bluuebunny/crossref_metadata_2025_split huggingface dataset:
Protein Characterization and Amino Acid Profiles of Various Wheat Varieties and Their Effect on Pasting Properties of Starch
Authors: Katyal Mehak , Singh Narpinder , Kathuria Deepika 
Published: August 2024

AbstractThe present study is to investigate amino acid compositions and protein characteristics of gluten extracted from different varieties of wheat and their influence on the pasting properties of starch. The starch and gluten are isolated separately from 12 wheat varieties categorized by grain hardness, i.e., hard (HW), medium hard (MHW), and soft (SW)with value ranged from 83 to 95, 72 to 80, and 17 to 29, respectively. Amino acid profiling reveals significant variations among the wheat varieties, with extraordinarily soft wheat (Ex‐SW) varieties exhibited higher levels of essential amino acid including methionine, phenylalanine, tryptophan, threonine, isoleucine, histidine, lysine, and valine. Protein fraction analysis indicates that gluten from HW varieties has a higher proportion of gliadin and lower glutenin content compared to MHW and Ex‐SW varieties. Furthermore, starch from Ex‐SW varieties possesses higher pasting parameters than MHW and HW varieties. Further, incorporation of gluten significantly reduced the pasting characteristics of starch. Flours with higher protein content demonstrate lower paste breakdown, highlighting the influence of protein content on viscoelastic properties. Furthermore, principal component analysis (PCA) reveals associations between specific amino acids and protein fractions with various pasting properties, highlighting the complex interplay between gluten composition and starch behavior.
Influence of fire severity and concrete properties on the thermo‐hygral behavior of concrete during fire exposure
Authors: Pulkit Umang , Adhikary Satadru Das , Kodur Venkatesh 
Published: August 2024

AbstractSince the world is transitioning toward performance based design, the study of thermo‐hygral behavior of concrete when subjected to real fire becomes crucial. Fire accidents have revealed that nominal fire curves cannot be applied because of varying severity of real fire. In 2008, traveling fire concept was developed in which severity is dependent on heat release rate and fire size. This study explores the effect of fire severity and other concrete properties like strength, aggregate type, and relative humidity. The proposed model has been developed by combining the principles of mechanics and thermodynamics and upon validation with the experimental results, a reasonable agreement has been observed. It can be concluded that severity of fire is directly related to thermo‐hygral behavior of concrete. On the other hand, this study also highlights the influence of type of aggregate and moisture content in addition to the traditional variables like volume fraction of solid and permeability. On studying the influence of type of aggregate, it can be concluded that recycled aggregate concrete performed better than conventional concrete. The integration of proposed model in the performance based design is a leap toward development of resilient structure subjected to dynamic fire conditions.
Global trends in opioid use for pain management in acute pancreatitis: A multicentre prospective observational study
Authors: Knoph Cecilie Siggaard , Lucocq James , Kamarajah Sivesh Kathir , Olesen Søren Schou , Jones Michael , Samanta Jayanta , Talukdar Rupjyoti , Capurso Gabriele , de‐Madaria Enrique , Yadav Dhiraj , Siriwardena Ajith K. , Windsor John , Drewes Asbjørn Mohr , Nayar Manu ,   PAINAP Collaborative, Pandanaboyana Sanjay 
Published: August 2024

AbstractBackgroundSince there is no current international consensus on the optimal approach for pain management in acute pancreatitis (AP), analgesic practices may vary across different healthcare settings.ObjectiveThis study explored global disparities in analgesic use, in particular opioids, during admission and at discharge in hospitalised AP patients.MethodsThis was a post hoc analysis of the prospective PAINAP database, which included all admissions for AP between April and June 2022 with a 1‐month follow‐up. Demographic details, analgesic use, and clinical outcomes were recorded during admission and at discharge. Odds ratios (ORs) for opioid use during admission and at discharge were identified using multivariable regression analyses.ResultsAmongst the 1864 patients (52% males, median age 56 (interquartile range, 41–71)) across three different continents, simple analgesics were predominantly used as the primary analgesic (70%). Opioid use during admission was lowest in European centres (67%). Admission in Asian (OR, 2.53 (95% confidence interval (CI), 1.59–4.04), p < 0.001), and Australian (OR, 5.81 (95% CI, 3.19–10.56), p < 0.001) centres was associated with opioid administration during admission compared with European centres. Increased pain severity, longer pre‐admission pain duration, organ failure, and longer length of admission increased opioid use during admission. At discharge, Asian (OR, 2.01 (95% CI, 1.40–2.88), p < 0.001) and Australian (OR, 1.91 (95% CI, 1.28–2.85), p = 0.002) centres were associated with opioid prescription compared with European centres. Increased pain severity, longer pre‐admission pain duration, acute necrotic collections, and walled‐off necrosis also increased the likelihood of opioid prescription at discharge.ConclusionThere are substantial intercontinental differences in opioid use for AP pain. Accordingly, there is a need for international guidelines on pain management in AP.
Entropy generation in Johnson–Segalman peristaltic flow with magnetic field and activation energy
Authors: Zahir Hina , Akram Javaria , Bayram Mustafa , Shakeel Mehnaz , Fatima Rabbia , Rezapour Shahram , Inc Mustafa 
Published: August 2024

AbstractThis study examines entropy generation in the peristaltic flow of Johnson–Segalman fluid through a curved channel, considering the effects of Hall and ion slip due to an externally applied magnetic field and activation energy. The fluid dynamics are modeled using a highly nonlinear mathematical framework, which is non‐dimensionalized and simplified with a lubrication approach. Numerical solutions are obtained using the shooting technique to analyze fluid flow properties. The results, presented graphically, provide a comprehensive understanding of the interactions between the non‐Newtonian characteristics of the Johnson–Segalman fluid, entropy generation, and activation energy effects. The study finds that increasing the Hall parameter enhances entropy generation. Higher activation energy increases the rate of chemical reactions and by‐products, raising system randomness. Additionally, reducing the channel curvature or increasing the curvature parameter elevates the system's entropy. These insights are valuable for biomedical and industrial applications.
Scholarly Question Answering Using Large Language Models in the NFDI4DataScience Gateway
Authors: Babaei Giglou Hamed , Taffa Tilahun Abedissa , Abdullah Rana , Usmanova Aida , Usbeck Ricardo , D’Souza Jennifer , Auer Sören 
Published: August 2024

AbstractThis paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing a Retrieval Augmented Generation-based (RAG) approach. The NFDI4DS Gateway, as a foundational framework, offers a unified and intuitive interface for querying various scientific databases using federated search. The RAG-based scholarly QA, powered by a Large Language Model (LLM), facilitates dynamic interaction with search results, enhancing filtering capabilities and fostering a conversational engagement with the Gateway search. The effectiveness of both the Gateway and the scholarly QA system is demonstrated through experimental analysis.
The Effect of Knowledge Graph Schema on Classifying Future Research Suggestions
Authors: Alivanistos Dimitrios , van der Bijl Seth , Cochez Michael , van Harmelen Frank 
Published: August 2024

AbstractThe output of research doubles at least every 20 years and in most research fields the number of research papers has become overwhelming. A critical task for researchers is to find promising future directions and interesting scientific challenges in the literature. To tackle this problem, we hypothesize that structured representations of information in the literature can be used to identify these elements. Specifically, we look at structured representations in the form of Knowledge Graphs (KGs) and we investigate how using different input schemas for extraction impacts the performance on the tasks of classifying sentences as future directions. Our results show that the MECHANIC-Granular schema yields the best performance across different settings and achieves state of the art performance when combined with pretrained embeddings. Overall, we observe that schemas with limited variation in the resulting node degrees and significant interconnectedness lead to the best downstream classification performance.
Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis
Authors: Ciuciu-Kiss Jenifer Tabita , Garijo Daniel 
Published: August 2024

AbstractScience Knowledge Graphs (SKGs) have emerged as a means to represent and capture research outputs (papers, datasets, software, etc.) and their relationships in a machine-readable manner. However, different SKGs use different taxonomies, making it challenging to understand their overlaps, gaps and differences. In this paper, we propose a quantitative bottom-up analysis to assess the overlap between two SKGs, based on the type annotations of their instances. We implement our methodology by assessing the category overlap of 100,000 publications present both in OpenAlex and OpenAIRE. As a result, our approach produces an alignment of 71 categories and discusses the level of agreement between both KGs when annotating research artefacts.
FoRC@NSLP2024: Overview and Insights from the Field of Research Classification Shared Task
Authors: Abu Ahmad Raia , Borisova Ekaterina , Rehm Georg 
Published: August 2024

AbstractThis article provides an overview of the Field of Research Classification (FoRC) shared task conducted as part of the Natural Scientific Language Processing Workshop (NSLP) 2024. The FoRC shared task encompassed two subtasks: the first was a single-label multi-class classification of scholarly papers across a taxonomy of 123 fields, while the second focused on fine-grained multi-label classification within computational linguistics, using a taxonomy of 170 (sub-)topics. The shared task received 13 submissions for the first subtask and two for the second, with teams surpassing baseline performance metrics in both subtasks. The winning team for subtask I employed a multi-modal approach integrating metadata, full-text, and images from publications, achieving a weighted F1 score of 0.75, while the winning team for the second subtask leveraged a weakly supervised X-transformer model enriched with automatically labelled data, achieving a micro F1 score of 0.56 and a macro F1 of 0.43.
Advancing Automatic Subject Indexing: Combining Weak Supervision with Extreme Multi-label Classification
Authors: Rajendram Bashyam Lakshmi , Krestel Ralf 
Published: August 2024

AbstractThe multi-label automatic classification of scientific publications based on a pre-defined taxonomy, also called automatic subject indexing is a continuing research endeavor with significant cross-domain applicability. In this paper, we assess the performance of X-transformer and its variants with other extreme multi-label classification models for the above task. Our model Weak X-transformer achieves a micro F1-score of 0.65 and 64% accuracy on the task outperforming all other methods. We also investigate the impact of incorporating additional unlabelled data and hierarchical structure into the models. Our findings demonstrate that the transformer-based model with weak supervision outperforms other approaches, providing insights into effective strategies for extreme multi-label classification in scholarly publications.
Enriched BERT Embeddings for Scholarly Publication Classification
Authors: Wolff Benjamin , Seidlmayer Eva , Förstner Konrad U. 
Published: August 2024

AbstractWith the rapid expansion of academic literature and the proliferation of preprints, researchers face growing challenges in manually organizing and labeling large volumes of articles. The NSLP 2024 FoRC Shared Task I addresses this challenge organized as a competition. The goal is to develop a classifier capable of predicting one of 123 predefined classes from the Open Research Knowledge Graph (ORKG) taxonomy of research fields for a given article. This paper presents our results.Initially, we enrich the dataset (containing English scholarly articles sourced from ORKG and arXiv), then leverage different pre-trained language Models (PLMs), specifically BERT, and explore their efficacy in transfer learning for this downstream task. Our experiments encompass feature-based and fine-tuned transfer learning approaches using diverse PLMs, optimized for scientific tasks, including SciBERT, SciNCL, and SPECTER2. We conduct hyperparameter tuning and investigate the impact of data augmentation from bibliographic databases such as OpenAlex, Semantic Scholar, and Crossref. Our results demonstrate that fine-tuning pre-trained models substantially enhances classification performance, with SPECTER2 emerging as the most accurate model. Moreover, enriching the dataset with additional metadata improves classification outcomes significantly, especially when integrating information from S2AG, OpenAlex and Crossref. Our best-performing approach achieves a weighted F1-score of 0.7415. Overall, our study contributes to the advancement of reliable automated systems for scholarly publication categorization, offering a potential solution to the laborious manual curation process, thereby facilitating researchers in efficiently locating relevant resources.
SOMD@NSLP2024: Overview and Insights from the Software Mention Detection Shared Task
Authors: Krüger Frank , Karmakar Saurav , Dietze Stefan 
Published: August 2024

AbstractSoftware is a central part of the scientific process and involved in obtaining, analysing, visualising and processing research data. Understanding the provenance of research requires an understanding of the involved software. However, software citations in scientific publications often are informal, what creates challenges when aiming at understanding software adoption. This paper provides an overview of the Software Mention Detection (SOMD) shared task conducted as part of the 2024 Natural Scientific Language Processing Workshop, aiming at advancing the state-of-the-art with respect to NLP methods for detecting software mentions and additional information in scholarly publications. The SOMD shared task encompasses three subtasks, concerned with software mention recognition (subtask I), recognition of additional information (subtask II) and classification of involved relations (subtask III). We present an overview of the tasks, received submissions and used techniques. The best submissions achieved F1 scores of 0.74 (subtask I), 0.838 (subtask II) and 0.911 (subtask III) indicating both task feasibility but also potential for further performance gains.
Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models
Authors: Otto Wolfgang , Upadhyaya Sharmila , Dietze Stefan 
Published: August 2024

AbstractThis paper describes our participation in the Shared Task on Software Mentions Disambiguation (SOMD), with a focus on improving relation extraction in scholarly texts through generative Large Language Models (LLMs) using single-choice question-answering. The methodology prioritises the use of in-context learning capabilities of LLMs to extract software-related entities and their descriptive attributes, such as distributive information. Our approach uses Retrieval-Augmented Generation (RAG) techniques and LLMs for Named Entity Recognition (NER) and Attributive NER to identify relationships between extracted software entities, providing a structured solution for analysing software citations in academic literature. The paper provides a detailed description of our approach, demonstrating how using LLMs in a single-choice QA paradigm can greatly enhance IE methodologies. Our participation in the SOMD shared task highlights the importance of precise software citation practices and showcases our system’s ability to overcome the challenges of disambiguating and extracting relationships between software mentions. This sets the groundwork for future research and development in this field.
RTaC: A Generalized Framework for Tooling
Authors: Bhavsar Nisarg , Thakur Abhinav , Singh Amrit Lal , Patwa Ashish 
Published: August 2024

AbstractIn the rapidly evolving domain of Large Language Models (LLMs), integrating tool usage remains a formidable challenge, particularly when it comes to the dynamic selection and sequencing of tools in response to complex queries. Addressing this, we introduce Reimagining Tooling as Coding (RTaC), a groundbreaking framework that transforms tool usage into a coding paradigm. Inspired by recent advancements [18], RTaC conceptualizes tools as Python functions within a dual-agent system [2], significantly enhancing LLMs’ tool usage efficiency. Our comprehensive experiments reveal that RTaC enables coding-based LLMs, such as DeepSeek and CodeLlama, to achieve and surpass GPT-4 benchmarks in cost-effectiveness and latency without compromising on handling intricate tool sequencing with conditional and iterative logic. This research not only sets a new benchmark for tooling efficiency in LLMs but also opens new avenues for the application of LLMs in complex problem-solving scenarios, heralding a significant leap forward in the functionality and versatility of LLMs across diverse domains.
Automated Extraction of Research Software Installation Instructions from README Files: An Initial Analysis
Authors: Utrilla Guerrero Carlos , Corcho Oscar , Garijo Daniel 
Published: August 2024

AbstractResearch Software code projects are typically described with a README files, which often contains the steps to set up, test and run the code contained in them. Installation instructions are written in a human-readable manner and therefore are difficult to interpret by intelligent assistants designed to help other researchers setting up a code repository. In this paper we explore this gap by assessing whether Large Language Models (LLMs) are able to extract installation instruction plans from README files. In particular, we define a methodology to extract alternate installation plans, an evaluation framework to assess the effectiveness of each result and an initial quantitative evaluation based on state of the art LLM models ( and ). Our results show that while LLMs are a promising approach for finding installation instructions, they present important limitations when these instructions are not sequential or mandatory.
Scattering on the supermembrane
Authors: Seibold Fiona K. , Tseytlin Arkady A. 
Published: August 2024

Abstract
We compute the one-loop 2 → 2 scattering amplitude of massless scalars on the world volume of an infinite D = 11 supermembrane quantized in the static gauge. The resulting expression is manifestly finite and turns out to be much simpler than in the bosonic membrane case in arXiv:2308.12189 being proportional to the tree-level scattering amplitude. We also consider the case of ℝ1, 1 × S1 membrane with one dimension compactified on a circle of radius R and demonstrate how the supermembrane scattering amplitude reduces to the one on an infinite D = 10 Green-Schwarz superstring in the limit of R→ 0.
Super Yang-Mills on branched covers and weighted projective spaces
Authors: Mauch Roman , Ruggeri Lorenzo 
Published: August 2024

Abstract
In this work we conjecture the Coulomb branch partition function, including flux and instanton contributions, for the $$ \mathcal{N} $$
N
= 2 vector multiplet on weighted projective space $$ {\mathbbm{CP}}_N^2 $$

CP
N
2

for equivariant Donaldson-Witten and “Pestun-like” theories. More precisely, we claim that this partition function agrees with the one computed on a certain branched cover of $$ {\mathbbm{CP}}^2 $$

CP
2

upon matching conical deficit angles with corresponding branch indices. Our conjecture is substantiated by checking that similar partition functions on spindles agree with their equivalent on certain branched covers of $$ {\mathbbm{CP}}^1 $$

CP
1

. We compute the one-loop determinant on the branched cover of $$ {\mathbbm{CP}}^2 $$

CP
2

for all flux sectors via dimensional reduction from the $$ \mathcal{N} $$
N
= 1 vector multiplet on a branched five-sphere along a free S1-action. This work paves the way for obtaining partition functions on more generic symplectic toric orbifolds.
One-dimensional quasi-uniform Kronecker sequences
Authors: Goda Takashi 
Published: August 2024

AbstractIn this short note, we prove that the one-dimensional Kronecker sequence $$i\alpha \bmod 1, i=0,1,2,\ldots ,$$

i
α

mod

1
,
i
=
0
,
1
,
2
,
…
,

is quasi-uniform over the unit interval [0, 1] if and only if $$\alpha $$
α
is a badly approximable number. Our elementary proof relies on a result on the three-gap theorem for Kronecker sequences due to Halton (Proc Camb Philos Soc, 61:665–670, 1965).
Validation of the implementation of phased-array heating systems in Plan2Heat
Authors: Kok H. P. , Crezee J 
Published: August 2024

Abstract

Background
Hyperthermia treatment planning can be supportive to ensure treatment quality, provided reliable prediction of the heating characteristics (i.e., focus size and effects of phase-amplitude and frequency steering) of the device concerned is possible. This study validates the predictions made by the treatment planning system Plan2Heat for various clinically used phased-array systems.

Methods
The evaluated heating systems were AMC-2, AMC-4/ALBA-4D (Med-Logix srl, Rome, Italy), BSD Sigma-30, and Sigma-60 (Pyrexar Medical, Salt Lake City, UT, USA). Plan2Heat was used for specific absorption rate (SAR) simulations in phantoms representing measurement set-ups reported in the literature. SAR profiles from published measurement data based on E‑field or temperature rise were used to compare the device-specific heating characteristics predicted by Plan2Heat.

Results
Plan2Heat is able to predict the correct location and size of the SAR focus, as determined by phase-amplitude settings and operating frequency. Measured effects of phase-amplitude steering on focus shifts (i.e., local SAR minima or maxima) were also correctly reflected in treatment planning predictions. Deviations between measurements and simulations were typically < 10–20%, which is within the range of experimental uncertainty for such phased-array measurements.

Conclusion
Plan2Heat is capable of adequately predicting the heating characteristics of the AMC‑2, AMC-4/ALBA-4D, BSD Sigma-30, and Sigma-60 phased-array systems routinely used in clinical hyperthermia.