The Red-Shift Magnitude Relation and Observational Data
Red shift-magnitude relation for universe model and observational data
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Red shift-magnitude relation for universe model and observational data
Here, a detailed study of the structural and magnetic properties of a spin-5/2 triangular lattice antiferromagnet RbFe(HPO 3 ) 2 is presented using x-ray diffraction, magnetization, heat capacity, and 31 P nuclear magnetic resonance (NMR) experiments on a polycrystalline sample. The crystal structure features an equilateral triangular lattice of Fe 3+ ions. The thermodynamic measurements reveal the onset of a magnetic long-range order at 𝑇 N1 ≃7.8K in zero field, followed by another low-temperature field-induced ordering at 𝑇 N2 in higher fields. The transition at 𝑇 N1 is further confirmed from the NMR spin lattice relaxation measurements. The value of the frustration ratio (𝑓≃7) implies moderate spin frustration in the compound. The 31 P NMR spectra exhibit two distinct spectral lines corresponding to two inequivalent phosphorus sites (P1 and P2), consistent with the crystal structure. The P1 site is strongly coupled with an isotropic hyperfine coupling of 𝐴$^{iso}_{hf}$ = 0.55(2) T/𝜇 B while the P2 site is weakly coupled with 𝐴$^{iso}_{hf}$ = 0.25(3) T/𝜇 B with the Fe 3+ ions. The magnetic susceptibility and NMR shift data are described well assuming a spin-5/2 isotropic triangular lattice antiferromagnetic model with an average exchange coupling of 𝐽/𝑘 B = 2.8(2)K. Below 𝑇 N1 , the spectra evolve into a nearly rectangular powder pattern, indicating a commensurate antiferromagnetic type order. The 31 P spin-lattice relaxation rate well below 𝑇N1 follows a 𝑇 3 temperature dependence, implying a two-magnon Raman scattering mechanism in the ordered state. Three well-defined phase regimes are clearly ascertained in the 𝐻−𝑇 phase diagram, reflecting a weak magnetic anisotropy in the compound.
Abstract The Bright Transient Survey (BTS) aims to obtain a classification spectrum for all bright ( m peak ≤ 18.5 mag) extragalactic transients found in the Zwicky Transient Facility (ZTF) public survey. BTS critically relies on visual inspection (“scanning”) to select targets for spectroscopic follow-up, which, while effective, has required a significant time investment over the past ∼5 yr of ZTF operations. We present BTSbot , a multimodal convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. BTSbot is able to eliminate the need for daily human scanning by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. BTSbot recovers all bright transients in our test split and performs on par with scanners in terms of identification speed (on average, ∼1 hr quicker than scanners). We also find that BTSbot is not significantly impacted by any data shift by comparing performance across a concealed test split and a sample of very recent BTS candidates. BTSbot has been integrated into Fritz and Kowalski , ZTF’s first-party marshal and alert broker, and now sends automatic spectroscopic follow-up requests for the new transients it identifies. Between 2023 December and 2024 May, BTSbot selected 609 sources in real time, 96% of which were real extragalactic transients. With BTSbot and other automation tools, the BTS workflow has produced the first fully automatic end-to-end discovery and classification of a transient, representing a significant reduction in the human time needed to scan.
A picture digitization grid based on logarithmic spirals rather than Cartesian coordinates is presented. Expressing this curvilinear grid as a conformal exponential mapping reveals useful image processing properties. The mapping induces a computational simplification that suggests parallel architectures in which most geometric transformations are effected by data shifting in memory rather than arithmetic on coordinates. These include fast, parallel noise-free rotation, scaling, and some projective transformations of pixel defined images. Conformality of the mapping preserves local picture-processing operations such as edge detection.
The final-state oxidation products and their distribution in thin native oxides (30-40 A) on GaAs have been studied using X-ray photoelectron spectroscopy in conjunction with chemical depth profiling. Extended room-temperature-oxidation conditions have been chosen to allow the native oxide to attain its equilibrium composition and structure. The work emphasizes the use of chemical depth-profiling methods which make it possible to examine the variation in chemical reactivity of the oxide structure. A minimum of two distinct regions of Ga2O3 with differing chemical reactivity is observed. Chemical shift data indicate the presence of As2O3 in the oxide together with an elemental As overlayer at the interface. A change in relative charge transfer between oxygen and both arsenic and gallium-oxide species is observed in the region of the interface.
Several approaches for implementing the register and multiplexer unit into two CMOS monolithic chip types were evaluated. The CMOS standard cell array technique was selected and implemented. Using this design automation technology, two LSI CMOS arrays were designed, fabricated, packaged, and tested for proper static, functional, and dynamic operation. One of the chip types, multiplexer register type 1, is fabricated on a 0.143 x 0.123 inch chip. It uses nine standard cell types for a total of 54 standard cells. This involves more than 350 transistors and has the functional equivalent of 111 gates. The second chip, multiplexer register type 2, is housed on a 0.12 x 0.12 inch die. It uses 13 standard cell types, for a total of 42 standard cells. It contains more than 300 transistors, the functional equivalent of 112 gates. All of the hermetically sealed units were initially screened for proper functional operation. The static leakage and the dynamic leakage were measured. Dynamic measurements were made and recorded. At 10 V, 14 megabit shifting rates were measured on multiplexer register type 1. At 5 V these units shifted data at a 6.6 MHz rate. The units were designed to operate over the 3 to 15 V operating range and over a temperature range of -55 to 125 C.
Some of the uses of holographic interferometry at Langley Research Center both for flow visualization and for density field determinations are described and tests in cryogenic flows at the Langley 0.3-Meter Transonic Cryogenic Tunnel are discussed. Experimental and theoretical fringe shift data are compared.
Measurement of mid spatial frequency figure error is critical to large precision optics for missions such as TPF-C. This presentation introduces a technique for increasing the spatial sampling resolution to meet these requirements using conventional video resolution phase-measuring interferometer. Technique involves sub-pixel data shifts, interlaced stitching and PSF deconvolution.
We report the discovery and analysis of a planet in the microlensing event OGLE-2018-BLG-0799. The planetary signal was observed by several ground-based telescopes, and the planet-host mass ratio is q = (2.65 ± 0.16) × 10(−3). The ground-based observations yield a constraint on the angular Einstein radius θE, and the microlensing parallax vector πE, is strongly constrained by the Spitzer data. However, the 2019 Spitzer baseline data reveal systematics in the Spitzer photometry, so there is ambiguity in the magnitude of the parallax. In our preferred interpretation, a full Bayesian analysis using a Galactic model indicates that the planetary system is composed of an Mplanet = 0.26+0.22 −0.11M(J) planet orbiting an Mhost = 0.093+0.082 −0.038 Mʘ , at a distance of DL = 3.71+3.24 −1.70 kpc. An alternate interpretation of the data shifts the localization of the minima along the arc-shaped microlens parallax constraints. This, in turn, yields a more massive host with median mass of 0.13 Mʘ at a distance of 6.3 kpc. This analysis demonstrates the robustness of the osculating circles formalism, but shows that further investigation is needed to assess how systematics affect the specific localization of the microlens parallax vector and, consequently, the inferred physical parameters.
This report examines how battery energy storage systems (BESS) can help utilities accommodate large load growth while protecting affordability for existing ratepayers. Rapid growth in electricity demand from artificial intelligence (AI) data centers is straining the U.S. grid. Furthermore, many new data centers are entering rural markets, which could offer economic benefits but may also pose implementation challenges for smaller utilities. At the same time, retail electricity prices are increasing faster than inflation, elevating customer affordability as a key challenge. While data centers have not been the primary driver of increases in residential prices to date, they have pushed wholesale energy and capacity prices higher in several markets. Fundamental utility cost-allocation principles show that data center growth can be rate-positive for existing customers only if new peak demand grows faster than the costs a utility must incur to serve it. Several factors, including a utility’s degree of wholesale market exposure, forecast uncertainty and stranded-asset risk, and tariff design can determine the outcome of load growth on retail rates. Energy storage can make several affordability contributions in the face of this landscape of uncertainty and market volatility, including deferral of higher-cost grid investments through improved utilization of existing assets and flexibility of new large loads, insulation from volatile wholesale prices through peak shaving, and reliability support to address grid risks stemming from the behavior of AI data center loads. Different potential BESS deployment pathways—utility-scale front-of-the-meter systems, aggregated small-scale storage installations, and data center-sited behind-the-meter storage—are compared against each other and against conventional capacity alternatives. This framework is intended as a conceptual resource to utilities, particularly smaller public utilities with rural service territories, who may be considering the role that energy storage can play in insulating existing ratepayers from data center cost shifts.
Not all zero shifted acceleration data can De-trended using this technique. DC shifts, improper AC coupling, Circuit noise/EMI/EMR, Equivalent RC circuit gain response/Circuit saturation(Slew Rate Limited), fixture grounding and wiring losses can all contribute to bad shock data being recorded. Some data that is zero-shifted or exhibit large instantaneous velocity shifts is inherently bad and a retest is warranted. Clean Acceleration-Time history data can be bad upon examining the Velocity & Displacement profiles. Laser Vibrometers provide a high level of accuracy for pyrotechnic shock testing. Engineering judgment and experience will determine the validity of Shock data.
An integrated-circuit quadriphase shift keying (QPSK) exciter and modulator have demonstrated excellent performance directly modulating a carrier frequency of 60 GHz with an output phase error of less than 3 degrees and maximum amplitude error of 0.5 dB. The circuit consists of a 60-GHz Gunn VCO phase-locked to a low-frequency reference source, a 4th subharmonic mixer, and a QPSK modlator packaged into a small volume of 1.8 x 2.5 x 0.35 in. The use of microstrip has the advantages of small size, light-weight, and low-cost fabrication. The unit has the potential for multigigabit data rate applications.
Simple loop first-in-first-out (FIFO) bubble memory shift register has continuous storage capability. Bubble shift register simplifies chip-control electronics by enabling all control functions to be alined at same bit. FIFO shift register is constructed from passive replicator and annihilator combinations.
While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series (> 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake.
In this research, three variations of time shift detection algorithms were tested for their ability to detect time shift issues (including daylight savings time and random time shifts) in measured PV data sets. Two algorithms from the Python PVAnalytics package were assessed, and one algorithm from the Solar-Data-Tools package was assessed. Each algorithm's ability to accurately detect and measure time shifts was assessed.
Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.
X-ray diffraction (XRD) is a well-established technique for analyzing materials at an atomic level. Dynamic compression experiments (DCE), in which materials are subject to extreme pressures, can provide fundamental understanding to pressure-induced phase transitions and compression of the crystal lattice. The analysis of XRD patterns from highly compressed samples is non-trivial given the sparsity of data, high experimental costs, and the fact that the data is often marred with X-ray background and other artifacts. While accurate computational frameworks exist, they solve the forward problem—from structures and orientations to XRD patterns. Solving the inverse problem for 2D experimental diffraction patterns is currently a complex manual process of matching and comparing experimentally observed patterns to computationally generated ones. Machine learning is a promising tool for automating the matching process but often requires data-intensive architectures. Here, in this study, we use a CycleGAN to translate the domain of limited experimental data to a domain in which there is readily available simulated data. This domain shift allows data-intensive machine learning models that have only been trained on simulated XRD patterns to be used in the analysis of experiments.
Electronic system collects, manipulates, and displays in real time results of manipulation of streams of data transmitted from remote scientific instrumentation. Interface circuit shifts data-and-clock signal from differential logic levels of multiplexer to single-ended logic levels of computer. System accommodates nonstandard data-transmission protocol. Software useful in applications where Macintosh computers used in real-time display and recording of data.