bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metastasis of pulmonary adenocarcinoma to the oral cavity: A case report and literatures review of the last 30 years
Authors: Kawaharada Masami , Maruyama Satoshi , Abé Tatsuya , Hayashi Takafumi , Kobayashi Tadaharu , Tanuma Jun‐ichi 
Published: January 2022

AbstractBackgroundMetastases of pulmonary adenocarcinomas (pADCs) into the oral cavity are rare and sometimes misdiagnosed because of clinical findings that are similar to benign or inflammatory lesions.Case presentationA 79‐year‐old man had a soft elastic mass in the oral cavity. Cytological and histological examinations revealed a metastasis of a pADC that was vimentin‐positive.ConclusionOral cytology may be valuable for the detection of metastatic lesions in the oral cavity. Palliative surgery for maintaining the oral function may be beneficial to improve quality of life, and it did not affect survival time in Kaplan–Meier analysis from the literature review.
International mobility activities predict research output: A longitudinal study
Authors: Song Yanan , Gan Yiqun 
Published: January 2022

AbstractA 4‐year data set of 274 scientists and technical staff in a Chinese university's physics department was constructed to study the impact of going on international visits (visit) and inviting international scholars (invite) on researchers' academic output. Results showed visit frequency significantly predicted output quantity and quality whereas invites significantly predicted output quantity, but not its quality. Visit/invite frequencies significantly predicted long‐term output through short‐term output, giving direct proof to their enduring promotion effect on innovation. Visits predicted far‐future output through near‐future visits whereas invites did not lead to more invites in promoting output. Age moderated the relationship between visits/invites and output. This study provided new evidence on the behavioral and psychological process of international mobility and its impact on research output.
Mechanistic modeling of a human IgG4 monoclonal antibody (tralokinumab) Fab‐arm exchange with endogenous IgG4 in healthy volunteers
Authors: Wang Bing , Goodman Jo , Roskos Lorin K. 
Published: January 2022

AbstractTherapeutic IgG4 antibodies engage in Fab‐arm exchange with endogenous human immunoglobulin G4 (IgG4) to form monovalent hybrid molecules. A mechanistic population model was developed to quantitatively characterize the dynamic Fab‐arm exchange of tralokinumab, a human IgG4 monoclonal antibody currently being developed for the treatment of atopic dermatitis, with endogenous IgG4 in healthy volunteers. The estimated pharmacokinetic parameters for IgG4 were similar to those of immunoglobulin G1 or immunoglobulin G2 in humans. However, the mechanistically modeled clearance of half molecules is 21‐fold higher, likely due to the loss of avidity for the neonatal Fc receptor. Half molecules of tralokinumab randomly associate with those of endogenous IgG4 to form monovalent hybrid molecules, which became the dominant form of tralokinumab within 1 day postdose in healthy volunteers. As the potency of monovalent tralokinumab is comparable with that of bivalent tralokinumab, the IgG4 Fab‐arm exchange with endogenous IgG4 is not expected to affect the potency of neutralization of interleukin‐13 in vivo.
Improving risk prediction for pulmonary embolism in COVID‐19 patients using echocardiography
Authors: Satoskar Monika A. , Metkus Thomas , Soleimanifard Alborz , Shade Julie K. , Trayanova Natalia A. , Michos Erin D. , Mukherjee Monica , Schiminger Madeline , Post Wendy S. , Hays Allison G. 
Published: January 2022

AbstractSARS‐CoV‐2 infection is associated with increased risk for pulmonary embolism (PE), a fatal complication that can cause right ventricular (RV) dysfunction. Serum D‐dimer levels are a sensitive test to suggest PE, however lacks specificity in COVID‐19 patients. The goal of this study was to identify a model that better predicts PE diagnosis in hospitalized COVID‐19 patients using clinical, laboratory, and echocardiographic imaging predictors. We performed a cross‐sectional study of 302 adult patients admitted to the Johns Hopkins Hospital (March 2020–February 2021) for COVID‐19 infection who underwent transthoracic echocardiography and D‐dimer testing; 204 patients had CT angiography. Clinical, laboratory and imaging predictors including, but not limited to, D‐dimer and RV dysfunction were used to build prediction models for PE using logistic regression. Model discrimination was assessed using area under the receiver operator curve (AUC) and calibration using Hosmer‐Lemeshow χ2 statistic. Internal validation was performed. The prevalence of PE was 7.6%. The model with positive D‐dimer above 5 mg/L, RV dysfunction on echocardiography, and troponin had an AUC of 0.77, and cross‐validated AUC of 0.74. D‐dimer (>5 mg/L) had a positive association with PE (adj odds ratio = 4.40; 95% confidence interval: [1.80, 10.78]). We identified a model including clinical, imaging and laboratory variables that predicted PE in hospitalized COVID‐19 patients. Positive D‐dimer >5, RV dysfunction on echocardiography, and troponin were important predictors for calculating likelihood of PE diagnosis. This approach may be useful to aid in clinical decision‐making related to diagnostic imaging and treatment. Prospective studies are needed to evaluate impact on patient outcomes.
The economic impact of school closures during the 2015 flood in Richland County, South Carolina
Authors: Gall Melanie , Sheldon Tamara L. , Collins Larianne 
Published: January 2022

AbstractResearch on the societal costs of disaster‐related US school closures has focused, and due to COVID‐19 will likely continue to focus, on pandemics, with very limited research on closures from natural hazards. This is surprising given that school closures occur frequently to protect children, teachers, and staff pre‐event as well as post‐disaster to convert facilities into emergency shelters, etc. This study investigates the secondary effects from post‐flooding, temporary school closures after the catastrophic 2015 flash flood in Richland County, South Carolina. Lost productivity from school closures was quantified using the Human Capital Method. Out of the 208 completed surveys, 75% of households had children that missed school. Post‐stratifying survey results on race produced an average of $437 in lost productivity due to school closures and an overall $2175 in lost productivity due to flood‐related impacts in general. Expressed in FEMA benefit‐cost analysis terms, our study shows that schools have a standard value of $215 per household and per day for the unweighted sample ($180 for the race‐weighted sample). Furthermore, households' disutility for a late start is almost as great as their disutility for a school closure. These exploratory findings suggest that unplanned school closures should be minimized, and community characteristics carefully considered to avoid unintended socioeconomic consequences.
Dynamic learning‐based event‐triggered control of strict‐feedback systems with finite‐time prescribed performance
Authors: Wang Lixue , Wang Min , Yang Chenguang 
Published: January 2022

AbstractThis paper is concerned with the event‐triggered neural learning control for strict‐feedback nonlinear systems (SFNSs) subject to predefined tracking performance. A system transformation approach is utilized to transform the original SFNSs into the normal form, which makes it easily to verify the convergence of neural weights since only one neural network (NN) approximator is employed. Then, a novel finite‐time performance function is first proposed to characterize the performance constraints of tracking error, thus guaranteeing the prescribed tracking performance. On this basis, a new lemma is given, which is crucial to ensure the stability of the closed‐loop system. By using the error transformation technique, the constrained tracking control problem is converted into the stabilization problem of unconstrained error system. Subsequently, by introducing the first‐order sliding mode differentiator into the backstepping procedure and with the help of the high‐gain observer, a novel adaptive neural tracking control scheme is presented to ensure that the prescribed tracking performance is satisfied and all closed‐loop signals are ultimately bounded. After ensuring the satisfaction of the partial persistent excitation condition for radial basis function NN, the neural weight estimates are verified to converge to the ideal values, and then the convergent weights are stored as the constant values. By utilizing the stored knowledge, an event‐triggered neural learning controller is constructed to accomplish the same or similar control tasks, which can effectively improve the transient control performance, save the communication resource, and relieve the online computation at the same time. Finally, the effectiveness of the presented scheme is testified by numerical and practical examples.
Wake expansion continuation: Multi‐modality reduction in the wind farm layout optimization problem
Authors: Thomas Jared J. , McOmber Spencer , Ning Andrew 
Published: January 2022

AbstractIn this paper we present a continuation optimization method for reducing multi‐modality in the wind farm layout optimization problem that we call wake expansion continuation (WEC). We achieve the reduction in multi‐modality by starting with an increased wake diameter while maintaining normal velocity deficits at the center of the wakes, and then reducing the wake diameter for each of a series of optimization runs until the accurate wake diameter is used. We applied and demonstrated the effectiveness of WEC with two different wake models. We tested WEC on four optimization case studies and compared the results with a gradient‐based optimization method and a gradient‐free optimization method. We found a significant improvement in the mean, standard deviation, and minimum wake loss for optimization with WEC compared to optimization without WEC for all test cases. We found the gradient‐free optimization algorithm resulted in less optimal layouts on average for all cases than the gradient‐based algorithm with WEC. We also applied WEC to the gradient‐free algorithm for one case study with significantly improved results, but there was more improvement when we applied WEC to a gradient‐based algorithm. WEC enables gradient‐based algorithms to search the wind farm layout optimization space more globally and provides more optimal results more consistently than optimization without WEC.
Blade planform design optimization to enhance turbine wake control
Authors: Allen Jeffery , Young Ethan , Bortolotti Pietro , King Ryan , Barter Garrett 
Published: January 2022

AbstractThis study considers optimizing the planform of wind turbine blades to ultimately enhance wind plant controls, namely, wake steering strategies. Adjoint‐enabled unsteady actuator line simulations are carried out to obtain gradients for optimization of several different performance objectives with respect to blade chord length at 10 locations along blade span. We demonstrate different blade design optimizations that can maximize time‐averaged lateral wake deflection, entrainment of kinetic energy, or total power of multiple turbines. Our optimized designs can produce a 4+% increase in wake deflection, a 4× increase in vertical kinetic energy entrainment, or a 3.6% increase in power when compared with the baseline case. While lateral wake deflection is only modestly sensitive to chord changes, we find that increasing the outboard chord length can dramatically increase kinetic energy entrainment, resulting in faster wake recovery and gains in net power. While this work develops only a few case studies emphasizing relative performance improvements and general trends, these results show the promise of a framework that combines mid‐fidelity computation with adjoint‐based optimization for control and design problems.
General Elements of Genomic Selection and Statistical Learning
Authors: Montesinos López Osval Antonio , Montesinos López Abelardo , Crossa Jose 
Published: January 2022

AbstractNowadays, huge data quantities are collected and analyzed for delivering deep insights into biological processes and human behavior. This chapter assesses the use of big data for prediction and estimation through statistical machine learning and its applications in agriculture and genetics in general, and specifically, for genome-based prediction and selection. First, we point out the importance of data and how the use of data is reshaping our way of living. We also provide the key elements of genomic selection and its potential for plant improvement. In addition, we analyze elements of modeling with machine learning methods applied to genomic selection and stress their importance as a predictive methodology. Two cultures of model building are analyzed and discussed: prediction and inference; by understanding modeling building, researchers will be able to select the best model/method for each circumstance. Within this context, we explain the differences between nonparametric models (predictors are constructed according to information derived from data) and parametric models (all the predictors take predetermined forms with the response) as well their type of effects: fixed, random, and mixed. Basic elements of linear algebra are provided to facilitate understanding the contents of the book. This chapter also contains examples of the different types of data using supervised, unsupervised, and semi-supervised learning methods.
Bayesian Genomic Linear Regression
Authors: Montesinos López Osval Antonio , Montesinos López Abelardo , Crossa Jose 
Published: January 2022

AbstractThe Bayesian paradigm for parameter estimation is introduced and linked to the main problem of genomic-enabled prediction to predict the trait of interest of the non-phenotyped individuals from genotypic information, environment variables, or other information (covariates). In this situation, a convenient practice is to include the individuals to be predicted in the posterior distribution to be sampled. We explained how the Bayesian Ridge regression method is derived and exemplified with data from plant breeding genomic selection. Other Bayesian methods (Bayes A, Bayes B, Bayes C, and Bayesian Lasso) were also described and exemplified for genome-based prediction. The chapter presented several examples that were implemented in the Bayesian generalized linear regression (BGLR) library for continuous response variables. The predictor under all these Bayesian methods includes main effects (of environments and genotypes) as well as interaction terms related to genotype × environment interaction.
Digitale Daten und Big Data
Authors: Lünich Marco 
Published: January 2022

ZusammenfassungUm die Bedeutung digitaler Daten im sozialen Kontext der Digitalisierung nachvollziehen zu können, muss nun zunächst ein grundlegendes Verständnis dafür hergestellt werden, was Daten und insbesondere digitale Daten ausmacht sowie warum im Kontext digitaler Daten oft von Big Data die Rede ist. Dabei steht zunächst erst einmal die eher technische Definition von Daten im Vordergrund. Doch bereits bei Betrachtung dessen, was gemeinhin unter Big Data verstanden wird, wird in der Literatur die rein technische Abgrenzung aufgeweicht und auch um sozio-technologische Aspekte der großen digitalen Datenbestände erweitert.
Das Big-Data-Glaubenssystem (BDGS)
Authors: Lünich Marco 
Published: January 2022

ZusammenfassungIn diesem Abschnitt wird nun mit Bezug auf die vorhergehenden Ausführungen zu Big Data argumentiert, dass sich das soeben beschriebene Wahrnehmungsmuster als ein Glaubenssystem beschreiben lässt: das Big-Data-Glaubenssystem (BDGS), das sich in seinen Glaubensüberzeugen insbesondere auf den Erkenntnis- und Nutzengewinn durch Big Data bezieht. Denn an dieser Stelle kommen die zuvor in den einzelnen Kapiteln gespannten konzeptuellen Fäden sozio-technischer Betrachtung des Phänomens Big Data zusammen und bereiten gleichzeitig ein genaueres Verständnis durch kognitions-psychologische Messung vor. Es muss daher zunächst die Bedeutung der zuvor beleuchteten Überlegungen für eine nachfolgende Analyse verdeutlicht werden.
Das Korpus
Authors: Ackermann Ulrike 
Published: January 2022

ZusammenfassungDas Ziel dieses Kapitels ist es, die Leser/innen mit der materialen Grundlage bekannt zu machen, damit ihnen die später folgenden theoretischen und methodischen Ausführungen plausibel sind. Angewandte qualitative Aufbereitungs- und Auswertungstechniken – also die Methode der Datenanalyse – um narrative Praktiken von Unternehmen im Zusammenspiel mit ihrer (S)D im Internet nachzuverfolgen, habe ich in direkter Nähe der Analysekapitel platziert. Mag die Trennung der Korpusbeschreibung von der Analysemethode ungewöhnlich wirken, erachte ich sie in Anbetracht der heterogenen Datenlage jedoch als zielführend.
Linguistische Spezifizierung des Untersuchungsgegenstandes
Authors: Ackermann Ulrike 
Published: January 2022

ZusammenfassungDa ich für die vorliegende Arbeit eine interdisziplinäre Betrachtung der Daten aufgrund ihres Entstehungskontextes als zentral erachte und aus diesem Grund neben den linguistischen Beschreibungsebenen insbesondere wirtschaftswissenschaftliche Hintergründe einbringe – und literaturwissenschaftliche Konzepte einbeziehe –, stelle ich den Analyseergebnissen ein Kapitel zur linguistischen Verortung des Untersuchungsgegenstandes voran. Dieses Kapitel umfasst maßgebende linguistische Merkmale der erhobenen Daten sowie die Zuordnung der Daten in den textlinguistischen Fachbereich. In dem daran anschließenden dritten Kapitel werden in Folge erzähltheoretische Ansätze in den Blick genommen.
Erzähltheoretische Grundlagen für uRE
Authors: Ackermann Ulrike 
Published: January 2022

ZusammenfassungNachdem im vorausgegangenen Kapitel der Untersuchungsgegenstand im Fachbereich der Textlinguistik verortet und anhand dessen beschrieben wurde, führe ich die Bestimmung der erhobenen uRE nun im erzähltheoretischen Bereich fort. In diesem Kapitel geht es also darum, (1) die uRE unter einer narratologischen Perspektive zu betrachten und (2) den narratologischen Rahmen vorzustellen, in dem sich die uRE bewegen. Einleitend greife ich Termini auf (Abschn. 5.1), die aufgrund ihrer vielfältigen Anwendung im Rahmen dieser Arbeit genauer gefasst werden müssen.
Situativer Kontext: „text“-externe und „text“-interne Einflussfaktoren
Authors: Ackermann Ulrike 
Published: January 2022

ZusammenfassungURE zur imagefördernden (S)D sind funktional angelegt, um ein Ziel zu erreichen. Sie entstehen daher unter bestimmten Einflussfaktoren. Diese bilden den spezifischen situativen Kontext, in dem sich die erhobenen uRE erst entwickeln. Die Einflussfaktoren sind darin verschieden, dass sie entweder „text“-extern oder „text“-intern wirksam werden. Gülich/Raible (1977: 46 f.) verwenden die beiden Termini für unterschiedliche Perspektiven, die man auf einen Text einnehmen kann. Auf diesen Kontext angewendet, bezieht sich eine „text“-externe Perspektive auf konzeptionelle Einflüsse auf die uRE (= „text“-externe Einflussfaktoren).
Narrativ gestützte Typen zur imagefördernden (Selbst)Darstellung
Authors: Ackermann Ulrike 
Published: January 2022

ZusammenfassungIm Mittelpunkt dieses Kapitels stehen vier Grundtypen imagefördernder (S)D von Unternehmen, die empirisch aus den erhobenen Erzähltexten methodisch (Abschn. 6.2.1) hervorgehen. Diese Grundtypen bilden immaterielle Werte der Unternehmen ab, die aus einem Selbstverständnis in ein gewünschtes Selbstbild geleitet werden, wie es aus Prozessen der Corporate Identity (Abschn. 1.1) hervorgeht.
General gauge-Yukawa-quartic β-functions at 4-3-2-loop order
Authors: Davies Joshua , Herren Florian , Thomsen Anders Eller 
Published: January 2022

Abstract
We determine the full set of coefficients for the completely general 4-loop gauge and 3-loop Yukawa β-functions for the most general renormalizable four-dimensional theories. Using a complete parametrization of the β-functions, we compare the general form to the specific β-functions of known theories to constrain the unknown coefficients. The Weyl consistency conditions provide additional constraints, completing the determination.
Fiducial cross sections for the lepton-pair-plus-photon decay mode in Higgs production up to NNLO QCD
Authors: Chen X. , Gehrmann T. , Glover E. W. N. , Huss A. 
Published: January 2022

Abstract
The rare three-body decay of a Higgs boson to a lepton-antilepton pair and a photon is starting to become experimentally accessible at the LHC. We investigate how higher-order QCD corrections to the dominant gluon-fusion production process impact on the fiducial cross sections in this specific Higgs decay mode for electrons and muons. Corrections up to NNLO QCD are found to be sizeable. They are generally uniform in kinematical variables related to the Higgs boson, but display several distinctive features in the kinematics of its individual decay products.
Lessons learned from terror attacks: thematic priorities and development since 2001—results from a systematic review
Authors: Schorscher Nora , Kippnich Maximilian , Meybohm Patrick , Wurmb Thomas 
Published: January 2022

Abstract
Purpose
The threat of national and international terrorism remains high. Preparation is the key requirement for the resilience of hospitals and out-of-hospital rescue forces. The scientific evidence for defining medical and tactical strategies often feeds on the analysis of real incidents and the lessons learned derived from them. This systematic review of the literature aims to identify and systematically report lessons learned from terrorist attacks since 2001.

Methods
PubMed was used as a database using predefined search strategies and eligibility criteria. All countries that are part of the Organization for Economic Cooperation and Development (OECD) were included. The time frame was set between 2001 and 2018.

Results
Finally 68 articles were included in the review. From these, 616 lessons learned were extracted and summarized into 15 categories. The data shows that despite the difference in attacks, countries, and casualties involved, many of the lessons learned are similar. We also found that the pattern of lessons learned is repeated continuously over the time period studied.

Conclusions
The lessons from terrorist attacks since 2001 follow a certain pattern and remained constant over time. Therefore, it seems to be more accurate to talk about lessons identified rather than lessons learned. To save as many victims as possible, protect rescue forces from harm, and to prepare hospitals at the best possible level it is important to implement the lessons identified in training and preparation.
Sooner or later? The impact of child education on household consumption
Authors: Aina Carmen , Sonedda Daniela 
Published: January 2022

AbstractWe study the impact of one more year of child’s education on household (non-durable) consumption. We exploit an exogenous shock generated by a university reform in Italy in the early 2000s. We find that families responded in a way that is consistent with education as a production good. The higher child’s education produced household positive, permanent income innovations. Hence, family non-durable consumption increased. Our findings suggest that education can be an insurance device against adverse permanent income shocks. The 2001 reform not only positively affected offspring’s years of schooling, but it also had a positive effect to boost household consumption.