bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Green Liquid Chromatography Method for Simultaneous Quantification of Caffeine and its Three Major Metabolites in Urine, Drinks, and Herbal Products
Authors: Goldner Débora M. B. , do Nascimento Fernando H. , Masini Jorge C. 
Published: June 2024

ABSTRACTA green reversed‐phase liquid chromatography method was developed to determine caffeine (CF), theobromine (TB), theophylline (TP), and paraxanthine (PX). The stationary phase was a silica‐based C18 monolithic column (100 × 3.0 mm2 i.d.), eluted with a gradient from 6% to 10% (v/v) of ethanol in water, at a flow rate of 0.75 mL/min (35°C) and UV detection at 272 nm. The compounds were separated in less than 6 min, with a total time of 10 min for column reconditioning. The effect of gradient composition, flow rate, injection volume, and column temperature was evaluated in the separation efficiency, especially regarding the critical pair PX/TP, for which the best resolution was 1.37. The linear range was between 0.5 and 100 µg/mL. The LODs were 0.065, 0.057, 0.040, and 0.044 mg/mL for TP, TB, PX, and CF, respectively. The method was applied to green tea and yerba mate infusions, soft drinks, energy drinks, and soluble coffee. Urine samples were analyzed before and after solid‐phase extraction. The accuracy was evaluated by the spiking/recovery strategy standing in the 79.6% to 116.4% range. The separation performance and the mobile phase consumption were compared to those of another method using a gradient of methanol in water.
Sequence Analysis in Education: Principles, Technique, and Tutorial with R
Authors: Saqr Mohammed , López-Pernas Sonsoles , Helske Satu , Durand Marion , Murphy Keefe , Studer Matthias , Ritschard Gilbert 
Published: June 2024

AbstractSequence analysis is a data mining technique that is increasingly gaining ground in learning analytics. Sequence analysis enables researchers to extract meaningful insights from sequential data, i.e., to summarize the sequential patterns of learning data and classify those patterns into homogeneous groups. In this chapter, readers will become familiar with sequence analysis techniques and tools through real-life step-by-step examples of sequential trace log data of students’ online activities. Readers will be guided on how to visualize the common sequence plots and interpret such visualizations. An essential part of sequence analysis is the discovery of patterns within sequences through clustering techniques. Therefore, this chapter will demonstrate the various sequence clustering methods, calculator of cluster indices, and evaluation of clustering results.
Factor Analysis in Education Research Using R
Authors: Vogelsmeier Leonie V. D. E. , Saqr Mohammed , López-Pernas Sonsoles , Jongerling Joran 
Published: June 2024

AbstractFactor analysis is a method commonly employed to reduce a large number of variables into fewer numbers of factors. The method is often used to identify which observable indicators are representative of latent, not directly-observed constructs. This is a key step in developing valid instruments to assess latent constructs in educational research (e.g., student engagement or motivation). The chapter describes the two main approaches for conducting factor analysis (and how to combine them in an integrated factor analysis strategy) and provides a tutorial on implementing both techniques in the R programming language. The first is confirmatory factor analysis (CFA), a more theory-driven approach, in which a researcher actively specifies the number of underlying constructs as well as the pattern of relations between these dimensions and observed variables. The second is exploratory factor analysis (EFA), a more data-driven approach, in which the number of underlying constructs is inferred from the data, and all underlying constructs are assumed to influence all observed variables (at least to some degree).
Cellphones Beyond the Workshop: Youth Researchers Owning Gender Transformative Change Through Participatory Visual Research in Rural India During COVID-19
Authors: MacEntee Katie , Panda Rukmini , Lowthers Megan , Mitchell Claudia 
Published: June 2024

AbstractParticipatory visual methods are a means for marginalized communities to engage in research for social change. However, the technology gap, especially for economically disadvantaged youth in the Global South, can exclude groups from sustained participation in project activities. This article explores the significance of providing 20 Youth Researchers (YRs) with cellphones that they could keep and the impact of this cellphone ownership on research activities during COVID-19. The YRs learned how to use cellphones for cellphilm and photovoice methods to research gender-based violence (GBV) and sexual and reproductive health and rights (SRHR) in Odisha, India. Beyond the workshop, when YRs returned to their rural communities, they navigated multiple waves of COVID-19. Being sensitized to issues of gender equality and social justice, they used their cellphones to draw attention to issues of GBV and SRHR, public health, social justice, and equality. The ‘beyond the workshop’ outputs were indicative of the intersectional impacts of youth in their rural contexts during this unprecedented time and demonstrate how acquiring cellphones can promote youth ownership of project activities, personal transformation for YRs and youth-led advocacy.
From Samuelson’s multiplier-accelerator to bifurcations and chaos in economic dynamics
Authors: Bischi Gian Italo 
Published: June 2024

AbstractThis piece in the series of Milestones starts from the short, clear and highly cited paper by Samuelson (Rev Econ Stat 21(2):75–78, 1939) entitled “Interactions between the Multiplier Analysis and the Principle of Acceleration”, in which the author proposes a linear dynamic model obtained by combining the Keynesian multiplier and the principle of acceleration, together with a proper structure of time lags. Although very simple, it generated a rich and interesting literature as it provides some answers to the question of endogenous business cycles. Moreover, it raises the question of sensitivity with respect to small variations of the model’s parameters, thus sparking the attention of economists and social scientists toward studies on the qualitative theory of non-linear dynamical systems, and the related work on deterministic chaos and bifurcations which appeared between the 1960 s and 1970 s, such as the paper by Lorenz (J Atmos Sci 20:130–141, 1963) in the Journal of Atmospheric Sciences or the one by May (Nature 26:459–467, 1976) in Nature, thereby stimulating an interdisciplinary mathematical approach to dynamic complexity in physics, biology and social sciences.
Accuracy of intra ocular lens calculation formulae in patients with pseudoexfoliation syndrome
Authors: Gazit Inbal , Gershevich Anna , Einan-Lifshitz Adi , Pras Eran , Barrett Graham D. , Or Lior 
Published: June 2024

Abstract
Background
The purpose of this study was to investigate the visual and refractive outcomes in patients with pseudoexfoliation (PXF) undergoing routine cataract surgery and to compare the accuracy of intraocular lens (IOL) power calculation formulae.

Methods
Retrospective case-series study from Shamir medical center, a public hospital, Israel. Medical records of patients who underwent routine cataract surgery between January 2019 and August 2021 were investigated. Postoperative visual acuity and manifest refraction were examined. The error in predicted refraction and IOL power calculation accuracy within a range of ± 0.50 to ± 1.00 diopters were compared between different IOL calculating formulae.

Results
151 eyes of 151 patients ages 73.9 ± 7.1 years were included in this study- 58 eyes in the PXF group and 93 eyes in the control group. The mean absolute error (MAE) for the BUII formula was 0.63D ± 0.87 for the PXF group and 0.36D ± 0.48 for the control group (p < 0.05). The MAE for the Hill-RBF 3.0 formula was 0.61D ± 0.84 for the PXF group and 0.42D ± 0.55 for the control group (p = 0.05). There were significant differences in MAE and MedAE between PXF group and control group measures (p < 0.05). In the PXF group there were no significant differences between the different formulae.

Conclusions
There were significant differences in accuracy of IOL power calculations in all formulae between PXF group and control group measures. PXF patients show hyperopic shift from predicted refraction. Barret universal II formula had the highest proportion of eyes with absolute error in prediction below or equal to 0.50 D in both PXF and control groups.
Revealing accuracy in climate dynamics: enhancing evapotranspiration
estimation using advanced quantile regression and machine learning models
Authors: Sharafi Saeed , Mohammadi Ghaleni Mehdi 
Published: June 2024

AbstractThis study examines the effectiveness of various quantile regression (QR) and machine learning (ML) methodologies developed for analyzing the relationship between meteorological parameters and daily reference evapotranspiration (ETref) across diverse climates in Iran spanning from 1987 to 2022. The analyzed models include D-vine copula-based quantile regression (DVQR), multivariate linear quantile regression (MLQR), Bayesian model averaging quantile regression (BMAQR), as well as machine learning algorithms such as extreme learning machine (ELM), random forest (RF), M5 model Tree (M5Tree), least squares support vector regression algorithm (LSSVR), and extreme gradient boosting (XGBoost). Additionally, empirical equations (EEs) such as Baier and Robertson (BARO), Jensen and Haise (JEHA), and Penman (PENM) models were considered. While the EEs demonstrated acceptable performance, the QR and ML models exhibited superior accuracy. Among these, the MLQR model displayed the highest accuracy compared to DVQR and BMAQR models. Moreover, LSSVR, XGBoost, and M5Tree models outperformed ELM and RF models. Notably, LSSVR, XGBoost, and MLQR models exhibited comparable performance (R2 and NSE > 0.92, MBE and RMSE < 0.5, and SI > 0.05) to M5Tree and BMAQR models across all climates. Importantly, these models significantly outperformed EEs, DVQR, ELM, and RF models in all climates. In conclusion, high-dimensional QR and ML models are recommended as promising alternatives for accurately estimating daily ETref in diverse global climate conditions.
Towards a Precision Model for Environmental Public Health: Wastewater-based Epidemiology to Assess Population-level Exposures and Related Diseases
Authors: Bowes Devin A. 
Published: June 2024

Abstract
Purpose of Review
Impacts from climate change and use of toxic chemicals that contaminate our environment continue to pose a threat to the health of human populations. The field of wastewater-based epidemiology (WBE) has evolved significantly in recent years due to the COVID-19 global pandemic, however, investigating the utility of this application to fit within a broader environmental public health framework remains relatively unexplored. This review offers a comprehensive summary of the historical progression of WBE and highlights recent notable advancements to support its use for assessing environmental exposures in human populations.

Recent Findings
Early pioneering studies confirmed feasibility of this application, including measuring pesticides, plasticizers, and flame retardants in influent wastewater, that offered foundational knowledge to support successful expansion in recent work, including exposure to heavy metals and mycotoxins. Collectively, it was identified that evaluating biomarker suitability (e.g., in-sewer degradation, specificity) and pharmacokinetic data of excreted metabolites are crucial for accurate interpretation of results. Additionally, measurements of contaminants differed between catchment areas, indicating disproportionate exposures across populations.

Summary
The use of WBE offers a near real-time approach to address public health priorities, with strong evidence suggesting it can be applied to generate population-level environmental exposure assessments. Research gaps such as biomarker selection, near real-time intervention efficacy assessment, and data analysis approaches are identified in this review and encouraged to be addressed in future work, informing key areas to support the use of WBE towards a precision-based model for environmental public health.
Allergy skin prick tests with COVID-19 vaccines and their contribution to improve vaccination readiness and reduce anxiety
Authors: Pindel Tobias , Brandstetter Susanne , Sieber Wolfgang , Kabesch Michael 
Published: June 2024

Abstract
Background
When coronavirus disease 2019 (COVID-19) vaccines were introduced, they were suspected of triggering severe allergic reactions disproportionately often. This contributed to the fear of vaccination, particularly among allergy patients.

Methods
In an allergy center in eastern Bavaria, we used a skin prick test to investigate how often sensitization to COVID-19 vaccines can be detected and whether appropriate testing could significantly reduce the fear of vaccination.

Results
Comirnaty® (n = 245 tested/6.93% clearly positive reaction; Biontec/Pfizer, Mainz, Germany/New York City, NY, USA), Spikevax® (56/14.28%; Moderna, Cambridge, MA, USA), Vaxzevria® (208/4.32%; Astra Zeneca, Cambridge, England) and Jcovden® (48/4.16%; Johnson & Johnson, New Brunswick, NJ, USA) were tested by skin prick test. Most participants tested were female (83.6%) and had a history of allergies (94.8%). Depending on the result of the skin prick test, the test subjects were advised on vaccination. In a questionnaire survey approximately 1 year after testing, 75.7% of the N = 70 respondents stated that their fear of vaccination had been greatly or very greatly reduced as a result of the testing and counseling. In the follow-up survey, 88.5% of all respondents had been vaccinated at least once. No notable allergic problems occurred during the COVID-19 vaccination in study participants.

Conclusion
The study shows that simple skin prick testing could reduce fears and concerns about allergic reactions to COVID-19 vaccines, and thus significantly increase the willingness to vaccinate in the population, especially among allergy patients.
Multi-UAV pursuit-evasion gaming based on PSO-M3DDPG schemes
Authors: Zhang Yaozhong , Ding Meiyan , Zhang Jiandong , Yang Qiming , Shi Guoqing , Lu Meiqu , Jiang Frank 
Published: June 2024

AbstractThe sample data for reinforcement learning algorithms often exhibit sparsity and instability, making the training results susceptible to falling into local optima. Mini-Max-Multi-agent Deep Deterministic Policy Gradient (M3DDPG) algorithm is a multi-agent reinforcement learning algorithm, which introduces the minimax theorem into Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. It also has unstable convergence caused by sparse sample data and randomization. However, the Particle Swarm Optimisation (PSO) algorithm, unlike traditional reinforcement learning methods, involves the construction of independent populations of policy networks to generate sample data, followed by training the reinforcement learning algorithm. PSO optimizes and updates the policy population based on a fitness function, aiming to enhance the efficiency and convergence speed of the algorithm in learning from the sample data. In order to address the multi-agent pursuit-evasion problem, we propose the PSO-M3DDPG algorithm, which combines the PSO algorithm with the M3DDPG algorithm. Through experimental simulations, the improved algorithm demonstrates superior training results and faster convergence speeds, thus validating its effectiveness.
Evaluation of mechanical properties characterization of additively manufactured components
Authors: Frölich Felix , Bechtloff Lennart , Scheuring Benedikt M. , Heuer Anselm L. , Wittemann Florian , Kärger Luise , Liebig Wilfried V. 
Published: June 2024

Abstract
Additive manufacturing by material extrusion offers innovative potential for component design and is driving advances in many industries. However, fully harnessing these advancements necessitates a thorough comprehension of the process-specific anisotropic structural properties. The complex interactions between process parameters and their direct influence on structural properties often lead to discrepancies between the mechanical properties of tested specimens at the coupon level and the inherent properties of additively manufactured components. In addition, there is no standardized method for preparing specimens that represent the mechanical properties within a given component. This further complicates the comparison of measured properties of different series of measurements and the investigation of manufacturing effects that may occur during the production of a component. Given these challenges, the present work addresses the fundamental question of what aspects need to be considered to ensure that the test specimens reflect the process conditions being tested. The studies look at the requirements for producing representative specimens and for the test methodology to characterize the mechanical properties of additively manufactured structures. The tests are carried out on specimens that were produced directly using the material extrusion process and on specimens that were cut from additively manufactured plates. Water jet cutting, milling, and laser cutting are investigated and compared as cutting methods. The influence of the specimen geometry and the size of the additively manufactured plate is considered. The orientation-dependent mechanical properties, the significance of the individual tests, the measurement scatter, and scanning electron micrographs of the cut edges and fracture surfaces are analyzed. Finally, guidelines for performing representative tests to characterize the mechanical properties of additively manufactured components are proposed.
Multi-view Heterogeneous Graph Neural Networks for Node Classification
Authors: Zeng Xi , Lei Fang-Yuan , Wang Chang-Dong , Dai Qing-Yun 
Published: June 2024

AbstractRecently, with graph neural networks (GNNs) becoming a powerful technique for graph representation, many excellent GNN-based models have been proposed for processing heterogeneous graphs, which are termed Heterogeneous graph neural networks (HGNNs). However, existing HGNNs tend to aggregate information from either direct neighbors or those connected by short metapaths, thereby neglecting the higher-order information and global feature similarity information in heterogeneous graphs. In this paper, we propose a Multi-View Heterogeneous graph neural network (MV-HGNN) to aggregate these information. Firstly, two auxiliary views, specifically a global feature similarity view and a graph diffusion view, are generated from the original heterogeneous graph. Secondly, MV-HGNN performs two message-passing strategies to get the representation of different views. Subsequently, a transformer-based aggregator is used to get the semantic information. Subsequently, the representations of the three views are fused into a final composite representation. We evaluate our method on the node classification task over three commonly used heterogeneous graph datasets, and the results demonstrate that our proposed MV-HGNN significantly outperforms state-of-the-art baselines.
Preliminary profile of the gut microbiota from amerindians in the Brazilian amazon experiencing a process of transition to urbanization
Authors: Alencar Rodrigo M. , Martínez José G. , Machado Valéria N. , Alzate Juan F. , Ortiz-Ojeda Cinthya P. , Matias Rosiane R. , Benzaquem Denise C. , Santos Maria C.F. , Assunção Enedina N. , Lira Evelyn C. , Astolfi-Filho Spartaco , Hrbek Tomas , Farias Izeni P. , Fantin Cleiton 
Published: June 2024

AbstractThe Yanomami are one of the oldest indigenous tribes in the Amazon and are direct descendants of the first people to colonize South America 12,000 years ago. They are located on the border between Venezuela and Brazil, with the Venezuelan side remaining uncontacted. While they maintain a hunter-gatherer society, they are currently experiencing contact with urbanized populations in Brazil. The human gut microbiota of traditional communities has become the subject of recent studies due to the Westernization of their diet and the introduction of antibiotics and other chemicals, which have affected microbial diversity in indigenous populations, thereby threatening their existence. In this study, we preliminarily characterized the diversity of the gut microbiota of the Yanomami, a hunter-gatherer society from the Amazon, experiencing contact with urbanized populations. Similarly, we compared their diversity with the population in Manaus, Amazonas. A metabarcoding approach of the 16 S rRNA gene was carried out on fecal samples. Differences were found between the two populations, particularly regarding the abundance of genera (e.g., Prevotella and Bacteroides) and the higher values of the phyla Bacteroidetes over Firmicutes, which were significant only in the Yanomami. Some bacteria were found exclusively in the Yanomami (Treponema and Succinivibrio). However, diversity was statistically equal between them. In conclusion, the composition of the Yanomami gut microbiota still maintains the profile characteristic of a community with a traditional lifestyle. However, our results suggest an underlying Westernization process of the Yanomami microbiota when compared with that of Manaus, which must be carefully monitored by authorities, as the loss of diversity can be a sign of growing danger to the health of the Yanomami.
Climatic and biogeographic factors show contrasted effects on continental and volcanic ISARs
Authors: Cáceres-Polgrossi L. , Buldrini F. , Bruzzaniti V. , Fernández-Palacios J. M. , Testolin R. , Zannini P. , Chiarucci A. 
Published: June 2024

Abstract
Aim
The study of islands biotas is fundamental to understand biodiversity patterns and process, both on evolutionary and ecological time scales. This study explores the influence of biogeographic and climatic factors on island species–area relationships (ISARs) in volcanic and continental islands, aiming to detect differences in slopes and intercepts between these island origins.

Methods
Data from 5049 vegetation plots on 58 Central Mediterranean Sea islands were collected from various sources. Islands were categorized as volcanic or continental based on their geological origin. area, isolation, maximum elevation, eccentricity, mean annual temperature and annual precipitation were calculated for each island. By using a moving window, we created groups of islands, and ISARs were fitted to each group using the Arrhenius power functions. Linear models and a permutation test were employed to examine how ISAR model parameters varied with the above-mentioned variables.

Results
While ISAR intercept values did not differ between island origins, volcanic islands showed higher ISAR slopes than continental islands. Whereas increasing island isolation and mean annual temperature increased ISAR intercepts on continental islands groups, it decreased on volcanic ones. Increasing annual precipitation decreased ISAR intercept on continental islands groups and increased on volcanic islands groups. Increasing island isolation, eccentricity and mean annual temperature increased the ISAR slope on volcanic islands groups while increasing annual precipitation decreased it. Increasing maximum elevation increased ISAR slope on continental islands groups.

Main conclusions
Our study provides evidence about the importance of island geological origin in determining the observed ISAR patterns. Biogeographic and climatic factors are pivotal in shaping species richness patterns on islands, exerting varying influences on both volcanic and continental islands.