bluuebunny/crossref_metadata_2025_split huggingface dataset:
COVID‐19 mortality in cirrhosis is determined by cirrhosis‐associated comorbidities and extrahepatic organ failure: Results from the multinational LEOSS registry
Authors: Brozat Jonathan F. , Hanses Frank , Haelberger Martina , Stecher Melanie , Dreher Michael , Tometten Lukas , Ruethrich Maria M. , Vehreschild Janne J. , Trautwein Christian , Borgmann Stefan , Vehreschild Maria J. G. T. , Jakob Carolin E. M. , Stallmach Andreas , Wille Kai , Hellwig Kerstin , Isberner Nora , Reuken Philipp A. , Geisler Fabian , Nattermann Jacob , Bruns Tony ,   the LEOSS study group
Published: April 2022

AbstractBackground and ObjectiveInternational registries have reported high mortality rates in patients with liver disease and COVID‐19. However, the extent to which comorbidities contribute to excess COVID‐19 mortality in cirrhosis is controversial.MethodsWe used the multinational Lean European Open Survey on SARS‐CoV‐2‐infected patients (LEOSS) to identify patients with cirrhosis documented between March 2020 and March 2021, when the wild‐type and alpha variant were predominant. We compared symptoms, disease progression and mortality after propensity score matching (PSM) for age, sex, obesity, smoking status, and concomitant diseases. Mortality was also compared with that of patients with spontaneous bacterial peritonitis (SBP) without SARS‐CoV‐2 infection, a common bacterial infection and well‐described precipitator of acute‐on‐chronic liver failure.ResultsAmong 7096 patients with SARS‐CoV‐2 infection eligible for analysis, 70 (0.99%) had cirrhosis, and all were hospitalized. Risk factors for severe COVID‐19, such as diabetes, renal disease, and cardiovascular disease were more frequent in patients with cirrhosis. Case fatality rate in patients with cirrhosis was 31.4% with the highest odds of death in patients older than 65 years (43.6% mortality; odds ratio [OR] 4.02; p = 0.018), Child‐Pugh class C (57.1%; OR 4.00; p = 0.026), and failure of two or more organs (81.8%; OR 19.93; p = 0.001). After PSM for demographics and comorbidity, the COVID‐19 case fatality of patients with cirrhosis did not significantly differ from that of matched patients without cirrhosis (28.8% vs. 26.1%; p = 0.644) and was similar to the 28‐day mortality in a comparison group of patients with cirrhosis and SBP (33.3% vs. 31.5%; p = 1.000).ConclusionsIn immunologically naïve patients with cirrhosis, mortality from wild‐type SARS‐CoV‐2 and the alpha variant is high and is largely determined by cirrhosis‐associated comorbidities and extrahepatic organ failure.
A reinforcement‐learning approach for individual pitch control
Authors: Coquelet Marion , Bricteux Laurent , Moens Maud , Chatelain Philippe 
Published: April 2022

AbstractIndividual pitch control has shown great capability of alleviating the oscillating loads experienced by wind turbine blades due to wind shear, atmospheric turbulence, yaw misalignment, or wake impingement. This work presents a novel controller structure that relies on the separation of low‐level control tasks and high‐level ones. It is based on a neural network that modulates basic periodic pitch angle signals. This neural network is trained with reinforcement learning, a trial and error way of acquiring skills, in a low‐fidelity environment exempt from turbulence. The trained controller is further deployed in large eddy simulations to assess its performances in turbulent and waked flows. Results show that the method enables the neural network to learn how to reduce fatigue loads and to exploit that knowledge to complex turbulent flows. When compared to a state‐of‐the‐art individual pitch controller, the one introduced here presents similar load alleviation capacities at reasonable turbulence intensity levels, while displaying very smooth pitching commands by nature.
Big Data Analytics in the Manufacturing Sector: Guidelines and Lessons Learned Through the Centro Ricerche FIAT (CRF) Case
Authors: Alexopoulos Andreas , Becerra Yolanda , Boehm Omer , Bravos George , Chatzigiannakis Vassilis , Cugnasco Cesare , Demetriou Giorgos , Eleftheriou Iliada , Fotis Spiros , Genchi Gianmarco , Ioannidis Sotiris , Jakovetic Dusan , Kallipolitis Leonidas , Katusic Vlatka , Kavakli Evangelia , Kopanaki Despina , Leventis Christoforos , Martínez Miquel , Mascolo Julien , Milosevic Nemanja , Montanera Enric Pere Pages , Ristow Gerald , Ruiz-Ocampo Hernan , Sakellariou Rizos , Sirvent Raül , Skrbic Srdjan , Spais Ilias , Spennacchio Giuseppe Danilo , Stamenkovic Dusan , Vasiliadis Giorgos , Vinov Michael 
Published: April 2022

AbstractManufacturing processes are highly complex. Production lines have several robots and digital tools, generating massive amounts of data. Unstructured, noisy and incomplete data have to be collected, aggregated, pre-processed and transformed into structured messages of a common, unified format in order to be analysed not only for the monitoring of the processes but also for increasing their robustness and efficiency. This chapter describes the solution, best practices, lessons learned and guidelines for Big Data analytics in two manufacturing scenarios defined by CRF, within the I-BiDaaS project, namely ‘Production process of aluminium die-casting’, and ‘Maintenance and monitoring of production assets’. First, it reports on the retrieval of useful data from real processes taking into consideration the privacy policies of industrial data and on the definition of the corresponding technical and business KPIs. It then describes the solution in terms of architecture, data analytics and visualizations and assesses its impact with respect to the quality of the processes and products.
Next-Generation Big Data-Driven Factory 4.0 Operations and Optimization: The Boost 4.0 Experience
Authors: Lázaro Oscar , Alonso Jesús , Ohlsson Philip , Tijsma Bas , Lekse Dominika , Volckaert Bruno , Kerkhove Sarah , Nielandt Joachim , Masera Davide , Patrimia Gaetano , Pittaro Pietro , Mulè Giuseppe , Pellegrini Edoardo , Köchling Daniel , Naskos Thanasis , Metaxa Ifigeneia , Leßmann Salome , von Enzberg Sebastian 
Published: April 2022

AbstractThis chapter presents the advanced manufacturing processes and big data-driven algorithms and platforms leveraged by the Boost 4.0 big data lighthouse project that allows improved digital operations within increasingly automated and intelligent shopfloors. The chapter illustrates how three different companies have been able to implement three distinct, open, yet sovereign cross-factory data spaces under a unified framework: the Boost 4.0 big data reference architecture and Digital Factory Alliance (DFA) service development framework.
Leveraging Data-Driven Infrastructure Management to Facilitate AIOps for Big Data Applications and Operations
Authors: McCreadie Richard , Soldatos John , Fuerst Jonathan , Argerich Mauricio Fadel , Kousiouris George , Totow Jean-Didier , Nieto Antonio Castillo , Navidad Bernat Quesada , Kyriazis Dimosthenis , Macdonald Craig , Ounis Iadh 
Published: April 2022

AbstractAs institutions increasingly shift to distributed and containerized application deployments on remote heterogeneous cloud/cluster infrastructures, the cost and difficulty of efficiently managing and maintaining data-intensive applications have risen. A new emerging solution to this issue is Data-Driven Infrastructure Management (DDIM), where the decisions regarding the management of resources are taken based on data aspects and operations (both on the infrastructure and on the application levels). This chapter will introduce readers to the core concepts underpinning DDIM, based on experience gained from development of the Kubernetes-based BigDataStack DDIM platform (https://bigdatastack.eu/). This chapter involves multiple important BDV topics, including development, deployment, and operations for cluster/cloud-based big data applications, as well as data-driven analytics and artificial intelligence for smart automated infrastructure self-management. Readers will gain important insights into how next-generation DDIM platforms function, as well as how they can be used in practical deployments to improve quality of service for Big Data Applications.This chapter relates to the technical priority Data Processing Architectures of the European Big Data Value Strategic Research & Innovation Agenda [33], as well as the Data Processing Architectures horizontal and Engineering and DevOps for building Big Data Value vertical concerns. The chapter relates to the Reasoning and Decision Making cross-sectorial technology enablers of the AI, Data and Robotics Strategic Research, Innovation & Deployment Agenda [34].
The DeepHealth Toolkit: A Key European Free and Open-Source Software for Deep Learning and Computer Vision Ready to Exploit Heterogeneous HPC and Cloud Architectures
Authors: Aldinucci Marco , Atienza David , Bolelli Federico , Caballero Mónica , Colonnelli Iacopo , Flich José , Gómez Jon A. , González David , Grana Costantino , Grangetto Marco , Leo Simone , López Pedro , Oniga Dana , Paredes Roberto , Pireddu Luca , Quiñones Eduardo , Silva Tatiana , Tartaglione Enzo , Zapater Marina 
Published: April 2022

AbstractAt the present time, we are immersed in the convergence between Big Data, High-Performance Computing and Artificial Intelligence. Technological progress in these three areas has accelerated in recent years, forcing different players like software companies and stakeholders to move quickly. The European Union is dedicating a lot of resources to maintain its relevant position in this scenario, funding projects to implement large-scale pilot testbeds that combine the latest advances in Artificial Intelligence, High-Performance Computing, Cloud and Big Data technologies. The DeepHealth project is an example focused on the health sector whose main outcome is the DeepHealth toolkit, a European unified framework that offers deep learning and computer vision capabilities, completely adapted to exploit underlying heterogeneous High-Performance Computing, Big Data and cloud architectures, and ready to be integrated into any software platform to facilitate the development and deployment of new applications for specific problems in any sector. This toolkit is intended to be one of the European contributions to the field of AI. This chapter introduces the toolkit with its main components and complementary tools, providing a clear view to facilitate and encourage its adoption and wide use by the European community of developers of AI-based solutions and data scientists working in the healthcare sector and others.
Forschungsdesign und Methoden
Authors: Kumkar Nils C. , Holubek-Schaum Stefan , Gottschall Karin , Hollstein Betina , Schimank Uwe 
Published: April 2022

ZusammenfassungIn diesem Kapitel stellen wir die zentralen Aspekte des Forschungsdesigns unserer empirischen Untersuchung vor und erläutern, wie wir methodisch vorgegangen sind: die Auswahl der untersuchten Fälle, den Einsatz biographisch-narrativer Interviews mit leitfadengestützten Nachfrageteilen und die Auswertung der Interviews mit der dokumentarischen Methode.
New dressing factors for AdS3/CFT2
Authors: Frolov Sergey , Sfondrini Alessandro 
Published: April 2022

Abstract
The worldsheet S matrix of strings on the AdS3× S3× T4 background is almost entirely fixed by symmetries, up to five functions — the dressing factors. These must satisfy several consistency conditions, in particular a set of crossing equations. We find that the existing proposal for the dressing factors, while crossing invariant, violates some of these consistency conditions. We put forward a new set of dressing factors and discuss in detail their analytic properties in the string and mirror region, as well as under bound-state fusion.
Frugal U(1)X models with non-minimal flavor violation for b → sℓℓ anomalies and neutrino mixing
Authors: Bhatia Disha , Desai Nishita , Dighe Amol 
Published: April 2022

Abstract
We analyze the class of models with an extra U(1)X gauge symmetry that can account for the b → sℓℓ anomalies by modifying the Wilson coefficients C9e and C9μ of the operators $$ {O}_{9\mathrm{\ell}}\equiv \left(\overline{b}{\gamma}_{\mu }{P}_Ls\right)\left(\overline{\mathrm{\ell}}{\gamma}^{\mu}\mathrm{\ell}\right) $$

O

9
ℓ

≡

b
¯

γ
μ

P
L

s

ℓ
¯

γ
μ

ℓ

from their standard model values. At the same time, these models generate appropriate quark mixing, and give rise to neutrino mixing via the Type-I seesaw mechanism. Apart from the gauge boson Z′, these frugal models only have three right-handed neutrinos for the seesaw mechanism, an additional SU(2)L scalar doublet for quark mixing, and a SM-singlet scalar that breaks the U(1)X symmetry. This set-up identifies a class of leptonic symmetries, and necessitates non-zero but equal charges for the first two quark generations. If the quark mixing beyond the standard model were CKM-like, all these symmetries would be ruled out by the latest flavor constraints on Wilson coefficients and collider constraints on Z′ parameters. However, we identify a single-parameter source of non-minimal flavor violation that allows a wider class of U(1)X symmetries to be compatible with all data. We show that the viable leptonic symmetries have to be of the form Le± 3Lμ− Lτ or Le− 3Lμ + Lτ, and determine the (MZ′, gZ′) parameter space that may be probed by the high-luminosity data at the LHC.
Teleparallelism in the algebraic approach to extended geometry
Authors: Cederwall Martin , Palmkvist Jakob 
Published: April 2022

Abstract
Extended geometry is based on an underlying tensor hierarchy algebra. We extend the previously considered L∞ structure of the local symmetries (the diffeomorphisms and their reducibility) to incorporate physical fields, field strengths and Bianchi identities, and identify these as elements of the tensor hierarchy algebra. The field strengths arise as generalised torsion, so the naturally occurring complex in the L∞ algebra is$$ \dots \leftarrow \mathrm{torsion}\ \mathrm{BI}'\mathrm{s}\leftarrow \mathrm{torsion}\leftarrow \mathrm{vielbein}\leftarrow \mathrm{diffeomorphism}\ \mathrm{parameters}\leftarrow \dots $$
…
←
torsion

BI
’
s
←
torsion
←
vielbein
←
diffeomorphism parameters
←
…
In order to obtain equations of motion, which are not in this complex, (pseudo-)actions, quadratic in torsion, are given for a large class of models. This requires considering the dual complex. We show how local invariance under the compact subgroup locally defined by a generalised metric arises as a “dual gauge symmetry” associated with a certain torsion Bianchi identity, generalising Lorentz invariance in the teleparallel formulation of gravity. The analysis is performed for a large class of finite-dimensional structure groups, with E5 as a detailed example. The continuation to infinite-dimensional cases is discussed.
Algorithmic fairness through group parities? The case of COMPAS-SAPMOC
Authors: Lagioia Francesca , Rovatti Riccardo , Sartor Giovanni 
Published: April 2022

AbstractMachine learning classifiers are increasingly used to inform, or even make, decisions significantly affecting human lives. Fairness concerns have spawned a number of contributions aimed at both identifying and addressing unfairness in algorithmic decision-making. This paper critically discusses the adoption of group-parity criteria (e.g., demographic parity, equality of opportunity, treatment equality) as fairness standards. To this end, we evaluate the use of machine learning methods relative to different steps of the decision-making process: assigning a predictive score, linking a classification to the score, and adopting decisions based on the classification. Throughout our inquiry we use the COMPAS system, complemented by a radical simplification of it (our SAPMOC I and SAPMOC II models), as our running examples. Through these examples, we show how a system that is equally accurate for different groups may fail to comply with group-parity standards, owing to different base rates in the population. We discuss the general properties of the statistics determining the satisfaction of group-parity criteria and levels of accuracy. Using the distinction between scoring, classifying, and deciding, we argue that equalisation of classifications/decisions between groups can be achieved thorough group-dependent thresholding. We discuss contexts in which this approach may be meaningful and useful in pursuing policy objectives. We claim that the implementation of group-parity standards should be left to competent human decision-makers, under appropriate scrutiny, since it involves discretionary value-based political choices. Accordingly, predictive systems should be designed in such a way that relevant policy goals can be transparently implemented. Our paper presents three main contributions: (1) it addresses a complex predictive system through the lens of simplified toy models; (2) it argues for selective policy interventions on the different steps of automated decision-making; (3) it points to the limited significance of statistical notions of fairness to achieve social goals.
Economic potential of abbreviated breast MRI for screening women with dense breast tissue for breast cancer
Authors: Tollens Fabian , Baltzer Pascal A. T. , Dietzel Matthias , Schnitzer Moritz L. , Schwarze Vincent , Kunz Wolfgang G. , Rink Johann , Rübenthaler Johannes , Froelich Matthias F. , Schönberg Stefan O. , Kaiser Clemens G. 
Published: April 2022

Abstract
Objectives
Abbreviated breast MRI (AB-MRI) was introduced to reduce both examination and image reading times and to improve cost-effectiveness of breast cancer screening. The aim of this model-based economic study was to analyze the cost-effectiveness of full protocol breast MRI (FB-MRI) vs. AB-MRI in screening women with dense breast tissue for breast cancer.

Methods
Decision analysis and a Markov model were designed to model the cumulative costs and effects of biennial screening in terms of quality-adjusted life years (QALYs) from a US healthcare system perspective. Model input parameters for a cohort of women with dense breast tissue were adopted from recent literature. The impact of varying AB-MRI costs per examination as well as specificity on the resulting cost-effectiveness was modeled within deterministic sensitivity analyses.

Results
At an assumed cost per examination of $ 263 for AB-MRI (84% of the cost of a FB-MRI examination), the discounted cumulative costs of both MR-based strategies accounted comparably. Reducing the costs of AB-MRI below $ 259 (82% of the cost of a FB-MRI examination, respectively), the incremental cost-effectiveness ratio of FB-MRI exceeded the willingness to pay threshold and the AB-MRI-strategy should be considered preferable in terms of cost-effectiveness.

Conclusions
Our preliminary findings indicate that AB-MRI may be considered cost-effective compared to FB-MRI for screening women with dense breast tissue for breast cancer, as long as the costs per examination do not exceed 82% of the cost of a FB-MRI examination.

Key Points
• Cost-effectiveness of abbreviated breast MRI is affected by reductions in specificity and resulting false positive findings and increased recall rates.
• Abbreviated breast MRI may be cost-effective up to a cost per examination of 82% of the cost of a full protocol examination.
• Abbreviated breast MRI could be an economically preferable alternative to full protocol breast MRI in screening women with dense breast tissue.
Serum albumin at resection predicts in-hospital death, while serum lactate and aPTT on the first postoperative day anticipate anastomotic leakage after Ivor-Lewis-esophagectomy
Authors: Scheufele Florian , Vogel Thomas , Gasiorek Melanie , Novotny Alexander , Friess Helmut , Demir Ihsan Ekin , Schorn Stephan 
Published: April 2022

Abstract

Background
Anastomotic leakage (AL) is a major complication after esophagectomy, potentiating morbidity and mortality. There are several patient risk factors associated with AL, but high-fidelity postoperative predictors are still under debate. The aim was to identify novel reliable predictors for AL after esophagectomy.

Methods
A high-volume single-center database study, including 138 patients receiving Ivor-Lewis-esophagectomy between 2017 and 2019, was performed. Serum levels of albumin, aPTT, and lactate before and after surgery were extracted to assess their impact on AL and in-hospital mortality.

Results
High serum lactate on postoperative day 1 (POD1) could be shown to predict AL after esophagectomy [AL vs. no AL: 1.2 (0.38) vs. 1.0 (0.37); p < 0.001]. Accordingly, also differences of serum lactate level between end (POD0-2) and start of surgery (POD0-1) (p < 0.001) as well as between POD1 and POD0-1 (p < 0.001) were associated with AL. Accordingly, logistic regression identified serum lactate on POD 1 as an independent predictor of AL [HR: 4.37 (95% CI: 1.28–14.86); p = 0.018]. Further, low serum albumin on POD0 [2.6 (0.53) vs. 3.1 (0.56); p = 0.001] and high serum lactate on POD 0–1 [1.1 (0.29) vs. 0.9 (0.30); p = 0.043] were associated with in-hospital death. Strikingly, logistic-regression (HR: 0.111; p = 0.008) and cox-regression analysis (HR: 0.118; p = 0.003) showed low serum albumin as an independently predictor for in-hospital death after esophagectomy.

Conclusions
This study identified high serum lactate as an independent predictor of AL and low serum albumin as a high-fidelity predictor of in-hospital death after esophagectomy. These parameters can facilitate improved postoperative treatment leading to better short-term as well as long-term outcomes.
Multisensory synchrony of contextual boundaries affects temporal order memory, but not encoding or recognition
Authors: van de Ven Vincent , Kleuters Guyon , Stuiver Joey 
Published: April 2022

AbstractWe memorize our daily life experiences, which are often multisensory in nature, by segmenting them into distinct event models, in accordance with perceived contextual or situational changes. However, very little is known about how multisensory boundaries affect segmentation, as most studies have focused on unisensory (visual or audio) segmentation. In three experiments, we investigated the effect of multisensory boundaries on segmentation in memory and perception. In Experiment 1, participants encoded lists of pictures while audio and visual contexts changed synchronously or asynchronously. After each list, we tested recognition and temporal associative memory for pictures that were encoded in the same audio-visual context or that crossed a synchronous or an asynchronous multisensory change. We found no effect of multisensory synchrony for recognition memory: synchronous and asynchronous changes similarly impaired recognition for pictures encoded at those changes, compared to pictures encoded further away from those changes. Multisensory synchrony did affect temporal associative memory, which was worse for pictures encoded at synchronous than at asynchronous changes. Follow up experiments showed that this effect was not due to the higher dimensionality of multisensory over unisensory contexts (Experiment 2), nor that it was due to the temporal unpredictability of contextual changes inherent to Experiment 1 (Experiment 3). We argue that participants formed situational expectations through multisensory synchronicity, such that synchronous multisensory changes deviated more strongly from those expectations than asynchronous changes. We discuss our findings in light of supportive and conflicting findings of uni- and multi-sensory segmentation.