bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pluricentricity AND pluriareality
Authors: Durgasingh Ryan , Meer Philipp 
Published: January 2025

Abstract
Moving beyond the contention which has characterized the pluricentricity and pluriareality debate, this
summative chapter draws together the argumentative strands in the chapters in this volume to build the case for their
complementarity. We show the various ways in which terminological differences, of geography and of sociolinguistics, have
somewhat obscured the underlying similarities of these models, and highlight many of the key findings of this book’s
contributors in doing so. The chapter ends by highlighting how some of the issues related to the pluri-concepts might lie in
the different assumptions and foci of different linguistic paradigms, and with a request that further work on pluricentricity
and pluriareality view the models as flexible constructs which can work together towards mutual benefits.
An enhanced BiLSTM recognition method for interfacial debonding damage distinguishing inherent classical nonlinear effects
Authors: Li Zhihang , Xiao Li , Qu Wenzhong 
Published: January 2025

Abstract
The integrity of composite bonded structures largely depends on the robust adhesion at their bonding interfaces. Detecting damage at these bonding interfaces is of significant engineering importance. However, distinguishing damage-induced nonlinearity from the structure’s inherent classical nonlinearity is challenging, which affects detection accuracy. To address this, we propose a robust intelligent detection method for interface debonding damage. This method integrates nonlinear vibro-acoustic modulation with a genetic algorithm (GA), bidirectional long short-term memory (BiLSTM), and an attention mechanism. The attention mechanism prioritizes key features, emphasizing the impact of damage-induced nonlinearities on the identification process. To verify the effectiveness of our approach, we conducted experiments on a composite bonded plate using the nonlinear vibro-acoustic modulation method for the interface debonding damage identification. The results indicate that when the interface is intact, the inherent classical nonlinearity of the structure introduces nonlinear damage characteristics into the damage spectrum, significantly affecting damage discrimination. However, after training the GA-BiLSTM-attention mechanism model, the discriminant results validate the model’s effectiveness in distinguishing interface debonding damage while accounting for inherent classical nonlinear effects.
LOO-PIT: A sensitive posterior test
Authors: Nguyen Alan B.H. , Bonici Marco , McGee Glen , Percival Will J. 
Published: January 2025

Abstract
With the advent of the next generation of astrophysics experiments, the volume of data
available to researchers will be greater than ever. As these projects will significantly drive
down statistical uncertainties in measurements, it is crucial to develop novel tools to assess
the ability of our models to fit these data within the specified errors. We introduce to
astronomy the Leave One Out-Probability Integral Transform (LOO-PIT) technique. This first
estimates the LOO posterior predictive distributions based on the model and likelihood
distribution specified, then evaluates the quality of the match between the model and data by
applying the PIT to each estimated distribution and data point, outputting a LOO-PIT
distribution. Deviations between this output distribution and that expected can be characterised
visually and with a standard Kolmogorov-Smirnov distribution test. We compare LOO-PIT and the
more common χ
2 test using both a simplified model and a more realistic astrophysics
problem, where we consider fitting Baryon Acoustic Oscillations in galaxy survey data with
contamination from emission line interlopers. LOO-PIT and χ
2 tend to find different signals
from the contaminants, and using these tests in conjunction increases the statistical power
compared to using either test alone. We also show that LOO-PIT outperforms χ
2 in certain
realistic test cases.
An optimized lens design using optical imaging for surveillance camera systems with minimal aberration
Authors: Rudiran Sureshkumar , Dhanalakshmi Samiappan , Maik Vivek 
Published: January 2025

Abstract
Modern security cameras are complex, operate in visible and near-infrared wavelength bands with a vast field of view, and are mounted on a motorized stage to scan and zoom the scene. Here, a low-cost aberration free security camera designed to record activity within a fixed-size field and provide surveillance in the visible band is discussed to reduce the cost of the design and also the fabrication of a custom sensor is highly prohibitive. Using global synthesis and automated design methods with the Code-V tool, we have designed and analyzed an on-screen display (OSD) camera with multiple camera lens parameters and suggested using the proposed camera system during the day in an open, well-lit area. For getting video graphics array format (640 × 480 pixels), the pixel size should be 7.5 µm. Considering the Nyquist criterion, the smallest line pair detected by the sensor would be two pixels wide,15 µm/lp or 66.67 c mm−1. The optimal parameters have been designed and optimized with multiple parameters like modulation transfer function (MTF), spot size diagrams, ray aberration with third-order and fifth-order aberrations curve, and error function. After optimization, the effective focal length value is 3.9881. The aperture size obtained after optimization is f/8, the entrance pupil diameter is 0.4985, and the overall length is 0.5184. The MTF obtained is 0.7563. The error function reduced to 0.2612. The aberration value lies from 0.018019 to −0.018019. The image height obtained at 1.8597. The designed OSD security camera eliminates spherical and chromatic aberration significantly and eliminates other third order and fifth order aberrations. The significant findings benefit the optics community in designing aberration free low-cost security camera systems.
OpenWFS—a library for conducting and simulating wavefront shaping experiments
Authors: Doornbos Jeroen H , Cox Daniël W S , Knop Tom , Sasikumar Harish , Vellekoop Ivo M 
Published: January 2025

Abstract
Wavefront shaping (WFS) is a technique for controlling the propagation of light. With applications ranging from microscopy to free-space telecommunication, this research field is expanding rapidly. As the field advances, it stands out that many breakthroughs are driven by the development of better software that incorporates increasingly advanced physical models and algorithms. Typical WFS software involves a complex combination of low-level hardware control, signal processing, calibration, troubleshooting, simulation, and the WFS algorithm itself. This complexity makes it hard to compare different algorithms and to extend existing software with new hardware or algorithms. Moreover, the complexity of the software can be a significant barrier for end users of microscopes to adopt WFS. OpenWFS addresses these challenges by providing a modular Python library that separates hardware control from the WFS algorithm itself. Using these elements, a WFS algorithm can be written in a minimal amount of code, with OpenWFS taking care of low-level hardware control, synchronization, and troubleshooting. Algorithms can be used on different hardware or in a completely simulated environment without changing the code. Moreover, we provide full integration with the Micro-Manager microscope control software, enabling WFS experiments to be executed from a user-friendly graphical user interface.
Research on interpretability of writer identification based on multi-label classification of writing style
Authors: Li Zhenjiang , Zhang Miao , Zhang Qianxue 
Published: January 2025

Abstract
Writer identification based on deep learning has shown great potential in fields such as forensic analysis and financial security due to its high efficiency and accuracy. However, the specificity of deep neural networks limits the acceptance and adoption of their identification results in these fields.This is due to the ‘opacity’ of deep neural networks. To address this issues, this paper proposes an interpretable framework for writer identification based on multi-label classification of writing styles, implemented using residual networks and attention mechanisms. Firstly, this study selects five writing style types commonly used based on the experience of manual writer identification.Based on the Chinese handwriting dataset HWDB2.0, multi-label writing style annotation was carried out to construct the writing style dataset HWDB-STYLE. Next, a residual convolutional neural network combined with a channel-spatial attention module is used to construct the backbone network. Finally, the number and structure of the classifiers in the backbone network are improved and a multi-task model is obtained which performs multi-label classification of both writer and writing styles. This model can provide both the identity of the writer and the classification of five different writing style types, and interpret the output writer identity results through the output style type. Experiments on the HWDB-STYLE dataset demonstrate that the model not only maintains high accuracy in writer identification but also accurately classifies the writing style of each sample. The results are consistent with human observations, providing a level of interpretability for writer identification results.
Double flux radial electrodynamic bearing with the radial air gap between permanent magnet rings and its optimization using artificial neural network for bearing characteristics
Authors: Supreeth D K , Bekinal Siddappa I , Shivamurthy R C 
Published: January 2025

Abstract
This paper presents the design and optimization of electrodynamic bearing (EDB) using Artificial neural networks (ANN). Initially, Finite element (FE) simulations were conducted to determine electric pole frequency

(

ω

rl

)

, and the results of the simulations were fitted to an analytical model to obtain the stiffnesses (k
1 and k
2) and damping coefficients (c
1 and c
2) of an EDB. An ANN model was utilized for designing, optimizing, and predicting the stiffnesses, damping coefficients, and electric pole frequency (

ω

rl

) of EDB. An ANN model with five different inputs, multiple types of algorithms, and different hidden neuron (HN) configurations was developed and trained to predict crucial bearing characteristics. The Bayesian Regularization (BR) algorithm with 10 HN demonstrated the lowest average error. Finally, the ANN model was used to optimize the EDB by utilizing MATLAB’s code of the Bonobo optimization algorithm. The optimization results were validated using the simulation results of COMSOL Multiphysics, and critical bearing characteristics were determined by curve-fitting an analytical model to the simulation outcomes. The results showed that ANN models could predict and enhance the EDB performance, as demonstrated by comparing results with the models’ predictions.
Temporal Neutrality and Temporal Bias
Authors: Scheffler Samuel 
Published: January 2025

Abstract
This chapter discusses what Derek Parfit calls “the bias toward the future”: our preference that certain kinds of painful experiences lie in our past rather than our future, and that certain kinds of pleasurable experiences lie in our future rather than our past. Parfit argues that this bias is bad for us and that adopting a kind of temporal neutrality in its place would be good for us. The chapter denies this: it argues that internalizing temporal neutrality would compromise our ability to sustain the relationships we most value. It then argues that neutrality is not, in any case, a general requirement of rationality. The chapter ends by suggesting that future bias is not a distinctive problem but rather a special case of a more general phenomenon, which is that various of our attitudes change with the passage of time in ways that may elude our understanding.
Plasmodium falciparum histidine-rich protein 2 exhibits cell penetration and cytotoxicity with autophagy dysfunction
Authors: Iwasaki Takashi , Shimoda Mayu , Kanayama Haru , Kawano Tsuyoshi 
Published: January 2025

Abstract
Plasmodium falciparum is a major cause of severe malaria. This protozoan infects human red blood cells and secretes large quantities of histidine-rich protein 2 (PfHRP2) into the bloodstream, making it a well-known diagnostic marker. Here, however, we identified PfHRP2 as a pathogenic factor produced by P. falciparum. PfHRP2 showed cell penetration and cytotoxicity against various human cells. PfHRP2 also exhibited significant cytotoxicity at concentrations found in P. falciparum-infected patients' blood (90-100 n m). We also showed that PfHRP2 binds to Ca2+ ions, localizes to intracellular lysosomes, increases lysosomal Ca2+ levels, and inhibits the basal level of autophagy by preventing autolysosome formation. Furthermore, the Ca2+-dependent cytotoxicity of PfHRP2 was suppressed by the metal ion chelator ethylenediaminetetraacetic acid. In summary, our findings suggest PfHRP2 as a crucial pathogenic factor produced by P. falciparum and its mode of action. Overall, this study provides preliminary insights into P. falciparum malaria pathogenesis.
Nanopore decoding with speed and versatility for data storage
Authors: Volkel Kevin D , Hook Paul W , Keung Albert , Timp Winston , Tuck James M 
Published: January 2025

Abstract

Motivation
As nanopore technology reaches ever higher throughput and accuracy, it becomes an increasingly viable candidate for reading out DNA data storage. Nanopore sequencing offers considerable flexibility by allowing long reads, real-time signal analysis, and the ability to read both DNA and RNA. We need flexible and efficient designs that match nanopore’s capabilities, but relatively few designs have been explored and many have significant inefficiency in read density, error rate, or compute time. To address these problems, we designed a new single-read per-strand decoder that achieves low byte error rates, offers high throughput, scales to long reads, and works well for both DNA and RNA molecules. We achieve these results through a novel soft decoding algorithm that can be effectively parallelized on a GPU. Our faster decoder allows us to study a wider range of system designs.

Results
We demonstrate our approach on HEDGES, a state-of-the-art DNA-constrained convolutional code. We implement one hard decoder that runs serially and two soft decoders that run on GPUs. Our evaluation for each decoder is applied to the same population of nanopore reads collected from a synthesized library of strands. These same strands are synthesized with a T7 promoter to enable RNA transcription and decoding. Our results show that the hard decoder has a byte error rate over 25%, while the prior state of the art soft decoder can achieve error rates of 2.25%. However, that design also suffers a low throughput of 183 s/read. Our new Alignment Matrix Trellis soft decoder improves throughput by 257× with the trade-off of a higher byte error rate of 3.52% compared to the state of the art. Furthermore, we use the faster speed of our algorithm to explore more design options. We show that read densities of 0.33 bits/base can be achieved, which is 4× larger than prior MSA-based decoders. We also compare RNA to DNA, and find that RNA has 85% as many error-free reads when compared to DNA.

Availability and implementation
Source code for our soft decoder and data used to generate figures is available publicly in the Github repository https://github.com/dna-storage/hedges-soft-decoder (10.5281/zenodo.11454877). All raw FAST5/FASTQ data are available at 10.5281/zenodo.11985454 and 10.5281/zenodo.12014515.
Working in biocuration: contemporary experiences and perspectives
Authors: Davies Sarah R 
Published: January 2025

Abstract
This perspective article synthesizes current knowledge regarding what is known regarding biocuration as a career and the challenges facing the field. It draws on existing literature and ongoing qualitative research to discuss the nature of biocuration, biocurators’ career trajectories, key challenges that biocurators face, and strategies for overcoming these. Overall, biocurators express a high degree of satisfaction with their work and see it as central to the wider biosciences. The central challenges that they face relate to the underfunding and under-recognition of this work, meaning that there is minimal stable funding for the field and that the work of human biocurators is often invisible to those who use curated resources. The article closes by critically discussing existing and potential strategies for responding to these challenges.
Media, Spillovers, and Social Norms: The Electoral Impact of Anti-Far Right Protests in the 2002 French Election
Authors: Lagios Nicolas , Méon Pierre-Guillaume , Tojerow Ilan 
Published: January 2025

Abstract
We study the electoral impact of protesting against the far right by investigating the demonstrations held during the 2002 French presidential elections against far-right candidate Jean-Marie Le Pen. Instrumenting rally attendance with rainfall while factoring in that some municipalities never host protests, we find that larger protests reduced the number of votes for Le Pen and abstention, while increasing the number of votes for the incumbent president, Jacques Chirac. We find that the effect spread out beyond the municipalities that hosted protests and worked through media exposure. Using survey data, we show that protests reduced support for the policies advocated by Le Pen. Moreover, the positive effect on voting for Chirac resulted from right-wing voters switching from Le Pen to Chirac and left-wing voters not casting a blank ballot, implying that some voters voted expressively. Finally, we show that protests reduced the social desirability of voting for Le Pen.
Ecotoxicological screening of an e-liquid mixture using computational tools
Authors: Massarsky Andrey , Bonin Adam , Atalay Yasemin , Jones Antony 
Published: January 2025

Abstract
E-liquids used in electronic nicotine delivery systems (ENDS) are mixtures composed of a variety of chemicals, with nicotine, propylene glycol, and glycerol typically representing the primary ingredients. The U.S. Food and Drug Administration (USFDA) requires manufacturers to evaluate the environmental impacts of manufacture, use, and disposal of ENDS, e-liquids, and other tobacco products as a component of the premarket tobacco product application (PMTA). Environmental impacts may be assessed by examining the potential ecotoxicological risks of e-liquid ingredients in National Environmental Policy Act (NEPA) of 1969 environmental assessments as part of the PMTA. Although ecotoxicological characteristics have been assessed for myriad industrial chemicals, pharmaceuticals, and personal care products, to date, no published study has evaluated the ecotoxicological characteristics of e-liquids. Hence, this study describes a novel framework to screen complex e-liquid mixtures for chemicals that exhibit the greatest potential ecological risk. Specifically, the U.S. Environmental Protection Agency (USEPA) Ecological Structure Activity Relationships program was used to predict toxicity of e-liquid ingredients to several aquatic organisms; the relative toxicity of each chemical was then calculated, accounting for toxicity thresholds and chemical concentration. The potential environmental fate of seven chemicals exhibiting the greatest toxicity was then evaluated using the USEPA Exposure and Fate Assessment Screening Tool. Concentrations of chemicals released to the environment under hypothetical manufacturing and end-use release scenarios were estimated in surface water and compared with toxicological benchmarks to determine the potential impacts to aquatic organisms. The described framework is a robust high-throughput method to screen the chemicals of greatest ecological concern in complex chemical mixtures, including but not limited to e-liquids, to enable a targeted assessment of potential ecological impacts.
Galaxy as a gateway to bioinformatics: Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for scRNA-seq
Authors: Goclowski Camila L , Jakiela Julia , Collins Tyler , Hiltemann Saskia , Howells Morgan , Loach Marisa , Manning Jonathan , Moreno Pablo , Ostrovsky Alex , Rasche Helena , Tekman Mehmet , Tyson Graeme , Videm Pavankumar , Bacon Wendi 
Published: January 2025

Abstract

Background
Bioinformatics is fundamental to biomedical sciences, but its mastery presents a steep learning curve for bench biologists and clinicians. Learning to code while analyzing data is difficult. The curve may be flattened by separating these two aspects and providing intermediate steps for budding bioinformaticians. Single-cell analysis is in great demand from biologists and biomedical scientists, as evidenced by the proliferation of training events, materials, and collaborative global efforts like the Human Cell Atlas. However, iterative analyses lacking reinstantiation, coupled with unstandardized pipelines, have made effective single-cell training a moving target.

Findings
To address these challenges, we present a Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for single-cell RNA sequencing (scRNA-seq) analysis, which offers parallel analytical methods using a graphical interface (buttons) or code. With clear, interoperable materials, MIGHTS facilitates smooth transitions between environments. Bridging the biologist–programmer gap, MIGHTS emphasizes interdisciplinary communication for effective learning at all levels. Real-world data analysis in MIGHTS promotes critical thinking and best practices, while FAIR data principles ensure validation of results. MIGHTS is freely available, hosted on the Galaxy Training Network, and leverages Galaxy interfaces for analyses in both settings. Given the ongoing popularity of Python-based (Scanpy) and R-based (Seurat & Monocle) scRNA-seq analyses, MIGHTS enables analyses using both.

Conclusions
MIGHTS consists of 11 tutorials, including recordings, slide decks, and interactive visualizations, and a demonstrated track record of sustainability via regular updates and community collaborations. Parallel pathways in MIGHTS enable concurrent training of scientists at any programming level, addressing the heterogeneous needs of novice bioinformaticians.