bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unveiling Axial Load Transfer Mechanism in Fully Encapsulated Rock Bolts
Authors: Nourizadeh Hadi , Mirzaghorbanali Ali , McDougall Kevin , Mitri Hani , Craig Peter , Rastegarmanesh Ashakan , Aziz Naj 
Published: January 2025

AbstractDetermining the axial load transfer mechanism of rock bolts under various conditions is paramount for ensuring efficient reinforcement in rock structures, advancing our understanding of rock support and ability to design robust engineering solutions. This paper presents the results of an experimental study aimed at investigating the factors affecting axial load transfer mechanisms in fully encapsulated rock bolts including embedment length, mechanical characteristics of bonding materials, and host rock conditions. The results show that increasing the embedment length improves the pullout capacity, but only up to a critical length, beyond which the ultimate strength and bond stress distribution remain constant. Amongst the mechanical characteristics, shear modulus of the bonding materials was found the most significant factor influencing axial load transfer and the bond stress distribution. Compressive strength of bonding materials, which is commonly used to assess performance, should not be the only factor considered, as shear properties were found more representative. The expansion characteristics of the bonding agents were also found to be effective in improving pullout performance. The study also provides a detailed explanation of how the mechanical properties of host rocks affect the pullout capacity of bolts. Specifically, it states that greater stiffness result in higher pullout strength.
Diagnostic value of glypican-1; a new marker differentiating pulmonary squamous cell carcinoma from adenocarcinoma: immunohistochemical study on Egyptian series
Authors: Adel Iman , Mahmoud Heba A , Khater Amira Ismail , Hafez Fatma S 
Published: January 2025

AbstractLung cancer is one of the major causes of cancer morbidity and mortality. Subtyping of non-small cell lung cancer is necessary owing to different treatment options. This study is to evaluate the value of immunohistochemical expression of glypican-1 in the diagnosis of lung squamous cell carcinoma (SCC). This retrospective study included a total of 68 cases, of which 36 were diagnosed as SCC and 32 as adenocarcinoma (ADC). Furthermore, glypican-1 expression was compared with the expressions of p63, thyroid transcription factor-1 (TTF-1), and napsin A. All cases of SCC except one showed positive immunostaining to glypican-1; 35/36 (97.2%) cases, and predominantly scored 3 + . While only 5 cases of ADC showed positive immunostaining to glypican-1, having a score of 1 + or 2 + . The difference between glypican-1 expression of the two tumor types was highly significant (p value < 0.001). The sensitivity, specificity, and overall accuracy of glypican-1 expression for differentiating lung SCC from ADC were 97.2%, 84.4%, and 91.2%, respectively. The sensitivity of glypican-1 is more than p63 in the diagnosis of lung SCC. Glypican-1 can be added as a new diagnostic marker to help in the accurate discrimination between poorly differentiated lung SCC and solid predominant adenocarcinoma cases.
Environmental drivers of seasonal and hourly fluxes of methane and carbon dioxide across a lowland stream network with mixed catchment
Authors: Olsen Benedichte Wiemann , Kragh Theis , Sø Jonas Stage , Polauke Emma , Sand-Jensen Kaj 
Published: January 2025

AbstractStreams serve as open windows for carbon emissions to the atmosphere due to the frequent supersaturation of carbon dioxide (CO2) and methane (CH4) that originates from large carbon input during runoff and associated in-stream processes. Due to the high spatial and temporal variability of the underlying environmental drivers (e.g., concentrations of dissolved CO2 and CH4, turbulence, and temperature), it has remained difficult to address the importance and upscale the emissions to annual whole-system and regional values. In this study, we measured concentrations and calculated emissions of CO2 and CH4 at diel and seasonal scales at 15 stations in a 1.4 km2 stream network that drains a mixed lowland catchment consisting of agriculture (210 km2), forest (56 km2), and lakes, ponds, and wetlands (22 km2) in the upper River Odense, Denmark to evaluate environmental drivers behind the spatiotemporal variability. We used automatically venting floating chambers to calculate hourly diffusive fluxes of CO2 and CH4 and CH4 ebullition. We found: 1) highly supersaturated CO2 and CH4 concentrations (median: 175 and 0.33 µmol L−1, respectively) and high diffusive fluxes of CO2 and CH4 (median: 3,608 and 19 µmol m−2 h−1, respectively); 2) lower daytime than nighttime diffusive emissions of CO2 in spring and summer, but no diel variability of CH4; 3) higher concentrations and emissions of CH4 at higher temperatures; and 4) higher emissions of CH4 at stations located in sub-catchments with higher agricultural coverage. Ebullition of CH4 peaked at two stations with soft organic sediment and low summer flow, and their ebullition alone constituted 30% of total annual CH4 emissions from the stream network. Mean annual CO2 emissions from the hydrological network (37.15 mol CO2 m−2 y−1) exceeded CH4 emissions 100-fold (0.43 mol CH4 m−2 y−1), and their combined warming potential was 1.83 kg CO2e m−2 y−1. Overall, agricultural sub-catchments had higher CH4 emissions from streams, while lakes and ponds likely reduced downstream CH4 and CO2 emissions. Our findings demonstrate that CO2 and CH4 emissions data at high spatial and temporal resolution are essential to frame the heterogeneous stream conditions, understand gas emissions regulation, and upscale to annual values for hydrological networks and larger regions.
Inferences, mastery, and objectivity: examining student understanding of mathematical concepts with inferentialism
Authors: Drimalla James 
Published: January 2025

AbstractInferentialism has emerged as a valuable theoretical resource in mathematics education. As a theory of meaning about the use and content of concepts, it offers a fresh perspective on traditional epistemological and linguistic questions in the field. Despite its emergence, important inferentialist ideas still need to be operationalized. In this paper, I operationalize multiple inferentialist concepts to analyze prospective secondary mathematics teachers’ learning about graphs and quantitative and covariational reasoning in a mathematics content course. I use Robert Brandom’s ideas about upstream, downstream, and incompatible inferences to illustrate the inferentialist mastering metaphor for learning and Brandom’s ideas about objectivity. My analysis shows (a) inferentialism’s capacity to address epistemological issues like objectivity and conceptual understanding, (b) the importance of attending to different types of inferences to assess learning from an inferentialist perspective, and (c) how the development of mastery is associated with virtues. I conclude by identifying the study’s potential limitations and trajectories for future research.
Large components in the subcritical Norros-Reittu model
Authors: Lienau Matthias , Schulte Matthias 
Published: January 2025

AbstractThe Norros-Reittu model is a random (multi-)graph with n vertices and i.i.d. weights assigned to them. The number of edges between any two vertices follows an independent Poisson distribution whose parameter is increasing in the weights of the two vertices. Choosing a suitable weight distribution leads to a power-law behaviour of the degree distribution as observed in many real-world complex networks. We study this model in the subcritical regime, i.e. in the absence of a giant component. For each component, we count all its vertices to determine the component sizes and show convergence of the corresponding point process of (rescaled) component sizes to a Poisson process. More generally, one can also count only specific vertices per component, like leaves. From this one can deduce asymptotic results on the size of the largest component or the maximal number of leaves in a single component. The results also apply to the Chung-Lu model and the generalised random graph.
The Impact of Water-Based Fire Suppression Systems on Combustion Products
Authors: Jiang Lei , Zeinali Davood , Arsava Kemal Sarp , Snersrud Dag Olav 
Published: January 2025

AbstractThis study aims to experimentally investigate the combustion products of fires suppressed/extinguished by two water-based fire suppression systems, namely, sprinklers and water mist systems. A total of thirteen experiments were conducted with various suppression system configurations, i.e., with a sprinkler, a low-pressure (LP) water mist, and a high-pressure (HP) water mist, at operating pressures ranging from 2 to 60 bar and water flow rate ranging from 10 to 206 L/min. Each experiment was conducted twice, except for the baseline experiment with no suppression. The fuel was a high-density polyethylene (HDPE) pallet placed on two wood pallets. During all the stages of fire development, suppression with water, and post-suppression, the combustion products were sampled through Fourier-Transform Infrared Spectroscopy (FTIR) using a gas analyzer capable of operating in high humidity. The main combustion products identified were CO2, CO, and H2O. In addition, NOx (nitrogen oxides), CxHy (light-weight hydrocarbons), and HCN were present in relatively high concentrations. All the tested fire suppression systems were effective in reducing the fire size and cooling down the gases. However, when the fire could not be immediately extinguished, the NOx, CxHy, and HCN concentrations were higher than those in the baseline experiment. Moreover, it is observed that the HP water mist system was more effective than the sprinkler and low-pressure water mist systems in reducing the amount of combustion gases during suppression, resulting in lower Fractional Effective Doses (FED). It was observed that the combustion products returned to ambient conditions after five minutes of deactivation. The results from this study provide much-needed validation data for the effectiveness of water-based suppression systems, not only in reducing the fire size but also in reducing the production of acute toxic gases, which is important for considerations regarding evacuation, firefighting, and post-extinguishment conditions.
Impact of Interactive Science Workshops Participation on Primary School Children's Emotions and Attitudes Towards Science
Authors: Muñoz-Losa Aurora , Corbacho-Cuello Isaac 
Published: January 2025

AbstractThis study investigates the impact of interactive science workshops participation on primary school children's emotions and attitudes towards science. The sample consisted of 186 students aged 9 to 13 from five different schools. The interactive science workshops consisted of a series of practical, active, and hands-on workshops where primary school students could learn about different aspects of science. These workshops, designed and conducted by prospective teachers from the Primary Education degree program, were non-competitive and did not involve participants in competitive activities. Using pre- and post-event questionnaires, changes in emotions and science-related attitudes were assessed. Results indicated a significant increase in positive emotions such as joy and enthusiasm, alongside a reduction in negative emotions like boredom and anxiety. Specifically, the post-test results showed a higher interest in discussing science, greater appreciation for experiments, and an increased desire for science-related careers. These changes were statistically significant, accentuating the transformative potential of interactive, hands-on learning experiences. The findings highlight the importance of integrating such activities into primary education curricula to enhance students' engagement and promote positive perceptions of science.
Beyond Macroethics: Developing an Ethical Framework for Interview Data Collection and Analysis to Acknowledge Participant Rights
Authors: Hosseini Mona , Haukås Åsta 
Published: January 2025

AbstractParticipant rights and voices are protected through institutional ethical considerations in the social sciences and applied linguistics. Yet, several ethical concerns remain. In addition to adhering to institutional macroethics, researchers should develop microethics to address contextual issues within their research. The need for the development of microethics is particularly important in the qualitative research process where researchers engage in studies with participants rather than treating them merely as data sources. Drawing on key concepts in philosophical hermeneutics, the current study discusses an ethical framework for data collection and analysis that embraces participants’ rights and voices through reflexive questioning, reciprocal dialogues, unbiased listening, and rightful analysis of participants’ data with and from two multilingual families in Norway. Participatory diagramming was employed in a collaborative participatory approach to present the collected data and initial data analysis to the participants, allowing them to review and validate the data analysis conducted by the researchers. Using thematic analysis to analyze participants’ experiences and reflections, our findings indicate that the ethical framework of interview and analysis enhanced researchers’ understanding, empowered participants in the research process, facilitated member-checking, created beneficial collaboration opportunities for the participants, and facilitated reaffirmation of informed consent.
The compliance to FAIR principles of shared data in addiction research
Authors: Sixto-Costoya Andrea , Ferrer-Sapena Antonia , Aleixandre-Benavent Rafael , Peset Fernanda , Valderrama-Zurián Juan Carlos , Petrosyan Luiza 
Published: January 2025

AbstractThe aim of this study is to assess the scientific data sharing in the field of addictions by applying FAIR principles. These principles play an important role, as they guarantee a minimum of findability, accessibility, interoperability and reusability of the shared data. They are one of the main measures to improve the integrity and quality of research data. For this study, three automated tools were used: the Data Citation Index (DCI) to capture datasets on addictions; Bibliometricos, proprietary software for data retrieval; and the F-UJI tool for the FAIR evaluation of datasets. The datasets on the most common addiction topics, such as alcohol, cannabis, tobacco, cocaine, opioids and stimulants, were downloaded by the DCI (5967 DOIs) and parsed into a database for subsequent analysis. In terms of datasets characteristics, alcohol, tobacco and opioids were the most productive. After assessment by F-UJI, none of the addictions analyzed reached an average of 30% FAIR compliance since all of them were between 20% and 29%. When analyzing each principle, Findable was the best scored principle (in a range of 40%–59%), followed by Accessible, Interoperable and Reusable. The results of our study show, first, an increasing number of shared datasets over the years, especially from basic studies. In terms of quality, there are issues that remain to be resolved, especially in relation to interoperability and reusability principles. This emphasizes the important role of adequate data sharing procedures in ensuring that datasets are FAIR compliant and usable in addiction research.
Comparing Above and Underwater Visibility of Fluorescent Quinine-Based Liquid and Solid Tracers to Estimate Suspended Sediment Concentrations Under Low Luminosity Conditions
Authors: Zehsaz Soheil , Lima João L. M. P. de , Lima M. Isabel P. de , Almeida Thayná A. B. , Lima Rui L. Pedroso de , Montenegro Abelardo A. de A. 
Published: January 2025

Abstract
While exploring the use and visibility of fluorescent tracers for estimating flow velocities under varying water turbidity, this study introduces a technique for estimating suspended sediment concentration (SSC) in water. Laboratory and field experiments were conducted using a dual above/underwater optical camera monitoring system to measure and compare the visibility (brightness intensity) of fluorescent tracers applied into water under varying SSC loads. Based on the fluorescent properties of quinine under low luminosity conditions, a quinine solution, in both liquid and solid (ice cube) states, was applied to the water surface. Its visibility was recorded using the monitoring system, and brightness intensity was quantified using image processing techniques such as grayscale conversion, object segmentation, and pixel intensity extraction. Results indicate that the tracer brightness intensity decreases as SSC loads increase, with the underwater camera recording lower visibility than the above-water camera, particularly at higher SSC loads. Empirical regression equations were developed to describe the relationship between the tracer brightness intensity recorded by the underwater camera and the SSC loads. These findings suggest the ability of the presented monitoring system, using quinine solution in both liquid and solid states, along with an underwater camera, to estimate a relevant range of SSC loads under low luminosity conditions.
Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles
Authors: Zhang Jie , Chen Yi , Li Guijun , Sun Zelin , Meng Zhengong , Wong Wai-Yeung 
Published: January 2025

Abstract
L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely, and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit islands. Benefiting from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.
Advances in multimodal imaging for adrenal gland disorders: integrating CT, MRI, and nuclear medicine
Authors: Yokoyama Kota , Matsuki Mitsuru , Isozaki Takanori , Ito Kimiteru , Imokawa Tomoki , Ozawa Akane , Kimura Koichiro , Tsuchiya Junichi , Tateishi Ukihide 
Published: January 2025

AbstractAdrenal diseases pose significant diagnostic challenges due to the wide range of neoplastic and non-neoplastic pathologies. Radiologists have a crucial role in diagnosing and managing these conditions by, leveraging advanced imaging techniques. This review discusses the vital role of computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine in adrenal imaging, and focuses on morphological and functional evaluations. First, the anatomy and physiology of the adrenal glands are described, followed by a discussion on ectopic adrenocortical adenomas and how they develop. The concepts and imaging findings of congenital diseases, such as congenital adrenal hyperplasia (CAH), adrenal rest tumors, and adrenocortical nodular disease, considering recent updates to the WHO Classification of Tumours (5th ed.) terminology are highlighted. The diagnostic value of dynamic contrast-enhanced CT and chemical-shift MRI for identifying adrenocortical adenomas are emphasized, alongside the use of adrenocortical scintigraphy such as 131I-adosterol scintigraphy for diagnosing Cushing’s disease, Cushing’s syndrome (CS), subclinical CS, and ectopic adrenocorticotropic hormone-producing tumors. Systemic complications associated with CS, and the diagnosis and treatment of pheochromocytomas, paragangliomas (PPGLs), and neuroblastomas, will also be discussed focusing on 123I-metaiodobenzylguanidine (MIBG) imaging and 131I-MIBG therapy. Pitfalls in 123I-MIBG imaging and the increasing importance of diagnosing hereditary PPGLs due to increased genetic testing are also be discussed. Additionally, the broad differential diagnosis for adrenal masses—including malignancies like adrenal carcinoma, metastases, and malignant lymphoma, as well as benign conditions like myelolipoma and ganglioneuroma, and complications, such as adrenal hemorrhage, infarction, and infections—will be outlined. The goal of this review was to provide an overview of adrenal diseases that includes the most recent information for radiologists to stay updated on the latest imaging techniques and advancements that can ensure accurate diagnosis and effective management.
AI image analysis as the basis for risk-stratified screening
Authors: Strand Fredrik 
Published: January 2025

AbstractArtificial intelligence (AI) has emerged as a transformative tool in breast cancer screening, with two distinct applications: computer-aided cancer detection (CAD) and risk prediction. While AI CAD systems are slowly finding its way into clinical practice to assist radiologists or make independent reads, this review focuses on AI risk models, which aim to predict a patient’s likelihood of being diagnosed with breast cancer within a few years after negative screening. Unlike AI CAD systems, AI risk models are mainly explored in research settings without widespread clinical adoption. This review synthesizes advances in AI-driven risk prediction models, from traditional imaging biomarkers to cutting-edge deep learning methodologies and multimodal approaches. Contributions by leading researchers are explored with critical appraisal of their methods and findings. Ethical, practical, and clinical challenges in implementing AI models are also discussed, with an emphasis on real-world applications. This review concludes by proposing future directions to optimize the adoption of AI tools in breast cancer screening and improve equity and outcomes for diverse populations.