Orientation Correlation in Simplified Models of Polymer Melts

We investigate mutual local chain order in systems of fully flexible polymer melts in a simple generic bead-spring model. The excluded-volume interaction together with the connectivity leads to local ordering effects which are independent of chain length between 25 and 700 monomers, i.e. in the Rouse as well as in the reptation regime. These ordering phenomena extend to a distance of about 3 to 4 monomer sizes and decay to zero afterwards.Comment: 5 pages, 3 figure

Local Structure and Dynamics of Trans-polyisoprene oligomers

Mono- and poly-disperse melts of oligomers (average length 10 monomers) of trans-1,4-polyisoprene are simulated in full atomistic detail. The force-field is developed by means of a mixture of ab initio quantum-chemistry and an automatic generation of empirical parameters. Comparisons to NMR and scattering experiments validate the model. The local reorientation dynamics shows that for C$-$H vectors there is a two-stage process consisting of an initial decay and a late-stage decorrelation originating from overall reorientation. The atomistic model can be successfully mapped onto a simple model including only beads for the monomers with bond springs and bond angle potentials. End-bridging Monte Carlo as an equilibration stage and molecular dynamics as the subsequent simulation method together prove to be a useful method for polymer simulations.Comment: 25 pages, 15 figures, accepted by Macromolecule

A Fuzzy-Logical Approach for Integrating Multi-Agent Estimators

This paper proposes a novel approach for integrating estimations from multiple agents. The approach is based on the fuzzy set theory. However, compared to existing fuzzy logical methods that use fuzzy if-then rules, this method is based on solving an over-determined fuzzy equation system. The result is either a global inconsistency message or the consistent core of the equation system. We demonstrate the approach with data from an actual case study undertaken by a German automotive manufacturer

DeepCause: Hypothesis Extraction from Information Systems Papers with Deep Learning for Theory Ontology Learning

This paper applies different deep learning architectures for sequence labelling to extract causes, effects, moderators, and mediators from hypotheses of information systems papers for theory ontology learning. We compared a variety of recurrent neural networks (RNN) architectures, like long short-term memory (LSTM), bidirectional LSTM (BiLSTM), simple RNNs, and gated recurrent units (GRU). We analyzed GloVe word embedding, character level vector representation of words, and part-of-speech (POS) tags. Furthermore, we evaluated various hyperparameters and architectures to achieve the highest performance scores. The prototype was evaluated on hypotheses from the AIS basket of eight. The F1 result for the sequence labelling task of causal variables on a chunk level was 80%, with a precision of 80% and a recall of 80%

Data Thinking: A Canvas for Data-Driven Ideation Workshops

New products and services increasingly follow a data-driven strategy, creating the need for designers, product developers, and teams of individuals to develop products and services with data in mind. This paper provides a data-informed ontology for visual collaboration tools. It presents a prototype of a canvas that could be used during data-oriented design thinking workshops. Using action design research, the Data Innovation Board is tested through iterative cycles of building, intervention, and evaluation and the results are analyzed using triangulation. The suggested data-informed ontology and the proposed canvas facilitate the development of data-driven products and services. The canvas helps teams sharpen their perspective on data challenges from the start and presents a more holistic view on data projects

Extracting Causal Claims from Information Systems Papers with Natural Language Processing for Theory Ontology Learning

The number of scientific papers published each year is growing exponentially. How can computational tools support scientists to better understand and process this data? This paper presents a software-prototype that automatically extracts causes, effects, signs, moderators, mediators, conditions, and interaction signs from propositions and hypotheses of full-text scientific papers. This prototype uses natural language processing methods and a set of linguistic rules for causal information extraction. The prototype is evaluated on a manually annotated corpus of 270 Information Systems papers containing 723 hypotheses and propositions from the AIS basket of eight. F1-results for the detection and extraction of different causal variables range between 0.71 and 0.90. The presented automatic causal theory extraction allows for the analysis of scientific papers based on a theory ontology and therefore contributes to the creation and comparison of inter-nomological networks

Preliminary speech recognition results after cochlear implantation in patients with unilateral hearing loss: a case series

<p>Abstract</p> <p>Introduction</p> <p>Cochlear implants known to provide support in individuals with bilateral hearing loss may also be of great benefit for individuals with unilateral hearing loss. This case report demonstrates the positive effects of cochlear implantation on speech understanding in noise conditions in patients with unilateral hearing loss and normal hearing on the contralateral side. To the best of our knowledge, the data presented here are from the first few cases to receive a cochlear implant for unilateral hearing loss.</p> <p>Case presentation</p> <p>Four Caucasian German men, two aged 48 and the others aged 51 and 57 years old, with post-lingual unilateral hearing loss and normal hearing on the contralateral side were implanted with a cochlear implant. All our patients were members of the German army. Before and after implantation, they were given a battery of speech tests in different hearing conditions to assess the effect of unilateral cochlear implantation on speech understanding in noise conditions. Test results showed that all patients benefited from unilateral cochlear implantation, particularly in terms of speech understanding in noise conditions.</p> <p>Conclusions</p> <p>Unilateral cochlear implantation might be a successful treatment method for patients with unilateral hearing loss not benefiting from alternative treatment options. The results of this case report open up the field of cochlear implantation for expanded criteria and new areas of research.</p

Multi-National Topics Maps for Parliamentary Debate Analysis

In recent years, automated political text processing became an indispensable requirement for providing automatic access to political debate. During the Covid-19 worldwide pandemic, this need became visible not only in social sciences but also in public opinion. We provide a path to operationalize this need in a multi-lingual topic-oriented manner. Using a publicly available data set consisting of parliamentary speeches, we create a novel process pipeline to identify a good reference model and to link national topics to the cross-national topics. We use design science research to create this process pipeline as an artifact