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Enhanced Separation of Intact Proteins and Proteoforms by CZE‐MS Using Sulfobetaine‐Modified Poly(α‐L‐lysine)‐Based Multilayer Coatings for EOF Adjustment
Full text linkHistidine-modified UiO-66(Zr) nanoparticles as an effective pH-responsive carrier for 5-fluorouracil drug delivery system
No full textNondestructive Analysis of Coercivity Distributions in Permanent Magnets with Locally Enhanced Magnetic Properties
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Analyse der Einstellung von Kontaktlinsenanpasser*innen zum Thema Nachhaltigkeit und Plastikverbrauch bei Kontaktlinsen
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VR-DeltaDebugging
Full text linkDebugging is a challenging activity involved in software development and maintenance processes. Delta Debugging (DD) is an automatic debugging algorithm and methodology that applies a scientific recurrent hypothesis, trial, and result loop to systematically reduce failure-inducing inputs to a minimal set. Yet, especially for larger (structured) input sets, how DD arrived at its results and its intermediate inputs and test results may not be intuitively evident to practitioners. This paper contributes our solution concept VR-DeltaDebugging for an immersive visualization in Virtual Reality to support comprehension, analysis, and collaboration. A prototype demonstrates its feasibility, and a cased-based evaluation on execution, comprehension and analysis, and scalability provides insights into its capabilities and potential
VR-ANN
Full text linkArtificial Neural Networks (ANNs or NNs) are used in Deep Learning (DL), a subset of Machine Learning (ML). And yet, especially to novices or infrequent users, ANNs can seem abstract and mathematical, and not readily accessible and understandable. Furthermore, a model’s configuration and output results may not be comprehensible and obvious. To make ANN models more accessible and support comprehension and analysis even for large models, this paper contributes our VR-ANN solution concept for immersive ANN visualization in Virtual Reality (VR). Its feasibility is demonstrated with a prototype, while a case-based evaluation provides insights into its capabilities and potential for supporting ANN model building, comprehension, analysis, and collaboration
