bluuebunny/crossref_metadata_2025_split huggingface dataset:
Workplace Learning Environment and Participation in Work Communities: A Qualitative Comparison of Stayers’ and Leavers’ Perceptions and Experiences
Authors: Schmid Evi , Nordlie Gøril Stokke , Jørstad Beate 
Published: August 2024

AbstractIn many countries with apprenticeship-based vocational education and training (VET), dropout from apprenticeship training is a major concern. Leaving an apprenticeship early can be problematic, particularly for young people who do not continue their training at another company or in another occupation, and drop out of the education system without obtaining a qualification. Previous research mostly has used a quantitative design focussing mainly on the perspective of apprentices who left training early and on attributes of the individual that may lead to dropout. Drawing on literature on quality of workplace learning environments, this study used a qualitative comparative approach to analyse the workplace learning environment from the perspectives of both young people who left their apprenticeships early and apprentices at the end of their training. The analysis revealed striking differences between the stayers and leavers in terms of two main characteristics of the workplace learning environment. The findings illustrate how being given responsibility can promote professional development and self-confidence, but also can lead to stress, exhaustion and insecurity if an early transfer of responsibility is not accompanied by support and guidance. Furthermore, the findings emphasise the importance of creating safe learning environments in which apprentices experience support and room for making mistakes. The study concludes that future research may include measures related to transfer and fulfilment of responsibility and handling of mistakes in workplaces to gain a more comprehensive understanding of the processes leading to early contract terminations.
Evaluation of monthly variations in electrometer calibration coefficients using a charge generator for radiation therapy
Authors: Kawashima Motohiro , Varnava Maria , Ozawa Shuichi , Higuchi Hiromitsu , Hoshino Yoshihiko , Tashiro Mutsumi 
Published: August 2024

AbstractElectrometers are important devices that are part of the standard dosimetry system. Therefore, we evaluated the variation of electrometer calibration coefficients (kelec) over 1 year in this study. We investigated two types of electrometers: a rate mode and an integrate mode. Each electrometer was connected to a charge generator, a constant charge was applied, and kelec was determined by measuring the current. The current measurements were repeated once a month. For electrometers with multiple ranges, measurements were taken at low and medium ranges. Almost all kelec measurements agreed within 0.2% of the initial measurements. However, the low range of the electrometer with an integrate mode showed seasonal variation, with a variation greater than 0.2%. This study shows that electrometers may exhibit errors that cannot be detected through annual inspections. The importance of quality assurance using a charge generator at one’s own institution was demonstrated.
Pd0–Ov–Ce3+ Interfacial Sites with Charge Redistribution for Enhanced Hydrogenation of Methyl Oleate to Methyl Stearate
Authors: Meng Zhaohui , Liao Ying , Liu Ling , Li Yaqian , Yan Hao , Feng Xiang , Chen Xiaobo , Liu Yibin , Yang Chaohe 
Published: August 2024

AbstractImproving the efficiency of metal/reducible metal oxide interfacial sites for hydrogenation reactions of unsaturated groups (e.g., C=C and C=O) is a promising yet challenging endeavor. In our study, we developed a Pd/CeO2 catalyst by enhancing the oxygen vacancy (OV) concentration in CeO2 through high-temperature treatment. This process led to the formation of an interface structure ideal for supporting the hydrogenation of methyl oleate to methyl stearate. Specifically, metal Pd0 atoms bonded to the OV in defective CeO2 formed Pd0–OV–Ce3+ interfacial sites, enabling strong electron transfer from CeO2 to Pd. The interfacial sites exhibit a synergistic adsorption effect on the reaction substrate. Pd0 sites promote the adsorption and activation of C=C bonds, while OV preferably adsorbs C=O bonds, mitigating competition with C=C bonds for Pd0 adsorption sites. This synergy ensures rapid C=C bond activation and accelerates the attack of active H* species on the semi-hydrogenated intermediate. As a result, our Pd/CeO2-500 catalyst, enriched with Pd0–OV–Ce3+ interfacial sites, demonstrated excellent hydrogenation activity at just 30 °C. The catalyst achieved a Cis–C18:1 conversion rate of 99.8% and a methyl stearate formation rate of 5.7 mol/(h·gmetal). This work revealed the interfacial sites for enhanced hydrogenation reactions and provided ideas for designing highly active hydrogenation catalysts.
Local Reinforcement of a Fuel Cell End Plate for Package Improvements Using Steel–Aluminium Hybrid-Casting Technology
Authors: Mielke Florian , Anand Sharath Christy , Fang Xiangfan 
Published: August 2024

AbstractIn this research work, a method for integrating a local reinforcement structure in a medium-pressure plate (MPP) for fuel cell electric vehicle (FCEV) applications was developed using steel–aluminium hybrid-casting technology. Using this technology, it is possible to create a bonded enclosure of a steel reinforcement patch with the cast aluminium pressure plate to increase its stiffness and achieve 15% package space savings. A load-compliant, manufacturable patch was chosen and optimised for maximum stiffness gains using non-linear static finite-element (FE) calculations. Special form and process requirements due to hybrid-casting technology were examined and secured with casting simulations. The reinforcement patch was manufactured and coated with a unique aluminium–silicon coating enabling a ductile material connection between the steel and aluminium, and casting trials were conducted to create prototypes. Additionally, the insulating plastic layer on top of the metallic pressure plate carrier was substituted from costly short-fibre-reinforced high-performance plastic to cheaper and stiffer glass-mat reinforced thermoplastic material. Finally, the new hybrid MPP was tested mechanically, and the FE-Model was verified. In summary, through the package gain, 2.1 kW more power output and 11% less weight could be achieved while remaining stiffness neutral.
From “stars” to nano: Porous poly(ethylene glycol) hydrogel films and nanosheets as a versatile platform for sensing and nanofabrication
Authors: Zharnikov Michael 
Published: August 2024

AbstractThe use of bioinert materials is crucially important for medicine and bioengineering. The most popular systems in this context are oligo- and poly(ethylene glycols) (OEGs and PEGs), applied generally in different forms as bulk materials, thin films, and functional molecular groups. Here, I review the fabrication, properties, and applications of porous hydrogel PEG films (PHFs) and nanosheets (PHNs) formed by thermally activated crosslinking of amino- and epoxy-terminated, star-branched PEG oligomers with variable molecular weight. These systems possess various useful characteristics, including tunable thickness and porosity, hydrogel properties, bioinertness, robustness, and extreme elasticity. They can serve as the basis for composite materials, advanced nanofabrication, and lithography, bioinert supports for high-resolution transmission electron microscopy, susceptible elements in micro-electromechanical systems, and basic building blocks of temperature, humidity, chemical, and biological sensors. Representative examples of the respective applications are provided. Even though these examples span a broad field-from nanoengineering to biosensing, the applications of the PHFs and PHNs are certainly not limited to these cases but can be specifically adapted and extended to other fields, such as tissue engineering and drug delivery, relying on versatility and tunability of these systems.
Outlier detection and selection of representative fluid samples using machine learning: a case study of Iranian oil fields
Authors: Hosseini Mahdi , Zaheri Seyed Hayan , Roosta Ali 
Published: August 2024

AbstractDuring the development of a field, many fluid samples are taken from wells. Selecting a robust fluid sample as the reservoir representative helps to have a better field characterization, reliable reservoir simulation, valid production forecast, efficient well placement and finally achieving optimized ultimate recovery. First, this paper aims to detect and separate the samples that have been collected under poor conditions or analyzed in a non-standard way. Moreover, it introduces a novel ranking method to score the samples based on the amount of coordination with other fluid samples in the region. The dataset includes 136 fluid samples from five reservoirs in Iranian fields, each of them consisting of 21 key parameters. Five acknowledged machine learning based anomaly detection techniques are implemented to compare fluid samples and detect those whose results deviate from others, indicating non-standard samples. To ensure the proper detection of outlier data, the results are compared with the traditional validation method of gas-oil ratio estimation. All five outlier detection methods demonstrate acceptable performance with average accuracy of 79% compared to traditional validation. Furthermore, the fluid samples with the highest scores in scoring-based algorithms are introduced as the best reservoir’s representative fluid. Finally, fuzzy logic is used to obtain a final score for each sample, taking the results of the six methods as input and ranking the samples based on their output score. The study confirms the robustness of the novel approach for fluid validation using outlier detection techniques and the value of machine learning and fuzzy logic for sample ranking, excelling in considering all critical fluid parameters simultaneously over traditional methods.
Performance Analysis of Embedding Methods for Deep Learning-Based Turkish Sentiment Analysis Models
Authors: Ba Alawi Abdulfattah , Bozkurt Ferhat 
Published: August 2024

AbstractThe complex syntactic structure of Turkish text makes sentiment analysis in natural language processing (NLP) a challenging task. Conventional sentiment analysis methods often fail to effectively identify attitudes in Turkish texts, creating an urgent need for more efficient approaches. To fill this need, our study investigates the effectiveness of embedding techniques including pre-trained Turkish models such as Word2Vec, GloVe, and FastText in addition to two character-level embedding methods, namely, character-integer embedding (CIE) and character one-hot encoding embedding (COE), in conjunction with deep learning models specifically long short-term memory (LSTM), convolution neural networks (CNNs), bidirectional LSTM (Bi-LSTM), and hybrid models, for Turkish short-texts sentiment analysis. DL-based models were investigated on two datasets (e.g., an original Twitter (X) dataset and an accessible hotel reviews dataset). In addition to providing an intensive performance analysis of different embedding strategies and assessing their efficacy in dealing with the linguistic intricacies of Turkish, this study proposed a previously unexplored method in Turkish text representation that relies on a character-level one-hot encoding technique. The obtained findings indicate positive progress using a novel approach utilizing a dual-pathway architecture for both character level and word level that constitutes a substantial contribution to the area of natural language processing (NLP), specifically in the context of complex morphological languages. By employing a hybrid strategy that combines character and word levels on Twitter (X) data, the LSTM model obtained an F1 score of $$0.835 \pm 0.005$$

0.835
±
0.005

concerning cross-validation while CNN-BiLSTM attained the highest F1 Score (0.8392) using holdout validation. This strategy consistently produced modest improvements across the second public dataset (hotel reviews dataset) by emerging as the runner-up embedding technique in effectiveness, surpassed only by FastText. Findings provide practical recommendations for practitioners on how to effectively use sentiment analysis to make informed decisions by introducing an extensive performance analysis of the use of embedding techniques and deep learning models for sentiment analysis in Turkish texts, which is crucial in the current age of data analysis.
A scale development study on the perception of the sustainable urban environment
Authors: Altanlar A. , Özdemir Z. 
Published: August 2024

Abstract
In order to assess sustainability, it is necessary to monitor social conditions on environmental, global, national and urban scales. Therefore, the objective of this study is to develop a scale to determine PSUE. In addition, it is also examined whether there is a significant difference between the components that determine the perception of sustainable urban environment according to the characteristics of the participants. For this purpose, Exploratory Factor Analysis, Spearman–Brown correlation test, Cronbach alpha test, Mann–Whitney test and Kruskal–Wallis test are implemented. According to the findings acquired, it has been detected that the components that determine PSUE are “spatial strategies related to sustainable environment and transportation”, “spatial strategies related to ecological sustainability and solid waste management” and “spatial strategies related to social and economic sustainability” respectively. In this study, differences have been identified in the perspectives on social and economic sustainability strategies according to gender. However, participant’s viewpoints regarding strategies related to sustainable environment and transportation, as well as ecological sustainability and solid waste management, are similar. Additionally, significant differences exist in social and economic sustainability strategies based on the age of the participants. The urban or rural character of the settlement where the students families live or grew up influences their perspectives on strategies based on social sustainability and economic relations.
Reconciling Signal-Detection Models of Criterion Learning with the Generalized Matching Law
Authors: Koß Christina , de la Cuesta-Ferrer Luis , Stüttgen Maik C. , Jäkel Frank 
Published: August 2024

AbstractTo make decisions that lead to favorable outcomes, animals have to consider both their perceptual uncertainty as well as uncertainty about the outcomes of their actions, such as reinforcements. There is a long tradition of research investigating how the reinforcement structure of a task controls animals’ response behavior. The relation between reinforcement and response rates has been described by the matching law and its generalizations for tasks with and without perceptual uncertainty. The influence of perceptual uncertainty on decision behavior is traditionally modeled with signal detection theory, which posits that a decision criterion is placed on an internal evidence axis. Where this criterion is placed and how it is updated based on reinforcements are open questions within signal detection theory. Various criterion learning models have been proposed; however, their steady-state behavior across different experimental conditions is not consistent with the aforementioned empirical matching laws. Here, we integrate models of criterion learning from signal detection theory with matching laws from animal learning theory to gain a better understanding of the mechanisms by which reinforcements and perceptual uncertainty jointly shape behavior. To do so, we first derive the criterion position that leads to behavior aligned with those laws. We then develop a model that updates the decision criterion trial by trial to learn this criterion position. Our model fits data from a previous experiment well and generates behavior in simulations that is in line with matching laws for perceptual tasks and the subjects’ behavior in the experiment.
Supervised machine learning-based Hall thruster scaling
Authors: Marianacci Alfredo , Mazouffre Stéphane 
Published: August 2024

AbstractThe scaling methodology described in this paper to find the geometry and working parameters of Hall Thrusters is based on algorithms of supervised Machine Learning. The approach considers the determination of the geometrical sizes, propellant mass flow rate and discharge voltage taking thrust and specific impulse as requirements. The magnetic field is also considered. The Gradient Boosting Regression is found as the most suitable algorithm for our purpose. Scaling relies on a specific database of 54 thrusters for the determination of all parameters. The database includes measurements carried out with xenon, krypton and argon as propellant. A unique analytical approach based on the GBR algorithm has been developed and validated to determine the suitable design for a Hall thruster according to space mission requirements.
Acusicola rochai n. sp. (Copepoda: Ergasilidae) parasitizing Anableps anableps (Anablepidae) from the Amazon Coast, with a key for Acusicola spp.
Authors: Couto João Victor , Silva Nunes Jorge Luiz , Paschoal Fabiano , Bisaggio Pereira Felipe 
Published: August 2024

Abstract
Ergasilid copepods represent one of the commonest groups of fish parasites in Brazil. Within Ergasilidae, three genera share a peculiar latching mechanism on the antenna that completely encircles the gill filament, one of which is Acusicola Cressey, 1970. During a survey of estuarine fish from the Brazilian Amazon Coast, a new species of Acusicola was found on the gills of the largescale foureyes Anableps anableps (Linnaeus, 1758) (Actinopterygii: Anablepidae) in São Marcos Bay, State of Maranhão. Acusicola rochai n. sp. can be distinguished from its closest congeners mainly by three protrusions on the dorsal surface of third and fourth pedigerous somites, and by smooth interpodal plates. This work is the first report of a parasitic copepod infesting a fish from Anablepidae and, consequently, the host An. anableps. The existing dichotomous key proposed for the genus Acusicola includes only ten species, excluding the eight species subsequently described. Therefore, in the present work, a new dichotomous key is provided based on reliable and well-documented features.
Robust Hydropower Planning Balances Energy Generation, Carbon Emissions and Sediment Connectivity in the Mekong River Basin
Authors: Tangi M. , Schmitt R. , Almeida R. , Bossi S. , Flecker A. , Sala F. , Castelletti A. 
Published: August 2024

AbstractWe present a framework for strategic dam planning under uncertainty, which includes GHG emissions mitigation as a novel objective. We focus on the Mekong River Basin, a fast‐developing region heavily relying on river‐derived ecosystem services. We employ a multi‐objective evolutionary algorithm to identify strategic dam portfolios for different hydropower expansion targets, using process‐related and statistical models to derive indicators of sediment supply disruption and GHG emissions. We introduce a robust optimization approach that explores variations in optimal portfolio compositions for more than 5,000 state‐of‐the‐world configurations, regarding sediment origins and trapping and GHG emissions. Thus, we can rank dam projects' attractiveness based on their frequency of inclusion in optimal portfolios and explore how uncertainty affects these rankings. Our results suggest that developing dams in the upper Mekong would be a more robust option for near‐term development than, for example, the lower Mekong and its tributaries, for both environmental and energy objectives. Our work presents a novel approach to better understand the basin‐scale cumulative impacts of dam development in high‐uncertainty, data‐scarce contexts like the Mekong Basin.
Response of a Terrestrial Polar Ecosystem to the March 2022 Antarctic Weather Anomaly
Authors: Barrett J. E. , Adams Byron J. , Doran Peter T. , Dugan Hilary A. , Myers Krista F. , Salvatore Mark R. , Power Sarah N. , Snyder Meredith D. , Wright Anna T. , Gooseff Michael N. 
Published: August 2024

AbstractRecord high temperatures were documented in the McMurdo Dry Valleys, Antarctica, on 18 March 2022, exceeding average temperatures for that day by nearly 30°C. Satellite imagery and stream gage measurements indicate that surface wetting coincided with this warming more than 2 months after peak summer thaw and likely exceeded thresholds for rehydration and activation of resident organisms that typically survive the cold and dry conditions of the polar fall in a freeze‐dried state. This weather event is notable in both the timing and magnitude of the warming and wetting when temperatures exceeded 0°C at a time when biological communities and streams have typically entered a persistent frozen state. Such events may be a harbinger of future climate conditions characterized by warmer temperatures and greater thaw in this region of Antarctica, which could influence the distribution, activity, and abundance of sentinel taxa. Here we describe the ecosystem responses to this weather anomaly reporting on meteorological and hydrological measurements across the region and on later biological observations from Canada Stream, one of the most diverse and productive ecosystems within the McMurdo Dry Valleys.
Large Isotopic Shift in Volcanic Plume CO2 Prior to a Basaltic Paroxysmal Explosion
Authors: D’Arcy Fiona , Aiuppa Alessandro , Grassa Fausto , Rizzo Andrea Luca , Stix John 
Published: August 2024

AbstractCarbon dioxide is a key gas to monitor at volcanoes because its concentration and isotopic signature can indicate changes to magma supply and degassing behavior prior to eruptions, yet carbon isotopic fluctuations at volcanic summits are not well constrained. Here we present δ13C results measured from plume samples collected at Stromboli volcano, Italy, by Uncrewed Aerial Systems (UAS). We found contrasting volcanic δ13C signatures in 2018 during quiescence (−0.36 ± 0.59‰) versus 10 days before the 3 July 2019 paroxysm (−5.01 ± 0.56‰). Prior to the eruption, an influx of CO2‐rich magma began degassing at deep levels (∼100 MPa) in an open‐system fashion, causing strong isotopic fractionation and maintaining high CO2/St ratios in the gas. This influx occurred between 10 days and several months prior to the event, meaning that isotopic changes in the gas could be detected weeks to months before unrest.