bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative Advantage Is an Old-Fashioned Trick
Authors: Lopes Carlos 
Published: July 2024

AbstractA convergence of economic imperatives, including the need for raw materials and markets, alongside inter-European power struggles and social factors such as rising unemployment kickstarted Europe’s scramble for Africa. The resulting colonial era has left an indelible impact on Africa’s economic, political, and social dimensions. Much of this legacy is now encoded in the international protocols that govern trade between Africa and the rest of the world. These systems tend to protect the interests of established leaders and have systematically penalised latecomers. This puts Africa at an enduring disadvantage despite its bountiful resources and much-vaunted potential.In this chapter, we revisit data and anecdotal evidence showing how countries that have ‘made it’ in the global trade arena often counted more on dynamic comparative advantages than on simplified comparative advantage theory interpretations based on their natural resources bounty. We also compare African and Asian countries after the independence movement of the 1960s regarding economic and social indicators, where they are now, and what it will take for Africa to chart a new course to a fairer trade system.
Estimation of Time-Dependent Transmission Rate for COVID-19 SVIRD Model Using Predictor–Corrector Algorithm
Authors: Luo Ruiyan , Herrera-Reyes Alejandra D. , Kim Yena , Rogowski Susan , White Diana , Smirnova Alexandra 
Published: July 2024

AbstractStable parameter estimation is an ongoing challenge within biomathematics, especially in epidemiology. Oftentimes epidemiological models are composed of large numbers of equations and parameters. Due to high dimensionality, classic parameter estimation approaches, such as least square fitting, are computationally expensive. Additionally, the presence of observational noise and reporting errors that accompany real-time data can make these parameter estimation problems ill-posed and unstable. The recent COVID-19 pandemic highlighted the need for efficient parameter estimation tools. In this chapter, we develop a modified version of a regularized predictor–corrector algorithm aimed at stable low-cost reconstruction of infectious disease parameters. This method is applied to a new compartmental model describing COVID-19 dynamics, which accounts for vaccination and immunity loss (from vaccinated and recovered populations). Numerical simulations are carried out with synthetic and real data for COVID-19 pandemic. Based on the reconstructed disease transmission rates (and known mitigation measures), observations on historical trends of COVID-19 in the states of Georgia and California are presented. Such observations can be used to provide insights into future COVID policies.
Natural Language Hypotheses in Scientific Papers and How to Tame Them
Authors: Heger Tina , Algergawy Alsayed , Brinner Marc , Jeschke Jonathan M. , König-Ries Birgitta , Mietchen Daniel , Zarrieß Sina 
Published: July 2024

AbstractHypotheses are critical components of scientific argumentation. Knowing established hypotheses is often a prerequisite for following and contributing to scientific arguments in a research field. In scientific publications, hypotheses are usually presented for specific empirical settings, whereas the related general claim is assumed to be known. Prerequisites for developing argumentation machines for assisting scientific workflows are to account for domain-specific concepts needed to understand established hypotheses, to clarify the relationships between specific hypotheses and general claims, and to take steps towards formalization. Here, we develop a framework for formalizing hypotheses in the research field of invasion biology. We suggest conceiving hypotheses as consisting of three basic building blocks: a subject, an object, and a hypothesized relationship between them. We show how the subject-object-relation pattern can be applied to well-known hypotheses in invasion biology and demonstrate that the contained concepts are quite diverse, mirroring the complexity of the research field. We suggest a stepwise approach for modeling them to be machine-understandable using semantic web ontologies. We use the SuperPattern Ontology to categorize hypothesized relationships. Further, we recommend treating every hypothesis as part of a hierarchical system with ‘parents’ and ‘children’. There are three ways of moving from a higher to a lower level in the hierarchy: (i) specification, (ii) decomposition, and (iii) operationalization. Specification involves exchanging subjects or objects. Decomposition means zooming in and making explicit assumptions about underlying (causal) relationships. Finally, operationalizing a hypothesis means providing concrete descriptions of what will be empirically tested.
“Do Not Disturb My Circles!” Identifying the Type of Counterfactual at Hand (Short Paper)
Authors: Willig Moritz , Zečević Matej , Kersting Kristian 
Published: July 2024

AbstractWhen the phenomena of interest are in need of explanation, we are often in search of the underlying root causes. Causal inference provides tools for identifying these root causes—by performing interventions on suitably chosen variables we can observe down-stream effects in the outcome variable of interest. On the other hand, argumentation as an approach of attributing observed outcomes to specific factors, naturally lends itself as a tool for determining the most plausible explanation. We can further improve the robustness of such explanations by measuring their likelihood within a mutually agreed-upon causal model. For this, typically one of in-principle two distinct types of counterfactual explanations is used: interventional counterfactuals, which treat changes as deliberate interventions to the causal system, and backtracking counterfactuals, which attribute changes exclusively to exogenous factors. Although both frameworks share the common goal of inferring true causal factors, they fundamentally differ in their conception of counterfactuals. Here, we present the first approach that decides when to expect interventional and when to opt for backtracking counterfactuals.
Automatic Analysis of Political Debates and Manifestos: Successes and Challenges
Authors: Ceron Tanise , Barić Ana , Blessing André , Haunss Sebastian , Kuhn Jonas , Lapesa Gabriella , Padó Sebastian , Papay Sean , Zauchner Patricia F. 
Published: July 2024

AbstractThe opinions of political actors (e.g., politicians, parties, organizations) expressed through claims are the core elements of political debates and decision-making. Political actors communicate through different channels: parties publish manifestos for major elections, while individual actors make statements on a day-to-day basis as reflected in the media. These two channels offer different approaches for analysis: Manifestos, on the one hand, are useful to characterize the parties’ positions at a global ideological level over time. In contrast, individual statements can be collected to analyze debates in particular policy domains on a fine-grained level, in terms of individual actors and claims. In this article, we summarize a series of studies we have carried out. We apply NLP-driven (semi-)automatic analyses on these two channels and compare their potentials and challenges. The fine-grained analysis yields rich insights into the communication but comes at the cost of three challenges: (a) a substantial hunger for manual annotation, introducing practical hurdles for analysis both within and across languages; (b) difficulties in claim classification arising from the uneven frequency distribution over the theory-based annotation schemas; (c) the need to map actor mentions onto canonical versions. Manifesto-based analysis avoids these challenges to a substantial extent when a more coarse-grained analysis of party positions is sufficient. We highlight the benefits and challenges of both approaches, and conclude by outlining perspectives for addressing the challenges in future research.
PolArg: Unsupervised Polarity Prediction of Arguments in Real-Time Online Conversations
Authors: Lenz Mirko , Bergmann Ralph 
Published: July 2024

AbstractThe increasing usage of social networks has led to a growing number of discussions on the Internet that are a valuable source of argumentation that occurs in real time. Such conversations are often made up of a large number of participants and are characterized by a fast pace. Platforms like X/Twitter and Hacker News (HN) allow users to respond to other users’ posts, leading to a tree-like structure. Previous work focused on training supervised models on datasets obtained from debate portals like Kialo where authors provide polarity labels (i.e., support/attack) together with their posts. Such classifiers may yield suboptimal predictions for the noisier posts from X or HN, so we propose unsupervised prompting strategies for large language models instead. Our experimental evaluation found this approach to be more effective for X conversations than a model fine-tuned on Kialo debates, but less effective for HN posts (which are more technical and less argumentative). Finally, we provide an open-source application for converting discussions on these platforms into argument graphs.
Integrating Mathematical Data and Resources: Advancements in zbMATH Open for Enhanced Mathematical Research Accessibility and Reproducibility
Authors: Azzouz-Thuderoz Maxence , Deb Madhurima , Petrera Matteo , Schubotz Moritz , Teschke Olaf 
Published: July 2024

AbstractWe report the ongoing efforts of swMATH, an integral part of zbMATH Open, to collect precise referencing software metadata. zbMATH Open is emerging as a unified platform offering a spectrum of mathematical resources, including mathematical software, formulas, reviews, and serial and mathematical item classification. zbMATH Open offers connection to external partners, DLMF and OEIS, via its Links API by indexing approximately 6,330 documents containing 65,069 references to OEIS sequences and 15,858 references to 2,053 DLMF functions. Significantly, the collection of 44,594 software entries from swMATH is entirely accessible through zbMATH Open. Here, we emphasize the accurate referencing of mathematical software in swMATH for maintaining integrity, advancing mathematical research, and enhancing reproducibility. We describe how swMATH is embedded into zbMATH open and elaborate on the relationship of software and other mathematical research data like OEIS and DLMF, ensuring a complete and FAIR resource for the mathematical research community.
Sharp Semiclassical Spectral Asymptotics for Local Magnetic Schrödinger Operators on $${\mathbb {R}}^d$$ Without Full Regularity
Authors: Mikkelsen Søren 
Published: July 2024

AbstractWe consider operators acting in $$L^2({\mathbb {R}}^d)$$

L
2

(

R

d

)

with $$d\ge 3$$

d
≥
3

that locally behave as a magnetic Schrödinger operator. For the magnetic Schrödinger operators, we suppose the magnetic potentials are smooth and the electric potential is five times differentiable and the fifth derivatives are Hölder continuous. Under these assumptions, we establish sharp spectral asymptotics for localised counting functions and Riesz means.
Mutations of noncommutative crepant resolutions in geometric invariant theory
Authors: Hara Wahei , Hirano Yuki 
Published: July 2024

AbstractLet X be a generic quasi-symmetric representation of a connected reductive group G. The GIT quotient stack $$\mathfrak {X}=[X^\text {ss}(\ell )/G]$$

X
=
[

X
ss

(
ℓ
)

/
G
]

with respect to a generic $$\ell $$
ℓ
is a (stacky) crepant resolution of the affine quotient X/G, and it is derived equivalent to a noncommutative crepant resolution (=NCCR) of X/G. Halpern-Leistner and Sam showed that the derived category $${{\textrm{D}}^{\textrm{b}}}({\text {coh}}\mathfrak {X})$$

D

b

(
coh
X
)

is equivalent to certain subcategories of $${{\textrm{D}}^{\textrm{b}}}({\text {coh}}[X/G])$$

D

b

(
coh

[
X
/
G
]

)

, which are called magic windows. This paper studies equivalences between magic windows that correspond to wall-crossings in a hyperplane arrangement in terms of NCCRs. We show that those equivalences coincide with derived equivalences between NCCRs induced by tilting modules, and that those tilting modules are obtained by certain operations of modules, which is called exchanges of modules. When G is a torus, it turns out that the exchanges are nothing but iterated Iyama–Wemyss mutations. Although we mainly discuss resolutions of affine varieties, our theorems also yield a result for projective Calabi-Yau varieties. Using techniques from the theory of noncommutative matrix factorizations, we show that Iyama–Wemyss mutations induce a group action of the fundamental group $$\pi _1(\mathbb {P}^1\,\backslash \{0,1,\infty \})$$

π
1

(

P

1

\

{
0
,
1
,
∞
}

)

on the derived category of a Calabi-Yau complete intersection in a weighted projective space.
Dreaming of AI: environmental sustainability and the promise of participation
Authors: Zehner Nicolas , Ullrich André 
Published: July 2024

AbstractThere is widespread consensus among policymakers that climate change and digitalisation constitute the most pressing global transformations shaping human life in the 21st century. Seeking to address the challenges arising at this juncture, governments, technologists and scientists alike increasingly herald artificial intelligence (AI) as a vehicle to propel climate change mitigation and adaptation. In this paper, we explore the intersection of digitalisation and climate change by examining the deployment of AI in government-led climate action. Building on participant observations conducted in the context of the “Civic Tech Lab for Green”—a government-funded public interest AI initiative—and eight expert interviews, we investigate how AI shapes the negotiation of environmental sustainability as an issue of public interest. Challenging the prescribed means–end relationship between AI and environmental protection, we argue that the unquestioned investment in AI curtails political imagination and displaces discussion of climate “problems” and possible “solutions” with “technology education”. This line of argumentation is rooted in empirical findings that illuminate three key tensions in current coproduction efforts: “AI talk vs. AI walk”, “civics washing vs. civics involvement” and “public invitation vs. public participation”. Emphasising the importance of re-exploring the innovative state in climate governance, this paper extends academic literature in science and technology studies that examines public participation in climate change adaptation by shedding light on the emergent phenomenon of public interest AI.
Exploring deep fully convolutional neural networks for surface defect detection in complex geometries
Authors: García Peña Daniel , García Pérez Diego , Díaz Blanco Ignacio , Juárez Jorge Marina 
Published: July 2024

AbstractIn this paper, we propose a machine learning approach for detecting superficial defects in metal surfaces using point cloud data. We compare the performance of two popular deep learning architectures, multilayer perceptron networks (MLPs) and fully convolutional networks (FCNs), with varying feature sets. Our results show that FCNs (F1=0.94) outperformed MLPs (F1=0.52) in terms of precision, recall, and F1-score. We found that transfer learning with pre-trained models can improve performance when the amount of available data is limited. Our study highlights the importance of considering the amount and quality of training data in developing machine learning models for defect detection in industrial settings with 3D images.
Metabolic versatility of anaerobic sludge towards platform chemical production from waste glycerol
Authors: Magalhães Carla P. , Alves Joana I. , Duber Anna , Oleskowicz-Popiel Piotr , Stams Alfons J. M. , Cavaleiro Ana J. 
Published: July 2024

Abstract
Waste glycerol is produced in excess by several industries, such as during biodiesel production. In this work, the metabolic versatility of anaerobic sludge was explored towards waste glycerol valorization. By applying different environmental (methanogenic and sulfate-reducing) conditions, three distinct microbial cultures were obtained from the same inoculum (anaerobic granular sludge), with high microbial specialization, within three different phyla (Thermodesulfobacteriota, Euryarchaeota and Pseudomonadota). The cultures are capable of glycerol conversion through different pathways: (i) glycerol conversion to methane by a bacterium closely related to Solidesulfovibrio alcoholivorans (99.8% 16S rRNA gene identity), in syntrophic relationship with Methanofollis liminatans (98.8% identity), (ii) fermentation to propionate by Propionivibrio pelophilus strain asp66 (98.6% identity), with a propionate yield of 0.88 mmol mmol−1 (0.71 mg mg−1) and a propionate purity of 80–97% and (iii) acetate production coupled to sulfate reduction by Desulfolutivibrio sulfoxidireducens (98.3% identity). In conclusion, starting from the same inoculum, we could drive the metabolic and functional potential of the microbiota towards the formation of several valuable products that can be used in industrial applications or as energy carriers.

Key points

Versatility of anaerobic cultures was explored for waste glycerol valorization

Different environmental conditions lead to metabolic specialization

Biocommodities such as propionate, acetate and methane were produced
Building up a model family for inflammations
Authors: Reisch Cordula , Nickel Sandra , Tautenhahn Hans-Michael 
Published: July 2024

AbstractThe paper presents an approach for overcoming modeling problems of typical life science applications with partly unknown mechanisms and lacking quantitative data: A model family of reaction–diffusion equations is built up on a mesoscopic scale and uses classes of feasible functions for reaction and taxis terms. The classes are found by translating biological knowledge into mathematical conditions and the analysis of the models further constrains the classes. Numerical simulations allow comparing single models out of the model family with available qualitative information on the solutions from observations. The method provides insight into a hierarchical order of the mechanisms. The method is applied to the clinics for liver inflammation such as metabolic dysfunction-associated steatohepatitis or viral hepatitis where reasons for the chronification of disease are still unclear and time- and space-dependent data is unavailable.
Higher levels of markers for early atherosclerosis in anti-citrullinated protein antibodies positive individuals at risk for RA, a cross sectional study
Authors: Hinkema Helma J. , Westra Johanna , Arends Suzanne , Brouwer Elisabeth , Mulder Douwe J. 
Published: July 2024

Abstract
Objective
To identify differences in levels of serum biomarkers associated with atherosclerosis between anti-citrullinated protein antibodies (ACPA) positive groups.

Methods
Cross-sectional data were used from the Dutch Lifelines Cohort Study combined with data derived from RA risk and early RA studies conducted at the University Medical Center Groningen (UMCG).
Serum biomarkers of inflammation, endothelial cell activation, tissue remodeling and adipokine, which were previously associated with atherosclerosis, were measured with Luminex in four ACPA positive groups with different characteristics: without joint complaints, with joint complaints, RA risk and early RA groups.

Results
Levels of C-reactive protein (CRP), Interleukin-6 (IL-6), Tumor Necrosis Factor Receptor 1 (TNFR1) and vascular endothelial growth factor (VEGF) were significantly higher in the RA risk and early RA groups compared to the joint complaints and the no joint complaints groups. The difference remained statistically significant after correcting for renal function, smoking and hypertension in multivariate logistic regression analysis, with focus on ACPA positive with joint complaints group versus RA risk group: CRP OR = 2.67, p = 0.033; IL-6 OR = 3.73, p = 0.019; TNFR1 OR = 1.003, p < 0.001; VGEF OR = 8.59, p = 0.019.

Conclusion
Individuals at risk for RA have higher levels of inflammatory markers and VEGF, which suggests that they might also have a risk of higher cardiovascular disease (CVD); however, this does not apply to individuals with ACPA positivity with self-reported joint complaints or without joint complaints only. Therefore, it is important that individuals with RA risk are referred to a rheumatologist to rule in or out arthritis/development of RA and discuss CVD risk.
Evaluation of ophthalmic vascular and neuroretinal alterations in fibromyalgia syndrome: a cross-sectional comparative study
Authors: Öztürk Gülşah Yaşa , Emekli Duygu Topaktaş , Sahutoglu Eda , Kocyigit Burhan Fatih 
Published: July 2024

Abstract
Introduction
Fibromyalgia syndrome (FMS) is a prevalent rheumatic disorder, and its pathogenesis includes genetic, neuroendocrine, and autonomic abnormalities, which may impact ocular structures. The aim was to conduct a comparative analysis of the ophthalmic vasculature and the retinal nerve fiber layer (RNFL) thickness between FMS and control groups using optical coherence tomography (OCT) and OCT angiography (OCTA).

Methods
This cross-sectional comparative study included 43 FMS patients and 40 healthy controls recruited from a tertiary education and research hospital between January 2024 and May 2024. All patients satisfied the 2016 American College of Rheumatology criteria for FMS and consented. OCT and OCTA were used to assess the RNFL thickness and the retinal microvasculature structure. The Fibromyalgia Impact Questionnaire (FIQ) was performed to evaluate disease severity.

Results
The study found significantly higher total retinal parafoveal thickness and foveal density in FMS patients (p = 0.017 and p = 0.044, respectively). Nevertheless, there were no significant differences among the groups concerning total retinal foveal thickness, foveal avascular zone characteristics, superficial and deep capillary plexus densities, choriocapillaris flow area, and outer retinal flow area values (p > 0.05). The RNFL thickness in all quadrants did not reveal significant differences between the groups (p > 0.05). Furthermore, there was no significant correlation between FIQ scores and OCTA parameters or RNFL thickness values (p > 0.05).

Conclusion
The study revealed slight differences in retinal parafoveal thickness and foveal density in FMS patients, but no substantial vascular or neurodegenerative alterations were observed compared to healthy controls. These data indicate that FMS may not substantially affect ocular structures, contrary to earlier hypotheses.
The pros and cons of lung cancer screening
Authors: Ledda Roberta Eufrasia , Funk Georg-Christian , Sverzellati Nicola 
Published: July 2024

Abstract
Several trials have shown that low-dose computed tomography-based lung cancer screening (LCS) allows a substantial reduction in lung cancer-related mortality, carrying the potential for other clinical benefits. There are, however, some uncertainties to be clarified and several aspects to be implemented to optimize advantages and minimize the potential harms of LCS.
This review summarizes current evidence on LCS, discussing some of the well-established and potential benefits, including lung cancer (LC)-related mortality reduction and opportunity for smoking cessation interventions, as well as the disadvantages of LCS, such as overdiagnosis and overtreatment.

Clinical relevance statement
Different perspectives are provided on LCS based on the updated literature.

Key Points

Lung cancer is a leading cancer-related cause of death and screening should reduce associated mortality.

This review summarizes current evidence related to LCS.

Several aspects need to be implemented to optimize benefits and minimize potential drawbacks of LCS.