bluuebunny/crossref_metadata_2025_split huggingface dataset:
A homeomorphism theorem for sums of translates
Authors: Farkas Bálint , Nagy Béla , Révész Szilárd Gy. 
Published: March 2023

AbstractFor a fixed positive integer n consider continuous functions $$K_1,\dots $$

K
1

,
⋯

, $$ K_n:[-1,1]\rightarrow {{\mathbb {R}}}\cup \{-\infty \}$$

K
n

:

[
-
1
,
1
]

→
R
∪

{
-
∞
}

that are concave and real valued on $$[-1,0)$$

[
-
1
,
0
)

and on (0, 1], and satisfy $$K_j(0)=-\infty $$

K
j

(
0
)

=
-
∞

. Moreover, let $$J:[0,1]\rightarrow {{\mathbb {R}}}\cup \{-\infty \}$$

J
:
[
0
,
1
]
→
R
∪
{
-
∞
}

be upper bounded and such that $$[0,1]\setminus J^{-1}(\{-\infty \})$$

[
0
,
1
]

\

J

-
1

(

{
-
∞
}

)

has at least $$n+1$$

n
+
1

elements, but it is arbitrary otherwise. For $$x_0:=0<x_1<\dots < x_n \le x_{n+1}:=1$$

x
0

:
=
0
<

x
1

<
⋯
<

x
n

≤

x

n
+
1

:
=
1

, so called nodes, and for $$t\in [0,1]$$

t
∈
[
0
,
1
]

consider the sum of translates function $$F(x_1,\ldots ,x_n,t):=J(t)+\sum _{j=1}^n K_j(t-x_j)$$

F

(

x
1

,
…
,

x
n

,
t
)

:
=
J

(
t
)

+

∑

j
=
1

n

K
j

(
t
-

x
j

)

, and the vector of interval maximum values $$m_j:=m_j(x_1,\ldots ,x_n):=\max _{t\in [x_j,x_{j+1}]}F(x_1,\ldots ,x_n,t)$$

m
j

:
=

m
j

(

x
1

,
…
,

x
n

)

:
=

max

t
∈
[

x
j

,

x

j
+
1

]

F

(

x
1

,
…
,

x
n

,
t
)

($$j=0,1,\ldots ,n$$

j
=
0
,
1
,
…
,
n

). We describe the structure of the arising interval maxima as the nodes run over the n-dimensional simplex. Applications presented here range from abstract moving node Hermite–Fejér interpolation for generalized algebraic and trigonometric polynomials via Bojanov’s problem to more abstract results of interpolation theoretic flavour.
Predicting Optimized Dissolution of Selected African Copperbelt Copper-cobalt-bearing Ores by Means of Neural Network Prediction and Response Surface Methodology Modeling
Authors: Mbuya Bienvenu , Mulaba-Bafubiandi Antoine F. 
Published: March 2023

AbstractWhile the uncertainty brought about by a varying feed mineralogy was taken into consideration, the paper investigated the modeling and prediction of the leaching behavior of complex copper-cobalt bearing ores, using an artificial neural network (ANN) with a backforward algorithm. The process optimization is further conducted using the response surface methodology (RSM) employing the Box-Behnken design (BBD). Seven (7) parameters were considered in a multiple linear regression according to the L12screening plan (27) of Plackett–Burman. From the seven parameters, four including solid percentage (15, 27.5, 40%), time (45, 90, 135 min), particle size passing (53, 75, 105 µm), and Fe2+ion concentration (2, 4, 6 g/L) are modeled with L27(34) BBD. With a composite desirability of 0.94, leaching yields of 93.46% Cu and 89.43% Co were obtained. The neural network algorithm used is the BFGS (Broyden, Fletcher, Goldfarb and Shanno) algorithm multilayer perceptron with the hyperbolic tangent activation function for the hidden layer and a linear activation function for the neural output. The Multilayer perceptron {4–7-1} structure was chosen as a suitable arrangement for Cu leaching. Comparing the predicted values and those obtained experimentally resulted with a correlation coefficient of 0.9552 for the data trained in the artificial neural network and 0.8742 for the data obtained with the response surface methodology. The synergy of these 2 techniques shows that the prediction can be achieved by means of the ANN giving the values of the root mean square errors (RMSE) of 0.0115, 0.00624, 0.0229, respectively, for the training, testing and validation sets for copper recovery while the correlational study between variables could be done through the RSM. The above includes only the 95% confidence interval while the remaining 5% would be uncertain. The above results and conclusion are accompanied by the relative uncertainty as the ore mineralogy varies. The combination of the synergistic use of ANN and RSM with the sensitivity analysis has approached the process to the physics of the Multi-criteria decision-making.Graphical Abstract
How to Prevent the Drop-Out: Understanding Why Adults Participate in Summative eHealth Evaluations
Authors: Hurmuz Marian Z. M. , Jansen-Kosterink Stephanie M. , van Velsen Lex 
Published: March 2023

AbstractThe aim of this study was to investigate why adults participate in summative eHealth evaluations, and whether their reasons for participating affect their (non-)use of eHealth. A questionnaire was distributed among adults (aged ≥ 18 years) who participated in a summative eHealth evaluation. This questionnaire focused on participants’ reason to enroll, their expectations, and on whether the study met their expectations. Answers to open-ended questions were coded by two researchers independently. With the generalized estimating equations method we tested whether there is a difference between the type of reasons in use of the eHealth service. One hundred and thirty-one adults participated (64.9% female; mean age 62.5 years (SD = 10.5)). Their reasons for participating were mainly health-related (e.g., being more active). Between two types of motivations there was a difference in the use of the eHealth service: Participants with an intellectual motivation were more likely to drop out, compared to participants with an altruistic motivation. The most prevalent expectations when joining a summative eHealth evaluation were health-related (like expecting to improve one’s health). 38.6% of the participants said their expectation was fulfilled by the study. In conclusion, We encourage eHealth evaluators to learn about adults’ motivation to participate in their summative evaluation, as this motivation is very likely to affect their results. Including altruistically motivated participants biases the results by their tendency to continue participating in a study.
Flexible Hybrid Table Recognition and Semantic Interpretation System
Authors: Namysł Marcin , Esser Alexander M. , Behnke Sven , Köhler Joachim 
Published: March 2023

AbstractExtracting information from documents containing quantitative data in tabular format is an important but still unsolved task due to the heterogeneity of document layouts. This work aims to take a step toward developing a solution to this problem. This paper proposes a flexible, hybrid table extraction system consisting of a deep learning-based table detection module, a heuristic-based structure recognition method, and a graph-based semantic interpretation component. The proposed system is modular and supports the most frequent table layouts. Moreover, it handles both the documents in image format and PDF files with embedded text. The proposed system outperforms the baseline method and achieves results on par with state-of-the-art approaches on the challenging benchmarks from ICDAR 2013 and ICDAR 2019 table interpretation competitions. Moreover, we correct an issue with the evaluation script used in the latter competition and report extended results of the proposed method in comparison with a leading commercial product. Finally, our table extraction system achieves a high F$$_1$$

1

score in the scenario where raw documents are given as input and the targeted information is contained in a subset of table columns. The presented system achieves results competitive with leading methods in the field. It has already been evaluated on general-purpose data and biomedical benchmarks. We intend to continuously improve our approach and process data from other domains, e.g., financial documents. To support future research on information extraction from documents, we make the evaluation scripts and results from our experiments publicly available at https://github.com/mnamysl/tabrec-sncs.
Near and Far Interaction for Outdoor Augmented Reality Tree Visualization and Recommendations on Designing Augmented Reality for Use in Nature
Authors: Lilligreen Gergana , Wiebel Alexander 
Published: March 2023

AbstractThe application of augmented reality in nature and in the context of environmental education is not common. Moreover, augmented reality is often used mainly for visualization, which puts the user in a rather passive state. To promote a more active user experience, we have created an interactive AR environment for outdoor scenarios. In this article, we investigate how near and far interaction using a head-mounted display can be combined with visualization on a tree. We compare both interaction techniques to decide which is more suitable for future use. We present some educational use cases and investigate the interaction with virtual leaves on the ground in combination with a virtual-real interaction with a physical tree. Parameters such as type of interaction, different real environments, and task performance time as well as the combination and interconnection between them are discussed and studied. In addition, process visualizations in nature, such as clouds and rain, and tree root growth are included in the augmented reality modules and are evaluated in user tests followed by questionnaires. The results show that both near and far interaction can be beneficial for a future educational application. We further present a number of outdoor-specific recommendations for AR design and usage to support future researchers and AR practitioners outdoors in the nature.
Experimental study of a periodic turbulent boundary layer in zero mean pressure gradient
Authors: Menendez A. N. , Ramaprian B. R. 
Published: March 2023

AbstractA turbulent boundary layer, subjected to a freestream velocity which changes sinusoidally with time around a spatially uniform mean value has been studied experimentally. The experiments were conducted in a specially designed unsteady-flow water tunnel. A two-component laser Doppler anemometer was used to measure the instantaneous velocities, and hence the turbulent stresses, in the periodic boundary layer. A surface-mounted heat-flux gauge was used to measure the unsteady wall shear stress. The experiments were conducted at relatively high amplitudes of oscillation (≈ 40% of the mean) and over a wide range of reduced frequencies (1.7-28). While the present data on the oscillatory flow properties generally confirm previously known trends the information on the turbulent shear stress and skin friction obtained in the present studies is more complete than previously available. The experimental data, all of which has been archived on magnetic tape, form a comprehensive base for the development and verification of predictive models for unsteady turbulent boundary layers.
Multivariate analysis from SSR and morphological data in chickpea (Cicer arietinum L.) for breeding purposes
Authors: Mariana Pocovi , Sosa Maximiliano , Delgado Romina , Castillo Verónica , Collavino Graciela , Carreras Julia 
Published: March 2023

AbstractIn order to enhance genetic potential of chickpea materials from the National University of Córdoba Breeding Programme and Germplasm collection (Argentina), a study for a comprehensive understanding of the amount and pattern of genetic variation within and between genotypes was carried out by applying a multivariate analysis form single simple repeats (SSR) and morphological data. Molecular data were also used to determine the discriminating power for genotype identification, and to find the optimal primer combination to ensure unambiguous identification. With the analysis of 15 SSR markers on 53 genotypes, a total of 58 alleles were detected with individual values ranging from one to nine alleles per locus. High values of discriminating power (Dj ⩾ 0.7, PIC ⩾ 0.7), and low values of confusion probability (Cj ⩽ 0.23) were obtained for at least four evaluated markers. The combination of TA113 + TA114 + H1B09 + TA106 primers was effective for discriminating the 53 chickpea genotypes with a cumulative confusion probability value (Ck) of 9.60 × 10−4. Except for some exceptions, individual chickpea genotypes within a cluster in the consensus tree were definitely more closely related with each other by the origin or pedigree. The results confirmed that both multivariate data analysis methods, ordination and clustering, were complementary. In most genotypes, discriminant principal component analysis classification was consistent with the original clusters defined by molecular data. Differences in results from molecular and morphological data indicate that they provide complementary and relevant information for establishing genetic relationships among chickpea materials and a better description and interpretation of the available variability in the germplasm collection.
Two Air Quality Regimes in Total Column NO2 Over the Gulf of Mexico in May 2019: Shipboard and Satellite Views
Authors: Thompson Anne M. , Kollonige Debra E. , Stauffer Ryan M. , Kotsakis Alexander E. , Abuhassan Nader , Lamsal Lok N. , Swap Robert J. , Blake Donald R. , Townsend‐Small Amy , Wecht Holli D. 
Published: March 2023

AbstractThe Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE) cruise in the Gulf of Mexico was conducted in May 2019 by NASA and the Bureau of Ocean Energy Management to determine the feasibility of using satellite data to measure air quality in a region of concentrated oil and natural gas (ONG) operations. SCOAPE addressed both technological and scientific issues related to measuring NO2 columns over the outer continental shelf. Featured were nitrogen dioxide (NO2) instruments (Pandora, Teledyne API analyzer) at Cocodrie, LA (29.26°, −90.66°), and on the Research Vessel Point Sur operating off the Louisiana coast with measurements of ozone, carbon monoxide, and volatile organic compounds (VOCs). The findings: (a) all NO2 observations revealed two atmospheric regimes over the Gulf, the first influenced by tropical air in 10–14 May, the second influenced by flow from urban areas on 15–17 May; (b) comparisons of OMI v4 and TROPOMI v1.3 TC (total column) NO2 data with shipboard Pandora NO2 column observations averaged 13% agreement with the largest difference during 15–17 May (∼20%). At Cocodrie, the satellite–Pandora agreement was ∼5%. (c) Three new‐model Pandora instruments displayed a TC NO2 precision of 0.01 Dobson Units (∼5%); (d) regions of smaller, older natural gas operations showed high methane readings from leakage; elevated VOCs were also detected. Neither satellite nor spectrometer captured the magnitude of ambient NO2 variability near ONG platforms. Given an absence of regular air quality monitoring over the Gulf of Mexico, SCOAPE data constitute a baseline against which future observations can be compared.
Controls on Terrestrial Carbon Fluxes in Simulated Networks of Connected Streams and Lakes
Authors: Vachon Dominic , Sponseller Ryan A. , Rosvall Martin , Karlsson Jan 
Published: March 2023

AbstractInland waters play a critical role in the carbon cycle by emitting significant amounts of land‐exported carbon to the atmosphere. While carbon gas emissions from individual aquatic systems have been extensively studied, how networks of connected streams and lakes regulate integrated fluxes of organic and inorganic forms remain poorly understood. Here, we develop a process‐based model to simulate the fate of terrestrial dissolved organic carbon (DOC) and carbon dioxide (CO2) in artificial inland water networks with variable topology, hydrology, and DOC reactivity. While the role of lakes is highly dependent on DOC reactivity, we find that the mineralization of terrestrial DOC is more efficient in lake‐rich networks. Regardless of typology and hydrology, terrestrial CO2 is emitted almost entirely within the network boundary. Consequently, the proportion of exported terrestrial carbon emitted from inland water networks increases with the CO2 versus DOC export ratio. Overall, our results suggest that CO2 emissions from inland waters are governed by interactions between the relative amount and reactivity of terrestrial DOC and CO2 inputs and the network configuration of recipient lakes and streams.
Magnetic Signature of Vertically Migrating Aggregations in the Ocean
Authors: Fu M. K. , Dabiri J. O. 
Published: March 2023

AbstractThe significance of fluid transport in the ocean due to migrating aggregations of zooplankton remains poorly understood due to practical challenges associated with predicting, identifying, and quantifying instances of enhanced biogenic mixing. We present a new approach to overcome these limitations by quantifying the biogenic transport via its distinct magnetic signatures. This present approach can be accomplished potentially without a priori knowledge of the precise location of the aggregation and without triggering animal avoidance caused by the introduction of instruments into the migration.
A 510,000‐Year Record of Mars' Climate
Authors: Buhler P. B. 
Published: March 2023

AbstractMars' polar layered deposits record its climate history. However, no deposit yet analyzed provides a global water cycle record that can be tied to a specific orbital history. Here, I fill this gap by analyzing H2O ice layer formation in Mars' south polar Massive CO2 Ice Deposit (MCID), a 510,000‐year climate record. Statistical analyses of ∼109 formation model runs compared to observed stratigraphy indicate a variable H2O deposition rate of ∼1, 0.1, and 0.01 mm yr−1 at 20, 24, and 28 obliquity, respectively—likely recording the obliquity‐dependent midlatitude‐to‐pole H2O transport rate. The MCID record allows unprecedented obliquity‐driven H2O ice deposition rate derivation because of its well‐defined age relative to other deposits and its CO2 cold‐trapping effect, which simplifies local seasonal and long‐term H2O flux. The recovery of an orbit‐resolved H2O transport rate is an essential step in elucidating Mars' global, orbit‐driven water cycle.
Sea Ice Melt Pond Fraction Derived From Sentinel‐2 Data: Along the MOSAiC Drift and Arctic‐Wide
Authors: Niehaus Hannah , Spreen Gunnar , Birnbaum Gerit , Istomina Larysa , Jäkel Evelyn , Linhardt Felix , Neckel Niklas , Fuchs Niels , Nicolaus Marcel , Sperzel Tim , Tao Ran , Webster Melinda , Wright Nicholas 
Published: March 2023

AbstractMelt ponds forming on Arctic sea ice in summer significantly reduce the surface albedo and impact the heat and mass balance of the sea ice. Therefore, their areal coverage, which can undergo rapid change, is crucial to monitor. We present a revised method to extract melt pond fraction (MPF) from Sentinel‐2 satellite imagery, which is evaluated by MPF products from higher‐resolution satellite and helicopter‐borne imagery. The analysis of melt pond evolution during the MOSAiC campaign in summer 2020, shows a split of the Central Observatory (CO) into a level ice and a highly deformed ice part, the latter of which exhibits exceptional early melt pond formation compared to the vicinity. Average CO MPFs are 17% before and 23% after the major drainage. Arctic‐wide analysis of MPF for years 2017–2021 shows a consistent seasonal cycle in all regions and years.
Ab Initio Melting Temperatures of Bcc and Hcp Iron Under the Earth’s Inner Core Condition
Authors: Sun Yang , Mendelev Mikhail I. , Zhang Feng , Liu Xun , Da Bo , Wang Cai‐Zhuang , Wentzcovitch Renata M. , Ho Kai‐Ming 
Published: March 2023

AbstractThere has been a long debate on the stable phase of iron under the Earth’s inner core conditions. Because of the solid‐liquid coexistence at the inner core boundary, the thermodynamic stability of solid phases directly relates to their melting temperatures, which remains considerable uncertainty. In the present study, we utilized a semi‐empirical potential fitted to high‐temperature ab initio data to perform a thermodynamic integration from classical systems described by this potential to ab initio systems. This method provides a smooth path for thermodynamic integration and significantly reduces the uncertainty caused by the finite‐size effect. Our results suggest the hcp phase is the stable phase of pure iron under the inner core conditions, while the free energy difference between the hcp and bcc phases is tiny, on the order of 10 s meV/atom near the melting temperature.
Distinct Role of a Spring Atmospheric Circulation Mode in the Arctic Sea Ice Decline in Summer
Authors: Bi Haibo , Liang Yu , Chen Xianyao 
Published: March 2023

AbstractA distinguishing feature of Arctic sea ice is the rapid declining in the Pacific Arctic Sector (PAS) in recent years. The atmospheric variability during spring (March‐May) plays a critical role in determining the sea ice extent (SIE) during the following summer. The summertime (June‐August) SIE reduction in the eastern part of PAS, including the East Siberian Sea and the Laptev Sea, is strongly related to the first two leading atmospheric circulation modes in spring, the Arctic Oscillation and the Arctic Dipole, retrieved from empirical orthogonal function analysis. Here, we show that the third leading mode of atmospheric circulation in spring, namely the Barents‐Beaufort Oscillation (BBO), can also change the summer SIE, especially on the western regime of the PAS across the Beaufort and Chukchi Seas. We find that a positive phase of springtime BBO (i.e., BBO+), relative to the BBO−, acts to decrease sea ice thickness via advection and generate sea ice deformation through divergence and shear processes, thereby preconditioning for a rapid sea ice loss within the PAS in the following summer. Furthermore, the occurrence of BBO is state‐dependent and potentially modulated by the phase shift of the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO). Since the mid‐1990s, the AMO (PDO) index has changed from a dominant negative (positive) to a positive (negative) phase. These phase shifts in AMO and PDO would have contributed to the subsequently increased appearance of anomalous BBO+ after 2000 and enhanced the summertime sea ice loss in the western regions of the PAS in recent years.
Morphology and Fractal Dimension of Size‐Resolved Soot Particles Emitted From Combustion Sources
Authors: Pang Yuner , Chen Minghao , Wang Yuanyuan , Chen Xiyao , Teng Xiaomi , Kong Shaofei , Zheng Zhonghua , Li Weijun 
Published: March 2023

AbstractDatabases of the morphological parameters of fresh size‐resolved soot particles and their systematic comparisons among various combustion sources are important to trace the soot aging process and evaluate their optical properties. Here, the mixing state, effective density (ρeff), monomer number and diameter (N and dp), and fractal dimension (Df) of size‐resolved soot particles from vehicle emissions (VE), biomass burning (BB), coal combustion (CC), tunnel air (TA) and urban air (UA) were characterized based on electron microscopy observations and analysis. We determined that freshly‐emitted soot particles from combustion sources contain not only bare‐like soot but also some coated‐soot particles. ρeff (157–689 kg/m3) decreased while N (46–1,500) and dp (24–42 nm) increased with the increased diameter of soot particles. The Df of BB and CC were independent of the diameter changes and fluctuated between 1.65 and 1.80, while the Df of VE and TA (1.62–1.71) increased and UA Df (1.87–1.80) decreased with increasing diameter. Based on Df obtained in this study, we found that the ensemble Df of VE, BB, CC, TA, and UA could not represent the finer (<150 nm) soot particles and coarser soot particles (>600 nm) in VE, TA, and UA. This study highlights that the morphological parameters of freshly‐emitted soot particles vary among different combustion sources and have a size effect, particularly for finer and coarser soot particles. The database of size‐resolved soot particles will be helpful to improve soot models based on numerical simulation and better evaluate atmospheric optical properties.