consist of three electromagnetic calorimeters, covering the combined pseudorapdity range from -3.7 to 3.8 and two hadronic calorimeters. Key calorimeter performances which include energy and position resolutions, reconstruction efficiency, and particle identification will be presented.Comment: 19 pages, 22 figures, 5 table

ECCE Sensitivity Studies for Single Hadron Transverse Single Spin Asymmetry Measurements

We performed feasibility studies for various single transverse spin measurements that are related to the Sivers effect, transversity and the tensor charge, and the Collins fragmentation function. The processes studied include semi-inclusive deep inelastic scattering (SIDIS) where single hadrons (pions and kaons) were detected in addition to the scattered DIS lepton. The data were obtained in {\sc pythia}6 and {\sc geant}4 simulated e+p collisions at 18 GeV on 275 GeV, 18 on 100, 10 on 100, and 5 on 41 that use the ECCE detector configuration. Typical DIS kinematics were selected, most notably $Q^2 > 1$ GeV$^2$, and cover the $x$ range from $10^{-4}$ to $1$. The single spin asymmetries were extracted as a function of $x$ and $Q^2$, as well as the semi-inclusive variables $z$, and $P_T$. They are obtained in azimuthal moments in combinations of the azimuthal angles of the hadron transverse momentum and transverse spin of the nucleon relative to the lepton scattering plane. The initially unpolarized MonteCarlo was re-weighted in the true kinematic variables, hadron types and parton flavors based on global fits of fixed target SIDIS experiments and $e^+e^-$ annihilation data. The expected statistical precision of such measurements is extrapolated to 10 fb$^{-1}$ and potential systematic uncertainties are approximated given the deviations between true and reconstructed yields. The impact on the knowledge of the Sivers functions, transversity and tensor charges, and the Collins function has then been evaluated in the same phenomenological extractions as in the Yellow Report. The impact is found to be comparable to that obtained with the parameterized Yellow Report detector and shows that the ECCE detector configuration can fulfill the physics goals on these quantities.Comment: 22 pages, 22 figures, to be submitted to joint ECCE proposal NIM-A volum

Open Heavy Flavor Studies for the ECCE Detector at the Electron Ion Collider

The ECCE detector has been recommended as the selected reference detector for the future Electron-Ion Collider (EIC). A series of simulation studies have been carried out to validate the physics feasibility of the ECCE detector. In this paper, detailed studies of heavy flavor hadron and jet reconstruction and physics projections with the ECCE detector performance and different magnet options will be presented. The ECCE detector has enabled precise EIC heavy flavor hadron and jet measurements with a broad kinematic coverage. These proposed heavy flavor measurements will help systematically study the hadronization process in vacuum and nuclear medium especially in the underexplored kinematic region.Comment: Open heavy flavor studies with the EIC reference detector design by the ECCE consortium. 11 pages, 11 figures, to be submitted to the Nuclear Instruments and Methods

AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider

The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the "glue" that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector.Comment: 16 pages, 18 figures, 2 appendices, 3 table

ECCE unpolarized TMD measurements

We performed feasibility studies for various measurements that are related to unpolarized TMD distribution and fragmentation functions. The processes studied include semi-inclusive Deep inelastic scattering (SIDIS) where single hadrons (pions and kaons) were detected in addition to the scattered DIS lepton. The single hadron cross sections and multiplicities were extracted as a function of the DIS variables $x$ and $Q^2$, as well as the semi-inclusive variables $z$, which corresponds to the momentum fraction the detected hadron carries relative to the struck parton and $P_T$, which corresponds to the transverse momentum of the detected hadron relative to the virtual photon. The expected statistical precision of such measurements is extrapolated to accumulated luminosities of 10 fb$^{-1}$ and potential systematic uncertainties are approximated given the deviations between true and reconstructed yields.Comment: 12 pages, 9 figures, to be submitted in joint ECCE proposal NIM-A volum

The status of the world's land and marine mammals: diversity, threat, and knowledge

Knowledge of mammalian diversity is still surprisingly disparate, both regionally and taxonomically. Here, we present a comprehensive assessment of the conservation status and distribution of the world's mammals. Data, compiled by 1700+ experts, cover all 5487 species, including marine mammals. Global macroecological patterns are very different for land and marine species but suggest common mechanisms driving diversity and endemism across systems. Compared with land species, threat levels are higher among marine mammals, driven by different processes (accidental mortality and pollution, rather than habitat loss), and are spatially distinct (peaking in northern oceans, rather than in Southeast Asia). Marine mammals are also disproportionately poorly known. These data are made freely available to support further scientific developments and conservation action

AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider

arXiv preprint [v2] Fri, 20 May 2022 03:23:44 UTC (2,296 KB) made available under a Creative Commons (CC BY) Attribution Licence, now in press, published by Elsevier: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, available online 17 November 2022 at: https://doi.org/10.1016/j.nima.2022.167748The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the "glue" that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector.Office of Nuclear Physics in the Office of Science in the Department of Energy; National Science Foundation, and the Los Alamos National Laboratory Laboratory Directed Research and Development (LDRD) 20200022DR

Design of the ECCE Detector for the Electron Ion Collider

Preprint submitted to Nuclear Instruments and Methods A. The file archived on this institutional repository has not been certified by peer review.32 pages, 29 figures, 9 tablesThe EIC Comprehensive Chromodynamics Experiment (ECCE) detector has been designed to address the full scope of the proposed Electron Ion Collider (EIC) physics program as presented by the National Academy of Science and provide a deeper understanding of the quark-gluon structure of matter. To accomplish this, the ECCE detector offers nearly acceptance and energy coverage along with excellent tracking and particle identification. The ECCE detector was designed to be built within the budget envelope set out by the EIC project while simultaneously managing cost and schedule risks. This detector concept has been selected to be the basis for the EIC project detector.Office of Science in the Department of Energy, the National Science Foundation, and the Los Alamos National Laboratory Laboratory Directed Research and Development (LDRD) 20200022DR; This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05- 00OR22725. The work of AANL group are supported by the Science Committee of RA, in the frames of the research project # 21AG-1C028. And we gratefully acknowledge that support of Brookhaven National Lab and the Thomas Jefferson National Accelerator Facility which are operated under contracts DESC0012704 and DE-AC05-06OR23177 respectivel