bluuebunny/crossref_metadata_2025_split huggingface dataset:
Completion of SysML state machines from Given–When–Then requirements
Authors: de Biase Maria Stella , Bernardi Simona , Marrone Stefano , Merseguer José , Palladino Angelo 
Published: November 2024

AbstractMDE enables the centrality of the models in semi-automated development processes. However, its level of usage in industrial settings is still not adequate for the benefits MDE can introduce. This paper proposes a semi-automatic approach for the completion of high-level models in the lifecycle of critical systems, which exhibit an event-driven behaviour. The proposal suggests a specification guideline that starts from a partial SysML model of a system and on a set of requirements, expressed in the well-known Given–When–Then paradigm. On the basis of such requirements, the approach enables the semi-automatic generation of new SysML state machines model elements. Accordingly, the approach focuses on the completion of the state machines by adding proper transitions (with triggers, guards and effects) among pre-existing states. Also, traceability modelling elements are added to the model. Two case studies demonstrate the feasibility of the proposed approach.
Engineered Osteochondral Scaffolds with Bioactive Cartilage Zone for Enhanced Articular Cartilage Regeneration
Authors: Golebiowska Aleksandra A. , Intravaia Jonathon T. , Sathe Vinayak , Kumbar Sangamesh G. , Nukavarapu Syam P. 
Published: November 2024

Abstract
Despite progress, osteochondral (OC) tissue engineering strategies face limitations in terms of articular cartilage layer development and its integration with the underlying bone tissue. The main objective of this study is to develop a zonal OC scaffold with native biochemical signaling in the cartilage zone to promote articular cartilage development devoid of cells and growth factors. Herein, we report the development and in vivo assessment of a novel gradient and zonal-structured scaffold for OC defect regeneration. The scaffold system is composed of a mechanically supportive 3D-printed template containing decellularized cartilage extracellular matrix (ECM) biomaterial in the cartilage zone that possesses bioactive characteristics, such as chemotactic activity and native tissue biochemical composition. OC scaffolds with a bioactive cartilage zone were implanted in vivo in a rabbit osteochondral defect model and assessed for gross morphology, matrix deposition, cellular distribution, and overall tissue regeneration. The scaffold system supported recruitment and infiltration of host cells into the cartilage zone of the graft, which led to increased ECM deposition and physiologically relevant articular cartilage tissue formation. Semi-quantitative ICRS scoring (overall score double for OC scaffold with bioactive cartilage zone compared to PLA scaffold) further confirm the bioactive scaffold enhanced articular cartilage engineering. This strategy of designing bioactive scaffolds to promote endogenous cellular infiltration can be a much simpler and effective approach for OC tissue repair and regeneration.
Engaging Marginalized Stakeholders: Towards a Dialogical Theorization of Effective Corporate-Rightsholder Remedy
Authors: Bianchi Lara , Caruana Robert , Shivji Alysha Kate 
Published: November 2024

AbstractIn the remediation of business-related human rights abuses, meaningful stakeholder engagement which culminates in effective access to remedy begins with forms of communication that enable the voice and agency of marginalized stakeholders, and value their lived experiences. Here, we consider how the development of adialogicaltheorization of stakeholder engagement is aligned with the practical and ethical goals of an effective access to human rights remedy. Drawing on dialogical theory, we discern four ethical criteria —power cognizance, polyphonic pluralism, generative agonism and discursive unfinalizability— that reveal three general approaches to stakeholder engagement —essentially monologic, seemingly dialogic and authentically dialogic— based on the extent to which they exhibit the criteria above. We propose and discuss an 'authentically dialogic' approach for organizations adopting morally expansive, victim-centric approaches to engagement in the design and implementation of company-led remedial mechanisms.
Textflows: an open science NLP evaluation approach
Authors: Martinc Matej , Perovšek Matic , Lavrač Nada , Pollak Senja 
Published: November 2024

AbstractWeb-based platforms offer suitable experimental environments enabling the construction and reuse of natural language processing (NLP) pipelines. However, systematic evaluation of NLP tools in an open science web-based setting is still a challenge, as suitable experimental environments for the construction and reuse of NLP pipelines are still rare. This paper presents TextFlows, an open-source web-based platform, which enables user-friendly construction, sharing, execution, and reuse of NLP pipelines. It demonstrates that TextFlows can be easily used for systematic evaluation of new NLP components by integrating seven publicly available open-source part of speech (POS) taggers from popular NLP libraries, and evaluating them on six annotated corpora. The integration of new tools into TextFlows supports tools reuse, while the use of precomposed algorithm comparison and evaluation workflows supports experiment reproducibility and testing of future algorithms in the same experimental environment. Finally, to showcase the variety of evaluation possibilities offered in the TextFlows platform, the influence of various factors, such as the training corpus length and the use of pre-trained models, have been tested.
Integrating binary classification and clustering for multi-class dysarthria severity level classification: a two-stage approach
Authors: Al-Ali Afnan S. , Haris Raseena M. , Akbari Younes , Saleh Moutaz , Al-Maadeed Somaya , Rajesh Kumar M. 
Published: November 2024

AbstractDysarthria, a motor speech disorder, poses challenges in accurate severity assessment. Recent research has excelled in classifying dysarthria based on severity levels, primarily utilizing annotated datasets and achieving high accuracies. However, these classification-based approaches may not readily translate to real-world scenarios without predefined labels. This study follows a different path by proposing a two-stage approach leveraging binary classification and clustering to comprehensively analyze and classify dysarthria severity levels. We begin by employing binary classification to differentiate control from dysarthric cases by experiencing eight different feature extraction techniques and two classifiers in order to support the largest amount of dysarthric cases to the second stage, where k-means clustering uncovers hidden patterns and boundaries within dysarthria severity levels, enabling a more nuanced understanding of the disorder. We applied our methodology to the TORGO dataset, a benchmark in dysarthria research, and evaluated it on the UA Speech dataset. After optimizing the number of clusters, our approach achieved an accuracy of 91% with sentence-based features and 85% with word-based features in clustering. This research extends previous studies by exploring unsupervised clustering to differentiate severity levels in unannotated cases, bridging the gap between controlled datasets and practical applications. Our findings highlight the effectiveness of clustering-driven two-stage analysis in improving dysarthria severity-level classification, with implications for real-world clinical settings.
Depression and Anxiety Among Arab Individuals in the United States: A Meta-analysis
Authors: El-Refaay Shaimaa Mosad , Kenny Christina , Weiss Sandra 
Published: November 2024

Abstract
Emerging research suggests that the increasing population of Arab immigrants and refugees living in the United States (U.S.) has a greater risk for depression and anxiety than other groups. The purpose of this meta-analysis was to estimate the prevalence of depression and anxiety for Arabs in the U.S., to examine the moderating effects of key demographic variables (gender, immigration status, ethnicity) and study characteristics (research design and quality) on the prevalence of anxiety and depression, and to evaluate the direct effects of additional demographic and study characteristics on prevalence rates. Using guidelines from the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P), we searched PubMed, Embase, CINHAL, ProQuest, and the Web of Science databases for articles published between January 2000 and March 2023. Two reviewers each individually extracted the articles’ data and evaluated the reported study’s methodological quality using a well-established checklist. Statistical analyses consisted of random effects models to estimate the pooled prevalence of depression and anxiety across all studies, separate random effects models for distinct demographic (e.g., immigration status) and methodologic (e.g., study quality) subgroups that might have unique prevalence rates, and a meta-regression to identify additional variables (e.g., age of individuals) that might influence prevalence rates. Our review included 17 studies with a total sample population size of 22, 558 participants. Results indicated that the estimated prevalence of depression overall was 48% (CI 34, 63%) and 58% (CI 33, 83%) for the prevalence of anxiety. Our subgroup analyses uncovered notable variations in prevalence rates based on gender composition, immigration status, ethnicity, and research quality while results of the meta-regressions demonstrated effects of publication year and age of the Arab individual on the estimated prevalence of depression and anxiety. The high prevalence rates for depression and anxiety identified through this meta-analysis are very concerning and underscore the urgent need to address mental health concerns in Arab communities. Results of subgroup and meta-regression analyses suggest that refugees, individuals from specific countries of origin, and younger Arabic persons may need particular attention by clinicians in screening for and treating their depression and anxiety. Our results also signify the need for further high-quality studies to more rigorously assess prevalence of these mental health problems, since we found evidence of potential publication bias and approximately one third of the studies in the review had less optimal methodological quality based on our evaluation. Ultimately, in-depth research regarding the causes of anxiety or depression among Arab individuals will be essential to the development of culturally competent interventions that may reduce their very high prevalence rates.
Psychometric Evaluation of the New Translated and Culturally Adapted Postpartum Bonding Questionnaire in German-Speaking Women
Authors: Steinbrueck Pia-Cecilia , Meyer Gabriele , Ayerle Gertrud 
Published: November 2024

Abstract

Background
This study aimed to evaluate the construct validity and reliability of the newly adapted German version of the Parental Bonding Questionnaire (PBQ) in a group of mothers drawn from the general population, with children aged 12–24 weeks. This assessment followed a thorough linguistic validation, which was conducted through a systematic, multi-step translation process.

Methods
363 women completed the PBQ online 12–24 weeks after delivery. Cronbach’s alpha and exploratory factor analysis (EFA) were used to assess internal consistency reliability and construct validity.

Results
The original 4-factor model could not be confirmed. The new PBQ provides a single factor solution. Ten items were removed from the original 25-item PBQ to produce the abbreviated German PBQ-15, which showed strong internal consistency. The Cronbach’s alpha for this version was 0.86.

Conclusion
While further research is needed to establish diagnostic thresholds and strengthen the construct validity of the shortened version, the German-language 15-item version of the PBQ holds promise as an accessible tool for recognizing bonding issues in a general population of German-speaking women 3–6 months postpartum.
Statistical Learning Theory and Occam’s Razor: The Core Argument
Authors: Sterkenburg Tom F. 
Published: November 2024

AbstractStatistical learning theory is often associated with the principle of Occam’s razor, which recommends a simplicity preference in inductive inference. This paper distills the core argument for simplicity obtainable from statistical learning theory, built on the theory’s central learning guarantee for the method of empirical risk minimization. This core “means-ends” argument is that a simpler hypothesis class or inductive model is better because it has better learning guarantees; however, these guarantees are model-relative and so the theoretical push towards simplicity is checked by our prior knowledge.
Multifunctional lightweight autonomous vehicles: an agent-based study
Authors: Coretti Sanchez Naroa , Larson Kent 
Published: November 2024

AbstractIn mobility-on-demand services, the number of vehicles needed is often determined by peak demand during rush hours, leading to prolonged vehicle idle times during off-peak periods. This surplus capacity presents an opportunity for vehicles to perform additional tasks, potentially enhancing system efficiency and reducing the overall number of vehicles needed in cities. Leveraging agent-based modeling, we evaluate the effectiveness of vehicles catering to on-demand rides and food deliveries in two real-life scenarios: Cambridge, MA, USA, and San Sebastian, Gipuzkoa, Spain. The results show that multifunctional behavior can lead to reduced fleet sizes, with context-specific exceptions. Additionally, a strategic dispatching algorithm is introduced that demonstrates reductions in wait times and overall distances traveled. This research contributes to the understanding of the performance of multifunctional fleets in diverse urban contexts, informing the development of sustainable and resource-efficient mobility systems.
Devising optimized maize nitrogen stress indices in complex field conditions from UAV hyperspectral imagery
Authors: Li Jiating , Ge Yufeng , Puntel Laila A. , Heeren Derek M. , Bai Geng , Balboa Guillermo R. , Gamon John A. , Arkebauer Timothy J. , Shi Yeyin 
Published: November 2024

Abstract
Nitrogen Sufficiency Index (NSI) is an important nitrogen (N) stress indicator for precision N management. It is usually calculated using variables such as leaf chlorophyll meter readings (SPAD) and vegetation indices (VIs). However, no consensus has been reached on the most preferred variable. Additionally, conventional NSI (NSIuni) calculation assumes N being the sole yield-limiting factor, neglecting other factors such as soil water variability. To tackle these issues, this study compared various variables for NSI calculation and evaluated two new N stress indicators in minimizing the impact of confounding water treatment. The following ground- and aerial-derived variables were compared for NSIuni calculation: SPAD, sampled leaf and canopy N content (LNC, CNC), LNC and CNC estimated using hyperspectral images acquired by an Unmanned Aerial Vehicle, and three VIs (Normalized Difference Vegetation Index (NDVI), Normalized Red Edge Index (NDRE), and Chlorophyll Index) from the hyperspectral images. Results demonstrated that ground-measured variables outperformed aerial-based variables in deriving N-responsive NSI. Especially, LNC derived NSIuni responded to N treatment significantly in ten out of thirteen site-date datasets. For the second objective, a modified NSI (NSIw) and the NDRE/NDVI ratio were compared to NSIuni. NSIw reduced water treatment effects in over 80% of the datasets where NSIuni showed evident impacts. NDRE/NDVI performed similarly to NSIw, with the notable advantage of not requiring prior knowledge of soil water spatial distribution. This research pioneers the optimization of N stress indicators by identifying the best variables for NSI and mitigating the effects of soil water variability. These advancements significantly contribute to precision N management in complex field conditions.
Combined Surface Flux Transport and Helioseismic Far-Side Active Region Model (FARM)
Authors: Yang Dan , Heinemann Stephan G. , Cameron Robert H. , Gizon Laurent 
Published: November 2024

AbstractMaps of the magnetic field at the Sun’s surface are commonly used as boundary conditions in space-weather modeling. However, continuous observations are only available from the Earth-facing part of the Sun’s surface. One commonly used approach to mitigate the lack of far-side information is to apply a surface flux transport (SFT) model to model the evolution of the magnetic field as the Sun rotates. Helioseismology can image active regions on the far side using acoustic oscillations and hence has the potential to improve the modeled surface magnetic field. In this study, we propose a novel approach for estimating magnetic fields of active regions on the Sun’s far side based on seismic measurements and then include them into an SFT model. To calibrate the conversion from helioseismic signal to magnetic field, we apply our SFT model to line-of-sight magnetograms from Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) to obtain reference maps of global magnetic fields (including the far side). The resulting magnetic maps are compared with helioseismic phase maps on the Sun’s far side computed using helioseismic holography. The spatial structure of the magnetic field within an active region is reflected in the spatial structure of the helioseismic phase shifts. We assign polarities to the unipolar magnetic-field concentrations based upon Hale’s law and require approximate flux balance between the two polarities. From 2010 to 2024, we modeled 859 active regions, with an average total unsigned flux of $7.84 \cdot 10^{21}$
7.84
⋅

10

21

Mx and an average area of $4.48 \cdot 10^{10}$
4.48
⋅

10

10

km2. Approximately $4.2\%$
4.2
%
of the active regions were found to have an anti-Hale configuration, which we manually corrected. Including these far-side active regions resulted in an average increase of $1.2\%$
1.2
%
(up to $25.3\%$
25.3
%
) in the total unsigned magnetogram flux. Comparisons between modeled open-field areas and EUV observations reveal a substantial improvement in agreement when far-side active regions are included. This proof of concept study demonstrates the potential of the “combined surface flux transport and helioseismic Far-side Active Region Model” (FARM) to improve space-weather modeling.
On the Instrumental Discrepancies in Lyman-Alpha Observations of Solar Flares
Authors: Greatorex Harry J. , Milligan Ryan O. , Dammasch Ingolf E. 
Published: November 2024

AbstractDespite the energetic significance of Lyman-alpha (Ly$\alpha $
α
; 1216 Å) emission from solar flares, regular observations of flare related Ly$\alpha $
α
have been relatively scarce until recently. Advances in instrumental capabilities and a shift in focus over previous solar cycles mean it is now routinely possible to take regular co-observations of Ly$\alpha $
α
emission in solar flares. Thus, it is valuable to examine how the instruments selected for flare observations may influence the conclusions drawn from the analysis of their unique measurements. Here, we examine three M-class flares each observed in Ly$\alpha $
α
by GOES-14/EUVS-E, GOES-15/EUVS-E, or GOES-16/EXIS-EUVS-B, and at least one other instrument from PROBA2/LYRA, MAVEN/EUVM, ASO-S/LST-SDI, and SDO/EVE-MEGS-P. For each flare, the relative and excess flux, contrast, total energy, and timings of the Ly$\alpha $
α
emission were compared between instruments. It was found that while the discrepancies in measurements of the relative flux between instruments may be considered minimal, the calculated contrasts, excess fluxes, and energetics may differ significantly – in some cases up to a factor of five. This may have a notable impact on multi-instrument investigations of the variable Ly$\alpha $
α
emission in solar flares and estimates of the contribution of Ly$\alpha $
α
to the radiated energy budget of the chromosphere. The findings presented in this study will act as a guide for the interpretation of observations of flare-related Ly$\alpha $
α
from upcoming instruments during future solar cycles and inform conclusions drawn from multi-instrument studies.
Effective Rates for Iterations Involving Bregman Strongly Nonexpansive Operators
Authors: Pischke Nicholas , Kohlenbach Ulrich 
Published: November 2024

AbstractWe develop the theory of Bregman strongly nonexpansive maps for uniformly Fréchet differentiable Bregman functions from a quantitative perspective. In that vein, we provide moduli witnessing quantitative versions of the central assumptions commonly used in this field on the underlying Bregman function and the Bregman strongly nonexpansive maps. In terms of these moduli, we then compute explicit and effective rates for the asymptotic regularity of Picard iterations of Bregman strongly nonexpansive maps and of the method of cyclic Bregman projections. Further, we also provide similar rates for the asymptotic regularity and metastability of a strongly convergent Halpern-type iteration of a family of such mappings and we use these new results to derive rates for various special instantiations like a Halpern-type proximal point algorithm for monotone operators in Banach spaces as well as Halpern-Mann- and Tikhonov-Mann-type methods.
Humean Sentimentalism, Sentimentalist Antinatalism, and the Metaphysics of Procreation
Authors: Szocik Konrad 
Published: November 2024

AbstractThere is no doubt that Humean sentimentalism provides a basis for thinking about future people. However, this thinking does not necessarily lead to concern about their coming into the world. This is possible on the basis of adopting a pronatalist metaphysics. Sentimentalism provides no less grounds for adopting an antinatalist metaphysics. Its consequence will be sympathy and moral sentiments towards future people, which will be full of concern that their lives may be insignificant. The paper shows that, depending on the type of metaphysics of procreation adopted, the conclusion resulting from moral sentiments toward future humans can either encourage bringing them into the world, or encourage a pro-extinctionist attitude.
Slowed epigenetic aging in Olympic champions compared to non-champions
Authors: Radák Zsolt , Aczél Dóra , Fejes Iván , Mozaffaritabar Soroosh , Pavlik Gabor , Komka Zsolt , Balogh László , Babszki Zsofia , Babszki Gergely , Koltai Erika , McGreevy Kristen M. , Gordevicius Juozas , Horvath Steve , Kerepesi Csaba 
Published: November 2024

AbstractThe lifestyle patterns of top athletes are highly disciplined, featuring strict exercise regimens, nutrition plans, and mental preparation, often beginning at a young age. Recently, it was shown that physically active individuals exhibit slowed epigenetic aging and better age-related outcomes. Here, we investigate whether the extreme intensity of physical activity of Olympic champions still has a beneficial effect on epigenetic aging. To test this hypothesis, we examined the epigenetic aging of 59 Hungarian Olympic champions and of the 332 control subjects, 205 were master rowers. We observed that Olympic champions exhibit slower epigenetic aging, applying seven state-of-the-art epigenetic aging clocks. Additionally, male champions who won any medal within the last 10 years showed slower epigenetic aging compared to other male champions, while female champions exhibited the opposite trend. We also found that wrestlers had higher age acceleration compared to gymnasts, fencers, and water polo players. We identified the top 20 genes that showed the most remarkable difference in promoter methylation between Olympic champions and non-champions. The hypo-methylated genes are involved in synaptic health, glycosylation, metal ion membrane transfer, and force generation. Most of the hyper-methylated genes were associated with cancer promotion. The data suggest that rigorous and long-term exercise from adolescence to adulthood has beneficial effects on epigenetic aging.