@book{EngelnMuellgesSchaeferTrippler2004, author = {Engeln-M{\"u}llges, Gisela and Sch{\"a}fer, Wolfgang and Trippler, Gisela}, title = {Kompaktkurs Ingenieurmathematik mit Wahrscheinlichkeitsrechnung und Statistik. - 3., neu bearb. und erw. Aufl.}, publisher = {Fachbuchverl. Leipzig im Carl-Hanser-Verl.}, address = {M{\"u}nchen [u.a.]}, isbn = {3-446-22864-0}, pages = {394 S. : graph. Darst.}, year = {2004}, language = {de} } @book{EngelnMuellgesReutter1991, author = {Engeln-M{\"u}llges, Gisela and Reutter, Fritz}, title = {Formelsammlung zur numerischen Mathematik mit Quick-BASIC-Programmen. Anh. Quick-BASIC-Programme / von J{\"u}rgen Dietel ... / von Gisela Engeln-M{\"u}llges und Fritz Reutter}, publisher = {BI-Wiss.-Verl.}, address = {Mannheim, Wien , Z{\"u}rich}, isbn = {3-411-14312-6}, pages = {XXIV, 1006 S., 24 cm}, year = {1991}, language = {de} } @book{EngelnMuellgesNiederdrenkWodicka2005, author = {Engeln-M{\"u}llges, Gisela and Niederdrenk, Klaus and Wodicka, Reinhard}, title = {Numerik-Algorithmen : Verfahren, Beispiele, Anwendungen. - 9., vollst. {\"u}berarb. und erw. Aufl.}, publisher = {Springer}, address = {Berlin [u.a.]}, isbn = {3-540-62669-7}, pages = {XXI, 677 S. : zahlr. graph. Darst. + 2 CD-ROMs}, year = {2005}, language = {de} } @book{EngelnMuellgesNiederdrenk1996, author = {Engeln-M{\"u}llges, Gisela and Niederdrenk, Klaus}, title = {Fortran 90 mit Fortran 95}, publisher = {Rowohlt}, address = {Reinbek bei Hamburg}, isbn = {3-499-19826-6}, pages = {351 S.}, year = {1996}, language = {de} } @book{EngelnMuellges2004, author = {Engeln-M{\"u}llges, Gisela}, title = {Numerische Mathematik und Statistik : NUMAS ; multimediales Lehr- und Lernsystem zur Numerischen Mathematik und Statistik ; Abschlussbericht ; Gemeinschaftsprojekt}, publisher = {Projektb{\"u}ro NUMAS, FH [u.a.]}, address = {Aachen}, year = {2004}, language = {de} } @article{EngelnMuellges1982, author = {Engeln-M{\"u}llges, Gisela}, title = {Entartungsbedingungen f{\"u}r Gleitfl{\"a}chennomogramme und Nomographierbarkeitsbedingungen f{\"u}r Fluchtebenennomogramme}, series = {ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift f{\"u}r Angewandte Mathematik und Mechanik}, volume = {62}, journal = {ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift f{\"u}r Angewandte Mathematik und Mechanik}, number = {2}, issn = {1521-4001}, doi = {10.1002/zamm.19820620203}, pages = {77 -- 93}, year = {1982}, language = {de} } @article{EmontsBuyel2023, author = {Emonts, Jessica and Buyel, Johannes Felix}, title = {An overview of descriptors to capture protein properties - Tools and perspectives in the context of QSAR modeling}, series = {Computational and Structural Biotechnology Journal}, journal = {Computational and Structural Biotechnology Journal}, number = {21}, publisher = {Research Network of Computational and Structural Biotechnology}, address = {Gotenburg}, issn = {2001-0370 (online-ressource)}, doi = {10.1016/j.csbj.2023.05.022}, pages = {3234 -- 3247}, year = {2023}, abstract = {Proteins are important ingredients in food and feed, they are the active components of many pharmaceutical products, and they are necessary, in the form of enzymes, for the success of many technical processes. However, production can be challenging, especially when using heterologous host cells such as bacteria to express and assemble recombinant mammalian proteins. The manufacturability of proteins can be hindered by low solubility, a tendency to aggregate, or inefficient purification. Tools such as in silico protein engineering and models that predict separation criteria can overcome these issues but usually require the complex shape and surface properties of proteins to be represented by a small number of quantitative numeric values known as descriptors, as similarly used to capture the features of small molecules. Here, we review the current status of protein descriptors, especially for application in quantitative structure activity relationship (QSAR) models. First, we describe the complexity of proteins and the properties that descriptors must accommodate. Then we introduce descriptors of shape and surface properties that quantify the global and local features of proteins. Finally, we highlight the current limitations of protein descriptors and propose strategies for the derivation of novel protein descriptors that are more informative.}, language = {en} } @phdthesis{Emonts2013, author = {Emonts, Jessica}, title = {Searching for many defective edges in hypergraphs}, publisher = {Rheinisch-Westf{\"a}lischen Technischen Hochschule Aachen}, address = {Aachen}, pages = {VIII, 104 Seiten : Ill.}, year = {2013}, language = {en} } @inproceedings{EichlerSkupinThurnetal.2019, author = {Eichler, Fabian and Skupin, Marco and Thurn, Laura and Kasch, Susanne and Schmidt, Thomas}, title = {Operating limits for beam melting of glass materials}, series = {Modern Technologies in Manufacturing (MTeM 2019)}, volume = {299}, booktitle = {Modern Technologies in Manufacturing (MTeM 2019)}, number = {Article 01004}, doi = {10.1051/matecconf/201929901004}, pages = {8 Seiten}, year = {2019}, abstract = {Laser-based Additive Manufacturing (AM) processes for the use of metals out of the powder bed have been investigated profusely and are prevalent in industry. Although there is a broad field of application, Laser Powder Bed Fusion (LPBF), also known as Selective Laser Melting (SLM) of glass is not fully developed yet. The material properties of glass are significantly different from the investigated metallic material for LPBF so far. As such, the process cannot be transferred, and the parameter limits and the process sequence must be redefined for glass. Starting with the characterization of glass powders, a parameter field is initially confined to investigate the process parameter of different glass powder using LPBFprocess. A feasibility study is carried out to process borosilicate glass powder. The effects of process parameters on the dimensional accuracy of fabricated parts out of borosilicate and hints for the post-processing are analysed and presented in this paper.}, language = {en} } @article{EichlerBalcBremenetal.2024, author = {Eichler, Fabian and Balc, Nicolae and Bremen, Sebastian and Nink, Philipp}, title = {Investigation of laser powder bed fusion parameters with respect to their influence on the thermal conductivity of 316L samples}, series = {Journal of Manufacturing and Materials Processing}, volume = {8}, journal = {Journal of Manufacturing and Materials Processing}, number = {4}, publisher = {MDPI}, address = {Basel}, issn = {2504-4494}, doi = {10.3390/jmmp8040166}, pages = {12 Seiten}, year = {2024}, abstract = {The thermal conductivity of components manufactured using Laser Powder Bed Fusion (LPBF), also called Selective Laser Melting (SLM), plays an important role in their processing. Not only does a reduced thermal conductivity cause residual stresses during the process, but it also makes subsequent processes such as the welding of LPBF components more difficult. This article uses 316L stainless steel samples to investigate whether and to what extent the thermal conductivity of specimens can be influenced by different LPBF parameters. To this end, samples are set up using different parameters, orientations, and powder conditions and measured by a heat flow meter using stationary analysis. The heat flow meter set-up used in this study achieves good reproducibility and high measurement accuracy, so that comparative measurements between the various LPBF influencing factors to be tested are possible. In summary, the series of measurements show that the residual porosity of the components has the greatest influence on conductivity. The degradation of the powder due to increased recycling also appears to be detectable. The build-up direction shows no detectable effect in the measurement series.}, language = {en} }