bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigation of the Effects of Filling Speed, Casting Temperature and Metallurgical Quality on Fluidity of Lamellar Graphite Cast Iron at Different Section Thicknesses
Authors: Köse Sami , Çolak Murat , Şüküroğlu Ebru Emine 
Published: March 2024

Abstract
In this study, the fluidity properties of the alloy were investigated at different casting temperatures, different section thicknesses, and varying casting parameters of lamellar graphite cast iron materials. To achieve our goal, we utilized sand molds that were created with specific parameters including pouring temperature, metallurgical quality, section thickness, and fluidity test model. These molds were used for casting. Thus, the effect of fluidity properties in changing casting conditions and liquid metal advance distances at determined section thicknesses was investigated. Modeling was carried out with FlowCast casting simulation software by determining the liquid metal advance distance depending on the section thickness in the castings made in sand molds under changing casting conditions. The fluidity and advance distance of the liquid metal was determined comparatively with experimental and modeling techniques under the changing casting conditions in the parameters determined in this study. When the outcomes were examined; it was observed that different liquid metal advance distances occur at different cross-section thicknesses depending on the changing conditions.
Narratives from GPT-derived networks of news and a link to financial markets dislocations
Authors: Miori Deborah , Petrov Constantin 
Published: March 2024

AbstractWe introduce a novel framework to study the dynamics of news narratives, by leveraging GPT3.5 advanced text analysis capabilities and graph theory. In particular, we focus on a corpus of economic articles from The Wall Street Journal and dynamically extract the main topics of discussion over time, in a completely systematic and scalable fashion. As a simple application of the suggested approach, we show how the structure of such topics of discussion has a statistically significant relationship with the contemporaneous state of financial markets, which can be used to construct an investment strategy or monitor financial risks. Our work is based on the intrinsic ability of GPT models to track the context of sentences within a document, thanks to which we can accurately extract a ranking of the most important entities discussed within each article, and evaluate their entity-specific sentiments. Then, we create a graph for each week of data, in which nodes are the entities retrieved and edges are built from the co-occurrence of such entities within articles. Graph centrality measures are computed over time to track the most representative keywords of topics of discussion, which result in an accurate summary view of the evolution of economic narratives. Fuzzy community detection is finally used to cluster linked entities into a more detailed representation of topics. Such groups of entities are mapped to the related journal articles, which are in turn summarised to reach a highly nuanced and interpretable view of the topics discussed within each week. Linking the features of these topics to the relevant financial market time series, we find that high fragmentation within our networks’ communities relates to moments of financial markets dislocations (i.e. dates with unusually high volatility across asset classes). This result should thus motivate stronger effort within financial research to move beyond ubiquitous sentiment analysis of news and delve deeper into broader and more holistic studies of textual data.
A Simulation Study of the Modulation of the Geomagnetic Field Configuration on the Seasonal Variation of Ionospheric Sq Currents
Authors: Liu Yunbo , Ren Zhipeng , Wei Yong , Zhou Xu 
Published: March 2024

AbstractBased on Global Coupled Ionosphere‐Thermosphere‐Electrodynamics Model, the solution of the 3‐dimensional current in the ionospheric region, the equivalent sheet current and filed‐aligned current are examined. The simulation study enables a comprehensive analysis of the effect of the geomagnetic field configuration, especially the non‐dipole component and tilt angle, on the ionospheric electrodynamics phenomena. Different geomagnetic field configurations are specified in the present work, including realistic geomagnetic field (RGF), tilted dipole geomagnetic field (TDGF) and zero‐declination dipole geomagnetic field (ZDGF). Our simulation focuses on the seasonal variation of Sq current, primarily governed by annual and semi‐annual variation. The modulation of the tilt angle of the geomagnetic field is globally distributed, whereas the modulation of the geomagnetic anomaly is localized. At mid‐latitudes, the annual mean and semi‐annual amplitudes of the Sq current are negatively correlated with the magnetic field strength, especially shown in geomagnetic anomaly area, while there is the opposite effect in the geomagnetic conjugate regions of the opposite hemispheres. The annual variation of Sq current system is more affected by the offset of the geomagnetic latitudes and geographical latitudes. The seasonal variation of the total Sq current is also modulated by the geomagnetic field. The annual mean, the annual and semi‐annual components of the total Sq current are negatively correlated with the magnetic field strength, while the annual variation is also controlled by the tilt angle of the geomagnetic field. The solar radiation affects the semi‐annual variation of the current more strongly than the annual variation.
Investigating the Main Features of the Correlation Between Electron Density and Temperature in the Topside Ionosphere Through Swarm Satellites Data
Authors: Pignalberi A. , Giannattasio F. , Truhlik V. , Coco I. , Pezzopane M. , Alberti T. 
Published: March 2024

AbstractElectron density (Ne) and electron temperature (Te) observations collected by Langmuir Probes on board the European Space Agency (ESA) Swarm B satellite are used to characterize their correlation in the topside ionosphere at an altitude of about 500 km. Spearman correlation coefficient values (RSpearman) are calculated on joint probability distributions between Ne and Te for selected conditions. The large data set of Swarm B observations at 2‐Hz rate, covering the years 2014–2022, allowed investigating the correlation properties of the topside ionospheric plasma on a global scale, for different diurnal and seasonal conditions, with both a coverage and a detail never reached before. Results are given as maps of RSpearman as a function of the Quasi‐Dipole (QD) magnetic latitude and magnetic local time (MLT) coordinates. The characterization of the correlation at high latitudes, along with the description of the diurnal trend at all latitudes, are the new findings of this study. The main correlation features point out a negative correlation at the morning overshoot, during daytime at mid latitudes, and during nighttime at the ionospheric trough and subauroral latitudes. Conversely, a positive correlation dominates the nighttime hours at mid and low latitudes and, to a minor extent, the low latitudes from 09 MLT onwards. A seasonal dependence of the correlation is noticeable only at very high latitudes where the general pattern of the negative correlation does not hold around ±75° QD latitude in the summer season. Results from Swarm B have been statistically compared and discussed with observations from the Arecibo, Jicamarca, and Millstone Hill incoherent scatter radars.
A New Eulerian Iceberg Module for Climate Studies
Authors: Erokhina Olga , Mikolajewicz Uwe 
Published: March 2024

AbstractIcebergs modulate the effective location of freshwater input from ice sheets into the ocean and therefore play an important role for the climate, especially during times of increased ice discharge (e.g., Heinrich events). None of the models participating in the Paleo Modeling Intercomparison Project simulations of the Last Glacial Maximum or the last deglaciation included icebergs. Here, we present a newly developed dynamic/thermodynamic iceberg module that was specifically designed to be incorporated in climate models used for long‐term climate simulations with interactive ice sheets. In contrast to the widely used Lagrangian iceberg models, it is formulated in an Eulerian framework. This simplifies coupling to ocean models and enhances computational efficiency for glacial climates. In a set of sensitivity experiments, where the module was implemented into an Earth System Model, we validate the model for present‐day climate conditions and test its sensitivity to key parameters. Further, we investigate the effect of iceberg hosing on the Atlantic meridional overturning circulation (AMOC) as compared to traditional freshwater hosing. Varying the hosing rate slowly in time yields a good approximation of the hysteresis curve of the AMOC. We find that the sensitivity of the AMOC to iceberg hosing is stronger than to freshwater hosing in the same ocean point, but weaker as compared to a latitude belt forcing in the North Atlantic. This emphasizes the necessity to include interactive icebergs in long‐term coupled climate simulations to realistically represent melt patterns and the response of the AMOC to freshwater input from melting ice sheets.
Investigation of the dynamical structures of double-chain deoxyribonucleic acid model in biological sciences
Authors: Yousaf Muhammad Zain , Abbas Muhammad , Nazir Tahir , Abdullah Farah Aini , Birhanu Asnake , Emadifar Homan 
Published: March 2024

AbstractThe present research investigates the double-chain deoxyribonucleic acid model, which is important for the transfer and retention of genetic material in biological domains. This model is composed of two lengthy uniformly elastic filaments, that stand in for a pair of polynucleotide chains of the deoxyribonucleic acid molecule joined by hydrogen bonds among the bottom combination, demonstrating the hydrogen bonds formed within the chain’s base pairs. The modified extended Fan sub equation method effectively used to explain the exact travelling wave solutions for the double-chain deoxyribonucleic acid model. Compared to the earlier, now in use methods, the previously described modified extended Fan sub equation method provide more innovative, comprehensive solutions and are relatively straightforward to implement. This method transforms a non-linear partial differential equation into an ODE by using a travelling wave transformation. Additionally, the study yields both single and mixed non-degenerate Jacobi elliptic function type solutions. The complexiton, kink wave, dark or anti-bell, V, anti-Z and singular wave shapes soliton solutions are a few of the creative solutions that have been constructed utilizing modified extended Fan sub equation method that can offer details on the transversal and longitudinal moves inside the DNA helix by freely chosen parameters. Solitons propagate at a consistent rate and retain their original shape. They are widely used in nonlinear models and can be found everywhere in nature. To help in understanding the physical significance of the double-chain deoxyribonucleic acid model, several solutions are shown with graphics in the form of contour, 2D and 3D graphs using computer software Mathematica 13.2. All of the requisite constraint factors that are required for the completed solutions to exist appear to be met. Therefore, our method of strengthening symbolic computations offers a powerful and effective mathematical tool for resolving various moderate nonlinear wave problems. The findings demonstrate the system’s potentially very rich precise wave forms with biological significance. The fundamentals of double-chain deoxyribonucleic acid model diffusion and processing are demonstrated by this work, which marks a substantial development in our knowledge of double-chain deoxyribonucleic acid model movements.
Alpha-pinene ameliorate behavioral deficit induced by early postnatal hypoxia in the rat: study the inflammatory mechanism
Authors: Bakhtazad Sharareh , Ghotbeddin Zohreh , Tabandeh Mohammad Reza , Rahimi Kaveh 
Published: March 2024

AbstractNeonatal hypoxia has a negative impact on the developing brain during the sensitive period. Inflammation plays a key role in the physiological response to hypoxic stress. Considering the anti-inflammatory properties of alpha-pinene, which has received a lot of attention in recent years, in this research we focused on the impact of alpha-pinene on the behavioral responses and proinflammatory factors in rats subjected to the neonatal hypoxia. This study involved Wistar rats (7-day-old) that were divided into six experimental groups, including a control group, groups receiving different doses of alpha-pinene (5 and 10 mg/kg), a hypoxia group receiving 7% O2 and 93% N2, 90 min duration for 7 days, and groups receiving alpha-pinene 30 min before hypoxia. All injections were done intraperitoneally. The rats were evaluated for proinflammatory factors 24 h after exposure to hypoxia (PND14) and at the end of the behavioral test (PND54). The results showed that hypoxia led to decreased motor activity, coordination, and memory, as well as increased inflammation. However, the rats that received alpha-pinene showed improved behavioral responses and reduced inflammation compared to the hypoxia group (all cases p < 0.05). This suggests that alpha-pinene may have a protective effect via anti-inflammatory properties against the negative impacts of hypoxia on the developing brain.
Raman spectroscopy of lymphocytes from patients with the Epstein–Barr virus infection
Authors: Pietruszewska Magdalena , Biesiada Grażyna , Czepiel Jacek , Birczyńska-Zych Malwina , Moskal Paulina , Garlicki Aleksander , Wesełucha-Birczyńska Aleksandra 
Published: March 2024

AbstractIn this study, Raman spectroscopy is applied to trace lymphocytes activation following contact with the Epstein–Barr virus (EBV) of the herpesvirus family. The biomarker of cell activation is found to be the 520 cm−1 band, indicating formation of immunoglobulins. The blood samples are obtained from patients diagnosed with infectious mononucleosis and treated at the University Hospital in Kraków. The lymphocytes’ Raman spectra are collected using a mapping technique, exciting samples with a 514.5 nm line of Ar + laser. Measurements are performed on the 1st, 4th, 6th, 12th and 30th day of hospitalization, until the patient has recovered. The highest intensity of the immunoglobulin marker is observed on the 4th day of hospitalization, while the results of the blood count of patients show the greatest increase in the number of lymphocytes at the beginning of hospitalization. No activated lymphocytes were observed in the blood of healthy volunteers. Some information is provided by the evaluation of B-cell activation by estimating the activated areas in the cells, which are determined by the presence of the Ig marker. The 900 cm−1 band and band around 1450 cm−1 are also analyzed as markers of the presence of the latent membrane protein, LMP2A (and 2B), of the EBV viral protein. The anomalous degree of depolarization observed in B-cells in the course of EBV infection appears to be due to the influence of a virus protein, disrupting BCR signal transduction.
Male manipulation impinges on social-dependent tumor suppression in Drosophila melanogaster females
Authors: Akiki Perla , Delamotte Pierre , Poidevin Mickael , van Dijk Erwin L. , Petit Apolline J. R. , Le Rouzic Arnaud , Mery Frederic , Marion-Poll Frederic , Montagne Jacques 
Published: March 2024

AbstractPhysiological status can influence social behavior, which in turn can affect physiology and health. Previously, we reported that tumor growth in Drosophila virgin females depends on the social context, but did not investigate the underlying physiological mechanisms. Here, we sought to characterize the signal perceived between tumorous flies, ultimately discovering that the tumor suppressive effect varies depending on reproductive status. Firstly, we show that the tumor suppressive effect is neither dependent on remnant pheromone-like products nor on the microbiota. Transcriptome analysis of the heads of these tumorous flies reveals social-dependent gene-expression changes related to nervous-system activity, suggesting that a cognitive-like relay might mediate the tumor suppressive effect. The transcriptome also reveals changes in the expression of genes related to mating behavior. Surprisingly, we observed that this social-dependent tumor-suppressive effect is lost in fertilized females. After mating, Drosophila females change their behavior—favoring offspring survival—in response to peptides transferred via the male ejaculate, a phenomenon called “male manipulation”. Remarkably, the social-dependent tumor suppressive effect is restored in females mated by sex-peptide deficient males. Since male manipulation has likely been selected to favor male gene transmission, our findings indicate that this evolutionary trait impedes social-dependent tumor growth slowdown.
Study of Urbach energy and Kramers–Kronig on Mn and Zn doped NiFe2O4 ferrite nanopowder for the determination of structural and optical characteristics
Authors: Nazari N. , Golzan M. M. , Mabhouti Kh. 
Published: March 2024

AbstractMxNi1-xFe2O4 spinel ferrite (M = Mn, Zn, and x = 0, 0.05) has been successfully synthesized by co-precipitation technique with hydrazine hydrate reduction agent (instead of NaOH) and Ethylene glycol surfactant. The XRD spectra of the samples illustrated high crystallinity. The structural characterization of pure and doped fcc NiFe2O4 were calculated by Scherrer, Modified Scherrer, Williamson–Hall, and SSP methods. In comparison of several methods, the Scherrer method is unreasonable method and W–H method has an acceptable range and can calculate both < L > and strain without restriction. The specific surface area in Zn-doped increased, demonstrate increment of adsorption properties in Ni ferrite structure. TEM images revealed the shape of grains is spherical, cubic, and irregular, with a grain size in the range of 35–65 nm. Hysteresis loops illustrated the magnetic behavior of samples. From the reflectance data, the band gap energies were estimated at 1.984, 1.954, and 1.973 eV for un-doped, Mn, and Zn-doped NiFe2O4 respectively (red shift). The almost low value of Urbach energy for pure, Mn, and Zn -doped NiFe2O4 indicates low structural disorder, which can approve the high crystallinity of samples. Direct band gap energy (Eg), refractive index, and extinction coefficient were estimated by the Kramers–Kronig method with linear optical evaluations. The Eg by K-K method is in good agreement with the Eg by Kubelka–Munk function.
Using shock generator for the fuel mixing of the extruded single 4-lobe nozzle at supersonic combustion chamber
Authors: Alizadeh As’ad , Jasim Dheyaa J. , Sohrabi Neaman , Ahmed Mohsen , Ameer S. Abdul , Ibrahim Safaa Mohammed , Dabis Hasan Khalid , Hussein Ali Adhab , Sultan Abbas J. 
Published: March 2024

AbstractThe importance of the fuel injection configuration on the propulsion efficiency of high-speed vehicles is apparent. In this article, the use of an annular extruded 4-lobe nozzle for the injection of fuel jet in a supersonic combustor of a scramjet engine in the existence of a shock generator is examined. The main aim of this study is to obtain the efficient jet arrangement for efficient fuel mixing inside the engine of hypersonic vehicles. A numerical approach is used to model the supersonic air stream and cross-jet flow with the SST turbulence model. The role of nozzle altitude and internal air jet on the fuel mixing of the hydrogen within the high-speed domain are disclosed. The importance of the horseshoe vortex and counter-rotating vortex on the fuel distribution is also presented. Our results show that the usage of a coaxial jet instead of an annular jet would increase fuel mixing by more than 40% in the combustion chamber.
Prediction of compressive strength of concrete based on improved artificial bee colony-multilayer perceptron algorithm
Authors: Li Ping , Zhang Yanru , Gu Jiming , Duan Shiwei 
Published: March 2024

AbstractThere are many factors that affect the compressive strength of concrete. The relationship between compressive strength and these factors is a complex nonlinear problem. Empirical formulas commonly used to predict the compressive strength of concrete are based on summarizing experimental data of several different mix proportions and curing periods, and their generality is poor. This article proposes an improved artificial bee colony algorithm (IABC) and a multilayer perceptron (MLP) coupled model for predicting the compressive strength of concrete. To address the shortcomings of the basic artificial bee colony algorithm, such as easily falling into local optima and slow convergence speed, this article introduces a Gaussian mutation operator into the basic artificial bee colony algorithm to optimize the initial honey source position and designs an MLP neural network model based on the improved artificial bee colony algorithm (IABC-MLP). Compared with traditional strength prediction models, the ABC-MLP model can better capture the nonlinear relationship of the compressive strength of concrete and achieve higher prediction accuracy when considering the compound effect of multiple factors. The IABC-MLP model built in this study is compared with the ABC-MLP and particle swarm optimization (PSO) coupling algorithms. The research shows that IABC can significantly improve the training and prediction accuracy of MLP. Compared with the ABC-MLP and PSO-MLP coupling models, the training accuracy of the IABC-MLP model is increased by 1.6% and 4.5%, respectively. This model is also compared with common individual learning algorithms such as MLP, decision tree (DT), support vector machine regression (SVR), and random forest algorithms (RF). Based on the comparison of prediction results, the proposed method shows excellent performance in all indicators and demonstrates the superiority of heuristic algorithms in predicting the compressive strength of concrete.
Benchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle
Authors: Mota Lucio F. M. , Arikawa Leonardo M. , Santos Samuel W. B. , Fernandes Júnior Gerardo A. , Alves Anderson A. C. , Rosa Guilherme J. M. , Mercadante Maria E. Z. , Cyrillo Joslaine N. S. G. , Carvalheiro Roberto , Albuquerque Lucia G. 
Published: March 2024

AbstractGenomic selection (GS) offers a promising opportunity for selecting more efficient animals to use consumed energy for maintenance and growth functions, impacting profitability and environmental sustainability. Here, we compared the prediction accuracy of multi-layer neural network (MLNN) and support vector regression (SVR) against single-trait (STGBLUP), multi-trait genomic best linear unbiased prediction (MTGBLUP), and Bayesian regression (BayesA, BayesB, BayesC, BRR, and BLasso) for feed efficiency (FE) traits. FE-related traits were measured in 1156 Nellore cattle from an experimental breeding program genotyped for ~ 300 K markers after quality control. Prediction accuracy (Acc) was evaluated using a forward validation splitting the dataset based on birth year, considering the phenotypes adjusted for the fixed effects and covariates as pseudo-phenotypes. The MLNN and SVR approaches were trained by randomly splitting the training population into fivefold to select the best hyperparameters. The results show that the machine learning methods (MLNN and SVR) and MTGBLUP outperformed STGBLUP and the Bayesian regression approaches, increasing the Acc by approximately 8.9%, 14.6%, and 13.7% using MLNN, SVR, and MTGBLUP, respectively. Acc for SVR and MTGBLUP were slightly different, ranging from 0.62 to 0.69 and 0.62 to 0.68, respectively, with empirically unbiased for both models (0.97 and 1.09). Our results indicated that SVR and MTGBLUBP approaches were more accurate in predicting FE-related traits than Bayesian regression and STGBLUP and seemed competitive for GS of complex phenotypes with various degrees of inheritance.