bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Gaussian process guide for signal regression in magnetic fusion
Authors: Michoski Craig , Oliver Todd A. , Hatch David R. , Diallo Ahmed , Kotschenreuther Mike , Eldon David , Waller Matthew , Groebner Richard , Nelson Andrew Oakleigh 
Published: January 2024

Abstract
Extracting reliable information from diagnostic data in tokamaks is critical for understanding, analyzing, and controlling the behavior of fusion plasmas and validating models describing that behavior. Recent interest within the fusion community has focused on the use of principled statistical methods, such as Gaussian process regression (GPR), to attempt to develop sharper, more reliable, and more rigorous tools for examining the complex observed behavior in these systems. While GPR is an enormously powerful tool, there is also the danger of drawing fragile, or inconsistent conclusions from naive GPR fits that are not driven by principled treatments. Here we review the fundamental concepts underlying GPR in a way that may be useful for broad-ranging applications in fusion science. We also revisit how GPR is developed for profile fitting in tokamaks. We examine various extensions and targeted modifications applicable to experimental observations in the edge of the DIII-D tokamak. Finally, we discuss best practices for applying GPR to fusion data.
Impact of microwave beam scattering by density fluctuations on the electron–cyclotron power deposition profile in tokamaks
Authors: Cazabonne J. , Coda S. , Decker J. , Krutkin O. , Kumar U. , Peysson Y. ,   the TCV Team
Published: January 2024

Abstract
Electron–cyclotron waves are a tool commonly used in tokamaks, in particular to drive current. Their ability to drive current in a very localized manner renders them an optimal tool for MHD mode mitigation. However, such applications require high accuracy and good control of the power deposition location to efficiently target the magnetic islands. It has been indirectly observed that the suprathermal electron distribution, resulting from the wave absorption, is broader than what is expected from experimentally-constrained forward drift-kinetic modeling. The present paper explores the possibility that beam scattering through the turbulent edge of the plasma may explain this observed discrepancy. In particular, full-wave studies exhibit three beam broadening regimes, from superdiffusive to diffusive, with an intermediate regime characterized by a Lorentzian beam profile with a slightly increased full-width at half maximum with respect to the quiet plasma case. In the tokamak à configuration variable, dedicated plasma scenarios have been developed to test this hypothesis. A realistic worst-case fluctuation scenario falls into this intermediate beam broadening regime. By comparing the experimental hard x-ray emission from suprathermal electron Bremmstrahlung with the emission calculated by coupling a full-wave model to a Fokker–Planck solver, it is shown that, in the tested cases, the beam broadening is not sufficient to explain the aforementioned discrepancy between simulation and experiment and that another mechanism must play the main role in broadening the suprathermal electron distribution.
Effect of stroke plane inclination on the hovering aerodynamic performance of tandem flapping foils
Authors: Tiwari Shubham , Chandel Sunil 
Published: January 2024

Abstract
The four-winged form of dragonfly and damselfly allows them to fly with great agility and endurance, which are accomplished by independently controlling the kinematics of each wing. In this study, we performed numerical simulations of two tandem airfoils oscillating along an inclined stroke plane at

R
e
=
157

. We investigated the effects of the stroke plane angle (

β
)

of forefoil and hindfoil on the aerodynamic performance of dragonflies (or damselflies) hovering flight. Simulations were conducted for parallel and non-parallel stroke planes of forefoil and hindfoil oscillating with three phase differences:

ϕ
=

0

o

,

90

o

and

180

o

. For parallel stroke planes, the results show that the total lift increases with

β

, whereas the total thrust decreases in same condition. In addition, the total lift and thrust decrease with an increase in

ϕ

. The forefoil performance is affected by the flow induced by hindfoil’s leading-edge vortices, and the hindfoil interactions with forefoil’s wake vortices significantly affect the hindfoil’s performance. The findings demonstrate that non-parallel stroke planes exert detrimental effects on the total lift for

ϕ
=

0

o

and

90

o

. However, for

ϕ
=

180

o

, the lift augmentation of 46% is obtained in the case where the forefoil stroke plane angle is smaller than the hindfoil. The results obtained during this investigation can help in optimizing the wing kinematics during the micro air vehicles development.
Biochar-composting substantially reduces methane and air pollutant emissions from dairy manure
Authors: Harrison Brendan P , Moo Zeyi , Perez-Agredano Evelyn , Gao Si , Zhang Xuan , Ryals Rebecca 
Published: January 2024

Abstract
Dairy manure is one of the largest sources of methane (CH4) emissions and air pollution from agriculture. In a previous study, we showed that composting dairy manure with biochar substantially reduces CH4 emissions and could help the dairy industry meet climate goals. However, it remained unclear whether biochar could also mitigate the emission of air pollutants and odor during composting. Here, we conducted a full-scale composting study at a dairy farm and monitored the emission of greenhouse gases (CO2, CH4, N2O) and air pollutants (H2S, VOCs, NO
x
, NH3) from compost piles amended with or without biochar. We found that amending compost with biochar significantly reduced total CH4 emissions by 58% (±22%) and cut H2S, VOCs, and NO
x
emissions by 67% (±24%), 61% (±19%) and 70% (±22%), respectively. We attribute this reduction in emissions to improved oxygen diffusion from the porous biochar and the adsorption of gas precursors to the biochar surface. Interestingly, NO
x
fluxes from the composting dairy manure were much higher than the few values reported in the literature, suggesting that dairy manure could also be a significant source of NO
x
emissions. We estimate that biochar-composting of dairy manure would reduce the social cost of manure emissions from this farm by over $66 000 annually. Results from this study suggest that composting dairy manure with biochar, in addition to reducing CH4 emissions, may help to improve air quality and the health and wellbeing of rural communities, but further studies are needed to test the quantitative impacts.
On a family of finitely many point interaction Hamiltonians free of ultraviolet pathologies
Authors: Figari Rodolfo , Saberbaghi Hamidreza , Teta Alessandro 
Published: January 2024

Abstract
We re-investigate the entire family of many center point interaction Hamiltonians. Under the assumption of exchange symmetry with respect to the point positions, we show that a large sub-family of point interaction Hamiltonian operators does not become either singular or trivial when the positions of two or more scattering centers tend to coincide. In this sense, they appear to be renormalized by default as opposed to the point interaction Hamiltonians usually considered in the literature. Functions in their domains satisfy regularized boundary conditions which turn out to be very similar to the ones proposed recently in many-body quantum mechanics to define three-particle system Hamiltonians with contact interactions bounded from below. In the two-center case, we study the behavior of the negative eigenvalues as a function of the center distance. The result is used to analyze a formal Born–Oppenheimer approximation of a three-particle system with two heavy bosons and one light particle. We demonstrate that this simplified model describes a stable system (no ‘fall to the center’ problem is present). Furthermore, in the unitary limit, the energy spectrum is characterized by an infinite sequence of negative energy eigenvalues accumulating at zero according to the geometrical Efimov law.
Conformists and contrarians on spheres
Authors: Crnkić Aladin , Jaćimović Vladimir , Niu Ben 
Published: January 2024

Abstract
We investigate the conformists–contrarians model of identical Kuramoto oscillators evolving on a sphere. Using group-theoretic and geometric approach, we reduce the model to the dynamical system on extended Ott–Antonsen manifold. Further reduction yields the system of three scalar ODE’s for global variables. This three-dimensional dynamical system is studied analytically in order to investigate an interplay between conformists and contrarians on spheres. Our study demonstrates that conformists–contrarians models on spheres display the same types of equilibria and dynamical phenomena in all dimensions. However, critical combination of parameters, for which particular equilibrium states arise, does depend on the dimension. In particular, models on spheres exhibit traveling waves consisting of contrarians. We derive an exact formula for the relation between parameter values for which such waves arise in different dimensions. Finally, we take a closer look at trajectories of traveling waves on spheres, demonstrating subtleties of this dynamical phenomenon.
Discovering interpretable physical models using symbolic regression and discrete exterior calculus
Authors: Manti Simone , Lucantonio Alessandro 
Published: January 2024

Abstract
Computational modeling is a key resource to gather insight into physical systems in modern scientific research and engineering. While access to large amount of data has fueled the use of machine learning to recover physical models from experiments and increase the accuracy of physical simulations, purely data-driven models have limited generalization and interpretability. To overcome these limitations, we propose a framework that combines symbolic regression (SR) and discrete exterior calculus (DEC) for the automated discovery of physical models starting from experimental data. Since these models consist of mathematical expressions, they are interpretable and amenable to analysis, and the use of a natural, general-purpose discrete mathematical language for physics favors generalization with limited input data. Importantly, DEC provides building blocks for the discrete analog of field theories, which are beyond the state-of-the-art applications of SR to physical problems. Further, we show that DEC allows to implement a strongly-typed SR procedure that guarantees the mathematical consistency of the recovered models and reduces the search space of symbolic expressions. Finally, we prove the effectiveness of our methodology by re-discovering three models of continuum physics from synthetic experimental data: Poisson equation, the Euler’s elastica and the equations of linear elasticity. Thanks to their general-purpose nature, the methods developed in this paper may be applied to diverse contexts of physical modeling.
Origin of off-centering effect and the influence on heat transport in thermoelectrics
Authors: Xie Hongyao , Zhao Li-Dong 
Published: January 2024

Abstract
Recently, off-centering behavior has been discovered in a series of thermoelectric materials. This behavior indicates that the constituent atoms of the lattice displace from their coordination centers, leading to the locally distorted state and local symmetry breaking, while the material still retains its original crystallographic symmetry. This effect has been proved to be the root cause of ultralow thermal conductivity in off-centering materials, and is considered as an effective tool to regulate the thermal conductivity and improve the thermoelectric performance. Herein, we present a collection of recently discovered off-centering compounds, discuss their electronic origins and local coordination structures, and illuminate the underlying mechanism of the off-centering effect on phonon transport and thermal conductivity. This paper presents a comprehensive view of our current understanding to the off-centering effect, and provides a new idea for designing high performance thermoelectrics.
H2Opred: a robust and efficient hybrid deep learning model for predicting 2’-O-methylation sites in human RNA
Authors: Pham Nhat Truong , Rakkiyapan Rajan , Park Jongsun , Malik Adeel , Manavalan Balachandran 
Published: January 2024

Abstract
2’-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high-throughput detection, the chemical stability of 2OM makes it difficult to detect and map in messenger RNA. Therefore, bioinformatics tools have been developed using machine learning (ML) algorithms to identify 2OM sites. These tools have made significant progress, but their performances remain unsatisfactory and need further improvement. In this study, we introduced H2Opred, a novel hybrid deep learning (HDL) model for accurately identifying 2OM sites in human RNA. Notably, this is the first application of HDL in developing four nucleotide-specific models [adenine (A2OM), cytosine (C2OM), guanine (G2OM) and uracil (U2OM)] as well as a generic model (N2OM). H2Opred incorporated both stacked 1D convolutional neural network (1D-CNN) blocks and stacked attention-based bidirectional gated recurrent unit (Bi-GRU-Att) blocks. 1D-CNN blocks learned effective feature representations from 14 conventional descriptors, while Bi-GRU-Att blocks learned feature representations from five natural language processing-based embeddings extracted from RNA sequences. H2Opred integrated these feature representations to make the final prediction. Rigorous cross-validation analysis demonstrated that H2Opred consistently outperforms conventional ML-based single-feature models on five different datasets. Moreover, the generic model of H2Opred demonstrated a remarkable performance on both training and testing datasets, significantly outperforming the existing predictor and other four nucleotide-specific H2Opred models. To enhance accessibility and usability, we have deployed a user-friendly web server for H2Opred, accessible at https://balalab-skku.org/H2Opred/. This platform will serve as an invaluable tool for accurately predicting 2OM sites within human RNA, thereby facilitating broader applications in relevant research endeavors.
Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning
Authors: Liu Yufan , Tian Boxue 
Published: January 2024

Abstract
Protein–DNA interaction is critical for life activities such as replication, transcription and splicing. Identifying protein–DNA binding residues is essential for modeling their interaction and downstream studies. However, developing accurate and efficient computational methods for this task remains challenging. Improvements in this area have the potential to drive novel applications in biotechnology and drug design. In this study, we propose a novel approach called Contrastive Learning And Pre-trained Encoder (CLAPE), which combines a pre-trained protein language model and the contrastive learning method to predict DNA binding residues. We trained the CLAPE-DB model on the protein–DNA binding sites dataset and evaluated the model performance and generalization ability through various experiments. The results showed that the area under ROC curve values of the CLAPE-DB model on the two benchmark datasets reached 0.871 and 0.881, respectively, indicating superior performance compared to other existing models. CLAPE-DB showed better generalization ability and was specific to DNA-binding sites. In addition, we trained CLAPE on different protein–ligand binding sites datasets, demonstrating that CLAPE is a general framework for binding sites prediction. To facilitate the scientific community, the benchmark datasets and codes are freely available at https://github.com/YAndrewL/clape.
SGCAST: symmetric graph convolutional auto-encoder for scalable and accurate study of spatial transcriptomics
Authors: Li Jinzhao , Wang Jiong , Lin Zhixiang 
Published: January 2024

Abstract
Recent advances in spatial transcriptomics (ST) have enabled comprehensive profiling of gene expression with spatial information in the context of the tissue microenvironment. However, with the improvements in the resolution and scale of ST data, deciphering spatial domains precisely while ensuring efficiency and scalability is still challenging. Here, we develop SGCAST, an efficient auto-encoder framework to identify spatial domains. SGCAST adopts a symmetric graph convolutional auto-encoder to learn aggregated latent embeddings via integrating the gene expression similarity and the proximity of the spatial spots. This framework in SGCAST enables a mini-batch training strategy, which makes SGCAST memory-efficient and scalable to high-resolution spatial transcriptomic data with a large number of spots. SGCAST improves the overall accuracy of spatial domain identification on benchmarking data. We also validated the performance of SGCAST on ST datasets at various scales across multiple platforms. Our study illustrates the superior capacity of SGCAST on analyzing spatial transcriptomic data.
MrGPS: an m6A-related gene pair signature to predict the prognosis and immunological impact of glioma patients
Authors: Zhang Ning , Yang Fengxia , Zhao Pengfei , Jin Nana , Wu Haonan , Liu Tao , Geng Qingshan , Yang Xiaojun , Cheng Lixin 
Published: January 2024

Abstract
N6-methyladenosine (m6A) RNA methylation is the predominant epigenetic modification for mRNAs that regulates various cancer-related pathways. However, the prognostic significance of m6A modification regulators remains unclear in glioma. By integrating the TCGA lower-grade glioma (LGG) and glioblastoma multiforme (GBM) gene expression data, we demonstrated that both the m6A regulators and m6A-target genes were associated with glioma prognosis and activated various cancer-related pathways. Then, we paired m6A regulators and their target genes as m6A-related gene pairs (MGPs) using the iPAGE algorithm, among which 122 MGPs were significantly reversed in expression between LGG and GBM. Subsequently, we employed LASSO Cox regression analysis to construct an MGP signature (MrGPS) to evaluate glioma prognosis. MrGPS was independently validated in CGGA and GEO glioma cohorts with high accuracy in predicting overall survival. The average area under the receiver operating characteristic curve (AUC) at 1-, 3- and 5-year intervals were 0.752, 0.853 and 0.831, respectively. Combining clinical factors of age and radiotherapy, the AUC of MrGPS was much improved to around 0.90. Furthermore, CIBERSORT and TIDE algorithms revealed that MrGPS is indicative for the immune infiltration level and the response to immune checkpoint inhibitor therapy in glioma patients. In conclusion, our study demonstrated that m6A methylation is a prognostic factor for glioma and the developed prognostic model MrGPS holds potential as a valuable tool for enhancing patient management and facilitating accurate prognosis assessment in cases of glioma.
A content-aware chatbot based on GPT 4 provides trustworthy recommendations for Cone-Beam CT guidelines in dental imaging
Authors: Russe Maximilian Frederik , Rau Alexander , Ermer Michael Andreas , Rothweiler René , Wenger Sina , Klöble Klara , Schulze Ralf K W , Bamberg Fabian , Schmelzeisen Rainer , Reisert Marco , Semper-Hogg Wiebke 
Published: January 2024

Abstract

Objectives
To develop a content-aware chatbot based on GPT-3.5-Turbo and GPT-4 with specialized knowledge on the German S2 Cone-Beam CT (CBCT) dental imaging guideline and to compare the performance against humans.

Methods
The LlamaIndex software library was used to integrate the guideline context into the chatbots. Based on the CBCT S2 guideline, 40 questions were posed to content-aware chatbots and early career and senior practitioners with different levels of experience served as reference. The chatbots’ performance was compared in terms of recommendation accuracy and explanation quality. Chi-square test and one-tailed Wilcoxon signed rank test evaluated accuracy and explanation quality, respectively.

Results
The GPT-4 based chatbot provided 100% correct recommendations and superior explanation quality compared to the one based on GPT3.5-Turbo (87.5% vs. 57.5% for GPT-3.5-Turbo; P = .003). Moreover, it outperformed early career practitioners in correct answers (P = .002 and P = .032) and earned higher trust than the chatbot using GPT-3.5-Turbo (P = 0.006).

Conclusions
A content-aware chatbot using GPT-4 reliably provided recommendations according to current consensus guidelines. The responses were deemed trustworthy and transparent, and therefore facilitate the integration of artificial intelligence into clinical decision-making.
A Love Triangle? Mapping Interactions between International Human Rights Institutions, Meta and Its Oversight Board
Authors: Tiedeke Anna Sophia , Fertmann Martin 
Published: January 2024

Abstract
Three years ago, the Oversight Board commenced its work ‘to make principled, independent, and binding decisions … based on respect for freedom of expression and human rights’ for Meta’s platforms Facebook and Instagram. From the very beginning, the vocabulary employed to talk about the Oversight Board was laden with court metaphors. Wary that these metaphors have stirred legal analysis into a specific direction, we move away from trying to fit the Oversight Board within established institutional categories. Instead, we shift the focus from institutions to interactions – that is, to the ‘in-between’. Rather than continuing to debate what the Oversight Board is, we focus on what the Oversight Board does. Our study maps different stages and modes of interaction between Meta, the Oversight Board and international human rights institutions. We show how different actors carefully craft entry points for constructing their respective semantic authority and what kind of strategies they pursue to contest semantic authority of others. Thereby, we uncover the first traces of emerging conversations between Meta, the Oversight Board and international human rights institutions and highlight who is included and excluded and who refuses to participate or to respond. With our intervention, we intend to offer empirically grounded insights into the dynamics at play and paint a more detailed picture of the various roles that novel actors, such as Meta and the Oversight Board, are beginning to assume in the protection of international human rights online.
Transforming nursing with large language models: from concept to practice
Authors: Woo Brigitte , Huynh Tom , Tang Arthur , Bui Nhat , Nguyen Giang , Tam Wilson 
Published: January 2024

Abstract
Large language models (LLMs) such as ChatGPT have emerged as potential game-changers in nursing, aiding in patient education, diagnostic assistance, treatment recommendations, and administrative task efficiency. While these advancements signal promising strides in healthcare, integrated LLMs are not without challenges, particularly artificial intelligence hallucination and data privacy concerns. Methodologies such as prompt engineering, temperature adjustments, model fine-tuning, and local deployment are proposed to refine the accuracy of LLMs and ensure data security. While LLMs offer transformative potential, it is imperative to acknowledge that they cannot substitute the intricate expertise of human professionals in the clinical field, advocating for a synergistic approach in patient care.