bluuebunny/crossref_metadata_2025_split huggingface dataset:
Static Response of Functionally Graded Porous Circular Plates via Finite Element Method
Authors: Doori Silda Ghazi Mohammed , Noori Ahmad Reshad , Etemadi Ali 
Published: March 2024

AbstractThe main purpose of this paper is to investigate the axisymmetric bending response of functionally graded porous (FGP) circular plates. The material properties are changed continuously in the thickness direction of the plate. Three distinct porosity distributions uniform, symmetric and monolithic are employed. The effect of porosity on the axisymmetric bending analysis of circular plates is examined parametrically. In this study, clamped and roller supports which commonly serve to achieve ideal boundary conditions in numerous engineering applications are used. The finite element method is employed for numerical analysis. The principal of the potential energy is used to obtain the governing equations. To generate the model of the FGP circular plates, an eight-node quadratic quadrilateral element with two degrees of freedom on each node is utilized. The results of this study are confirmed by the existing published literature. A good agreement between the results of the presented model and the previous literature has been observed. The results of the present study show that plate deflection increases with the increase of the porosity coefficient and the ratio of radius to thickness of circular plates. By increasing the porosity coefficient, the displacement values of the plates made of uniform porosity distribution is effected more than those of other porosity distributions.
SARS-CoV-2 journey: from alpha variant to omicron and its sub-variants
Authors: Hattab Dima , Amer Mumen F. A. , Al-Alami Zina M. , Bakhtiar Athirah 
Published: March 2024

AbstractThe COVID-19 pandemic has affected hundreds of millions of individuals and caused more than six million deaths. The prolonged pandemic duration and the continual inter-individual transmissibility have contributed to the emergence of a wide variety of SARS-CoV-2 variants. Genomic surveillance and phylogenetic studies have shown that substantial mutations in crucial supersites of spike glycoprotein modulate the binding affinity of the evolved SARS-COV-2 lineages to ACE2 receptors and modify the binding of spike protein with neutralizing antibodies. The immunological spike mutations have been associated with differential transmissibility, infectivity, and therapeutic efficacy of the vaccines and the immunological therapies among the new variants. This review highlights the diverse genetic mutations assimilated in various SARS-CoV-2 variants. The implications of the acquired mutations related to viral transmission, infectivity, and COVID-19 severity are discussed. This review also addresses the effectiveness of human neutralizing antibodies induced by SARS-CoV-2 infection or immunization and the therapeutic antibodies against the ascended variants.
An optimization method of human skeleton keyframes selection for action recognition
Authors: Chen Hao , Pan Yuekai , Wang Chenwu 
Published: March 2024

AbstractIn the action recognition field based on the characteristics of human skeleton joint points, the selection of keyframes in the skeleton sequence is a significant issue, which directly affects the action recognition accuracy. In order to improve the effectiveness of keyframes selection, this paper proposes inflection point frames, and transforms keyframes selection into a multi-objective optimization problem based on it. First, the pose features are extracted from the input skeleton joint point data, which used to construct the pose feature vector of each frame in time sequence; then, the inflection point frames in the sequence are determined according to the flow of momentum of each body part. Next, the pose feature vectors are input into the keyframes multi-objective optimization model, with the fusion of domain information and the number of keyframes; finally, the output keyframes are input to the action classifier. To verify the effectiveness of the method, the MSR-Action3D, the UTKinect-Action and Florence3D-Action, and the 3 public datasets, are chosen for simulation experiments and the results show that the keyframes sequence obtained by this method can significantly improve the accuracy of multiple action classifiers, and the average recognition accuracy of the three data sets can reach 94.6%, 97.6% and 94.2% respectively. Besides, combining the optimized keyframes with deep learning classifier on the NTU RGB + D dataset can make the accuracies reaching 83.2% and 93.7%.
E pluribus, quaedam. Gross Domestic Product out of a Dashboard of Indicators
Authors: Guerini Mattia , Vanni Fabio , Napoletano Mauro 
Published: March 2024

AbstractIs aggregate income enough to summarize well-being? We address this long-standing question by exploiting a quantitative approach that studies the relationship between gross domestic product (GDP) and a set of economic, social and environmental indicators for nine developed economies. We introduce a mathematical approach to the analysis of economic indicators. By employing dimensionality reduction and time series reconstruction techniques, we quantify the share of variability stemming from a large set of different indicators that can be compressed into a univariate index. We also evaluate how well this variability can be explained if the univariate index is assumed to be respectively the gross domestic product, national income, household income, or household spending. Our results indicate that all the four univariate measures are doomed to fail in accounting for the variability of all the domains. Even if GDP emerges as the best option among the four economic variables, its quality in synthesizing the variability of indicators belonging to other domains is poor (about 35%). Our approach provides additional support for policy makers interested in measuring the trade offs between income and other relevant social, health and ecological dimensions. Finally, our work adds new quantitative evidence to the vast literature criticizing the usage of GDP as a measure of well-being.
Inverse problem solver for epidemiological geographic profiling
Authors: Maeno Yoshiharu 
Published: March 2024

AbstractThis work studies machine-learning-based inverse problem solvers for a reaction–diffusion process. The study focus is on the performance of a state-of-the-art convolutional neural network in discovering the source of disease spreading. This problem is called epidemiological geographic profiling. The performance is investigated with synthetic datasets for SIR epidemiological compartments on a square grid geo-space. The convolutional neural network works effectively in discovering a single source and achieves the largest time average of accuracy for growing infection in a heterogeneous geo-space. The hit score remains near the lower bound over time. Discovering multiple sources is feasible potentially as well by learning the dataset for a single source.
Cooperation in public goods experiments with random and finite stopping rules
Authors: Anderson Lisa R. , Hicks Robert L. , Turscak Andrew 
Published: March 2024

AbstractWe contribute to a large literature that explores prosocial behavior in public goods experiments. We adopt an experimental design that allows full contribution to the public good to be sustained in equilibrium. We study the effect of the time horizon on a subject's propensity to contribute to a public good by varying the stopping rule for the game. While many studies examine the effect of a random stopping rule in prisoner's dilemma games, to our knowledge, only two other studies have directly compared behavior in public goods experiments with finite and random stopping rules. Consistent with existing studies, we find that contribution rates are similar across treatments in early rounds of play, and contribution rates are higher with random verses finite stopping rules in later rounds. Overall, we find significantly higher contributions to the public good when donors face a known probability of future interactions with the same group of participants compared to interactions with a finite endpoint. Further, the difference in cooperative behavior is driven primarily by the stopping rule, rather than the length of the game.
An extension of the best–worst method based on the spherical fuzzy sets for multi-criteria decision-making
Authors: Haseli Gholamreza , Sheikh Reza , Ghoushchi Saeid Jafarzadeh , Hajiaghaei-Keshteli Mostafa , Moslem Sarbast , Deveci Muhammet , Kadry Seifedine 
Published: March 2024

AbstractThe ambiguous information in multi-criteria decision-making (MCDM) and the vagueness of decision-makers for qualitative judgments necessitate accurate tools to overcome uncertainties and generate reliable solutions. As one of the latest and most powerful MCDM methods for obtaining criteria weight, the best–worst method (BWM) has been developed. Compared to other MCDM methods, such as the analytic hierarchy process, the BWM requires fewer pairwise comparisons and produces more consistent results. Consequently, the main objective of this study is to develop an extension of BWM using spherical fuzzy sets (SFS) to address MCDM problems under uncertain conditions. Hesitancy, non-membership, and membership degrees are three-dimensional functions included in the SFS. The presence of three defined degrees allows decision-makers to express their judgments more accurately. An optimization model based on nonlinear constraints is used to determine optimal spherical fuzzy weight coefficients (SF-BWM). Additionally, a consistency ratio is proposed for the SF-BWM to assess the reliability of the proposed method in comparison to other versions of BWM. SF-BWM is examined using two numerical decision-making problems. The results show that the proposed method based on the SF-BWM provided the criteria weights with the same priority as the BWM and fuzzy BWM. However, there are differences in the criteria weight values based on the SF-BWM that indicate the accuracy and reliability of the obtained results. The main advantage of using SF-BWM is providing a better consistency ratio. Based on the comparative analysis, the consistency ratio obtained for SF-BWM is threefold better than the BWM and fuzzy BWM methods, which leads to more accurate results than BWM and fuzzy BWM.
Nutritional value and functional properties of an underexploited Tunisian wild beet (Beta macrocarpa Guss.) in relation to soil characteristics
Authors: Mahmoud K. Ben , Abdelkefi F. , Mezzapesa G. N. , Perrino E. V. 
Published: March 2024

AbstractThe study centers on the valorization of Beta macrocarpa Guss., an endangered Mediterranean wild plant that grows in Tunisia. This plant is disappearing due to a reduction in marginal areas and a lack of awareness of this important crop wild relative (CWR). This prompted us to carry out work to assess the nutritional and functional value of its plant shoots in relation to physicochemical soil properties at three different Tunisian sites covering the north (Sijoumi), the center (Enfidha) and the south (Kerkennah) of the country. All soil samples showed an alkaline pH and high salinity. Sijoumi, Enfidha and Kerkennah soils were classified as loamy, silty clay loamy and sandy, respectively. Chemical analysis revealed that all soils, especially the sandy one, were low in total nitrogen, organic matter and microelements. Plant analysis showed that shoots harvested from the loamy soil presented the highest levels of carbohydrate (19.1 g/100 g FW) and fiber (6.1 g/100 g FW) and the greatest energetic value (94 kcal/100 g FW), whereas shoots collected from the sandy soil showed the highest contents of protein (4.1 g/100 g FW), ash (5.2 g/100 g FW), total polyphenols and flavonoids (39.01 mg GAE/g DW; 27.8 mg CE/g DW), and the greatest DPPH scavenging capacity (IC50 = 0.74 mg/ml). The results suggest that Beta macrocarpa, which naturally grows in poor and salt-affected soils, could play a crucial role in maintaining the biodiversity and sustainability of agro-ecosystems, particularly in marginal areas, and could also provide an alternative source of food with significant nutritional value and health benefits.
Graphical abstract
Learning deep news sentiment representations for macro-finance
Authors: Groß-Klußmann Axel 
Published: March 2024

AbstractThis paper introduces custom neural network techniques to the problem of latent economic factor extraction for voluminous news analytics data. In the context of macro-financial news, we derive low-dimensional representations of time series that arise in textual sentiment analyses spanning various topics. We explore three applications for compressed news sentiment data: nowcasting GDP growth, explaining asset class returns in a panel data analysis, and time series momentum investment. Our empirical study shows that nonlinear data representations based on supervised autoencoder architectures compare favorably to alternatives across all applications. In specific, we demonstrate that augmenting autoencoders with supervision tasks based on common asset class returns and market characteristics disciplines the dimension reduction and naturally supports the transparency of resulting representations. Taken together, our findings position supervised autoencoders as attractive competitor models alongside PCA and PLS approaches.
EONS: A New Biogeochemical Model of Earth's Oxygen, Carbon, Phosphorus, and Nitrogen Systems From the Archean to the Present
Authors: Horne J. E. , Goldblatt C. 
Published: March 2024

AbstractWe present Earth's Oxygenation and Natural Systematics (EONS): a new, fully coupled biogeochemical model of the atmosphere, ocean, and their interactions with the geosphere, which can reproduce major features of Earth's evolution following the origin of life to the present day. The model, consisting of 257 unique fluxes between 96 unique chemical reservoirs, includes an interactive biosphere, cycles of carbon, nitrogen, phosphorus, and oxygen, and climate. A nominal model run initialized in the Eoarchean resolves emergent surface oxygenation, nutrient limitations, and climate feedbacks. The modeled atmosphere oxygenates in stepwise fashion over the course of the Proterozoic; a nearly billion year lag after the evolution of photosynthesis at 3.5 Ga is followed by a great oxidation event at 2.4 Ga, which appears to be caused by the gradual buildup of organic matter on the continents imposing nutrient limitation on the biosphere by removing key nutrients from the ocean system. The simple climate system shows significant temperature shifts punctuate the oxygenation process, implying that major biological transitions possibly destabilized Earth's climate. This work demonstrates that forward modeling the entirety of Earth's history with relatively few imposed boundary forcings is feasible, that the Earth system is not at steady state, and that our understanding of coupled C‐N‐P‐O cycling as it functions today can explain much of the Earth's evolution.
On the Wave‐Normal Distribution of Lightning‐Generated Whistlers and Their Propagation Modes
Authors: Xia Zhiyang , Chen Lunjin , Horne Richard B. , Liu Xu , He Jiabei , Bortnik Jacob 
Published: March 2024

AbstractObservations from Van Allen Probes are analyzed to obtain the statistical wave normal distribution of lightning‐generated whistlers (LGWs). An automatic algorithm is developed to identify burst mode waveform data with LGW signals and analyze the wave polarization information for these LGW signals. The spatial distribution of the LGW occurrence and the probabilities of different propagation types demonstrate that most LGWs can be observed in the low L‐shell region (L < 3), where the dominant propagation type is oblique outward. Parallel propagation dominates in the high L‐shell region, but the LGW occurrence is very small. Additionally, a small group of oblique but inwardly propagating LGWs are observed at low altitudes (<0.2RE). A ray tracing simulation is performed not only to confirm predominant oblique and outward wave vectors in the vast region of the plasmasphere but also to verify the existence of these inward propagating LGW signals at low altitudes near the topside ionosphere.
The Lithosphere and Upper Mantle of the Western‐Central Mediterranean Region From Integrated Geophysical‐Geochemical Modeling
Authors: Zhang Wentao , Jiménez‐Munt Ivone , Torne Montserrat , Vergés Jaume , Bravo‐Gutiérrez Estefanía , Negredo Ana M. , García‐Castellanos Daniel 
Published: March 2024

AbstractThis study integrates geophysical‐geochemical data to investigate the thermochemical structure of the lithosphere and sublithospheric mantle, along the Southern Tyrrhenian Basin, Apennines, Adriatic Sea, Dinarides, and Carpathians‐Balkanides. We present the lithospheric structure of the Adria microplate and the two opposing mantle slabs along its NE and SW margins. The modeling shows the presence of two asthenospheric mantle wedges aligning with the Apenninic and Dinaric continental mantle slab rollback, along with cold (−200°C) sublithospheric anomalies beneath Adria's NE and SW margins. In the northern Adria region, the lithosphere undergoes synchronous thinning in the Tyrrhenian domain and thickening toward the forefront of the northern Apennines. This is associated with the northeastward rollback of the SW Adriatic slab, leading to subsequent delamination of the continental mantle. In the southern Adria region, the complex deep structure results from the variably oriented lithospheric slabs, and nearly 90‐degree shift of the tectonic grain between the southern Apennines and the Calabrian Arc. At the SW Adria margin, beneath the northern Apennines, the thermal sublithospheric anomaly is attached to the shallower lithosphere, while a slab gap is modeled in the southern Apennines. One possibility is that the gap is due to a recent horizontal slab tear. Along the NE margin of Adria, the thermal anomaly penetrates to depths of about 200 km in the northern Dinarides and 280 km in the southern Dinarides, shallower than the SW Adria anomaly, which extends to at least 400 km depth.
Spatiotemporal Variability of Extreme Precipitation Events and Associated Atmospheric Processes Over Dronning Maud Land, East Antarctica
Authors: Simon Sibin , Turner John , Thamban Meloth , Wille Jonathan D. , Deb Pranab 
Published: March 2024

AbstractWe investigate the spatial and temporal variability of extreme precipitation events (EPEs) in the Dronning Maud Land (DML) sector of Antarctica using high‐resolution ECMWF ERA5 reanalysis data. This study examines the spatial occurrence of EPEs across DML, focusing particularly on six locations spanning the coastal and interior parts of the area. The largest snowfall amounts are usually found on eastward‐facing slopes in the coastal zone. EPEs occur predominantly in north‐easterly to easterly flows, leading to enhanced precipitation on the windward side of the orographic features with a steep gradient. Wind during EPEs was found to be more directionally consistent in the coastal area than in the interior. An east‐west couplet of a mid‐tropospheric ridge and low‐pressure center is essential for steering warm moist maritime airmasses into the DML region before EPEs. Approximately 40% of EPEs result from atmospheric rivers (ARs), narrow bands of moist air originating at subtropical latitudes, which provide the greatest daily precipitation amounts. From 1979 to 2018, much of the DML experienced a statistically significant (p < 0.05) increase in the number of EPEs per year, along with increased precipitation from the EPEs. These trends were associated with significant changes in moisture availability and poleward meridional winds in the Atlantic sector of the Southern Ocean. The inter‐annual variability in the number of EPEs is primarily dictated by regional atmospheric variability, while the influence of the Southern Oscillation Index and Southern Annular Mode is limited.
Observational Quantification of Tropical High Cloud Changes and Feedbacks
Authors: Raghuraman Shiv Priyam , Medeiros Brian , Gettelman Andrew 
Published: March 2024

AbstractThe response of tropical high clouds to surface warming and their radiative feedbacks are uncertain. For example, it is uncertain whether their coverage will contract or expand in response to surface warming and whether such changes entail a stabilizing radiative feedback (iris feedback) or a neutral feedback. Global satellite observations with passive and active remote sensing capabilities over the last two decades can now be used to address such effects that were previously observationally limited. Using these observations, we show that the vertically averaged coverage exhibits no significant contraction or expansion. However, we find a reduction in coverage at the altitude where high clouds peak and are particularly radiatively‐relevant. This results in a negative longwave (LW) feedback and a positive shortwave (SW) feedback which cancel to yield a near‐zero high‐cloud amount feedback, providing observational evidence against an iris feedback. Next, we find that tropical high clouds have risen but have also warmed, leading to a positive, but small, high‐cloud altitude feedback dominated by the LW feedback. Finally, we find that high clouds have been thinning, leading to a near‐zero high‐cloud optical depth feedback from a cancellation between negative LW and positive SW feedbacks. Overall, high clouds lead the total tropical cloud feedback to be small due to the negative LW‐positive SW feedback cancellations.
A Survey on Gravity Waves in the Brazilian Sector Based on Radiosonde Measurements From 32 Aerodromes
Authors: Brhian Alysson , Ridenti Marco A. , Roberto Marisa , de Abreu Alessandro J. , Abalde Guede José R. , de Campos Elson 
Published: March 2024

AbstractIn this paper, we applied a variety of statistical methods to study gravity waves in the troposphere and lower stratosphere in the Brazilian sector, using a large database from Instituto de Controle do Espaço Aéreo (ICEA) of radiosonde measurements carried out in 2014 at 32 locations in the Brazilian territory totaling 49,652 wind and temperature profiles. The average wind profiles were computed and classified by means of a hierarchical cluster analysis. The kinetic and potential energy densities of gravity waves were determined using a detrending technique based on the Least Squares Method and the Fast Fourier Transform. By analyzing the energy density time series it was found that tropospheric average values are consistently larger in the months of winter, late autumn and early spring. Stratospheric average values of variability and kinetic energy density are also consistently larger in this period. A systematic search for quasi monochromatic waves was carried out and their main characteristics such as horizontal/vertical wavelengths and velocities were determined both in the troposphere and lower stratosphere. A correlation analysis between the troposphere and the lower stratosphere based on the measured parameters was used to investigate the wave coupling between the two layers, and no significant correlation was found. Finally, a spatial correlation analysis between energy densities measured at different aerodromes in the same atmospheric layer was carried out, showing that energy densities are spatially correlated for distances less than 3,000–4,000 km.