@article{27023,
  abstract     = {{<jats:title>Abstract</jats:title>
               <jats:sec>
                  <jats:title>Background</jats:title>
                  <jats:p>Blood immunoreactive biomarkers, such as C-reactive protein (CRP), and metabolic abnormalities have been associated with schizophrenia. Studies comprehensively and bidirectionally probing possible causal links between such blood constituents and liability to schizophrenia are lacking.</jats:p>
               </jats:sec>
               <jats:sec>
                  <jats:title>Methods</jats:title>
                  <jats:p>To disentangle putative causal links between CRP blood levels and schizophrenia in both directions, we conducted multiple univariable Mendelian-randomization (MR) analyses, ranging from fixed-effect to inverse variance-weighted (IVW), weighted-median, MR Egger and generalized summary-data-based Mendelian-randomization (GSMR) models. To prioritize metabolic risk factors for schizophrenia, a novel multivariable approach was applied: multivariable Mendelian-randomization–Bayesian model averaging (MR-BMA).</jats:p>
               </jats:sec>
               <jats:sec>
                  <jats:title>Results</jats:title>
                  <jats:p>All forward univariable MR analyses consistently showed that CRP has a protective effect on schizophrenia, whereas reverse MR analyses consistently suggested absent causal effects of schizophrenia liability on CRP blood levels. Using MR-BMA, as the top protective factors for schizophrenia we prioritized leucine and as the prime risk-factor triglycerides in medium very-low-density lipoprotein (VLDL). The five best-performing MR-BMA models provided one additional risk factor: triglycerides in large VLDL; and two additional protective factors: citrate and lactate.</jats:p>
               </jats:sec>
               <jats:sec>
                  <jats:title>Conclusions</jats:title>
                  <jats:p>Our results add to a growing body of literature hinting at metabolic changes—in particular of triglycerides—independently of medication status in schizophrenia. We also highlight the absent effects of genetic liability to schizophrenia on CRP levels.</jats:p>
               </jats:sec>}},
  author       = {{Lin, Bochao D and Alkema, Anne and Peters, Triinu and Zinkstok, Janneke and Libuda, Lars and Hebebrand, Johannes and Antel, Jochen and Hinney, Anke and Cahn, Wiepke and Adan, Roger and Luykx, Jurjen J}},
  issn         = {{0300-5771}},
  journal      = {{International Journal of Epidemiology}},
  pages        = {{1505--1514}},
  title        = {{{Assessing causal links between metabolic traits, inflammation and schizophrenia: a univariable and multivariable, bidirectional Mendelian-randomization study}}},
  doi          = {{10.1093/ije/dyz176}},
  year         = {{2019}},
}

@article{27024,
  author       = {{Kalhoff, Hermann and Mesch, Christina M. and Stimming, Madlen and Israel, Andreas and Spitzer, Christoph and Beganovic, Latifa and Perez, Rocio Estella and Koletzko, Berthold and Warschburger, Petra and Kersting, Mathilde and Libuda, Lars}},
  issn         = {{0954-3007}},
  journal      = {{European Journal of Clinical Nutrition}},
  pages        = {{682--690}},
  title        = {{{Effects of LC-PUFA supply via complementary food on infant development—a food based intervention (RCT) embedded in a total diet concept}}},
  doi          = {{10.1038/s41430-019-0491-0}},
  year         = {{2019}},
}

@inproceedings{27101,
  author       = {{Drewel, Marvin and Gausemeier, Jürgen and Vaßholz, Mareen and Homburg, Nils}},
  booktitle    = {{Symposium für Vorausschau und Technologieplanung, Band 15}},
  editor       = {{Gausemeier, Jürgen and Bauer, Wilhelm and Dumitrescu, Roman}},
  publisher    = {{Heinz Nixdorf Institut}},
  title        = {{{Einstieg in die Plattformökonomie}}},
  year         = {{2019}},
}

@article{27102,
  abstract     = {{Mithilfe von Industrie 4.0-Reifegradmodellen können Unternehmen ihren Leistungsstand im Kontext Industrie 4.0 systematisch erfassen. Mit der Ermittlung des Status Quos ist in aller Regel die Frage verbunden „Wo wollen wir zukünftig hin?“. Vor dem Hintergrund, dass Unternehmen aus unterschiedlichen Gründen nicht immer das grundsätzlich Mögliche einführen können, ist die Beantwortung dieser Frage nicht trivial. Ist sich ein Unternehmen über seine I4.0-Zielposition vermeintlich im Klaren, führen äußere Einflüsse häufig dazu, dass die Zielerreichung erschwert wird, was oftmals eine Anpassung der Zielposition zur Folge hat. Es gilt also, diese Umstände bereits in der Planung zu berücksichtigen. Der vorliegende Beitrag zeigt auf, wie Umfeldentwicklungen bei der Ermittlung einer Erfolg versprechenden I4.0-Zielposition von Unternehmen einbezogen werden können.}},
  author       = {{Pierenkemper, Christoph and Reinhold, Jannik and Dumitrescu, Roman and Gausemeier, Jürgen}},
  journal      = {{Industrie 4.0 Management (5)}},
  pages        = {{30--34}},
  title        = {{{Erfolg versprechende Industrie 4.0-Zielposition - Ermittlung unter Berücksichtigung zukünftiger Umfeldentwicklungen}}},
  year         = {{2019}},
}

@inproceedings{27103,
  abstract     = {{One of the notable drivers of the fourth industrial revolution is the collection of vast amounts of data along the entire lifecycle of a product. The analysis of product lifecycle data in conjunction with product hypotheses leads to promising potentials in strategic product planning. In this thesis paper, we postulate the need for data-driven product generation and retrofit planning as an interdisciplinary field of research. We define and analyze the key concepts and derive requirements in a structured way. Based on an exhaustive research of existing approaches, we structure open research questions and propose a roadmap in order to shape future research efforts.
}},
  author       = {{Massmann, Melina and Meyer, Maurice and Dumitrescu, Roman and von Enzberg, Sebastian and Frank, Maximilian and Koldewey, Christian and Kühn, Arno and Reinhold, Jannik}},
  booktitle    = {{Proceedings of the CIRP DESIGN}},
  publisher    = {{Scientific Technical Committee Design of the International Academy for Production Engineering (CIRP)}},
  title        = {{{Significance and Challenges of Data-driven Product Generation and Retrofit Planning}}},
  year         = {{2019}},
}

@inproceedings{27104,
  abstract     = {{Today's manufacturing industry is confronted with fundamental changes in value creation. The tension between the two megatrends of digitization and servitization leads to new hybrid market offerings, so-called smart services. Corresponding value networks fundamentally differ from traditional ones. Developing smart services requires new competences in young disciplines, while their provision requires new internal and external organizational structures or processes. To strengthen their competitive position, manufacturing companies need to adapt their value networks. However, the highly complex transformation of value crea-tion especially challenges small and medium-sized companies due to limited competences and resources. They must consider opening their boundaries and collaborating with partners. In this paper, we introduce a basic framework for planning smart services and present a methodology for competence-based plan-ning of value networks for smart services in three phases: Smart service analysis, competence analysis and value creation planning. The methodology is explained by an example from tooling machine industry.}},
  author       = {{Reinhold, Jannik and Frank, Maximilian and Koldewey, Christian and Dumitrescu, Roman and Gausemeier, Jürgen}},
  booktitle    = {{Proceedings of the ISPIM Connects}},
  publisher    = {{International Society for Professional Innovation Management (ISPIM)}},
  title        = {{{Competence-based Planning of Value Networks for Smart Services}}},
  year         = {{2019}},
}

@inproceedings{27105,
  author       = {{Wortmann, Fabio and Joppen, Robert and Drewel, Marvin and Kühn, Arno and Dumitrescu, Roman}},
  booktitle    = {{IAMOT 2019 – Proceedings of the 28th International Association for Management of Technology Conference}},
  editor       = {{Jain, K. and Sangle, S. and Gupta, R. and Persis, J. and Mukundan, R.}},
  publisher    = {{International Association for Management of Technology (IAMOT)}},
  title        = {{{Developing and Evaluating Concepts for a Digital Platform}}},
  year         = {{2019}},
}

@inproceedings{27107,
  abstract     = {{Smart Services are digital, IT-based services that are using data of a corresponding physical product, e.g. predictive maintenance or automated reordering of consumables for a production machine. This contribution shows a study about 265 services from the German manufacturing industry. We derive hypothesis for identifying Smart Services as well as eleven variables and their 47 characteristics to systematically describe them. A clustering of the found Smart Services resulted in four distinguishable groups. Furthermore, we show statistics of how companies combine the characteristics of their market offerings. Our findings reflect where the theoretical possibilities were fully met by the implementations of the German manufacturing companies and where the industry is still lagging behind. Finally, we derived six bundles of highly consistent combinations of the characteristics. These bundles can be starting points for developing Smart Services for one’s specific use case.}},
  author       = {{Frank, Maximilian and Rabe, Martin and Koldewey, Christian and Dumitrescu, Roman and Gausemeier, Jürgen and Hennig-Cardinal von Widdern, Nils and Reinhold, Jannik}},
  booktitle    = {{Proceedings of the ISPIM connects}},
  publisher    = {{ International Society for Professional Innovation Management (ISPIM)}},
  title        = {{{Classification-based Planning of Smart Service Portfolios}}},
  year         = {{2019}},
}

@inbook{27108,
  author       = {{Dangelmaier, Wilhelm and Gausemeier, Jürgen}},
  booktitle    = {{Intelligente Technische Systeme – Lösungen aus dem Spitzencluster it’s OWL}},
  publisher    = {{Springer-Verlag GmbH Deutschland}},
  title        = {{{Intelligente Arbeitsvorbereitung auf Basis virtueller Werkzeugmaschinen}}},
  year         = {{2019}},
}

@book{27109,
  author       = {{Gausemeier, Jürgen and Dumitrescu, Roman and Echterfeld, Julian and Pfänder, Tomas and Steffen, Daniel and Thielemann, Frank}},
  publisher    = {{Carl Hanser Verlag}},
  title        = {{{Innovationen für die Märkte von morgen - Strategische Planung von Produkten, Dienstleistungen und Geschäftsmodellen}}},
  year         = {{2019}},
}

@techreport{27110,
  author       = {{Gausemeier, Jürgen and Guggemos, Michael and Kreimeyer, Andreas and Lange, Thomas and Behrens, Jan Henning and Seitz, Ralph and Ortloff, Luise and Frey, Anna and Dachsberger, Stephanie and Drewel, Marvin and Frank, Maximilian}},
  title        = {{{Nationales Kompetenz-Monitoring - Bericht: Energiespeichersysteme (Fokus Lithium-Ionen-Speicher)}}},
  year         = {{2019}},
}

@inbook{27379,
  author       = {{Schlegel-Matthies, Kirsten}},
  booktitle    = {{Verbraucherbildung: Ein weiter Weg zum mündigen Verbraucher }},
  editor       = {{Bala, Christian and Buddensiek, Marit and Maier, Petra and Schuldzinski, Wolfgang}},
  isbn         = {{978-3-86336-924-8 }},
  pages        = {{41--60}},
  publisher    = {{Kompetenzzentrum Verbraucherforschung NRW, Verbraucherzentrale Nordrhein-Westfalen e.V.}},
  title        = {{{Verbraucherbildung als Bildung für Lebensführung}}},
  doi          = {{10.15501/978-3-86336-924-8_3}},
  year         = {{2019}},
}

@article{27380,
  abstract     = {{<jats:p>Der Beitrag geht der Frage nach, wie haushaltsbezogene Bildung als Ernährungs- und Verbraucherbildung umgesetzt werden kann, damit Jugendliche selbstbestimmt und verantwortlich ihre individuellen Vorstellungen von einem „guten“ und „gelingenden“ Leben umsetzen können. Die Auseinandersetzung mit dem Zusammenwirken von gesellschaftlicher Lebensweise, privater Lebensführung und individuellen Lebensstilen erweist sich dabei als bedeutsam.</jats:p>}},
  author       = {{Schlegel-Matthies, Kirsten}},
  issn         = {{2196-1662}},
  journal      = {{Haushalt in Bildung & Forschung}},
  pages        = {{88--106}},
  title        = {{{Haushaltsbezogene Bildung – quo vadis? Daseinsvorsorge und Lebensführung im Wandel}}},
  doi          = {{10.3224/hibifo.v8i2.07}},
  year         = {{2019}},
}

@phdthesis{27648,
  author       = {{Pohl, Max}},
  title        = {{{High-Speed-Extrusion amorpher Polymere am Beispiel von Polycarbonat (PC) und Polymethylmethacrylat (PMMA)}}},
  year         = {{2019}},
}

@phdthesis{27649,
  author       = {{Meilwes , Peter}},
  title        = {{{Simulation und Modellierung des Druckverlustes industrieller Polymerschmelzefilter in Abhängigkeit verschiedener Prozessbedingungen sowie deren verschmutzung }}},
  year         = {{2019}},
}

@phdthesis{27653,
  author       = {{Landgräber, Björn}},
  title        = {{{Experimentelle und modellbasierte Analyse der Prozessphasen des Spritzgießsonderverfahrens GITBlow }}},
  year         = {{2019}},
}

@phdthesis{27654,
  author       = {{Fiebig, Isabel}},
  title        = {{{Beitrag zur Erhöhung der Wirksamkeit der Faserverstärkung in der Schweißnaht faserverstärkter Thermoplaste }}},
  year         = {{2019}},
}

@phdthesis{27655,
  author       = {{Nordmeyer, Timo }},
  title        = {{{Verfahrenstechnische Entwicklung des Direktinjektion-Plasmaverfahrens im Spritzgießprozess }}},
  year         = {{2019}},
}

@article{27758,
  abstract     = {{<jats:p>Published meta-analyses indicate significant but inconsistent incident type-2 diabetes (T2D)-dietary glycemic index (GI) and glycemic load (GL) risk ratios or risk relations (RR). It is now over a decade ago that a published meta-analysis used a predefined standard to identify valid studies. Considering valid studies only, and using random effects dose–response meta-analysis (DRM) while withdrawing spurious results (p &lt; 0.05), we ascertained whether these relations would support nutrition guidance, specifically for an RR &gt; 1.20 with a lower 95% confidence limit &gt;1.10 across typical intakes (approximately 10th to 90th percentiles of population intakes). The combined T2D–GI RR was 1.27 (1.15–1.40) (p &lt; 0.001, n = 10 studies) per 10 units GI, while that for the T2D–GL RR was 1.26 (1.15–1.37) (p &lt; 0.001, n = 15) per 80 g/d GL in a 2000 kcal (8400 kJ) diet. The corresponding global DRM using restricted cubic splines were 1.87 (1.56–2.25) (p &lt; 0.001, n = 10) and 1.89 (1.66–2.16) (p &lt; 0.001, n = 15) from 47.6 to 76.1 units GI and 73 to 257 g/d GL in a 2000 kcal diet, respectively. In conclusion, among adults initially in good health, diets higher in GI or GL were robustly associated with incident T2D. Together with mechanistic and other data, this supports that consideration should be given to these dietary risk factors in nutrition advice. Concerning the public health relevance at the global level, our evidence indicates that GI and GL are substantial food markers predicting the development of T2D worldwide, for persons of European ancestry and of East Asian ancestry.</jats:p>}},
  author       = {{Livesey, Geoffrey and Taylor, Richard and Livesey, Helen F. and Buyken, Anette and Jenkins, David J. A. and Augustin, Livia S. A. and Sievenpiper, John L. and Barclay, Alan W. and Liu, Simin and Wolever, Thomas M. S. and Willett, Walter C. and Brighenti, Furio and Salas-Salvadó, Jordi and Björck, Inger and Rizkalla, Salwa W. and Riccardi, Gabriele and Vecchia, Carlo La and Ceriello, Antonio and Trichopoulou, Antonia and Poli, Andrea and Astrup, Arne and Kendall, Cyril W. C. and Ha, Marie-Ann and Baer-Sinnott, Sara and Brand-Miller, Jennie C.}},
  issn         = {{2072-6643}},
  journal      = {{Nutrients}},
  title        = {{{Dietary Glycemic Index and Load and the Risk of Type 2 Diabetes: A Systematic Review and Updated Meta-Analyses of Prospective Cohort Studies}}},
  doi          = {{10.3390/nu11061280}},
  year         = {{2019}},
}

@article{27759,
  abstract     = {{<jats:p>While dietary factors are important modifiable risk factors for type 2 diabetes (T2D), the causal role of carbohydrate quality in nutrition remains controversial. Dietary glycemic index (GI) and glycemic load (GL) have been examined in relation to the risk of T2D in multiple prospective cohort studies. Previous meta-analyses indicate significant relations but consideration of causality has been minimal. Here, the results of our recent meta-analyses of prospective cohort studies of 4 to 26-y follow-up are interpreted in the context of the nine Bradford-Hill criteria for causality, that is: (1) Strength of Association, (2) Consistency, (3) Specificity, (4) Temporality, (5) Biological Gradient, (6) Plausibility, (7) Experimental evidence, (8) Analogy, and (9) Coherence. These criteria necessitated referral to a body of literature wider than prospective cohort studies alone, especially in criteria 6 to 9. In this analysis, all nine of the Hill’s criteria were met for GI and GL indicating that we can be confident of a role for GI and GL as causal factors contributing to incident T2D. In addition, neither dietary fiber nor cereal fiber nor wholegrain were found to be reliable or effective surrogate measures of GI or GL. Finally, our cost–benefit analysis suggests food and nutrition advice favors lower GI or GL and would produce significant potential cost savings in national healthcare budgets. The high confidence in causal associations for incident T2D is sufficient to consider inclusion of GI and GL in food and nutrient-based recommendations.</jats:p>}},
  author       = {{Livesey, Geoffrey and Taylor, Richard and Livesey, Helen F. and Buyken, Anette and Jenkins, David J. A. and Augustin, Livia S. A. and Sievenpiper, John L. and Barclay, Alan W. and Liu, Simin and Wolever, Thomas M. S. and Willett, Walter C. and Brighenti, Furio and Salas-Salvadó, Jordi and Björck, Inger and Rizkalla, Salwa W. and Riccardi, Gabriele and Vecchia, Carlo La and Ceriello, Antonio and Trichopoulou, Antonia and Poli, Andrea and Astrup, Arne and Kendall, Cyril W. C. and Ha, Marie-Ann and Baer-Sinnott, Sara and Brand-Miller, Jennie C.}},
  issn         = {{2072-6643}},
  journal      = {{Nutrients}},
  title        = {{{Dietary Glycemic Index and Load and the Risk of Type 2 Diabetes: Assessment of Causal Relations}}},
  doi          = {{10.3390/nu11061436}},
  year         = {{2019}},
}