TY - JOUR A1 - Abbasi, Ali A1 - Xu, Yaolin A1 - Khezri, Ramin A1 - Etesami, Mohammad A1 - Lin, C. A1 - Kheawhom, Soorathep A1 - Lu, Yan T1 - Advances in characteristics improvement of polymeric membranes/separators for zinc-air batteries JF - Materials Today Sustainability N2 - Zinc-air batteries (ZABs) are gaining popularity for a wide range of applications due to their high energy density, excellent safety, and environmental friendliness. A membrane/separator is a critical component of ZABs, with substantial implications for battery performance and stability, particularly in the case of a battery in solid state format, which has captured increased attention in recent years. In this review, recent advances as well as insight into the architecture of polymeric membrane/separators for ZABs including porous polymer separators (PPSs), gel polymer electrolytes (GPEs), solid polymer electrolytes (SPEs) and anion exchange membranes (AEMs) are discussed. The paper puts forward strategies to enhance stability, ionic conductivity, ionic selectivity, electrolyte storage capacity and mechanical properties for each type of polymeric membrane. In addition, the remaining major obstacles as well as the most potential avenues for future research are examined in detail. KW - Ionic selectivity KW - Ionic conductivity KW - Gel polymer KW - Ion exchange KW - Porous KW - polymer Y1 - 2022 U6 - https://doi.org/10.1016/j.mtsust.2022.100126 SN - 2589-2347 VL - 18 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Abdelwahab, Ahmed A1 - Landwehr, Niels T1 - Deep Distributional Sequence Embeddings Based on a Wasserstein Loss JF - Neural processing letters N2 - Deep metric learning employs deep neural networks to embed instances into a metric space such that distances between instances of the same class are small and distances between instances from different classes are large. In most existing deep metric learning techniques, the embedding of an instance is given by a feature vector produced by a deep neural network and Euclidean distance or cosine similarity defines distances between these vectors. This paper studies deep distributional embeddings of sequences, where the embedding of a sequence is given by the distribution of learned deep features across the sequence. The motivation for this is to better capture statistical information about the distribution of patterns within the sequence in the embedding. When embeddings are distributions rather than vectors, measuring distances between embeddings involves comparing their respective distributions. The paper therefore proposes a distance metric based on Wasserstein distances between the distributions and a corresponding loss function for metric learning, which leads to a novel end-to-end trainable embedding model. We empirically observe that distributional embeddings outperform standard vector embeddings and that training with the proposed Wasserstein metric outperforms training with other distance functions. KW - Metric learning KW - Sequence embeddings KW - Deep learning Y1 - 2022 U6 - https://doi.org/10.1007/s11063-022-10784-y SN - 1370-4621 SN - 1573-773X PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Abdou, Nicole A1 - Alonso, Bruno A1 - Brun, Nicolas A1 - Landois, Perine A1 - Taubert, Andreas A1 - Hesemann, Peter A1 - Mehdi, Ahmad T1 - Ionic guest in ionic host BT - ionosilica ionogel composites via ionic liquid confinement in ionosilica supports JF - Materials chemistry frontiers N2 - Ionosilica ionogels, i.e. composites consisting of an ionic liquid (IL) guest confined in an ionosilica host matrix, were synthesized via a non-hydrolytic sol-gel procedure from a tris-trialcoxysilylated amine precursor using the IL [BMIM]NTf2 as solvent. Various ionosilica ionogels were prepared starting from variable volumes of IL in the presence of formic acid. The resulting brittle and nearly colourless monoliths are composed of different amounts of IL guests confined in an ionosilica host as evidenced via thermogravimetric analysis, FT-IR, and C-13 CP-MAS solid-state NMR spectroscopy. In the following, we focused on confinement effects between the ionic host and guest. Special host-guest interactions between the IL guest and the ionosilica host were evidenced by H-1 solid-state NMR, Raman spectroscopy, and broadband dielectric spectroscopy (BDS) measurements. The three techniques indicate a strongly reduced ion mobility in the ionosilica ionogel composites containing small volume fractions of confined IL, compared to conventional silica-based ionogels. We conclude that the ionic ionosilica host stabilizes an IL layer on the host surface; this then results in a strongly reduced ion mobility compared to conventional silica hosts. The ion mobility progressively increases for systems containing higher volume fractions of IL and finally reaches the values observed in conventional silica based ionogels. These results therefore point towards strong interactions and confinement effects between the ionic host and the ionic guest on the ionosilica surface. Furthermore, this approach allows confining high volume fractions of IL into self-standing monoliths while preserving high ionic conductivity. These effects may be of interest in domains where IL phases must be anchored on solid supports to avoid leaching or IL spilling, e.g., in catalysis, in gas separation/sequestration devices or for the elaboration of solid electrolytes for (lithium-ion) batteries and supercapacitors. Y1 - 2022 U6 - https://doi.org/10.1039/d2qm00021k SN - 2052-1537 VL - 6 IS - 7 SP - 939 EP - 947 PB - Royal Society of Chemistry CY - Cambridge ER - TY - JOUR A1 - Abramova, Olga T1 - No matter what the name, we're all the same? BT - examining ethnic online discrimination in ridesharing marketplaces JF - Electronic markets N2 - Sharing marketplaces emerged as the new Holy Grail of value creation by enabling exchanges between strangers. Identity reveal, encouraged by platforms, cuts both ways: While inducing pre-transaction confidence, it is suspected of backfiring on the information senders with its discriminative potential. This study employs a discrete choice experiment to explore the role of names as signifiers of discriminative peculiarities and the importance of accompanying cues in peer choices of a ridesharing offer. We quantify users' preferences for quality signals in monetary terms and evidence comparative disadvantage of Middle Eastern descent male names for drivers and co-travelers. It translates into a lower willingness to accept and pay for an offer. Market simulations confirm the robustness of the findings. Further, we discover that females are choosier and include more signifiers of involuntary personal attributes in their decision-making. Price discounts and positive information only partly compensate for the initial disadvantage, and identity concealment is perceived negatively. KW - sharing economy KW - discrimination KW - racism KW - discrete choice experiment KW - stated preferences KW - social inclusion Y1 - 2022 U6 - https://doi.org/10.1007/s12525-021-00505-z SN - 1019-6781 SN - 1422-8890 VL - 32 SP - 1419 EP - 1446 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Abramova, Olga A1 - Batzel, Katharina A1 - Modesti, Daniela T1 - Collective response to the health crisis among German Twitter users BT - a structural topic modeling approach JF - International Journal of Information Management Data Insights N2 - We used structural topic modeling to analyze over 800,000 German tweets about COVID-19 to answer the questions: What patterns emerge in tweets as a response to a health crisis? And how do topics discussed change over time? The study leans on the goals associated with the health information seeking (GAINS) model, discerning whether a post aims at tackling and eliminating the problem (i.e., problem-focused) or managing the emotions (i.e., emotion-focused); whether it strives to maximize positive outcomes (promotion focus) or to minimize negative outcomes (prevention focus). The findings indicate four clusters salient in public reactions: 1) “Understanding” (problem-promotion); 2) “Action planning” (problem-prevention); 3) “Hope” (emotion-promotion) and 4) “Reassurance” (emotion-prevention). Public communication is volatile over time, and a shift is evidenced from self-centered to community-centered topics within 4.5 weeks. Our study illustrates social media text mining's potential to quickly and efficiently extract public opinions and reactions. Monitoring fears and trending topics enable policymakers to rapidly respond to deviant behavior, like resistive attitudes toward containment measures or deteriorating physical health. Healthcare workers can use the insights to provide mental health services for battling anxiety or extensive loneliness from staying home. KW - social media KW - Twitter KW - modeling KW - regulatory focus theory KW - crisis management KW - text mining Y1 - 2022 U6 - https://doi.org/10.1016/j.jjimei.2022.100126 SN - 2667-0968 VL - 2 IS - 2 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Abramova, Olga A1 - Wagner, Amina A1 - Olt, Christian M. A1 - Buxmann, Peter T1 - One for all, all for one BT - social considerations in user acceptance of contact tracing apps using longitudinal evidence from Germany and Switzerland JF - International Journal of Information Management N2 - We propose a conceptual model of acceptance of contact tracing apps based on the privacy calculus perspective. Moving beyond the duality of personal benefits and privacy risks, we theorize that users hold social considerations (i.e., social benefits and risks) that underlie their acceptance decisions. To test our propositions, we chose the context of COVID-19 contact tracing apps and conducted a qualitative pre-study and longitudinal quantitative main study with 589 participants from Germany and Switzerland. Our findings confirm the prominence of individual privacy calculus in explaining intention to use and actual behavior. While privacy risks are a significant determinant of intention to use, social risks (operationalized as fear of mass surveillance) have a notably stronger impact. Our mediation analysis suggests that social risks represent the underlying mechanism behind the observed negative link between individual privacy risks and contact tracing apps' acceptance. Furthermore, we find a substantial intention–behavior gap. KW - digital contact tracing KW - privacy calculus KW - longitudinal study KW - privacy risks KW - surveillance KW - intention-behavior gap Y1 - 2022 U6 - https://doi.org/10.1016/j.ijinfomgt.2022.102473 SN - 0268-4012 SN - 1873-4707 VL - 64 SP - 1 EP - 16 PB - Elsevier CY - Kidlington ER - TY - JOUR A1 - Adair, Gigi A1 - McLaughlin, Carly T1 - Beyond humanitarianism BT - reading counternarratives of forced migration from the global south JF - Narrating Flight and Asylum Y1 - 2022 SN - 978-3-86821-965-4 SP - 165 EP - 182 PB - Trier CY - WVT Wissenschaftlicher Verlag Trier ER - TY - JOUR A1 - Adamik, Verena T1 - Alien Horrors BT - lovecraft and the racialized underclass in the age of trump JF - The Aliens Within : danger, disease, and displacement in representations of the racialized poor N2 - H. P. Lovecraft’s oeuvre abounds with stereotypes of the racialized poor. As scholars have noted, Lovecraft’s work turns those he viewed as ‘Others’ into ‘aliens.’ Poor people of color (as opposed to the orderly White rural population and White working class) in Lovecraft’s stories are foreign, diseased, and criminal, and they threaten social and cosmic orders as they are in league with a nebulous entity that waits to wreak indescribable havoc. This chapter analyzes three ‘Lovecraftian’ novels published in 2016 - Cassandra Khaw’s Hammers on Bone,Victor LaValle’s The Ballad of Black Tom, and Matt Ruff’s Lovecraft Country. These works elucidate the connection of Trump’s 2016 rhetoric in campaign and presidential speeches and the White supremacist imagery used by Lovecraft. In these novels, the racialized poor have a special connection to an astronomical, evil entity à la Lovecraft. As carriers of numinous genes or parasitic entities (literally having ‘an alien within’) they become empowered. They thus occupy a pivotal position in forestalling or bringing about the destruction of societal order; that is, of White supremacy. Exploring the alleged risk posed by this ‘underclass,’ these works seem to foretell current representations of protesters as ‘riotous mobs’ that threaten the body politic Trump sought to make great (and White) again. Y1 - 2022 SN - 978-3-11-078974-4 SN - 978-3-11-078984-3 SN - 978-3-11-078979-9 U6 - https://doi.org/10.1515/9783110789799-006 SN - 0340-5435 SP - 113 EP - 131 PB - de Gruyter CY - Berlin ER - TY - JOUR A1 - Adolfs, Marjolijn A1 - Hoque, Mohammed Mainul A1 - Shprits, Yuri Y. T1 - Storm-time relative total electron content modelling using machine learning techniques JF - Remote sensing N2 - Accurately predicting total electron content (TEC) during geomagnetic storms is still a challenging task for ionospheric models. In this work, a neural-network (NN)-based model is proposed which predicts relative TEC with respect to the preceding 27-day median TEC, during storm time for the European region (with longitudes 30 degrees W-50 degrees E and latitudes 32.5 degrees N-70 degrees N). The 27-day median TEC (referred to as median TEC), latitude, longitude, universal time, storm time, solar radio flux index F10.7, global storm index SYM-H and geomagnetic activity index Hp30 are used as inputs and the output of the network is the relative TEC. The relative TEC can be converted to the actual TEC knowing the median TEC. The median TEC is calculated at each grid point over the European region considering data from the last 27 days before the storm using global ionosphere maps (GIMs) from international GNSS service (IGS) sources. A storm event is defined when the storm time disturbance index Dst drops below 50 nanotesla. The model was trained with storm-time relative TEC data from the time period of 1998 until 2019 (2015 is excluded) and contains 365 storms. Unseen storm data from 33 storm events during 2015 and 2020 were used to test the model. The UQRG GIMs were used because of their high temporal resolution (15 min) compared to other products from different analysis centers. The NN-based model predictions show the seasonal behavior of the storms including positive and negative storm phases during winter and summer, respectively, and show a mixture of both phases during equinoxes. The model's performance was also compared with the Neustrelitz TEC model (NTCM) and the NN-based quiet-time TEC model, both developed at the German Aerospace Agency (DLR). The storm model has a root mean squared error (RMSE) of 3.38 TEC units (TECU), which is an improvement by 1.87 TECU compared to the NTCM, where an RMSE of 5.25 TECU was found. This improvement corresponds to a performance increase by 35.6%. The storm-time model outperforms the quiet-time model by 1.34 TECU, which corresponds to a performance increase by 28.4% from 4.72 to 3.38 TECU. The quiet-time model was trained with Carrington averaged TEC and, therefore, is ideal to be used as an input instead of the GIM derived 27-day median. We found an improvement by 0.8 TECU which corresponds to a performance increase by 17% from 4.72 to 3.92 TECU for the storm-time model using the quiet-time-model predicted TEC as an input compared to solely using the quiet-time model. KW - ionosphere KW - relative total electron content KW - geomagnetic storms KW - neural KW - networks KW - NTCM KW - European storm-time model Y1 - 2022 U6 - https://doi.org/10.3390/rs14236155 SN - 2072-4292 VL - 14 IS - 23 PB - MDPI CY - Basel ER - TY - JOUR A1 - Aga-Barfknecht, Heja A1 - Soultoukis, George A. A1 - Stadion, Mandy A1 - Garcia-Carrizo, Francisco A1 - Jähnert, Markus A1 - Gottmann, Pascal A1 - Vogel, Heike A1 - Schulz, Tim Julius A1 - Schürmann, Annette T1 - Distinct adipogenic and fibrogenic differentiation capacities of mesenchymal stromal cells from pancreas and white adipose tissue JF - International journal of molecular sciences N2 - Pancreatic steatosis associates with beta-cell failure and may participate in the development of type-2-diabetes. Our previous studies have shown that diabetes-susceptible mice accumulate more adipocytes in the pancreas than diabetes-resistant mice. In addition, we have demonstrated that the co-culture of pancreatic islets and adipocytes affect insulin secretion. The aim of this current study was to elucidate if and to what extent pancreas-resident mesenchymal stromal cells (MSCs) with adipogenic progenitor potential differ from the corresponding stromal-type cells of the inguinal white adipose tissue (iWAT). miRNA (miRNome) and mRNA expression (transcriptome) analyses of MSCs isolated by flow cytometry of both tissues revealed 121 differentially expressed miRNAs and 1227 differentially expressed genes (DEGs). Target prediction analysis estimated 510 DEGs to be regulated by 58 differentially expressed miRNAs. Pathway analyses of DEGs and miRNA target genes showed unique transcriptional and miRNA signatures in pancreas (pMSCs) and iWAT MSCs (iwatMSCs), for instance fibrogenic and adipogenic differentiation, respectively. Accordingly, iwatMSCs revealed a higher adipogenic lineage commitment, whereas pMSCs showed an elevated fibrogenesis. As a low degree of adipogenesis was also observed in pMSCs of diabetes-susceptible mice, we conclude that the development of pancreatic steatosis has to be induced by other factors not related to cell-autonomous transcriptomic changes and miRNA-based signals. KW - MSCs KW - fatty pancreas KW - WAT KW - lineage commitment KW - transcriptomics KW - miRNAs Y1 - 2022 U6 - https://doi.org/10.3390/ijms23042108 SN - 1422-0067 VL - 23 IS - 4 PB - Molecular Diversity Preservation International CY - Basel ER -