Rethinking the Design of Ionic Conductors Using Meyer?Neldel?Conductivity Plot
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Gao, Yirong↗
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Dalitz plot analyses of J / ψ → π + π - π 0 , J / ψ → K + K - π 0 , and J / ψ → K s 0 K ± π ∓ produced via e + e - annihilation with initial-state radiation
Here, we study the processes e + e - → γ ISR J/ψ , where J/ψ → π + π - π 0 , J/ψ → K + K - π 0 , and J / ψ → K$0\atop{S}$ K ± π ∓ using a data sample of 519 fb -1 recorded with the BABAR detector operating at the SLAC PEP-II asymmetric-energy e + e - collider at center-of-mass energies at and near the Υ (nS) (n = 2 , 3 , 4) resonances. We measure the ratio of branching fractions R 1 = $B(J/ψ →K^+K^- π^0)\atop{B(J/ψ →π^+π^- π^0)}$ and R 2 = $B(J/ψ →K^0_SK^±π^∓)\atop{B(J/ψ →π^+π^- π^0)}$. We perform Dalitz plot analyses of the three J/ψ decay modes and measure fractions for resonances contributing to the decays. We also analyze the J/ψ → $π^+π^- π^0$ decay using the Veneziano model. We observe structures compatible with the presence of ρ (1450) in all three J/ψ decay modes and measure the relative branching fraction: R (p(1450)) = $Bp(1450)→K^+K^-)\atop{B(p(1450)→π^+π^-)}$ +0.307 ± 0.084 (stat) ± 0.082 (sys).
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Using Median Point in Keeling Plot to Reduce the Uncertainty of the Isotopic Composition of Evapotranspiration
Abstract The isotopic composition of evapotranspirationδ ET is a crucial parameter in isotope-based evapotranspiration (ET) partitioning and moisture recycling studies. The Keeling plot method is the most prevalent method to calculateδ ET , though it contains large extrapolated uncertainties from the least squares regression. Traditional Keeling regression uses the mean point of individual measurements. Here, a modified Keeling plot framework was proposed using the median point of individual measurements. We tested theδ ET uncertainty using the mean point [σ ET (mean)] and median point [σ ET (median)]. Multiple resolutions of input and output data from six independent sites were used to test the performance of the two methods. Theσ ET (mean) would be greater thanσ ET (median) when the mean value of inverse vapor concentration () is greater than the median value of inverse vapor concentration []. When applying the filter ofr 2 > 0.8, around 70% ofσ ET (mean) was greater thanσ ET (median). This phenomenon might be due to the normality of the vapor concentrationC υ producing the asymmetric distribution of 1/C υ . The median method could perform significantly better than the mean method when inputting high-resolution measurements (e.g., 1 Hz) and when the water vapor concentrationC υ is relatively low. Compared to the mean method, applying the median method could on average reduce 6.88% of ET partitioning uncertainties and could on average reduce 9.00% of moisture recycling uncertainties. This study provided a new insight of the Keeling plot method and emphasized handling model output uncertainty from multiple perspectives instead of only from input parameters.
Meteorology & Atmospheric Sciences↗
Loran-C plotting program for plotting lines of position on standard charts
A set of programs designed to be run on the IBM System/370 computer is described. These programs are used to plot Loran-C lines of position (LOP) on any common map or standard aviation sectional chart. The Loran-C plotting program JRPLOT FORTRAN uses a standard Calcomp-compatible plotting subroutine package for the Hewlett-Packard 7203A graphic plotter. A general description of the features of the Loran-C plotting program. This program involves a simple add/substrate method to calculate the LOP. A description on how to use the program and some methods of operation are included.
Roman, J. P.↗
Loran-C plotting program for plotting lines of position on standard charts
A set of programs used to plot Loran C lines of position on any common map or standard aviation sectional chart are given. The Loran C plotting program JRPLOT FORTRAN uses a standard Calcomp compatible plotting subroutine package for the Hewlett-Packard 7203A graphic plotter. The programs are designed to be run on an IBM System 370 computer. A simple add subtract method is used to calculate the lines of position. A description of how to use the program and some methods of operation are given.
Roman, J. P.↗
Measurement of the CKM angle φ 1 in B 0 → D ¯ ( * ) 0 h 0 , D ¯ 0 → K S 0 π + π - decays with time-dependent binned Dalitz plot analysis
Here, we show a measurement of the C P -violation parameter φ 1 obtained in a time-dependent analysis of B 0 → D ¯ ( * ) 0 h 0 decays followed by D ¯ 0 → K S 0 π + π - decay. A model-independent measurement is performed using the binned Dalitz plot technique. The measured value is φ 1 = 11.7 ° ± 7.8 ° ( stat ) ± 2.1 ° ( syst ) . Treating sin 2 φ 1 and cos 2 φ 1 as independent parameters, we obtain sin 2 φ 1 = 0.43 ± 0.27 ( stat ) ± 0.08 ( syst ) and cos 2 φ 1 = 1.06 ± 0.33 ( stat ) - 0.15 + 0.21 ( syst ) . The findings are obtained with a full data sample of 772 × 1 0 6 B B ¯ pairs collected near the Υ ( 4 S ) resonance with the Belle detector at the KEKB asymmetric-energy e + e - collider.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Admittance-Based Stability Analysis: Bode Plots, Nyquist Diagrams or Eigenvalue Analysis?
Not provided.
Engineering↗
SPRUCE Soil Volumetric Water Content in Experimental Plots, Marcell Experimental Forest, Minnesota, 2017-2025
This dataset contains soil volumetric water content (VWC) measurements from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) Experiment. Measurements were made inside SPRUCE experimental plots in the S1 Bog at the US Forest Service Marcell Experimental Forest in northern Minnesota, USA from 2018-2025 (2018-06-14 to 2025-12-31). Observations were made with METER 10HS soil moisture sensors. To reduce bulk density related variability, 10HS sensors were placed inside mesh tubes filled with peat at a standard bulk density. The observations under standard bulk density represent relative differences in water content between hummock and hollow positions, and in response to experimental treatments. Productivity of peatlands, and their keystone species sphagnum, are highly dependent on water availability, which is affected by lateral inputs, precipitation, ground water depth and evapotranspiration. Knowledge of near surface and sphagnum water content is useful to understand peatland function. This dataset contains 8 data files in comma-separate values (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma-separate values (*.csv) format and a user guide in PDF (*.pdf) format.
Warren, Jeffrey [ORNL] (ORCID:0000000206804697)↗
SPRUCE Tree Growth Assessments of Picea and Larix in S1-Bog Plots and SPRUCE Experimental Plots, Beginning in 2011, Version 2
This data set reports tree growth measurements of mature Picea mariana and Larix laricina located in the S1-Bog permanent plots and the SPRUCE experimental study plots. Data have been collected annually from 2011 to 2024 (2011-02-23 to 2024-02-26) in late February/ early March to capture the productivity of the previous growing season (i.e. the February 2024 measurements track the 2023 growing season). Data collections are anticipated to continue through February of 2026 and this data set will be appended annually. Initial observations in 2011 included measurements of circumference at 1.3 m (diameter at breast height assessments; DBH) above the nominal bog hollow surface, tree heights and crown diameters. Subsequent annual measurements have focused on the measures of circumference at DBH. Supplemental tree height data collected via terrestrial lidar (Graham et al. 2019) were initiated in 2016 and have been maintained annually. Circumference measurements to the nearest 0.1 cm are converted to DBH in cm and basal area at DBH in (cm2). Tree height and crown diameter are measured to the nearest 0.1 m. This dataset contains one data file in comma separate (*.csv) format. User Note: In this Version 2, some previously released ‘Circum_Status’ values were updated to fix misread field measurements in previous years. The new data file, SPRUCE_Tree_Growth_Archive_20240620.csv, supersedes and replaces the previous, SPRUCE_Tree_Growth_Archive_20230327.csv.
54 ENVIRONMENTAL SCIENCES↗
Plotting XPS Narrow Scans (and Other data) with MATLAB, 1. Using the 'plot' and 'subplot' functions.
As researchers, we often focus more on data collection than data work-up. However, skillfully plotting data so that it can be effectively visualized is often an important step toward making it impactful. Data visualization in academic writing takes the form of plots, figures, graphs, spectra, etc., which are often combined with cartoons and schematics. An important goal of data visualization is to make complex data and concepts more accessible, understandable, and usable, and to do this in a concise manner – ‘a picture is worth a thousand words’ as the old cliché goes. Data visualization is both art and science, where, of course, data visualization depends on the type of data in question. Herein, we discuss ways of representing/plotting data using MATLAB from the MathWorks ® .
97 MATHEMATICS AND COMPUTING↗
η → 3 π : Study of the Dalitz plot and extraction of the Quark Mass Ratio Q [The decay η → 3 π : Study of the Dalitz plot and extraction of the quark mass ratio Q ]
The $\eta\to 3\pi$ amplitude is sensitive to the quark mass difference $m_u-m_d$ and offers a unique way to determine the quark mass ratio $Q^2\equiv (m_s^2-m_{ud}^2)/(m_d^2-m_u^2)$ from experiment. We calculate the amplitude dispersively and fit the KLOE data on the charged mode, varying the subtraction constants in the range allowed by chiral perturbation theory. The parameter-free predictions obtained for the neutral Dalitz plot and the neutral-to-charged branching ratio are in excellent agreement with experiment. Lastly, our representation of the transition amplitude implies $Q = 22.0 \pm 0.7$.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
SPRUCE Shrub-Layer Growth Assessments in S1-Bog Plots and SPRUCE Experimental Plots beginning in 2010
This data set reports shrub layer growth assessments for the S1-Bog on the Marcell Experimental Forest in Minnesota from 2010 through 2023 (2010-08-01 to 2023-08-31, no data were collected in 2020 and 2022). Data were obtained by destructively harvesting selected plots of varying number and size. In early years a distinct hummock and a hollow sampling square were both collected, but in later years unsampled hollow areas became unavailable due to prior sampling or instrument installations. Harvested samples were broken down species, tissue type (leaf or stem/), and by whether tissue was from the current year’s growth or growth from previous year’s growth. Dry mass of the leaf and stem fractions are provided. For 2011, 2012, 2017, 2018 and 2023, tissue carbon (C) and nitrogen (N) measurements are available for select samples. Future growth assessments are planned for 2025 and will be appended to this dataset. This dataset contains one data file in comma-separate (*.csv) format.
54 ENVIRONMENTAL SCIENCES↗
Measurement of CP-violating asymmetries in B^0 \to (rho-pi)^0 Decays using a Time-dependent Daltiz Plot Analysis
Abstract Not Provided
Experiment-HEP,HEPEX↗
Plotting XPS Narrow Scans (and other Data) with MATLAB’s Powerful Plotting Capabilities, 2. An Introduction to Handle Graphics
The graphical representation of numerical data plays a vital role in chemistry, materials science, and every other scientific field. No matter how good your results are, poorly represented data can ultimately make your work confusing. Indeed, with an increase in the amount of data that is being gathered, e.g., by the Materials Genome Initiative, or in X-ray photoelectron spectroscopy (XPS) or time-of-flight secondary ion mass spectrometry (ToF-SIMS) depth profiles, the importance of effectively representing data has increased. Good figures also matter because many researchers will not look at more than the title, abstract, and perhaps a few key figures from a paper.
42 ENGINEERING↗
Model-independent plotting of the cosmological scale factor as a function of lookback time
In this work we describe a model-independent method of developing a plot of scale factor a(t) versus lookback time t{sub L} from the usual Hubble diagram of modulus data against redshift. This is the first plot of this type. We follow the model-independent methodology of Daly and Djorgovski used for their radio-galaxy data. Once the a(t)data plot is completed, any model can be applied and will display as described in the standard literature. We then compile an extensive data set to z = 1.8 by combining Type Ia supernovae (SNe Ia) data from SNLS3 of Conley et al., high-z SNe data of Riess et al., and radio-galaxy data of Daly and Djorgovski to validate the new plot. We first display these data on a standard Hubble diagram to confirm the best fit for ΛCDM cosmology, and thus validate the joined data set. The scale factor plot is then developed from the data and the ΛCDM model is again displayed from a least-squares fit. The fit parameters are in agreement with the Hubble diagram fit confirming the validity of the new plot. Of special interest is the transition time of the universe, which in the scale factor plot will appear as an inflection point in the data set. Noise is more visible in this presentation, which is particularly sensitive to inflection points of any model displayed in the plot, unlike on a modulus-z diagram, where there are no inflection points and the transition-z is not at all obvious by inspection. We obtain a lower limit of z ≥ 0.6. It is evident from this presentation that there is a dearth of SNe data in the range z = 1-2, exactly the range necessary to confirm a ΛCDM transition-z around z = 0.76. We then compare a 'toy model' wherein dark matter is represented as a perfect fluid with an equation of state p = –(1/3) ρ to demonstrate the plot sensitivity to model choice. Its density varies as 1/t {sup 2} and it enters the Friedmann equations as Ω{sub dark}/t {sup 2}, replacing only the Ω{sub dark}/a {sup 3} term. The toy model is a close match to ΛCDM, but separates from it on the scale factor plot for similar ΛCDM density parameters. It is described in the Appendix. A more complete transition time analysis will be presented in a future paper.
79 ASTRONOMY AND ASTROPHYSICS↗
Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling
Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.
59 BASIC BIOLOGICAL SCIENCES↗
SPRUCE Vegetation Community Survey Data from SPRUCE Experimental Plots, 2014-2020
This dataset provides vegetation survey data collected across vegetation community plots within the 10 chambered treatment plots (6, 19, 11, 20, 4, 13, 8, 16, 10, 17) and 3 unchambered ambient plots (7, 14, 21) from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. Collections occurred from 2014-2020 (2014-04-27 to 2020-08-06). The vegetation community plots, three in each experimental plot, were established in 2014 and designated to be undisturbed and reserved for long-term sampling. Vegetation survey data were collected at the peak of each growing season by placing a 1x2 meter rectangular grid over each community plot. The 1x2 meter grid is subset into 50 20x20 cm grid cells. Observations are of the presence of each species in each grid cell for each subplot. That’s a total of 39 subplots across the 13 experimental plots. From 2018 to 2020, shrub stem density was counted and percent shrub cover was estimated in five selected grid cells in each vegetation community plot. Also included are photographs of each community plot taken at the time of data collection for each measurement year. These data provide insight into the responses of plant community composition to warming and increased carbon dioxide. These data were use in analyses in McPartland et al (2018; 2020). This dataset contains three data files in comma-separate values (*.csv) format and seven compressed (*.zip) files containing 877 JPEG (*.jpg) files. Additional metadata are provided: a data dictionary and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format.
54 ENVIRONMENTAL SCIENCES↗