Excited states of Ce-138
Ce 138 excited states identification in beta decay spectra of Pr 138m and Pr 138 sources, using proton target bombardment
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Ce 138 excited states identification in beta decay spectra of Pr 138m and Pr 138 sources, using proton target bombardment
It is pointed out that the JACEE-3 experiment combined electronic counters and an emulsion chamber to study nucleus-nucleus interactions between 20 and 100 GeV/n. In the present paper, a description is given of the use of the passive detector stack. The electronic position detectors facilitate the event location, taking into account a determination of the incident angles. CR-39 plastic plates were placed just below the proportional counters to enhance tracking accuracy. The JACEE-3 emulsion chamber consists of three parts, including the charge module, the target module, and the calorimeter module. The charge module is mainly used in track identification. The obtained results are also discussed, giving attention to event number and tracking efficiency, interaction measurements, and the inelasticity distribution.
The Impactor for Surface and Interior Science (ISIS) mission concept is a kinetic asteroid impactor mission to the target of NASA's OSIRIS-REx (Origins-Spectral Interpretation-Resource Identification-Security-Regolith Explorer) asteroid sample return mission. The ISIS mission concept calls for the ISIS spacecraft, an independent and autonomous smart impactor, to guide itself to a hyper-velocity impact with 1999 RQ36 while the OSIRIS-REx spacecraft observes the collision. Later the OSIRIS-REx spacecraft descends to reconnoiter the impact site and measure the momentum imparted to the asteroid through the impact before departing on its journey back to Earth. In this paper we discuss the planetary science, human exploration and impact mitigation drivers for mission, and we describe the current mission concept and flight system design.
The GlueX experiment at Jefferson Lab has been designed to study photoproduction reactions with a 9-GeV linearly polarized photon beam. The energy and arrival time of beam photons are tagged using a scintillator hodoscope and a scintillating fiber array. The photon flux is determined using a pair spectrometer, while the linear polarization of the photon beam is determined using a polarimeter based on triplet photoproduction. Charged-particle tracks from interactions in the central target are analyzed in a solenoidal field using a central straw-tube drift chamber and six packages of planar chambers with cathode strips and drift wires. Electromagnetic showers are reconstructed in a cylindrical scintillating fiber calorimeter inside the magnet and a lead-glass array downstream. Charged particle identification is achieved by measuring energy loss in the wire chambers and using the flight time of particles between the target and detectors outside the magnet. The signals from all detectors are recorded with flash ADCs and/or pipeline TDCs into memories allowing trigger decisions with a latency of 3.3 $μs$. The detector operates routinely at trigger rates of 40 kHz and data rates of 600 megabytes per second. Here, we describe the photon beam, the GlueX detector components, electronics, data-acquisition and monitoring systems, and the performance of the experiment during the first three years of operation.
The NASA Clouds and the Earth's Radiant Energy System (CERES) project provides observed flux and cloud products to the climate science community. The CERES instruments, along with the MODIS and VIIRS imagers, are onboard the Terra, Aqua, NPP, and NOAA20 satellites. In order to produce seamless multi-platform integrated products, long-term sensor stability and inter-calibration are required. Inter-calibration between sensors within the same sun-synchronous orbit relies on Earth invariant targets because simultaneous nadir overpasses are not possible. To facilitate inter-calibration efforts, the CERES Imager and Geostationary Calibration Group (IGCG) has improved the Libya-4 target characterization. Improvements include full scan angle characterization to enable daily observations, clear-sky identification using individual scan angle dynamic spatial homogeneity thresholds, and atmospheric corrections. The water vapor correction was found to be effective across the full scan, whereas the ozone and aerosol corrections were less effective. The atmospheric-corrected normalized radiance temporal fluctuations are similar across scan angles, spectral bands, and between the MODIS and VIIRS imagers, suggesting that the fluctuations are a result of the natural variability of the Libya-4 surface reflectance. The Libya-4 surface variability is more than likely caused by changes in the prevailing winds that alter sand dune orientation and resulting shadows. The full-scan-imager atmosphere and angle corrected reflected solar band radiance trend standard errors are between 0.6% and 1.0%, and for near-nadir observations, are between 0.5% and 0.8%. The advanced characterization suggests that the Libya-4 short-term surface reflectance anomalies may need to be considered for imager stability monitoring and inter-calibration efforts.
This thesis describes the search for two rare Higgs processes. The first analysis describes the CMS Run 2 search for $H$ $\rightarrow$ $\mu$$\mu$ decays, with 137.3 fb$^{-1}$ of data at $\sqrt{s}$ = 13 TeV. The analysis targeted four different Higgs production modes: the gluon fusion (ggH), the vector boson fusion (VBF), the Higgs-strahlung process (VH), and the production in association with a pair of top quarks (ttH). Each category used a dedicated machine learning based classifier to separate the signal from the background processes. A combined fit from all these categories saw a slight excess in the data corresponding to 3.0 standard deviations at $M$$_{H}$ = 125.38 GeV, and gave the first evidence for the Higgs boson decay to second-generation fermions. The best-fit signal strength and the corresponding 68% CL interval was found to be +0.17?????? = 1.19 $_{-0.39}^{+0.41}$ (stat)$_{-0.16}^{+0.17}$(syst) at $M$$_{H}$ = 125.38 GeV. The second analysis describes the CMS Run 2 search for 𝐻𝐻 → 𝑏𝑏𝑏𝑏 with highly boosted Higgs bosons. This analysis used a dedicated jet identification algorithm based on graph neural networks (ParticleNet) to identify boosted H→ bb jets. This search targeted the gluon fusion and the vector boson fusion HH production modes, and put constraints on the allowed values of the various Higgs couplings as: 𝜅𝜆 ∈ [−9.9, 16.9] when 𝜅𝑉 = 1, 𝜅2𝑉 = 1; 𝜅𝑉 ∈ [−1.17, −0.79] ∪ [0.81, 1.18] when 𝜅𝜆 = 1, 𝜅2𝑉 = 1; 𝜅2𝑉 ∈ [0.62, 1.41] when 𝜅𝜆 = 1, 𝜅𝑉 = 1. A scenario with 𝜅2𝑉 = 0 was excluded with a significance of 6.3 standard deviations for the first time, when other H couplings are fixed to their SM values. The combined observed (expected) 95% upper limit on the HH production cross section was found to be 9.9 (5.1) × SM. Finally, this thesis also discusses the planned MIP Timing Detector (MTD) upgrade for CMS at the HL-LHC. The MTD will be a time-of-flight (TOF) detector, designed to provide a precision timing information for charged particles using SiPMs + LYSO scintillating crystals, with a time resolution of ∼30 ps. This thesis describes several R&D tests that have been performed for characterizing the sensor properties (time resolution, light yield, etc.) and optimizing the sensor design geometry. This thesis also contains a description of mock test setups for cooling the sensors, since it is known to be an effective way of mitigating the increased dark current rates in the sensors due to radiation damage.
The thrust of this paper is to present a new approach to multi-target tracking for the mid-course stage of the Strategic Defense Initiative (SDI). This approach is based upon a continuum representation of a cluster of flying objects. We assume that the velocities of the flying objects can be embedded into a smooth velocity field. This assumption is based upon the impossibility of encounters in a high density cluster between the flying objects. Therefore, the problem is reduced to an identification of a moving continuum based upon consecutive time frame observations. In contradistinction to the previous approaches, here each target is considered as a center of a small continuous neighborhood subjected to a local-affine transformation, and therefore, the target trajectories do not mix. Obviously, their mixture in plane of sensor view is apparent. The approach is illustrated by an example.
Production of 99 Mo by the 100 Mo (γ, n) 99 Mo reaction through the bremsstrahlung process using an electron accelerator is one of the feasible options currently pursued by several countries. In this work, we report experimental results on identification of side-reaction products after the irradiation of natural and enriched 100 Mo targets. Side-reaction products identified include various Mo, Nb and Zr isotopes. Comparison of experimentally determined reaction production rates with those determined based on theoretical cross-sections will be presented. Moreover, activation products formed due to presence of impurities introduced during the manufacturing of the Mo targets will also be discussed.
Here, we report on the improved operational stability of unencapsulated perovskite solar cells (PSCs) aged in an ambient atmosphere at elevated temperatures (70 °C) for >1000 h under constant illumination and bias at 30–50% relative humidity. We identify a previously unseen interfacial degradation mechanism concerning the use of a MoO x interlayer, which was originally added to increase operational stability. Specifically, the hole-transport layer/MoO x interface buckles under illumination at 70 °C, which leads to delamination and rapid losses of short-circuit current density corresponding to an average $t_{80}$ of ~55 h. By judiciously evaluating various hole-transport layers, interlayers, and contacts, we find that replacing the MoO x with a VO x interlayer, regardless of the other components in the solar cell, alleviates this buckling issue due to its higher activation barrier toward crystallization, leading to significant gains in PSC operational stability. Unencapsulated devices aged in an ambient atmosphere with a VO x interlayer retain 71% of their initial PCE on average after constant illumination and bias at 70 °C for 1100 h ($t_{80}$ ~ 645 h). Currently, this is the highest temperature reported for the operational stability of unencapsulated n-i-p PSCs aged in air. Identification of a new facet of the complex degradation mechanisms in PSCs will allow for targeted acceleration testing to speed the deployment of low-cost, long-lasting electricity generation under realistic operating temperatures.
Ransomware attacks are one of the most dangerous cyber-attacks which can disrupt the operation of photovoltaic (PV) systems and incur an enormous economic loss. This paper introduces a ransomware security threat modeling method that identifies potential vulnerabilities, threats, and impacts of ransomware attacks targeting a PV system. Here, the security threat modeling consists of three steps: 1) system identification, 2) threat modeling that finds existing vulnerabilities, 3) attack modeling that designs attack profiles to succeed ransomware attacks, and 4) penetration testing that performs authorized cyber-attacks and analyzes impacts of the ransomware attack profiles using a real-time hardware-in-the-loop (HIL) PV system security testbed.
This library includes in-progress code for the optimization of materials and molecules using reinforcement learning. This project seeks to develop a generalized machine learning approach for optimizing targeted, and often complex, functional properties over the space of possible material structures, allowing faster identification of promising candidates. Electronic and transport properties of materials are determined by their molecular structure (for molecules) or their crystal structure (for in-organic crystalline materials). The atomic composition, bonding, and 3-D-spatial arrangement in these systems therefore represent a vast, discontinuous search space for optimal candidates. This library implements AlphaZero style reinforcement learning applied to two worked examples: the optimization of stable organic radicals for redox flow batteries, and the determination of stable electrolyte species for solid state batteries. The current code contains proof-of-concept results run on the Eagle HPC system, while the completed package will be a flexible tool for material optimization across of number of energy-relevant fields, which leverages cloud compute resources for the majority of the computational heavy lifting.
The development of imaging radar polarimetry at JPL is considered. Advances in the correlation of polarimetric SAR and in the synthesis of arbitrary polarization are discussed. The capabilities of the polarization workstation are described. Various applications for polarimetric SAR data include: the identification of scatter, the evaluation of scattering theories, the measurement of the roughness of geologic targets, the classification of forest stands, and the characterization of trees within a particular stand. The development and testing of the DC-8 SAR and the SIR-C system are examined.
The DSN (Deep Space Network) Scheduling Engine targets all space missions that use DSN services. It allows clients to issue scheduling, conflict identification, conflict resolution, and status requests in XML over a Java Message Service interface. The scheduling requests may include new requirements that represent a set of tracks to be scheduled under some constraints. This program uses a heuristic local search to schedule a variety of schedule requirements, and is being infused into the Service Scheduling Assembly, a mixed-initiative scheduling application. The engine resolves conflicting schedules of resource allocation according to a range of existing and possible requirement specifications, including optional antennas; start of track and track duration ranges; periodic tracks; locks on track start, duration, and allocated antenna; MSPA (multiple spacecraft per aperture); arraying/VLBI (very long baseline interferometry)/delta DOR (differential one-way ranging); continuous tracks; segmented tracks; gap-to-track ratio; and override or block-out of requirements. The scheduling models now include conflict identification for SOA(start of activity), BOT (beginning of track), RFI (radio frequency interference), and equipment constraints. This software will search through all possible allocations while providing a best-effort solution at any time. The engine reschedules to accommodate individual emergency tracks in 0.2 second, and emergency antenna downtime in 0.2 second. The software handles doubling of one mission's track requests over one week (to 42 total) in 2.7 seconds. Further tests will be performed in the context of actual schedules.
The Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx; or OREx) spacecraft arrived at its target, near-Earth asteroid (101955) Bennu, on December 3, 2018. The OSIRIS-REx spacecraft has since collected a wealth of scientific information in order to select a suitable site for sampling. Shortly after insertion into orbit on December 31, 2018, particles were identified in starfield images taken by the navigation camera (NavCam 1). Several groups within the OSlRlS-REx team analyzed the particle data in an effort to better understand this newfound activity of Bennu and to investigate the potential sensitivity of the particles to Bennu's geophysical parameters. A number of particles were identified through automatic and manual methods in multiple images, which could be turned into short sequences of optical tracking observations. Here, we discuss the precision orbit determination (OD) effort focused on these particles at NASA GSFC, which involved members of the Independent Navigation Team (INT) in particular. The particle data are combined with other OSIRIS-REx tracking data (radiometric from OSN and optical landmark data) using the NASA GSFC GEODYN orbit determination and geodetic parameter estimation software. We present the results of our study, particularly those pertaining to the gravity field of Bennu. We describe the force modeling improvements made to GEODYN specifically for this work, e.g., with a raytracing-based modeling of solar radiation pressure. The short-lived, low-flying moonlets enable us to determine a gravity field model up to a relatively high degree and order: at least degree 6 without constraints, and up to degree 10 when applying Kaula-like regularization. We can backward- and forward-integrate the trajectory of these particles to the ejection and landing sites on Bennu. We assess the recovered field by its impact on the OSIRIS-REx trajectory reconstruction and prediction quality in the various mission phases (e.g., Orbital A, Detailed Survey, and Orbital B).
Formation flying of satellites is at the beginning of an explosive growth curve. Spacecraft buses are shrinking to the point where we will soon be able to launch 10 micro-satellites or 100 nano-satellites on a single launch vehicle. Simultaneously, spectrometers are just beginning to be flown in space by both the U.S. and Europe. On-board programmable band aggregation will soon allow exactly the spectral bands desired to be returned to Earth. Further efforts are being devoted to radically shrink spectrometers both in size and weight. And GPS positioning and attitude determination, plus new technologies for attitude control, will allow fleets of satellites to all point at the same Earth target. All these advances, in combination, make possible for the first time the proper measurement of Bidirectional Reflectance Distribution (BRDF) form space. Previously space BDRF's were mere composites, built up over time by viewing different types of scenes at different times, then creating catalogs of BDRF functions whose use relied upon correct "scene identification" --the weak link. Formation-flying micro-satellites, carrying programmable spectrometers and precision-pointing at the same Earth target, can measure the full BDRF simultaneously, in real time. This talk will review these technological advances and discuss an actual proposed concept, based on these advances, to measure Earth-target BDRF's (clouds as well as surface) across the full solar spectrum in the 2010 timeframe. This concept is part of a larger concept called Leonardo for properly measuring the radiative forcing of Earth for climate purposes; lack of knowing of BDRF and of diurnal cycle are at present the two limiting factors preventing improved estimates of this forcing.
The vast majority of intracellular protein targets are refractory toward small-molecule therapeutic engagement, and additional therapeutic modalities are needed to overcome this deficiency. Here, the identification and characterization of a natural product, WDB002, reveals a therapeutic modality that dramatically expands the currently accepted limits of druggability. WDB002, in complex with the FK506-binding protein (FKBP12), potently and selectively binds the human centrosomal protein 250 (CEP250), resulting in disruption of CEP250 function in cells. The recognition mode is unprecedented in that the targeted domain of CEP250 is a coiled coil and is topologically featureless, embodying both a structural motif and surface topology previously considered on the extreme limits of “undruggability” for an intracellular target. Structural studies reveal extensive protein–WDB002 and protein–protein contacts, with the latter being distinct from those seen in FKBP12 ternary complexes formed by FK506 and rapamycin. Outward-facing structural changes in a bound small molecule can thus reprogram FKBP12 to engage diverse, otherwise “undruggable” targets. The flat-targeting modality demonstrated here has the potential to expand the druggable target range of small-molecule therapeutics. As CEP250 was recently found to be an interaction partner with the Nsp13 protein of the SARS-CoV-2 virus that causes COVID-19 disease, it is possible that WDB002 or an analog may exert useful antiviral activity through its ability to form high-affinity ternary complexes containing CEP250 and FKBP12.
Introduction Dogs with naturally occurring diabetes mellitus represent a potential model for human type 1 diabetes, yet signifcant knowledge voids exist in terms of the pathogenic mechanisms underlying the canine disorder. Untargeted metabolomic studies from a limited number of diabetic dogs identifed similarities to humans with the disease. Objective To expand and validate earlier metabolomic studies, identify metabolites that difer consistently between diabetic and healthy dogs, and address whether certain metabolites might serve as disease biomarkers. Methods Untargeted metabolomic analysis via liquid chromatography-mass spectrometry was performed on serum from diabetic (n=15) and control (n=15) dogs. Results were combined with those of our previously published studies using identical methods (12 diabetic and 12 control dogs) to identify metabolites consistently diferent between the groups in all 54 dogs. Thirty-two candidate biomarkers were quantifed using targeted metabolomics. Biomarker concentrations were compared between the groups using multiple linear regression (corrected P<0.0051 considered signifcant). Results Untargeted metabolomics identifed multiple persistent diferences in serum metabolites in diabetic dogs compared with previous studies. Therefore, targeted metabolomics showed increases in gamma amino butyric acid, valine, leucine, isoleucine, citramalate, and 2-hydroxyisobutyric acid in diabetic versus control dogs while indoxyl sulfate, N-acetyl-L-aspartic acid, kynurenine, anthranilic acid, tyrosine, glutamine, and tauroursodeoxycholic acid were decreased. Conclusion Several of these fndings parallel metabolomic studies in both human diabetes and other animal models of this disease. Given recent studies on the role of GABA and branched chain amino acids in human diabetes, the increase in serum concentrations in canine diabetes warrants further study of these metabolites as potential biomarkers, and to identify similarity in mechanisms underlying this disease in humans and dogs.
Introduction: Microalgae constitute a prominent feedstock for producing biofuels and biochemicals by virtue of their prolific reproduction, high bioproduct accumulation, and the ability to grow in brackish and saline water. However, naturally occurring wild type algal strains are rarely optimal for industrial use; therefore, bioengineering of algae is necessary to generate superior performing strains that can address production challenges in industrial settings, particularly the bioenergy and bioproduct sectors. One of the crucial steps in this process is deciding on a bioengineering target: namely, which gene/protein to differentially express. These targets are often orthologs which are defined as genes/proteins originating from a common ancestor in divergent species. Although bioinformatics tools for the identification of protein orthologs already exist, processing the output from such tools is nontrivial, especially for a researcher with little or no bioinformatics experience. Methods: The present study introduces AlgaeOrtho, a user-friendly tool that builds upon the SonicParanoid orthology inference tool (based on an algorithm that identifies potential protein orthologs based on amino acid sequences) and the PhycoCosm database from JGI (Joint Genome Institute) to help researchers identify orthologs of their proteins of interest in multiple diverse algal species. Results: The output of this application includes a table of the putative orthologs of their protein of interest, a heatmap showing sequence similarity (%), and an unrooted tree of the putative protein orthologs. Notably, the tool would be instrumental in identifying novel bioengineering targets in different algal strains, including targets in not-fully annotated algal species, since it does not depend on existing protein annotations. We tested AlgaeOrtho using three case studies, for which orthologs of proteins relevant to bioengineering targets, were identified from diverse algal species, demonstrating its ease of use and utility for bioengineering researchers. Discussion: This tool is unique in the protein ortholog identification space as it can visualize putative orthologs, as desired by the user, across several algal species.