bluuebunny/crossref_metadata_2025_split huggingface dataset:
Who’s bored in school?
Authors: Golle Jessika , Flaig Maja , Jaggy Ann-Kathrin , Göllner Richard 
Published: December 2022

AbstractIn the literature, general cognitive ability has been discussed as one critical factor that shapes students’ boredom. In this study, we investigated the relationship between academic boredom and general cognitive ability in primary school students while also considering intrinsic value. We analyzed data from N = 2849 third graders from 188 German classrooms. We used structural equation models with latent variables to test for whether general cognitive ability and interest showed linear as well as nonlinear relationships with students’ boredom. The results showed that the association between general cognitive ability and boredom was almost negligible after controlling for students’ achievement as well as gender, age, and family background. However, both the linear and quadratic terms for students’ intrinsic value were statistically significant, indicating that students with low and students with high interest reported more boredom in class than students with average interest. The findings are discussed in light of expectations based on control-value theory and the domain specificity and situational specificity of control and value appraisals.
Multiple solutions of the Dirichlet problem in multidimensional billiard spaces
Authors: Gabor Grzegorz , Tomeček Jan 
Published: December 2022

AbstractDirichlet problem in an n-dimensional billiard space is investigated. In particular, the system of ODEs $$\ddot{x}(t) = f(t,x(t))$$

x
¨

(
t
)

=
f

(
t
,
x

(
t
)

)

together with Dirichlet boundary conditions $$x(0) = A$$

x
(
0
)
=
A

, $$x(T) = B$$

x
(
T
)
=
B

in an n-dimensional interval K with elastic impact on the boundary of K is considered. The existence of multiple solutions having prescribed number of impacts with the boundary is proved. As a consequence the existence of infinitely many solutions is proved, too. The problem is solved by reformulating it into non-impulsive problem with a discontinuous right-hand side. This auxiliary problem is regularized and the Schauder Fixed Point Theorem is used.
Investigation of the Potential Correlation Between RNA-Binding Proteins in the Evolutionarily Conserved MEX3 Family and Non-small-Cell Lung Cancer
Authors: Zhang Ming , Cao Linfeng , Hou Gouxin , Lv Xiaodong , Deng Jingjing 
Published: December 2022

AbstractMembers of the MEX3 (muscle excess 3) family, uniquely characterised as mRNA binding proteins, play emerging roles in the post-transcriptional regulation of programmed biological processes, including tumour cell death and immune mechanisms, and have been shown to be involved in a variety of diseases. However, the role of MEX3 in non-small cell lung cancer (NSCLC) has not been fully elucidated. In this study, we found no significant changes in the sequence and copy number of the MEX3 gene through analysis using the COSMIC database, revealing its stability during malignancy development. Its expression in NSCLC was examined using the Oncomine™ database, and the prognosis of each member gene was analysed by Kaplan–Meier. The results showed that overexpression of MEX3A, MEX3B, MEX3C and MEX3D was associated with significantly worse OS in patients with LUAD, while overexpression of MEX3D was also associated with significantly worse OS in patients with LUSC. Afterwards, we applied the Tumour Immunology Estimation Resource (TIMER) tool to assess the correlation between different MEX3 and infiltrative immune cell infiltration. Ultimately, we found that most MEX3 members were highly expressed in NSCLC, with high expression suggesting poor prognosis and correlating with immune cell infiltration. The complexity and heterogeneity of NSCLC was understood through MEX3, setting the framework for the prognostic impact of MEX3 in NSCLC patients and the development of new targeted therapeutic strategies in the future.
Junge Menschen in der Politikberatung
Authors: Kutscher Nadia , Schröer Wolfgang 
Published: December 2022

ZusammenfassungDiese Einleitung in den Schwerpunkt „Junge Menschen, Politikberatung und Beteiligung“ skizziert kurz die Probleme, vor denen der Wunsch nach politischer Beteiligung von Kindern und Jugendlichen während der Covid-Pandemie stand und stellt dann die Beiträge des Schwerpunkts vor.
On the association between students’ (domain-specific) subjective well-being and academic achievement—disentangling mixed findings
Authors: Maechel Lena , Steinmayr Ricarda , Christiansen Hanna , Wirthwein Linda 
Published: December 2022

Abstract
Today, there is wide recognition of schools as places that should foster children’s learning and well-being alike. The past two decades have thus witnessed an upsurge in research on children’s subjective well-being (SWB) in school and how it relates to their academic achievement. The first meta-analysis on the SWB/achievement relation revealed substantial, yet unexplained variability in effect sizes across samples and studies. In this context, in the present investigation we examined three potential sources of differential SWB/achievement relations: The domain-specificity of measures (global/school-specific/math-specific), the type of SWB component (cognitive/affective), and the type of achievement indicator (test-based/grade-based). Data from N = 767 8th and 9th grade students from German secondary schools were assessed via latent factor analyses. Our findings generally point towards stronger SWB/achievement relations with a more specific and specificity-matching rather than a less specific and specificity-mismatched SWB measure, with SWB’s cognitive rather than its affective component, and with a grade- rather than test-based indicator of academic achievement. We discuss differences in our findings by type of SWB component as well as by level of domain-specificity, and we provide initial empirical evidence supporting the relevance of SWB’s subject-specific measures.
Biomarkers and translational research approaches in breast cancer—an update
Authors: Starzer Angelika M. , Berghoff Anna S. , Bartsch Rupert 
Published: December 2022

SummaryDiagnosis and decision-making in the treatment of breast cancer patients is vastly dependent on the exploration of biomarkers. Estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 are long-standing biomarkers, which determine the breast cancer subtype. In current practice, gene expression analyses further define the molecular breast cancer subtype and give additional information on disease characteristics. Prognostic biomarkers provide information regarding recurrence risk and survival. Predictive biomarkers, such as programmed cell death ligand 1 expression, are tools for identifying patients who can benefit from specific therapy regimens in order to choose the best treatment option for the patient. While some biomarkers are affordable and readily available, others remain technically complex to access. Translational research builds the bridge from discovering novel biomarkers in preclinical studies to testing their application utility in the clinical setting. Integrating translational studies into clinical trials is therefore essential to find novel and reliable biomarkers for an optimal personalized treatment approach for patients with breast cancer.
Linking gender, climate change and security in the Pacific Islands Region: A systematic review
Authors: Howard Elise 
Published: December 2022

AbstractThis systematic review aims to address gaps in understanding how concepts of gender, climate change and security are given meaning and linked in empirical scholarship within the Pacific Islands Region. The review assesses the 53 articles returned through Web of Science, SCOPUS and ProQuest databases that are derived from empirical research and refer to gender, climate change and security. The findings indicate that this is an emerging topic in a region that is one of the most vulnerable to climate change across the globe. Most frequently gender analysis is given superficial treatment; there is limited literature that connects gendered vulnerabilities to historical legacies and structural inequalities; and women’s critical roles that create security are often overlooked and devalued. The review indicates that greater work is needed to question perceived threats to security and to reveal how climate change, gendered institutions, systems and spaces, historical legacies and politics interact to construct security in the Pacific Islands Region.
Strategies to Build Trust and COVID-19 Vaccine Confidence and Engagement among Minority Groups in Scotland
Authors: Adekola Josephine , Fischbacher-Smith Denis , Okey-Adibe Thelma , Audu Jamila 
Published: December 2022

AbstractAs countries continue to deal with the global COVID-19 pandemic and its consequences, policymakers recognize that science, technology, and innovation (STI) practices offer a means of addressing many of the health problems that arise from the ongoing pandemic. Such recognition has given rise to many STI policy initiatives across various areas of science and policy, leading to a better understanding of coronavirus and the development of COVID-19 vaccines, treatments, and diagnostics. However, the practical implementation of vaccine and treatment strategies within local communities extends well beyond the laboratory. This study explored how misinformation and trust amplify or attenuate coronavirus and COVID-19 vaccine perceptions of those from ethnic minority groups deemed more susceptible to the impacts of the virus. Primary data in this study were collected in Scotland through semistructured interviews with 26 expert and nonexpert members from Scotland’s minority ethnic communities. The study findings show that risk perception is fluid and dependent on the information and evidential environment in which people find themselves. Misinformation, fake news, conspiracies, and trust or distrust (from prior experiences and historic practices) influence the perception of coronavirus and how risk messages are received, including the acceptance of coronavirus vaccines. This article reflects on Scotland’s approach to building trust and COVID-19 vaccine confidence and engagement based on the findings of this study, identifying areas of strength and areas for further improvement or research. The authors believe, as shown by our research, that vaccine engagement will be more impactful if developed by and with the public, and reflects public values, concerns, and priorities.
Degradation of amaranth by persulfate activated with zero-valent iron: influencing factors, response surface modeling
Authors: Yu Changye , Lu Xian , Lu Jinyu , Zhang Yinjiang 
Published: December 2022

AbstractIn this study, zero-valent iron (ZVI) is applied to activate persulfate (PDS) for the degradation of amaranth (AMR). The effects of PDS concentration, ZVI concentration, solution pH  , temperature, and reaction time on the degradation of AMR by the ZVI/PDS advance oxidation process are investigated. Sulfate and hydroxyl radicals are involved in the main reaction pathway of AMR and sulfate radical acts as a dominant oxidant. The CCD (central composite design) plan is chosen to build the RSM model for the prediction of AMR degradation. ANOVA analysis shows that the secondary fitting model had great fitness with R2 = 0.997, $$R_{{{\text{adj}}}}^{2}$$

R

adj

2

= 0.936, p-value of lack of fit = 0.107. Optimum conditions for 98% AMR removal given by RSM are PDS concentration = 7.33 mM, ZVI dosage = 17.79 mM, initial pH   4.62, temperature = 59.49 °C, reaction time = 9.88 min which is proved to be very closed to the real removal rate of 96.78%. Sensitivity analysis indicates that the relative importance of the influencing parameters is of the following order: temperature, PDS concentration, pH  , ZVI dosage, and reaction time. The PDS/ZVI system shows an acceptable RSE of about 75% and TOC removal of 85% on AMR oxidation. Finally, the possible pathway of AMR degradation is proposed.
From 2D projections to the 3D rotation matrix: an attempt for finding a machine learning approach for the efficient evaluation of mechanical joining elements in X-ray computed tomography volume data
Authors: Schromm T. M. , Grosse C. U. 
Published: December 2022

AbstractDestructive and predominantly manual procedures are commonly used in the automotive industry for the testing of mechanical joints, such as rivets or screws. Combining X-ray computed tomography (CT) and machine learning (ML) bears the potential of a non-destructive and largely automated methodology. Assuming the desired result is a comprehensible and documentable evaluation, three basic steps need to be automatized: First, a joint must be detected and identified as such in a CT scan of the joined parts. Second, the detected region containing the joint is rotated to a predefined orientation. Third, key measures in cross-sections from the newly oriented joint are dimensioned and documented. This work deals only with the second step, the rotation. On the one hand, we present a methodology for creating a well-curated data set for the contextual machine learning application. On the other, we evaluate its performance on the well-known ResNet50. More concretely, we investigate if it is possible for a deep convolutional neural network (CNN) to learn the respective rotation matrix from three volume projections that are perpendicular to each other. Two scenarios are investigated: In one scenario we assume that future data that is presented to the network has similar rivet demographics to historic data. We therefore do not employ hold-out sets for the network evaluation. In the other scenario we assume the opposite and therefore evaluating the networks performance with hold-out sets. We show that from a machine learning point of view, a CNN like ResNet50 is well able to learn this relationship with acceptable accuracy. In most cases the validation loss dropped below 0.1 after only a couple of epochs. In one particular case, we even reached both mean and median errors lower than 0.2 for approximately 80% of the entire test set of 1600 examples using our methodology. From an application point of view, however, these low test set errors should be treated with caution since small deviations from the intended rotation matrix can cause volume warping and translation. In another case, in which we used a hold-out set, only a fraction of the median errors were below 0.2.
Application of an Electric Impulse Technology tool for removing concrete or mineral surfaces and cutting concrete within existing structures
Authors: Voigt Matthias , Anders Erik , Hammel Lukas , Otto Jens , Will Frank 
Published: December 2022

AbstractWith the help of the Electric Impulse Technology, it is possible to remove concrete or mineral surfaces in existing building structures with low vibrations and emissions and to make openings by slotting and cutting. Very fast high-voltage impulses are generated and led to an electrode arrangement positioned on the material to be destroyed. A breakdown is generated in the material and thus the material is destroyed. On this basis, investigations into the removal of concrete as well as mineral structures are carried out and a machine concept for the use of the Electric Impulse Technology for operations in existing buildings is developed.
Modeling of CNC machine tools’ spatial geometric error based on two-dimensional angle error
Authors: Zhang Xiuqin , Chen Guohua , Zhang Lin , Hu Yongwen , Mao Jie , Zhang Zhiyang 
Published: December 2022

Abstract
A modeling method of CNC machine tools’ spatial geometric error is proposed based on two-dimensional angle error in this paper. Different from traditional modeling method, this method takes into account the effect of additional angle errors because the machine tool’s angle errors in the system of the lower guide rail will be passed on to slip board structure to the upper guide rail system. Firstly, after analyzing the principle of two-dimensional angle error, an optimized error compensation model of CNC machine tools’ spatial geometric error contains two-dimensional angle error is established through the traditional homogeneous coordinate transformation matrix method (HTM). Further, by applying the experiment, the compensation effect of the model is verified through comparing the body diagonal errors of CNC machine tools before and after compensation. Experiment results show that the body-diagonal accuracy after compensation based on the optimized model is 87.73% higher than that before compensation, and 54.82% higher than that of the traditional model after compensation.

Article Highlights
The highlight of the study reported here is threefold: Firstly, the principle of two-dimensional angle error is analyzed. Secondly, an optimized spatial geometric error compensation model of CNC machine tool is established based on the principle of two-dimensional angle error. Thirdly, the compensation effect of the optimized compensation model is verified by comparing the bodies diagonal precision of the machine tool.
Forging of an age-hardenable Mg–Al–Ca–Mn–Zn alloy on industrial scale
Authors: Papenberg Nikolaus , Hatzenbichler Thomas , Grabner Florian , Uggowitzer Peter J. , Pogatscher Stefan 
Published: December 2022

AbstractWeight reduction plays an important role in transportation industries, directly impacting on fuel consumption and vehicle range. The use of multi-material mixes is common practice, allowing for an optimum application of specific material properties. Light metals, primarily aluminum alloys, are used in both, cast and wrought state, to good effect. On the other hand, magnesium alloys, which are still lighter by one third, are used in castings exclusively. While scientific research and development on Mg wrought alloys is progressing steadily, industrial implementation is still scarce. As a result, safety relevant and structural applications made from Mg wrought products are nearly nonexistent. To increase acceptance and facilitate industrial application for this interesting class of materials we investigated the forging process of an original-sized automotive control arm. To ease industrial access, the used age hardenable Mg–Al–Ca–Zn–Mn lean alloy, can be processed similarly to Al alloys, e.g. 6xxx series. This work describes the development sequence, starting with the analysis of the forming window, followed by laboratory forging trials and industrial sized part production, providing information on forming characteristics as well as possible difficulties.
Performance of a seismically isolated building in Albania during the Durrës sequence of 2019 and a feasibility study of pendulum isolators in the region
Authors: Hima Nikolin , Çaushi Ergys , Murtaj Luan , Castellano Maria Gabriella 
Published: December 2022

AbstractThis work evaluates the behaviour of the only seismically isolated building in Albania during the Durrës sequence of 2019 and analyses the efficiency and feasibility of pendulum isolators in the region. The behaviour of the existing building is analysed through observational data gathered during on-site inspection and is verified through numerical simulations using the impact of the main shock of 26th of November 2019. The technical efficiency and the economic feasibility of seismic isolation through pendulum isolators for non-strategic buildings are analysed by designing, detailing, and estimating the cost of an ordinary six story residential building in both approaches (capacity design and seismic isolation approach), accounting for the local factors such as typical architecture, soil characterization, the level of the seismic hazard and the cost of the construction materials and workmanship in the densely populated region of central Albania. The structural behaviour assessment highlighted the superiority of the seismic isolation, and the feasibility analysis revealed that the initial cost for delivering seismically isolated buildings is higher than that of the conventional approach by some percentage. However, the gains in structural performance and the relatively high seismic hazard of the country make seismic isolation of buildings a relevant alternative in achieving seismic protection for buildings in the region.
Markdown Optimization with Generalized Weighted Least Squares Estimation
Authors: Hekimoğlu Mustafa 
Published: December 2022

AbstractRetailers increasingly apply price markdowns for their seasonal products. Efficiency of these markdown applications is driven by the accuracy of empirical models, especially toward the end of a selling season. In the literature, recent sales are recognized to be more important than older sales data for estimating the current period’s demand for a given markdown level. The importance difference between the weeks of a selling season is addressed by weighted least squares (WLS) method with continuous weight functions of time. This study suggests a generalization of the weight functions and a method for optimizing their shape and discretization parameters to stimulate the predictive accuracy of models. We find that addressing the importance difference of recent sales observations using our generalized weight functions improves the forecast accuracy by up to 20%, and most of the improvement stems from our weight discretization method.
The unique value proposition for using drones to map coastal ecosystems
Authors: Joyce Karen E. , Fickas Kate C. , Kalamandeen Michelle 
Published: December 2022

AbstractCoasts are undeniably regions of critical importance for a range of environmental, sociocultural, and economic reasons. Yet they are also areas of intense anthropogenic impact and are particularly susceptible to climate change-related concerns. As such, it is imperative that we have the means to monitor and manage them in a sustainable manner. Drone technology has emerged as providing a unique value proposition in coastal environments to support data-driven monitoring and management decisions. With their highly detailed data capture capability, drones are particularly well suited to map the spatial heterogeneity, structural complexity, and temporally dynamic nature of coasts. Further, they are readily accessible to coastal populations and can promote grassroots action by the very people whose lives and livelihoods the coasts support. Herein, we cover several of the leading innovations in using aerial drones to map coastal ecosystems. We then consider how general trends and technology projections including artificial intelligence, as well as cloud and edge computing offer opportunities for the future of drone mapping and monitoring in a coastal context. While the challenge of change is inevitable, embracing the opportunities it provides will allow us to better understand and live sustainably with and within our coastal ecosystems.