bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intralabyrinthine schwannomas: a two-case series and literature review with a focus on hearing rehabilitation
Authors: Curatoli Luigi , Pontillo Vito , Quaranta Nicola 
Published: January 2023

Abstract
Purpose
Intralabyrinthine schwannomas (ILSs) are an uncommon finding. Diagnosis is challenging and no gold standard treatment exists yet. In this article, we present a two-cases series and review the latest available literature to assess the best diagnostic and therapeutic scheme.

Methods
We reviewed the latest available literature assessing most frequent and relevant sets of symptoms, clinical features of the disease, diagnostic tests and imaging, possible treatments and after-surgery hearing rehabilitation techniques. We then compared literature data to our own series ones.

Results
ILSs clinical presentation and development may overlap with other, more common otological conditions. Full audiometric battery test, electrophysiological study of VEMPS and MRI with contrast enhancement all appear to be critical to correctly diagnose these tumors. Several treatments exist: radiological follow-up, radiation therapy, full or partial surgical excision. Hearing rehabilitation is mostly accomplished through simultaneous cochlear implantation.

Conclusions
Our case-series data matches the available literature. ILSs are a rare type of vestibular schwannomas. Diagnosis in challenging and delayed in time as all the diagnostic tests, yet sensitive, are not specific for ILSs. The most suitable treatment seems to be surgical excision of these tumors followed by simultaneous cochlear implantation to restore hearing.
Systematic literature review of immunoglobulin trends for anti-CD20 monoclonal antibodies in multiple sclerosis
Authors: Saidha Shiv , Bell Judith , Harold Sydney , Belisario Jose Marcano , Hawe Emma , Shao Qiujun , Wyse Kerri , Maiese Eric M. 
Published: January 2023

AbstractObjectiveTo exp lore changes in immunoglobulin (Ig) levels for people with relapsing-multiple sclerosis (RMS) treated with ocrelizumab or ofatumumab and the relationship between Ig levels and infections.MethodsA systematic literature review (SLR) was conducted to identify clinical trials and real-world evidence (RWE) studies on Ig levels over time and studies on associations with infections for ocrelizumab and ofatumumab for people with RMS through 10 September 2021. Searches were conducted in Embase, MEDLINE, Cochrane Library, trial registries, and recent conference abstracts.ResultsOf 1,580 articles identified, 30 reporting on 11 trials and 5 RWE studies were included. Ocrelizumab trials (n = 4) had 24–336 weeks of follow-up and reported decreasing Ig G (IgG) levels, while RWE (n = 5) had 52–78 weeks of follow-up and reported IgG to be stable or decrease only slightly. IgG levels were stable in ofatumumab trials (n = 5; 104–168 weeks of follow-up), but no RWE or longer-term studies were identified. No apparent association between decreased Ig levels and infections was observed during ofatumumab treatment (ASCLEPIOS I/II), while for ocrelizumab, the only data on apparent associations between decreased IgG levels and serious infection rates were for a pooled population of people with RMS or primary progressive MS.ConclusionDecreasing IgG levels have been correlated with increased infection risk over time. IgG levels appeared to decrease over time in ocrelizumab trials but remained relatively stable over time in ofatumumab trials. Additional research is needed to understand differences between ocrelizumab and ofatumumab and identify people at risk of decreasing IgG levels and infection.
Construction and demolition waste management in Kosovo: a survey of challenges and opportunities on the road to circular economy
Authors: Alite Martin , Abu-Omar Heba , Agurcia Maria Teresa , Jácome Myriam , Kenney Jolina , Tapia Andrea , Siebel Maarten 
Published: January 2023

AbstractManaging Construction and demolition waste (CDW) is a severe and growing urban challenge, particularly in post-conflict countries. Though Kosovo has significantly rebuilt and developed after the Liberation War, these initiatives have accompanied suboptimal waste management. This research evaluates CDW management in Kosovo by assessing current practices and operations vis-a-vis the legal framework and EU requirements. It identifies instruments and policies capable of ameliorating gaps and proposes a more sustainable and circular CDW management system for Kosovo. Information was primarily collected during on-site visits to Pristina and the surroundings by a cohort of Urban Management Students from the Technische Universität Berlin, students from Kosovo, and the local office of the Gesellschaft für Internationale Zusammenarbeit, GIZ. The analysis identified gaps in Kosovo's CDW management and its observance and enforcement of existing CDW legislation, including an ambiguous licensing system impeding sustainable demolition, storage, and transport; the absence of approved CDW storage options leading to uncontrolled disposal; and an underdeveloped market for recycling and reuse, deterring stakeholders from further pursuit of circular practices. These gaps were compounded by poor recordkeeping, obscuring precise information on CDW streams. Possible instruments and incentives to support Kosovo's transition to a more sustainable CDW management system were then identified.
A graph-based framework for model-driven optimization facilitating impact analysis of mutation operator properties
Authors: John Stefan , Kosiol Jens , Lambers Leen , Taentzer Gabriele 
Published: January 2023

AbstractOptimization problems in software engineering typically deal with structures as they occur in the design and maintenance of software systems. In model-driven optimization (MDO), domain-specific models are used to represent these structures while evolutionary algorithms are often used to solve optimization problems. However, designing appropriate models and evolutionary algorithms to represent and evolve structures is not always straightforward. Domain experts often need deep knowledge of how to configure an evolutionary algorithm. This makes the use of model-driven meta-heuristic search difficult and expensive. We present a graph-based framework for MDO that identifies and clarifies core concepts and relies on mutation operators to specify evolutionary change. This framework is intended to help domain experts develop and study evolutionary algorithms based on domain-specific models and operators. In addition, it can help in clarifying the critical factors for conducting reproducible experiments in MDO. Based on the framework, we are able to take a first step toward identifying and studying important properties of evolutionary operators in the context of MDO. As a showcase, we investigate the impact of soundness and completeness at the level of mutation operator sets on the effectiveness and efficiency of evolutionary algorithms.
Uncertainty in firm valuation and a cross-sectional misvaluation measure
Authors: Bottazzi Giulio , Cordoni Francesco , Livieri Giulia , Marmi Stefano 
Published: January 2023

AbstractThe degree of uncertainty associated with the value of a company plays a relevant role in valuation analysis. We propose an original and robust methodology for company market valuation, which replaces the traditional point estimate of the conventional Discounted Cash Flow model with a probability distribution of fair values that convey information about both the expected value of the company and its intrinsic uncertainty. Our methodology depends on two main ingredients: an econometric model for company revenues and a set of firm-specific balance sheet relations that are estimated using historical data. We explore the effectiveness and scope of our methodology through a series of statistical exercises on publicly traded U.S. companies. At the firm level, we show that the fair value distribution derived with our methodology constitutes a reliable predictor of the company’s future abnormal returns. At the market level, we show that a long-short valuation (LSV) factor, built using buy-sell recommendations based on the fair value distribution, contains information not accessible through the traditional market factors. The LSV factor significantly increases the explanatory and the predictive power of factor models estimated on portfolios and individual stock returns.
A survey of Bayesian Network structure learning
Authors: Kitson Neville Kenneth , Constantinou Anthony C. , Guo Zhigao , Liu Yang , Chobtham Kiattikun 
Published: January 2023

AbstractBayesian Networks (BNs) have become increasingly popular over the last few decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology, epidemiology, economics and the social sciences. This is especially true in real-world areas where we seek to answer complex questions based on hypothetical evidence to determine actions for intervention. However, determining the graphical structure of a BN remains a major challenge, especially when modelling a problem under causal assumptions. Solutions to this problem include the automated discovery of BN graphs from data, constructing them based on expert knowledge, or a combination of the two. This paper provides a comprehensive review of combinatoric algorithms proposed for learning BN structure from data, describing 74 algorithms including prototypical, well-established and state-of-the-art approaches. The basic approach of each algorithm is described in consistent terms, and the similarities and differences between them highlighted. Methods of evaluating algorithms and their comparative performance are discussed including the consistency of claims made in the literature. Approaches for dealing with data noise in real-world datasets and incorporating expert knowledge into the learning process are also covered.
Descent for internal multicategory functors
Authors: Prezado Rui , Nunes Fernando Lucatelli 
Published: January 2023

AbstractWe give sufficient conditions for effective descent in categories of (generalized) internal multicategories. Two approaches to study effective descent morphisms are pursued. The first one relies on establishing the category of internal multicategories as an equalizer of categories of diagrams. The second approach extends the techniques developed by Ivan Le Creurer in his study of descent for internal essentially algebraic structures.
Insecticidal activity of Bacillus thuringiensis strains isolated from tropical greenhouses towards Cydia pomonella and Spodoptera exigua larvae
Authors: Baranek Jakub , Pluskota Mateusz , Rusin Monika , Konecka Edyta , Kaznowski Adam , Wiland-Szymańska Justyna 
Published: January 2023

Abstract
Three Bacillus thuringiensis strains were isolated from a specific habitat of tropical greenhouses located in the Botanical Garden of Adam Mickiewicz University in Poznań, Poland. The BG11, BG12 and BG15 strains harbour genes encoding a specific set of insecticidal proteins (cry1Ba, cry1Ia, cry2Ab, vip3Aa)—entirely different from those found in commercial isolates, currently used as bioinsecticides. Despite high genetic similarity of the new strains, each of them produces unique Cry1Ba toxin as a main component of the parasporal crystals. Moreover, the tested entomopathogens contain genetic determinants encoding two types of chitinolytic enzymes ChiA and ChiB. The tested strains display insecticidal activity against two distinct, economically important pest insects, Cydia pomonella L. (Lepidoptera: Tortricidae) and Spodoptera exigua Hübner (Lepidoptera: Noctuidae). However, BG12 and BG15 strains are significantly more active than BG11 towards both pests. The BG12 and BG15 strains can be considered as candidates for the production of new lepidopteran-active bioinsecticides with high potential to augment the existing biocontrol strategies.
Short attribute-based signatures for arbitrary Turing machines from standard assumptions
Authors: Datta Pratish , Dutta Ratna , Mukhopadhyay Sourav 
Published: January 2023

AbstractThis paper presents the first attribute-based signature () scheme supporting signing policies representable by Turing machines (), based on well-studied computational assumptions. Our work supports arbitrarys as signing policies in the sense that the s can accept signing attribute strings of unbounded polynomial length and there is no limit on their running time, description size, or space complexity. Moreover, we are able to achieve input-specific running time for the signing algorithm. All other known expressive schemes could at most support signing policies realizable by either arbitrary polynomial-size circuits or s having a pre-determined upper bound on the running time. Consequently, those schemes can only deal with signing attribute strings whose lengths are a priori bounded, as well as suffers from the worst-case running time problem. On a more positive note, for the first time in the literature, the signature size of our scheme only depends on the size of the signed message and is completely independent of the size of the signing policy under which the signature is generated. This is a significant achievement from the point of view of communication efficiency. Our construction makes use of indistinguishability obfuscation () for polynomial-size circuits and certain -compatible cryptographic tools. Note that, all of these building blocks including for polynomial-size circuits are currently known to be realizable under well-studied computational assumptions.
Large Isotropic Elastic Deformations: On a Comprehensive Model to Correlate the Theory and Experiments for Incompressible Rubber-Like Materials
Authors: Anssari-Benam Afshin 
Published: January 2023

AbstractA comprehensive model, i.e., a model that: (i) suitably captures the mechanical behaviour of various types of rubber-like materials; (ii) describes the constitutive behaviour of a subject rubber-like specimen under different deformation modes via a single set of model parameter values, and (iii) is parent to many of the existing models of distinct types, is presented in this paper for application to the finite deformation of incompressible isotropic rubber-like materials. The model breaks away from Rivlin’s principal invariants $I_{i}$

I
i

and Valanis-Landel’s separable function of principal stretches $\lambda _{i}$

λ
i

representations, and instead adopts a general non-separable functional form $W( \lambda _{1}, \lambda _{2}, \lambda _{3} )$
W
(

λ
1

,

λ
2

,

λ
3

)
, subject to the incompressibility constraint. By way of example, the application of the model to extant datasets from four types of rubber-like materials that exhibit discernible mechanical behaviours, namely natural unfilled and filled rubbers, hydrogels and (extremely) soft tissue specimens, will be considered. The favourable correlation between the model predictions and the considered experimental datasets, obtained via simultaneous fitting of the model to the data of various deformations, will be demonstrated. It will be shown that most of the landmark models in the literature including the $W( I_{1}, I_{2} )$
W
(

I
1

,

I
2

)
form Mooney-Rivlin, the Gent-like limiting chain extensibility, the nonaffine tube and the separable principal stretches-based type Ogden models are all a special sub-set of the presented parent model, and are all recovered from this model. An important implication of the non-separable functional form of the model will be conferred, through a specific dataset, where the predictions of the separable functions prove inherently inadequate. The consequences of these improvements for a more accurate modelling of the finite deformation of incompressible isotropic rubber-like materials will also be discussed.
From Groups to Communities: A Resource Mobilization Theory Perspective on the Emergence of Communities
Authors: Angelopoulos Spyros , Canhilal Kubra S. , Hawkins Matthew A. 
Published: January 2023

Abstract
Groups and communities have been key topics in the information systems (IS) research agenda. While communities are assumed to emerge at the intersection of overlapping groups and their practices, prior research has mainly focused on their dynamics and evolution. This has resulted to limited empirical support regarding the emergence of communities. We address that lacuna by tracing the emergence of communities through the prism of resource mobilization theory. In doing so, we make use of a unique longitudinal dataset and incorporate Topic Modelling, Bipartite Network Analysis, and Community Detection. We show that new communities are formed at the intersection of overlapping groups and practices. In addition, we contribute to the IS literature by demonstrating that their emergence occurs due to resource mobilization that gives rise to a shared mindset. We also reveal that multiple resources are incorporated into the practices of an emerging community. By combining large datasets and innovative computational approaches, we help IS theory and practice to move away from traditional "what" questions towards the more insightful "how" ones. We discuss the theoretical and practical implications of our work and delineate an agenda for future research on the topic.
Learning Linearized Assignment Flows for Image Labeling
Authors: Zeilmann Alexander , Petra Stefania , Schnörr Christoph 
Published: January 2023

AbstractWe introduce a novel algorithm for estimating optimal parameters of linearized assignment flows for image labeling. An exact formula is derived for the parameter gradient of any loss function that is constrained by the linear system of ODEs determining the linearized assignment flow. We show how to efficiently evaluate this formula using a Krylov subspace and a low-rank approximation. This enables us to perform parameter learning by Riemannian gradient descent in the parameter space, without the need to backpropagate errors or to solve an adjoint equation. Experiments demonstrate that our method performs as good as highly-tuned machine learning software using automatic differentiation. Unlike methods employing automatic differentiation, our approach yields a low-dimensional representation of internal parameters and their dynamics which helps to understand how assignment flows and more generally neural networks work and perform.
How expressing one’s likes and dislikes affects enjoyment: a replication
Authors: Wittich Julia Sophia , Landwehr Jan R. , Wentzel Daniel 
Published: January 2023

AbstractA recent article by He, Melumad, and Pham (Journal of Consumer Research 46(3):545–563, 2019) showed that consumers experienced greater enjoyment when they were asked to perform an evaluative task relative to a non-evaluative task. In this research, we intend to replicate this finding and to examine to what extent the strength of the effect is contingent on the sample employed. Using a sample from Amazon MTurk, study 1 replicates the indirect effect but not the total experimental effect. Study 2 draws on a sample that is unacquainted with the experimental paradigm and replicates the indirect as well as the total experimental effect. These findings suggest that the sampling population of MTurk may have become desensitized to the basic experimental paradigm.
Complete mitochondrial genome of the Spanish toothcarp, Aphanius iberus (Valenciennes, 1846) (Actinopterygii, Aphaniidae) and its phylogenetic position within the Cyprinodontiformes order
Authors: López-Solano Alfonso , Nester Tessa Lynn , Perea Silvia , Doadrio Ignacio 
Published: January 2023

Abstract
Background
The Spanish toothcarp (Aphanius iberus Valenciennes, 1846) is a small fish endemic to the eastern coastline of the Iberian Peninsula and is currently listed as “Endangered” (category IUCN: EN). It mainly inhabits brackish waters which can exhibit large fluctuations in temperature and salinity throughout the year. The genetics of A. iberus are not well-known since most studies have only evaluated the genetic structure of the species under a conservation framework in order to identify its potential conservation units. Different phylogenetic relationships of Aphanius have been published based on some particular genes. In the present study, the entire mitochondrial genome of A. iberus was obtained for the first time in the context of an A. iberus reference genome and a hypothesis regarding its phylogenetic position was considered.

Methods and results
The mitogenome (a circular doble-stranded DNA sequence of 16,708 bp) was reconstructed and aligned against 83 Cyprinodontiformes and two outgroup taxa to identify the phylogenetic position of A. iberus. PartitionFinder was first used to test for the best evolutionary model and the phylogenetic analyses were performed using two methods: Maximun-Likelihood Approximation (IQ-Tree) and Bayesian inference (MrBayes). Our results show that A. iberus forms a sister group with Orestias ascotanensis, a cyprinodontiform species native to South America.

Conclusions
The results were congruent with the traditional morphometric reconstructed trees and with a geological vicariant hypothesis involving Cyprinodontiformes where Aphaniidae is shown as a monophyletic family separated from the family Cyprinodontidae. The information gathered from this study is not only valuable for improving our understanding of the evolutionary history of A. iberus, but for future genomic studies involving the species.
Reporting non-consensual pornography: clarity, efficiency and distress
Authors: De Angeli Antonella , Falduti Mattia , Menendez-Blanco Maria , Tessaris Sergio 
Published: January 2023

AbstractAccording to recent legislative initiatives, non-consensual pornography is a crime in several countries and social media providers have a duty to provide their users easy to use mechanisms to report abuses. In this paper, we analyse the state of the art of the interfaces for reporting non-consensual pornography from the victim’s perspective. Firstly, we analysed 45 content sharing platforms where aggressors might post non-consensual pornography. The analysis identified three distinct interaction styles for reporting the crime: Scriptum (a text-field where the user verbally describes the abuse), Bonam (a multilayered menu that includes a correct option), and Malam (a multilayered menu that does not include a correct option). Secondly, we conducted a within-subject study to evaluate the experience elicited by these interaction styles. Participants (N = 39) were given a scenario and asked to report six blurred images as non-consensual pornography using a medium-fidelity prototype. The results exposed complex trade-offs between clarity, efficiency, and distress among the different interaction styles. These trade-offs open foundational research directions transcending boundaries between human-computer interaction and multimedia studies and interfacing computer science research with the law.
Population diversity and inheritance in genetic programming for symbolic regression
Authors: Burlacu Bogdan , Yang Kaifeng , Affenzeller Michael 
Published: January 2023

AbstractIn this work we aim to empirically characterize two important dynamical aspects of GP search: the evolution of diversity and the propagation of inheritance patterns. Diversity is calculated at the genotypic and phenotypic levels using efficient similarity metrics. Inheritance information is obtained via a full genealogical record of evolution as a directed acyclic graph and a set of methods for extracting relevant patterns. Advances in processing power enable our approach to handle previously infeasible graph sizes of millions of arcs and vertices. To enable a more comprehensive analysis we employ three closely-related but different evolutionary models: canonical GP, offspring selection and age-layered population structure. Our analysis reveals that a relatively small number of ancestors are responsible for producing the majority of descendants in later generations, leading to diversity loss. We show empirically across a selection of five benchmark problems that each configuration is characterized by different rates of diversity loss and different inheritance patterns, in support of the idea that each new problem may require a unique approach to solve optimally.