TY - JOUR A1 - Trauth, Martin H. T1 - TURBO2 - a MATLAB simulation to study the effects of bioturbation on paleoceanographic time series JF - Computers & geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology N2 - Bioturbation (or benthic mixing) causes significant distortions in marine stable isotope signals and other palaeoceanographic records. Although the influence of bioturbation on these records is well known it has rarely been dealt systematically. The MATLAB program called TURBO2 can be used to simulate the effect of bioturbation on individual sediment particles. It can therefore be used to model the distortion of all physical, chemical, and biological signals in deep-sea sediments, such as Mg/Ca ratios and UK37-based sea-surface temperature (SST) variations. In particular, it can be used to study the distortions in paleoceanographic records that are based on individual sediment particles, such as SST records based on foraminifera assemblages. Furthermore. TURBO2 provides a tool to study the effect of benthic mixing of isotope signals such as C-14, delta O-18, and delta C-13, measured in a stratigraphic carrier such as foraminifera shells. KW - Bioturbation KW - Modeling KW - MATLAB KW - Deep-sea records KW - Foraminifera KW - Stable oxygen isotopes Y1 - 2013 U6 - https://doi.org/10.1016/j.cageo.2013.05.003 SN - 0098-3004 SN - 1873-7803 VL - 61 IS - 12 SP - 1 EP - 10 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Schneider, Anne-Kathrin A1 - Schröder-Esselbach, Boris T1 - Perspectives in modelling earthworm dynamics and their feedbacks with abiotic soil properties JF - Applied soil ecology : a section of agriculture, ecosystems & environment N2 - Effects of earthworms on soil abiotic properties are well documented from several decades of laboratory and mesocosm experiments, and they are supposed to affect large-scale soil ecosystem functioning. The prediction of the spatiotemporal occurrence of earthworms and the related functional effects in the field or at larger scales, however, is constrained by adequate modelling approaches. Correlative, phenomenological methods, such as species distribution models, facilitate the identification of factors that drive species' distributions. However, these methods ignore the ability of earthworms to select and modify their own habitat and therefore may lead to unreliable predictions. Understanding these feedbacks between earthworms and abiotic soil properties is a key requisite to better understand their spatiotemporal distribution as well as to quantify the various functional effects of earthworms in soil ecosystems. Process-based models that investigate either effects or responses of earthworms on soil environmental conditions are mostly applied in ecotoxicological and bioturbation studies. Process-based models that describe feedbacks between earthworms and soil abiotic properties explicitly are rare. In this review, we analysed 18 process-based earthworm dynamic modelling studies pointing out the current gaps and future challenges in feedback modelling. We identify three main challenges: (i) adequate and reliable process identification in model development at and across relevant spatiotemporal scales (individual behaviour and population dynamics of earthworms), (ii) use of information from different data sources in one model (laboratory or field experiments, earthworm species or functional type) and (iii) quantification of uncertainties in data (e.g. spatiotemporal variability of earthworm abundances and soil hydraulic properties) and derived parameters (e.g. population growth rate and hydraulic conductivity) that are used in the model. KW - Oligochaeta KW - Feedback biotic-abiotic KW - Functional effect KW - Population dynamics KW - Modeling KW - Ecosystem engineer Y1 - 2012 U6 - https://doi.org/10.1016/j.apsoil.2012.02.020 SN - 0929-1393 VL - 58 IS - 1 SP - 29 EP - 36 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Romero-Mujalli, Daniel A1 - Jeltsch, Florian A1 - Tiedemann, Ralph T1 - Individual-based modeling of eco-evolutionary dynamics BT - state of the art and future directions JF - Regional environmental change N2 - A challenge for eco-evolutionary research is to better understand the effect of climate and landscape changes on species and their distribution. Populations of species can respond to changes in their environment through local genetic adaptation or plasticity, dispersal, or local extinction. The individual-based modeling (IBM) approach has been repeatedly applied to assess organismic responses to environmental changes. IBMs simulate emerging adaptive behaviors from the basic entities upon which both ecological and evolutionary mechanisms act. The objective of this review is to summarize the state of the art of eco-evolutionary IBMs and to explore to what degree they already address the key responses of organisms to environmental change. In this, we identify promising approaches and potential knowledge gaps in the implementation of eco-evolutionary mechanisms to motivate future research. Using mainly the ISI Web of Science, we reveal that most of the progress in eco-evolutionary IBMs in the last decades was achieved for genetic adaptation to novel local environmental conditions. There is, however, not a single eco-evolutionary IBM addressing the three potential adaptive responses simultaneously. Additionally, IBMs implementing adaptive phenotypic plasticity are rare. Most commonly, plasticity was implemented as random noise or reaction norms. Our review further identifies a current lack of models where plasticity is an evolving trait. Future eco-evolutionary models should consider dispersal and plasticity as evolving traits with their associated costs and benefits. Such an integrated approach could help to identify conditions promoting population persistence depending on the life history strategy of organisms and the environment they experience. KW - Modeling KW - Individual-based models KW - Ecology KW - Evolution KW - Eco-evolutionary dynamics Y1 - 2018 U6 - https://doi.org/10.1007/s10113-018-1406-7 SN - 1436-3798 SN - 1436-378X VL - 19 IS - 1 SP - 1 EP - 12 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Li, Yuanqing A1 - Chen, Li A1 - Nofal, Issam A1 - Chen, Mo A1 - Wang, Haibin A1 - Liu, Rui A1 - Chen, Qingyu A1 - Krstić, Miloš A1 - Shi, Shuting A1 - Guo, Gang A1 - Baeg, Sang H. A1 - Wen, Shi-Jie A1 - Wong, Richard T1 - Modeling and analysis of single-event transient sensitivity of a 65 nm clock tree JF - Microelectronics reliability N2 - The soft error rate (SER) due to heavy-ion irradiation of a clock tree is investigated in this paper. A method for clock tree SER prediction is developed, which employs a dedicated soft error analysis tool to characterize the single-event transient (SET) sensitivities of clock inverters and other commercial tools to calculate the SER through fault-injection simulations. A test circuit including a flip-flop chain and clock tree in a 65 nm CMOS technology is developed through the automatic ASIC design flow. This circuit is analyzed with the developed method to calculate its clock tree SER. In addition, this circuit is implemented in a 65 nm test chip and irradiated by heavy ions to measure its SER resulting from the SETs in the clock tree. The experimental and calculation results of this case study present good correlation, which verifies the effectiveness of the developed method. KW - Clock tree KW - Modeling KW - Single-event transient (SET) Y1 - 2018 U6 - https://doi.org/10.1016/j.microrel.2018.05.016 SN - 0026-2714 VL - 87 SP - 24 EP - 32 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Klose, Tim A1 - Guillemoteau, Julien A1 - Simon, Francois-Xavier A1 - Tronicke, Jens T1 - Toward subsurface magnetic permeability imaging with electromagnetic induction sensors BT - Sensitivity computation and reconstruction of measured data JF - Geophysics N2 - In near-surface geophysics, small portable loop-loop electro-magnetic induction (EMI) sensors using harmonic sources with a constant and rather small frequency are increasingly used to investigate the electrical properties of the subsurface. For such sensors, the influence of electrical conductivity and magnetic permeability on the EMI response is well-understood. Typically, data analysis focuses on reconstructing an electrical conductivity model by inverting the out-of-phase response. However, in a variety of near-surface applications, magnetic permeability (or susceptibility) models derived from the in-phase (IP) response may provide important additional information. In view of developing a fast 3D inversion procedure of the IP response for a dense grid of measurement points, we first analyze the 3D sensitivity functions associated with a homogeneous permeable half-space. Then, we compare synthetic data computed using a linear forward-modeling method based on these sensitivity functions with synthetic data computed using full nonlinear forward-modeling methods. The results indicate the correctness and applicability of our linear forward-modeling approach. Furthermore, we determine the advantages of converting IP data into apparent permeability, which, for example, allows us to extend the applicability of the linear forward-modeling method to high-magnetic environments. Finally, we compute synthetic data with the linear theory for a model consisting of a controlled magnetic target and compare the results with field data collected with a four-configuration loop-loop EMI sensor. With this field-scale experiment, we determine that our linear forward-modeling approach can reproduce measured data with sufficiently small error, and, thus, it represents the basis for developing efficient inversion approaches. KW - Electromagnetics KW - Imaging KW - Magnetic+Susceptibility KW - Near+Surface KW - Modeling Y1 - 2018 U6 - https://doi.org/10.1190/GEO2017-0827.1 SN - 0016-8033 SN - 1942-2156 VL - 83 IS - 5 SP - E335 EP - E345 PB - Society of Exploration Geophysicists CY - Tulsa ER - TY - JOUR A1 - Hoffmann, Falk A1 - Machatschek, Rainhard Gabriel A1 - Lendlein, Andreas T1 - Analytical model and Monte Carlo simulations of polymer degradation with improved chain cut statistics JF - Journal of materials research : JMR N2 - The degradation of polymers is described by mathematical models based on bond cleavage statistics including the decreasing probability of chain cuts with decreasing average chain length. We derive equations for the degradation of chains under a random chain cut and a chain end cut mechanism, which are compared to existing models. The results are used to predict the influence of internal molecular parameters. It is shown that both chain cut mechanisms lead to a similar shape of the mass or molecular mass loss curve. A characteristic time is derived, which can be used to extract the maximum length of soluble fragments l of the polymer. We show that the complete description is needed to extract the degradation rate constant k from the molecular mass loss curve and that l can be used to design polymers that lose less mechanical stability before entering the mass loss phase. KW - Modeling KW - Degradable KW - Polymer KW - Molecular weight KW - Simulation Y1 - 2022 U6 - https://doi.org/10.1557/s43578-022-00495-4 SN - 0884-2914 SN - 2044-5326 VL - 37 IS - 5 SP - 1093 EP - 1101 PB - Springer CY - Heidelberg ER - TY - CHAP A1 - Haase, Jennifer A1 - Thim, Christof A1 - Bender, Benedict T1 - Expanding modeling notations BT - requirements for creative process modeling T2 - Business Process Management Workshops. BPM 2021 / Lecture Notes in Business Information Processing N2 - Creativity is a common aspect of business processes and thus needs a proper representation through process modeling notations. However, creative processes constitute highly flexible process elements, as new and unforeseeable outcome is developed. This presents a challenge for modeling languages. Current methods representing creative-intensive work are rather less able to capture creative specifics which are relevant to successfully run and manage these processes. We outline the concept of creative-intensive processes and present an example from a game design process in order to derive critical process aspects relevant for its modeling. Six aspects are detected, with first and foremost: process flexibility, as well as temporal uncertainty, experience, types of creative problems, phases of the creative process and individual criteria. By first analyzing what aspects of creative work modeling notations already cover, we further discuss which modeling extensions need to be developed to better represent creativity within business processes. We argue that a proper representation of creative work would not just improve the management of those processes, but can further enable process actors to more efficiently run these creative processes and adjust them to better fit to the creative needs. KW - Modeling KW - Requirements KW - Pockets of creativity KW - Creative process Y1 - 2022 SN - 978-3-030-94342-4 SN - 978-3-030-94343-1 U6 - https://doi.org/10.1007/978-3-030-94343-1_15 IS - 436 SP - 193 EP - 196 PB - Springer CY - Cham ER - TY - THES A1 - Grum, Marcus T1 - Construction of a concept of neuronal modeling N2 - The business problem of having inefficient processes, imprecise process analyses, and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating, and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS), and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes. N2 - Die vorliegende Arbeit addressiert das Geschäftsproblem von ineffizienten Prozessen, unpräzisen Prozessanalysen und -simulationen sowie untransparenten künstlichen neuronalen Netzwerken, indem ein Modellierungskonzept zum Neuronalen Modellieren konstruiert wird. Dieses neuartige Konzept des Neuronalen Modellierens (CoNM) fungiert als flexibler und effizienter Ansatz zum Modellieren, Simulieren und Optimieren von Prozessen mit Hilfe von neuronalen Netzwerken und wird mittels einer Modellierungssprache, dessen mathematischen Formalisierung und technischen Substanziierung sowie einer Sammlung von neuartigen Subartefakten beschrieben. In der Verwendung derer Implementierung als CoNM-Werkzeuge können somit neue Arten einer Neuronalen-Prozess-Modellierung (NPM), Neuronalen-Prozess-Simulation (NPS) sowie Neuronalen-Prozess-Optimierung (NPO) realisiert werden. Die Wirksamkeit der erstellten Artefakte wurde anhand von sechs Experimenten demonstriert sowie in einem Simulator in realen Produktionsprozessen gezeigt. T2 - Konzept des Neuronalen Modellierens KW - Deep Learning KW - Artificial Neuronal Network KW - Explainability KW - Interpretability KW - Business Process KW - Simulation KW - Optimization KW - Knowledge Management KW - Process Management KW - Modeling KW - Process KW - Knowledge KW - Learning KW - Enterprise Architecture KW - Industry 4.0 KW - Künstliche Neuronale Netzwerke KW - Erklärbarkeit KW - Interpretierbarkeit KW - Geschäftsprozess KW - Simulation KW - Optimierung KW - Wissensmanagement KW - Prozessmanagement KW - Modellierung KW - Prozess KW - Wissen KW - Lernen KW - Enterprise Architecture KW - Industrie 4.0 Y1 - 2021 ER - TY - JOUR A1 - de Abreu e Lima, Francisco Anastacio A1 - Leifels, Lydia A1 - Nikoloski, Zoran T1 - Regression-based modeling of complex plant traits based on metabolomics data JF - Plant Metabolomics N2 - Bridging metabolomics with plant phenotypic responses is challenging. Multivariate analyses account for the existing dependencies among metabolites, and regression models in particular capture such dependencies in search for association with a given trait. However, special care should be undertaken with metabolomics data. Here we propose a modeling workflow that considers all caveats imposed by such large data sets. KW - Metabolomics KW - Plants KW - Trait KW - Regression KW - Prediction KW - Modeling KW - R programing language KW - R software packages Y1 - 2018 SN - 978-1-4939-7819-9 SN - 978-1-4939-7818-2 U6 - https://doi.org/10.1007/978-1-4939-7819-9_23 SN - 1064-3745 SN - 1940-6029 VL - 1778 SP - 321 EP - 327 PB - Humana Press Inc. CY - New York ER -