bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mind the naive forecast! a rigorous evaluation of forecasting models for time series with low predictability
Authors: Beck Nico , Dovern Jonas , Vogl Stefanie 
Published: February 2025

Abstract
In the field of time series forecasting, numerous machine learning studies have assessed the performance of new methods on highly volatile data from macroeconomics and finance. Unlike in other domains, where models are also compared to simpler statistical or naive baselines, they mostly compare the performance solely relative to other complex models. This approach may lead to limited conclusions and reduce the practical significance of the results, as it overlooks the unpredictability of some highly volatile time series in the datasets used. We apply state-of-the-art methods from time-series econometrics and machine learning, including autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), Bayesian vector autoregressive model (BVAR), long-short term memory neural networks (LSTM), historical consistent neural networks (HCNN), deep vector autoregressive neural networks (DeepVAR), temporal fusion transformers (TFT), and extreme gradient boosting (XGBoost). Our results demonstrate that no method consistently outperforms the naive (no-change) forecast for highly volatile time series from two popular datasets containing exchange rates and stock prices, rendering comparative analysis between complex models less meaningful. In contrast, when applied to more predictable macroeconomic price indices, many of the methods significantly outperform naive forecasts. We find that the performance of machine learning models deteriorates more than that of statistical models for high-volatility time series. This study highlights the critical importance of using appropriate benchmark models, including cost-effective, simple approaches, on datasets that permit meaningful conclusions.
A comparative evaluation of clustering-based outlier detection
Authors: Sánchez Vinces Braulio V. , Schubert Erich , Zimek Arthur , Cordeiro Robson L. F. 
Published: February 2025

Abstract
We perform an extensive experimental evaluation of clustering-based outlier detection methods. These methods offer benefits such as efficiency, the possibility to capitalize on more mature evaluation measures, more developed subspace analysis for high-dimensional data and better explainability, and yet they have so-far been neglected in literature. To our knowledge, our work is the first effort to analytically and empirically study their advantages and disadvantages. Our main goal is to evaluate whether or not clustering-based techniques can compete in efficiency and effectiveness against the most studied state-of-the-art algorithms in the literature. We consider the quality of the results, the resilience against different types of data and variations in parameter configuration, the scalability, and the ability to filter out inappropriate parameter values automatically based on internal measures of clustering quality. It has been recently shown that several classic, simple, unsupervised methods surpass many deep learning approaches and, hence, remain at the state-of-the-art of outlier detection. We therefore study 14 of the best classic unsupervised methods, in particular 11 clustering-based methods and 3 non-clustering-based ones, using a consistent parameterization heuristic to identify the pros and cons of each approach. We consider 46 real and synthetic datasets with up to 125k points and 1.5k dimensions aiming to achieve plausibility with the broadest possible diversity of real-world use cases. Our results indicate that the clustering-based methods are on par with (if not surpass) the non-clustering-based ones, and we argue that clustering-based methods like KMeans−− should be included as baselines in future benchmarking studies, as they often offer a competitive quality at a relatively low run time, besides several other benefits.
Experimental Compressed Liquid Density Measurements and Correlation of the Binary Mixture {n-Pentane (R601) + Trans-1-chloro-3,3,3-trifluoro-1-propene (R1233zd(E))}
Authors: Menegazzo Davide , Lombardo Giulia , Vallese Laura , Bobbo Sergio 
Published: February 2025

Abstract
Regulations like the F-gas Regulation (EU) 2024/573 and the Kigali Amendment to the Montreal Protocol, along with efforts to mitigate climate change, drive research into alternatives to fluorinated greenhouse gases for high-temperature heat pumps and power generation. Ideal refrigerants should have low GWP, high efficiency, non-flammability, non-toxicity, material compatibility and cost effectiveness. HCFOs have emerged as promising candidates, both as pure fluids and in mixtures with HCs. These blends show efficiency and potential non-flammability for high-temperature applications, but experimental data on their thermophysical properties remain scarce. This study presents the first experimental measurements on the {n-pentane (R601) + trans-1-chloro-3,3,3-trifluoro-1-propene (R1233zd(E))} binary system. In particular, the compressed liquid density of three mixture compositions have been measured employing a vibrating tube densimeter within the temperature range from 283.15 K to 423.15 K and at pressures ranging from 1 MPa to 12 MPa. A novel technique was applied to ensure a combined uncertainty (k=2) not greater than 0.0003 mol

$$\cdot$$

·

mol

$$^{-1}$$

-
1

in the mixture composition, leading to final combined uncertainty (k=2) on the liquid density of no more than 0.2%. Finally, a new mixture model based on the Helmholtz-energy-explicit Equation of State has been developed from such experimental data. This model accurately represents the behaviour of the binary mixture, enhancing the available understanding of its thermodynamic properties.
Teaching to whom and with whom: the role of context in developing preservice teachers’ self-efficacy for teaching engineering and coding via robotics
Authors: Kidd Jennifer , Gutierrez Kristie , Lee Min Jung , Rhemer Danielle , Pazos Pilar , Kaipa Krishna , Ringleb Stacie , Ayala Orlando 
Published: February 2025

Abstract
Due to mandates for the inclusion of engineering and computer science standards for K-6 schools nationwide, there is a need to understand how teacher educators can help develop preservice teachers’ (PSTs’) teaching self-efficacy in these areas. To provide experience teaching and learning engineering and coding, PSTs in an instructional technology course were partnered with undergraduate engineering students in an electromechanical systems course to teach robotics lessons to fifth graders (10–11 year olds) over Zoom. A multi-case study approach explored teaching self-efficacy development for three preservice teachers during their robotics project experiences using multiple data sources, including surveys, reflections, interviews, and Zoom recordings, which were examined to identify how the project's social and intrapersonal context influenced the development of each PST’s teaching self-efficacy for engineering and coding. The PSTs gained teaching self-efficacy through all four sources of teaching self-efficacy, although not all PSTs benefited from all four types, nor did they benefit equally. These sources also influenced the PSTs’ intention to integrate engineering and coding into their future classrooms. This study demonstrates the potential of providing PSTs with the opportunity to teach robotics to children during their teacher preparation programs to support the development of their teaching self-efficacy for engineering and coding. When conducted in the context of a college course, such opportunities can be thoughtfully structured to leverage positive interactions with peers and elementary students and to take advantage of low-stakes environments, like afterschool clubs, offering PSTs settings rich in sources of self-efficacy information.
Decoding sustainable entrepreneurship current research and future direction through application of machine learning-based structured topic modeling on intellectual corpus
Authors: Abbas Muhammad Hassan , Bullut Mehmet , Ali Hassnian 
Published: February 2025

Abstract
This study leverages structured topic modeling (STM) to decode the expansive intellectual corpus on sustainable entrepreneurship, utilizing a dataset of 363 peer-reviewed articles from Scopus over a decade. Focused on “sustainable entrepreneurship” and related terms, the STM method integrated document-specific metadata to enhance the analysis of thematic developments. The findings revealed ten distinct topics, such as innovation in firm performance, sustainability in business models, and the role of education in sustainable intentions, highlighting the interplay between these themes and their evolution. This research identifies key thematic areas and examines the influence of source titles and publication years on topic prevalence, indicating shifts in academic focus and identifying emerging trends. The study’s implications suggest integrating sustainability into core business and educational strategies, enhancing the understanding of sustainable entrepreneurship’s dynamic nature, and providing a foundation for future scholarly and practical efforts.
Non-equilibrium Phase Coexistence in Boundary-Driven Diffusive Systems
Authors: Sasa Shin-ichi , Nakagawa Naoko 
Published: February 2025

Abstract
Liquid–gas phase coexistence in a boundary-driven diffusive system is studied by analyzing fluctuating hydrodynamics of a density field defined on a one-dimensional lattice with a space interval

$$\varLambda $$

Λ

. When an interface width

$$\ell $$

ℓ

is much larger than

$$\varLambda $$

Λ

, the discrete model becomes the standard fluctuating hydrodynamics, where the phase coexistence condition is given by the local equilibrium thermodynamics. In contrast, when

$$\ell < \varLambda $$

ℓ
<
Λ

, the most probable density profile is determined by a new variational principle, where the chemical potential at the interface is found to deviate from the equilibrium coexistence chemical potential. This means that metastable states at equilibrium stably appear near the interface as the influence of the particle current. The variational function derived in the theoretical analysis is also found to be equivalent to the variational function formulated in an extended framework of thermodynamics called global thermodynamics. Finally, the validity of the theoretical result is confirmed by numerical simulations.
Exploring sex-specific hematological changes and their impact on quality of life in patients with prolactinoma
Authors: Detomas Mario , Deutschbein Timo , Dolce Pasquale , Möhres Yvonne , Fassnacht Martin , Altieri Barbara 
Published: February 2025

Abstract

Context
Despite prolactin´s (PRL) role in stimulating hematopoiesis, anemia is commonly observed in men with macroprolactinomas. However, hematological changes in men with microprolactinomas and women with prolactinomas remain unexplored, and the impact of erythropoietic alterations on quality of life (QoL) is still unclear.

Objective
To explore sex-related changes in red blood cell (RBC) parameters and their potential impact on QoL at initial diagnosis of prolactinoma and after normalization of PRL under dopamine agonists.

Design
Retrospective, monocentric study involving 205 patients with prolactinoma (127 women, 62%). The SF-36 QoL questionnaire was administered to 57 women and 34 men.

Results
In women, no significant changes in RBC parameters were observed at diagnosis or after PRL normalization, regardless the adenoma size. Conversely, men with microprolactinoma showed a significant increase in hematocrit (HCT) and hemoglobin (Hb) levels after PRL normalization (median HCT 42.3 vs.44.0%; Hb 14.5 vs. 15.1 g/dL; both p < 0.005). Men with macroprolactinoma exhibited similar improvements (HCT 40.2 vs. 43.9%; Hb 14.0 vs. 15.1 g/dL; both p < 0.0001). In men, hypogonadism was observed in 73% of patients at baseline, and in 11% after PRL normalization. In male patients where SF-36 was administered at diagnosis and after PRL normalization, energy improvement was observed (median 50 vs. 60, p < 0.05). While changes in Hb and HCT were not significantly impacting the QoL of women and men, persistence of hypogonadism after PRL normalization, negatively impacted all the QoL scores of men.

Conclusion
Patients with prolactinoma show sex-dependent changes in RBC parameters. Unlike women, men exhibit decreased HCT and Hb levels irrespective of adenoma size. Of note, the failure to recover from hypogonadism significantly affected the QoL of men.
The role of BRAF testing of Rathke’s cleft cysts to identify missed papillary craniopharyngioma
Authors: Candy Nicholas G. , Mignone E. , Quick E. , Koszyca B. , Brown A. , Chapman I. M. , Torpy D. J. , Vrodos N. , Santoreneos S. , De Sousa S. M. C. 
Published: February 2025

Abstract

Aim
The differential diagnosis of cystic sellar/suprasellar lesions includes craniopharyngioma (CP) and Rathke’s cleft cyst (RCC). Histological differentiation between cystic papillary craniopharyngioma (pCP) and RCC using light microscopy alone is challenging. A major point of difference is that virtually all pCPs are clonal for the BRAF V600E variant, whereas RCCs are not. Noting that BRAF testing of RCCs is not current standard practice, we hypothesised that routinely performing BRAF studies in RCCs might uncover otherwise missed pCPs.

Method
We performed a retrospective cohort study of all RCCs operated on at Flinders Medical Centre, the Memorial and Royal Adelaide Hospitals, between 2001 and 2023. In cases with sufficient tissue, we performed BRAF V600E immunohistochemistry (IHC) and BRAF next generation sequencing (NGS) of extracted tumour DNA.

Results
Of eleven patients with suitable operative specimens, one patient with an initial diagnosis of RCC was revised to pCP following BRAF testing with equivocal positivity on BRAF IHC and clear identification of the V600E variant on NGS. The patient’s subsequent clinical course was aggressive and more compatible with pCP than RCC.

Conclusion
This study highlights the potential value of BRAF testing in RCCs to identify missed pCP, which is an especially timely finding given the advent of primary medical therapy with BRAF inhibition for pCP. In the absence of guidelines advising on the use of BRAF studies in sellar lesions, we suggest consideration of BRAF testing of all RCCs, particularly if there is squamous metaplasia or disease recurrence.
Home as mind: AI extenders and affective ecologies in dementia care
Authors: Krueger Joel 
Published: February 2025

Abstract
I consider applications of “AI extenders” to dementia care. AI extenders are AI-powered technologies that extend minds in ways interestingly different from old-school tech like notebooks, sketch pads, models, and microscopes. I focus on AI extenders as ambiance: so thoroughly embedded into things and spaces that they fade from view and become part of a subject’s taken-for-granted background. Using dementia care as a case study, I argue that ambient AI extenders are promising because they afford richer and more durable forms of multidimensional integration than do old-school extenders like Otto’s notebook. They can be tailored, in fine-grained ways along multiple timescales, to a user’s particular needs, values, and preferences—and crucially, they can do much of this self-optimizing on their own. I discuss why this is so, why it matters, and its potential impact on affect and agency. I conclude with some worries in need of further discussion.
Toxicity Assessment of River Sediments Impacted by Open-Pit Coal Mining in Colombia Using Caenorhabditis elegans
Authors: Duran-Izquierdo Margareth , de la Rosa Jesus , Olivero-Verbel Jesus 
Published: February 2025

Abstract
Coal mining is a critical economic for Colombia. However, mineral extraction is usually carried out near rivers that provide ecosystem services to riverside populations. Cesar River receives discharges from several open-pit coal mines, as well as from other anthropogenic sources. The aim of this work was to assess the chemical and the toxicity profile of the sediments from this river. Bottom sediment samples were collected from 12 points along the river, including tributaries and a Ramsar site, the Zapatosa Marsh. Trace elements were quantified employing ICP-MS, and mercury (Hg) was measured using a direct Hg analyzer. Aqueous extracts (K-medium) were obtained from dried sediments (1:3 ratio) and tested using Caenorhabditis elegans, assessing mortality, locomotion and growth as end points. Transcriptional effects associated with various toxicity mechanisms were evaluated using GFP-related transgenic strains (mtl-2, sod-4 and gst-1). Some trace metals enriched along the course of the river, especially Hg and V. Sediment extract-induced lethality was low (1.5–6.4%); however, nematode growth and locomotion decreased downstream the river, showing inhibition rates up to 23.3 and 35.4%, respectively. Extracts from downstream points increased the mRNA expression of tested genes compared to that elicited by the most upstream site, with greater values on stations receiving domestic sewage and mining outputs. Cobalt and lead were positively associated with metallothioneins and gst-1 expression. In short, coal mining areas should be closely monitored for trace-element release and their impact on biota. The Colombian government should implement laws and programs to protect key ecosystems from mining activities, as a commitment to sustainable development goals.
Genomic exploration of Thuja plicata indicates no loss of diversity from adults to offspring in UK Woodlands
Authors: Guillardín Laura , Glover Ella , Kerr Gary , MacKay John J. 
Published: February 2025

Abstract

Thuja plicata is a conifer tree that is valued for its cultural, ecological and wood quality features in its natural range in western North America and is used in Europe as an exotic timber species. It is increasingly used in mixed species forests in the UK that are managed using natural regeneration, raising questions about genetic diversity of both the adult trees and offspring of the species in these stands. We studied population structure and genetic diversity in four UK woodlands of both adults and naturally regenerated offspring of T. plicata. We discovered 194,154 Single Nucleotide Polymorphisms (SNPs) using Genotyping-by-Sequencing (GBS) and retained 655 SNPs across 598 genome sequence scaffolds for analysis. The majority of these scaffolds included only one SNP, suggesting that; the selected SNPs were widely distributed within the genome. We found many monomorphic sites, most of which were restricted to adults in a single woodland, indicative of a genetic differentiation among woodlands. We found three ancestral populations (K = 3) and low levels of admixture across the four sites, indicating that the starting materials were largely from single populations. Estimates of genetic diversity using heterozygosity (Ho) and nucleotide diversity (π) were low (overall Ho = 0.174, π = 0.00226) across all sites but slightly higher in juveniles at two sites. Our genotyping methods and results on standing genetic diversity in this important conifer provide insight into the ability of planted woodlands in the UK to face environmental shifts and disease threats.
K-12 Teachers’ Acceptance and Resistance Perceptions of Learning Analytics Adoption: A Mixed-Methods Approach
Authors: Tzimas Dimitrios , Demetriadis Stavros 
Published: February 2025

Abstract
Learning analytics (LA) is an educational innovation that enhances teaching practices and facilitates student learning. However, the degree of LA adoption across schools remains limited, and teachers who adopt LA do not engage with it consistently. Based on the unified theory of acceptance and use of technology (UTAUT) as a framework, we conducted a perception survey and five focus group interviews with K-12 teachers, aiming to identify their expectations, needs, and perceptions of LA adoption. This study focuses on aspects related to LA’s technology acceptance in school education. The research question is, “Using the UTAUT framework as a theoretical perspective, which factors explain the adoption of LA in schools? How do teachers describe their motivation and readiness for LA adoption?” The findings indicated that the factors facilitating LA adoption were performance expectancy, social influence, and feelings. Furthermore, the factors inhibiting the adoption of LA were effort expectancy, self-efficacy, facilitating conditions, and culture change. The implications and potential future research for adopting LA in schools are discussed.
Immigrant Single Mothers in Germany: An Intersectional Perspective on a Group at Risk
Authors: Maciejewski Linda , Harder Niklas 
Published: February 2025

Abstract
The risk of poverty is not evenly distributed in the German population, and immigrants and single mothers are particularly at risk. According to the Family Stress Model, poverty can impact all family members and their relationships. We use the AID:A 2019 dataset on young families and apply an intersectional perspective to study the situation of immigrant single mothers and their children. We focus on how single mothers and in particular immigrant single mothers differ from mothers in two-parent families and non-immigrants in terms of poverty risk, reported life satisfaction, and mother-reported psychosocial difficulties (SDQ) of their children. Our results show that immigrant single mothers, especially first-generation non-naturalized immigrant mothers, face the highest risk of economic stress and lower life satisfaction. This supports the Family Stress Model. However, contrary to the extended Family Stress Model, children of immigrant single mothers are not more likely to show mother-reported psychosocial difficulties than their peers from two-parent or non-immigrant families. Following an intersectional perspective, we do not assume that the reasons for these differences necessarily lie only in the individuals. Instead, we also interpret the effects of social positions as effects of the political and social dynamics creating these social positions and thus discuss the German institutional settings that might explain our results.
Generative artificial intelligence acceptance and artificial intelligence anxiety among university students: the sequential mediating role of attitudes toward artificial intelligence and literacy
Authors: Cengiz Serkan , Peker Adem 
Published: February 2025

Abstract
The use of artificial intelligence (AI) applications is increasing in daily life. However, despite its remarkable capabilities, the widespread integration of this technology into all aspects of human life also carries the risk of causing unforeseen psychological difficulties for humanity. For this reason, it is necessary to identify the factors that may particularly affect artificial intelligence anxiety. We examined the sequential mediation effect of AI literacy and attitudes towards AI on the relationship between generative AI acceptance and AI anxiety. The study was conducted with 494 university students (M

age
= 21.84, SD = 1.24). The research model was designed according to the PROCESS Macro model 80 proposed by Hayes. Data were analyzed using the SPSS v.26 program, Pearson’s correlations, and Hayes’s PROCESS macro method for mediation. The results revealed that AI anxiety has significant relationships with generative AI acceptance, AI literacy, and attitudes toward AI. Indirect effect tests supported the mediating relationship between attitudes toward AI and AI literacy. These results help us understand the underlying mechanism in the relationship between generative AI acceptance and AI anxiety.