bluuebunny/crossref_metadata_2025_split huggingface dataset:
Eine empirische Studie zum Zusammenhang von ökonomischer Kompetenz und Finanzverhalten unter Berücksichtigung individueller Präferenzen
Authors: Eberle Mira 
Published: October 2024

ZusammenfassungBereits 2008 hielt die Kultusministerkonferenz (KMK) fest, dass ökonomische Bildung zur Allgemeinbildung gehört (KMK 2008). Zu ihren Zielen gehört es nicht nur, Verständnis für wirtschaftliche Zusammenhänge und ökonomisches Handeln zu entwickeln, sondern auch Handeln anzuleiten (KMK 2008). Dennoch konnte sich die Politik noch nicht zu einer nationalen Strategie für eine curriculare Umsetzung durchringen. Im Zuge der Diskussion um die ökonomische Bildung in Deutschland sind Forschungsprojekte für eine evidenzbasierte Entscheidungsfindung unablässig. Eine solche Evidenz liegt für einen Effekt von finanzieller Bildung auf Finanzverhalten bereits vor. Demgegenüber ist die Studienlage in Bezug auf die Wirkung einer umfassenderen ökonomischen Bildung, wie 2008 von der KMK eruiert, noch rar. Dieser Beitrag liefert Erkenntnisse zum Zusammenhang zwischen dem breiten Konstrukt der ökonomischen Kompetenz und finanziellen Verhaltensweisen einerseits sowie individuellen Präferenzen, wie Geduld, andererseits. Dazu wurden Daten einer Befragung inklusive eines Kompetenztests von 2929 Lernenden der Sekundarstufe 1 aus Baden-Württemberg ausgewertet. Die Lösungen zu den Fragen des Tests wurden mit Hilfe der Item-Response-Theorie in eine auswertungsfähige Intervallskala transformiert. In Bezug auf die finanziellen Verhaltensweisen und individuellen Präferenzen wurde auf in der Forschung bereits erprobte und anerkannte Items, wie beispielsweise die CAEd Autonomy Scale von Micarello et al. (2012) oder zur Risikobereitschaft von Dohmen et al. (2011), zurückgegriffen. Die Ergebnisse zeigen, dass ökonomische Kompetenz einen kleinen, aber statistisch hoch signifikanten Effekt auf Finanzverhalten und Präferenzen hat. Ökonomisch kompetentere Schülerinnen und Schüler in Baden-Württemberg zeigen sich gewissenhafter im Umgang mit ihren Finanzen.
Imputation Strategies in Time Series Based on Language Models
Authors: Jacobsen Michel , Tropmann-Frick Marina 
Published: October 2024

AbstractIncomplete time series present a significant challenge for downstream analysis. In the field of time series, Large Language Models are already being used for prediction, classification, and, in rare cases, imputation. This study thoroughly examines the imputation of time series using Large Language Models. Within a defined experimental setup, current state-of-the-art time series imputation methods are compared with the performance of Large Language Models. Parameter-efficient fine-tuning methods are applied to adapt the Large Language Models to the imputation task. The results indicate that the models are suitable for time series imputation. The performance of these models depends on the number of parameters and the type of pre-training. Small specialized models, such as BERT, compete with models like Llama2 and outperform them on selected datasets. Furthermore, it becomes clear that the attention and feedforward network components of Large Language Models are particularly well-suited for adaptation to imputation, and parameter-efficient methods are also performance-enhancing.
Groundwater modelling for decision-support in practice: Insights from Sweden
Authors: Höglund Nikolas Benavides , Sparrenbom Charlotte , Barthel Roland , Haraldsson Emil 
Published: October 2024

AbstractGroundwater is an essential resource for drinking water, food production, and industrial applications worldwide. Over-exploitation and pollution pose significant risks to groundwater sustainability. Groundwater models can be powerful tools for optimizing use, managing risks, and aiding decision-making. For this purpose, models should assimilate pertinent data and quantify uncertainties in outcomes. We examine applied modelling for characterization and decision support in Sweden from 2010 to 2023. We also review syllabi of water-related courses in Swedish higher education to assess the inclusion and extent of groundwater modelling education. We find that important academic advances in groundwater modelling over the past two decades have not translated into practical application within Sweden’s industry, that uncertainty quantification is rarely undertaken, and that groundwater modelling remains a low priority in higher education. Based on these findings, we offer recommendations that, while informed by the Swedish context, hold relevance for educational institutions, industry, and decision-makers internationally.
Factors impacting curricula in maritime simulator-based education
Authors: Karahalil Meric , Lützhöft Margareta , Scanlan Joel 
Published: October 2024

AbstractThe International Convention on Standards of Training, Certification, and Watchkeeping for Seafarers (STCW) provides a foundational framework for maritime education and training (MET). However, variations in its interpretation across different nations and institutions lead to diverse teaching practices and strategies. This diversity stems from differences in national regulations, resources, cultures, and the perspectives of institutions and instructors. This study introduces a concept map to scaffold the key concepts influencing maritime simulator-based education. By examining both the education system and student factors, the concept map offers insights into these various factors through observations from simulator-based teaching sessions and discussions with maritime educators and students. This tool can help identify differences and highlight good practices. It is a valuable resource for stakeholders, promoting a novel approach to developing an effective, comprehensive, coherent maritime simulator-based education.
Less can be more: representational vs. stereotypical gender bias in facial expression recognition
Authors: Dominguez-Catena Iris , Paternain Daniel , Jurio Aranzazu , Galar Mikel 
Published: October 2024

Abstract
Machine learning models can inherit biases from their training data, leading to discriminatory or inaccurate predictions. This is particularly concerning with the increasing use of large, unsupervised datasets for training foundational models. Traditionally, demographic biases within these datasets have not been well-understood, limiting our ability to understand how they propagate to the models themselves. To address this issue, this paper investigates the propagation of demographic biases from datasets into machine learning models. We focus on the gender demographic component, analyzing two types of bias: representational and stereotypical. For our analysis, we consider the domain of facial expression recognition (FER), a field known to exhibit biases in most popular datasets. We use Affectnet, one of the largest FER datasets, as our baseline for carefully designing and generating subsets that incorporate varying strengths of both representational and stereotypical bias. Subsequently, we train several models on these biased subsets, evaluating their performance on a common test set to assess the propagation of bias into the models’ predictions. Our results show that representational bias has a weaker impact than expected. Models exhibit a good generalization ability even in the absence of one gender in the training dataset. Conversely, stereotypical bias has a significantly stronger impact, primarily concentrated on the biased class, although it can also influence predictions for unbiased classes. These results highlight the need for a bias analysis that differentiates between types of bias, which is crucial for the development of effective bias mitigation strategies.
On partially observed jump diffusions III: regularity of the filtering density
Authors: Germ Fabian , Gyöngy István 
Published: October 2024

AbstractThe filtering equations associated to a partially observed jump diffusion model $$(Z_t)_{t\in [0,T]}=(X_t,Y_t)_{t\in [0,T]}$$

(

Z
t

)

t
∈
[
0
,
T
]

=

(

X
t

,

Y
t

)

t
∈
[
0
,
T
]

, driven by Wiener processes and Poisson martingale measures are considered. Building on results from two preceding articles on the filtering equations, the regularity of the conditional density of the signal $$X_t$$

X
t

, given observations $$(Y_s)_{s\in [0,t]}$$

(

Y
s

)

s
∈
[
0
,
t
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, is investigated, when the conditional density of $$X_0$$

X
0

given $$Y_0$$

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0

exists and belongs to a Sobolev space, and the coefficients satisfy appropriate smoothness and growth conditions.
Explicit Multi-slit Loewner Flows and Their Geometry
Authors: Theodosiadis E. K. 
Published: October 2024

AbstractIn this paper we present explicit solutions to the radial and chordal Loewner PDEs and we make an extensive study of their geometry. Specifically, we study multi-slit Loewner flows, driven by the time-dependent point masses $$\mu _{t}:=\sum _{j=1}^{n}b_j \delta _{\{\zeta _j e^{iat}\}}$$

μ
t

:
=

∑

j
=
1

n

b
j

δ

{

ζ
j

e

iat

}

in the radial case and $$\nu _t:=\sum _{j=1}^{n}b_j\delta _{\{k_j\sqrt{1-t}\}}$$

ν
t

:
=

∑

j
=
1

n

b
j

δ

{

k
j

1
-
t

}

in the chordal case, where all the above parameters are chosen arbitrarily. Furthermore, we investigate their close connection to the semigroup theory of holomorphic functions, which also allows us to map the chordal case to the radial one.
From Empirical Problem-Solving to Theoretical Problem-Finding Perspectives on the Cognitive Sciences
Authors: Adolfi Federico , van de Braak Laura , Woensdregt Marieke 
Published: October 2024

AbstractMeta-theoretical perspectives on the research problems and activities of (cognitive) scientists often emphasize empirical problems and problem-solving as the main aspects that account for scientific progress. While certainly useful to shed light on issues of theory-observation relationships, these conceptual analyses typically begin when empirical problems are already there for researchers to solve. As a result, the role of theoretical problems and problem-finding remain comparatively obscure. How do the scientific problems of Cognitive Science arise, and what do they comprise, empirically and theoretically? Here, we attempt to understand the research activities that lead to adequate explanations through a broader conception of the problems researchers must attend to and how they come about. To this end, we bring theoretical problems and problem-finding out of obscurity to paint a more integrative picture of how these complement empirical problems and problem-solving to advance cognitive science.
Modular momentum: assessing the efficacy of modular construction in alleviating the UK housing crisis
Authors: Parisi Luana , Donyavi Sohrab 
Published: October 2024

AbstractThis manuscript critically evaluates the potential of modular construction as a viable remedy to the ongoing housing crisis in the UK. In light of the escalating construction costs and the long-standing lack of productivity gains in the residential construction sector, there is in fact an urgent need to explore innovative solutions to address the scarcity of affordable housing. The study delves then into the feasibility and effectiveness of modular construction in providing cost-efficient and sustainable housing alternatives, particularly for the working-class population. Through a comprehensive analysis of construction trends and methodologies, and stakeholder perspectives, this research aims to illuminate the advantages and limitations of modular construction compared to traditional building techniques. Drawing upon data sourced from construction professionals, and government records, the study assesses crucial factors such as construction timelines, financial implications, design adaptability, and overall efficacy in tackling the housing crisis. The findings of this study are poised to offer valuable insights into the potential role of modular construction in transforming housing solutions in the UK. By examining the intersection of housing demand, construction practices, and technological advancements, the manuscript seeks to inform decision-making processes in both public and private sectors. Ultimately, the objective is to identify strategies that can facilitate the widespread adoption of modular construction as a sustainable and scalable solution to the housing crisis, in line also with the principles of the circular economy, thereby enhancing access to safe, affordable, and high-quality housing for all segments of society.
Building trust in large-scale water quality models: 13 alternative strategies beyond validation
Authors: Strokal Maryna , Wang Mengru , Micella Ilaria , Janssen Annette B. G. 
Published: October 2024

AbstractValidating large-scale water quality models is challenging because of the variety of water quality constituents, and scales for which observations are limited. Here, in this perspective, we propose 13 alternative strategies to build trust in large-scale water quality models beyond validation and discuss their strengths and weaknesses regarding their validity, reliability, and applicability. Our alternative strategies aim to evaluate separately model inputs (Strategies 1–4), outputs (Strategies 5–6) and structures (Strategy 7) as well as these aspects together (Strategies 8–13). This is done via methods such as comparisons (Strategies 1–3, 6–8, 12–13), sensitivity analysis (Strategy 5), use of innovations (Strategy 9), expert knowledge (Strategy 11) and local models (Strategy 13). The proposed strategies vary in their validity, reliability, and applicability. Validation is an important starting point but should be used in combination with other strategies. Our proposed list opens the discussion to improve methods to evaluate global water quality models.
Performing electrocatalytic CO2 reduction reactions at a high pressure
Authors: Chen Boxu , Feng Manshuo , Chen Yi , Yang Jirui , Liu Ya 
Published: October 2024

AbstractElectrocatalytic CO2 reduction technology offers an effective way to convert CO2 into valuable chemicals and fuels, presenting a sustainable solution for carbon emissions. Current electrocatalytic CO2 reduction technologies encounter significant issues such as salt precipitation and hydrogen evolution, which prevent energy conversion efficiency, selectivity, current density, and stability from simultaneously meeting industrial standards. In recent years, researchers have discovered that increasing the CO2 pressure on the gas supply could enhance the coverage of the catalyst and activate more CO2 reduction reaction sites on the catalyst surface, which provides a practical and effective approach for optimizing the energy conversion and mass transfer. In this review, we provide a comprehensive review of the development history and current status of high-pressure CO2 electrocatalytic reduction technology, focusing on its reaction devices, catalytic performance, and reaction mechanisms. Furthermore, we summarize and offer insights into the most promising research avenues to propel the field forward.
Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement
Authors: Xu Haowen , Omitaomu Femi , Sabri Soheil , Zlatanova Sisi , Li Xiao , Song Yongze 
Published: October 2024

AbstractThe digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.
Data-driven assessment of business process resilience
Authors: Kraus Alexander , Rehse Jana-Rebecca , van der Aa Han 
Published: October 2024

AbstractProcess resilience represents a core competence for organizations in light of an increasing number of process disruptions, such as sudden increases in case arrivals or absences in the workforce. It reflects an organization’s ability to restore a process to its acceptable performance level after a disruption. In this regard, the first key step for organizations towards achieving resilience is to understand how resilient their processes actually are. Although recognized as important, few works focus on such resilience assessment in a data-driven manner, thus barring organizations from gaining the necessary insights into how much their processes are affected by disruptions and how long it takes them to recover. To address this problem, we propose an approach for automated resilience assessment, based on recorded event data. Our approach interprets relevant process characteristics, such as the average lead time or arrival rate, as time series, which capture the development of the process execution over time. Based on these time series, it uses statistical modeling, specifically a vector autoregressive model, to determine the inter-relations between those characteristics and assess how the process performance responds to a disruption, i.e., a significant and temporal change in one of the process characteristics. We validate our approach by comparing its accuracy with a what-if analysis using a simulation model and demonstrate its effectiveness by assessing the resilience of the same process to diverse disruptions across different organizations.
The effect of combination treatment with casirivimab and imdevimab versus standard antiviral therapy on clinical outcomes in hospitalized COVID-19 patients
Authors: Hegazy Sahar K. , Hassan Ahmed H. 
Published: October 2024

Abstract
Background
The Coronavirus Disease of 2019 (COVID-19) pandemic motivated research efforts to address this catastrophe from the beginning of 2020 to the present. The year 2021 is coming to an end, and several developments in pharmacotherapy for COVID-19 have occurred. In terms of antiviral therapy, monoclonal antibody combinations have become one type of COVID-19 immunotherapy. Remdesivir and favipiravir are two antiviral medications typically used to treat COVID-19. Paxlovid (nirmatrelvir plus ritonavir) and molnupiravir are other antiviral agents used in COVID-19. The purpose of this study was to compare the effects of combinations of antibodies (casirivimab and imdevimab), remdesivir, and favipiravir on COVID-19 swab results in hospitalized patients with COVID-19 at discharge.

Methods
This research is a phase IV non-randomized controlled trial (< 300 patients as sample size) with no comparison to placebo. In this study, 265 COVID-19 patients confirmed by polymerase chain reaction (PCR) and indications for antiviral medication were non-randomized into three groups at a ratio of (1:2:2): REGN3048-3051 (group A), remdesivir (group B), and favipiravir (group C). A nonrandomized single-blind controlled study design was used. The study's medication was provided by the Hospital of Mansoura University. Following ethical approval, the study was conducted for approximately 6 months.

Results
Compared to remdesivir and favipiravir, casirivimab and imdevimab cause less hepatotoxicity, less renal impairment, and more negative swab results at discharge. The three tested antivirals also had no significant adverse effects. All three antivirals are considered safe based on these findings.

Conclusion
Compared with remdesivir and favipiravir, casirivimab and imdevimab result in better clinical outcomes. All three antivirals are safe and have no significant toxicity.
Trial registration: Clinicaltrials.gov, NCT05502081, 16/08/2022.
Does Adoption of Improved Variety Encourage Farmers to Invest in Modern Inputs and Use Good Practices? Evidence from Rice Farmers in Guinea
Authors: Arouna Aminou , Barry Mamadou L. , Kamano Pierre , Yabi Jacob A. 
Published: October 2024

AbstractIron toxicity is one of the constraints limiting rice production in Africa. This study used a randomized controlled trial to assess the impact of an iron toxicity-tolerant variety, named ARICA 6, on different outcomes and investment in modern inputs by smallholder farmers. Two rounds of data were collected from 520 rice-farming households in Guinea. Results showed that the use of ARICA 6 increased rice yield by 330 kg ha−1 and net income by US$ 120 ha−1. However, adoption of improved variety may not be enough to crowd in investment in modern inputs because farmers face other constraints.
Explaining the Comparatively Less Robust Human Rights Impact of the ECOWAS Court on Legislative and Judicial Decision-making, Process, and Action in Nigeria
Authors: Okafor Obiora Chinedu , Owie Udoka , Effoduh Okechukwu , Zarma Rahina 
Published: October 2024

Abstract
This article outlines and tackles two inter-related puzzles regarding the comparatively much less robust human rights impact that the ECOWAS Court (in effect, West Africa’s international human rights court) has had on the generally more democratic legislative/judicial branch of decision-making and action in Nigeria vis-à-vis the generally more authoritarian executive branch within Nigeria, the country that is the source of most of the cases filed before the court. The article then discusses and analyzes the examples and extent of the court’s human rights impact on legislative/judicial branch decision-making and action in that key country. This is followed by the development of a set of analytical, multi-factorial, explanations for the two inter-connected puzzles that animate the enquiry in this article. In the end, the article argues that several factors have combined to produce the comparatively much less robust human rights impact that the ECOWAS Court has had on domestic legislative and judicial decision-making, process, and action in Nigeria, through restricting the extent to which the latter could mobilize more robustly the court’s human rights-relevant processes and rulings.