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[25th International Conference on Material Forming, ESAFORM 2022, 2022-04-27 - 2022-04-29, Braga, Portugal] 25th International Conference on Material Forming, ESAFORM 2022, Braga, Portugal, 27 Apr 2022 - 29 Apr 2022; Uetikon a.S. : Trans Tech Publications, Key engineering materials 926, 2137-2145 (2022). doi:10.4028/p-d17700, Published by Trans Tech Publications, Uetikon a.S.

Treatment concepts in oncology are becoming increasingly personalized and diverse. Successively, changes in standards of care mandate continuous monitoring of patient pathways and clinical outcomes based on large, representative real-world data. The German Cancer Consortium’s (DKTK) Clinical Communication Platform (CCP) provides such opportunity. Connecting fourteen university hospital-based cancer centers, the CCP relies on a federated IT-infrastructure sourcing data from facility-based cancer registry units and biobanks. Federated analyses resulted in a cohort of 600,915 patients, out of which 232,991 were incident since 2013 and for which a comprehensive documentation is available. Next to demographic data (i.e., age at diagnosis: 2.0% 0–20 years, 8.3% 21–40 years, 30.9% 41–60 years, 50.1% 61–80 years, 8.8% 81 + years; and gender: 45.2% female, 54.7% male, 0.1% other) and diagnoses (five most frequent tumor origins: 22,523 prostate, 18,409 breast, 15,575 lung, 13,964 skin/malignant melanoma, 9,005 brain), the cohort dataset contains information about therapeutic interventions and response assessments and is connected to 287,883 liquid and tissue biosamples. Focusing on diagnoses and therapy-sequences, showcase analyses of diagnosis-specific sub-cohorts (pancreas, larynx, kidney, thyroid gland) demonstrate the analytical opportunities offered by the cohort’s data. Due to its data granularity and size, the cohort is a potential catalyst for translational cancer research. It provides rapid access to comprehensive patient groups and may improve the understanding of the clinical course of various (even rare) malignancies. Therefore, the cohort may serve as a decisions-making tool for clinical trial design and contributes to the evaluation of scientific findings under real-world conditions.

Background Numerous predictive clinical scores with varying discriminatory performance have been developed in the context of the current coronavirus disease 2019 (COVID-19) pandemic. To support clinical application, we test the transferability of the frequently applied 4C mortality score (4C score) to the German prospective Cross-Sectoral Platform (SUEP) of the National Pandemic Cohort Network (NAPKON) compared to the non COVID-19 specific quick sequential organ failure assessment score (qSOFA). Our project aims to externally validate these two scores, stratified for the most prevalent variants of concerns (VOCs) of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) in Germany. Methods A total of 685 adults with polymerase chain reaction (PCR)-detected SARS-CoV-2 infection were included from NAPKON-SUEP. Patients were recruited from 11/2020 to 03/2022 at 34 university and non-university hospitals across Germany. Missing values were complemented using multiple imputation. Predictive performance for in-hospital mortality at day of baseline visit was determined by area under the curve (AUC) with 95%-confidence interval (CI) stratified by VOCs of SARS-CoV-2 (alpha, delta, omicron) (Figure 1). Figure 1:Study flow chart with inclusion criteria and methodological workflow. Results Preliminary results suggest a high predictive performance of the 4C score for in-hospital mortality (Table 1). This applies for the overall cohort (AUC 0.813 (95%CI 0.738-0.888)) as well as the VOC-strata (alpha: AUC 0.859 (95%CI 0.748-0.970); delta: AUC 0.769 (95%CI 0.657-0.882); omicron: AUC 0.866 (95%CI 0.724-1.000)). The overall mortality rates across the defined 4C score risk groups are 0.3% (low), 3.2% (intermediate), 11.6% (high), and 49.5% (very high). The 4C score performs significantly better than the qSOFA (Chi2-test: p=0.001) and the qSOFA does not seem to be a suitable tool in this context. Table 1:Discriminatory performance of the 4C Mortality Score and the qSOFA score within the validation cohort NAPKON-SUEP stratified by the Variant of Concerns of SARS-CoV-2. Conclusion Despite its development in the early phase of the pandemic and improved treatment, external validation of the 4C score in NAPKON-SUEP indicates a high predictive performance for in-hospital mortality across all VOCs. However, since the qSOFA was not specifically designed for this predictive issue, it shows low discriminatory performance, as in other validation studies. Any interpretations regarding the omicron stratum are limited due to the sample size. Disclosures Daniel Pape, Dr., Advanz Pharma Germany: Support for attending meetings and/or travel for ECCMID 2021 Martin Hower, n/a, MSD: Advisor/Consultant|Trogarzo: Advisor/Consultant|ViiV Healthcare: Advisor/Consultant Björn-Erik O. Jensen, Dr. med., GILEAD: Advisor/Consultant|GILEAD: Lectures, Travel|GSK: Lectures, Travel Jörg J. Vehreschild, Univ.-Prof. Dr. med., Ärztekammer Nordrhein: Honoraria|Academy for Infectious Medicine, University Manchester: Honoraria|Astellas Pharma: Grant/Research Support|Astellas Pharma: Honoraria|Back Bay Strategies: Honoraria|Basilea: Grant/Research Support|Basilea: Honoraria|Deutsches Zetrum für Luft- und Raumfahrt (DLR): Grant/Research Support|German Centre for Infection Research (DZIF): Grant/Research Support|German Centre for Infection Research (DZIF): Honoraria|German Federal Ministry of Education and Research (BMBF): Grant/Research Support|German Society for Infectious Diseases (DGI): Honoraria|German Society for Internal Medicine (DGIM): Honoraria|GILEAD: Advisor/Consultant|GILEAD: Grant/Research Support|GILEAD: Honoraria|Janssen: Honoraria|Merck / MSD: Grant/Research Support|Merck / MSD: Honoraria|Molecular Health: Honoraria|Netzwerk Universitätsmedizin: Honoraria|NordForsk: Honoraria|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Pfizer: Honoraria|Rigshospitalet Copenhagen: Grant/Research Support|Shionogi: Advisor/Consultant|Shionogi: Honoraria|University Hospital Aachen: Honoraria|University Hospital Freiburg/ Congress and Communication: Honoraria|University of Bristol: Grant/Research Support.

The purpose of this article is to identify a potential influence of a Chief Digital Officer (CDO) on company’s success. To do so, the CDO position is analyzed and put in context with existing research regarding upper echelons and consequences of digital activities. The research approach is theoretical and analytical and provides an intensive literature analysis concerning the connecting topics. Firstly, general facts and characteristics of CDOs will be presented. Secondly, the monetary influence of digital activities in former studies will be examined. Thirdly, upper echelon theory and attention-based view theory will be discussed with regard to organizational issues. In a final step, the topics mentioned are put into context with each other. The aim is to prove that the employment of a CDO might be able to influence the financial outcome of a company. As an intermediate finding it can be stated that the CDO Position has an impact on company’s success. But further research is needed to clearly prove the extent of influence. Keywords: Chief Digital Officer (CDO), Upper Echelon, Digital Transformation, Artificial Intelligence (AI) JEL Codes: O10, O11, O14

The optimal design of the tools in bulk metal forming is a crucial task in the early design phase and greatly affects the final accuracy of the parts. The process of tool geometry assessment is resource- and time-consuming, as it consists of experience-based procedures. In this paper, a compensation method is developed with the aim to reduce geometrical deviations in hot forged parts. In order to simplify the transition process between the discrete finite-element (FE) mesh and the computer-aided-design (CAD) geometry, a strategy featuring an equivalent surrogate model is proposed. The deviations are evaluated on a reduced set of reference points on the nominal geometry and transferred to the FE nodes. The compensation approach represents a modification of the displacement-compatible spring-forward method (DC-SF), which consists of two elastic FE analyses. The compatible stress originating the deviations is estimated and subsequently applied to the original nominal geometry. After stress relaxation, an updated nominal geometry of the part is obtained, whose surfaces represent the compensated tools. The compensation method is verified by means of finite element simulations and the robustness of the algorithm is demonstrated with an additional test geometry. Finally, the compensation strategy is validated experimentally.

While scholarly attention has been devoted to social media's potential mobilizing function, they may also contribute to demobilization discourses: social communication actively promoting nonvoting. This paper examines discourses around mobilization vs. demobilization in the context of the municipal elections in Jerusalem. As the sweeping majority of East Jerusalem Palestinians have continuously been boycotting Jerusalem's municipal elections, this is a potent case through which to examine how demobilization functions in action, through social media conversations. Using a mixed-methods analysis of Twitter contents as structured by different languages, our findings show how mobilization and demobilization discourses can co-occur during the same election event. Users of different languages - reflecting different social and political identities - interpret the elections in contrasting ways, with tangible implications for (in)equality in political participation. The study thus contributes theoretically to several domains of political communication, including election studies, local politics, and language fragmentation in online political discourse.

Topic modeling enables researchers to explore large document corpora. However, accurate model specification requires the calculation of multiple models, which can become infeasibly costly in terms of time and computing resources. In order to circumvent this problem, we test and propose a strategy introducing two easy-to-implement modifications to the modeling process: Instead of modeling the full corpus and the whole vocabulary, we (1) use random document samples and (2) an extensively pruned vocabulary. Using three empirical corpora with different origins and characteristics (news articles, websites, and Tweets), we investigate how different sample sizes and pruning strategies affect the resulting topic models as compared to fully modeled corpora. Our test provides evidence that sampling and pruning are cheap and viable strategies to accelerate model specification. Sample-based topic models closely resemble corpus-based models, if the sample size is large enough (usually >10%). Also, extensive pruning does not compromise the quality of the resulting topics. Altogether, pruning and sample-based modeling leads to increased performance without impairing model quality.

This work presents a new GNSS array processing algorithm to estimate simultaneously the antenna array attitude and the direction of incoming multipath wave fronts. The multipath and attitude estimating phase lock loop (MAEPLL) is a post-correlation technique based on the parallel processing of the prompt correlator values from the antenna elements, forming double-differenced prompt correlator (DDPC) values between antenna elements and satellites. With a geometrical based model of the derived DDPC values the attitude and the multipath direction of arrival (DOA) can be estimated. Non-adaptive beamforming techniques can use the estimated platform attitude and multipath DOA to obtain conventional (not differenced) correlation values. With classical DLL/FLL/PLL filters the position, velocity and timing (PVT) solution is derived after the beamforming process. On the basis of a self developed wideband multipath channel simulation framework we present our first results for the attitude and multipath DOA estimation accuracy. The results show, that the attitude accuracy is quite independent of the environmental conditions due to the double-differencing. It is also shown, that it is possible to jointly estimate antenna array attitude and multipath direction of arrival. This work outlines the mathematical foundations of a combined multipath and attitude estimation based on DDPC values. The simulation results show attitude accuracies below one degree in all three spatial directions, as well as high robustness in terms of initially assumed attitudes, multipath environments and signal strengths. The MAEPLL technique has the potential to extend the DDPC model to include an estimation of the antenna element phase center corrections (PCCs), enabling the use of cheap, supoptimal, uncalibrated and integrated antenna arrays in mass-market applications with on-the-fly antenna phase center variation calibration.

In hot forging processes, geometry of the formed workpieces deviate from the desired target geometry, due to complex interactions between tools and billets which result in inhomogeneous temperature and stress fields. The resulting deviation can only be mapped insufficiently by using numerical simulation which makes it difficult to be considered when designing the tool. Therefore, the development of forging tools requires an iterative adaptation process through a large number of revisions in the tool geometry, which escalates the resulting costs. To compensate the deviations and reduce the number of tool revisions, a holistic view of the influencing factors on the geometrical deviation is necessary. In order to address this issue, a hot forging process was developed, whose geometry is prone to high deviations, and a stress-based compensation model was applied. For this, forging experiments were carried out and a comparison was made between the actual geometry and the desired one by means of 3D coordinate measurements. The compensation methodology, which directly takes into account the complex 3D stress states during forming, allows to determine a compensating tool geometry. This opened up the possibility of validating the simulation results and testing a compensation model while eliminating deterministic deviations in hot forging processes.

Materials 14(16), 4549 (2021). doi:10.3390/ma14164549, Published by MDPI, Basel

In this article, we focus on noise in the sense of irrelevant information in a data set as a specific methodological challenge of web research in the era of big data. We empirically evaluate several methods for filtering hyperlink networks in order to reconstruct networks that contain only webpages that deal with a particular issue. The test corpus of webpages was collected from hyperlink networks on the issue of food safety in the United States and Germany. We applied three filtering strategies and evaluated their performance to exclude irrelevant content from the networks: keyword filtering, automated document classification with a machine-learning algorithm, and extraction of core networks with network-analytical measures. Keyword filtering and automated classification of webpages were the most effective methods for reducing noise, whereas extracting a core network did not yield satisfying results for this case.