bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fifty-four thousand deaths, zero electoral impact
Authors: Demirdogen Alper , Olhan Emine 
Published: January 2025

Abstract
The February 2023 earthquake in Turkiye resulted in 54,000 fatalities and over $100 billion in economic losses. We examine whether this catastrophic event influenced incumbent-party support in the subsequent election. In contrast with previous researchers, we find no significant effect on voter turnout or the incumbent party’s vote share, even after accounting for population displacement and infrastructure damage. We argue that this null effect is due to several factors: effective electoral management policies that reduced voting costs despite significant population displacement; a compressed timeline between disaster and elections, which constrained politically motivated aid distribution; extreme political polarization, which led the public to filter their interpretation of the disaster response through partisan lenses; different information flows in state-controlled media and social media; and offsetting electoral responses across regions. Our results offer valuable insights for policy makers, political scientists, and economists studying the political consequences of natural disasters.
Global metabolomic alterations associated with endocrine-disrupting chemicals among pregnant individuals and newborns
Authors: Puvvula Jagadeesh , Song Lucie C. , Zalewska Klaudia J. , Alexander Ariel , Manz Kathrine E. , Braun Joseph M. , Pennell Kurt D. , DeFranco Emily A. , Ho Shuk-Mei , Leung Yuet-Kin , Huang Shouxiong , Vuong Ann M. , Kim Stephani S. , Percy Zana , Bhashyam Priyanka , Lee Raymund , Jones Dean P. , Tran Vilinh , Kim Dasom V. , Calafat Antonia M. , Botelho Julianne C. , Chen Aimin 
Published: January 2025

Abstract

Background
Gestational exposure to non-persistent endocrine-disrupting chemicals (EDCs) may be associated with adverse pregnancy outcomes. While many EDCs affect the endocrine system, their effects on endocrine-related metabolic pathways remain unclear. This study aims to explore the global metabolome changes associated with EDC biomarkers at delivery.

Methods
This study included 75 pregnant individuals who delivered at the University of Cincinnati Hospital from 2014 to 2017. We measured maternal urinary biomarkers of paraben/phenol (12), phthalate (13), and phthalate replacements (4) from the samples collected during the delivery visit. Global serum metabolome profiles were analyzed from maternal blood (n = 72) and newborn (n = 63) cord blood samples collected at delivery. Fifteen of the 29 urinary biomarkers were excluded due to low detection frequency or potential exposures during hospital stay. We assessed metabolome-wide associations between 14 maternal urinary biomarkers and maternal/newborn metabolome profiles. Additionally, performed enrichment analysis to identify potential alterations in metabolic pathways.

Results
We observed metabolome-wide associations between maternal urinary concentrations of phthalate metabolites (mono-isobutyl phthalate), phthalate replacements (mono-2-ethyl-5-carboxypentyl terephthalate, mono-2-ethyl-5-hydroxyhexyl terephthalate) and phenols (bisphenol-A, bisphenol-S) and maternal serum metabolome, using q-value < 0.2 as a threshold. Additionally, associations of phthalate metabolites (mono-n-butyl phthalate, monobenzyl phthalate) and phenols (2,5-dichlorophenol, BPA) with the newborn metabolome were noted. Enrichment analyses revealed associations (p-gamma < 0.05) with amino acid, carbohydrate, lipid, glycan, vitamin, and other cofactor metabolism pathways.

Conclusion
Maternal paraben, phenol, phthalate, and phthalate replacement biomarker concentrations at delivery were associated with maternal and newborn serum global metabolome.
Land use dynamics and their impact on hydrology and water quality of a river catchment: a comprehensive analysis and future scenario
Authors: Shiferaw Natnael , Habte Lulit , Waleed Mirza 
Published: January 2025

Abstract
Land use changes profoundly affect hydrological processes and water quality at various scales, necessitating a comprehensive understanding of sustainable water resource management. This paper investigates the implications of land use alterations in the Gap-Cheon watershed, analyzing data from 2012 and 2022 and predicting changes up to 2052 using the Future Land Use Simulation (FLUS) model. The study employs the Hydrological Simulation Program-FORTRAN (HSPF) model to assess water quantity and quality dynamics. Seven land use classes were identified, and their evolution was examined, revealing significant shifts in urban, agricultural, grassland, wetland, and forested areas. The model performance across observed data was evaluated using coefficient of determination (R
2), percent bias (PBAIS), and mean absolute error (MAE). Results show the dynamic nature of land use changes, highlighting shifts in urbanization, agriculture, and forested areas. Notably, the study explores the consequences of these changes on water quantity and quality, scrutinizing surface runoff, evapotranspiration, stream flow, and nutrient loads. Urban green spaces emerge as key mitigators, regulating runoff and enhancing water absorption. Forests (vegetation) also play a crucial role in maintaining water balance, while wetlands act as natural filters for flood mitigation and water quality improvement. The findings underscore the importance of informed land use planning, recognizing urban green spaces, forests, and wetlands as integral components for sustainable watershed management. As society navigates environmental challenges, this research contributes to a deeper understanding of the complex interactions between human activities and the natural environment emphasizing the need for nature-based solutions in land use planning for resilient and balanced ecosystems.
T Cell Dysregulation in Rheumatoid Arthritis: from Genetic Susceptibility to Established Disease
Authors: Chin Athena , Small Annabelle , Wong Soon Wei , Wechalekar Mihir D. 
Published: January 2025

Abstract

Purpose of Review
Rheumatoid arthritis (RA) is a complex autoimmune disease characterized by chronic inflammation of the synovial tissue, where T cells play a central role in pathogenesis. Recent research has identified T peripheral helper (Tph) cells as critical mediators of local B cell activation in inflamed tissues. This review synthesizes the latest advancements in our understanding the of the role of T cells in RA, from initiation to established disease.

Recent Findings
We explore recent advances regarding the genetic and epigenetic factors that predispose individuals to RA, the mechanisms of T cell activation and differentiation, and the interactions between T cells and other immune and stromal cells within the synovial microenvironment. The emergence of Tph cells as key drivers of RA pathobiology is highlighted, along with their potential as therapeutic targets. We also discuss the heterogeneity of T cell responses and their interplay with synovial cells, while addressing critical research gaps such as the drivers of T cell recruitment and the plasticity of synovial phenotypes.

Summary
A deeper understanding of T cell dynamics in RA will provide valuable insights for developing targeted therapies to modulate T cell-mediated inflammation and improve patient outcomes.
Multiple Lurias. Reconstructing Alexander Romanovich’s Life-Writing
Authors: Kölbl Carlos , Métraux Alexandre 
Published: January 2025

Abstract
While widely considered Alexander Luria’s (1902–1977) autobiography, The Making of Mind. A Personal Account of Soviet Psychology, published posthumously in 1979, is not a true autobiography but rather an autobiography with heterobiographic elements. However, the largely overlooked Spanish book, Mirando hacia atrás. La vida de un psicólogo soviético en retrospección, published in the same year, may be regarded as an authentic autobiography written by Luria. Based on the close reading of previously unknown archival sources, including Luria’s autobiographic typescripts, the central argument of the article shows that it is likely that the history of this key figure in modern Soviet neuropsychology is embodied in various instances of his life-writing. Our reconstruction of Luria’s life-writing is transnational in scope and multi-language based. It aims at drawing an overall account, which may later be followed by a series of contributions dealing with details of the history of Luria’s life-writing.
Automated Classification of Biscuit Quality Using YOLOv8 Models in Food Industry
Authors: Kılcı Oya , Koklu Murat 
Published: January 2025

Abstract
It is of great importance for food safety and consumer satisfaction that industrial food products are durable, hygienic, and flawless. Robust products protect the physical integrity of the product by preventing damage that may occur during the production and transportation processes, which meets the expectations of the consumer. Hygienic production conditions prevent foodborne diseases by minimizing the risk of microbial contamination and protect consumer health. Perfect products strengthen the brand image with their aesthetic and satisfactory features and increase consumer loyalty. In the study conducted in this context, the classification of defect and no defect conditions of biscuits in the food industry was examined using YOLOv8 models. A summary dataset consisting of 4990 biscuit images was created and the biscuits were initially divided into two categories: defect and no defect. Later, defect biscuits were classified into three subcategories: not complete, overcooked, and texture defect. As a result of experiments with YOLOv8 models, binary classification (defect, no defect), the highest accuracy rate was achieved in the YOLOv8-m, YOLOv8-l, and YOLOv8-x models with 96.78%, while the highest accuracy rate in the triple classification (not complete, overcooked, and texture defect) performance was achieved in the YOLOv8-m model with 96.99%.
Variation in Mangrove Geometric Traits Among Genera and Climate Zones
Authors: Twomey Alice J. , Lovelock Catherine E. 
Published: January 2025

Abstract
Coastal engineering models frequently represent vegetation as rigid cylinders to simulate the geometry of mangrove trees, including their roots, trunks, and canopies. However, mangrove geometry varies widely across different species, ages, geographic locations, and environmental conditions. Despite numerous studies on mangrove morphology, comprehensive data describing these geometric variations for accurate coastal protection models remain scarce. These geometric variations, particularly in root structures, play a crucial role in influencing functions such as flow modifications, sediment deposition, surface elevation changes, and wave reduction. Acknowledging the ecological complexities often neglected in engineering models, our meta-analysis examines the differences in mangrove geometry among genera and climatic zones. We systematically reviewed the literature to compile published values of mangrove geometry, detailing the height, diameter, and densities of roots, trunks, and canopies for 28 mangrove genera across four climatic regions (tropical, subtropical, semi-arid, and temperate). Our findings offer a valuable resource for coastal protection modellers aiming to improve the precision and reliability of mangrove-based defence models. Additionally, the dataset reveals gaps and biases in data collection for certain genera in specific climatic regions, suggesting areas for future research to address these deficiencies.
Identifying silver ore sources for the earliest coins of Athens
Authors: Davis Gillan , Blichert-Toft Janne , Gentelli Liesel , Gore Damian B. , Sheedy Kenneth A. , Albarède Francis 
Published: January 2025

Abstract
This study addresses longstanding questions concerning the ore sources used in the first series of coins of ancient Athens known as the Wappenmϋnzen (c.540-c.500 BCE) by combining comprehensive numismatic data on 22 coins (16 new and 6 legacy analyses) with lead isotope and surface elemental measurements (MC-ICP-MS and XRF). It finds usage of ores from Spain to Romania and Türkiye and frequent mixing. This upends current thinking based on a (mis)interpretation of historical sources which argues that the tyrant Peisistratos and his sons, who ruled Athens during the period, sourced most silver from the districts of Mt Pangaion and Strymon River in northern Greece and that silver did not flow from the western Mediterranean into their coinage. The data suggest that domestic ‘Lavrion’ mines of Athens did not contribute to the ore stock of the Wappenmϋnzen until the subsequent production of the ‘owl’ series when it was also used in some Wappenmϋnzen fractions and show that there is no correlation between coin types and ore sources. Elemental compositions nuance our understanding of the coins, but do not shed light on provenance. Together, these new findings force a reappraisal of numismatic and historical perceptions of the period of the Athenian tyranny in the lead up to democracy, not least because the multiple silver sources point to trading relationships with a greater variety of regions than previously contemplated.
SynDEc: A Synthetic Data Ecosystem
Authors: Karst Fabian Sven , Li Mahei Manhai , Leimeister Jan Marco 
Published: January 2025

Abstract
Given the critical role of data availability for growth and innovation in financial services, especially small and mid-sized banks lack the data volumes required to fully leverage AI advancements for enhancing fraud detection, operational efficiency, and risk management. With existing solutions facing challenges in scalability, inconsistent standards, and complex privacy regulations, we introduce a synthetic data sharing ecosystem (SynDEc) using generative AI. Employing design science research in collaboration with two banks, among them UnionBank of the Philippines, we developed and validated a synthetic data sharing ecosystem for financial institutions. The derived design principles highlight synthetic data setup, training configurations, and incentivization. Furthermore, our findings show that smaller banks benefit most from SynDEcs and our solution is viable even with limited participation. Thus, we advance data ecosystem design knowledge, show its viability for financial services, and offer practical guidance for privacy-resilient synthetic data sharing, laying groundwork for future applications of SynDEcs.
Adaptive model based on ESN for anomaly detection in industrial systems
Authors: Rodríguez-Ossorio José Ramón , Morán Antonio , Fuertes Juan J. , Prada Miguel A. , Díaz Ignacio , Domínguez Manuel 
Published: January 2025

Abstract
Modeling real industrial systems is a challenging task due to the complex nature of process data. Data-driven models commonly employ machine learning algorithms, but they often lack the ability to adapt to changes in the system over time. This paper proposes a method that uses Echo State Networks (ESN), a simplified version of Recurrent Neural Networks (RNN), to model an industrial plant. The ESN model incorporates online adaptation to system changes and enables the visualisation of deviations or errors in the operation of the plant. This adaptive model acknowledges acceptable changes within the original system while identifying potential problems or errors. The advantage of this approach is that the same model can be applied to other systems with the same design, eliminating the need for algorithm retraining. Firstly, its successful offline application in visualising anomalies applied to the reference plant is assessed. Secondly, the model is tested for online adaptation to changes in another plant with an identical design but slight differences, while still observing the generated faults. Residual colour maps are used for the visualisation of anomalies.
Glycerol Immobilises Anaerobic Digestate Supplied Nitrogen
Authors: van Midden Christina , Shaw Liz , Harris Jim , Sizmur Tom , Morgan Hayden , Pawlett Mark 
Published: January 2025

Abstract
Anaerobic digestate, a nutrient rich by-product of the biogas industry, is frequently applied to agricultural land as a fertiliser. However, nitrogen losses from its application negatively impact air and water quality. Therefore, methods are needed to reduce these losses. The aim of this study was to test the efficacy of applying digestate with glycerol, an organic carbon rich by-product of the biodiesel industry, on microbial nitrogen immobilisation and the soil microbial community. Soil was incubated with digestate, applied at a rate equivalent to 250 kg-N ha-1, in a laboratory experiment over 50 days with glycerol additions at either 0, 12, 24 or 36 kg-C m3 of digestate. The addition of glycerol resulted in significantly higher microbial biomass carbon and increased the relative abundance of Gram-negative bacteria. The 24 and 36 kg-C m3 doses of glycerol resulted in similarly greater and longer lasting effect on microbial biomass carbon, indicating that beyond 24 kg-C m3 digestate that nitrogen (or other essential nutrients) became the limiting factor for microbial growth instead of carbon. Soil available nitrogen decreased throughout the study and remained at lower concentrations in glycerol treatments than the digestate only treatment by the end of the study. These results demonstrate that glycerol has the potential to reduce nitrogen losses from digestate application by immobilising nitrogen in the microbial biomass. Therefore, the co-application of digestate and glycerol to soil is a potential mechanism for the biogas and biofuel industries to valorise their respective by-products. Further research is needed to verify that this method is viable under field conditions.
Assessment of silica sand behavior around rotating square rod in cylindrical container via ultrasonic velocity profiling
Authors: Shoji Naruki , Oishi Yoshihiko , Osanai Shou , Kusumoto Kenta , Kikura Hiroshige , Kawai Hideki 
Published: January 2025

Abstract
This study aims to establish a test method for simultaneously evaluating material erosion and corrosion, and rotating-material wear tests were conducted using a slurry pot. The erosion and corrosion scars of specimens were assessed from photographic images, and wear condition diagrams organized by dimensionless particle size (d

p
/d) and Reynolds number (Re) were constructed. Furthermore, the flow structure and particle behaviour around each specimen were visualized via ultrasonic velocity profiling to confirm the mechanism of the ratio change between erosion and corrosion. Results showed that a vortex flow formed around the specimen near the bottom of the container, which caused erosion. The outward flow height due to the vortex varied at Reynolds numbers of 5 × 103 ≤ Re ≤ 1 × 104 and sand particle concentrations of 2.24 ≤ C

v
≤ 40 vol%. It decreased with increasing Re and increased with C

v
, which was consistent with the trend in the surface wear images. The proposed wear condition diagram was supported by flow visualization, which showed that erosion- and corrosion-dominated regions could be distinguished from the amount of remaining rust.

Graphical abstract
Magnetic nanocomposites: innovative adsorbents for antibiotics removal from aqueous environments–a narrative review
Authors: Abolghasemi Sahar , Nasiri Alireza , Hashemi Majid , Rajabi Saeed , Rahimi Fatemeh 
Published: January 2025

Abstract
Pharmaceutical pollutants represent a significant class of industrial contaminants known for their persistence and adverse effects on the environment, posing a substantial concern for global ecosystems. Addressing the removal of these pollutants has become imperative in contemporary environmental management strategies. Among the various remediation techniques, the adsorption process stands out as an effective method for treating diverse wastewater streams. In this context, the utilization of adsorbents plays a pivotal role in facilitating the removal of pharmaceutical contaminants from aquatic environments. This review aims to explore the efficacy of magnetic nanocomposites in removing pharmaceutical pollutants from water matrices through the adsorption process. The findings underscore the successful application of magnetic nanocomposites endowed with enhanced properties in eliminating pharmaceutical contaminants from aqueous settings. Notably, advancements in the physical and chemical characteristics of these nanocomposites have been shown to significantly augment their removal efficiency during the adsorption process. Key attributes contributing to the effectiveness of magnetic nanocomposites include their tailored functional groups, large specific surface area, robust magnetic strength, and thermal stability. These features endow magnetic nanocomposites with several advantages, including high adsorption efficiency, reduced adsorbent dosage requirements, ease of accessibility and reusability, shortened process durations, and rapid adsorption kinetics. The utilization of magnetic nanocomposites in the adsorption process emerges as a promising approach for water purification due to its notable advantages. This technique demonstrates exceptional efficiency in removing pharmaceutical contaminants, offering swift and effective purification within short timeframes. Importantly, the implementation of magnetic nanocomposites in water treatment processes carries minimal environmental impact, aligning with sustainability goals. In conclusion, the utilization of magnetic nanocomposites holds significant promise as a viable strategy for addressing pharmaceutical pollutant contamination in water environments. Continued research efforts aimed at refining the synthesis and application of these nanomaterials are essential for advancing their efficacy and ensuring sustainable water management practices in the face of growing environmental challenges.
Statistical models versus machine learning approach for competing risks in proctological surgery
Authors: Romano Lucia , Manno Andrea , Rossi Fabrizio , Masedu Francesco , Attanasio Margherita , Vistoli Fabio , Giuliani Antonio 
Published: January 2025

Abstract
Clinical risk prediction models are ubiquitous in many surgical domains. The traditional approach to develop these models involves the use of regression analysis. Machine learning algorithms are gaining in popularity as an alternative approach for prediction and classification problems. They can detect non-linear relationships between independent and dependent variables and incorporate many of them. In our work, we aimed to investigate the potential role of machine learning versus classical logistic regression for the preoperative risk assessment in proctological surgery. We used clinical data from a nationwide audit: the database consisted of 1510 patients affected by Goligher’s grade III hemorrhoidal disease who underwent elective surgery. We collected anthropometric, clinical, and surgical data and we considered ten predictors to evaluate model-predictive performance. The clinical outcome was the complication rate evaluated at 30-day follow-up. Logistic regression and three machine learning techniques (Decision Tree, Support Vector Machine, Extreme Gradient Boosting) were compared in terms of area under the curve, balanced accuracy, sensitivity, and specificity. In our setting, machine learning and logistic regression models reached an equivalent predictive performance. Regarding the relative importance of the input features, all models agreed in identifying the most important factor. Combining and comparing statistical analysis and machine learning approaches in clinical field should be a common ambition, focused on improving and expanding interdisciplinary cooperation.