bluuebunny/crossref_metadata_2025_split huggingface dataset:
What does the general population know about nonmelanoma skin cancer? Representative cross-sectional data from Germany
Authors: Brokmeier Luisa L. , Görig Tatiana , Spähn Bianca A. , Breitbart Eckhard W. , Heppt Markus , Diehl Katharina 
Published: May 2024

Abstract
Aim
Nonmelanoma skin cancer (NMSC) is the most common form of cancer in white-skinned populations. However, there is a lack of comprehensive research on the general population’s knowledge about NMSC.

Subjects and methods
As part of wave 8 of the German National Cancer Aid Monitoring (NCAM), 4000 participants aged 16–65 years (49.3% female) were interviewed by telephone about NMSC. Questions included awareness of (colloquial) terms for NMSC, its signs, consequences, risk factors, prevalence, and severity.

Results
Colloquial terms for skin cancer were heard of more often than medical terms (60.9–82.1% vs. 22.6–51.5%). In our sample, there was little familiarity with signs (15.8–36.4%), possible consequences (14.7–56.3%), prevalence (27.4%), and severity (29.0%) of NMSC. Most participants knew about UV-related risk factors for NMSC (73.0–78.7%). Women, those with higher education levels, full-time employment, lighter skin type, presence of more than 40 nevi, frequent childhood sunburns, or frequent intentional tanners achieved a higher knowledge score. Concern about NMSC was associated with more NMSC knowledge.

Conclusion
We found knowledge gaps regarding signs, consequences, and prevalence of NMSC. To ensure early recognition and treatment, the general population should be further educated, specifically targeting male, less educated, and unemployed individuals.
Asset pricing and hedging in financial markets with fixed and proportional transaction costs
Authors: Babaei Esmaeil 
Published: May 2024

AbstractWe establish the asset pricing and hedging principle in a financial market model, which is a specific case of the von Neumann-Gale dynamical system, with both fixed and proportional transaction costs and trading constraints. The main results are hedging criteria stated in terms of consistent valuation systems, generalizing the notion of an equivalent martingale measure.
NoiseNet, a fully automatic noise assessment tool that can identify non-diagnostic CCTA examinations
Authors: Palmquist Emma , Alvén Jennifer , Kercsik Michael , Larsson Måns , Lundqvist Niklas , Hjelmgren Ola , Fagman Erika 
Published: May 2024

AbstractImage noise and vascular attenuation are important factors affecting image quality and diagnostic accuracy of coronary computed tomography angiography (CCTA). The aim of this study was to develop an algorithm that automatically performs noise and attenuation measurements in CCTA and to evaluate the ability of the algorithm to identify non-diagnostic examinations. The algorithm, “NoiseNet”, was trained and tested on 244 CCTA studies from the Swedish CArdioPulmonary BioImage Study. The model is a 3D U-Net that automatically segments the aortic root and measures attenuation (Hounsfield Units, HU), noise (standard deviation of HU, HUsd) and signal-to-noise ratio (SNR, HU/HUsd) in the aortic lumen, close to the left coronary ostium. NoiseNet was then applied to 529 CCTA studies previously categorized into three subgroups: fully diagnostic, diagnostic with excluded parts and non-diagnostic. There was excellent correlation between NoiseNet and manual measurements of noise (r = 0.948; p < 0.001) and SNR (r = 0.948; <0.001). There was a significant difference in noise levels between the image quality subgroups: fully diagnostic 33.1 (29.8–37.9); diagnostic with excluded parts 36.1 (31.5–40.3) and non-diagnostic 42.1 (35.2–47.7; p < 0.001). Corresponding values for SNR were 16.1 (14.0–18.0); 14.0 (12.4–16.2) and 11.1 (9.6–14.0; p < 0.001). ROC analysis for prediction of a non-diagnostic study showed an AUC for noise of 0.73 (CI 0.64–0.83) and for SNR of 0.80 (CI 0.71–0.89). In conclusion, NoiseNet can perform noise and SNR measurements with high accuracy. Noise and SNR impact image quality and automatic measurements may be used to identify CCTA studies with low image quality.
Predictors and Mechanisms of Resilience for High School Students with ADHD: A Prospective Longitudinal Study
Authors: Chan Elizabeth S. M. , Dvorsky Melissa R. , Green Cathrin D. , Breaux Rosanna , Becker Stephen P. , Langberg Joshua M. 
Published: May 2024

AbstractAttention-deficit/hyperactivity disorder (ADHD) has primarily been studied from a deficit-focused perspective. However, there are individuals with ADHD who exhibit resilience or a pattern of positive adaptation despite the risks associated with their diagnosis. The present study evaluated whether peer acceptance predicted resilience for adolescents with ADHD and if self-efficacy or a stress-is-enhancing mindset served as mechanisms of those relations. Participants included 113 comprehensively evaluated adolescents with ADHD (67% male) across three time-points (10th–12th grade). Mediation analyses revealed higher T1 peer acceptance significantly predicted higher resilience (β = 0.24) 1.5–2 years later, with higher T2 self-efficacy (β = 0.08) demonstrating a significant indirect effect of the association. A stress-is-enhancing mindset directly predicted resilience (β = 0.15) but was not associated with peer acceptance nor mediated the association between peer acceptance and resilience. Present results are the first to provide longitudinal evidence for peer acceptance, self-efficacy, and a stress-is-enhancing mindset as important for promoting resilience among adolescents with ADHD.
Preservation of sentiment in machine translation of low-resource languages: a case study on Slovak movie subtitles
Authors: Reichel Jaroslav , Benko Ľubomír 
Published: May 2024

AbstractThis research explores the effectiveness of machine translation from Slovak to English for sentiment analysis, specifically focusing on the translation of movie subtitles. The study employs a parallel corpus of segmented movie subtitles in both languages and utilizes IBM Watson™ Natural Language Understanding service and Google Translate. The research aims to assess the correlation between human-generated text and machine-translated text in sentiment analysis. A comparative analysis was also conducted using OpenAI to evaluate the sentiment of the Slovak text directly, without translation into English. The findings reveal a strong correlation between human text and machine translation, with a Pearson correlation coefficient of 0.86, and a correlation with OpenAI’s GPT model evaluation at 0.72. Despite the relatively high accuracy of the end-to-end solution using OpenAI, the methodology comprising machine translation followed by sentiment analysis in English was found to be significantly more precise. The research further investigates the challenges in translating specific language nuances, such as humor and vulgarism, and their impact on sentiment analysis. The study concludes that machine translation can be effectively used for sentiment analysis in Slovak, a flective language, and highlights the potential of advanced language models in low-resource languages. Future research directions include expanding the study to other text types and comparable languages beyond Slovak.
An autonomous blockchain-based workflow execution broker for e-science
Authors: Alimoğlu Alper , Özturan Can 
Published: May 2024

AbstractScientific workflows are essential for many applications, enabling the configuration and execution of complex tasks across distributed resources. In this paper, we contribute an Ethereum blockchain-based scientific workflow execution manager, which distributes workflows to run on cluster computing providers that utilize the Slurm workload manager to execute them. We extended our blockchain-based autonomous resource broker called eBlocBroker, which is a DAO-based decentralized coordinator, by providing distributed workflow execution via blockchain. Through various tests, we demonstrate how our eBlockBroker autonomous organization, which is programmed as a smart contract, can manage scientific workflow submission, scheduling, and execution on cluster computing providers. The utilization of blockchain for distributed workflow execution is a new concept. We are motivated because our system has been developed with e-Science in mind where scientific workflows are widely utilized.
Exploring cannabidiol variations, investigation of genetic diversity, population structure and unveiling male-specific genetic marker in industrial hemp (Cannabis sativa L.)
Authors: Alsaleh Ahmad , Yılmaz Güngör 
Published: May 2024

Abstract

Cannabis sativa played a pivotal role across different industries. Recently, industrial hemp, particularly in the case of Cannabidiol (CBD), gained attention for its therapeutic potential. This study evaluated cannabinoid variability and genetic diversity within 43 industrial hemp individuals, primarily Turkish, across various plant parts and growth stages, and inflorescences of females showed significant CBD content. The highest contents were observed in Turkish landraces (0.55–8.05% with an average of 3.26%), making them valuable genetic resources for high CBD. Genetic structure revealed distinct populations based on gender and influenced by geographical origin. Analysis of Molecular Variance showed 92% of genetic variation observed within populations and indicated a promising source of novel allelic diversity in the Turkish gene pool. Turkish females showed significant genetic diversity [No. of Different Alleles (Na) 1.507, No. of Effective Alleles (Ne) 1.226, Shannon’s Information Index (I) 0.258, and Percentage of Polymorphic Loc (%P) 74.91], exhibiting richer genetic variation than their international peers. Principal Coordinate Analysis unveiled gender-specific genetic differences, and admixture clusters shed light on genetic interactions and historical connections among diverse populations. Unweighted Pair-Group Method with Arithmetic Averaging highlighted unique genetic profiles and distinct genetic lineages. Genome-wide association study revealed a highly significant male-specific genetic marker explained 50% of the phenotypic variation. These findings inform future breeding strategies and conservation efforts and contribute to varietal identification methods, Marker-Assisted Selection, and efficient cultivar development in upcoming programs.
PADRE – A Repository for Research on Fault Detection and Isolation of Unmanned Aerial Vehicle Propellers
Authors: Puchalski Radosław , Ha Quang , Giernacki Wojciech , Nguyen Huynh Anh Duy , Nguyen Lanh Van 
Published: May 2024

AbstractUnmanned aerial vehicles are being used increasingly in a variety of applications. They are more and more often operating in close proximity to people and equipment. This necessitates ensuring maximum stability and flight safety. A fundamental step to achieving this goal is timely and effective diagnosis of possible defects. Popular data-based methods require a large amount of data collected during flights in various conditions. This paper describes an open PADRE database of such measurements for the detection and classification of the most common faults - multirotor propeller failures. It presents the procedure of data acquisition, the structure of the repository and ways to use the various types of data contained therein. The repository enables research on drone fault detection to be undertaken without time-consuming preparation of measurement data. The database is available on GitHub at https://github.com/AeroLabPUT/UAV_measurement_data. The article also introduces new and universal quality indicators for evaluating classifiers with non-uniform parameters, are proposed. They allow comparison of methods tested for a variety of fault classes and with different processing times.
Enhanced Traffic Light Guidance for Safe and Energy-Efficient Driving: A Study on Multiple Traffic Light Advisor (MTLA) and 5G Integration
Authors: Khayyat Michael , Gabriele Alberto , Mancini Francesca , Arrigoni Stefano , Braghin Francesco 
Published: May 2024

AbstractThis paper presents Multiple Traffic Light Advisor (MTLA), a novel Green Light Optimal Speed Advisory (GLOSA) system that leverages 5G communication technology. GLOSA systems are emerging as a key component in intelligent transportation systems, thanks to the development of effective communication technologies. At its core, MTLA serves as a guidance system for drivers, providing real-time instructions to adjust vehicle speed to optimize the utilization of current and future states of traffic lights along their route.The work addresses several limitations in the current state-of-the-art approaches, including the use of an overly simplified velocity profile, the omission of potential grip and jerk in problem formulation, and the absence of a detailed description of the algorithm’s implementation aspects. Initially, we comprehensively present an optimization-free implementation of the overall control architecture based on an unconventional speed profile. Subsequently, MTLA is improved within a non-linear Model Predictive Control (MPC) framework which uses the latter nonoptimal solution as an initial guess and considers potential grip and jerk in the problem formulation. The developed systems are numerically tested and compared within a high-fidelity simulation environment using the IPG CarMaker simulator. The results demonstrate promising performance in terms of energy savings, with a significant reduction of 37% in energy usage, as well as improved overall comfort with respect to the case where no guidance is given to the driver. These findings suggest a high potential for future developments in this domain.
Evidence for Magnetic Crystallization Waves at the Surface of $$^3$$He Crystal
Authors: Todoshchenko I. , Savin A. , Hakonen P. J. 
Published: May 2024

AbstractUltralow temperature crystals of the helium isotopes $$^3$$

3

He and $$^4$$

4

He are intriguing quantum systems. Deciphering the complex features of these unusual materials has been made possible in large part by Alexander Andreev’s groundbreaking research. In 1978, Andreev and Alexander Parshin predicted the existence of melting/freezing waves at the surface of a solid $$^4$$

4

He crystal, which was subsequently promptly detected. Successively, for the fermionic $$^3$$

3

He superfluid/solid interface, even more intricate crystallization waves were anticipated, although they have not been observed experimentally so far. In this work, we provide preliminary results on $$^3$$

3

He crystals at the temperature $$T = 0.41$$

T
=
0.41

mK, supporting the existence of spin supercurrents in the melting/freezing waves on the crystal surface below the antiferromagnetic ordering temperature $$T_N= 0.93$$

T
N

=
0.93

mK, as predicted by Andreev. The spin currents that accompany such a melting-freezing wave make it a unique object, in which the inertial mass is distinctly different from the gravitational mass.
Cu (II)-based metal-organic framework functionalized with graphene oxide as a sorbent for the dispersive micro-solid-phase extraction of losartan potassium from water
Authors: Valenzuela Ivon E. , Muñoz-Acevedo Juan Carlos , Pabón Elizabeth , Paim Ana Paula Silveira 
Published: May 2024

AbstractIn this study, a new metal–organic framework (MOF) based on copper (II) functionalized with graphene oxide (MOF-Cu@GO) was successfully synthesized and applied as an efficient sorbent for dispersive micro-solid phase extraction (D-μSPE) of losartan potassium (LP) from water. The MOF-Cu@GO sorbent was characterized using DRX, FTIR, XPS, TGA, RAMAN, BET, SEM, EDX, and potential Z. The influence of different parameters in the D-μSPE method was studied and optimized using the fractional factorial design 25–1 and central composite design. The results indicated that the adsorption process was spontaneous and endothermic followed the pseudo-second order model kinetics and the Freundlich isotherm. The maximum adsorption capacity of MOF-Cu@GO sorbent was 415 mg g−1. The adsorption mechanism proposed of LP onto MOF-Cu@GO proceeded via electrostatic interaction, hydrogen bonds, unsaturated sites of the ligand, and π-π interactions. The microextraction procedure was followed by determination of LP with high performance liquid chromatography (HPLC–UV-Vis). Under optimized conditions, the limits of detection and quantitation were found to be 25 and 80 ng ml−1 respectively, the method exhibited a linear response (r = 0.998) in the concentration range of 0.1–50 µg ml−1 of LP, with a relative standard deviation less than 2% (n = 5). The D-μSPE method showed preconcentration factor of 684.9 and high percentage of LP extraction of 99.78% ± 2.62, the accuracy of the method was demonstrated by studying the recovery of LP from water samples of 100.3 ± 1.06. The material obtained can be used up to 3 cycles with time for the sorption and determination of 30 min indicating good stability and reusability. The MOF-Cu@GO proposed is an efficient and fast sorbent in the D-μSPE for determination of LP from aqueous solutions.
Repeat endoscopic endonasal transsphenoidal surgery for residual or recurrent Cushing’s disease: safety, feasibility, and success
Authors: Hanalioglu Sahin , Gurses Muhammet Enes , Gecici Neslihan Nisa , Baylarov Baylar , Isikay Ilkay , Gürlek Alper , Berker Mustafa 
Published: May 2024

Abstract
Purpose
The success and outcomes of repeat endoscopic transsphenoidal surgery (ETS) for residual or recurrent Cushing’s disease (CD) are underreported in the literature. This study aims to address this gap by assessing the safety, feasibility, and efficacy of repeat ETS in these patients.

Methods
A retrospective analysis was conducted on 56 patients who underwent a total of 65 repeat ETS performed by a single neurosurgeon between January 2006 and December 2020. Data including demographic, clinical, laboratory, radiological, and operative details were collected from electronic medical records. Logistic regression was utilized to identify potential predictors associated with sustained remission.

Results
Among the cases, 40 (61.5%) had previously undergone microscopic surgery, while 25 (38.5%) had prior endoscopic procedures. Remission was achieved in 47 (83.9%) patients after the first repeat ETS, with an additional 9 (16.1%) achieving remission after the second repeat procedure. During an average follow-up period of 97.25 months, the recurrence rate post repeat surgery was 6.38%. Sustained remission was achieved in 48 patients (85.7%), with 44 after the first repeat ETS and 4 following the second repeat ETS. Complications included transient diabetes insipidus (DI) in 5 (7.6%) patients, permanent (DI) in 2 (3%) patients, and one case (1.5%) of panhypopituitarism. Three patients (4.6%) experienced rhinorrhea necessitating reoperation. A serum cortisol level > 5 µg/dL on postoperative day 1 was associated with a reduced likelihood of sustained remission.

Conclusion
Repeat ETS is a safe and effective treatment option for residual or recurrent CD with satisfactory remission rates and low rates of complications.
Socio-emotional experiences of primary school students: Relations to teachers’ underestimation, overestimation, or accurate judgment of their cognitive ability
Authors: Gnas Jessica , Urban Julian , Feuchter Markus Daniel , Preckel Franzis 
Published: May 2024

AbstractPrevious research revealed that students who are overestimated in their ability by their teachers experience school more positively than underestimated students. In the present study, we compared the socio-emotional experiences of N = 1516 students whose cognitive abilities were overestimated, accurately judged, or underestimated by their teachers. We applied propensity score matching using students’ cognitive ability, gender, language, parental education, and teacher’s acquaintance with them as covariates for building the three student groups. Matching students on these variables, reduced the original sample size to subsamples with n1 = 348, and n2 = 312 with exact matching including classroom. We compared overestimated, accurately judged, or underestimated students in both matching samples in their socio-emotional profiles (comprised of academic self-concept, joy of learning, attitude towards school, willingness to make an effort, social integration, perceived class climate, and feeling of being accepted by the teacher) by linear discriminant analyses. Groups significantly differed in their profiles. Overestimated students had the most positive socio-emotional experiences of school, followed by accurately judged students. Underestimated students experienced school most negatively. Differences in experiences were most pronounced for the learning environment (medium to large effects for academic self-concept, joy of learning, and willingness to make an effort; negligible effect for attitude towards school) and less for the social environment (medium effects for feeling of being accepted by the teacher; negligible effects for social integration and perceived class climate).
A survey of appearance-based approaches for human gait recognition: techniques, challenges, and future directions
Authors: Güner Şahan Pınar , Şahin Suhap , Kaya Gülağız Fidan 
Published: May 2024

AbstractGait recognition has become an important biometric feature for human identification, in addition to data such as face, iris, and fingerprint. The goal of human gait recognition is to identify people based on walking images. Artificial intelligence technologies have revolutionized the field of gait recognition by enabling computers to automatically learn and extract intricate patterns. These techniques examine video recordings to determine key features in an individual's gait, and these features are used to identify the person. This paper examines the existing appearance-based gait recognition methods that have been published in recent years. The primary objective of this paper is to provide an informative survey of the state-of-the-art in appearance-based gait recognition techniques, highlighting their applications, strengths, and limitations. Through our analysis, we aim to highlight the significant advance that has been made in this field, draw attention to the challenges that have been faced, and identify areas of prospective future research and advances in technology. Furthermore, we comprehensively examine common datasets used in gait recognition research. By analyzing the latest developments in appearance-based gait recognition, our study aims to be a helpful resource for researchers, providing an extensive overview of current methods and guiding future attempts in this dynamic field.