bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optical UV–visible, Raman spectroscopy, and gamma radiation shielding properties of borate glass systems; B2O3 + Na2O + Al2O3 / MgO/ Li2O
Authors: Hussein E. M. Abou , Rammah Y. S. 
Published: January 2024

AbstractThree borate glass systems with chemical formula 65 B2O3 +  30 Na2O + 5 (x) wt%; x = Al2O3, MgO, and Li2O were fabricated by the common melting-annealing method and coded as BNAl, BNMg, and BNLi, respectively. XRD pattern revealed the non-crystalline form and the glassy states of the prepared samples. UV-optical absorption spectra showed UV cutoff peaks in the range 320–340 nm and optical energy gap (Eopt) by Tauc’s model (ETauc’s) and ASF method (EASF) revealed quite similar values ranged from 2.60 to 3 eV before irradiation and 2.23 to 2.60 eV after 20 kGy of gamma irradiation. Raman spectra show three detectable regions at; (i) 250–600 cm−1, (ii) 500–1000 cm−1 and (iii) 1000–2000 cm−1 correlated to different borate forms. Many radiation shielding parameters were theoretically calculated using Phy-X/PSD and simulated via Monte Carlo code (MCNP-5) in photon energy range (0.015–15 MeV). Mass attenuation coefficient (MAC), linear gamma attenuation coefficient (LAC), effective atomic number (Zeff), fast neutron macroscopic cross section (∑R), exposure buildup factor (EBF) and energy absorption buildup factor (EABF) at various penetration depths PD, as well as half (HVL) and tenth value layers (TVL) and mean free path (MFP) were also estimated. The shielding parameters showed the order of (MAC, LAC)BNAl > (MAC,LAC)BNMg > (MAC, LAC)BNLi. The stability of the glasses optical, and structural properties against gamma irradiation indicate the capable use of the glasses for radiation shielding applications specially Al2O3 glass with the heaviest atomic weight and the highest capacity for radiation shielding protection.
In Vitro Screening and Lipid-Lowering Effect of Prickly Pear (Opuntia Ficus-Indica L. Mill.) Fruit Extracts in 3T3-L1 Pre-Adipocytes and Mature Adipocytes
Authors: Eseberri Itziar , Gómez-Maqueo Andrea , Trepiana Jenifer , Gómez-López Iván , Proença Carina , Cano M. Pilar , Portillo Maria P. 
Published: January 2024

AbstractOpuntia ficus-indica fruits have been widely used due to their nutritional composition and beneficial effects on health, particularly against chronic diseases such as diabetes, obesity, cardiovascular diseases and cancer, among others. In recent years, prickly pear peel and pulp extracts have been characterised, and a high number of bioactive compounds have been identified. This study aimed to analyse the triglyceride-lowering effect of prickly pear peel and pulp extracts obtained from fruits of three varieties (Pelota, Sanguinos, and Colorada) in 3T3-L1 maturing and mature adipocytes. At a concentration of 50 µg/mL, peel extracts from Colorada reduced triglyceride accumulation in pre-adipocytes and mature adipocytes. Additionally, at 25 µg/mL, Pelota peel extract decreased triglyceride content in mature adipocytes. Moreover, maturing pre-adipocytes treated with 50 and 25 µg/mL of Sanguinos pulp extract showed a reduction of triglyceride accumulation. In addition, the lipid-lowering effect of the main individual betalain and phenolic compounds standards were assayed. Piscidic acid and isorhamnetin glycoside (IG2), found in Colorada peel extract, were identified as the bioactive compounds that could contribute more notably to the triglyceride-lowering effect of the extract. Thus, the betalain and phenolic-rich extracts from Opuntia ficus indica fruits may serve as an effective tool in obesity management.
The effect of bariatric surgery on the expression of gastrointestinal taste receptors: A systematic review
Authors: Walmsley Rosalind , Chong Lynn , Hii Michael W. , Brown Robyn M. , Sumithran Priya 
Published: January 2024

AbstractGastrointestinal nutrient sensing via taste receptors may contribute to weight loss, metabolic improvements, and a reduced preference for sweet and fatty foods following bariatric surgery. This review aimed to investigate the effect of bariatric surgery on the expression of oral and post-oral gastrointestinal taste receptors and associations between taste receptor alterations and clinical outcomes of bariatric surgery. A systematic review was conducted to capture data from both human and animal studies on changes in the expression of taste receptors in oral or post-oral gastrointestinal tissue following any type of bariatric surgery. Databases searched included Medline, Embase, Emcare, APA PsychInfo, Cochrane Library, and CINAHL. Two human and 21 animal studies were included. Bariatric surgery alters the quantity of many sweet, umami, and fatty acid taste receptors in the gastrointestinal tract. Changes to the expression of sweet and amino acid receptors occur most often in intestinal segments surgically repositioned more proximally, such as the alimentary limb after gastric bypass. Conversely, changes to fatty acid receptors were observed more frequently in the colon than in the small intestine. Significant heterogeneity in the methodology of included studies limited conclusions regarding the direction of change in taste receptor expression induced by bariatric surgeries. Few studies have investigated associations between taste receptor expression and clinical outcomes of bariatric surgery. As such, future studies should look to investigate the relationship between bariatric surgery-induced changes to gut taste receptor expression and function and the impact of surgery on taste preferences, food palatability, and eating behaviour.Registration code in PROSPERO: CRD42022313992
Scientometric rules as a guide to transform science systems in the Middle East and North Africa
Authors: El-Ouahi Jamal 
Published: January 2024

AbstractThis study explores how scientometric data and indicators are used to transform science systems in a selection of countries in the Middle East and North Africa. I propose that scientometric-based rules inform such transformation. First, the research shows how research managers adopt scientometrics as ‘global standards’. I also show how several scientometric data and indicators are adopted following a ‘glocalization’ process. Finally, I demonstrate how research managers use this data to inform decision-making and policymaking processes. This study contributes to a broader understanding of the usage of scientometric indicators in the context of assessing research institutions and researchers based on their publishing activities. Related to these assessments, I also discuss how such data transforms and adapts local science systems to meet so-called ‘global standards’.
Manner Matters: Linguistic Equity Through a Court Interpreter in Australia
Authors: Yi Ran 
Published: January 2024

Abstract
Linguistic equity through an interpreter is not merely a fundamental human right but also an integral part of procedural justice. As codified in the professional code of conduct, interpreters should faithfully interpret everything that has been said in the exact same manner as the original speakers. Much has been researched about the content. Little has been known about the interpretations of the manner. Drawing on one hundred questionnaire responses, this article examines the interpreters’ awareness of the manner of speech in Australian virtual courts and remote interpreting settings. The results suggest a general lack of understanding of the manner of speech, as reflected by interpreters’ inadequate knowledge of definitions and specific types of manner-related features. Furthermore, the insufficient understanding of the manner of speech impacts the interpreters’ perceptions of the manner of speech in the original utterances, and their views of the faithful reproduction of the se feature in courtroom discursive practices. The qualitative and quantitative analyses of written responses provided by professional interpreters provide insights into the practical aspects of reproducing the manner of speech in court.
Image-based hemodynamic simulations for intracranial aneurysms: the impact of complex vasculature
Authors: Gaidzik Franziska , Korte Jana , Saalfeld Sylvia , Janiga Gábor , Berg Philipp 
Published: January 2024

Abstract
Purpose
Hemodynamics play an important role in the assessment of intracranial aneurysm (IA) development and rupture risk. The purpose of this study was to examine the impact of complex vasculatures onto the intra-vessel and intra-aneurysmal blood flow.

Methods
Complex segmentation of a subject-specific, 60-outlet and 3-inlet circle of Willis model captured with 7T magnetic resonance imaging was performed. This model was trimmed to a 10-outlet model version. Two patient-specific IAs were added onto both models yielding two pathological versions, and image-based blood flow simulations of the four resulting cases were carried out. To capture the differences between complex and trimmed model, time-averaged and centerline velocities were compared. The assessment of intra-saccular blood flow within the IAs involved the evaluation of wall shear stresses (WSS) at the IA wall and neck inflow rates (NIR).

Results
Lower flow values are observed in the majority of the complex model. However, at specific locations (left middle cerebral artery 0.5 m/s, left posterior cerebral artery 0.25 m/s), higher flow rates were visible when compared to the trimmed counterpart. Furthermore, at the centerlines the total velocity values reveal differences up to 0.15 m/s. In the IAs, the reduction in the neck inflow rate and WSS in the complex model was observed for the first IA (IA-A δNIRmean =  − 0.07ml/s, PCA.l δWSSmean =  − 0.05 Pa). The second IA featured an increase in the neck inflow rate and WSS (IA-B δNIRmean = 0.04 ml/s, PCA.l δWSSmean = 0.07 Pa).

Conclusion
Both the magnitude and shape of the flow distribution vary depending on the model’s complexity. The magnitude is primarily influenced by the global vessel model, while the shape is determined by the local structure. Furthermore, intra-aneurysmal flow strongly depends on the location in the vessel tree, emphasizing the need for complex model geometries for realistic hemodynamic assessment and rupture risk analysis.
Distributed Deep Reinforcement Learning: A Survey and a Multi-player Multi-agent Learning Toolbox
Authors: Yin Qiyue , Yu Tongtong , Shen Shengqi , Yang Jun , Zhao Meijing , Ni Wancheng , Huang Kaiqi , Liang Bin , Wang Liang 
Published: January 2024

AbstractWith the breakthrough of AlphaGo, deep reinforcement learning has become a recognized technique for solving sequential decision-making problems. Despite its reputation, data inefficiency caused by its trial and error learning mechanism makes deep reinforcement learning difficult to apply in a wide range of areas. Many methods have been developed for sample efficient deep reinforcement learning, such as environment modelling, experience transfer, and distributed modifications, among which distributed deep reinforcement learning has shown its potential in various applications, such as human-computer gaming and intelligent transportation. In this paper, we conclude the state of this exciting field, by comparing the classical distributed deep reinforcement learning methods and studying important components to achieve efficient distributed learning, covering single player single agent distributed deep reinforcement learning to the most complex multiple players multiple agents distributed deep reinforcement learning. Furthermore, we review recently released toolboxes that help to realize distributed deep reinforcement learning without many modifications of their non-distributed versions. By analysing their strengths and weaknesses, a multi-player multi-agent distributed deep reinforcement learning toolbox is developed and released, which is further validated on Wargame, a complex environment, showing the usability of the proposed toolbox for multiple players and multiple agents distributed deep reinforcement learning under complex games. Finally, we try to point out challenges and future trends, hoping that this brief review can provide a guide or a spark for researchers who are interested in distributed deep reinforcement learning.
Estimating vertical ground reaction forces from plantar pressure using interpretable high-dimensional approximation
Authors: Weidensager Laura , Krumm Dominik , Potts Daniel , Odenwald Stephan 
Published: January 2024

AbstractSeveral studies have reported methods for capturing ground reaction forces in the field. However, these forces are usually measured indirectly with non-interpretable “black box” models. Here we report an interpretable model with a function approximation algorithm. The model uses time series of plantar pressure from instrumented insoles to estimate vertical ground reaction forces. The study included data from 16 persons moving at different speeds on a two-belt treadmill equipped with force plates. The introduced regression model, based on a high-dimensional approximation, using only low-dimensional variable interactions, demonstrated its ability to estimate vertical ground reaction forces. The normalized mean square error for velocities between 3 and $$9\,\hbox {km}\,\hbox {h}^{-1}$$

9

km

h

-
1

ranged from 10.6 to $$24.4\,\%$$

24.4

%

. The accuracy of the presented approach, which can be used to analyze and interpret the learned model, was comparable to that reported in the literature. Furthermore, the evaluation of the learned model is particularly suitable for embedded and portable systems and, after a one-time calibration measurement, allows permanent and laboratory-independent measurement of vertical ground reaction forces.
Facilitators and Barriers of Implementation of Evidence-Based Parenting Support in Educational Settings
Authors: Moller Nicholas , Tellegen Cassandra L. , Ma Tianyi , Sanders Matthew R. 
Published: January 2024

AbstractImproving child behavior and promoting family well-being is a key objective of evidence-based parenting programs, such as the Triple P–Positive Parenting Program. To achieve this goal, parenting programs are delivered using a multidisciplinary workforce. Previous researchers have collectively examined the entire workforce of parenting practitioners to determine the factors that influence program delivery, primarily using self-report measures. However, these findings did not highlight the unique factors relevant to specific practitioner disciplines. Educators are one practitioner discipline that play an integral role in delivering parenting programs through schools and early childhood learning settings. This study aimed at exploring the facilitators and barriers that impact frequency of program use for educator practitioners using both qualitative and quantitative analyses. Data from 404 Triple P educator practitioners were extracted from a larger dataset of 1202 practitioners from English-speaking countries who completed self-report questionnaires and responded to three open-ended questions. Hierarchical multiple regressions were conducted using eight independent variables (with participant characteristics as control variables), revealing seven positive and one negative predictor for frequency of use. A thematic analysis was then conducted on the qualitative responses, producing 11 themes and 28 subthemes. The quantitative analysis revealed organisational support, perceived usefulness, and practitioner self-regulation were the most important positive predictors. The qualitative analysis supported these findings and revealed novel barriers including Covid-19/work from home, online delivery, parent factors, and specific organisational factors. These findings highlight the need for online resources, reliable virtual delivery methods, improved ways to reach and engage families, and additional trained education practitioners to distribute high workloads.
Vertical distribution of epibenthic megafauna of a large seamount west of Cape Verde islands (tropical North Atlantic)
Authors: Scepanski Dominik , Augustin Nico , Dünn Manon , Scherwaß Anja , Xavier Joana R. , Werner Johannes , Waldvogel Ann-Marie , Arndt Hartmut 
Published: January 2024

AbstractSeamounts are thought to function as hotspots of megafauna diversity due to their topology and environmental characteristics. However, assessments of megafauna communities inhabiting seamounts, including diversity and density, are scarce. In this study, we provide megafauna diversity and density estimates for a recently discovered, not yet characterized seamount region (Boetius seamounts) west of Cape Verde (N17° 16′, W29° 26′). We investigated the distribution of epibenthic megafauna over a large depth gradient from the seamount’s summit at 1400 m down to 3200 m water depth and provided qualitative and quantitative analyses based on quantified video data. In utilizing an ocean floor observation system (OFOS), calibrated videos were taken as a horizontal transect from the north-eastern flank of the seamount, differentiating between an upper, coral-rich region (−1354/−2358 m) and a deeper, sponge-rich region (−2358/−3218 m). Taxa were morphologically distinguished, and their diversity and densities were estimated and related to substrate types. Both the upper and deeper seamount region hosted unique communities with significantly higher megafauna richness at the seamount’s summit. Megafauna densities differed significantly between the upper (0.297 ± 0.167 Ind./m2) and deeper community (0.112 ± 0.114 Ind./m). The seamount showed a vertical zonation with dense aggregations of deep-sea corals dominating the seamount’s upper region and colonies of the glass sponges Poliopogon amadou dominating the deeper region. The results are discussed in light of detected substrate preferences and co-occurrence of species and are compared with findings from other Atlantic seamounts.
Studying Drowsiness Detection Performance While Driving Through Scalable Machine Learning Models Using Electroencephalography
Authors: Hidalgo Rogel José Manuel , Martínez Beltrán Enrique Tomás , Quiles Pérez Mario , López Bernal Sergio , Martínez Pérez Gregorio , Huertas Celdrán Alberto 
Published: January 2024

AbstractDriver drowsiness is a significant concern and one of the leading causes of traffic accidents. Advances in cognitive neuroscience and computer science have enabled the detection of drivers’ drowsiness using Brain-Computer Interfaces (BCIs) and Machine Learning (ML). However, the literature lacks a comprehensive evaluation of drowsiness detection performance using a heterogeneous set of ML algorithms, being also necessary to study the performance of scalable ML models suitable for groups of subjects. To address these limitations, this work presents an intelligent framework employing BCIs and features based on electroencephalography for detecting drowsiness in driving scenarios. The SEED-VIG dataset is used to evaluate the best-performing models for individual subjects and groups. Results show that Random Forest (RF) outperformed other models used in the literature, such as Support Vector Machine (SVM), with a 78% f1-score for individual models. Regarding scalable models, RF reached a 79% f1-score, demonstrating the effectiveness of these approaches. This publication highlights the relevance of exploring a diverse set of ML algorithms and scalable approaches suitable for groups of subjects to improve drowsiness detection systems and ultimately reduce the number of accidents caused by driver fatigue. The lessons learned from this study show that not only SVM but also other models not sufficiently explored in the literature are relevant for drowsiness detection. Additionally, scalable approaches are effective in detecting drowsiness, even when new subjects are evaluated. Thus, the proposed framework presents a novel approach for detecting drowsiness in driving scenarios using BCIs and ML.
Microstructure and thermal properties of dissimilar M300-CuCr1Zr alloys by multi-material laser-based powder bed fusion
Authors: Li Xiaoshuang , Sukhomlinov Dmitry , Que Zaiqing 
Published: January 2024

AbstractMulti-material laser-based powder bed fusion (PBF-LB) allows manufacturing of parts with 3-dimensional gradient and additional functionality in a single step. This research focuses on the combination of thermally-conductive CuCr1Zr with hard M300 tool steel. Two interface configurations of M300 on CuCr1Zr and CuCr1Zr on M300 were investigated. Ultra-fine grains form at the interface due to the low mutual solubility of Cu and steel. The material mixing zone size is dependent on the configurations and tunable in the range of 0.1–0.3 mm by introducing a separate set of parameters for the interface layers. Microcracks and pores mainly occur in the transition zone. Regardless of these defects, the thermal diffusivity of bimetallic parts with 50vol% of CuCr1Zr significantly increases by 70%–150% compared to pure M300. The thermal diffusivity of CuCr1Zr and the hardness of M300 steel can be enhanced simultaneously by applying the aging heat treatment.
Do users adopt extremist beliefs from exposure to hate subreddits?
Authors: Schmitz Matheus , Muric Goran , Hickey Daniel , Burghardt Keith 
Published: January 2024

Abstract
Social media offers an avenue for like-minded individuals to interact in ways that were previously not possible. Yet, it can also be a breeding ground for hate and extremism to spread. Despite research into hate speech on social media, its influence on users adopting extremist beliefs is less understood. In this study, we use causal analysis to quantify extremist adoption resulting from users becoming active in hate online communities known as subreddits. Using an interrupted time series research design, we compare users who became involved in hate subreddits (treatment group) to those who did not (control group). This analysis is reproduced across ten different subreddits covering four different topics: Alt-Right, Racism, Sexism, and Fat-Shaming. From these analyses, we uncover a causal link between a user becoming active in a hate community and using more hate speech both within hate subreddits and across the wider platform. The results are consistent and replicate across communities. Our findings are tentative evidence that users adopt extremist ideas from exposure to hate subreddits.
When remediating one artifact results in another: control, confounders, and correction
Authors: Colaço David 
Published: January 2024

AbstractScientists aim to remediate artifacts in their experimental datasets. However, the remediation of one artifact can result in another. Why might this happen, and what does this consequence tell us about how we should account for artifacts and their control? In this paper, I explore a case in functional neuroimaging where remediation appears to have caused this problem. I argue that remediation amounts to a change to an experimental arrangement. These changes need not be surgical, and the arrangement need not satisfy the criterion of causal modularity. Thus, remediation can affect more than just the factor responsible for the artifact. However, if researchers can determine the consequences of their remediation, they can make adjustments that control for the present artifact as well as for previously controlled ones. Current philosophical accounts of artifacts and the factors responsible for them cannot adequately address this issue, as they do not account for what is needed for artifact remediation (and specifically correction). I support my argument by paralleling it with ongoing concerns regarding the transparency of complex computational systems, as near future remediation across the experimental life sciences will likely make greater use of AI tools to correct for artifacts.