bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring microrotational effects and temperature variations in electroosmotic peristalsis in tapered microchannel
Authors: Batool Sidra , Noreen Saima , Chamkha Ali J. 
Published: April 2024

AbstractThe current study is based on the effects of microrotational dynamics, microinertial effects, and temperature changes on electroosmotic peristalsis in a tapered microchannel. This has been addressed by an analytical study of heat transfer in the setting of electroosmotic peristaltic flow involving a micropolar fluid, specifically considering a symmetrically tapered channel. The Navier–Stokes equation, the Poisson–Boltzmann equation, the energy equation, and the micropolar fluid model are all included in the mathematical model. On the flow and temperature fields, a thorough parametric analysis is carried out, investigating the impacts of numerous variables, including the micropolar parameter, Prandtl number, Brinkman number, Grashof number, thermal conductivity ratio, and channel aspect ratio. The findings show that peristalsis and electroosmosis both contribute to higher heat transfer rates. Notably, the electroosmotic parameter and Brinkman number have a substantial impact on the distribution of temperature. The micropolar parameter and Brinkman number have a significant effect on the flow and temperature fields. Furthermore, electrokinetic phenomena are crucial in controlling the axial and spin velocities of the micropolar fluid. These findings have significant ramifications for the design and optimization of microfluidic devices in engineering and biomedical applications that employ the electroosmotic peristaltic flow of micropolar fluids.
Seismic Discrimination Between Nuclear Explosions and Natural Earthquakes using Multi-Machine Learning Techniques
Authors: Elkhouly Shimaa. H. , Ali Ghada 
Published: April 2024

AbstractIn the field of seismic signal analysis, it is of utmost importance to accurately differentiate between earthquakes and underground nuclear explosions. As a contribution for the verification regime of the Comprehensive Nuclear Test Ban Treaty (CTBT), Various methods have been employed for this purpose, including Complexity, Spectral ratio, mb—Ms (body wave and surface wave magnitudes), and corner frequency of P and S waves. These discrimination techniques have been examined to manually identify natural seismic events from nuclear explosions across different regions worldwide, such as China, India, Pakistan, North Korea, and the United States. To gather the necessary data, a comprehensive dataset comprising nuclear explosions and earthquakes of the same magnitude range (4 ≤ mb ≤ 6.5) of 35 seismic events from 1945 to 2017 has been compiled from the International Research Institute for Seismology (IRIS) using broadband and long period seismic stations. The objective of this study is to employ a range of linear and nonlinear Machine Learning (ML) models with the aim of automatically distinguishing between underground nuclear explosions and large earthquakes to enhance the accuracy of manual feature extraction. For this purpose, time domain waveforms and different classifier techniques focused on feature extraction have been used. The ML models employed include logistic regression, K-nearest neighbours classifier, decision tree classifier, random forest classifier, voting classifier, and Naive Bayes. The outcomes of the ROC and AUC analyses were employed to validate the validity of our proposed discrimination algorithm. The results show that the Random Forest Classifier is the most effective model, obtaining 100% accuracy in the case of feature extraction, while the best model for the time domain waveform classifier that achieved 75.5% accuracy is the voting classifier.
Cranio-cervical and traumatic brain injury patterns—do they differ between electric bicycle, bicycle, and motorcycle-induced accidents?
Authors: Rauer Thomas , Klingebiel Felix Karl-Ludwig , Lühring Adrian , Küffer Alexander , Hofer Anna-Sophie , Ferrari Raphael Marco , Kupka Michael , Pape Hans-Christoph 
Published: April 2024

Abstract
Purpose
With the growing technical options of power transmission and energy-saving options in electric drives, the number of E-bike-related accidents especially in an elderly population has increased. The aim of the current study was to compare if the increased velocity in comparison to conventional bikes translates into different injury patterns in the cranio-cervical and head region.

Methods
A retrospective cohort study was performed in patients admitted to our level one trauma center between 2009 and 2019 after being involved in an accident with either an E-bike, bicycle, or motorcycle and suffered cranio-cervical or traumatic brain injury. Outcomes: cranio-cervical/intracranial injury pattern. Data interpretation was conducted in an interdisciplinary approach.

Results
From 3292 patients treated in this period, we included 1068 patients. E-bikers were significantly older than bicyclists (or motorcyclists) and lay between the other two groups in terms of helmet use. Overall injury patterns of E-bikers resembled those found in motorcyclists rather than in bicyclists. E-bikers had a higher incidence of different cerebral bleedings, especially if no helmet was worn. Helmet protection of E-bikers resulted in a comparable frequency of intracranial bleeding to the helmeted bicyclists.

Conclusion
The overall pattern of head and cervical injuries in E-bikers resembles more to that of motorcyclists than that of bicyclists. As they are used by a more senior population, multiple risk factors apply in terms of complications and secondary intracranial bleeding. Our study suggests that preventive measures should be reinforced, i.e., use of helmets to prevent from intracranial injury.
Erhöhte Befundvollständigkeit und gesteigerte Zuweiserzufriedenheit bei strukturierter neurootologischer Befunderhebung in der interdisziplinären Schwindelabklärung
Authors: Lasrich M. , Helling K. , Strieth S. , Bahr-Hamm K. , Vogt T. J. , Fröhlich L. , Send T. , Hill K. , Nitsch L. , Rader T. , Bärhold F. , Becker S. , Ernst B. P. 
Published: April 2024

Zusammenfassung
Hintergrund
Befundberichte neurootologischer Funktionsdiagnostik im Rahmen der interdisziplinären Schwindelabklärung werden meist als Freitextbefunde („free text reports“, FTR) formuliert. Diese unterliegen häufig einer großen Variabilität, sodass hier Informationsverluste möglich sind. Ziel der vorliegenden Studie war es, die Befundvollständigkeit strukturierter Befunde („structured reports“, SR) und die Zuweiserzufriedenheit im Rahmen der neurootologischen Funktionsdiagnostik zu evaluieren.

Material und Methoden
Retrospektiv wurden konsiliarisch durchgeführte neurootologische Funktionsdiagnostiken (n = 88) ausgewertet. Anhand der vorliegenden Rohdaten erfolgte mittels einer spezifischen Befunderhebungsmaske für neurootologische Funktionsdiagnostik die Erstellung korrespondierender SR zu den FTR aus der klinischen Routine. FTR und SR wurden auf Vollständigkeit und die Zufriedenheit der zuweisenden Ärzte (n = 8) mittels eines Fragebogens mit visueller Analogskala (VAS) untersucht.

Ergebnisse
Im Vergleich zu den FTR zeigten die SR eine signifikant erhöhte Gesamtvollständigkeit (73,7 vs. 51,7 %; p < 0,001), insbesondere in Bezug auf die Anamnese (92,5 vs. 66,7 %; p < 0,001), Beschreibung von Vorbefunden (87,5 vs. 38 %; p < 0,001) und die neurootologische (33,5 vs. 26,7 %; p < 0,001) und audiometrische Funktionsdiagnostik (58 vs. 32,3 %; p < 0,001). Zudem zeigte sich mittels SR eine deutlich gesteigerte Zuweiserzufriedenheit (VAS 8,8 vs. 4,9; p < 0,001).

Schlussfolgerung
Neurootologische SR ermöglichen eine deutlich gesteigerte Vollständigkeit der Befunde bei höherer Zufriedenheit der Zuweiser im Kontext der interdisziplinären Schwindelabklärung. Darüber hinaus eignen sich SR ideal zur wissenschaftlichen Datenanalyse, insbesondere im Rahmen von Big-Data-Analysen.
Lunar ground segmentation using a modified U-net neural network
Authors: Petrakis Georgios , Partsinevelos Panagiotis 
Published: April 2024

AbstractSemantic segmentation plays a significant role in unstructured and planetary scene understanding, offering to a robotic system or a planetary rover valuable knowledge about its surroundings. Several studies investigate rover-based scene recognition planetary-like environments but there is a lack of a semantic segmentation architecture, focused on computing systems with low resources and tested on the lunar surface. In this study, a lightweight encoder-decoder neural network (NN) architecture is proposed for rover-based ground segmentation on the lunar surface. The proposed architecture is composed by a modified MobilenetV2 as encoder and a lightweight U-net decoder while the training and evaluation process were conducted using a publicly available synthetic dataset with lunar landscape images. The proposed model provides robust segmentation results, allowing the lunar scene understanding focused on rocks and boulders. It achieves similar accuracy, compared with original U-net and U-net-based architectures which are 110–140 times larger than the proposed architecture. This study, aims to contribute in lunar landscape segmentation utilizing deep learning techniques, while it proves a great potential in autonomous lunar navigation ensuring a safer and smoother navigation on the moon. To the best of our knowledge, this is the first study which propose a lightweight semantic segmentation architecture for the lunar surface, aiming to reinforce the autonomous rover navigation.
Manifold turnpikes of nonlinear port-Hamiltonian descriptor systems under minimal energy supply
Authors: Karsai Attila 
Published: April 2024

AbstractTurnpike phenomena of nonlinear port-Hamiltonian descriptor systems under minimal energy supply are studied. Under assumptions on the smoothness of the system nonlinearities, it is shown that the optimal control problem is dissipative with respect to a manifold. Then, under controllability assumptions, it is shown that the optimal control problem exhibits a manifold turnpike property.
Non-convex scenario optimization
Authors: Garatti Simone , Campi Marco C. 
Published: April 2024

Abstract

Scenario optimization is an approach to data-driven decision-making that has been introduced some fifteen years ago and has ever since then grown fast. Its most remarkable feature is that it blends the heuristic nature of data-driven methods with a rigorous theory that allows one to gain factual, reliable, insight in the solution. The usability of the scenario theory, however, has been restrained thus far by the obstacle that most results are standing on the assumption of convexity. With this paper, we aim to free the theory from this limitation. Specifically, we focus on the body of results that are known under the name of “wait-and-judge” and show that its fundamental achievements maintain their validity in a non-convex setup. While optimization is a major center of attention, this paper travels beyond it and into data-driven decision making. Adopting such a broad framework opens the door to building a new theory of truly vast applicability.
Large language models in cryptocurrency securities cases: can a GPT model meaningfully assist lawyers?
Authors: Trozze Arianna , Davies Toby , Kleinberg Bennett 
Published: April 2024

AbstractLarge Language Models (LLMs) could be a useful tool for lawyers. However, empirical research on their effectiveness in conducting legal tasks is scant. We study securities cases involving cryptocurrencies as one of numerous contexts where AI could support the legal process, studying GPT-3.5’s legal reasoning and ChatGPT’s legal drafting capabilities. We examine whether a) GPT-3.5 can accurately determine which laws are potentially being violated from a fact pattern, and b) whether there is a difference in juror decision-making based on complaints written by a lawyer compared to ChatGPT. We feed fact patterns from real-life cases to GPT-3.5 and evaluate its ability to determine correct potential violations from the scenario and exclude spurious violations. Second, we had mock jurors assess complaints written by ChatGPT and lawyers. GPT-3.5’s legal reasoning skills proved weak, though we expect improvement in future models, particularly given the violations it suggested tended to be correct (it merely missed additional, correct violations). ChatGPT performed better at legal drafting, and jurors’ decisions were not statistically significantly associated with the author of the document upon which they based their decisions. Because GPT-3.5 cannot satisfactorily conduct legal reasoning tasks, it would be unlikely to be able to help lawyers in a meaningful way at this stage. However, ChatGPT’s drafting skills (though, perhaps, still inferior to lawyers) could assist lawyers in providing legal services. Our research is the first to systematically study an LLM’s legal drafting and reasoning capabilities in litigation, as well as in securities law and cryptocurrency-related misconduct.
Efficient fog node placement using nature-inspired metaheuristic for IoT applications
Authors: Naouri Abdenacer , Nouri Nabil Abdelkader , Khelloufi Amar , Sada Abdelkarim Ben , Ning Huansheng , Dhelim Sahraoui 
Published: April 2024

AbstractManaging the explosion of data from the edge to the cloud requires intelligent supervision, such as fog node deployments, which is an essential task to assess network operability. To ensure network operability, the deployment process must be carried out effectively regarding two main factors: connectivity and coverage. The network connectivity is based on fog node deployment, which determines the network’s physical topology, while the coverage determines the network accessibility. Both have a significant impact on network performance and guarantee the network quality of service. Determining an optimum fog node deployment method that minimizes cost, reduces computation and communication overhead, and provides a high degree of network connection coverage is extremely hard. Therefore, maximizing coverage and preserving network connectivity is a non-trivial problem. In this paper, we propose a fog deployment algorithm that can effectively connect the fog nodes and cover all edge devices. Firstly, we formulate fog deployment as an instance of multi-objective optimization problems with a large search space. Then, we leverage Marine Predator Algorithm (MPA) to tackle the deployment problem and prove that MPA is well-suited for fog node deployment due to its rapid convergence and low computational complexity, compared to other population-based algorithms. Finally, we evaluate the proposed algorithm on a different benchmark of generated instances with various fog scenario configurations. Our algorithm outperforms state-of-the-art methods, providing promising results for optimal fog node deployment. It demonstrates a 50% performance improvement compared to other algorithms, aligning with the No Free Lunch Theorem (NFL Theorem) Theorem’s assertion that no algorithm has a universal advantage across all problem domains. This underscores the significance of selecting tailored algorithms based on specific problem characteristics.
‘Don’t Pick, Don’t Lick’: Connecting Young Children’s Risky Play in Nature to Science Education in Australian Bush Kinders
Authors: Speldewinde Christopher 
Published: April 2024

AbstractThe forest school approach to nature learning has gathered momentum in the UK and across parts of Europe including Scandinavia for well over 50 years. In other contexts that include Canada, China, New Zealand and Australia, nature-based early childhood education and care settings, influenced by European forest school approaches, are in a rapid expansion phase as educators and policy makers acknowledge the benefits for children from time spent in nature. It is known that science education opportunities exist in these nature-based settings, yet this area has only garnered limited research attention to date. An example of a nature-based approach to early childhood education which emerged in the 2010s is the Australian ‘bush kinder’. Four- to five-year-old preschool children experience and learn from nature through play in bush kinders. This paper highlights the intersections that occur between risky play and science teaching and learning in the context of bush kinders. Through analysing research in early years science education, guiding curriculum frameworks and early childhood learning, I propose the importance of children’s risky play to early childhood science education. Drawing on vignettes from ethnographic fieldwork data, the merits of risky play in bush kinders to embed science knowledge is illustrated here. Participant observation was used to build a profile of each site and to gather data relating to how educators draw on children’s risky play to seek out opportunities to teach children about physical, chemical and biological science.
Exploring the link between CO2 emissions, health expenditure, and economic growth in Türkiye: evidence from the ARDL model
Authors: Çobanoğulları Gökhan 
Published: April 2024

AbstractIn recent times, the literature has seen considerable growth in research at the intersection of CO2 emission, health expenditure, and economic growth. But looking at the literature, it appears that the relationship between health expenditures, CO2 emissions, and economic growth is unclear. To resolve this uncertainty, this study was conducted with different data, countries, and methods. To this end, the present study analyzed the nexus between CO2 emissions, health expenditure, and economic growth in Türkiye from 1975 to 2020 using the Autoregressive Distributed Lagged (ARDL) model developed by Pesaran et al. (J Appl Econ 16(3):289–326, 2001). The study reveals a connection between CO2 emissions, health spending, and economic development in Türkiye over the long term. It also highlights a short-term correlation among these factors. The study indicates that a 1% increase in economic growth results in a 0.553 and 0.297 rise in CO2 emissions in the short and long term, respectively. That is, it suggests that if economic growth in Türkiye doesn’t involve renewable energy, it could negatively affect CO2 emissions both in the short and long term. To address this, substantial efforts are needed to transition to low-carbon technologies like renewable energy and energy efficiency, aiming to reduce emissions and support long-term economic growth. The study further demonstrates that a 1% growth in health expenditure leads to a 0.124% decrease in CO2 emissions over the long term. This implies that Türkiye’s health sector could benefit from utilizing more renewable energy or using fossil fuels more efficiently. Additionally, the study warns that long-term population growth could negatively affect CO2 emissions in Türkiye.
The “Spirit” of New Atheism and Religious Activism in the Post-9/11 God Debate
Authors: Rosenfeldt Adrian 
Published: April 2024

AbstractIn this article I examine the contemporary discourses and debates that surround the sociology of spirituality, with especial attention to the term “spirituality”. To counter the widespread belief that this term lacks clarity and utility, I suggest reconsidering Max Weber’s use of the term “spirit,” as it refers to a recognisable ethic that results in specific behaviour, while still retaining its religious and spiritual connotations. Through focusing on two influential English figures in the post 9/11 God debate in the West, Richard Dawkins and Karen Armstrong, I provide a brief case study of how Weber’s understanding of “spirit” serves great utility in illuminating what drives the ideas, identity-making and behaviour of contemporary atheists and those defending religion. By utilising Weber’s “spirit,” rather than the term spirituality, I demonstrate that this enables us to dig deep into the social context and backgrounds of these two individuals, and to avoid taking their statements at face value – a common criticism of sociology of spirituality studies. I argue that the use of “spirit,” in terms of a recognisable ethic that results in specific behaviour, would benefit the sociology of spirituality. This is because it grounds the God debaters’ ideas and beliefs in a recognisable human experience that eludes reductive distinctions and disembodied abstractions.
Rapid time-domain simulation of fractional capacitors with SPICE
Authors: Wilson Marcus , Cowie Logan , Farrow Vance , Cree Michael , Scott Jonathan 
Published: April 2024

AbstractFractional capacitors, commonly called constant-phase elements or CPEs, are used in modeling and control applications, for example, for rechargeable batteries. Unfortunately, they are not natively supported in the well-used circuit simulator SPICE. This manuscript presents and demonstrates a modeling approach that allows users to incorporate these elements in circuits and model the response in the time domain. The novelty is that we implement for the first time a particular configuration of RC elements in parallel in a Foster-type network with SPICE in order to simulate a constant-phase element across a defined frequency range. We demonstrate that the circuit produces the required impedance spectrum in the frequency domain, and shows a power-law voltage response to a step change in current in the time domain, consistent with theory, and is able to reproduce the experimental voltage response to a complicated current profile in the time domain. The error depends on the chosen frequency limits and the number of RC branches, in addition to very small SPICE numerical errors. We are able to define an optimum circuit description that minimizes error while maintaining a short computation time. The scientific value is that the work permits rapid and accurate evaluation of the response of CPEs in the time domain, faster than other methods, using open source tools.
Mindsets and politically motivated reasoning about fake news
Authors: Ludwig Jonas , Sommer Joseph 
Published: April 2024

AbstractFalse information may be published with the intention of misleading the public, and such fake news is often difficult to detect. Ideological fake news may pose a particular challenge, as people may be less able to detect false information that supports their prior beliefs. The difficulty of detecting fake news with an ideological slant may be compounded if people are motivated to defend their beliefs. Building on the mindset theory of action phases, we investigated motivational states as moderators of people’s ability to detect fake news. We tested two competing predictions to study the cognitive and motivational processes implicated in fake news detection. Both predictions concern an ideological belief bias, where people tend to accept information that agrees with their partisan identities and to reject information that disagrees with them. First, motivated reasoning accounts posit that deliberation should reinforce the ideological belief bias because reasoning primarily serves to defend and rationalize one’s own position. An opposing view, based on dual-process theory, assumes that deliberation attenuates the ideological belief bias by facilitating an unbiased assessment of new information. An online experiment (N = 497) tested these competing accounts. Participants were induced with deliberative/implemental/control mindsets prior to rating the veracity of (true/fake) news headlines. Some headlines favored a Republican view; others leaned toward a Democrat perspective. Based on self-reported political preference (Democrat vs. Republican), headlines were categorized as congruent or incongruent with participants’ political views. Consistent with an ideological belief bias, participants accepted more congruent than incongruent news, and they were more likely to fail to detect favorable fake news. In the main analysis, mindsets did not moderate the ideological belief bias, but showed interesting relationships with cognitive reflection and dishonest behavior. Further exploration using signal-detection theory suggested that the deliberative mindset might be associated with increased skepticism, thereby promoting fake news detection.