bluuebunny/crossref_metadata_2025_split huggingface dataset:
Scientific evidence and specific context: leveraging large language models for health fact-checking
Authors: Ni Zhenni , Qian Yuxing , Chen Shuaipu , Jaulent Marie-Christine , Bousquet Cedric 
Published: September 2024

PurposeThis study aims to evaluate the performance of LLMs with various prompt engineering strategies in the context of health fact-checking.Design/methodology/approachInspired by Dual Process Theory, we introduce two kinds of prompts: Conclusion-first (System 1) and Explanation-first (System 2), and their respective retrieval-augmented variations. We evaluate the performance of these prompts across accuracy, argument elements, common errors and cost-effectiveness. Our study, conducted on two public health fact-checking datasets, categorized 10,212 claims as knowledge, anecdotes and news. To further analyze the reasoning process of LLM, we delve into the argument elements of health fact-checking generated by different prompts, revealing their tendencies in using evidence and contextual qualifiers. We conducted content analysis to identify and compare the common errors across various prompts.FindingsResults indicate that the Conclusion-first prompt performs well in knowledge (89.70%,66.09%), anecdote (79.49%,79.99%) and news (85.61%,85.95%) claims even without retrieval augmentation, proving to be cost-effective. In contrast, the Explanation-first prompt often classifies claims as unknown. However, it significantly boosts accuracy for news claims (87.53%,88.60%) and anecdote claims (87.28%,90.62%) with retrieval augmentation. The Explanation-first prompt is more focused on context specificity and user intent understanding during health fact-checking, showing high potential with retrieval augmentation. Additionally, retrieval-augmented LLMs concentrate more on evidence and context, highlighting the importance of the relevance and safety of retrieved content.Originality/valueThis study offers insights into how a balanced integration could enhance the overall performance of LLMs in critical applications, paving the way for future research on optimizing LLMs for complex cognitive tasks.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2024-0111
Numerical simulation and analytical modelling of temperature and morphology of melt pool in electron beam powder bed fusion of copper
Authors: Sharabian Elmira , Khorasani Mahyar , Gulizia Stefan , Ghasemi Amir Hossein , MacDonald Eric , Downing David , Rolfe Bernard , Brandt Milan , Leary Martin 
Published: September 2024

Purpose
This study aims to comprehensively investigate the electron beam powder bed fusion (EB-PBF) process for copper, offering validated estimations of melt pool temperature and morphology through numerical and analytical approaches. This work also assesses how process parameters influence the temperature fluctuations and the morphological changes of the melt pool.

Design/methodology/approach
Two distinct methods, an analytical model and a numerical simulation, were used to assess temperature profiles, melt pool morphology and associated heat transfer mechanisms, including conduction and keyhole mode. The analytical model considers conduction as the dominant heat transfer mechanism; the numerical model also includes convection and radiation, incorporating specific parameters such as beam power, scan speed, thermophysical material properties and powder interactions.

Findings
Both the analytical model and numerical simulations are highly correlated. Results indicated that the analytical model, emphasising material conduction, exhibited exceptional precision, although at substantially reduced cost. Statistical analysis of numerical outcomes underscored the substantial impact of beam power and scan speed on melt pool morphology and temperature in EB-PBF of copper.

Originality/value
This numerical simulation of copper in EB-PBF is the first high-fidelity model to consider the interaction between powder and substrate comprehensively. It accurately captures material properties, powder size distribution, thermal dynamics (including heat transfer between powder and substrate), phase changes and fluid dynamics. The model also integrates advanced computational methods such as computational fluid dynamics and discrete element method. The proposed model and simulation offer a valuable predictive tool for melt pool temperature, heat transfer processes and morphology. These insights are critical for ensuring the bonding quality of subsequent layers and, consequently, influencing the overall quality of the printed parts.
Enhanced strength, toughness and reliability in crab exoskeleton–inspired 3D-printed porous thermoplastics
Authors: Keleş Özgür , Anderson Eric H. , Tan Timothy L. , Wu Cheng-Lun , Karakoc Alp 
Published: September 2024

Purpose
Fused deposition modeling enables multiscale structure control. However, most of this structural space is unexplored. Specifically, the impact of biomimetic porous structures on the mechanical behavior and reliability of common thermoplastics are unclear. In this work, porous structures inspired by the multifunctional crab exoskeleton were 3D-printed with different raster orientations, including fully rotating rasters similar to Bouligand structures found in biological materials. Tensile tests and simulations were performed to observe the stochastic behavior of fracture properties and to reveal the underlying origins of mechanical reliability in biomimetic porous systems.

Design/methodology/approach
Tensile tests were performed on 3D-printed porous structures with four different rasters. These rasters were biomimetic Bouligand, semi-Bouligand, 00 raster and 45°/−45° raster. In addition, two different sets were manufactured to observe the impact of contours on the mechanical behavior. A total of 137 tensile tests were performed. A total of 88 finite element simulations were executed using Abaqus built-in Hashin damage initiation criterion and energy-based damage evolution law. Weibull analyses were performed to quantify the stochastic properties.

Findings
Biomimetic Bouligand structure is effective in increasing fracture strength. Average fracture strength of the Bouligand structure was 33% higher compared to the default 45°/−45° and 10% higher compared to 00 rasters. Variations in strength were lower in Bouligand structure compared to the default 45°/−45° raster. However, 00 raster had the highest Weibull modulus m = 54 compared to Bouligand m = 25 and 45°/−45° m = 17. Simulations showed that Bouligand structure is effective in increasing the mechanical reliability through local damage accumulation around the holes. The simulated Weibull modulus of the Bouligand structure was 40 compared to the moduli of other rasters that ranged from 18 to 25.

Practical implications
The mechanical reliability of porous Bouligand structures is higher compared to other rasters, which makes the biomimetic structure a better choice for industrial applications. Contours decrease the strength and strain at failure for 3D-printed porous structures. Bouligand structures with rotating raster orientations increased strength and strain at failure when contours are present in the porous structure.

Originality/value
To the best of the authors’ knowledge, this is the first study showing the effects of biomimetic raster orientations on the mechanical behavior and the effects of contours on the tensile fracture properties of 3D-printed porous acrylonitrile butadiene styrene using tensile tests and fracture simulations. This is the first study applying composite fracture model to anisotropic porous 3D-printed polymers.
Herding behaviour in the cryptocurrency market: the role of uncertainty and return of classical financial markets
Authors: Ansari Hojjat , Peymany Moslem 
Published: September 2024

Purpose
The purpose of the study is to examine the impact of uncertainty and return of classical financial assets on herding behaviour in the cryptocurrency market. Also, herding in this market and the impact of the COVID-19 pandemic have been investigated.

Design/methodology/approach
The study uses quantile regression to estimate the models. Daily data from ten major cryptocurrencies, the CCI30 index and three volatility indices (VIX, EVZ and GVZ), spot gold price, the MSCI and the US dollar indices from January 2018 to December 2023 have been used.

Findings
The findings show evidence of anti-herding during periods of simultaneous high volatility in stock and currency markets, as well as in the gold and currency markets. However, the results support herding in the whole sample period, which reduces when including the COVID-19 pandemic effect. In addition, the study does not support the relationship between returns of traditional financial assets and herding in the cryptocurrency market.

Practical implications
The result of the study can be useful for investors, particularly the managers of the novel class of ETFs, to make their investment decisions more consciously, regarding uncertainty in other financial markets. Also, the findings provide some insight to regulators regarding the herding behaviour in the cryptocurrency market and its influences on the financial system’s stability.

Originality/value
To the best of the authors’ knowledge, for the first time, this study examines the impact of concurrent high uncertainty conditions in classical financial markets on herding behaviour in the cryptocurrency market.
RFID-transformer recognition system (RTRS): enhancing privacy in facial recognition with transformer models
Authors: Wang Zeyuan , Xu He , Zhang Manman , Cai Zhaorui , Chen Yongyuan 
Published: September 2024

Purpose
This paper aims to present a novel approach to facial recognition that enhances privacy by using radio frequency identification (RFID) technology combined with transformer models, eliminating the need for visual data and thus reducing privacy risks associated with traditional image-based systems.

Design/methodology/approach
The proposed RFID-transformer recognition system (RTRS) uses RFID technology to capture signal features such as phase and received signal strength indicator, which are then processed by a transformer model. The model is specifically designed to handle structured RFID data, capturing subtle patterns and dependencies to achieve accurate biometric recognition. The system’s performance was validated through comprehensive experiments involving different environmental conditions and user scenarios.

Findings
The experimental results demonstrate that the RTRS system achieves a recognition accuracy of 98.91%, maintaining robust performance across various challenging conditions, including low-light environments and changes in face orientation. In addition, the system provides a high level of privacy preservation by avoiding the collection and storage of visual data.

Originality/value
To the best of the authors’ knowledge, this work introduces the first RFID-based facial recognition system that fully leverages transformer models, offering a privacy-preserving alternative to traditional image-based methods. The system’s ability to perform accurately in diverse scenarios while ensuring user privacy makes it a significant advancement in biometric technology.
Cross‐equalization for time‐lapse sparker seismic data
Authors: Lee Soojin , Won Jongpil , Jun Hyunggu 
Published: September 2024

AbstractTime‐lapse seismic data processing is an important technique for observing subsurface changes over time. The conventional time‐lapse seismic exploration has been conducted using a large‐scale exploration system. However, for efficient monitoring of shallow subsurface, time‐lapse monitoring based on the small‐scale exploration system is required. Small‐scale exploration system using a sparker source offers high vertical resolution and cost efficiency, but it faces challenges, such as inconsistent waveforms of sparker sources, inaccurate positioning information and a low signal‐to‐noise ratio. Therefore, this study proposes a data processing workflow to preserve the signal and enhance the repeatability of small‐scale time‐lapse seismic data acquired using a sparker source. The proposed workflow has three stages: pre‐stack, post‐stack and machine learning–based data processing. Conventional seismic data processing methods were applied to enhance the quality of the sparker seismic data during the pre‐stack data processing stage. In the post‐stack processing stage, the positions and energy correction were performed, and the machine learning–based data processing stage attenuated random noise and applied a matched filter. The data processing was performed using only the seismic signals recorded near the seafloor, and the results confirmed the improvement in the repeatability of the entire seismic profile, including that of the target area. According to the repeatability quantification results, the predictability increased and the normalized root mean square decreased during data processing, indicating improved repeatability. In particular, the repeatability of the data was greatly improved through vertical correction, energy correction and matched filtering approaches. The processing results demonstrate that the data processing method proposed in this study can effectively enhance the repeatability of high‐resolution time‐lapse seismic data. Consequently, this approach could contribute to a more accurate understanding of temporal changes in subsurface structure and material properties.
Deep‐learning‐based Q model building for high‐resolution imaging
Authors: Ju Xin , Xu Jincheng , Zhang Jianfeng 
Published: September 2024

AbstractBuilding a macro Q model for deabsorption migration using surface reflection data is challenging owing to interferences of the reflections resulting from stacked thin layers. The effective Q approach gives an alternative way to overcome this difficulty. However, manual processing is involved for effective Q estimation. This restricts the use of denser grids in building an inhomogeneous Q model. We therefore incorporate deep learning into the effective Q approach, thus yielding a deep learning‐based Q model building scheme. The resulting scheme improves the manual effective Q estimation by simultaneously accounting for the imaging resolution and induced noises using two networks. Moreover, most manual processing is reduced in spite of denser grids in building a 3D Q model. One of the networks used is a 1D convolutional neural network that determines the optimal upper cut‐off frequency for a selected Q with an input of multi‐channel amplitude spectra, and another is a residual neural network that determines the optimal Q for a series of Q values with an input of multi‐channel imaging sections inside the selected small window filtered under the corresponding upper cut‐off frequencies. As a result, a Q model that improves the imaging resolution in the absence of amplification of noises is gained. Transfer learning is used, thus reducing the training cost when applied to different geological targets. We test our scheme using 3D field data. Higher resolution images without induced noises are obtained by a deabsorption migration using the Q model built and compared to those obtained by the migration without absorption compensation.
Establishment of an immortalized sheep mammary epithelial cell line for studying milk fat and protein synthesis
Authors: Liu Shujuan , Wang Zhanhang , Cao Heran , Tong Jiashun , Cui Jiuzeng , Li Long , Bu Qiqi , Li Yan , Jin Tianqi , An Xiaopeng , Zhang Lei , Song Yuxuan 
Published: September 2024

AbstractThe mammary gland, crucial for milk production in mammals, presents challenges for in vitro study due to its complex structure and limited cell lifespan. We addressed this by introducing the SV40 large T antigen into primary mammary epithelial cells (MECs) from sheep, creating an immortalized T‐tag MEC line. This line, stable for over 50 passages, maintained typical epithelial cell morphology during long‐term culture. Through transcriptome sequencing and validation, we discovered 3833 differentially expressed genes between MECs and T‐tag MEC line, encompassing key biological processes and signaling pathways like cell cycle, p53, and cancer. The cell line, expressing MEC markers (KRT8, KRT18, proliferating cell nuclear antigen, SV40, CSN2, and acetyl‐CoA carboxylase alpha), proved capable of synthesizing milk fat and protein. Despite its infinite proliferation potential, the T‐tag MEC line showed no tumor formation in mice or cell migration in vitro, indicating stability. This development offers a valuable resource for studying MECs in dairy sheep, facilitating the advancement of long‐term culture systems and in vitro lactation bioreactors.
Estimation of the maximum potential cost saving from reducing serious adverse events in hospitalized patients
Authors: Larsen Arendse Tange , Sopina Liza , Aasvang Eske Kvanner , Meyhoff Christian Sylvest , Kristensen Søren Rud , Kjellberg Jakob 
Published: September 2024

AbstractPurposeThe increasing use of advanced medical technologies to detect adverse events, for instance, artificial intelligence‐assisted technologies, has shown promise in improving various aspects within health care but may also come with substantial expenses. Therefore, understanding the potential economic benefits can guide decision‐making processes regarding implementation. We aimed to estimate the potential cost savings associated with reducing length of stay and avoiding readmissions within the framework of an artificial intelligence‐assisted vital signs monitoring system.MethodsWe used data from Danish national registries and coarsened exact matching to estimate the difference in length of stay and probability of readmission among adult in‐hospital patients exposed to and not exposed to serious adverse events. We used these estimates to calculate the maximum potential savings that could be achieved by early detection of adverse events to reduce length of stay and avoid readmissions.ResultsPatients exposed to serious adverse events during admission had 2.4 (95% CI: 2.4–2.5) additional hospital bed days and had 14% (95% CI 11%–17%) higher odds of readmissions compared with patients not exposed to such events. A base case scenario yielded maximum potential savings if one patient avoided a serious adverse event of EUR 2040 due to reduced length of stay and EUR 43 due to avoidance of readmissions caused by serious adverse events.ConclusionReductions in serious adverse events are associated with decreased healthcare costs due to reduced length of stay and avoided readmissions. Artificial intelligence‐assisted vital signs monitoring systems are one potential approach to reduce serious adverse events, however, the ability of this technology to reduce adverse events remains unclear. Comprehensive prospective analyses of such systems including the intervention and implementation costs are necessary to understand their full economic impact.
Temperature‐dependent lifespan extension is achieved in miR‐80‐deleted Caenorhabditis elegans by NLP‐45 to modulate endoplasmic reticulum unfolded protein responses
Authors: Zhao Chunlin , Luo Jintao , Zhang Yuqiang , Yu Yong 
Published: September 2024

AbstractMicroRNA plays a crucial role in post‐transcriptional gene regulation and has recently emerged as a factor linked to aging, but the underlying regulatory mechanisms remain incompletely understood. In this study, we observed lifespan‐extending effects in miR‐80‐deficient Caenorhabditis elegans at 20°C but not 25°C. At 20°C, miR‐80 deletion leads to NLP‐45 upregulation, which positively correlates to increased abu transcripts and extended lifespan. Supportively, we identified miR‐80 binding regions in the 5′ and 3’ UTR of nlp‐45. As the temperature rises to 25°C, wildtype increases miR‐80 levels, but removal of miR‐80 is accompanied by decreased nlp‐45 expression, suggesting intervention from other temperature‐sensitive mechanisms. These findings support the concept that microRNAs and neuropeptide‐like proteins can form molecular regulatory networks involving downstream molecules to regulate lifespan, and such regulatory effects vary on environmental conditions. This study unveils the role of an axis of miR‐80/NLP‐45/UPRER components in regulating longevity, offering new insights on strategies of aging attenuation and health span prolongation.
How type‐2 dendritic cells induce Th2 differentiation: Instruction, repression, or fostering T cell‐T cell communication?
Authors: Ronchese Franca , Webb Greta R. , Ochiai Sotaro , Lamiable Olivier , Brewerton Maia 
Published: September 2024

AbstractAllergic disease is caused by the activation of allergen‐specific CD4+ type‐2 T follicular helper cells (Tfh2) and T helper 2 (Th2) effector cells that secrete the cytokines IL‐4, IL‐5, IL‐9, and IL‐13 upon allergen encounter, thereby inducing IgE production by B cells and tissue inflammation. While it is accepted that the priming and differentiation of naïve CD4+ T cells into Th2 requires allergen presentation by type 2 dendritic cells (DC2s), the underlying signals remain unidentified. In this review we focus on the interaction between allergen‐presenting DC2s and naïve CD4+ T cells in lymph node (LN), and the potential mechanisms by which DC2s might instruct Th2 differentiation. We outline recent advances in characterizing DC2 development and heterogeneity. We review mechanisms of allergen sensing and current proposed mechanisms of Th2 differentiation, with specific consideration of the role of DC2s and how they might contribute to each mechanism. Finally, we assess recent publications reporting a detailed analysis of DC‐T cell interactions in LNs and how they support Th2 differentiation. Together, these studies are starting to shape our understanding of this key initial step of the allergic immune response.
Israel's War on Gaza in a Global Frame
Authors: El‐Shewy Mohamed , Griffiths Mark , Jones Craig 
Published: September 2024

AbstractThe objective of this article is to set out lines of international complicity in Israel's war on Gaza towards establishing not merely a refreshed agenda for research but also strategic sites of accountability and intervention. The article surveys Israeli military activities in Gaza since the 7 October 2023 Hamas attacks, drawing focus on three key points of international military collaboration: the F‐16 fighter jet; the GBU type bomb; and the weapons manufacturer, Elbit Systems. We then turn to multiple other geographies of exchange that are visible through a global frame, including military aid, ideological support, and the deployment of military personnel from overseas. The article thus substantiates an argument that Israeli military violence in Gaza depends on a global network of supply, demand, and complicity whose extraneous relationship with the state indicates politically urgent sites of critical inquiry and intervention.