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Zirconium present in large quantities in criticality safety applications such as fuel manufacturing and processing facilities, and in commercial thermal nuclear reactors. ICSBEP lacks benchmarks optimized for zirconium sensitivity to validate zirconium evaluations. Evaluators lack isotopic differential nuclear data in regions of high importance to fission systems.
In the last decade, there has been a renaissance of fission research resulting in new high-precision experiments and advanced fission modeling. For instance, the Chi-Nu and CEA teams supplied, for the first time, the 239 Pu prompt fission neutron spectrum (PFNS) for broad ranges of incident and outgoing neutron energies. The CEA team also measured 239 Pu average prompt neutron multiplicities, ν ̄ p , with lower statistical uncertainties and a technique significantly different than the one used in the past. The NIFFTE collaboration provided 239 Pu( n ,f)/ 235 U( n ,f) cross section shape ratios with uncertainties below 1% utilizing a novel detector type. Advanced fission event generators were developed, among them CGMF, FIFRELIN, FREYA, and GEF, which calculate post-scission fission observables in a correlated manner. These new experimental data and more consistent fission models change the evaluated PFNS, ν ̄ p , and ( n ,f) cross sections, some only modestly, compared to ENDF/B-VIII.0. In turn, the individual new nuclear data distinctly change simulated effective neutron multiplication factors of fast critical assemblies, but their combined impact is small, while affecting the prediction of LLNL pulsed sphere neutron leakage spectra and reaction rates only within experimental uncertainties. Also, the parameters obtained from fitting to ν ̄ p reproduce various post-scission fission observables within the uncertainties of experimental data. This indicates that new differential experiments and consistent fission modeling reduce compensating errors present in ENDF/B-VIII.0.
Following a highly dynamic and complex dialogue between the epithelium and the surrounding microenvironment, the mammary gland develops into a branching structure during puberty, buds during pregnancy, forms intricate polar acini during lactation and, once the babies are weaned, remodels and involutes. At every stage of menstrual and pregnancy cycles, interactions between the cells and the extracellular matrix (ECM) and homotypic and heterotypic cell–cell interactions give rise to the architecture and function of the gland at that junction. These orchestrated programs would not be possible without the important role of the ECM receptors, integrins being the prime examples. The ECM–integrin axis regulates many crucial cellular functions including survival, migration and quiescence; the imbalance in any of these processes could contribute to oncogenesis. In this review we spotlight the involvement of two prominent integrin subunits, β1 and β4 integrins, in cross-talk with tyrosine kinase receptors, and we discuss the roles of these integrin subunits in the biology of normal breast differentiation and as potential prognostic and therapeutic targets in breast cancer.
Suniva, Inc., in collaboration with the University Center for Excellence in Photovoltaics (UCEP) at the Georgia Institute of Technology (GIT) proposed this comprehensive three year program to enable the development of an advanced high performance product that will help the US regain its competitive edge in PV. This project was designed to overcome cost and efficiency barriers through advances in PV science, technology innovation, low-cost manufacturing and full production of ~22.5% efficient n-type Si cells in Norcross, GA. At the heart of the project is the desire to complement the technology being developed concurrently under the Solarmat and ARPAe initiatives to develop a differentiated product superior in both performance and cost effectiveness to the competing alternatives available on the market, and push towards achieving SunShot objectives while ensuring a sustainable business model based on US manufacturing. A significant reduction of the costs in modules produced today will need to combine reductions in wafer costs, cell processing costs as well as module fabrication costs while delivering a product that is not only more efficient under test conditions but also increases the energy yield in outdoor operations. This project will result in a differentiated high performance product and technology that is consistent with sustaining PV manufacturing in the US for a longer term and further highlights the need for continued support for developing the next generation concepts that can keep US manufacturing thriving to support the growing demand for PV in the US and consistent with the US government’s mandates for energy independence.
We present a study of the dust properties of 12 classical and superbubble H II regions in the Large Magellanic Cloud. We use infrared photometry from Spitzer (8, 24, 70, and 160 {mu} m bands), obtained as part of the Surveying the Agents of a Galaxy's Evolution (SAGE) program, along with archival spectroscopic classifications of the ionizing stars to examine the role of stellar sources on dust heating and processing. Our infrared observations show surprisingly little correlation between the emission properties of the dust and the effective temperatures or bolometric magnitudes of stars in the H II regions, suggesting that the H II region evolutionary timescale is not on the order of the dust processing timescale. We find that the infrared emission of superbubbles and classical H II regions shows little differentiation between the two classes, despite the significant differences in age and morphology. We do detect a correlation of the 24 {mu} m emission from hot dust with the ratio of 70-160 {mu} m flux. This correlation can be modeled as a trend in the temperature of a minority hot dust component, while a majority of the dust remains significantly cooler.
With currently available RNA-Seq pipelines, expression estimates for most genes are very noisy. We here introduce MapAl, a tool for RNA-Seq expression profiling that builds on the established programs Bowtie and Cufflinks. In the post-processing of RNA-Seq reads, it incorporates gene models already at the stage of read alignment, increasing the number of reliably measured known transcripts consistently by 50%. Adding genes identified de novo then allows a reliable assessment of double the total number of transcripts compared to other available pipelines. This substantial improvement is of general relevance: Measurement precision determines the power of any analysis to reliably identify significant signals, such as in screens for differential expression, independent of whether the experimental design incorporates replicates or not.
An extensive program is underway at Jefferson Lab to study the eletromagnetic excitations of baryon states. We report the analysis of exclusive single ?0 electroproduction in the resonance region at Jefferson Lab in the Q2 range of 2 to 6 GeV2/c2. A longitudinally polarized 5.75 GeV electron beam was incident on a 5 cm long liquid Hydrogen target. The CLAS spectrometer at Jefferson Lab was used to detect the final state particles. The data was taken between October 2001 and January 2002. Preliminary results for differential cross sections over the entire 4? c.m. solid angle will be presented. This high precision measurement will allow us to access the structure and dynamics of nucleon excitations with masses up to 2 GeV.
A phase space is a mathematical representation of all possible physical states of a system. Particle beams at Fermilab exist within a six-dimensional (6D) phase space defined by three positional components, (x, y, z) and three momentum components, (px, py, pz). To reconstruct this space implies taking measurement data from detectors and mapping out particle behavior using computational methods. The beam detectors, however, are only able to detect spatial distribution among the events of the beam, therefore being limited to positional data. Also, due to the vast number of events in a particle beam, it is extremely difficult to analyze and differentiate every single one’s behavior. However, with Machine Learning (ML), which can distinguish between patterns and map out particle behavior more efficiently. We first used the particle beam software, G4beamline, to simulate a 10,000-event muon beam, adjusting parameters such as initial momentum magnitude (p¬0) and virtual detector position. Using ten virtual detectors, we analyzed p0 values such that minimum 9,990 events were analyzed by every detector. We then input the data from these beam simulations to a C++ program, that randomly selects 100 events, and creates a 2D histogram based on spatial distribution, detector position, and event intensity. This process is repeated 100 times to create 100 histograms per p0 value. These images were then input to a modified ResNet18 Convolutional Neural Network (CNN) for training, and to predict p0 from some unseen set of histograms. The model was accurate when trained on momentum increments of 5 MeV/c and provided with denser training samples around highly variable test values. These results displayed machine learning being able to accurately predict p0 from being trained on different particle behaviors.
Active control of the toroidal current density profile is among those plasma control milestones that the National Spherical Torus eXperiment-Upgrade (NSTX-U) program must achieve to realize its next-step operational goals. Motivated by the coupled, nonlinear, multivariable, distributed-parameter plasma dynamics, the first step towards control design is the development of a physics-based, control-oriented model for the current profile evolution in response to non-inductive current drives and heating systems. The evolution of the toroidal current density profile is closely related to the evolution of the poloidal magnetic flux profile, whose dynamics is modeled by a nonlinear partial differential equation (PDE) referred to as the magnetic-flux diffusion equation (MDE). The proposed control-oriented model predicts the spatial-temporal evolution of the current density profile by combining the nonlinear MDE with physics-based correlations obtained at NSTX-U for the electron density, electron temperature, and non-inductive current drives (neutral beams). The resulting first-principles-driven, control-oriented model is tailored for NSTX-U based on predictions by the time-dependent transport code TRANSP. Furthermore, main objectives and possible challenges associated with the use of the developed model for the design of both feedforward and feedback controllers are also discussed.
Although the structure and function of the AhR are conserved, emerging evidence suggests that downstream effects are species-specific. In this study, rat hepatic gene expression data from the DrugMatrix database (National Toxicology Program) were compared to mouse hepatic whole-genome gene expression data following treatment with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). For the DrugMatrix study, male Sprague–Dawley rats were gavaged daily with 20 μg/kg TCDD for 1, 3 and 5 days, while female C57BL/6 ovariectomized mice were examined 1, 3 and 7 days after a single oral gavage of 30 μg/kg TCDD. A total of 649 rat and 1386 mouse genes (|fold change| ≥ 1.5, P1(t) ≥ 0.99) were differentially expressed following treatment. HomoloGene identified 11,708 orthologs represented across the rat Affymetrix 230 2.0 GeneChip (12,310 total orthologs), and the mouse 4 × 44K v.1 Agilent oligonucleotide array (17,578 total orthologs). Comparative analysis found 563 and 922 orthologs differentially expressed in response to TCDD in the rat and mouse, respectively, with 70 responses associated with immune function and lipid metabolism in common to both. Moreover, QRTPCR analysis of Ceacam1, showed divergent expression (induced in rat; repressed in mouse) functionally consistent with TCDD-elicited hepatic steatosis in the mouse but not the rat. Functional analysis identified orthologs involved in nucleotide binding and acetyltransferase activity in rat, while mouse-specific responses were associated with steroid, phospholipid, fatty acid, and carbohydrate metabolism. These results provide further evidence that TCDD elicits species-specific regulation of distinct gene networks, and outlines considerations for future comparisons of publicly available microarray datasets. - Highlights: ► We performed a whole-genome comparison of TCDD-regulated genes in mice and rats. ► Previous species comparisons were extended using data from the DrugMatrix database. ► Less than 15% of TCDD-regulated orthologs were common to mice and rats. ► Considerations for the comparison of publicly available datasets are described.
We utilize a two-color Lyman-break selection criterion to search for z ∼ 9-10 galaxies over the first 19 clusters in the CLASH program. A systematic search yields three z ∼ 9-10 candidates. While we have already reported the most robust of these candidates, MACS1149-JD, two additional z ∼ 9 candidates are also found and have H {sub 160}-band magnitudes of ∼26.2-26.9. A careful assessment of various sources of contamination suggests ≲1 contaminants for our z ∼ 9-10 selection. To determine the implications of these search results for the luminosity function (LF) and star formation rate density at z ∼ 9, we introduce a new differential approach to deriving these quantities in lensing fields. Our procedure is to derive the evolution by comparing the number of z ∼ 9-10 galaxy candidates found in CLASH with the number of galaxies in a slightly lower-redshift sample (after correcting for the differences in selection volumes), here taken to be z ∼ 8. This procedure takes advantage of the fact that the relative volumes available for the z ∼ 8 and z ∼ 9-10 selections behind lensing clusters are not greatly dependent on the details of the lensing models. We find that the normalization of the UV LF at z ∼ 9 is just 0.28{sub −0.20}{sup +0.39}× that at z ∼ 8, which is ∼1.4{sub −0.8}{sup +3.0}× lower than extrapolating z ∼ 4-8 LF results. While consistent with the evolution in the UV LF seen at z ∼ 4-8, these results marginally favor a more rapid evolution at z > 8. Compared to similar evolutionary findings from the HUDF, our result is less insensitive to large-scale structure uncertainties, given our many independent sightlines on the high-redshift universe.
The success of the ambitious programs of both long- and short-baseline neutrino-oscillation experiments employing liquid-argon time-projection chambers will greatly rely on the precision with which the weak response of the argon nucleus can be estimated. In the E12-14-012 experiment at Jefferson Lab Hall A, we have studied the properties of the argon nucleus by scattering a high-quality electron beam off a high-pressure gaseous argon target. Here, we present the measured 40 Ar(e,e') double differential cross section at incident electron energy E = 2:222 GeV and scattering angle = 15:541 deg. The data cover a broad range of energy transfers, where quasi-elastic scattering and delta production are the dominant reaction mechanisms. The result for argon is compared to our previously reported cross sections for titanium and carbon, obtained in the same kinematical setup.
ButterflyPACK is a mathematical software for rapidly solving large-scale dense linear systems that exhibit off-diagonal rank-deficiency properties. These targeted systems are either matrices arising from discretized boundary integral equations, or frontal matrices in multi-frontal solvers for differential equations. ButterflyPACK relies on low-rank and butterfly formats under Hierarchical matrix, HODLR or HSS frameworks to compress, factor/precondition and solve the linear system in quasi-linear time. ButterflyPACK is particularly useful for large-scale ill-conditioned matrices arising from high-frequency wave equations involving multiple right-hand-sides. ButterflyPACK is based on Fortran 2003, it also has interface written in C++. In addition, ButterflyPACK can be readily invoked from the software STRUMPACK. ButterflyPACK supports hybrid MPI/OpenMP programming models.
We summarize the phenomenological description of electron-deuteron deep-inelastic scattering (DIS) with detection of a nucleon in the nuclear fragmentation region, eD → e' + N + X ("spectator tagging"), at collision energies accessible with an Electron-Ion Collider (EIC), √s eD ~ several 10 GeV. This includes (a) the kinematic variables, differential cross section, conditional structure functions, and phase space elements; (b) a cross section model based on the light-front impulse approximation and an analytic parametrization of the deuteron wave function; (c) counting rate estimates for simulations of cross section measurements and on-shell extrapolation in the recoil nucleon momentum; (d) a FORTRAN 77 code package for computing the cross section and performing counting rate estimates. The document summarizes the definitions and results without derivation and is intended as a reference for programming and simulations.
Employing quantum mechanical resources in computing, information processing, and networking opens the door to potential exponential advantages over classical counterparts. However, quantifying and realizing such advantages poses extensive scientific and engineering challenges. Department of Energy (DOE) investments have driven steady progress in addressing such challenges. Recently developed quantum algorithms offer asymptotic exponential advantages in speed or accuracy for fundamental scientific problems. These problems include simulating physical systems, solving systems of linear equations, differential equations, and optimization problems. Empirical demonstrations on nascent quantum hardware suggest better performance on contrived computational tasks than classical analogs. However, the requirements for a quantum computer or network to demonstrate an end-to-end rigorously quantifiable performance improvement over classical analogs remains a grand challenge, especially for problems of practical value. In particular, what will be required for quantum technology to ultimately exhibit scalable, rigorous, and transformative performance advantages for practical applications? In July 2023, DOE’s Advanced Scientific Computing Research program in the Office of Science convened the Workshop on Basic Research Needs in Quantum Computing and Networking, where major opportunities and grand challenges were identified. The following five priority research directions (PRDs) were identified as a result of the workshop.
A new cold neutron moderator system was developed and constructed at the Rensselaer Polytechnic Institute (RPI) Gaerttner Linear Accelerator. This system was then used to measure the neutron total cross section in the thermal energy range for polyethylene, polystyrene, Lucite, and yttrium hydride. In tandem with these measurements, a fitting procedure was developed at Oak Ridge National Laboratory to vary computationally simulated phonon density of states until a fit with differential and integral data was achieved. This model, combined with the new RPI total cross section data, was used to generate thermal scattering law files for the materials measured at RPI.
This report summarizes a research effort undertaken by Argonne National Laboratory to accelerate the development of creep data package for ASME code qualification. Progress on the fabrication of a 3-sample load train and the application of a direct current potential drop (DCPD) technique to measure large creep strains are reported. Specifically: (1) The 3-sample load train, with three sets of Linear Variable Differential Transformers (LVDT), has been fabricated and assembled. (2) A DCPD method for creep-strain measurement up to 50% has been demonstrated. The DCPD data matches very well to LVDT results, suggesting its potential application in future creep testing. The technical developments contribute towards shortening the time needed to generate a creep data package for an ASME code case of the laser powder bed fusion 316H stainless steel, an effort under the US Department of Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program.
Using three design fields we develop an optimization environment that can simultaneously optimize material, shape and topology. We use the implicit representation of the boundaries with level-set functions that define the shape and topology. Differentiable R-functions allow us to combine these shapes and topology descriptions with Boolean operations. Additionally, we incorporate design dependent-stiffness materials with another design field. Notably, this framework accommodates design dependent loads, has the ability to introduce holes, and ensures the satisfaction of optimality criteria. It builds upon the fictitious domain, ersatz material, material interpolation and level-set methods. Additionally, it also borrows from parameterized density-based topology optimization methods. Since analytical sensitivities can be computed, we use efficient nonlinear programming algorithms to update the design instead of the Hamilton–Jacobi’s scheme of level-set methods. We illustrate the features of our framework by designing a cantilever beam with octet truss microlattice, a dam with design-dependent loads, and a composite clevis plate.