TY - INPR A1 - Greiner, Lasse A1 - Jeromin, Günter Erich A1 - Sithole, Patience A1 - Petersen, Soenke T1 - Preprint: Studies on the enzymatic reduction of levulinic acid using Chiralidon-R and Chiralidon-S T2 - ChemRxiv N2 - The enzymatic reduction of levulinic acid by the chiral catalysts Chiralidon-R and Chiralidon-S which are commercially available superabsorbed alcohol dehydrogenases is described. The Chiralidon®-R/S reduces the levulinic acid to the (R,S)-4-hydroxy valeric acid and the (R)- or (S)- gamma-valerolactone. KW - Levulinic acid KW - Chiralidon-R KW - Chiralidon-S KW - 4-hydroxy valeric acid KW - (R)- or (S)- gamma-valerolactone Y1 - 2023 U6 - https://doi.org/10.26434/chemrxiv-2023-jlvcv ER - TY - JOUR A1 - Grajewski, Matthias A1 - Kleefeld, Andreas T1 - Detecting and approximating decision boundaries in low-dimensional spaces JF - Numerical Algorithms N2 - A method for detecting and approximating fault lines or surfaces, respectively, or decision curves in two and three dimensions with guaranteed accuracy is presented. Reformulated as a classification problem, our method starts from a set of scattered points along with the corresponding classification algorithm to construct a representation of a decision curve by points with prescribed maximal distance to the true decision curve. Hereby, our algorithm ensures that the representing point set covers the decision curve in its entire extent and features local refinement based on the geometric properties of the decision curve. We demonstrate applications of our method to problems related to the detection of faults, to multi-criteria decision aid and, in combination with Kirsch’s factorization method, to solving an inverse acoustic scattering problem. In all applications we considered in this work, our method requires significantly less pointwise classifications than previously employed algorithms. KW - MCDA KW - Inverse scattering problem KW - Fault approximation KW - Fault detection Y1 - 2023 SN - 1572-9265 N1 - Corresponding author: Matthias Grajewski VL - 93 IS - 4 PB - Springer Science+Business Media CY - Dordrecht ER - TY - JOUR A1 - Gaigall, Daniel A1 - Gerstenberg, Julian T1 - Cramér-von-Mises tests for the distribution of the excess over a confidence level JF - Journal of Nonparametric Statistics N2 - The Cramér-von-Mises distance is applied to the distribution of the excess over a confidence level. Asymptotics of related statistics are investigated, and it is seen that the obtained limit distributions differ from the classical ones. For that reason, quantiles of the new limit distributions are given and new bootstrap techniques for approximation purposes are introduced and justified. The results motivate new one-sample goodness-of-fit tests for the distribution of the excess over a confidence level and a new confidence interval for the related fitting error. Simulation studies investigate size and power of the tests as well as coverage probabilities of the confidence interval in the finite sample case. A practice-oriented application of the Cramér-von-Mises tests is the determination of an appropriate confidence level for the fitting approach. The adoption of the idea to the well-known problem of threshold detection in the context of peaks over threshold modelling is sketched and illustrated by data examples. KW - Cramér-von-Mises test KW - conditional excess distribution KW - confidence interval KW - goodness-of-fit test Y1 - 2023 U6 - https://doi.org/10.1080/10485252.2023.2173958 SN - 1048-5252 (Print) SN - 1029-0311 (Online) PB - Taylor & Francis ER - TY - THES A1 - Gaigall, Daniel T1 - On selected problems in multivariate analysis N2 - Selected problems in the field of multivariate statistical analysis are treated. Thereby, one focus is on the paired sample case. Among other things, statistical testing problems of marginal homogeneity are under consideration. In detail, properties of Hotelling‘s T² test in a special parametric situation are obtained. Moreover, the nonparametric problem of marginal homogeneity is discussed on the basis of possibly incomplete data. In the bivariate data case, properties of the Hoeffding-Blum-Kiefer-Rosenblatt independence test statistic on the basis of partly not identically distributed data are investigated. Similar testing problems are treated within the scope of the application of a result for the empirical process of the concomitants for partly categorial data. Furthermore, testing changes in the modeled solvency capital requirement of an insurance company by means of a paired sample from an internal risk model is discussed. Beyond the paired sample case, a new asymptotic relative efficiency concept based on the expected volumes of multidimensional confidence regions is introduced. Besides, a new approach for the treatment of the multi-sample goodness-of-fit problem is presented. Finally, a consistent test for the treatment of the goodness-of-fit problem is developed for the background of huge or infinite dimensional data. KW - Paired sample KW - Marginal homogeneity KW - Incomplete data KW - Asymptotic relative efficiency KW - Volumes of confidence regions Y1 - 2023 U6 - https://doi.org/10.15488/14304 N1 - Gottfried Wilhelm Leibniz Universität Hannover ER - TY - JOUR A1 - Gaigall, Daniel ED - AitSahlia, Farid T1 - Allocating and forecasting changes in risk JF - Journal of risk N2 - We consider time-dependent portfolios and discuss the allocation of changes in the risk of a portfolio to changes in the portfolio’s components. For this purpose we adopt established allocation principles. We also use our approach to obtain forecasts for changes in the risk of the portfolio’s components. To put the approach into practice we present an implementation based on the output of a simulation. Allocation is illustrated with an example portfolio in the context of Solvency II. The quality of the forecasts is investigated with an empirical study. KW - portfolio risk KW - allocation KW - forecast KW - covariance principle KW - conditional expectation principle Y1 - 2023 U6 - https://doi.org/10.21314/JOR.2022.048 SN - 1755-2842 SN - 1465-1211 VL - 25 IS - 3 SP - 1 EP - 24 PB - Infopro Digital Risk CY - London ER - TY - JOUR A1 - Gaigall, Daniel T1 - On the applicability of several tests to models with not identically distributed random effects JF - Statistics : A Journal of Theoretical and Applied Statistics N2 - We consider Kolmogorov–Smirnov and Cramér–von-Mises type tests for testing central symmetry, exchangeability, and independence. In the standard case, the tests are intended for the application to independent and identically distributed data with unknown distribution. The tests are available for multivariate data and bootstrap procedures are suitable to obtain critical values. We discuss the applicability of the tests to random effects models, where the random effects are independent but not necessarily identically distributed and with possibly unknown distributions. Theoretical results show the adequacy of the tests in this situation. The quality of the tests in models with random effects is investigated by simulations. Empirical results obtained confirm the theoretical findings. A real data example illustrates the application. KW - central symmetry test KW - exchangeability test KW - independence test KW - random effects KW - not identically distributed Y1 - 2023 SN - 0323-3944 U6 - https://doi.org/10.1080/02331888.2023.2193748 SN - 1029-4910 VL - 57 PB - Taylor & Francis CY - London ER - TY - CHAP A1 - Funke, Harald A1 - Beckmann, Nils A1 - Stefan, Lukas A1 - Keinz, Jan T1 - Hydrogen combustor integration study for a medium range aircraft engine using the dry-low NOx “Micromix” combustion principle T2 - Proceedings of the ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition. Volume 1: Aircraft Engine. N2 - The feasibility study presents results of a hydrogen combustor integration for a Medium-Range aircraft engine using the Dry-Low-NOₓ Micromix combustion principle. Based on a simplified Airbus A320-type flight mission, a thermodynamic performance model of a kerosene and a hydrogen-powered V2530-A5 engine is used to derive the thermodynamic combustor boundary conditions. A new combustor design using the Dry-Low NOx Micromix principle is investigated by slice model CFD simulations of a single Micromix injector for design and off-design operation of the engine. Combustion characteristics show typical Micromix flame shapes and good combustion efficiencies for all flight mission operating points. Nitric oxide emissions are significant below ICAO CAEP/8 limits. For comparison of the Emission Index (EI) for NOₓ emissions between kerosene and hydrogen operation, an energy (kerosene) equivalent Emission Index is used. A full 15° sector model CFD simulation of the combustion chamber with multiple Micromix injectors including inflow homogenization and dilution and cooling air flows investigates the combustor integration effects, resulting NOₓ emission and radial temperature distributions at the combustor outlet. The results show that the integration of a Micromix hydrogen combustor in actual aircraft engines is feasible and offers, besides CO₂ free combustion, a significant reduction of NOₓ emissions compared to kerosene operation. KW - emission index KW - nitric oxides KW - aircraft engine KW - Micromix KW - combustion KW - hydrogen Y1 - 2023 SN - 978-0-7918-8693-9 U6 - https://doi.org/10.1115/GT2023-102370 N1 - Paper No. GT2023-102370, V001T01A022 N1 - ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition June 26–30, 2023, Boston, Massachusetts, USA PB - ASME CY - New York ER - TY - CHAP A1 - Freyer, Nils A1 - Thewes, Dustin A1 - Meinecke, Matthias ED - Gusikhin, Oleg ED - Hammoudi, Slimane ED - Cuzzocrea, Alfredo T1 - GUIDO: a hybrid approach to guideline discovery & ordering from natural language texts T2 - Proceedings of the 12th International Conference on Data Science, Technology and Applications DATA - Volume 1 N2 - Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low. KW - Natural Language Processing KW - Text Mining KW - Process Model Extraction KW - Business Process Intelligence Y1 - 2023 SN - 978-989-758-664-4 U6 - https://doi.org/10.5220/0012084400003541 SN - 2184-285X N1 - 12th International Conference on Data Science, Technology and Applications, July 11-13, 2023, in Rome, Italy. SP - 335 EP - 342 ER - TY - CHAP A1 - Freyer, Nils A1 - Kempt, Hendrik ED - Bhakuni, Himani ED - Miotto, Lucas T1 - AI-DSS in healthcare and their power over health-insecure collectives T2 - Justice in global health N2 - AI-based systems are nearing ubiquity not only in everyday low-stakes activities but also in medical procedures. To protect patients and physicians alike, explainability requirements have been proposed for the operation of AI-based decision support systems (AI-DSS), which adds hurdles to the productive use of AI in clinical contexts. This raises two questions: Who decides these requirements? And how should access to AI-DSS be provided to communities that reject these standards (particularly when such communities are expert-scarce)? This chapter investigates a dilemma that emerges from the implementation of global AI governance. While rejecting global AI governance limits the ability to help communities in need, global AI governance risks undermining and subjecting health-insecure communities to the force of the neo-colonial world order. For this, this chapter first surveys the current landscape of AI governance and introduces the approach of relational egalitarianism as key to (global health) justice. To discuss the two horns of the referred dilemma, the core power imbalances faced by health-insecure collectives (HICs) are examined. The chapter argues that only strong demands of a dual strategy towards health-secure collectives can both remedy the immediate needs of HICs and enable them to become healthcare independent. Y1 - 2023 SN - 9781003399933 U6 - https://doi.org/10.4324/9781003399933-4 SP - 38 EP - 55 PB - Routledge CY - London ER - TY - JOUR A1 - Fayyazi, Mojgan A1 - Sardar, Paramjotsingh A1 - Thomas, Sumit Infent A1 - Daghigh, Roonak A1 - Jamali, Ali A1 - Esch, Thomas A1 - Kemper, Hans A1 - Langari, Reza A1 - Khayyam, Hamid T1 - Artificial intelligence/machine learning in energy management systems, control, and optimization of hydrogen fuel cell vehicles N2 - Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed. KW - optimization system KW - intelligent control KW - fuel cell vehicle KW - machine learning KW - artificial intelligence KW - intelligent energy management Y1 - 2023 U6 - https://doi.org/10.3390/su15065249 N1 - This article belongs to the Special Issue "Circular Economy and Artificial Intelligence" VL - 15 IS - 6 SP - 38 PB - MDPI CY - Basel ER -