bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical implementation of axial angulation of incisors in the course of routine fixed appliance treatment — a retrospective cohort study
Authors: Pour Ramtin Davoudi , Papageorgiou Spyridon N. , Safi Sema , Eble Oliver-Steffen , Jäger Andreas , Gölz Lina 
Published: December 2022

Abstract

Purpose
To identify clinically relevant factors for changes in axial angulation of incisors during routine fixed appliance orthodontic treatment.

Methods
A total of 106 patients (grades 1–2 of IOTN, 64 females, 42 males; mean age: 15.5 years) from a private practice and treated with metal or ceramic brackets were included in this retrospective cohort study. The axial angulation of the upper and lower incisors was measured on lateral cephalograms before insertion of the first rectangular 0.016 × 0.022-in NiTi archwire (T0) and at the end of treatment about 8 weeks after insertion of the working 0.019 × 0.025-in stainless steel archwire (T1). Treatment-related changes according to bracket type, initial situation, premolar extraction, angle class, and skeletal vertical configuration were analyzed.

Results
Although statistically significant treatment-related changes were seen for both the upper incisors (+ 1.3°) and the lower incisors (− 5.2°), only in ten patients (9.4%) was the prescribed torque value of 17° for the upper incisors and in no patient for the lower incisors achieved. A negative association between the induced change of axial angulation of incisors and the initial values was detected for the upper incisors as well as for the lower incisors. A comparison of the angle classes revealed significant differences in incisor changes. At the end of therapy, only a slight change for the upper central incisors in patients in angle class I cases and a significantly greater change in patients with angle class II/2 was observed. Cases with premolar extraction ended with lower axial angulation of the incisor than cases without extraction. The individual analysis of possible influencing factors also revealed an association with the vertical skeletal configuration.

Conclusions
For the first time, the presented data show clinically relevant influencing factors for incisor axial angulation changes of the upper and lower incisors in relation to the torque value of the applied brackets in the course of routine clinical practice. For the orthodontist, it remains mandatory to decide whether a customized system must be individualized in order to achieve individual therapy goals.
Leaching of rare earths from Abu Tartur (Egypt) phosphate rock with phosphoric acid
Authors: Roshdy O. E. , Haggag E. A. , Masoud A. M. , Bertau M. , Haneklaus N. , Pavón S. , Hussein A. E. M. , Khawassek Y. M. , Taha M. H. 
Published: December 2022

AbstractThe leaching of rare earth elements (REEs) from Egyptian Abu Tartur phosphate rock using phosphoric acid has been examined and was subsequently optimized to better understand if such an approach could be industrially feasible. Preliminary experiments were performed to properly define the design of experiments. Afterward, 24 full factorial design was implemented to optimize the leaching process. Optimum REEs leaching efficiency (96.7 ± 0.9%) was reached with the following conditions: phosphoric acid concentration of 30 wt.-% P2O5, liquid/solid ratio, mL/g, of 5:1, at 20 °C, and 120 min of leaching time. The apparent activation energy of the dissolution of REEs from phosphate rock using the phosphoric acid solution was -19.6 kJ/mol. D2EHPA was subsequently applied as an organic solvent for REEs separation from the acquired leach liquor. REEs stripping and precipitation were conducted, and finally, rare earth oxides with a purity of 88.4% were obtained. The leach liquor was further treated with concentrated sulfuric acid to recover the used phosphoric acid and produce gypsum with a purity of >95% at the same time. A flow diagram for this innovative cleaner production process was developed, and larger-scale experiments are proposed to further understand this promising approach to comprehensive phosphate rock processing.
On the Computation of Geometric Features of Spectra of Linear Operators on Hilbert Spaces
Authors: Colbrook Matthew J. 
Published: December 2022

AbstractComputing spectra is a central problem in computational mathematics with an abundance of applications throughout the sciences. However, in many applications gaining an approximation of the spectrum is not enough. Often it is vital to determine geometric features of spectra such as Lebesgue measure, capacity or fractal dimensions, different types of spectral radii and numerical ranges, or to detect gaps in essential spectra and the corresponding failure of the finite section method. Despite new results on computing spectra and the substantial interest in these geometric problems, there remain no general methods able to compute such geometric features of spectra of infinite-dimensional operators. We provide the first algorithms for the computation of many of these long-standing problems (including the above). As demonstrated with computational examples, the new algorithms yield a library of new methods. Recent progress in computational spectral problems in infinite dimensions has led to the solvability complexity index (SCI) hierarchy, which classifies the difficulty of computational problems. These results reveal that infinite-dimensional spectral problems yield an intricate infinite classification theory determining which spectral problems can be solved and with which type of algorithm. This is very much related to S. Smale’s comprehensive program on the foundations of computational mathematics initiated in the 1980s. We classify the computation of geometric features of spectra in the SCI hierarchy, allowing us to precisely determine the boundaries of what computers can achieve (in any model of computation) and prove that our algorithms are optimal. We also provide a new universal technique for establishing lower bounds in the SCI hierarchy, which both greatly simplifies previous SCI arguments and allows new, formerly unattainable, classifications.
On the Severi problem in arbitrary characteristic
Authors: Christ Karl , He Xiang , Tyomkin Ilya 
Published: December 2022

AbstractIn this paper, we show that Severi varieties parameterizing irreducible reduced planar curves of a given degree and geometric genus are either empty or irreducible in any characteristic. Following Severi’s original idea, this gives a new proof of the irreducibility of the moduli space of smooth projective curves of a given genus in positive characteristic. It is the first proof that involves no reduction to the characteristic zero case. As a further consequence, we generalize Zariski’s theorem to positive characteristic and show that a general reduced planar curve of a given geometric genus is nodal.
An effective zero-shot learning approach for intelligent fault detection using 1D CNN
Authors: Zhang Siyu , Wei Hua-Liang , Ding Jinliang 
Published: December 2022

AbstractData-driven fault detection techniques have attracted extensive attention in engineering, industry and many other areas in recent years. In many real applications, the following situation often occurs: data for certain types of faults (unseen faults) are not available to train models that are used for fault detection. Such a scenario can occur when data collection becomes highly time-consuming or destructive. To address this challenging problem, a novel fault detection method using zero-shot learning (ZSL) is proposed in this paper, which contains three phases: feature extraction, label embedding, and feature embedding. The method first extracts features from raw signals by applying a one-dimensional convolutional neural network (1D CNN), then builds semantic descriptions (human-defined) as fault attributes shared between seen faults and unseen faults, and finally uses a bi-linear compatibility function to find the highest-ranking fault type. The proposed semantic space based zero-shot learning with 1D CNN is called SSB-ZSL-1DCNN. The cosine distance is used to measure the similarity between feature embeddings and fault attributes. An important characteristic of SSB-ZSL-1DCNN is that the model, trained using only samples of seen faults, can be used to detect unseen defects. To evaluate the proposed method, two case studies are designed based on two well-known benchmarks (the Tennessee-Eastman chemical control process and the rolling bearing experiments at the Case Western Reserve University, respectively). The results demonstrate that the proposed method shows remarkable performance in detecting unseen faults.
Development and Validation of the Sexual Minority Adolescent Rejection Sensitivity Scale
Authors: Kiekens Wouter J. , Baams Laura , Feinstein Brian A. , Veenstra René 
Published: December 2022

AbstractBecause no measure for sexual orientation-related rejection sensitivity (RS) for adolescents exists, we aimed to develop and validate the Sexual Minority Adolescent Rejection Sensitivity Scale (SMA-RSS). In Study 1, interviews with 22 sexual minority youth were conducted (M age = 18.86, SD = 3.03). Based on these interviews, 29 scenarios were developed as potential items for the SMA-RSS. In Study 2, exploratory factor analyses were conducted on these 29 scenarios in a sample of 397 sexual minority adolescents (M age = 16.63, SD = 1.07). The 14 best performing items were selected and a two-factor structure best fit the data. In Study 3, a confirmatory factor analysis was conducted and the test–retest reliability, criterion validity, convergent validity, and incremental validity of the SMA-RSS were assessed in a sample of 499 sexual minority adolescents (M age = 16.61, SD = 1.34). A bifactor model best fit the data and evidence was provided for a strong enough general factor to justify unidimensionality. For criterion validity, the SMA-RSS evidenced small to moderate correlations with minority stressors and mental health indicators. For convergent validity, we found a moderate correlation with general RS. For incremental validity, the SMA-RSS was associated with mental health indicators over and above minority stressors and general RS. Participants were moderately stable in their scores on the SMA-RSS over a one-month period. Taken together, the SMA-RSS captured unique situations in which sexual minority adolescents anxiously expect rejection and can aid in better understanding health disparities among sexual minority adolescents.
Gender wage gap, quality of earnings and gender digital divide in the European context
Authors: Picatoste Xose , Mesquita Anabela , González-Laxe Fernando 
Published: December 2022

AbstractOne of the leading national and international objectives is to achieve more egalitarian societies. Avoiding gender or digital gaps are priorities generally assumed as concerns of governments and international organizations. This paper evaluates the digital gender divide in its three stages: access, use and results, relating it to gender and salary gaps in the context of the European Union. Cluster analysis was conducted to classify the countries according to their gender digital divide. The influence of age and studies level of males and females was revised. Based on the OECD and EUROSTAT data, an empirical analysis was conducted. By comparison of means, the significant variables influencing the gender digital divide were identified, probing that the educational level significantly influences it, especially in what refers to the third stage. Finally, through a regression analysis, it was proved that the influence of the insecurity and the gender wage gap goes beyond the women and affects society.
Transferring makerspace activities to the classroom: a tension between two learning cultures
Authors: Walan Susanne , Gericke Niklas 
Published: December 2022

AbstractThis case study aimed to investigate teachers’ reflections on the transfer of makerspace activities into classrooms. Primary and secondary STEM teachers participated in a Continuous Professional Development programme about makerspaces. Data were collected in the form of written reflections and semi-structured interviews after the teachers conducted makerspace activities in their classes. A thematic approach was used for data analysis. The results showed that teachers identified possibilities: Connections to learning objectives in STEM subjects; Motivating and engaging students; Stimulating collaboration; Stimulating creativity; and challenges: Problem of assessment; Lack of digital competence; Lack of high-tech equipment. However, the teachers did not reflect upon the cultural, ontological, and epistemological differences between makerspaces and formal schooling. Thus, we argue that it is difficult ‘to eat the cake and have it too’, i.e. to fully reconcile both the maker-culture and demands of formal schooling. Rather, we suggest three ways to connect makerspace culture with formal education.
Tools for supporting teacher noticing about classroom video in online professional development
Authors: Larison Sarah , Richards Jennifer , Sherin Miriam Gamoran 
Published: December 2022

AbstractTeachers’ noticing of students’ mathematical thinking plays an important role in supporting student learning. Yet little is known about how online professional development (PD)—a growing setting for PD in the USA—can cultivate this noticing. Here, we explore the potential of using two online tools for engaging video to support K-2 teachers’ noticing of students’ mathematical thinking in the context of a six-week online video-based PD program. While participating in the program, teachers used a commenting tool that allowed them to view video in its entirety and write a summary note, as well as a tagging tool that allowed them to mark moments of video while viewing and associate notes with those moments. We found that teachers’ regular use of the tagging tool promoted their increased noticing of students’ mathematical thinking in video. Further, the tagging tool and commenting tool appeared to function in complementary ways to support teachers’ noticing. These findings contribute to the field’s growing understanding of how technological advances can support the development and study of mathematics teacher noticing, with implications for the design of online teacher learning environments.
Classification of Located Acoustic Emission Events Using Neural Network
Authors: Manthei Gerd , Guckert Michael 
Published: December 2022

AbstractLocation of acoustic emission (AE) events is one of the main evaluation tools in AE analysis. Reliable location of AE sources enables accurate investigation of the mechanisms that led to a crack in the material. It is known that the location errors are influenced by several factors, including the accuracy of the elastic wave arrival time reading, the geometric distribution of the AE sensors, and most importantly, by the physical properties of the propagation medium. The aim of this study is the application of a neural network to classify clustered AE events, which were detected during six hydraulic fracturing tests in massive salt rock. A fully connected feed forward network was used for pattern recognition and classification of the input events according to target classes. For input data the signal arrival time profiles of the longitudinal (L) and transversal (T) elastic waves were used to train, to test, and to validate the neural network. In total 765 AE events were classified in various target classes. Receiver operating characteristic analysis (ROC) was applied for analyzing the result of the neural network approach. The neural network classified clustered events correctly, while few spatially scattered events outside the region of a cluster could not be matched to any cluster. Bootstrap analysis showed that the results are robust and demonstrates the high potential of Deep Learning (DL) methods in the location of AE events.
Can language models automate data wrangling?
Authors: Jaimovitch-López Gonzalo , Ferri Cèsar , Hernández-Orallo José , Martínez-Plumed Fernando , Ramírez-Quintana María José 
Published: December 2022

AbstractThe automation of data science and other data manipulation processes depend on the integration and formatting of ‘messy’ data. Data wrangling is an umbrella term for these tedious and time-consuming tasks. Tasks such as transforming dates, units or names expressed in different formats have been challenging for machine learning because (1) users expect to solve them with short cues or few examples, and (2) the problems depend heavily on domain knowledge. Interestingly, large language models today (1) can infer from very few examples or even a short clue in natural language, and (2) can integrate vast amounts of domain knowledge. It is then an important research question to analyse whether language models are a promising approach for data wrangling, especially as their capabilities continue growing. In this paper we apply different variants of the language model Generative Pre-trained Transformer (GPT) to five batteries covering a wide range of data wrangling problems. We compare the effect of prompts and few-shot regimes on their results and how they compare with specialised data wrangling systems and other tools. Our major finding is that they appear as a powerful tool for a wide range of data wrangling tasks. We provide some guidelines about how they can be integrated into data processing pipelines, provided the users can take advantage of their flexibility and the diversity of tasks to be addressed. However, reliability is still an important issue to overcome.
Factors impacting on the retention of students from under-represented groups in initial teacher education in Ireland
Authors: Keane Elaine , Heinz Manuela , Lynch Andrea 
Published: December 2022

AbstractInitial teacher education (ITE) programmes in Ireland have seen continued high demand for places, reflecting high regard for the teaching profession. However, there is a dearth of diversity amongst Ireland’s student teacher and teaching populations, with the vast majority of entrants to ITE being from majority ethnic groups and a high proportion from higher socio-economic backgrounds. Diversifying the teaching profession has come to be of research and policy significance in recent decades in Ireland. This paper explores issues of retention in ITE in Ireland, with a focus on supporting the engagement and completion of student teachers from under-represented groups. We draw on data collected as part of the Access to Post-primary Teaching (APT) project, funded under the Higher Education Authority’s Programme for Access to Higher Education (PATH): Strand 1 (Equity of Access to Initial Teacher Education), including interviews with ‘working class’ student teachers and a focus group interview with teacher educators. While the paper highlights high retention rates in ITE in Ireland, it reports key barriers that particularly negatively impact student teachers from under-represented groups, in relation to identity and belonging, and finance and part-time work. The paper also observes the necessity of a high level of support by programme staff in maintaining high retention rates more generally in ITE programmes. Given the significant barriers student teachers from under-represented groups encounter in ITE, their high levels of retention merits further research, particularly the roles played by their motivation and by the specific additional supports provided by ITE staff and teacher diversity projects.
Soziale Unterstützung und Informationsaneignung von ungewollt Schwangeren
Authors: Bomert Christiane 
Published: December 2022

ZusammenfassungAusgangspunkt des hier vorgestellten partizipativen Praxisforschungsprojekts mit Schwangerschaftskonfliktberatungsstellen aus der Region Stuttgart sind die anhaltenden gesellschaftlichen Auseinandersetzungen über Schwangerschaftsabbrüche. Mithilfe einer standardisierten wie teilstandardisierten Befragung der am Beratungsgespräch beteiligten Personen fragt das Projekt, welche soziale Unterstützung im Umfeld ungewollt schwangerer Frauen besteht und welche Möglichkeiten einer medizinisch korrekten und moralisch neutralen Informationsaneignung über den Abbruch es gibt. Die Ergebnisse zeigen, dass sich die gesellschaftlichen Auseinandersetzungen z. T. in Richtung einer Individualisierung des Themas hin auswirken.
Von Baumdiagrammen über Doppelbäume zu Häufigkeitsnetzen – kognitive Überlastung oder didaktische Unterstützung?
Authors: Binder Karin , Steib Nicole , Krauss Stefan 
Published: December 2022

ZusammenfassungIn stochastischen Situationen mit zwei dichotomen Merkmalen erlauben weder die schulüblichen Baumdiagramme noch Vierfeldertafeln die simultane Darstellung sämtlicher in der Situation möglicher Wahrscheinlichkeiten. Das im vorliegenden Beitrag vorgestellte Netz hat die Kapazität, alle vier möglichen Randwahrscheinlichkeiten, alle vier Schnittwahrscheinlichkeiten sowie alle acht bedingten Wahrscheinlichkeiten gleichzeitig darzustellen. Darüber hinaus ist – aufgrund der Knoten-Ast-Struktur des Netzes – die simultane Darstellung von Wahrscheinlichkeiten und absoluten Häufigkeiten mit dieser Visualisierung ebenfalls möglich. Bei der sukzessiven Erweiterung des typischen Baumdiagramms zunächst zum Doppelbaum und schließlich zum Netz sinkt der Inferenzgrad (d. h. weniger kognitive Schritte sind erforderlich) z. B. für Fragen nach bedingten Wahrscheinlichkeiten, aber gleichzeitig steigt die Komplexität der Darstellung und somit die extrinsische kognitive Belastung. Im vorliegenden Artikel erfolgt zunächst ein theoretischer Vergleich dieser Knoten-Ast-Strukturen. Eine anschließende Studie illustriert, dass sich die sukzessive Erweiterung bereits vollständig ausgefüllter Diagramme positiv auf die Performanz von N = 269 Schülerinnen und Schülern auswirkt. Obwohl Häufigkeitsdoppelbäume und Häufigkeitsnetze den Schülerinnen und Schülern gänzlich unbekannt waren, unterstützten diese Visualisierungen die Schülerinnen und Schüler bei der Bearbeitung der Aufgaben am meisten.
Photoelectrochemical energy conversion using hybrid photoelectrodes
Authors: Shiyani T. , Dube Charu Lata 
Published: December 2022

AbstractWe demonstrated the basil sensitized hybrid photoelectrodes for photocurrents and fuel generation. Hybrid photoelectrochemical cells (PECs) were proposed for direct solar energy conversion. The biohybrid device allows tunable control of energy conversion through the chemically stable photoelectrode. Biohybrid PEC was prepared by integrating organic and inorganic layers on fluorine doped tin oxide substrate. This integrated assembly produces electricity upon the illumination of visible light and drives overall water splitting reaction to generate solar fuel. The basil layer enhances the overall absorption with wide spectrum range and hence, a strong increment in generation of photocurrent is observed in the biohybrid PEC device. This hybrid PEC device can also be used to generate solar fuels and solar power.