bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling of COVID‐19 death rate using various air pollutants: A multiple linear regression approach
Authors: Teja Kambhampati , Laskar Nirban , Mozumder Ruhul Amin 
Published: February 2023

AbstractAir pollution is a significant health risk, especially for vulnerable populations such as children, people with chronic illnesses, the elderly, and the economically and socially disadvantaged. Furthermore, air pollution has enormous social costs that we all bear in the form of premature deaths, low productivity, sick leave, and other strains on the healthcare system. The primary sources of air pollution are traffic, home fires, and industry. Measuring NO2 levels in air pollution reveals the extent of pollution caused by traffic, particularly diesel vehicles, which are the primary source of NO2. COVID‐19 rates are rising in areas with high levels of air pollution, according to mounting evidence. Toxic contaminants can make people more susceptible to COVID‐19. The causal relationship between air pollution and COVID‐19 cases has yet to be established, but experts warn that long‐term exposure will undoubtedly make people more susceptible to lung infections. Air pollution has been linked to an increase in cancer, heart disease, stroke, diabetes, asthma, and other comorbidities by inducing cellular damage and inflammation throughout the body. All of these factors increase the risk of death in COVID‐19 patients. As a result, air quality parameters must be predicted and monitored. To predict results, this study proposes a statistical‐based machine learning approach. Using multiple linear regression (MLR), Decision Tree (D.T.), and Random Forest (R.F.), the experimental results achieved 80%, 73%, and 65% accuracy on the dataset, respectively.
Smoke suppression, flame retardancy, and fire toxicity of polypropylene containing melamine salt of pentaerythritol phosphate halloysite
Authors: Abdelkhalik Aksam , Mahmoud Manar , Nour Mohamed A. , Hassan Mohamed A. , Souaya Eglal R. 
Published: February 2023

AbstractPolypropylene (PP) was melt blended with a new mono molecular intumescent flame retardant, melamine salt of pentaerythritol phosphate halloysite (MPPH) to enhance its thermal stability, flame retardancy, and smoke suppression properties. The structure of MPPH was elucidated by Fourier transform infrared (FTIR), 1H NMR, X‐ray diffraction (XRD), and energy dispersive X‐ray (EDX) analysis. PP composites results showed that MPPH increased the thermal stability of PP at high temperatures in all PP composites. The horizontal flammability test (UL94H) showed that MPPH stopped flame propagation in PP composites. Vertical burning rate test (UL94V) revealed that PP composites can attain V0 rating at loading levels 25, 30, and 35 wt.% of MPPH. Limiting oxygen index (LOI) data indicated that adding 20, 25, 30, and 35 wt.% of MPPH to PP increased the LOI value of PP (19.2%) to 27.1%, 32.5%, 35.4%, and 38.7%. MPPH succeeded in reducing the maximum specific optical density (Dsmax), mass specific optical density (MOD), and rate of smoke generation during the first 4 min (VOF4) of PP composites compared to PP alone. FTIR gas analyzer results revealed that MPPH decreased the emission of CO and CO2 in the gas phase during the combustion process. Digital photos and scanning electron microscope (SEM) images of char residues remained after the smoke density test revealed that MPPH succeeded in forming a cellular and cohesive char layer on the PP surface. The new data is expected to increase the use of PP in rigid packaging applications.
A Data-Driven Analysis of Formula 1 Car Races Outcome
Authors: Patil Ankur , Jain Nishtha , Agrahari Rahul , Hossari Murhaf , Orlandi Fabrizio , Dev Soumyabrata 
Published: February 2023

AbstractThere are a range of factors that affect the outcome of Formula 1 (F1) car races. Today, it is reasonable to say that F1 races are first won at the factory, and then on the track. F1 teams accumulate enormous amounts of data during races. In this paper, we propose a data-driven approach to identify the most important factors that contribute to the overall points scored by each driver in a F1 season. We perform a correlation analysis along with a principal components analysis (PCA) to identify the factors that are closely related. Furthermore, using PCA, we efficiently reduce our 21 input variables into a lower-dimensional subspace, that can explain most of the variance in our data and which is easier to comprehend. We obtain 5 years (2015–2019) of data explaining the F1 car characteristics from a publicly available website https://www.racefans.net/. We use this web-scrapped F1 race study to understand the impact of the different car features on the total points scored by a driver in the season. To the best of our knowledge, our work is the first of its kind in the area of F1 car races.
Identity Term Sampling for Measuring Gender Bias in Training Data
Authors: Sobhani Nasim , Delany Sarah Jane 
Published: February 2023

AbstractPredictions from machine learning models can reflect biases in the data on which they are trained. Gender bias has been identified in natural language processing systems such as those used for recruitment. The development of approaches to mitigate gender bias in training data typically need to be able to isolate the effect of gender on the output to see the impact of gender. While it is possible to isolate and identify gender for some types of training data, e.g. CVs in recruitment, for most textual corpora there is no obvious gender label. This paper proposes a general approach to measure bias in textual training data for NLP prediction systems by providing a gender label identified from the textual content of the training data. The approach is compared with the identity term template approach currently in use, also known as Gender Bias Evaluation Datasets (GBETs), which involves the design of synthetic test datasets which isolate gender and are used to probe for gender bias in a dataset. We show that our Identity Term Sampling (ITS) approach is capable of identifying gender bias at least as well as identity term templates and can be used on training data that has no obvious gender label.
How Augmented Reality Beauty Filters Can Affect Self-perception
Authors: Isakowitsch Clara 
Published: February 2023

AbstractAugmented reality is used on visual social media platforms such as Snapchat and Instagram with filters that can be applied to the user’s face. These filters detect and transform facial features by overlaying digital masks on moving faces. Augmented reality beauty filters (ARB filters) alter the appearance of the face by conforming it to current beauty ideals. Prior to the development of ARB filters selfies could only be enhanced by retroactive photo editing. However, ARB filters adapt to facial features in real time, resulting in a unique digital beautifying process. This qualitative study explores how the use of ARB filters impacts people’s perceptions of themselves. It is based on online interviews that were conducted with eight individuals. The results are analysed within the frameworks of Extended Mind Theory and Enactivism and indicate that ARB filters may have a greater impact on people’s self-perception than retroactive photo editing.
Bayesian Optimization with Multi-objective Acquisition Function for Bilevel Problems
Authors: Dogan Vedat , Prestwich Steven 
Published: February 2023

AbstractA bilevel optimization problem consists of an upper-level and a lower-level optimization problem connected to each other hierarchically. Efficient methods exist for special cases, but in general solving these problems is difficult. Bayesian optimization methods are an interesting approach that speed up search using an acquisition function, and this paper proposes a modified Bayesian approach. It treats the upper-level problem as an expensive black-box function, and uses multiple acquisition functions in a multi-objective manner by exploring the Pareto-front. Experiments on popular bilevel benchmark problems show the advantage of the method.
Variable-Relationship Guided LNS for the Car Sequencing Problem
Authors: Souza Filipe , Grimes Diarmuid , O’Sullivan Barry 
Published: February 2023

AbstractLarge Neighbourhood Search (LNS) is a powerful technique that applies the “divide and conquer” principle to boost the performance of solvers on large scale Combinatorial Optimization Problems. In this paper we consider one of the main hindrances to the LNS popularity, namely the requirement of an expert to define a problem specific neighborhood. We present an approach that learns from problem structure and search performance in order to generate neighbourhoods that can match the performance of domain specific heuristics developed by an expert. Furthermore, we present a new objective function for the optimzation version of the Car Sequencing Problem, that better distinguishes solution quality.Empirical results on public instances demonstrate the effectiveness of our approach against both a domain specific heuristic and state-of-the-art generic approaches.
Latent Space Cartography for Geometrically Enriched Latent Spaces
Authors: O’ Mahony Niall , Awasthi Anshul , Walsh Joseph , Riordan Daniel 
Published: February 2023

AbstractThere have been many developments in recent years on the exploitation of non-Euclidean geometry for the better representation of the relation between subgroups in datasets. Great progress has been made in this field of Disentangled Representation Learning, in leveraging information geometry divergence, manifold regularisation and geodesics to allow complex dynamics to be captured in the latent space of the representations produced. However, interpreting the high-dimensional latent spaces of the modern deep learning-based models involved is non-trivial. Therefore, in this paper, we investigate how techniques in Latent Space Cartography can be used to display abstract and representational 2D visualisations of manifolds.Additionally, we present a multi-task metric learning model to capture in its output representations as many metrics as is available in a multi-faceted fine-grained change detection dataset. We also implement an interactive visualisation tool that utilises cartographic techniques that allow dimensions and annotations of graphs to be representative of the underlying factors affecting individual scenarios the user can morph and transform to focus on an individual/sub-group to see how they are performing with respect to said metrics.
Multi-Graph Convolutional Neural Network for Breast Cancer Multi-task Classification
Authors: Ibrahim Mohamed , Henna Shagufta , Cullen Gary 
Published: February 2023

AbstractMammography is a popular diagnostic imaging procedure for detecting breast cancer at an early stage. Various deep-learning approaches to breast cancer detection incur high costs and are erroneous. Therefore, they are not reliable to be used by medical practitioners. Specifically, these approaches do not exploit complex texture patterns and interactions. These approaches warrant the need for labelled data to enable learning, limiting the scalability of these methods with insufficient labelled datasets. Further, these models lack generalisation capability to new-synthesised patterns/textures. To address these problems, in the first instance, we design a graph model to transform the mammogram images into a highly correlated multigraph that encodes rich structural relations and high-level texture features. Next, we integrate a pre-training self-supervised learning multigraph encoder (SSL-MG) to improve feature presentations, especially under limited labelled data constraints. Then, we design a semi-supervised mammogram multigraph convolution neural network downstream model (MMGCN) to perform multi-classifications of mammogram segments encoded in the multigraph nodes. Our proposed frameworks, SSL-MGCN and MMGCN, reduce the need for annotated data to 40% and 60%, respectively, in contrast to the conventional methods that require more than 80% of data to be labelled. Finally, we evaluate the classification performance of MMGCN independently and with integration with SSL-MG in a model called SSL-MMGCN over multi-training settings. Our evaluation results on DSSM, one of the recent public datasets, demonstrate the efficient learning performance of SSL-MNGCN and MMGCN with 0.97 and 0.98 AUC classification accuracy in contrast to the multitask deep graph (GCN) method Hao Du et al. (2021) with 0.81 AUC accuracy.
A Transformer Architecture for Online Gesture Recognition of Mathematical Expressions
Authors: Ramo Mirco , Silvestre Guénolé C. M. 
Published: February 2023

AbstractThe Transformer architecture is shown to provide a powerful framework as an end-to-end model for building expression trees from online handwritten gestures corresponding to glyph strokes. In particular, the attention mechanism was successfully used to encode, learn and enforce the underlying syntax of expressions creating latent representations that are correctly decoded to the exact mathematical expression tree, providing robustness to ablated inputs and unseen glyphs. For the first time, the encoder is fed with spatio-temporal data tokens potentially forming an infinitely large vocabulary, which finds applications beyond that of online gesture recognition. A new supervised dataset of online handwriting gestures is provided for training models on generic handwriting recognition tasks and a new metric is proposed for the evaluation of the syntactic correctness of the output expression trees. A small Transformer model suitable for edge inference was successfully trained to an average normalised Levenshtein accuracy of 94%, resulting in valid postfix RPN tree representation for 94% of predictions.
Einführung in das Untersuchungsfeld
Authors: Rieck Louise Ina Hannah 
Published: February 2023

ZusammenfassungIm einleitenden Kapitel skizziere ich den Untersuchungsgegenstand. Ich erkläre meine Präkonzepte und spezifiziere meine Forschungsziele. Das Forschungsdesign wird vorgestellt und methodologisch begründet. Im letzten Abschnitt beschreibe ich den inhaltlichen Aufbau der Arbeit.
Aufstellung 5: Narrativ
Authors: Rieck Louise Ina Hannah 
Published: February 2023

ZusammenfassungZum vorläufigen Abschluss meiner Forschungsreise erkunde ich mittels Aufstellung das Wirkpotenzial von Marketing für transformative Narrative: Kann Marketing dabei unterstützen, dass ein neues, zukunftsorientiertes und gesellschaftlich anschlussfähiges Konsumnarrativ entsteht?
Zusammenfassung: Orientierungswissen
Authors: Rieck Louise Ina Hannah 
Published: February 2023

ZusammenfassungIm explorativen Hauptteil sind – zumindest auf meiner inneren Landkarte und soweit ich es überblicke auch auf der von Forschung und Praxis – eine ganze Reihe gehaltvoller Unterschiede entstanden, die vorher nicht greifbar waren. Diese neuen Perspektiven für Marketing fasse ich im Folgenden zusammen.
Methodenreflexion
Authors: Rieck Louise Ina Hannah 
Published: February 2023

ZusammenfassungSystemische Erkundungen mittels Aufstellung führen den:die Beobachtende wieder näher an die Realität des Ganzen heran dadurch, dass die „Wirklichkeit“ im Aufstellungsraum ihre Autonomie zurückerhält. Dies macht die Methode besonders in der Transformationsforschung zu einem nützlichen Werkzeug.
Methodendiskussion Systemische Aufstellungen
Authors: Rieck Louise Ina Hannah 
Published: February 2023

ZusammenfassungIn diesem Kapitel wird die Wirkungsweise von Aufstellungsarbeit dargestellt. Eine theoretische Beschreibung kann das eigene Erleben einer Aufstellung zwar nicht ersetzen, doch sie ist notwendig, um Aufstellungen als Forschungsinstrument methodologisch einzuordnen und zu begründen.
Aufstellung 1: Das Konsumsystem
Authors: Rieck Louise Ina Hannah 
Published: February 2023

ZusammenfassungMein Erkenntnisinteresse bei dieser Aufstellung gilt dem systemischen Beziehungsgefüge im Konsumsystem: Wer verfolgt welche Interessen, welche Akteure interagieren, welche Kräfteverteilung zeigt sich? Ein weiterer initialer Analysefokus lag zudem auf Veränderungen im Konsumsystem, die sich bei der – erwarteten – „Entwicklung hin zu Nachhaltigkeit“ vollziehen würden.
Aufstellung 3: Kommunikationsstrategien
Authors: Rieck Louise Ina Hannah 
Published: February 2023

ZusammenfassungBei der Aufstellung nehme ich einige der Kommunikationsstrategien in den Blick, die im Mittelpunkt des aktuellen fachpolitischen Diskurses stehen und auch von Unternehmen im „Nachhaltigkeitsmarketing“ eingesetzt werden: Nudging, Suffizienzbotschaften sowie Regional-Label für „Produkte aus der Heimat“. Mein Analysefokus liegt auf den Reaktionen von Kund:innen auf diese unterschiedlichen Ansätze.
Einleitung
Authors: Villioth Lisa 
Published: February 2023

ZusammenfassungEine ganze Bandbreite von Online- und Offline-Protestpraktiken erlaubt es Bürger*innen, sich in politische Prozesse einzumischen, Öffentlichkeit für bestimmte Themen zu erzeugen und Politiker*innen und Unternehmen unter Druck zu setzen. Dabei reicht die Auswahl der Praktiken von niedrigschwelligen Angeboten wie dem Unterschreiben einer Online-Petition bis zu zeitintensiven Praktiken wie der aktiven Mitarbeit in einer lokalen Ortsgruppe eines Umweltverbandes. Warum entschließen sich Bürger*innen, ganz spezifischen Protestpraktiken – online wie offline – nachzugehen und anderen Praktiken nicht? Welche Vor- und Nachteile benennen die Subjekte für die einzelnen ihnen zur Verfügung stehenden Kanälen und Kommunikationsformaten? Ziel der vorliegenden Arbeit ist, mit Fokus auf einzelne Subjekte, fallspezifisch und empirisch den Handlungs- und Wirkungszusammenhang von Straßenprotest und Online-Aktivismus im Bereich der Umweltschutz-Bewegung zu untersuchen.
Forschungsgegenstand und historische Einordnung
Authors: Villioth Lisa 
Published: February 2023

ZusammenfassungIm nun folgenden vierten Kapitel wird die Geschichte der Umweltschutz-Bewegung in Deutschland mit ihren verschiedenen Entwicklungsphasen skizziert, es werden Ziele und Strategien der Bewegung vorgestellt und Akteure sowie relevante Organisationen benannt. Außerdem wird die gegenwärtige Situation der Umweltschutz-Bewegung mit zuletzt besonders relevanten Protestaktionen und zum Teil neuen Kollektivakteuren beschrieben. Anschließend folgt eine Vorstellung der in der Analyse des Interviewmaterials relevanten Organisationen MoveOn, Campact, Change.org und BUND mit ihren jeweils spezifischen Konzepten, Positionen und Strategien.
Augmented Reality in Schülerversuchen – Entwicklung und Evaluierung der Applikation PUMA: Magnetlabor
Authors: Schwanke Hagen , Trefzger Thomas 
Published: February 2023

ZusammenfassungExperimente stehen im naturwissenschaftlichen Unterricht nach wie vor im Zentrum des Unterrichtsgeschehens. Durch die Weiterentwicklungen im informationstechnischen Bereich ergänzen inzwischen kostengünstige digitale Technologien und Werkzeuge das Experiment im Unterricht. Die Sekundarstufe I bietet in der 10. Jahrgangsstufe in Bayern zum Thema der Elektrizitätslehre viele Möglichkeiten zur Anwendung einer Augmented-Reality-Lernumgebung. Zum Beispiel können die Applikationen die Modelle der magnetischen Felder sichtbar machen. Sie können jedoch auch zur Darstellung des „Unsichtbaren“, wie z. B. Atome, Elektronen oder Raumladungen, genutzt werden. In diesem Artikel wird das Vorgehen zur Erstellung einer Augmented-Reality-Applikation beschrieben. Ein wichtiger Punkt stellt das Konstrukt der Usability dar, deren Unterkategorie der Nutzerzufriedenheit mittels einer explorativen Mixed-Methods-Studie quantitativ und qualitativ evaluiert wurde. Anhand der Ergebnisse der Interviews wird dargestellt, welche Konsequenzen für die Applikation gezogen wurden.
Symmetries of Calabi-Yau prepotentials with isomorphic flops
Authors: Lukas Andre , Ruehle Fabian 
Published: February 2023

Abstract
Calabi-Yau threefolds with infinitely many flops to isomorphic manifolds have an extended Kähler cone made up from an infinite number of individual Kähler cones. These cones are related by reflection symmetries across flop walls. We study the implications of this cone structure for mirror symmetry, by considering the instanton part of the prepotential in Calabi-Yau threefolds. We show that such isomorphic flops across facets of the Kähler cone boundary give rise to symmetry groups isomorphic to Coxeter groups. In the dual Mori cone, non-flopping curve classes that are identified under these groups have the same Gopakumar-Vafa invariants. This leads to instanton prepotentials invariant under Coxeter groups, which we make manifest by introducing appropriate invariant functions. For some cases, these functions can be expressed in terms of theta functions whose appearance can be linked to an elliptic fibration structure of the Calabi-Yau manifold.
Line defects in three dimensional mirror symmetry beyond ADE quivers
Authors: Dey Anindya 
Published: February 2023

Abstract
Understanding the map of line defects in a Quantum Field Theory under a given duality is generically a difficult problem. This paper is the second in a series which aims to address this question in the context of 3d $$ \mathcal{N} $$
N
= 4 mirror symmetry. A general prescription for constructing vortex defects and their mirror maps in quiver gauge theories beyond the A-type was presented by the author in an earlier paper [1], where specific examples involving D-type and affine D-type quivers were discussed. In this paper, we apply the aforementioned prescription to construct a family of vortex defects as coupled 3d-1d systems in quiver gauge theories beyond the ADE-type, and study their mirror maps. Specifically, we focus on a class of quiver gauge theories involving unitary gauge nodes with edge multiplicity greater than 1, i.e. two gauge nodes in these theories may be connected by multiple bifundamental hypermultiplets. Quiver gauge theories of this type arise as 3d mirrors of certain Argyres-Douglas theories compactified on a circle. Some of these quiver gauge theories are known to have a pair of 3d mirrors, which are themselves related by an IR duality, discussed recently in [2]. For a concrete example where a pair of 3d mirrors do exist, we study how the vortex defects constructed using our prescription map to Wilson defects in each mirror theory.
Handlungsempfehlungen zur individuellen Risikoermittlung von Komorbidität bei erwachsenen Patienten mit Psoriasis
Authors: Wohlrab Johannes , Körber Andreas , Adler Georg , Blüher Matthias , Zirlik Andreas , Gerdes Sascha 
Published: February 2023

ZusammenfassungEs ist seit Langem bekannt, dass chronisch entzündliche Systemerkrankungen wie die Psoriasis ein hohes Risiko für die Entwicklung von Komorbidität bieten. Im klinischen Alltag ist es deshalb von besonderer Bedeutung, Patient:innen zu identifizieren, die ein individuell erhöhtes Risikoprofil bieten. Bei Menschen mit Psoriasis konnten in epidemiologischen Studien in Abhängigkeit von Krankheitsdauer und -schwere die Komorbiditätsmuster „metabolisches Syndrom“, „kardiovaskuläre Komorbidität“ und „psychische Erkrankungen“ als besonders relevant identifiziert werden. In der alltäglichen Versorgung von Menschen mit Psoriasis in der dermatologischen Praxis haben sich der Einsatz einer interdisziplinär inhaltlich abgestimmten Checkliste für die Risikoanalyse und die Bahnung einer professionellen Anschlussversorgung bewährt. Auf der Basis einer existierenden Checkliste wurden die Inhalte von einer interdisziplinären Expertengruppe kritisch bewertet, und eine leitlinienorientierte Aktualisierung wurde vorgenommen. Der nun vorgelegte Analysebogen stellt nach Auffassung der Autoren ein praktikables, sachbezogen fokussiertes und inhaltlich aktualisiertes Werkzeug für die Risikoermittlung von Komorbidität bei Patient:innen mit mittelschwerer bis schwerer Psoriasis dar.