Scale Resolution Research Articles

The effect of urban form parameters on annual and diurnal cycles of land surface temperature with 30-meter hourly resolution

Understanding dynamic changes in urban land surface temperature (U-LST) is vital for improving urban thermal environment. Previous studies noted a close association between two/three-dimensional (2D/3D) urban form parameters (UFPs) and U-LST, but divergent conclusions emerged due to study scale and data spatiotemporal resolution. This study investigated the effect of 2D/3D UFPs on multi-temporal (hourly, diurnal, and annual) U-LSTs with 30-m spatial resolution, which is the first exploration of such dynamics at this fine scale. In addition, the influence of local climate zones (LCZs) was considered. Results showed that 2D/3D vegetation UFPs have the greatest summer cooling effect at 21:00, while 3D building UFP sky view factor (SVF) impacts minimum nighttime temperature more (5:00). The independently total explained variances (R2) of 2D UFPs (0.2–0.7) on seasonal average U-LSTs were higher than that of 3D UFPs (0.1–0.4) in most LCZs. LCZ 3 and LCZ 2 had the highest seasonal and daytime U-LST, respectively. Interestingly, SVF replaced normalized difference vegetation index as the dominant factor in LCZs 4–6 and LCZs A-C after summer. Findings of this study are useful for guiding urban planning, formulating urban heat island mitigation policies, and promoting sustainable development goals (SDGs 11, 13, 15) of cities and communities.

Pion gravitational form factors in the QCD instanton vacuum. I

The pion form factors of the QCD energy-momentum tensor (EMT) are studied in the instanton liquid model (ILM) of the QCD vacuum. In this approach, the breaking of conformal symmetry is encoded in the form of stronger-than-Poisson fluctuations in the number of instantons. For the trace of the EMT, it is shown that the gluonic trace anomaly term contributes half the pion mass, with the other half coming from the quark-mass-dependent σ term. The Q2 dependence of the form factors is governed by glueball and scalar meson exchanges. For the EMT, the spin-0 (trace) and spin-2 (traceless rank-2 tensor) form factors are computed at next-to-leading order in the instanton density using effective quark operators. Relations between the gluon and quark contributions to the EMT form factors are derived. The form factors are also expressed in terms of the pion light-front wave functions in the ILM. The results at the low resolution scale of the inverse instanton size are evolved to higher scales using the renormalization group equation. The ILM results compare well with those of recent lattice QCD calculations. Published by the American Physical Society 2024

FRUGIVORY CAMTRAP: A dataset of plant-animal interactions recorded with camera traps.

Ecological interactions are a key component of biodiversity, essential for understanding ecosystem services and functioning. Recording and quantifying ecological interactions is challenging, frequently requiring complex logistics and substantial effort in the field. Camera traps are routinely used in ecology for various applications, and have proven to be an excellent method for passive and non-invasive sampling of plant-animal interactions. We implemented a standardized camera trap protocol to document vertebrate frugivores-fleshy fruitedplants interactions in Doñana National Park, SW Spain, with the central objective of inventorying the diversity of plant-animal ecological interactions providing seed dispersal services. From 2018 to 2023 we recorded pairwise interactions from which we obtained qualitative (presence-absence) and quantitative (frequency of visits) information. Each record in the dataset contains information of a visit by an individual animal to an individual plant, resulting in any form of fleshy-fruit use and provides information on visitation phenology, visit length, and feeding behavior. The dataset presented here includes 10,659 frugivory interaction events for 59 vertebrate species (46 birds, 13 mammals) recorded on 339 plant individuals from 13 different plant species which dominate the fleshy-fruited plant assemblage in the Doñana National Park. The most recorded animal species consuming fruits and playing a legitimate seed dispersal role was Curruca melanocephala (1678 records) among birds and Vulpes vulpes among mammals (751 records). Cervus elaphus, a fruit consumer with a marginal role as legitimate seed disperser, was the most recorded mammal species (1508 records). Avian frugivores, particularly those from the Sylviidae and Turdidae families, are widespread in the region and play a crucial role in maintaining the dispersal service for the fleshy-fruited plant populations in the area. The dataset offers highly versatile quantitative information that can be used to investigate frugivory from the highest resolution scale, the interaction event between pairs of individuals. In addition, other information that can be extracted includes the timing of interactions of animals and plants (their phenological couplings), activity periods of the animals, behavior during the events and preferences for individual plants within populations. There are no copyright restrictions on the data. When using the data from this data paper in publications, we kindly request that you cite the paper accordingly. Additionally, we encourage researchers and educators to inform us about how they are using this data, as we value feedback and would like to be aware of its various applications.

Computer vision for kinematic metrics of the drinking task in a pilot study of neurotypical participants

Assessment of the upper limb is critical to guiding the rehabilitation cycle. Drawbacks of observation-based assessment include subjectivity and coarse resolution of ordinal scales. Kinematic assessment gives rise to objective quantitative metrics, but uptake is encumbered by costly and impractical setups. Our objective was to investigate feasibility and accuracy of computer vision (CV) for acquiring kinematic metrics of the drinking task, which are recommended in stroke rehabilitation research. We implemented CV for upper limb kinematic assessment using modest cameras and an open-source machine learning solution. To explore feasibility, 10 neurotypical participants were recruited for repeated kinematic measures during the drinking task. To investigate accuracy, a simultaneous marker-based motion capture system was used, and error was quantified for the following kinematic metrics: Number of Movement Units (NMU), Trunk Displacement (TD), and Movement Time (MT). Across all participant trials, kinematic metrics of the drinking task were successfully acquired using CV. Compared to marker-based motion capture, no significant difference was observed for group mean values of kinematic metrics. Mean error for NMU, TD, and MT were − 0.12 units, 3.4 mm, and 0.15 s, respectively. Bland–Altman analysis revealed no bias. Kinematic metrics of the drinking task can be measured using CV, and preliminary findings support accuracy. Further study in neurodivergent populations is needed to determine validity of CV for kinematic assessment of the post-stroke upper limb.

Temporal and spatial resolution of magnetosome degradation at the subcellular level in a 3D lung carcinoma model

Magnetic nanoparticles offer many exciting possibilities in biomedicine, from cell imaging to cancer treatment. One of the currently researched nanoparticles are magnetosomes, magnetite nanoparticles of high chemical purity synthesized by magnetotactic bacteria. Despite their therapeutic potential, very little is known about their degradation in human cells, and even less so of their degradation within tumours. In an effort to explore the potential of magnetosomes for cancer treatment, we have explored their degradation process in a 3D human lung carcinoma model at the subcellular level and with nanometre scale resolution. We have used state of the art hard X-ray probes (nano-XANES and nano-XRF), which allow for identification of distinct iron phases in each region of the cell. Our results reveal the progression of magnetite oxidation to maghemite within magnetosomes, and the biosynthesis of magnetite and ferrihydrite by ferritin.Graphical

Near-Inertial Response of a Salinity-Stratified Ocean

Abstract We study the near-inertial response of the salinity-stratified north Bay of Bengal to monsoonal wind forcing using 6 years of hourly observations from four moorings. The mean annual energy input from surface winds to near-inertial mixed layer currents is 10–20 kJ m−2, occurring mainly in distinct synoptic “events” from April–September. A total of fifteen events are analyzed: Seven when the ocean is capped by a thin layer of low-salinity river water (fresh) and eight when it is not (salty). The average near-inertial energy input from winds is 40% higher in the fresh cases than in the salty cases. During the fresh events, 1) mixed layer near-inertial motions decay about two times faster and 2) near-inertial kinetic energy below the mixed layer is reduced by at least a factor of three relative to the salty cases. The near-inertial horizontal wavelength was measured for one fresh and one salty event; the fresh was about three times shorter initially. A linear model of near-inertial wave propagation tuned to these data reproduces 2); the thin (10 m) mixed layers during the fresh events excite high modes, which propagate more slowly than the low modes excited by the thicker (40 m) mixed layers in the salty events. The model does not reproduce 1); the rapid decay of the mixed layer inertial motions in the fresh events is not explained by the linear wave propagation at the resolved scales; a different and currently unknown set of processes is likely responsible.

Predicting plants in the wild: Mapping arctic and boreal plants with UAS-based visible and near infrared reflectance spectra

Biophysical changes in the Arctic and boreal zones drive shifts in vegetation, such as increasing shrub cover from warming soil or loss of living mat species due to fire. Understanding current and future responses to these factors requires mapping vegetation at a fine taxonomic resolution and landscape scale. Plants vary in size and spectral signatures, which hampers mapping of meaningful functional groups at coarse spatial resolution. Fine spatial grain of remotely sensed data (<10 cm pixels) is often necessary to resolve patches of many Arctic and boreal plant groups, such as bryophytes and lichens, which are significant components of terrestrial vegetation cover. Separation of co-occurring small vegetation patches in images also requires high spectral resolution. Our goal here was to test the capabilities of UAS-based imaging spectroscopy for mapping plant functional types (PFT) using high spatial and spectral resolution data over Arctic and boreal vegetation at four sites in central Alaska. We then tested several Machine and Deep learning models of PFT cover using the reflectance spectra. The best models were very simple, balancing both bias (overfitting caused by imbalance sample sizes) and variance (fit to the independent validation data), explaining > 50 % of the independent ground cover estimation and > 84 % accuracy in estimating validation pixels. We explored the impact of spectral resolution on PFT mapping by including vegetation indices and a gradient of narrow (5 nm) to wide (50 nm) band features in our classification models across. Vegetation indices were the most important predictors for classifying PFTs, while including band features improved models, with narrow and wide bandwidths having similar importance but models with wide bandwidths performing slightly better. We conclude that Arctic and boreal PFT reflectance can be pooled across sites for mapping with relatively few labeled pixels. Underfit, simple algorithms outperformed deep learning, at least with these small sample sizes, in classifying PFTs by balancing bias and variance. Future work should aim to increase the number of labeled pixels and the detail of labels to further improve mapping taxonomic precision.

Large eddy simulation analysis of a model reactive tracer through spatial filtering

Large eddy simulations (LES) provide a methodology for both analyzing and simulating multi-scale flows when the smallest scales of motion cannot be resolved. Within environmental flows there exist numerous biogeochemical processes involving tracers undergoing reactions. In this study, we perform an a posteriori LES analysis on a direct numerical simulation of an idealized model reactive tracer subjected to three-dimensional turbulence induced by a Rayleigh–Taylor instability. The governing equations, including an advection–diffusion–reaction equation for the reactive tracer, are filtered, and the resulting sub-filter-scale terms are expressed in terms of interactions between scales. The procedure is demonstrated for a generalized degree N polynomial reaction function. Various spectral filters are applied to the data and compared. The preferential choice is to use the widest filter possible with a smoothed cutoff. The sub-filter-scale reaction term that results from filtering the reaction function is considered for each of the filter choices. When using a particularly harsh filter, local balances are found for the resolved scale and cross-scale components of the sub-filter-scale reaction term. The same result is shown for the vertical sub-filter-scale flux for both a reactive and a passive tracer. The components of the sub-filter-scale reaction and vertical flux terms involving interactions at the sub-filter-scale do not show any evidence of local balances and are distributed around the fine turbulent structures in the flow. This suggests that parameterizations for the sub-filter-scale terms would benefit from considering event specific dynamics.

Late-Holocene sea-level markers preserved in a beach ridge system on Phra Thong Island, Thailand

Instrumental sea-level records are insufficient to understand the response of sea-level changes to global temperature on centennial timescales. Sea-level histories spanning at least the Common Era have not been widely studied in Southeast Asia. This period is crucial for providing a pre-industrial context to understand sea-level change with climate. Linking sea-level change to climate change is proxy dependant, with most proxies only providing decadal to centennial scale resolution of both sea level and climate. This study examines the efficiency of beach ridge stratigraphy as a proxy for reconstructing regional sea-level histories in the tropics. We present topographically corrected Ground Penetrating Radar (GPR) profiles from a prograded coast in Phra Thong Island, Thailand, where we identify downlap point marking the boundary between the foreshore and shoreface subzones and use this as a past low-tide marker. The low-tide markers were corrected (considering the tidal range) and then connected to approximate the past sea level. Optically Stimulated Luminescence (OSL) samples collected at locations slightly offset from the shore-normal GPR profile line were incorporated to create a relative sea-level record. Overall, the shore-normal GPR record shows a ∼ 1.06 m fall in sea level between ∼2660 and ∼ 370 years ago. The study also highlighted stepwise fluctuations in sea level that were not identified in previous studies. Between ∼2600 and ∼ 2200 years ago, the record indicates a steep fall in sea level, followed by a phase of relatively stable to slightly falling sea level between ∼2200 and ∼ 550 years ago. Finally, for the seaward side, between ∼550 and ∼ 350 years ago, the record indicates an accelerating sea-level fall. This study confirms that the investigation of tropical beach ridge systems using GPR and OSL techniques can be a highly efficient and effective means for reconstructing regional sea-level trends throughout the Common Era and beyond.

Microscale Temperature-Humidity Index (THI) Distribution Estimated at the City Scale: A Case Study in Maebashi City, Gunma Prefecture, Japan

Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island (SUHI) phenomenon, these studies often focus on small to large geographic regions using low-to-moderate-resolution data, highlighting general thermal trends across large administrative areas. However, there is a growing need for methods that can detect microscale thermal patterns in environments familiar to urban residents, such as streets and alleys. The temperature-humidity index (THI), which incorporates both temperature and humidity data, serves as a critical measure of human-perceived heat. However, few studies have explored microscale THI variations within urban settings and identified potential THI hotspots at a local level where SUHI effects are pronounced. This research aims to address this gap by estimating THI at a finer resolution scale using data from multiple sensor platforms. We developed a model with the random forest algorithm to assess THI trends at a resolution of 0.5 m, utilizing various variables from different sources, including Landsat 8 land surface temperature (LST), unmanned aerial system (UAS)-derived LST, Sentinel-2 NDVI and NDMI, a wind exposure index, solar radiation modeled from aircraft and UAS-derived Digital Surface Models, and vehicle density and building floor area from social big data. Two models were constructed with different variables: Modelnatural, which includes variables related to only natural factors, and Modelmix, which includes all variables, including anthropogenic factors. The two models were compared to reveal how each source contributes to the model development and SUHI effects. The results show significant improvements, as Modelnatural had a fitting R2 = 0.5846, a root mean square error (RMSE) = 0.5936 and a mean absolute error (MAE) = 0.4294. Moreover, when anthropogenic factors were introduced, Modelmix performed even better, with R2 = 0.9638, RMSE = 0.1751, and MAE = 0.1065 (n = 923). This study contributes to the future of microscale SUHI analysis and offers important insights into urban planning and smart city development.

One step 4x and 12x 3D-ExM: robust super-resolution microscopy in cell biology.

Super-resolution microscopy has become an indispensable tool across diverse research fields, offering unprecedented insights into biological architectures with nanometer scale resolution. Compared to traditional nanometer-scale imaging methods such as electron microscopy, super-resolution microscopy offers several advantages, including the simultaneous labeling of multiple target biomolecules with high specificity and simpler sample preparation, making it accessible to most researchers. In this study, we introduce two optimized methods of super-resolution imaging: 4-fold and 12-fold 3D-isotropic and preserved Expansion Microscopy (4x and 12x 3D-ExM). 3D-ExM is a straightforward expansion microscopy method featuring a single-step process, providing robust and reproducible 3D isotropic expansion for both 2D and 3D cell culture models. With standard confocal microscopy, 12x 3D-ExM achieves a lateral resolution of under 30 nm, enabling the visualization of nanoscale structures, including chromosomes, kinetochores, nuclear pore complexes, and Epstein-Barr virus particles. These results demonstrate that 3D-ExM provides cost-effective and user-friendly super-resolution microscopy, making it highly suitable for a wide range of cell biology research, including studies on cellular and chromatin architectures.

Development of the basic architecture of neocortical circuitry in the human fetus as revealed by the coupling spatiotemporal pattern of synaptogenesis along with microstructure and macroscale in vivo MR imaging.

In humans, a quantifiable number of cortical synapses appears early in fetal life. In this paper, we present a bridge across different scales of resolution and the distribution of synapses across the transient cytoarchitectonic compartments: marginal zone (MZ), cortical plate (CP), subplate (SP), and in vivo MR images. The tissue of somatosensory cortex (7-26 postconceptional weeks (PCW)) was prepared for electron microscopy, and classified synapses with a determined subpial depth were used for creating histograms matched to the histological sections immunoreacted for synaptic markers and aligned to in vivo MR images (1.5T) of corresponding fetal ages (maternal indication). Two time periods and laminar patterns of synaptogenesis were identified: an early and midfetal two-compartmental distribution (MZ and SP) and a late fetal three-compartmental distribution (CP synaptogenesis). During both periods, a voluminous, synapse-rich SP was visualized on the in vivo MR. Another novel finding concerns the phase of secondary expansion of the SP (13 PCW), where a quantifiable number of synapses appears in the upper SP. This lamina shows a T2 intermediate signal intensity below the low signal CP. In conclusion, the early fetal appearance of synapses shows early differentiation of putative genetic mechanisms underlying the synthesis, transport and assembly of synaptic proteins. "Pioneering" synapses are likely to play a morphogenetic role in constructing of fundamental circuitry architecture due to interaction between neurons. They underlie spontaneous, evoked, and resting state activity prior to ex utero experience. Synapses can also mediate genetic and environmental triggers, adversely altering the development of cortical circuitry and leading to neurodevelopmental disorders.

Fluid flow zones along fracture corridors inferred from collapse dolines in carbonates of the Irecê Basin, Brazil

AbstractDolines are a widespread karst landform, and understanding their genesis can provide critical information about the evolution of subsurface karstification and groundwater flow zones. Swarms of collapse dolines characterise the karst in the southern portion of the Irecê Basin, northeastern Brazil. The basin comprises Neoproterozoic carbonate rocks approximately 100 m thick, bounded by less soluble older quartzites. The distribution of dolines is not uniform, as they cluster along NW‐SE axes and are aligned with cave systems and permeability (fracture) corridors mostly associated with the Almas and Água de Rega ephemeral rivers. Detailed analysis of sectors with a higher density of dolines was performed using LiDAR and unmanned aerial vehicle (UAV) imagery at two different scales to obtain morphometric and orientation patterns. As expected, scale dependency is observed, with an increase in density and circularity index at a higher resolution scale, but a decrease in mean area and perimeter. However, Voronoi polygon indices are similar at both scales (6.86 ± 1); the departure from the more stable number of six due to the clustering of dolines associated with NW‐SE oriented structural trends. Doline density trends closely follow the orientation of caves, but they are not always spatially connected, since doline evolution may either occlude the caves or be related to a non‐identified void. The Irecê Basin karst system displays a continuum of landforms evolving initially from high‐density fracture zones (fracture corridors), which work as favourable dissolution zones, grading into more integrated flow routes, cave systems and eventually clusters of collapse dolines. Collapse dolines are widespread karst landforms worldwide, and their genesis requires the presence of dissolution voids at depth, which may pass undetected from the surface. In some sectors of Irecê Basin, the existence of doline alignments but the absence of known caves indicates the potential of collapse dolines to be used as proxies for subsurface karstification. Such an approach may allow the prediction of the location and orientation of subsurface zones of enhanced dissolution that can potentially represent productive aquifers or favourable zones for geofluids.

Flight patterns of monocularly occluded homing pigeons further reveal a left eye/right cerebral hemispheric advantage in the visual processing of familiar landscape/landmark features

Studying pigeons during homing has offered an opportunity to investigate visual–functional brain lateralization in the context of free flying and long distance navigation. In the current study we examine at a high scale of spatial resolution the flight paths of pigeons, which vary with respect to monocular and binocular treatment, as they familiarise themselves with landscape and landmark features during repeated flights home from two locations. The analysis of the flight paths of monocularly occluded pigeons revealed that pigeons using the right eye/left hemisphere visual system were more likely to display more tortuous paths over tracts of 500 m along the flight path home compared to both pigeons using the left eye/right hemisphere visual system and control birds. Accompanying this finding was the observation that pigeons using the right eye/left hemisphere system were more likely to perform flight “loops”, suggesting that, without right hemispheric visual processing, pigeons are more motivated to seek out visual input, behaviourally expanding the now monocularly reduced visual field. Taken together, the data are consistent with a more important role of the left eye/right hemisphere system in processing visual features of the landscape, which may contribute to the construction of the familiar landmark-based map used for navigation. More broadly, our data are consistent with the hypothesis that the right cerebral hemisphere of the avian brain plays a more important role in memorising and using the relational properties of visual stimuli to guide performance on spatial tasks.

A Super-Resolution and 3D Reconstruction Method Based on OmDF Endoscopic Images.

In the field of endoscopic imaging, challenges such as low resolution, complex textures, and blurred edges often degrade the quality of 3D reconstructed models. To address these issues, this study introduces an innovative endoscopic image super-resolution and 3D reconstruction technique named Omni-Directional Focus and Scale Resolution (OmDF-SR). This method integrates an Omnidirectional Self-Attention (OSA) mechanism, an Omnidirectional Scale Aggregation Group (OSAG), a Dual-stream Adaptive Focus Mechanism (DAFM), and a Dynamic Edge Adjustment Framework (DEAF) to enhance the accuracy and efficiency of super-resolution processing. Additionally, it employs Structure from Motion (SfM) and Multi-View Stereo (MVS) technologies to achieve high-precision medical 3D models. Experimental results indicate significant improvements in image processing with a PSNR of 38.2902 dB and an SSIM of 0.9746 at a magnification factor of ×2, and a PSNR of 32.1723 dB and an SSIM of 0.9489 at ×4. Furthermore, the method excels in reconstructing detailed 3D models, enhancing point cloud density, mesh quality, and texture mapping richness, thus providing substantial support for clinical diagnosis and surgical planning.

Quantifying Quantum Chaos through Microcanonical Distributions of Entanglement

A characteristic feature of “quantum chaotic” systems is that their eigenspectra and eigenstates display universal statistical properties described by random matrix theory (RMT). However, eigenstates of local systems also encode structure beyond RMT. To capture this feature, we introduce a framework that allows us to compare the properties of eigenstates in local systems with those of pure random states. In particular, our framework defines a notion of distance between quantum state ensembles that utilizes the Kullback-Leibler divergence to compare the microcanonical distribution of entanglement entropy (EE) of eigenstates with a reference RMT distribution generated by pure random states (with appropriate constraints). This notion gives rise to a quantitative metric for quantum chaos that not only accounts for averages of the distributions but also higher moments. The differences in moments are compared on a highly resolved scale set by the standard deviation of the RMT distribution, which is exponentially small in system size. As a result, the metric can distinguish between chaotic and integrable behaviors and, in addition, quantify and compare the of chaos (in terms of proximity to RMT behavior) between two systems that are assumed to be chaotic. We implement our framework in local, minimally structured, Floquet random circuits, as well as a canonical family of many-body Hamiltonians, the mixed-field Ising model (MFIM). Importantly, for Hamiltonian systems, we find that the reference random distribution must be appropriately constrained to incorporate the effect of energy conservation in order to describe the ensemble properties of midspectrum eigenstates. The metric captures deviations from RMT across all models and parameters, including those that have been previously identified as strongly chaotic, and for which other diagnostics of chaos such as level spacing statistics look strongly thermal. In Floquet circuits, the dominant source of deviations is the second moment of the distribution, and this persists for all system sizes. For the MFIM, we find significant variation of the KL divergence in parameter space. Notably, we find a small region where deviations from RMT are minimized, suggesting that “maximally chaotic” Hamiltonians may exist in fine-tuned pockets of parameter space. Published by the American Physical Society 2024

Grey‐zone simulations of shallow‐to‐deep convection transition using dynamic subgrid‐scale turbulence models

AbstractWe examine the ability of two dynamic turbulence closure models to simulate the diurnal development of convection and the transition from dry to shallow cumuli and then to deep convection. The dynamic models are compared with the conventional Smagorinsky scheme at a range of cloud‐resolving and grey‐zone resolutions. The dynamic schemes include the Lagrangian‐averaged, scale‐dependent dynamic Smagorinsky model and a Lagrangian‐averaged, dynamic mixed model. The conventional Smagorinsky model fails to reproduce the shallow convection stage beyond the large‐eddy simulation regime, continuously building up the convective available potential energy that eventually leads to an unrealistic deep convection phase. The dynamic Smagorinsky model significantly improves the representation of shallow and deep convection; however, it exhibits issues similar to the conventional scheme at coarser resolutions. In contrast, the dynamic mixed model closely follows the large‐eddy simulation results across the range of sub‐kilometre simulations. This is achieved by the combined effect of an adaptive length scale and the inclusion of the Leonard terms, which can produce counter‐gradient fluxes through the backscatter of energy from the subgrid to the resolved scales and enable appropriate non‐local contributions. A further sensitivity test on the inclusion of the Leonard terms on all hydrometeor fluxes reveals the strong interaction between turbulent transport and microphysics and the possible need for further optimisation of the dynamic mixed model coefficients together with the microphysical representation.

Improving CONUS Convective‐Scale Forecasting With Simultaneous Multiscale Data Assimilation

AbstractAccurate initialization of CONUS convective‐scale forecasting requires a proper estimate of all resolved scales. This study further develops and examines a simultaneous multiscale data assimilation (MDA) approach in EnVar with modulated cross‐scale and cross‐variable covariances. The method is examined using 10 retrospective cases with the assimilation of both in situ and radar reflectivity observations (hereafter, SimMDA). The necessity of the modulated and therefore weakened cross‐covariances in simultaneous MDA for CONUS convective‐scale forecasting is first demonstrated. The relative benefits of increasing the decomposed‐scale number with increased computational cost in SimMDA are also discussed. The impact of the further developed simultaneous MDA method is revealed by comparing it with a commonly adopted DA approach (Baseline), which separately assimilates in situ and reflectivity observations using individual single‐scale localization. During DA cycling, SimMDA improves analysis accuracy for temperature and reflectivity and reduces biases in all variables compared to Baseline. SimMDA yields significantly better forecasts than Baseline for most lead times. Additional experiments are conducted to attribute such improvements in a case study. Specifically, an experiment the same as Baseline except using simultaneous MDA for reflectivity assimilation enhances cold pools and inflows and thus improves storms by making larger‐scale increments. An experiment the same as Baseline except using simultaneous MDA for in situ assimilation more properly constrains small‐scale covariances, leading to more reasonable correlations along the front and more accurate moisture near the dryline and consequently improved analyses and forecasts. Both effects together largely contribute to the overall improvements of SimMDA compared to Baseline.

Patient, family, and community factors associated with medication adherence among people with hypertension or diabetes: A cross-sectional analysis

BackgroundWhile suboptimal medication adherence remains an obstacle to the management of hypertension and diabetes in China, few studies have investigated associated factors with medication adherence on different dimensions simultaneously. ObjectiveTo systematically examine associated patient, family, and community factors with suboptimal medication adherence among people with hypertension and/or type 2 diabetes in China. MethodsThe study stratified a random sample of 622 adults aged 45 years or older with hypertension and/or type 2 diabetes from three southeast cities in China in 2019. Trained interviewers used the Morisky Green Levine Medication Adherence Scale, Self-Efficacy to Manage Chronic Disease (SEMCD) Scale, and the Family Adaptability, Partnership, Growth, Affection, and Resolve (APGAR) Scale to assess medication adherence, self-efficacy, and family function, respectively. Participants also reported their perceived satisfaction with community health services (quantity, quality, affordability, and overall acceptance). The study used the multivariable logistic regression to assess the association of patient, family, and community factors with suboptimal medication adherence. ResultsAmong the participants, 42.9% reported suboptimal medication adherence. In the multivariable logistic regression model, male participants (odds ratio [OR] = 0.55, p = 0.001) had higher medication adherence compared to females. Having a self-efficacy score that was lower than or equal to the sample mean was significantly associated with lower adherence (OR = 1.44, p = 0.039). Participants unsatisfied with the affordability of community health services and medicine had lower adherence (OR = 2.18, p = 0.028) than those neutral or satisfied. There were no significant associations between family function and medication adherence. ConclusionsSex, self-efficacy, and perceived affordability of community health services were important factors associated with medication adherence. Healthcare professionals are recommended to consider multiple factors and leverage services and resources in community health centers when promoting medication adherence.

Jet classification using high-level features from anatomy of top jets

Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to understand the decision-making process and to identify the high-level features (HLFs) crucial for explaining jet classification. To address this, we consider the top jet tagging problems and introduce an analysis model (AM) that analyzes selected HLFs designed to capture important features of top jets. Our AM mainly consists of the following three modules: a relation network analyzing two-point energy correlations, mathematical morphology and Minkowski functionals for generalizing jet constituent multiplicities, and a recursive neural network analyzing subjet constituent multiplicity to enhance sensitivity to subjet color charges. We demonstrate that our AM achieves performance comparable to the Particle Transformer (ParT) while requiring fewer computational resources in a comparison of top jet tagging using jets simulated at the hadronic calorimeter angular resolution scale. Furthermore, as a more constrained architecture than ParT, the AM exhibits smaller training uncertainties because of the bias-variance tradeoff. We also compare the information content of AM and ParT by decorrelating the features already learned by AM. Lastly, we briefly comment on the results of AM with finer angular resolution inputs.