bluuebunny/crossref_metadata_2025_split huggingface dataset:
Scientometric indicators in research evaluation and research misconduct: analysis of the Russian university excellence initiative
Authors: Lovakov Andrey , Teixeira da Silva Jaime A. 
Published: February 2025

Abstract
This study aimed to examine the impact of the Russian University Excellence Initiative (RUEI), also known as Project 5–100, on research misconduct in Russian higher education. Launched in 2013, the RUEI incentivized universities to increase the number of publications in internationally indexed journals. The analysis compares the prevalence of retracted publications—as a proxy for research misconduct—between universities that participated in the RUEI and a control group of universities that did not. A total of 2621 retracted papers affiliated with at least one Russian institution were identified. Of which 203 papers were indexed in Web of Science (WoS) and/or Scopus databases. The results showed that RUEI universities had a significantly higher number of retracted publications in WoS- and Scopus-indexed journals, suggesting that pressure to meet quantitative scientometric indicators may have encouraged unethical research practices and research misconduct. In addition, different reasons for retraction were found between publications indexed and not indexed in WoS and/or Scopus databases. These findings suggest that the direct and irresponsible use of scientometric indicators as performance measures may have unintended negative consequences that may undermine research integrity.
LMD: Light-Weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds
Authors: Riedlinger Tobias , Schubert Marius , Penquitt Sarina , Kezmann Jan-Marcel , Colling Pascal , Kahl Karsten , Roese-Koerner Lutz , Arnold Michael , Zimmermann Urs , Rottmann Matthias 
Published: February 2025

Abstract
Object detection on Lidar point cloud data is a promising technology for autonomous driving and robotics which has seen a significant rise in performance and accuracy during recent years. Particularly uncertainty estimation is a crucial component for down-stream tasks and deep neural networks remain error-prone even for predictions with high confidence. Previously proposed methods for quantifying prediction uncertainty tend to alter the training scheme of the detector or rely on prediction sampling which results in vastly increased inference time. In order to address these two issues, we propose LidarMetaDetect (LMD), a light-weight post-processing scheme for prediction quality estimation. Our method can easily be added to any pre-trained Lidar object detector without altering anything about the base model and is purely based on post-processing, therefore, only leading to a negligible computational overhead. Our experiments show a significant increase of statistical reliability in separating true from false predictions. We show that this improvement carries over to object detection performance when replacing the objectness score native to the object detector. We propose and evaluate an additional application of our method leading to the detection of annotation errors. Explicit samples and a conservative count of annotation error proposals indicates the viability of our method for large-scale datasets like KITTI and nuScenes. On the widely-used nuScenes test dataset, 43 out of the top 100 proposals of our method indicate, in fact, erroneous annotations.
Quantifying Molecular Changes in the Preeclamptic Rat Placenta with Targeted Contrast-Enhanced Ultrasound Imaging
Authors: Shi Lili , Alencar Allan K. N. , Swan Kenneth F. , Lawrence Dylan J. , Pridjian Gabriella , Bayer Carolyn L. 
Published: February 2025

Abstract

Purpose
Abnormal placental remodeling is linked to various pregnancy-related diseases, including preeclampsia (PE). This study applies a bicompartmental (BCM) model to quantify molecular expression changes in the placenta, indicative of abnormal placental remodeling, and evaluates the effectiveness of targeted contrast-enhanced ultrasound (T-CEUS) in detecting the abnormal placental vasculature. The BCM model provides high temporal resolution and differentiation of anatomical artery structures within the placenta by analyzing the distribution of contrast agents.

Methods
A targeted contrast agent (TCA) composed of gas-filled microbubbles (MB), with a surface-conjugated peptide to target ανβ3 integrin, a biomarker for angiogenesis, was used for quantifying placental vascular development. CEUS images were acquired from timed pregnant Sprague Dawley rats with experimentally-induced reduced uterine perfusion pressure (RUPP) placental insufficiency. On gestational day (GD) 18 of a 21-day gestation, CEUS images were acquired from both Normal pregnant (NP; n = 6) and RUPP (n = 6) dams after injection of the TCA. The BCM model was used to estimate the binding dynamics of the TCA, providing a parametric map of the binding constant (

$${K}_{b}$$

K
b

) of the placenta.

Results
The RUPP group showed a significant reduction in the value of

$${K}_{b}$$

K
b

compared to the NP group (p < 0.05). A histogram of the placental

$${K}_{b}$$

K
b

was compared to alternative analyses (differential target enhancement, dTE and late enhancement, LE) to demonstrate that it can differentiate between anatomical artery structures with a higher contrast-to-background ratio.

Conclusions
The BCM method differentiates molecular changes associated with the abnormal placental development associated with PE. It also reveals more intricate internal anatomical structures of the placenta in comparison to dTE and LE, suggesting that the BCM could enhance early detection and monitoring of PE.
Comparison of three different zeolites to activate peroxymonosulfate for the degradation of the pharmaceutical ciprofloxacin in water
Authors: Serna-Galvis Efraím A. , Mendoza-Merlano Carlos , Arboleda-Echavarría Johana , Torres-Palma Ricardo A. , Echavarría-Isaza Adriana 
Published: February 2025

Abstract
Zeolites are typically used as adsorbents for the removal of organic pollutants from water but recently are gaining attention as catalysts for the activation of persulfates toward contaminants degradation. In this work, the capability of a zeolite Y (FAU-type) and two zeolites beta (BEA-type) to activate peroxymonosulfate (PMS) toward the degradation of one representative pollutant of a pharmaceutical nature (i.e., ciprofloxacin) was tested and compared. Initially, the characterization of the considered zeolites was carried out, evidencing that they had different Si/Al, surface area, and basicity. Then, the main degradation pathway involved in the target pollutant degradation was determined and the activating ability of three zeolites was compared. It was found that among the three tested materials, zeolite Y had the highest activating capability toward PMS for ciprofloxacin degradation (showing ~ 90% degradation after 10 min of treatment). The synergy (S) of the systems followed the order: zeolites beta/PMS (S, 0.5–1.4) < zeolite Y/PMS (S, 3.9), revealing that the Si/Al ratio has a determinant role in the zeolite/peroxymonosulfate combination, being convenient lower values of such a ratio. In the most adequate combination (i.e., zeolite Y/PMS), the pharmaceutical was attacked by singlet oxygen (coming from the PMS activation by the zeolite via basic sites), which modified ciprofloxacin on its piperazyl ring, producing two intermediates. Theoretical analyses based on the structure suggested that the two intermediates have low toxicity against mammals. Additionally, experimental tests showed that the zeolite Y/PMS process led to a resultant solution without antimicrobial activity against S. aureus. Finally, it can be mentioned that ZY/PMS was used to deal with ciprofloxacin in synthetic hospital wastewater, achieving ~ 40% pollutant elimination after 60 min of treatment.
Scientific habits of arguing minds: enhancing online argumentation to foster SHOM
Authors: Çakiroğlu Ünal , Güven Onurhan , Yaman Havva , Güven Ebru Mazlum 
Published: February 2025

Abstract
This paper reports on a study aiming at examining whether online argumentation enhance the teacher candidates’ scientific habits of mind (SHOM). The study was carried out as a pre-experimental design including pre and post-test. Scientific Habits of Mind scale, interviews and written arguments were used as data collection tools. Google Classroom, sentence openers, prompt questions and videos were used to facilitate the online argumentation process. Five different SSIs were given as discussion and argumentation tasks for the participants who worked in online groups. The results indicated that, the SHOM scores of the teacher candidates were slightly decreased while the argumentation skills were significantly increased. In addition, there was a medium, positive correlation between the levels of scientific habits of mind and levels of argumentation skills. Participants’ perspectives also showed the contributions of virtual argumentation strategy implemented in this study. Sentence openers, prompt questions, videos and feedback were the prominent factors shaped the online argumentation process. We believe that the findings of this study would assist in future design and implementation of online tools for enhancing argumentation.
Enhanced self-supervised monocular depth estimation with self-attention and joint depth-pose loss for laparoscopic images
Authors: Li Wenda , Hayashi Yuichiro , Oda Masahiro , Kitasaka Takayuki , Misawa Kazunari , Mori Kensaku 
Published: February 2025

Abstract

Purpose
Depth estimation is a powerful tool for navigation in laparoscopic surgery. Previous methods utilize predicted depth maps and the relative poses of the camera to accomplish self-supervised depth estimation. However, the smooth surfaces of organs with textureless regions and the laparoscope’s complex rotations make depth and pose estimation difficult in laparoscopic scenes. Therefore, we propose a novel and effective self-supervised monocular depth estimation method with self-attention-guided pose estimation and a joint depth-pose loss function for laparoscopic images.

Methods
We extract feature maps and calculate the minimum re-projection error as a feature-metric loss to establish constraints based on feature maps with more meaningful representations. Moreover, we introduce the self-attention block in the pose estimation network to predict rotations and translations of the relative poses. In addition, we minimize the difference between predicted relative poses as the pose loss. We combine all of the losses as a joint depth-pose loss.

Results
The proposed method is extensively evaluated using SCARED and Hamlyn datasets. Quantitative results show that the proposed method achieves improvements of about 18.07

$$\%$$

%

and 14.00

$$\%$$

%

in the absolute relative error when combining all of the proposed components for depth estimation on SCARED and Hamlyn datasets. The qualitative results show that the proposed method produces smooth depth maps with low error in various laparoscopic scenes. The proposed method also exhibits a trade-off between computational efficiency and performance.

Conclusion
This study considers the characteristics of laparoscopic datasets and presents a simple yet effective self-supervised monocular depth estimation. We propose a joint depth-pose loss function based on the extracted feature for depth estimation on laparoscopic images guided by a self-attention block. The experimental results prove that all of the proposed components contribute to the proposed method. Furthermore, the proposed method strikes an efficient balance between computational efficiency and performance.
Digitalization against tax evasion: evidence on the role of company size
Authors: Ván Bálint , Lovics Gábor , Tóth Csaba G. , Szőke Katalin 
Published: February 2025

Abstract
To reduce tax evasion, in 2013 and 2014 the Hungarian government introduced mandatory online cash registers (OCR) in some sectors. As a result, almost 200,000 OCRs have been installed by 100,000 enterprises. We assume that OCR installation does not change the company’s operating model, so the increase in reported turnover around the installation date reflects a reduction in tax evasion. In this paper, we use microdata to estimate the effect of OCR introduction on reported turnover and tax liabilities using fixed-effects panel and event study models. We identify strong size-related heterogeneity in the retail and the accommodation and food services sectors: smaller companies increased their reported turnover more than larger ones. Since large companies pay the dominant part of value-added tax, the effects on the payment of this tax were mitigated. We find significant spillover effects to suppliers in both sectors, which are slightly stronger among larger companies.
Potential use of the ATR-FTIR spectroscopy as an almond cultivar recognition tool: impact of sample and spectral pre-treatments
Authors: Lamas Sandra , Rodrigues Nuno , Santamaria-Echart Arantzazu , Palu Igor , Manhique Jocyla R. , Herrero Baudilio , López-Cortés Isabel , Pereira José Alberto , Peres António M. 
Published: February 2025

Abstract
Three almond cultivars (Lauranne, Marinada, and Vairo) were studied, considering morphological parameters that showed statistical cultivar-dependence but not enabled accurate cultivar recognition. Alternatively, attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) was applied to whole endocarp, kernel, and ground almonds. Both transmittance spectra and respective derivatives (3400–2800 and 1900–600 cm−1) of the three matrices were used to establish multivariate linear discriminant models, based on subsets of selected wavenumbers (6–33), allowing 90.5–99.7% of correct cultivar classification for repeated K-fold cross-validation. Ground almonds yielded the best results regardless of spectrum pre-treatment. While epicarp analysis offers less invasiveness, the use of raw transmittance spectra of ground almonds resulted into the most practical approach due to the need of fewer independent variables (less complex models), proving effective for cultivar identification via ATR-FTIR -chemometric tools. Overall, the findings point out that ATR-FTIR is a reliable tool for almond cultivar traceability.
Deep Learning for Time Series Forecasting: Review and Applications in Geotechnics and Geosciences
Authors: Mojtahedi F. Fazel , Yousefpour N. , Chow S. H. , Cassidy M. 
Published: February 2025

Abstract
This paper presents a detailed review of existing and emerging deep learning algorithms for time series forecasting in geotechnics and geoscience applications. Deep learning has shown promising results in addressing complex prediction problems involving large datasets and multiple interacting variables without requiring extensive feature extraction. This study provides an in-depth description of prominent deep learning methods, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), generative adversarial network, deep belief network, reinforcement learning, attention and transformer algorithms as well as hybrid networks using a combination of these architectures. In addition, this paper summarizes the applications of these models in various fields, including mining and tunnelling, railway and road construction, seismology, slope stability, earth retaining and stabilizing structures, remote sensing, as well as scour and erosion. This review reveals that RNN-based models, particularly Long Short-Term Memory networks, are the most commonly used models for time series forecasting. The advantages of deep learning models over traditional machine learning, including their superior ability to handle complex patterns and process large-scale data more effectively, are discussed. Furthermore, in time series forecasting within the fields of geotechnics and geosciences, studies frequently reveal that deep learning methods tend to surpass traditional machine learning techniques in effectiveness.
Echocardiography and Heart Failure: An Echocardiographic Decision Aid for the Diagnosis and Management of Cardiomyopathies
Authors: Pender Ashley , Lewis-Owona Jessica , Ekiyoyo Abdulmojeed , Stoddard Marcus 
Published: February 2025

Abstract

Purpose of Review
The purpose of this review is to highlight the utility of echocardiography in the diagnosis and management of cardiomyopathies.

Recent Findings
Echocardiographic parameters function synergistically to guide decision-making ranging from early detection of disease and screening to risk stratification of complex disease.

Summary
The collective wealth of information available from 2D/3D assessment, Doppler, diastology and strain makes echocardiography an invaluable decision aid.
Exploring Habits in Anorexia Nervosa: Promise, Pitfalls, and Progress
Authors: Foerde Karin 
Published: February 2025

Abstract

Purpose of Review
Habits, characterized by automaticity and insensitivity to outcomes, may be key to the persistence of maladaptive behaviors in anorexia nervosa (AN). This review examines the status of habit research in AN, focusing on insights from task-based assessments.

Recent Findings
Findings indicate dysfunction in the frontostriatal circuits associated with habitual and goal-directed behaviors, with some studies linking neural disturbances to habit measures or clinically relevant behaviors. Heightened habitual tendencies in AN have consistently been reported using self-reports, while research utilizing experimental paradigms has yielded mixed results and efforts to capture real-world habits in AN remain limited.

Summary
Some experimental paradigms appear more sensitive than others, but all face challenges associated with studying habits in the lab. Promising new approaches will need to be adopted and efforts made to capture real-world habits. Understanding which habits are problematic, when in illness and for whom they dominate, could make good on the promise of habit-focused treatments for AN.
Effects of Epicatechin on the Expression of MyomiRs−31, −133, −136, −206, −296, and −486 in the Skeletal Muscle of the Offspring of Obese Mothers
Authors: Zárate-Segura Paola B. , Alvarez-Chávez Ana Luisa , De los Santos Sergio , Bastida-Gonzalez Fernando G. , Hernández-Hernández José Manuel , Zambrano Elena , Coral-Vázquez Ramón Mauricio , Canto Patricia 
Published: February 2025

Abstract

Specific myogenic microRNAs termed “myomiRNAs” are involved in skeletal muscle development and regeneration, and an obesogenic environment in utero may affect these processes. The present study aimed to determine whether this environment induced variations in the expression levels of myomiRs-31, −133, −136, −206, and −296 and whether the administration of (-)-epicatechin (Epi), an exercise mimetic, could modify these variations. Rat Wistar male offspring from control mothers (C) or obese mothers (MO) were treated (C+Epi and MO+Epi) or not treated with Epi (C and MO). MyomiRNA expression in the gastrocnemius and soleus muscles was analyzed via RT‒qPCR, and bioinformatic analysis was used to predict the participation of these miRNAs in different skeletal muscle signal transduction pathways. The expression of myomiRNA-31-5p in the gastrocnemius and soleus was significantly lower in the Epi-treated groups (C+Epi and MO+Epi vs. C and MO). The expression of myomiRNA-206 increased in the gastrocnemius muscles of the MO and MO+Epi groups but decreased in the soleus muscles of the MO and MO+Epi groups. The expression of myomiRNA-296 was increased in the MO group in the gastrocnemius and soleus but was reduced in the Epi stimulus group. The expression of myomiRNA-486 increased in the gastrocnemius of the C+Epi group and decreased in the soleus of the MO+Epi group (p = 0.028 vs. MO). In conclusion, we show that an intrauterine obesogenic environment differentially affects the expression levels of some myomiRNAs and that this effect is modified by epicatechin.
A Gene-Expression Based Comparison of Murine and Human Inhibitory Interneurons in the Cerebellar Cortex and Nuclei
Authors: Schilling Karl 
Published: February 2025

Abstract
Cerebellar information processing is critically shaped by several types of inhibitory interneurons forming various intra-cerebellar feed-forward and feed-back loops. Evidence gathered over the past decades has focused interest on a non-uniform set of cortical inhibitory interneurons distinct from “classical” Golgi, basket or stellate cells, summarily referred to as PLIs (for Purkinje cell layer interneurons). Similarly, cerebellar nuclear inhibitory interneurons have gained increasing attention. Our understanding of the functions of these cells is still fragmentary. For humans, we lack functional data, and even any dependable morphological classification for these cells. Here, I used publicly available single cell based gene expression data to compare inhibitory interneurons from the cerebellar cortex and inhibitory nuclear neurons of humans and mice. Integration of nuclear and cortical cells revealed transcriptomic similarities between subsets of these cells and suggest known characteristics of cortical cell types may be helpful to devise strategies for the further characterization of nuclear inhibitory interneurons. Comparison of human and murine PLIs indicate that these strongly differ by the expression of genes used to characterize these cells in mice. This limits their utility to identify and classify human PLIs, and leaves the question open as to the number and characteristics of non-Golgi inhibitory interneurons resident in the cerebellar granule cell and Purkinje cell layers in humans.
Toegankelijkheid van digitale zorg: signalen uit de e‑healthmonitor 2023
Authors: van Tuyl Lilian , Keuper Jelle , Standaar Lucille , Alblas Eva , Keij Brigitta 
Published: February 2025

Samenvatting
Digitalisering en technologische innovaties spelen een steeds prominentere rol in de gezondheidszorg. Het gebruik van digitale technologieën in de gezondheidszorg, ook wel bekend als e‑health of digitale zorg, heeft de potentie om de toegankelijkheid, efficiëntie en kwaliteit van zorg te verbeteren. Om de potentie van e‑health te kunnen realiseren, is het essentieel dat technologieën voor zo veel mogelijk inwoners toegankelijk zijn. Recente nationale gegevens van de E‑healthmonitor van 2023 laten zien dat hier nog verbetering mogelijk is.
Data en de praktijk van gehandicaptenzorg: wat zijn thema’s voor data-ondersteund werken?
Authors: Winkelmolen Christijn , Sloot Bart , Boon Brigitte , van der Poel Agnes , de Nooij Ruud 
Published: February 2025

Samenvatting
Data-ondersteund werken betekent een verandering in zorg- en werkprocessen. Het kan bijdragen aan de toekomstbestendigheid, en de kwaliteit van de zorg en de kwaliteit van leven van mensen met een beperking vergroten. Binnen het programma Leren Werken met Data in de gehandicaptenzorg zijn in zestien sessies met individuele gehandicaptenzorgorganisaties vier overkoepelende thema’s opgehaald waar kansen liggen voor data-ondersteund werken. Gehandicaptenzorgorganisaties beschikken over databronnen en data om ermee aan de slag te gaan, al is nog niet aan alle randvoorwaarden daarvoor voldaan. Ze hebben een grote behoefte en motivatie om data-ondersteund werken rond thema’s voor de cliënt en zorgmedewerker te implementeren. In dit artikel presenteren we de vier thema’s waar de gehandicaptenzorg kansen ziet als het gaat om data-ondersteund werken.