bluuebunny/crossref_metadata_2025_split huggingface dataset:
Role of geographic risk factors and social determinants of health in COVID-19 epidemiology: Longitudinal geospatial analysis in a midwest rural region
Authors: Wheeler Philip H. , Patten Christi A. , Wi Chung-Il , Bublitz Joshua T. , Ryu Euijung , Ristagno Elizabeth H. , Juhn Young J. 
Published: December 2021

Abstract

Background:
Studies examining the role of geographic factors in coronavirus disease-2019 (COVID-19) epidemiology among rural populations are lacking.

Methods:
Our study is a population-based longitudinal study based on rural residents in four southeast Minnesota counties from March through October 2020. We used a kernel density estimation approach to identify hotspots for COVID-19 cases. Temporal trends of cases and testing were examined by generating a series of hotspot maps during the study period. Household/individual-level socioeconomic status (SES) was measured using the HOUSES index and examined for association between identified hotspots and SES.

Results:
During the study period, 24,243 of 90,975 residents (26.6%) were tested for COVID-19 at least once; 1498 (6.2%) of these tested positive. Compared to other rural residents, hotspot residents were overall younger (median age: 40.5 vs 43.2), more likely to be minorities (10.7% vs 9.7%), and of higher SES (lowest HOUSES [SES] quadrant: 14.6% vs 18.7%). Hotspots accounted for 30.1% of cases (14.5% of population) for rural cities and 60.8% of cases (27.1% of population) for townships. Lower SES and minority households were primarily affected early in the pandemic and higher SES and non-minority households affected later.

Conclusion:
In rural areas of these four counties in Minnesota, geographic factors (hotspots) play a significant role in the overall burden of COVID-19 with associated racial/ethnic and SES disparities, of which pattern differed by the timing of the pandemic (earlier in pandemic vs later). The study results could more precisely guide community outreach efforts (e.g., public health education, testing/tracing, and vaccine roll out) to those residing in hotspots.
Variation, mosaicism and degeneracy in the hominin foot
Authors: McClymont J. , Davids K. , Crompton R.H. 
Published: December 2021

AbstractThe fossil record is scarce and incomplete by nature. Animals and ecological processes devour soft tissue and important bony details over time and, when the dust settles, we are faced with a patchy record full of variation. Fossil taxa are usually defined by craniodental characteristics, so unless postcranial bones are found associated with a skull, assignment to taxon is unstable. Naming a locomotor category based on fossil bone morphology by analogy to living hominoids is not uncommon, and when no single locomotor label fits, postcrania are often described as exhibiting a ‘mosaic’ of traits. Here, we contend that the unavoidable variation that characterises the fossil record can be described far more rigorously based on extensive work in human neurobiology and neuroanatomy, movement sciences and motor control and biomechanics research. In neurobiology,degeneracyis a natural mechanism of adaptation allowing system elements that are structurally different to perform the same function. This concept differs fromredundancyas understood in engineering, where the same function is performed by identical elements. Assuming degeneracy, structurally different elements are able to produce different outputs in a range of environmental contexts, favouring ecological robusticity by enabling adaptations. Furthermore, as degeneracy extends to genome level, genetic variation is sustained, so that genes which might benefit an organism in a different environment remain part of the genome, favouring species’ evolvability.
Another World? East Africa, Decolonisation, and the Global History of the Mid-Twentieth Century
Authors: Milford Ismay , McCann Gerard , Hunter Emma , Branch Daniel 
Published: December 2021

AbstractThis article proposes that there is a gap in our current understanding of the globalising and deglobalising dynamics of mid-twentieth-century East Africa, one that might be addressed by consolidating and taking forward recent developments in the historiography of decolonisation. Recent work by international historians has recovered the connected world of the 1940s to 1960s: the era of new postcolonial states, the ‘Bandung moment’, pan-African cooperation, and the early Cold War. Yet East Africa is less prominent in these histories than we might expect, despite the vibrancy of current work on this period in Kenya, Uganda, and Tanzania. Bringing these two fields into dialogue, through an explicitly regional East African framework and with a particular focus on individual lives, expands our understanding not only of the ‘globalisation of decolonisation’ but also of the deglobalising dynamics of the following decades that are frequently reduced to a history of global economic crisis.
Stride segmentation of inertial sensor data using statistical methods for different walking activities
Authors: Jain Rahul , Semwal Vijay Bhaskar , Kaushik Praveen 
Published: December 2021

AbstractHuman gait data can be collected using inertial measurement units (IMUs). An IMU is an electronic device that uses an accelerometer and gyroscope to capture three-axial linear acceleration and three-axial angular velocity. The data so collected are time series in nature. The major challenge associated with these data is the segmentation of signal samples into stride-specific information, that is, individual gait cycles. One empirical approach for stride segmentation is based on timestamps. However, timestamping is a manual technique, and it requires a timing device and a fixed laboratory set-up which usually restricts its applicability outside of the laboratory. In this study, we have proposed an automatic technique for stride segmentation of accelerometry data for three different walking activities. The autocorrelation function (ACF) is utilized for the identification of stride boundaries. Identification and extraction of stride-specific data are done by devising a concept of tuning parameter (
$t_{p}$
) which is based on minimum standard deviation (
$\sigma$
). Rigorous experimentation is done on human activities and postural transition and Osaka University – Institute of Scientific and Industrial Research gait inertial sensor datasets. Obtained mean stride duration for level walking, walking upstairs, and walking downstairs is 1.1, 1.19, and 1.02 s with 95% confidence interval [1.08, 1.12], [1.15, 1.22], and [0.97, 1.07], respectively, which is on par with standard findings reported in the literature. Limitations of accelerometry and ACF are also discussed. stride segmentation; human activity recognition; accelerometry; gait parameter estimation; gait cycle; inertial measurement unit; autocorrelation function; wearable sensors; IoT; edge computing; tinyML.
The metaethical dilemma of epistemic democracy
Authors: Schamberger Christoph 
Published: December 2021

AbstractEpistemic democracy aims to show, often by appeal to the Condorcet Jury Theorem, that democracy has a high chance of reaching correct decisions. It has been argued that epistemic democracy is compatible with various metaethical accounts, such as moral realism, conventionalism and majoritarianism. This paper casts doubt on that thesis and reveals the following metaethical dilemma: if we adopt moral realism, it is doubtful that voters are, on average, more than 0.5 likely to track moral facts and identify the correct alternative. By contrast, if we adopt conventionalism or majoritarianism, we cannot expect that voters are both competent and sincere. Either way, the conditions for the application of Condorcet’s theorem are not met.
Roadsides are key habitats for birds in the Argentine Pampas: conservation and management implications
Authors: Depalma Daniela María , Lacoretz Mariela Verónica , Zilli Cecilia , Charnelli Emilio Martín , Mermoz Myriam Emilia 
Published: December 2021

SummaryUnexploited public areas such as roadsides could provide habitat to help preserve biodiversity in South America, as in other regions. Our objective was to determine the importance of the roadsides of the Argentine Pampas for native birds and to suggest management strategies. We surveyed birds inhabiting roadsides in all seasons and determined whether roadsides were used as habitat. We recorded a total of 95 species on roadsides, which represents 55% of those species described from the area. Species included specialists of grassland, wetland and woodland, 4 vulnerable species and 19 declining species. Bird richness decreased in winter, as well as grassland specialists’ abundances. Most individuals used roadsides for foraging and performing reproduction-related behaviours, mainly on native trees; these and tall grass were the main substrates. We conclude that many species of birds use the habitat provided by roadsides, and we recommend management strategies such as favouring seed availability in winter, restoring trees and tall grass and increasing vegetation diversity to maximize roadside conservation value.
Genetic and Environmental Architecture of Five Factor Model and Super-Factors: An Italian Twin Study
Authors: Gigantesco Antonella , Fagnani Corrado , Alessandri Guido , Carluccio Enrica , Stazi Maria Antonietta , Medda Emanuela 
Published: December 2021

Abstract
No previous research explored the genetic and environmental structure of Big Five dimensions of personality and higher-order factors in a single twin study, except, in part, for just one study. We used the twin design to estimate the effects of genes and environment on both Five Factor model and related second- and third-order factors (i.e., Alpha [stability], Beta [plasticity], and GFP [general factor of personality]). We analyzed data from 314 adult twins (157 pairs: 83 monozygotic, 74 dizygotic; mean age: 52 years) enrolled in the Italian Twin Register. Participants underwent clinical and instrumental evaluations, and completed a 25-adjective list drawn from the Short Adjectives Checklist to Measure Big Five (SACBIF). We applied quantitative genetic models to unravel the sources of variation and covariation for the Big Five and higher-order factors. We found a similar etiological architecture across the different levels of analysis, with moderate to substantial non-additive genetic and unique environmental influences on all the personality traits, and no shared environmental contribution for any of them. We also detected significant genetic correlations for the Big Five dimensions and the Alpha and Beta super-factors. With some limitations, our results suggest that the etiological architecture of personality may be invariant to the factor level of analysis.
Surveys of weed management on flooded rice yields in southern Brazil
Authors: Silva Anelise L. , Streck Nereu A. , Zanon Alencar J. , Ribas Giovana G. , Fruet Bruno L. , Ulguim André R. 
Published: December 2021

AbstractOne of the main limiting factors for high yields of flooded rice (Oryza sativa L.) is the presence of weeds, especially herbicide-resistant weeds. The aim of this study was to evaluate the association of weed management practices adopted by flooded rice farmers in the state of Rio Grande do Sul (RS), Brazil, with grain yield. For this purpose, 324 interview surveys were administered to farmers who supplied information about the history of weed management and yields. The answers to the survey indicated that weedy rice (Oryza sativa L.) and Echinochloa spp. were the most important weeds that occurred in flooded rice areas in RS. Advanced growth stage of weeds and inadequate environmental conditions such as air temperature and relative humidity were listed as the main reasons for low weed control efficacy. Farmers achieved greater rice yields when they adopted rice–soybean [Glycine max (L.) Merr.] (9,140 kg ha−1 average yield) and herbicide site of action rotations (8,801 kg ha−1 average yield) along with tank mixes (8,580 kg ha−1 average yield) as specific management practices for resistant weed control. The use of glyphosate with residual herbicides in a tank mix in the rice spiking stage is the main factor related to greater yields. The postemergence applications and their relationship to delaying of flooding in rice is a factor that reduces rice yield when no spiking glyphosate application was made. Identification of the most important weeds in terms of occurrence and knowledge of the main agronomic practices adopted by farmers are essential so that recommendations for integrated management practices can be adopted in an increasingly accurate and sustainable manner in flooded rice areas in southern Brazil.
An Integer Programming Model for the Selection of Pumped‐Hydro Storage Projects
Authors: Andrade T. , Kelman R. , Cunha T. M. , Albuquerque L. R. , Calili R. F. 
Published: December 2021

AbstractEnergy storage systems—in particular, Pumped Hydropower Storage (PHS)—will be increasingly important to support the transition of power systems toward zero emissions. The reason is that PHS can mitigate the variability and uncertainty of renewable energy production from solar and wind power to balance electricity demand with supply. In this paper, we propose an integer programming problem for PHS siting that uses a Digital Elevation Model (DEM) to meet an energy storage requirement. It assumes the existence of a reservoir, lake, or river, and decides where to build a reservoir that will constitute the PHS with the existing body of water. This model finds minimum‐cost project candidates given parameters such as desired head, power, and operation time. The paper discusses different solution methods to assure reservoir closure and avoid its fragmentation. A heuristic explores the representations of the DEM, from more aggregate to more precise, to sequentially refine the solution based on the last selected site, which reduces computational effort. The formulation is general and the objective function includes both construction and equipment costs. Constraints are related to the energy storage target and reservoir closure based on the DEM. We illustrate the methodology by selecting multiple PHS projects next to the reservoir of the Sobradinho hydropower plant in Brazil. The result of this model can be seen as a bottom‐up step that prepares PHS candidate projects to be considered by an integrated resource planning model in a top‐down step, that would select from these shortlisted projects.
Comparison of Frequency‐Domain Electromagnetic Responses at Different Offsets Based on Exact Calculation of Grounded‐Wire Source
Authors: Xue Guoqiang , Zhou Nannan , Hou Dongyang 
Published: December 2021

AbstractSubsurface geoelectric structures and resources can be detected based on the propagation of the electromagnetic field. Near‐source electromagnetic methods have recently become the most popular survey mode. However, calculation of the electromagnetic field in the near‐source region with artificial sources has always been performed based on far‐source dipole approximation. Owing to the introduction of artificial sources in the near‐source region, it is challenging to obtain accurate solution of the electromagnetic field based on the relationship between the observation point and the source, thereby affecting the detection accuracy. Specifically, the main problem lies in the position vector of the transmitting source, which is generally neglected in the response calculation based on the plane‐wave theory. Moreover, only far‐source data can be observed in the plane‐wave theory. However, near‐source data have been collected in a near‐source survey, with the dipole approximation resulting in a large error. We introduce a point charge element to decrease or eliminate the error caused by the dipole approximation. The non‐homogeneous Helmholtz equation is first solved using the Green's function. Then, the response expression of the real source, including the source position vector, is obtained by integrating the response of the harmonic point charge element. The substitution of the dipole elements with the point‐charge elements not only reduces the dipole approximation errors but also displays the electromagnetic characteristics of the entire zone, which are verified by the forward modeling and field survey results. The near‐source (offset is 0.8 of the target depth) survey can be realized based on the point charge element.
First Observations of the New MEXART's Digital System
Authors: Gonzalez‐Esparza J. A. , Mejia‐Ambriz J. C. , Aguilar‐Rodriguez E. , Villanueva P. , Andrade E. , Magro A. , Chiello R. , Cutajar D. , Borg J. , Zarb‐Adami K. 
Published: December 2021

AbstractThe Mexican Array Radio Telescope (MEXART) is a transit instrument mainly dedicated to performing interplanetary scintillation (IPS) observations with a central operating frequency of 139.65 MHz. The main scientific objective is to perform studies of solar wind properties and space weather effects. MEXART initially operated with an analog beam former (16 × 16 Butler matrix), which produced 16 fixed latitudinal beams. MEXART began operations and reported the first measurements of IPS sources. MEXART's beam forming system had several problems, however. The North‐South beams had poor directivity, with large side lobes, and the instrument did not achieve the expected performance. Therefore, we commissioned the design and construction of a digital back‐end. The digital system solved the problems with the beam forming, increases the bandwidth, and significantly improves the instrument's sensitivity. In this paper, we present the first light of MEXART's digital system. We describe the new technical capabilities of the instrument, and we show some preliminary results: an estimation of the radio telescope's sensitivity (ΔSmin = 2.28 ± 0.23 Jy), the transit of the Galaxy at 140 MHz with the simultaneous tracking of 62 latitudinal beams, and an example of an IPS observation and the single‐station methodology for calculating the solar wind speed. The new technical capabilities of the radio telescope will provide the potential for participating in several scientific studies. These include solar wind properties, space weather forecasting, ionospheric perturbations, and astrophysical aims such as the monitoring of repeating fast radio bursts and pulsars' observations.
An Improved Zhang's Dynamic Water Balance Model Using Budyko‐Based Snow Representation for Better Streamflow Predictions
Authors: Hwang Jeongwoo , Devineni Naresh 
Published: December 2021

AbstractUnderstanding the water balance of a catchment in relation to its regional climate forcings and catchment characteristics is critical for predicting current and future water resources amid changing climate and land cover. This study intends to improve Zhang's monthly water balance model (a physics‐based conceptual hydrologic model) that reflects the physical partitioning process of the hydrological cycle at the basin level based on regional climate and catchment characteristics. The existing model does not include snow process and has confronted evident limitations in snow‐affected areas, which is a critical aspect since snowmelt water has been a significant source of water resources for many regions, especially in the temperate and frigid zones. We introduce a snow module based on surface energy balance and Budyko‐limits on melting and combine it with the existing water balance equations. Moreover, monthly parameterization is applied to the model to better explain the time‐varying hydrological characteristics of a catchment. The proposed model involves five different monthly parameters, which determine the physical partitioning process of the hydrological cycle, and they are regionally calibrated and validated under Budyko‐type constraints. The model is applied to 1,210 basins across the continental United States (CONUS), and the simulated streamflow is compared to the observed data. The proposed model significantly outperformed the original model, improving the median NSE by 31% (from 0.51 to 0.67) and increasing the number of catchments with an acceptable NSE by 58%. The spatial variability of the basin characteristics across the CONUS is also investigated based on the calibrated parameters.
Dynamic Flow Alteration Index for Complex River Networks With Cascading Reservoir Systems
Authors: Kumar Hemant , Hwang Jeongwoo , Devineni Naresh , Sankarasubramanian A. 
Published: December 2021

AbstractLarge dams degrade the river’s health by heavily regulating the natural flows. Despite a long history of research on flow regulation due to dams, most studies focused only on the impact of a single dam and ignored the combined impact of flow regulation on a river network. We propose a new Dynamic Flow Alteration Index (DFAI) to quantify the local and cumulative degree of regulation by comparing the observed controlled flows with the naturalized flows based on a moving time horizon for the highly regulated Colorado River Basin. The proposed DFAI matches closely to dam’s localized regulation for headwater gages and starts to diverge as we move downstream due to increase in cumulative impact of the dams. DFAI considers the impact of dam operations on flow characteristics such as shifting of peak flow occurrence and dampening of peak flows. DFAI estimates the degree of regulation to be small for upstream dams and finds the maximum network regulation to be 2.52 years at Glen Canyon reservoir. DFAI also successfully captures the reduction in cumulative regulation when dam operations (e.g., Hoover Dam) bring the altered flow in synchronization with natural regime due to downstream flow requirements. The impact of San Juan River Basin Recovery Implementation Program is also captured by DFAI as the reduction in network regulation drops by 1.5 years for Navajo Dam. Our findings using DFAI suggest the need to develop naturalized flows for major river basins to quantify the flow alteration under continually changing climate and human influences.
Model‐Reduced Adjoint‐Based Inversion Using Deep‐Learning: Example of Geological Carbon Sequestration Modeling
Authors: Xiao Cong , Zhang Shicheng , Ma Xinfang , Jin Jianbing , Zhou Tong 
Published: December 2021

AbstractAs an extension of our previous research on deep‐learning‐based adjoint‐state approach (Xiao, Deng, & Wang, 2021, https://doi.org/10.1029/2020wr027400), we present a two‐level hybrid neural network architecture to efficiently derive a model‐reduced adjoint‐based optimization workflow with a large‐scale inverse modeling example in geological carbon‐storage applications. The first level employs a nonlinear dimensionality reduction technique, that is, deep convolutional autoencoder, owing to its high scalability and the availability of high‐performance deep‐learning libraries for flexible implementation. Analogous to the reduced‐order linear model, the second level emulates a neural‐network‐powered linear transition unit based on the reduced subspace. This hybrid framework provides a readily available model‐reduced adjoint derivation with negligible computational cost and computer storage. Once the gradient is obtained in reduced space, it is correspondingly projected back to full space for the inverse modeling. The performance of the method is verified by estimating the space‐dependent stochastic permeability field using two geological carbon storage (GCS) process‐based multiphase flow models with an increasing complexity. The experimental results indicate that the surrogate model can accurately characterize the spatial‐temporal evolution of the CO2 saturation fields using a relatively small number of training data. Although the proposed inversion workflow achieves comparable results similar to the conventional finite‐difference method, it is faster than high‐fidelity model simulations in several orders of magnitude; hence, it will be a beneficial simulation and modeling tool for engineers to manage the uncertainty of GCS processing.