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At least 163 records · Page 9

The impact of urban configuration types on urban heat islands, air pollution, CO 2 emissions, and mortality in Europe: a data science approach

The world is becoming increasingly urbanized. As cities around the world continue to grow, it is important for urban planners and policymakers to understand how different urban configuration patterns affect the environment and human health. We aimed at identifying European urban configuration types, based on the Local Climate Zones categories and street design variables from Open Street Map, and evaluating their association with motorized traffic flows, Surface Urban Heat Island (SUHI) intensities, tropospheric nitrogen dioxide (NO 2 ), CO 2 per capita emissions and age-standardized mortality. We considered 946 European cities from 31 countries for the analysis defined in the 2018 Urban Audit database, of which 919 European cities were analysed. Data were collected at a 250 m × 250 m grid cell resolution. We divided all cities into five concentric rings based on the Burgess concentric urban planning model and calculated the mean values of all variables for each ring. First, to identify distinct urban configuration types, we applied the Uniform Manifold Approximation and Projection for Dimension Reduction method, followed by the k-means clustering algorithm. Next, statistical differences in exposures (including SUHI) and mortality between the resulting urban configuration types were evaluated using a Kruskal–Wallis test followed by a post-hoc Dunn's test. We identified four distinct urban configuration types characterising European cities: compact high density (n=246), open low-rise medium density (n=245), open low-rise low density (n=261), and green low density (n=167). Compact high density cities were a small size, had high population densities, and a low availability of natural areas. In contrast, green low-density cities were a large size, had low population densities, and a high availability of natural areas and cycleways. The open low-rise medium and low-density cities were a small to medium size with medium to low population densities and low to moderate availability of green areas. Motorised traffic flows and NO 2 exposure were significantly higher in compact high density and open low rise medium density cities when compared with green low density and open low-rise low density cities. Additionally, green low-density cities had a significantly lower SUHI effect compared with all other urban configuration types. Per person CO 2 emissions were significantly lower in compact high density cities compared with green low density cities. Lastly, green low density cities had significantly lower mortality rates when compared with all other urban configuration types. Our findings indicate that, although the compact city model is more sustainable, European compact cities still face challenges related to poor environmental quality and health. Our results have notable implications for urban and transport planning policies in Europe and contribute to the ongoing discussion on which city models can bring the greatest benefits for the environment, climate, and health.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

High-throughput Single-Cell Proteomics and Transcriptomics from the Same Cells with a Nanoliter-Scale Spin-Transfer Approach

Single-cell multiomic platforms provide a comprehensive snapshot of cellular states and cell types by offering critical insights into the spatiotemporal regulation of biomolecular networks at a systems level, thereby defining the basis of multicellularity. Here, we introduce nanoSPINS, an advanced platform that enables high-throughput profiling and integrative analysis of the transcriptome and proteome from the same single cells using RNA sequencing and isobaric labeling LC-MS-based proteomics, respectively. NanoSPINS can efficiently transfer mRNA-containing droplets across two microarrays via a centrifugation-based approach, while proteins are retained on the initial platform. Benchmarking of nanoSPINS on two cell lines demonstrates its ability to generate global proteomic and transcriptomic profiles that align well with previously established methodologies/platforms. The incorporation of isobaric TMTpro labeling into this single-cell multiomics platform significantly enhances the throughput of single-cell proteomic analyses. Through the high-throughput quantification of the proteome and transcriptome, nanoSPINS not only facilitates the identification of molecular features at both mRNA and protein level but also provides larger sample sizes for improved statistical power in clustering and differential abundance. Given the broad applicability of single-cell multiomics in biological research and clinical settings, we believe nanoSPINS represents a powerful platform for the characterization of heterogeneous cell populations.

multi 'omics↗

Synthetic light-cone catalogues of modern redshift and weak lensing surveys with abacussummit

ABSTRACT The joint analysis of different cosmological probes, such as galaxy clustering and weak lensing, can potentially yield invaluable insights into the nature of the primordial Universe, dark energy, and dark matter. However, the development of high-fidelity theoretical models is a necessary stepping stone. Here, we present public high-resolution weak lensing maps on the light-cone, generated using the N-body simulation suite abacussummit, and accompanying weak lensing mock catalogues, tuned to the Early Data Release small-scale clustering measurements of the Dark Energy Spectroscopic Instrument. Available in this release are maps of the cosmic shear, deflection angle, and convergence fields at source redshifts ranging from z = 0.15 to 2.45 as well as cosmic microwave background convergence maps for each of the 25 base-resolution simulations ($L_{\rm box} = 2000\, h^{-1}\, {\rm Mpc}$ and Npart = 69123) as well as for the two huge simulations ($L_{\rm box} = 7500\, h^{-1}\, {\rm Mpc}$ and Npart = 86403) at the fiducial abacussummit cosmology. The pixel resolution of each map is 0.21 arcmin, corresponding to a healpix Nside of 16 384. The sky coverage of the base simulations is an octant until z ≈ 0.8 (decreasing to about 1800 deg2 at z ≈ 2.4), whereas the huge simulations offer full-sky coverage until z ≈ 2.2. Mock lensing source catalogues are sampled matching the ensemble properties of the Kilo-Degree Survey, Dark Energy Survey, and Hyper Suprime-Cam data sets. The mock catalogues are validated against theoretical predictions for various clustering and lensing statistics, such as correlation multipoles, galaxy–shear, and shear–shear, showing excellent agreement. All products can be downloaded via a Globus endpoint (see Data Availability section).

79 ASTRONOMY AND ASTROPHYSICS↗

Scalar Field Comparison with Topological Descriptors: Properties and Applications for Scientific Visualization

In topological data analysis and visualization, topological descriptors such as persistence diagrams, merge trees, contour trees, Reeb graphs, and Morse–Smale complexes play an essential role in capturing the shape of scalar field data. Herein we present a state–of–the–art report on scalar field comparison using topological descriptors. We provide a taxonomy of existing approaches based on visualization tasks associated with three categories of data: single fields, time–varying fields, and ensembles. These tasks include symmetry detection, periodicity detection, key event/feature detection, feature tracking, clustering, and structure statistics. Our main contributions include the formulation of a set of desirable mathematical and computational properties of comparative measures, and the classification of visualization tasks and applications that are enabled by these measures.

97 MATHEMATICS AND COMPUTING↗

Data and scripts associated with “Moisture content modulates DOM thermodynamic regulation of oxygen consumption in drying streambed sediments”

This data package is associated with the publication “Moisture content modulates DOM thermodynamic regulation of oxygen consumption in drying streambed sediments” published in Scientific Reports (Garayburu-Caruso et al., 2026). The package contains processed data products and scripts used to quantify how drying and re-inundation of riverbed sediments influence dissolved organic matter (DOM) thermodynamic properties and their relationship with sediment oxygen (O₂) consumption across 33 stream sites in the contiguous United States. The data package contains DOM thermodynamic metrics (e.g., Gibbs free energy of carbon oxidation and thermodynamic efficiency), and O₂ consumption along with watershed-scale climate and land-cover metrics used as explanatory variables in the analyses. Underlying unprocessed and processed ultrahigh-resolution mass spectrometry data, oxygen consumption rates from laboratory moisture-manipulation experiments, within-sample environmental properties, sediment moisture content and contextual field measurements are archived separately at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2428003 (Laan et al., 2024) and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689 (Forbes et al.,2023). A preliminary version of this data package was published in February 2026 at the time of manuscript submission. It was updated in June 2026, at the time of manuscript acceptance, to include the finalized data and additional metadata (readme, data dictionary, and file level metadata). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. At the top level, the data package is organized into five main folders: (1) Data, (2)Figures, (3) Map, (4) GAM_Reulsts, and (5) src. The Data folder contains analysis-ready tabular files with oxygen consumption rates, DOM thermodynamic properties by site and treatment, site-level environmental variables, watershed-scale metrics, and other derived variables referenced in the manuscript. The Figures folder contains static image files associated with the main text and supplemental figures, while the Map folder includes spatial data and map-layer files used to create the sampling-location map. The GAM results folder contains the results for each of the general additive model (GAM).The src folder contains R scripts used to perform data processing, statistical analyses (including clustering, generalized additive models, and threshold analysis), and figure generation. This data package is associated with a GitHub repository found at https://github.com/WHONDRS-Hub/ECA_DOM_Thermodynamics.

Dissolved organic matter↗

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

Supervised and cluster block training statistics were used to analyze the thematic mapper simulation MSS data (both 1979 and 1980 data sets). Cover information classes identified on SAR imagery include: hardwood, pine, mixed pine hardwood, clearcut, pasture, crops, emergent crops, bare soil, urban, and water. Preliminary analysis of the HH and HV polarized SAR data indicate a high variance associated with each information class except for water and bare soil. The large variance for most spectral classes suggests that while the means might be statistically separable, an overlap may exist between the classes which could introduce a significant classification error. The quantitative values of many cover types are much larger on the HV polarization than on the HH, thereby indicating the relative nature of the digitized data values. The mean values of the spectral classes in the areas with larger look angles are greater than the means of the same cover type in other areas having steeper look angles. Difficulty in accurately overlaying the dual polarization of the SAR data was resolved.

Hoffer, R. M.↗

Characterization of surficial geologic units on Venus from Pioneer Venus radar data: A progress report

A classification database using the reflectivity (derived from the altimetry data), rms slope, and the first principal component of altimetry and topographic slope is presented. The resultant clustered data is examined qualitatively as well as quantitatively, to establish the statistical integrity of each cluster by use of an interactive, ternary plotting algorithm. This algorithm plots, for a cluster, the position of each of its pixels within a ternary diagram whose apices represent reflectivity, rms slope, and the first principal component. The digital values in these three databases are normalized such that unity is represented by a value of 255 in each database. The frequencies of each plotted point within the ternary diagram are recorded in order to establish the mode of each cluster. The pixels of each cluster are displayed as one separate color; their ternary plot will show not only the interrelations between clusters, but also the presence of any anomalous points within a cluster. Existing lunar and terrestrial analog radar data is used to establish fields within this ternary diagram that are indicative of as many different geologic materials and tectonics settings as possible. The resultant fields are used to determine empirically the geologic significance of the clusters resulting from the cluster analysis.

Davis, P. A.↗

Automated clustering-based workload characterization

The demands placed on the mass storage systems at various federal agencies and national laboratories are continuously increasing in intensity. This forces system managers to constantly monitor the system, evaluate the demand placed on it, and tune it appropriately using either heuristics based on experience or analytic models. Performance models require an accurate workload characterization. This can be a laborious and time consuming process. It became evident from our experience that a tool is necessary to automate the workload characterization process. This paper presents the design and discusses the implementation of a tool for workload characterization of mass storage systems. The main features of the tool discussed here are: (1)Automatic support for peak-period determination. Histograms of system activity are generated and presented to the user for peak-period determination; (2) Automatic clustering analysis. The data collected from the mass storage system logs is clustered using clustering algorithms and tightness measures to limit the number of generated clusters; (3) Reporting of varied file statistics. The tool computes several statistics on file sizes such as average, standard deviation, minimum, maximum, frequency, as well as average transfer time. These statistics are given on a per cluster basis; (4) Portability. The tool can easily be used to characterize the workload in mass storage systems of different vendors. The user needs to specify through a simple log description language how the a specific log should be interpreted. The rest of this paper is organized as follows. Section two presents basic concepts in workload characterization as they apply to mass storage systems. Section three describes clustering algorithms and tightness measures. The following section presents the architecture of the tool. Section five presents some results of workload characterization using the tool.Finally, section six presents some concluding remarks.

Pentakalos, Odysseas I.↗

Soil Moisture and Snow Cover: Active or Passive Elements of Climate

A key question is the extent to which surface effects such as soil moisture and snow cover are simply passive elements or whether they can affect the evolution of climate on seasonal and longer time scales. We have constructed ensembles of predictability studies using the NCAR CCM3 in which we compared the relative roles of initial surface and atmospheric conditions over the central and western U.S. in determining the subsequent evolution of soil moisture and of snow cover. Results from simulations with realistic soil moisture anomalies indicate that internal climate variability may be the strongest factor, with some indication that the initial atmospheric state is also important. Model runs with exaggerated soil moisture reductions (near-desert conditions) showed a much larger effect, with warmer surface temperatures, reduced precipitation, and lower surface pressures; the latter indicating a response of the atmospheric circulation. These results suggest the possibility of a threshold effect in soil moisture, whereby an anomaly must be of a sufficient size before it can have a significant impact on the atmospheric circulation and climate. Results from simulations with realistic snow cover anomalies indicate that the time of year can be crucial. When introduced in late winter, these anomalies strongly affected the subsequent evolution of snow cover. When introduced in early winter, however, little or no effect is seen on the subsequent snow cover. Runs with greatly exaggerated initial snow cover indicate that the high reflectivity of snow is the most important process by which snow cover can impact climate, through lower surface temperatures and increased surface pressures. The results to date were obtained for model runs with present-day conditions. We are currently analyzing runs made with projected forcings for the 21st century to see if these results are modified in any way under likely scenarios of future climate change. An intriguing new statistical technique involving 'clustering' is developed to assist in this analysis.

Oglesby, Robert J.↗

An ALMA Glimpse of Dense Molecular Filaments Associated with High-mass Protostellar Systems in the Large Magellanic Cloud

Recent millimeter/submillimeter facilities have revealed the physical properties of filamentary molecular clouds in relation to high-mass star formation. A uniform survey of the nearest, face-on star-forming galaxy, the Large Magellanic Cloud (LMC), complements the Galactic knowledge. We present ALMA survey data with a spatial resolution of ∼0.1 pc in the 0.87 mm continuum and HCO+ (4–3) emission toward 30 protostellar objects with luminosities of 104–105.5L⊙ in the LMC. The spatial distributions of the HCO+ (4–3) line and thermal dust emission are well correlated, indicating that the line effectively traces dense, filamentary gas with an H2 volume density of ≳105 cm−3 and a line mass of ∼103–104M⊙ pc−1. Furthermore, we obtain an increase in the velocity line widths of filamentary clouds, which follows a power-law dependence on their H2 column densities with an exponent of ∼0.5. This trend is consistent with observations toward filamentary clouds in nearby star-forming regions within ≲1 kpc from us and suggests enhanced internal turbulence within the filaments due to surrounding gas accretion. Among the 30 sources, we find that 14 are associated with hub-filamentary structures, and these complex structures predominantly appear in protostellar luminosities exceeding ∼5 × 104L⊙. The hub-filament systems tend to appear in the latest stages of their natal cloud evolution, often linked to prominent H II regions and numerous stellar clusters. Our preliminary statistics suggest that the massive filaments accompanied by hub-type complex features may be a necessary intermediate product in forming extremely luminous high-mass stellar systems capable of ultimately dispersing the parent cloud.

Marta M Sewilo↗

Statistical Descriptors of Composite Fiber Aggregation

This study introduces a method of characterizing fiber aggregation and resin rich regions in composite microstructures. Microscale models of representative elements (RVE) need to be indicative of the extend of clustering (i.e. close fiber-to-fiber interaction) and resin rich “pools” which may impact the overall strength and performance of a composite structure. This algorithm was used to evaluate different unidirectional 2-D microstructure scans, which will be compared to their manufacturing method or any special treatment processes. These cluster and pool scan statistics can be used as criteria to judge statistical equivalency of artificially constructed microstructures.

Statistics↗

Constraining Galaxy-Halo connection using machine learning

We investigate the potential of machine learning (ML) methods to model small-scale galaxy clustering for constraining Halo Occupation Distribution (HOD) parameters. Our analysis reveals that while many ML algorithms report good statistical fits, they often yield likelihood contours that are significantly biased in both mean values and variances relative to the true model parameters. This highlights the importance of careful data processing and algorithm selection in ML applications for galaxy clustering, as even seemingly robust methods can lead to biased results if not applied correctly. ML tools offer a promising approach to exploring the HOD parameter space with significantly reduced computational costs compared to traditional brute-force methods if their robustness is established. Using our ANN-based pipeline, we successfully recreate some standard results from recent literature. Properly restricting the HOD parameter space, transforming the training data, and carefully selecting ML algorithms are essential for achieving unbiased and robust predictions. Among the methods tested, artificial neural networks (ANNs) outperform random forests (RF) and ridge regression in predicting clustering statistics, when the HOD prior space is appropriately restricted. We demonstrate these findings using the projected two-point correlation function (w p (r p )), angular multipoles of the correlation function (ξ ℓ (r)), and the void probability function (VPF) of Luminous Red Galaxies from Dark Energy Spectroscopic Instrument mocks. Our results show that while combining w p (r p ) and VPF improves parameter constraints, adding the multipoles ξ 0 , ξ 2 , and ξ 4 to w p (r p ) does not significantly improve the constraints.

cosmology↗

The HEAO A-2 survey of Abell clusters and the X-ray luminosity function

A statistically complete sample of 225 Abell clusters has been examined. Twelve new identifications of clusters with X-ray sources have been made. It is found that (1) Lx is roughly correlated with richness for richness classes 0, 1, and 2; (2) Lx is well correlated with Rood-Sastry type and only loosely correlated with Bautz-Morgan type; and (3) the cluster contribution to the X-ray background is less than or equal to 5% between 2 and 10 keV.

Mckee, J. D.↗

Discovery of a Cool, Metal-rich Gas Reservoir in the Outskirts of z ≈ 0.5 Clusters

We built the first-ever statistically significant sample of ≈80,000 background quasar–foreground cluster pairs to study the cool, metal-rich gas in the outskirts (>R 500 ) of z ≈ 0.5 clusters with a median mass of ≈10 14.2 M ⊙ . The sample was obtained by crossmatching the Sloan Digital Sky Survey (SDSS) cluster catalog of Wen & Han and the SDSS quasar catalog of Lyke et al. The median impact parameter (ρcl) of the clusters from the quasar sightlines is 2.4 Mpc (median ρ cl /R 500 = 3.6). A strong Mg II, along with marginal Fe II, absorption is detected in the mean and median stacked spectra of the quasars with a total Mg II rest-frame equivalent width (${W}_{r}^{2796+2803}$) of 0.034 ± 0.005 Å (7σ) and 0.010 ± 0.003 Å (3σ), respectively. The ${W}_{r}^{2796+2803}$ shows a declining trend with increasing ρcl and ρcl/R 500 , but does not show any significant trend with mass (M 500 ) or redshift (z cl ) within the small M500 and z cl ranges probed here. The Mg II absorption signal and the trends persist even if we exclude the quasar–cluster pairs where the background quasars may be probing the circumgalactic medium of bright galaxies with impact parameters <300 kpc. The Mg II (and Fe II) absorption reported here is the first detection of its kind. It indicates the presence of a cool, metal-rich gas reservoir surrounding galaxy clusters out to several R 500 . We suggest that the metal-rich gas in the cluster outskirts arise from stripped materials and that gas stripping may be important out to large clustocentric distances (>3R 500 ).

Intergalactic medium↗

Three-dimensional super line-localization in low signal-to-noise microscope images via prior-apprised unsupervised learning (PAUL)

Biological processes such as processive enzyme turnover and intracellular cargo tracking involve the dynamic motion of a small "article" along a curvilinear biopolymer track. To understand these processes that occur across multiple length and time scales, one must acquire both the trajectory of the particle and the position of the track along which it moves, possibly by combining high-resolution single-particle tracking with conventional microscopy. Yet, usually there is a significant resolution mismatch between these modalities: while the tracked particle is localized with a precision of 10 nm, the image of the surroundings is limited by optical difraction, with 200 nm lateral and 500 nm axial resolutions. Compared to the particle's trajectory, the surrounding curvilinear structure appears as a blurred and noisy image. This disparity in the spatial resolutions of the particle trajectory and the surrounding curvilinear structure image makes data reconstruction, as well as interpretation, particularly challenging. Analysis is further complicated when the curvilinear structures are oriented arbitrarily in 3D space. Here, we present a prior-apprised unsupervised learning (PAUL) approach to extract information from 3D images where the underlying features resemble a curved line such as a filament or microtubule. This three-stage framework starts with a Hessian-based feature enhancement, which is followed by feature registration, where local line segments are detected on repetitively sampled subimage tiles. In the final stage, statistical learning, segments are clustered based on their geometric relationships. Principal curves are then approximated from each segment group via statistical tools including principal component analysis, bootstrap and kernel transformation. This procedure is characterized on simulated images, where sub-voxel medium deviations from true curves have been achieved. The 3D PAUL approach has also been implemented for successful line localization in experimental 3D images of gold nanowires obtained using a multifocal microscope. Lastly, this work not only bridges the resolution gap between two microscopy modalities, but also allows us to conduct 3D super line-localization imaging experiments, without using super-resolution techniques.

42 ENGINEERING↗

Evidence for a high-energy tail in the gamma-ray spectra of globular clusters

Millisecond pulsars are very likely the main source of gamma-ray emission from globular clusters. However, the relative contributions of two separate emission processes – curvature radiation from millisecond pulsar magnetospheres versus inverse Compton emission from relativistic pairs launched into the globular cluster environment by millisecond pulsars – have long been unclear. To address this, we search for evidence of inverse Compton emission in 8-yr Fermi–LAT data from the directions of 157 Milky Way globular clusters. We find a mildly statistically significant (3.8σ) correlation between the measured globular cluster gamma-ray luminosities and their photon field energy densities. However, this may also be explained by a hidden correlation between the photon field densities and the stellar encounter rates of globular clusters. Analysed in toto, we demonstrate that the gamma-ray emission of globular clusters can be resolved spectrally into two components: (i) an exponentially cut-off power law and (ii) a pure power law. The latter component – which we uncover at a significance of 8.2σ – has a power index of 2.79 ± 0.25. It is most naturally interpreted as inverse Compton emission by cosmic-ray electrons and positrons injected by millisecond pulsars. Here, we find the luminosity of this power-law component is comparable to, or slightly smaller than, the luminosity of the curved component, suggesting the fraction of millisecond pulsar spin-down luminosity into relativistic leptons is similar to the fraction of the spin-down luminosity into prompt magnetospheric radiation.

79 ASTRONOMY AND ASTROPHYSICS↗

The Effects of Nearby Clusters of Galaxies on the Microwave Background Radiation

This project proposed to use the COBE (Cosmic Background Explorer) DMR sky-maps to measure the anisotropies introduced into the microwave background radiation by the Sunyaev-Zel'dovich and Rees-Sciama effects of nearby clusters and superclusters of galaxies. We intended to seek these effects by making maps of the best-fit anisotropies on particular angular scales and comparing the apparent anisotropies near target clusters and superclusters with the statistical noise and sky variance. The locations of the clusters and superclusters were to be found using HEAO-1 (High Energy Astronomy Observatory) A2 and Einstein X-ray maps. Checks against biases were to be made using radio and X-ray sky-maps as guides to the properties of the clusters and superclusters. Any signals detected would have implications for the gas properties and baryonic masses of clusters and superclusters. The scientific background, project activities and references to published papers are included.

Birkinshaw, M.↗

Galaxy Clustering with LSST: Effects of Number Count Bias from Blending

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will survey the southern sky to create the largest galaxy catalog to date, and its statistical power demands an improved understanding of systematic effects such as source overlaps, also known as blending. In this work we study how blending introduces a bias in the number counts of galaxies (instead of the flux and colors), and how it propagates into galaxy clustering statistics. We use the 300 deg 2 DC2 image simulation and its resulting galaxy catalog (LSST Dark Energy Science Collaboration et al. 2021) to carry out this study. We find that, for a LSST Year 1 (Y1)-like cosmological analyses, the number count bias due to blending leads to small but statistically significant differences in mean redshift measurements when comparing an observed sample to an unblended calibration sample. In the two-point correlation function, blending causes differences greater than 3σ on scales below approximately 10', but large scales are unaffected. We fit Ω m and linear galaxy bias in a Bayesian cosmological analysis and find that the recovered parameters from this limited area sample, with the LSST Y1 scale cuts, are largely unaffected by blending. Our main results hold when considering photometric redshift and a LSST Year 5 (Y5)-like sample.

79 ASTRONOMY AND ASTROPHYSICS↗