bluuebunny/crossref_metadata_2025_split huggingface dataset:
The optimal grouting area and cable distribution design for guaranteeing roadway stability while considering fluid mechanical effects via the CMOEAD algorithm
Authors: Li Peng , Guo Yinghai , Cheng Yinghao , Zhang Jiaming 
Published: January 2025

AbstractWater inrush in roadways frequently occurs in coal mines when the rock mass is enriched with underground water. To avoid underground water flow into the roadway and guarantee the stability of the roadway, grouting and cables are commonly used to prevent water inrush and guarantee the stability of the roadway. In this work, FLAC3D (fast lagrangian analysis of continua 3 dimension) numerical simulation software was used, and the fluid‒mechanical coupling effects were considered. In combination with the CMOEAD (constrained multi-objective evolution algorithm based on decomposition) optimization method, the optimal grouting area and cable distribution were determined: the center point of the ellipse (grouting area) is (0.01, 1.59), the long axis length is 4.73 m, the short axis length is 4.60 m, and the inclination angle of the ellipse is 53.15°. The cable length is 6.51 m, the total number of cables is 11. The grouting area and cable distribution design from numerical simulation results were applied to engineering practice, the degree of water inrush was markedly reduced, and the displacement of the roadway was within control, indicating that the proposed method is workable and reliable.
Advancing macromolecular structure determination with microsecond X-ray pulses at a 4th generation synchrotron
Authors: Orlans Julien , Rose Samuel L. , Ferguson Gavin , Oscarsson Marcus , Homs Puron Alejandro , Beteva Antonia , Debionne Samuel , Theveneau Pascal , Coquelle Nicolas , Kieffer Jerome , Busca Paolo , Sinoir Jeremy , Armijo Victor , Lopez Marrero Marcos , Felisaz Franck , Papp Gergely , Gonzalez Herve , Caserotto Hugo , Dobias Fabien , Gigmes Jonathan , Lebon Guillaume , Basu Shibom , de Sanctis Daniele 
Published: January 2025

AbstractSerial macromolecular crystallography has become a powerful method to reveal room temperature structures of biological macromolecules and perform time-resolved studies. ID29, a flagship beamline of the ESRF 4th generation synchrotron, is the first synchrotron beamline in the world capable of delivering high brilliance microsecond X-ray pulses at high repetition rate for the structure determination of biological macromolecules at room temperature. The cardinal combination of microsecond exposure times, innovative beam characteristics and adaptable sample environment provides high quality complete data, even from an exceptionally small amount of crystalline material, enabling what we collectively term serial microsecond crystallography (SµX). After validating the use of different sample delivery methods with various model systems, we applied SµX to an integral membrane receptor, where only a few thousands diffraction images were sufficient to obtain a fully interpretable electron density map for the antagonist istradefylline-bound A2A receptor conformation, providing access to the antagonist binding mode. SµX, as demonstrated at ID29, will quickly find its broad applicability at upcoming 4th generation synchrotron sources worldwide and opens a new frontier in time-resolved SµX.
Two types of motifs enhance human recall and generalization of long sequences
Authors: Wu Shuchen , Thalmann Mirko , Schulz Eric 
Published: January 2025

AbstractWhether it is listening to a piece of music, learning a new language, or solving a mathematical equation, people often acquire abstract notions in the sense of motifs and variables—manifested in musical themes, grammatical categories, or mathematical symbols. How do we create abstract representations of sequences? Are these abstract representations useful for memory recall? In addition to learning transition probabilities, chunking, and tracking ordinal positions, we propose that humans also use abstractions to arrive at efficient representations of sequences. We propose and study two abstraction categories: projectional motifs and variable motifs. Projectional motifs find a common theme underlying distinct sequence instances. Variable motifs contain symbols representing sequence entities that can change. In two sequence recall experiments, we train participants to remember sequences with projectional and variable motifs, respectively, and examine whether motif training benefits the recall of novel sequences sharing the same motif. Our result suggests that training projectional and variables motifs improve transfer recall accuracy, relative to control groups. We show that a model that chunks sequences in an abstract motif space may learn and transfer more efficiently, compared to models that learn chunks or associations on a superficial level. Our study suggests that humans construct efficient sequential memory representations according to the two types of abstraction we propose, and creating these abstractions benefits learning and out-of-distribution generalization. Our study paves the way for a deeper understanding of human abstraction learning and generalization.
Teletraining to Teach Communication Partners to Support Students With Multiple Disabilities Including Cortical Visual Impairment and Emerging Symbolic Communication in Communicating Choices
Authors: McCarty Tara V. , Light Janice C. 
Published: January 2025

Purpose:
The purpose of the current study was to investigate the impact of a teletraining to teach adult communication partners the “Communicating Choices–Cortical Visual Impairment (CVI)” strategy to support participation for students with multiple disabilities.

Method:
A nonconcurrent, multiple-probe, across-participants design was implemented with four adult communication partner (i.e., speech-language pathologist, paraeducators, and parent) and student dyads to determine the effects of a teletraining administered over a video-calling platform on the partner's implementation of the substeps from the Communicating Choice–CVI strategy. Dyads completed a randomly predetermined number of baseline sessions, two teletraining sessions to instruct the partners in the strategy, and five intervention probes.

Results:
Results of the study indicated that communication partners successfully implemented an increased number of the strategy substeps following two short teletraining sessions and that all students communicated choices in every opportunity when they were provided with a structured opportunity. Communication partners reported that the strategy was successful for the students and that the strategy could be applied to other contexts or students.

Conclusions:
This study provides evidence for the importance of communication partner training for students with complex needs in domains such as vision and communication. Furthermore, the Communicating Choices–CVI strategy may lead to increased opportunities for academic participation, social engagement, and self-determination for students whose opportunities to take on active roles in educational settings are often severely restricted.

Supplemental Material:

https://doi.org/10.23641/asha.28108139
Cognitive Predictors of Perception and Adaption to Dysarthric Speech in Older Adults
Authors: Lansford Kaitlin L. , Hirsch Micah E. , Barrett Tyson S. , Borrie Stephanie A. 
Published: January 2025

Purpose:
In effortful listening conditions, speech perception and adaptation abilities are constrained by aging and often linked to age-related hearing loss and cognitive decline. Given that older adults are frequent communication partners of individuals with dysarthria, the current study examines cognitive–linguistic and hearing predictors of dysarthric speech perception and adaptation in older listeners.

Method:
Fifty-eight older adult listeners (aged 55–80 years) completed a battery of hearing and cognitive tasks administered via the National Institutes of Health Toolbox. Participants also completed a three-phase familiarization task (pretest, training, and posttest) with one of two speakers with dysarthria. Elastic net regression models of initial intelligibility (pretest) and intelligibility improvement (posttest) were constructed for each speaker with dysarthria to identify important cognitive and hearing predictors.

Results:
Overall, the regression models indicated that intelligibility outcomes were optimized for older listeners with better words-in-noise thresholds, vocabulary knowledge, working memory capacity, and cognitive flexibility. Despite some convergence across models, unique constellations of cognitive–linguistic and hearing parameters and their two-way interactions predicted speech perception and adaptation outcomes for the two speakers with dysarthria, who varied in terms of their severity and perceptual characteristics.

Conclusion:
Here, we add to an extensive body of work in related disciplines by demonstrating age-related declines in speech perception and adaptation to dysarthric speech can be traced back to specific hearing and cognitive–linguistic factors.
Conflicting Evidence for a Motor Timing Theory of Stuttering: Choral Speech Changes the Rhythm of Both Neurotypical and Stuttering Talkers, but in Opposite Directions
Authors: Meekings Sophie , Eijk Lotte , Stankova Stefany , Maruthy Santosh , Scott Sophie Kerttu 
Published: January 2025

Purpose:
Talking in unison with a partner, otherwise known as choral speech, reliably induces fluency in people who stutter (PWS). This effect may arise because choral speech addresses a hypothesized motor timing deficit by giving PWS an external rhythm to align with and scaffold their utterances onto. This study tested this theory by comparing the choral speech rhythm of people who do and do not stutter to assess whether both groups change their rhythm in similar ways when talking chorally.

Method:
Twenty adults who stutter and 20 neurotypical controls read a passage on their own and then a second passage chorally with a neurotypical partner. Their speech rhythm was evaluated using Envelope Modulation Spectrum (EMS) analysis to derive peak frequency, a measure of the dominant rate of modulation in the sound envelope, as well as peak amplitude (the amplitude of the peak frequency), across several octave bands associated with different features of speech.

Results:
The two groups displayed opposing patterns of rhythmic change during choral reading. People with a stutter increased their EMS peak frequency when they read chorally, while neurotypical talkers' choral speech was characterized by reduced peak frequency compared to solo reading.

Conclusions:
Our findings show that the choral speech rhythm of PWS differs from that of neurotypical talkers. This indicates limited support for the hypothesis that choral speech addresses a motor timing deficit by giving PWS a rhythmic cue with which to align.
Speech-Language Pathologists' Language Practices During Speech-Language Therapy for Bilingual Children
Authors: Gross Megan C. , Dubé Kylie 
Published: January 2025

Purpose:
Previous research and clinical practice guidelines indicate that it is best practice to support bilingual children with communication disorders in both of their languages. However, there is a shortage of bilingual speech-language pathologists (SLPs) in the United States. Therefore, it is important to examine how SLPs with varying linguistic backgrounds serve their bilingual clients. This study investigated the language practices of SLPs during intervention with bilingual children and identified needed resources.

Method:
This convergent mixed-methods study included a survey and semistructured interviews, with interviewees sampled purposively based on survey responses. Quantitative analyses were examined alongside qualitative themes to address each research question.

Results:
Overall, 21.5% of the bilingual clients described by survey respondents were reported to receive intervention in both of their languages. SLPs who shared both languages with their clients and had a professional level of proficiency were more likely to provide services in both languages and to engage in code-switching during sessions. Qualitative analysis revealed creative strategies that SLPs used to overcome their own linguistic limitations when they lacked skills in a client's heritage language. Although practices varied based on the SLP's linguistic background, respondents identified similar priorities, including more bilingual providers and access to training, multilingual materials, and interpreters.

Conclusions:
This study contributes insights into how SLPs serve their bilingual clients and what resources are needed to improve services. Although this study focused on Massachusetts and had a small sample, the findings may apply to other states, and there may be benefits to addressing these questions at a state level to provide targeted advocacy, training, and resources.
Enhancement of first carbon hit rate in converter steelmaking through integrated learning‐based data cleansing
Authors: Yang Lingyun , Zhao Qianchuan , Li Tan , Gu Mu , Yang Kaiwu , Song Weining 
Published: January 2025

AbstractFirst carbon hit rate (FCHR) is an essential indicator of steel converter smelting, reflecting the proportion of steel tapping completed without additional oxygen blowing. However, significant data loss has occurred due to equipment ageing and worker operations, resulting in difficulties in analysing the FCHR. This paper uses mechanism analysis and feature screening to determine the model input, predicts and fills in abnormal data through ensemble learning, and then optimises it through data transformation. Finally, the Stacking model predicts the FCHR, with a training accuracy of up to 94.5% and a test set accuracy of 90.5%. In addition, the authors also conducted a predictive study on oxygen consumption, and the hit rate performed well under different error thresholds, with a maximum of 97.9%. These results provide powerful decision support for steel production and effectively overcome the challenges of data missingness.
Enhancing intrusion detection against denial of service and distributed denial of service attacks: Leveraging extended Berkeley packet filter and machine learning algorithms
Authors: Anand Nemalikanti , M A Saifulla , Aakula Pavan Kumar , Ponnuru Raveendra Babu , Patan Rizwan , Reddy Chegireddy Rama Prakasha 
Published: January 2025

AbstractAs organizations increasingly rely on network services, the prevalence and severity of Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks have emerged as significant threats. The cornerstone of effectively addressing these challenges lies in the timely and precise detection capabilities offered by advanced intrusion detection systems (IDS). Hence, an innovative IDS framework is introduced that seamlessly integrates the extended Berkeley Packet Filter (eBPF) with powerful machine learning algorithms—specifically Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and TwinSVM—enabling unparalleled real‐time detection of DDoS attacks. This cutting‐edge solution provides a robust and scalable IDS framework to combat DoS and DDoS threats with high efficiency, leveraging eBPF's capabilities within the Linux kernel to bypass typical user space constraints. The methodology encompasses several key steps: (a) Collection of data from the renowned CIC‐IDS‐2017 repository; (b) Processing the raw data through a meticulous series of steps, including transmission, cleaning, reduction, and discretization; (c) Utilizing an ANOVA F‐test for the extraction of critical features from the preprocessed data; (d) Application of various ML algorithms (DT, RF, SVM, and TwinSVM) to analyze the extracted features for potential intrusion; (e) Implementing an eBPF program to capture network traffic and harness trained model parameters for efficient attack detection directly within the kernel. The experimental results reveal outstanding accuracy rates of 99.38%, 99.44%, 88.73%, and 93.82% for DT, RF, SVM, and TwinSVM, respectively, alongside remarkable precision values of 99.71%, 99.65%, 84.31%, and 98.49%. This high‐speed, accurate detection model is ideally suited for high‐traffic environments such as data centers. Furthermore, its foundational architecture paves the way for future advancements, including the potential integration of eBPF with XDP to achieve even lower‐latency packet processing. The experimental code is available at the GitHub repository link: https://github.com/NemalikantiAnand/Project.
KLMS‐Net: Deep unrolling for kernel least mean square algorithm
Authors: Tang Yu , Zhang Tingsen , Gao Jinrong , Bi Dongjie , Li Xifeng , Xie Yongle 
Published: January 2025

AbstractThe performance of the kernel least mean square (KLMS) algorithm heavily depends on the utilized kernel function and its associated parameters. To address this inherent limitation, this letter proposes a novel network framework based on the deep unrolling of KLMS (KLMS‐Net). KLMS‐Net transforms the iterative process of KLMS into the forward propagation of deep neural networks (DNNs), which learn the implicit feature mappings in a model‐driven manner, providing DNNs with explicit interpretability. Compared to standalone DNNs, KLMS‐Net compresses the size of the solution space of DNNs within the mathematical framework of KLMS. Through prediction experiments on nonlinear systems across various scenarios, KLMS‐Net exhibits significantly enhanced convergence speed and prediction accuracy, achieving improvements of approximately two orders of magnitude compared to the original KLMS and standalone DNNs. These findings highlight the substantial potential of KLMS‐Net in processing data with complex nonlinear structures and connect the fields of kernel adaptive filtering and deep learning.
YOLOv7‐SFWC: A detection algorithm for illegal manned trucks
Authors: Wu Xuan , Wang Yanan , Nie Tengtao , Pan Wenlin 
Published: January 2025

AbstractAutomatic analysis and evidence collection of obvious traffic violations, such as illegal manned trucks, is one of the critical operational challenges of the traffic police department's business. For the enormous volume of road surveillance images generated daily, traditional manual screening is highly time‐intensive and resource‐draining. Therefore, this article proposes an improved detection model YOLOv7‐SFWC for illegally manned trucks. First of all, the pictures of illegal manned vehicles obtained by relevant departments are expanded and labeled, and the dataset of illegal manned vehicles is created. Building upon the foundational YOLOv7 model, this study replaces the traditional convolution module with the FasterNet convolution module and SCConv module, and introduces the Wise‐IoU (WIoU) loss function algorithm and Coordinate Attention (CA) mechanism. The results show that the mAP value of the YOLOv7‐SFWC model is improved by 4.15% and FPS by 7.6 compared with the original YOLOv7 model, and the computational complexity is reduced to adapt to the deployment. Moreover, the model's effectiveness is validated through extensive comparison experiments. Finally, the visual results show the accurate performance of the model and verify the progress of YOLOv7‐SFWC. This advancement has the potential to transform traffic violation enforcement by reducing reliance on manual screening, effectively combating traffic violations, and purifying traffic order.
Cultural information dynamics and the rise of women in Norway’s state and military
Authors: Girard Chris 
Published: January 2025

AbstractThe growing presence of women in the Norwegian state and military heralds an epoch-making, worldwide transformation. A key challenge is to explain why institutions which excluded women for more than a millennium no longer promote all-male membership. This tectonic shift is investigated with a data-based synthetical methodology. Multidisciplinary evidence going back five thousand years is combined with a graphical analysis of two centuries of time series data. The guiding theory is that historical pathways for cultural information flow have coevolutionary spatial and energetic sociodynamics. Accordingly, women’s exclusion from warfare and politics in agrarian-era Norway coevolved with three interconnected constraints: oral communication, dependence on musculoskeletal energy, and the spatial limitations of person-to-person contact. The contemporary relaxation of such constraints is investigated using two centuries of data culled from Norway’s statistical yearbooks. These data show that women’s entry into Norway’s national legislature, pushed by women’s organizations, roughly coevolved with literacy-based communication and education, industrial-era extrasomatic energy, and distance-closing motorization. Multidisciplinary evidence also indicates that women’s military and political careers were spatiotemporally handicapped by inflexible work hours and worksites far from childrearing locations. The Norwegian military prioritized physical endurance rather than the competencies that women would later bring to a 21st-century rapid reaction force. Today, with new information pathways forming, the digitalized knowledge economy is reversing the human-capital advantage of men compared to women. Instantaneous information exchange and high-tech energetics are reducing spatiotemporal barriers via remote work. Cross-disciplinary and time-series evidence suggest that these digital-age dynamics contribute to a more gender-neutral state and military.
Beyond borders or building new walls?
Authors: Tran Hao , Stell Annita 
Published: January 2025

Abstract
Generative Artificial Intelligence (GenAI) has been offering unprecedented opportunities for language education.
However, its capacity to embrace linguistic diversity, particularly for learners of dialect-rich languages like Vietnamese and
Mandarin, remains underexamined. Without careful consideration, GenAI risks reinforcing language hegemonies, thereby contributing
to the recolonization of language learning landscapes by marginalizing minority dialects in favour of preferred standards.
Adopting sociolinguistics interview, this study explores GenAI’s (namely ChatGPT’s) ability to recognize and generate
dialect-specific content in discussing several pre-determined questions in both Vietnamese dialects (i.e., Northern, Southern, and
Central) and Mandarin varieties (i.e., Mainland Standard Mandarin, Taiwanese Mandarin, and Singaporean Mandarin). A multi-stage
role prompt, focusing on the topic of food, was used for both Vietnamese dialects and Mandarin varieties to generate responses.
Our study reveals major inconsistencies in the representation of Vietnamese dialects and Chinese varieties within AI-generated
output, raising critical questions about generative AI’s role in perpetuating linguistic hierarchies. We conclude by emphasizing
the need for tailored language learning approaches that leverage generative AI’s capabilities to not only accommodate but also
celebrate the rich tapestry of global dialects and languages, ensuring equitable access to language education for all
learners.
Environmental discourse on social media
Authors: Sancho Ortiz Ana E. 
Published: January 2025

Abstract
The multifaceted character of environmental discourse becomes evident on social media, where non-governmental
organisations (NGOs) engage in public environmental discourses. Focused on X/Twitter for environmental purposes, this paper
investigates engagement as an interpersonal phenomenon comprising two dimensions: self-presentation and audience projection. It
qualitatively and quantitatively analyses 100 X/Twitter posts from WWF and Greenpeace (50 per account) with the tool NVivo 14.
Findings reveal how the NGOs rely on verbal (self-mentions, lexical items) and non-verbal resources (emoji and multimedia
elements) to present themselves to their audience, whilst exploiting verbal resources (second-person pronouns and directives) to
construct their audience. Results underscore how NGOs leverage X/Twitter to assert their own legitimacy and that of their audience
as integral members of the environmentalist community.