bluuebunny/crossref_metadata_2025_split huggingface dataset:
An acrylate-based quasi-solid polymer electrolyte incorporating a novel dinitrile poly(ethylene glycol) plasticizer for lithium-ion batteries
Authors: Leš Kristian , Schönewerk Jens , Glenneberg Jens , Jordan Carmen-Simona 
Published: July 2022

AbstractThe performance of solid polymer electrolytes is characterized by lower ionic conductivity than conventional liquid electrolytes but provides advantages in terms of operational safety. A quasi-solid polymer electrolyte (QSPE) based on a new plasticizer 4,7,10,13-tetraoxahexadecane-1,16-dinitrile (bCN-PEG4) and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) incorporated into a polyacrylates matrix was successfully prepared via UV-induced copolymerization. The matrix consists of units of trimethylolpropane ethoxylate triacrylate (ETPTA), poly(ethylene glycol) diacrylate (PEGDA), and the monoacrylate poly(ethylene glycol) methyl ether acrylate (mPEGa). The QSPE containing 55 wt% bCN-PEG4 exhibits highly uniform morphology, thermal stability > 200 °C, ionic conductivity of 1.8 × 10−4 S cm−1 at 30 °C, and 1.3 × 10−3 S cm−1 at 80 °C, coupled with very high electrochemical stability (> 5 V vs. Li/Li+) and a low glass transition temperature (− 55.7 °C). A cycling experiment in a Li/QPSE/Li cell setup demonstrated the compatibility toward lithium metal additionally. The bCN-PEG4 offers an overall satisfying performance as a plasticizer in a poly(ethylene oxide)-based solid polymer electrolyte. The new QSPE is an alternative to dinitrile-based (e.g., succinonitrile) or glycol ether-based (e.g., tetraglyme) plasticizers with application potential in high-voltage lithium-ion batteries.
Graphical abstract
Evaluation of Laboratory and Sonographic Parameters for Detection of Portal Hypertension in Patients with Common Variable Immunodeficiency
Authors: Globig Anna-Maria , Strohmeier Valentina , Surabattula Rambabu , Leeming Diana J. , Karsdal Morten A. , Heeg Maximilian , Kindle Gerhard , Goldacker Sigune , von Spee-Mayer Caroline , Proietti Michele , Bausch Birke , Bettinger Dominik , Schultheiß Michael , Thimme Robert , Schuppan Detlef , Warnatz Klaus 
Published: July 2022

AbstractTimely detection of portal hypertension as a manifestation in a subgroup of patients with common variable immunodeficiency (CVID) represents a challenge since it is usually not associated with liver cirrhosis. To identify relevant markers for portal hypertension, we evaluated clinical history, laboratory parameters, and abdominal ultrasound including liver elastography and biomarkers of extracellular matrix formation. Twenty seven (6%) of 479 CVID patients presented with clinically significant portal hypertension as defined by either the presence of esophageal varices or ascites. This manifestation occurred late during the course of the disease (11.8 years after first diagnosis of CVID) and was typically part of a multiorgan disease and associated with a high mortality (11/27 patients died during follow up). The strongest association with portal hypertension was found for splenomegaly with a longitudinal diameter of > 16 cm. Similarly, most patients presented with a liver stiffness measurement (LSM) of above 6.5 kPa, and a LSM above 20 kPa was always indicative of manifest portal hypertension. Additionally, many laboratory parameters including Pro-C4 were significantly altered in patients with portal hypertension without clearly increasing the discriminatory power to detect non-cirrhotic portal hypertension in CVID. Our data suggest that a spleen size above 16 cm and an elevated liver stiffness above 6.5 kPa should prompt further evaluation of portal hypertension and its sequelae, but earlier and better liquid biomarkers of this serious secondary complication in CVID are needed.
Machine unlearning: linear filtration for logit-based classifiers
Authors: Baumhauer Thomas , Schöttle Pascal , Zeppelzauer Matthias 
Published: July 2022

AbstractRecently enacted legislation grants individuals certain rights to decide in what fashion their personal data may be used and in particular a “right to be forgotten”. This poses a challenge to machine learning: how to proceed when an individual retracts permission to use data which has been part of the training process of a model? From this question emerges the field of machine unlearning, which could be broadly described as the investigation of how to “delete training data from models”. Our work complements this direction of research for the specific setting of class-wide deletion requests for classification models (e.g. deep neural networks). As a first step, we propose linear filtration as an intuitive, computationally efficient sanitization method. Our experiments demonstrate benefits in an adversarial setting over naive deletion schemes.
Estimation of tire-road contact forces through a model-based approach employing strain measurements
Authors: Garcia-Pozuelo Daniel , Olatunbosun Oluremi , Palli Gianluca , Strano Salvatore , Terzo Mario , Tordela Ciro 
Published: July 2022

AbstractThe employment of intelligent tires to test the vehicle performances is ever-increasing in the last years. Many research activities have been made to correlate measurements provided by sensors to the tire dynamics. In this paper, a novel tire-road forces estimation technique is presented. The developed estimator is based on an approximation of the well-known Flexible Ring Tire Model (FRTM) and therefore, it is suitable for real-time normal and tangential forces identification. Only the tread band strain measurements are employed in the developed algorithm which can estimate the tire-road forces at every wheel revolution. The tool is validated through both numerical and experimental tests. The results indicate that the developed estimation algorithm, obtained by a mathematical inversion of the FRTM, can be employed as a monitoring tool for tires and vehicles.
Mitigating adversarial evasion attacks by deep active learning for medical image classification
Authors: Ahmed Usman , Lin Jerry Chun-Wei , Srivastava Gautam 
Published: July 2022

AbstractIn the Internet of Medical Things (IoMT), collaboration among institutes can help complex medical and clinical analysis of disease. Deep neural networks (DNN) require training models on large, diverse patients to achieve expert clinician-level performance. Clinical studies do not contain diverse patient populations for analysis due to limited availability and scale. DNN models trained on limited datasets are thereby constraining their clinical performance upon deployment at a new hospital. Therefore, there is significant value in increasing the availability of diverse training data. This research proposes institutional data collaboration alongside an adversarial evasion method to keep the data secure. The model uses a federated learning approach to share model weights and gradients. The local model first studies the unlabeled samples classifying them as adversarial or normal. The method then uses a centroid-based clustering technique to cluster the sample images. After that, the model predicts the output of the selected images, and active learning methods are implemented to choose the sub-sample of the human annotation task. The expert within the domain takes the input and confidence score and validates the samples for the model’s training. The model re-trains on the new samples and sends the updated weights across the network for collaboration purposes. We use the InceptionV3 and VGG16 model under fabricated inputs for simulating Fast Gradient Signed Method (FGSM) attacks. The model was able to evade attacks and achieve a high accuracy rating of 95%.
Do Kernel and Neural Embeddings Help in Training and Generalization?
Authors: Rahbar Arman , Jorge Emilio , Dubhashi Devdatt , Haghir Chehreghani Morteza 
Published: July 2022

AbstractRecent results on optimization and generalization properties of neural networks showed that in a simple two-layer network, the alignment of the labels to the eigenvectors of the corresponding Gram matrix determines the convergence of the optimization during training. Such analyses also provide upper bounds on the generalization error. We experimentally investigate the implications of these results to deeper networks via embeddings. We regard the layers preceding the final hidden layer as producing different representations of the input data which are then fed to the two-layer model. We show that these representations improve both optimization and generalization. In particular, we investigate three kernel representations when fed to the final hidden layer: the Gaussian kernel and its approximation by random Fourier features, kernels designed to imitate representations produced by neural networks and finally an optimal kernel designed to align the data with target labels. The approximated representations induced by these kernels are fed to the neural network and the optimization and generalization properties of the final model are evaluated and compared.
The Perpetual Trouble with Network Products Why IT Firms Choose Partial Compatibility
Authors: Stadler Manfred , Trexler Céline Tobler , Unsorg Maximiliane 
Published: July 2022

AbstractCompatibility of network products is an important issue in markets for communication technology as well as hard- and software products. Empirical findings suggest that firms competing in these markets typically choose intermediate degrees of product compatibility. We present a strategic two-stage game of two firms deciding independently or commonly on the degree of product compatibility in the first stage and on prices in the second stage. Indeed, partial compatibility constitutes a subgame perfect Nash equilibrium when coordination costs of standardization are high and the installed bases are low - conditions that typically characterize IT markets.
Five Solar Cycles of Solar Corona Investigations
Authors: Antonucci Ester 
Published: July 2022

AbstractThese are the memoirs of fifty years of research in solar physics, closely related to the history of three of the major solar space missions, from the Solar Maximum Mission, SMM, to Solar Orbiter, at present in navigation toward vantage points closer and closer to the Sun. My interest in solar physics was stimulated by the studies on cosmic rays at the University of Turin, and the research in this field initiated at Stanford University as a postdoctoral fellow in the team of John Wilcox with studies on the large-scale corona and its rotation. Thanks to Alan Gabriel, during my first space mission, SMM, I was involved in the operations and scientific data analysis of the Soft X-ray Polychromator. Together with Giancarlo Noci and Giuseppe Tondello, I participated in the realization of the UltraViolet Coronagraph Spectrometer, NASA/ASI, flown on-board SOHO. After this experience there was the opportunity to participate in the formulation of the proposal of the Solar Orbiter mission, and to guide the team, which for this mission developed the Metis coronagraph, up to the delivery of the instrument to the European Space Agency in 2017.
The metaphysical burden of Millianism
Authors: Mahant Nikhil 
Published: July 2022

AbstractThe Millian semantic view of names relies on a metaphysical view of names—often given the label ‘common currency conception’ (‘CCC’)—on which the names of distinct individuals count as distinct names. While even defenders of the Millian view admit that the CCC ‘does not agree with the most common usage’ (Kripke in Naming & Necessity, Harvard University Press, 1980), I will argue further that the CCC makes names exceptional amongst the class of linguistic expressions: if the CCC is correct, then names must have a sui-generis metaphysical nature, distinct from the metaphysics of every other kind of linguistic expression. Such metaphysical exceptionalism would be justified if the Millian view had a clear, uncontested theoretical advantage over its rivals. However, in the context of a semantic debate about names in which the closest competitors of the Millian view—the Predicate view and Indexicalism—do not result in such exceptionalism, it counts as a strike against the Millian view.
Deploying a robotic positive psychology coach to improve college students’ psychological well-being
Authors: Jeong Sooyeon , Aymerich-Franch Laura , Arias Kika , Alghowinem Sharifa , Lapedriza Agata , Picard Rosalind , Park Hae Won , Breazeal Cynthia 
Published: July 2022

AbstractDespite the increase in awareness and support for mental health, college students’ mental health is reported to decline every year in many countries. Several interactive technologies for mental health have been proposed and are aiming to make therapeutic service more accessible, but most of them only provide one-way passive contents for their users, such as psycho-education, health monitoring, and clinical assessment. We present a robotic coach that not only delivers interactive positive psychology interventions but also provides other useful skills to build rapport with college students. Results from our on-campus housing deployment feasibility study showed that the robotic intervention showed significant association with increases in students’ psychological well-being, mood, and motivation to change. We further found that students’ personality traits were associated with the intervention outcomes as well as their working alliance with the robot and their satisfaction with the interventions. Also, students’ working alliance with the robot was shown to be associated with their pre-to-post change in motivation for better well-being. Analyses on students’ behavioral cues showed that several verbal and nonverbal behaviors were associated with the change in self-reported intervention outcomes. The qualitative analyses on the post-study interview suggest that the robotic coach’s companionship made a positive impression on students, but also revealed areas for improvement in the design of the robotic coach. Results from our feasibility study give insight into how learning users’ traits and recognizing behavioral cues can help an AI agent provide personalized intervention experiences for better mental health outcomes
Surgical Tool Datasets for Machine Learning Research: A Survey
Authors: Rodrigues Mark , Mayo Michael , Patros Panos 
Published: July 2022

AbstractThis paper is a comprehensive survey of datasets for surgical tool detection and related surgical data science and machine learning techniques and algorithms. The survey offers a high level perspective of current research in this area, analyses the taxonomy of approaches adopted by researchers using surgical tool datasets, and addresses key areas of research, such as the datasets used, evaluation metrics applied and deep learning techniques utilised. Our presentation and taxonomy provides a framework that facilitates greater understanding of current work, and highlights the challenges and opportunities for further innovative and useful research.
Tackling Marine Microplastics Pollution: an Overview of Existing Solutions
Authors: Fiore Melania , Fraterrigo Garofalo Silvia , Migliavacca Alessandro , Mansutti Alessandro , Fino Debora , Tommasi Tonia 
Published: July 2022

Abstract
Microplastics pollution is one of the main environmental challenges of our time, even though microplastics were observed for the first time almost 50 years ago. Microplastics—little plastic fragments smaller than 5 mm in size—are released from bigger plastic objects during their use, maintenance, or disposal. As their release is uncontrolled and mostly uncontrollable, microplastics end up in the environment and are easily transported across the world, polluting nearly every ecosystem, especially the aquatic ones. Hence, microplastics represent a huge menace for many living species: they are ingested unintentionally by smaller animals and transferred along the food chain up to human beings, even threatening our health. It is therefore vital to take action against microplastics and many technologies have been designed in recent years with this purpose in mind. This paper provides an overview of the main solutions developed thus far to reduce further microplastic emissions and to collect those already released.
Die Generalisierung, Ausweitung und Rivalität sozialer Perspektiven. Zur Rekonstruktion und Aktualisierung von Meads Theorie der Normativität
Authors: Nungesser Frithjof 
Published: July 2022

ZusammenfassungDer Aufsatz untersucht die normativitätstheoretische Bedeutung von George Herbert Meads Arbeiten. Er konzentriert sich dabei auf zwei zentrale Stränge des Werks. Rekonstruiert werden zum einen Meads am Spielverhalten entwickelte Argumente zur stufenweisen Aneignung normativer Kompetenzen im Laufe der Sozialisation. Im Rückgriff auf aktuelle entwicklungspsychologische Studien wird gezeigt, dass Meads Theorie an Plausibilität gewinnt, wenn man Rollen- („play“) und Regelspiele („game“) nicht als Phasen, sondern als Grundformen des Sozialverhaltens versteht, die parallel zueinander im Zeitverlauf an normativer Bindungskraft und Abstraktheit gewinnen. Rekonstruiert werden zum anderen Meads wenig bekannte gesellschaftstheoretische Überlegungen zu den gegenläufigen Dynamiken generalisierter Perspektiven. In Studien zur Strafjustiz, zu den internationalen Beziehungen oder zu sozialen Konflikten und Reformbewegungen argumentiert Mead, dass die Fähigkeit zur Perspektivenübernahme die Ausweitung des sozialen Dialogs ebenso ermöglicht wie die aggressive Abgrenzung von Anderen. Anhand des Vergleichs mit Luc Boltanskis und Laurent Thévenots Theorie der Rechtfertigungsordnungen wird im Anschluss herausgearbeitet, dass Mead zwar Konflikten zentrale Bedeutung beimisst, insgesamt aber die Pluralität normativer Perspektiven ausblendet, einer Universalisierungsteleologie zuneigt und spezifische Konfliktformen pathologisiert.
Konsensus-Statement der Österreichischen Gesellschaft für Schlafmedizin (ÖGSM/ASRA) zum Management der residualen exzessiven Tagesschläfrigkeit (rETS) bei obstruktiver Schlafapnoe
Authors: Amouzadeh-Ghadikolai O. , Popovic R. , Heidbreder A. , Kugi A. , Saletu M. 
Published: July 2022

Zusammenfassung
Hintergrund
Sechs bis zehn Prozent aller Patienten mit einer obstruktiven Schlafapnoe leiden trotz adäquater nächtlicher Überdrucktherapie oder einer effektiven Alternativtherapie an einer residualen exzessiven Tagesschläfrigkeit (rETS). Die Differentialdiagnose der rETS stellt im klinischen Alltag eine interdisziplinäre Herausforderung dar.

Fragestellung
Entwicklung eines übersichtlichen Leitfadens für die Erkennung, die differentialdiagnostischen Abwägungen und die Möglichkeiten der weiterführenden Behandlung der rETS in der klinischen Praxis.

Material und Methode
MeSH-Analyse basierte Recherche und interdisziplinäre Abstimmung von Fachvertreter:innen der Inneren Medizin und Pneumologie, Neurologie sowie Psychiatrie und Psychotherapie.

Ergebnisse
SPAIN-Checkliste zur systematischen differentialdiagnostischen Exploration der rETS mit den Parametern: S chlafverhalten, P sychische Ursachen, A namnese der Medikation, I nternistische Ursachen und N eurologische Ursachen.

Schlussfolgerungen
Die rETS sollte als behandlungswürdiges Symptom erkannt werden. Sie verlangt nach einer interdisziplinären Abklärung und der individuellen Abstimmung der Behandlung auf die Bedürfnisse der Betroffenen.
The Lagrangian Atmospheric Radionuclide Transport Model (ARTM) — development, description and sensitivity analysis
Authors: Hanfland Robert , Pattantyús-Ábrahám Margit , Richter Cornelia , Brunner Dominik , Voigt Christiane 
Published: July 2022

AbstractAtmospheric dispersion models are applied to describe and predict the dispersion of emitted plumes. Here, we describe the Lagrangian Atmospheric Radionuclide Transport Model (ARTM) 2.8.0 which was developed to simulate the atmospheric dispersion of the emissions of nuclear facilities under routine operation for regulatory purposes over annual time scales. ARTM includes a diagnostic wind field model and a particle dispersion model. It simulates size-dependent wet and dry deposition, plume rise and γ-cloud shine of radioactive exhaust plumes in the simulation domain. This work presents an extensive overview of the different components of the model and of the physical and mathematical concepts of ARTM. We investigate the dependence of the plume dispersion in terms of plume volume, position of maximum concentration and dry deposition rates on key input parameters such as atmospheric stability, surface roughness, zero plane displacement height, source height and the particle size in the case of particulate matter tracers. The results indicate a strong dependence of plume volume and position of the maximum concentration on the stability as well as a minor influence on surface roughness. The source height above ground level has a low impact on the plume volume as the zero plane displacement only slightly affects the position of maximum concentration. Strong turbulence under unstable conditions tends to reduce the impact of sedimentation and decreases deposition in general. This computational model serves to advance the understanding of the dispersion of radioactive plumes in the boundary layer.
Role of Chemokines in the Development and Progression of Alzheimer’s Disease
Authors: Wojcieszak Jakub , Kuczyńska Katarzyna , Zawilska Jolanta B. 
Published: July 2022

AbstractAlzheimer’s disease (AD) is a progressive neurogenerative disorder manifested by gradual memory loss and cognitive decline due to profound damage of cholinergic neurons. The neuropathological hallmarks of AD are intracellular deposits of neurofibrillary tangles (NFTs) and extracellular aggregates of amyloid β (Aβ). Mounting evidence indicates that intensified neuroinflammatory processes play a pivotal role in the pathogenesis of AD. Chemokines serve as signaling molecules in immune cells but also in nerve cells. Under normal conditions, neuroinflammation plays a neuroprotective role against various harmful factors. However, overexpression of chemokines initiates disruption of the integrity of the blood–brain barrier, facilitating immune cells infiltration into the brain. Then activated adjacent glial cells–astrocytes and microglia, release massive amounts of chemokines. Prolonged inflammation loses its protective role and drives an increase in Aβ production and aggregation, impairment of its clearance, or enhancement of tau hyperphosphorylation, contributing to neuronal loss and exacerbation of AD. Moreover, chemokines can be further released in response to growing deposits of toxic forms of Aβ. On the other hand, chemokines seem to exert multidimensional effects on brain functioning, including regulation of neurogenesis and synaptic plasticity in regions responsible for memory and cognitive abilities. Therefore, underexpression or complete genetic ablation of some chemokines can worsen the course of AD. This review covers the current state of knowledge on the role of particular chemokines and their receptors in the development and progression of AD. Special emphasis is given to their impact on forming Aβ and NFTs in humans and in transgenic murine models of AD.