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  • 2020-2024  (192)
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  • 101
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    On generalized surrogate duality in mixed-integer nonlinear programming (2022)
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    Publication Date: 2023-11-03
    Description: The most important ingredient for solving mixed-integer nonlinear programs (MINLPs) to global ϵ-optimality with spatial branch and bound is a tight, computationally tractable relaxation. Due to both theoretical and practical considerations, relaxations of MINLPs are usually required to be convex. Nonetheless, current optimization solvers can often successfully handle a moderate presence of nonconvexities, which opens the door for the use of potentially tighter nonconvex relaxations. In this work, we exploit this fact and make use of a nonconvex relaxation obtained via aggregation of constraints: a surrogate relaxation. These relaxations were actively studied for linear integer programs in the 70s and 80s, but they have been scarcely considered since. We revisit these relaxations in an MINLP setting and show the computational benefits and challenges they can have. Additionally, we study a generalization of such relaxation that allows for multiple aggregations simultaneously and present the first algorithm that is capable of computing the best set of aggregations. We propose a multitude of computational enhancements for improving its practical performance and evaluate the algorithm’s ability to generate strong dual bounds through extensive computational experiments.
    Language: English
    Type: article , doc-type:article
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  • 102
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    Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches (2022)
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    Publication Date: 2023-11-03
    Description: Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein–protein interfaces. Here in this review, we provide an overview of the progress that has been made in virtual screening methodology and technology on multiple fronts in recent years. The advent of ultra-large virtual screens, in which hundreds of millions to billions of compounds are screened, has proven to be a powerful approach to discover highly potent hit compounds. However, these developments are just the tip of the iceberg, with new technologies and methods emerging to propel the field forward. Examples include novel machine-learning approaches, which can reduce the computational costs of virtual screening dramatically, while progress in quantum-mechanical approaches can increase the accuracy of predictions of various small molecule properties.
    Language: English
    Type: article , doc-type:article
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  • 103
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    ULD Build-Up Scheduling with Dynamic Batching in an Air Freight Hub (2022)
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    Publication Date: 2023-11-03
    Description: Air freight is usually shipped in standardized unit load devices (ULDs). The planning process for the consolidation of transit cargo from inbound flights or locally emerging shipments into ULDs for outbound flights is called build-up scheduling. More specifically, outbound ULDs must be assigned a time and a workstation subject to both workstation capacity constraints and the availability of shipments which in turn depends on break-down decisions for incoming ULDs. ULDs scheduled for the same outbound flight should be built up in temporal and spatial proximity. This serves both to minimize overhead in transportation times and to allow workers to move freight between ULDs. We propose to address this requirement by processing ULDs for the same outbound flight in batches. For the above build-up scheduling problem, we introduce a multi-commodity network design model. Outbound flights are modeled as commodities; transit cargo is represented by cargo flow volume and unpack and batch decisions are represented as design variables. The model is solved with a standard MIP solver on a set of benchmark data. For instances with a limited number of resource conflicts, near-optimal solutions are found in under two hours for a whole week of operations.
    Language: English
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  • 104
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    Three-dimensional structure of kinetochore-fibers in human mitotic spindles (2022)
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    Publication Date: 2023-11-03
    Description: During cell division, kinetochore microtubules (KMTs) provide a physical linkage between the chromosomes and the rest of the spindle. KMTs in mammalian cells are organized into bundles, so-called kinetochore-fibers (k-fibers), but the ultrastructure of these fibers is currently not well characterized. Here we show by large-scale electron tomography that each k-fiber in HeLa cells in metaphase is composed of approximately nine KMTs, only half of which reach the spindle pole. Our comprehensive reconstructions allowed us to analyze the three-dimensional (3D) morphology of k-fibers and their surrounding MTs in detail. We found that k-fibers exhibit remarkable variation in circumference and KMT density along their length, with the pole-proximal side showing a broadening. Extending our structural analysis then to other MTs in the spindle, we further observed that the association of KMTs with non-KMTs predominantly occurs in the spindle pole regions. Our 3D reconstructions have implications for KMT growth and k-fiber self-organization models as covered in a parallel publication applying complementary live-cell imaging in combination with biophysical modeling (Conway et al., 2022). Finally, we also introduce a new visualization tool allowing an interactive display of our 3D spindle data that will serve as a resource for further structural studies on mitosis in human cells.
    Language: English
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  • 105
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    An Algorithm-independent Measure of Progress for Linear Constraint Propagation (2022)
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    Publication Date: 2023-11-03
    Language: English
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  • 106
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    Low-Rank Tensor Decompositions of Quantum Circuits (2022)
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    Publication Date: 2023-11-03
    Description: Quantum computing is arguably one of the most revolutionary and disruptive technologies of this century. Due to the ever-increasing number of potential applications as well as the continuing rise in complexity, the development, simulation, optimization, and physical realization of quantum circuits is of utmost importance for designing novel algorithms. We show how matrix product states (MPSs) and matrix product operators (MPOs) can be used to express certain quantum states, quantum gates, and entire quantum circuits as low-rank tensors. This enables the analysis and simulation of complex quantum circuits on classical computers and to gain insight into the underlying structure of the system. We present different examples to demonstrate the advantages of MPO formulations and show that they are more efficient than conventional techniques if the bond dimensions of the wave function representation can be kept small throughout the simulation.
    Language: English
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  • 107
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    Strengthening SONC Relaxations with Constraints Derived from Variable Bounds (2022)
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    Publication Date: 2023-11-03
    Description: Certificates of polynomial nonnegativity can be used to obtain tight dual bounds for polynomial optimization problems. We consider Sums of Nonnegative Circuit (SONC) polynomials certificates, which are well suited for sparse problems since the computational cost depends only on the number of terms in the polynomials and does not depend on the degrees of the polynomials. This work is a first step to integrating SONC-based relaxations of polynomial problems into a branch-and-bound algorithm. To this end, the SONC relaxation for constrained optimization problems is extended in order to better utilize variable bounds, since this property is key for the success of a relaxation in the context of branch-and-bound. Computational experiments show that the proposed extension is crucial for making the SONC relaxations applicable to most constrained polynomial optimization problems and for integrating the two approaches.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
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  • 108
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    Parameter estimation on multivalent ITC data sets (2022)
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    Publication Date: 2023-11-03
    Description: The Wiseman fitting can be used to extract binding parameters from ITC data sets, such as heat of binding, number of binding sites, and the overall dissociation rate. The classical Wiseman fitting assumes a direct binding process and neglects the possibility of intermediate binding steps. In principle, it only provides thermodynamic information and not the kinetics of the process. In this article we show that a concentration dependent dissociation constant could possibly stem from intermediate binding steps. The mathematical form of this dependency can be exploited with the aid of the Robust Perron Cluster Cluster Analysis method. Our proposed extension of the Wiseman fitting rationalizes the concentration dependency, and can probably also be used to determine the kinetic parameters of intermediate binding steps of a multivalent binding process. The novelty of this paper is to assume that the binding rate varies per titration step due to the change of the ligand concentration and to use this information in the Wiseman fitting. We do not claim to produce the most accurate values of the binding parameters, we rather present a novel method of how to approach multivalent bindings from a different angle.
    Language: English
    Type: article , doc-type:article
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  • 109
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    データ格付けサービス実現のための数理基盤の構築 (2022)
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    Publication Date: 2023-11-03
    Language: Japanese
    Type: proceedings , doc-type:Other
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  • 110
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    Construction of Mathematical Basis for Realizing Data Rating Service (2022)
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    Publication Date: 2023-11-03
    Language: English
    Type: proceedings , doc-type:Other
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  • 111
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    Solving the time-independent Schrödinger equation for chains of coupled excitons and phonons using tensor trains (2022)
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    Publication Date: 2023-11-03
    Description: We demonstrate how to apply the tensor-train format to solve the time-independent Schrödinger equation for quasi-one-dimensional excitonic chain systems with and without periodic boundary conditions. The coupled excitons and phonons are modeled by Fröhlich–Holstein type Hamiltonians with on-site and nearest-neighbor interactions only. We reduce the memory consumption as well as the computational costs significantly by employing efficient decompositions to construct low-rank tensor-train representations, thus mitigating the curse of dimensionality. In order to compute also higher quantum states, we introduce an approach that directly incorporates the Wielandt deflation technique into the alternating linear scheme for the solution of eigenproblems. Besides systems with coupled excitons and phonons, we also investigate uncoupled problems for which (semi-)analytical results exist. There, we find that in the case of homogeneous systems, the tensor-train ranks of state vectors only marginally depend on the chain length, which results in a linear growth of the storage consumption. However, the central processing unit time increases slightly faster with the chain length than the storage consumption because the alternating linear scheme adopted in our work requires more iterations to achieve convergence for longer chains and a given rank. Finally, we demonstrate that the tensor-train approach to the quantum treatment of coupled excitons and phonons makes it possible to directly tackle the phenomenon of mutual self-trapping. We are able to confirm the main results of the Davydov theory, i.e., the dependence of the wave packet width and the corresponding stabilization energy on the exciton–phonon coupling strength, although only for a certain range of that parameter. In future work, our approach will allow calculations also beyond the validity regime of that theory and/or beyond the restrictions of the Fröhlich–Holstein type Hamiltonians.
    Language: English
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  • 112
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    Predicting cognitive scores with graph neural networks through sample selection learning (2022)
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    Publication Date: 2023-11-06
    Description: Analyzing the relation between intelligence and neural activity is of the utmost importance in understanding the working principles of the human brain in health and disease. In existing literature, functional brain connectomes have been used successfully to predict cognitive measures such as intelligence quotient (IQ) scores in both healthy and disordered cohorts using machine learning models. However, existing methods resort to flattening the brain connectome (i.e., graph) through vectorization which overlooks its topological properties. To address this limitation and inspired from the emerging graph neural networks (GNNs), we design a novel regression GNN model (namely RegGNN) for predicting IQ scores from brain connectivity. On top of that, we introduce a novel, fully modular sample selection method to select the best samples to learn from for our target prediction task. However, since such deep learning architectures are computationally expensive to train, we further propose a \emph{learning-based sample selection} method that learns how to choose the training samples with the highest expected predictive power on unseen samples. For this, we capitalize on the fact that connectomes (i.e., their adjacency matrices) lie in the symmetric positive definite (SPD) matrix cone. Our results on full-scale and verbal IQ prediction outperforms comparison methods in autism spectrum disorder cohorts and achieves a competitive performance for neurotypical subjects using 3-fold cross-validation. Furthermore, we show that our sample selection approach generalizes to other learning-based methods, which shows its usefulness beyond our GNN architecture.
    Language: English
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  • 113
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    A Hierarchical Geodesic Model for Longitudinal Analysis on Manifolds (2022)
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    Publication Date: 2023-11-06
    Description: In many applications, geodesic hierarchical models are adequate for the study of temporal observations. We employ such a model derived for manifold-valued data to Kendall's shape space. In particular, instead of the Sasaki metric, we adapt a functional-based metric, which increases the computational efficiency and does not require the implementation of the curvature tensor. We propose the corresponding variational time discretization of geodesics and employ the approach for longitudinal analysis of 2D rat skulls shapes as well as 3D shapes derived from an imaging study on osteoarthritis. Particularly, we perform hypothesis test and estimate the mean trends.
    Language: English
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  • 114
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    A Nonlinear Hierarchical Model for Longitudinal Data on Manifolds (2022)
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    Publication Date: 2023-11-06
    Description: Large longitudinal studies provide lots of valuable information, especially in medical applications. A problem which must be taken care of in order to utilize their full potential is that of correlation between intra-subject measurements taken at different times. For data in Euclidean space this can be done with hierarchical models, that is, models that consider intra-subject and between-subject variability in two different stages. Nevertheless, data from medical studies often takes values in nonlinear manifolds. Here, as a first step, geodesic hierarchical models have been developed that generalize the linear ansatz by assuming that time-induced intra-subject variations occur along a generalized straight line in the manifold. However, this is often not the case (e.g., periodic motion or processes with saturation). We propose a hierarchical model for manifold-valued data that extends this to include trends along higher-order curves, namely Bézier splines in the manifold. To this end, we present a principled way of comparing shape trends in terms of a functional-based Riemannian metric. Remarkably, this metric allows efficient, yet simple computations by virtue of a variational time discretization requiring only the solution of regression problems. We validate our model on longitudinal data from the osteoarthritis initiative, including classification of disease progression.
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 115
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    Model-Chain Validation for Estimating the Energy Yield of Bifacial Perovskite/Silicon Tandem Solar Cells (2022)
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    Publication Date: 2023-11-06
    Language: English
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  • 116
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    An Interactive Approach for Identifying Structure Definitions (2022)
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    Publication Date: 2023-11-06
    Description: Our ability to grasp and understand complex phenomena is essentially based on recognizing structures and relating these to each other. For example, any meteorological description of a weather condition and explanation of its evolution recurs to meteorological structures, such as convection and circulation structures, cloud fields and rain fronts. All of these are spatiotemporal structures, defined by time-dependent patterns in the underlying fields. Typically, such a structure is defined by a verbal description that corresponds to the more or less uniform, often somewhat vague mental images of the experts. However, a precise, formal definition of the structures or, more generally, concepts is often desirable, e.g., to enable automated data analysis or the development of phenomenological models. Here, we present a systematic approach and an interactive tool to obtain formal definitions of spatiotemporal structures. The tool enables experts to evaluate and compare different structure definitions on the basis of data sets with time-dependent fields that contain the respective structure. Since structure definitions are typically parameterized, an essential part is to identify parameter ranges that lead to desired structures in all time steps. In addition, it is important to allow a quantitative assessment of the resulting structures simultaneously. We demonstrate the use of the tool by applying it to two meteorological examples: finding structure definitions for vortex cores and center lines of temporarily evolving tropical cyclones. Ideally, structure definitions should be objective and applicable to as many data sets as possible. However, finding such definitions, e.g., for the common atmospheric structures in meteorology, can only be a long-term goal. The proposed procedure, together with the presented tool, is just a first systematic approach aiming at facilitating this long and arduous way.
    Language: English
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  • 117
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    The Impact of Neuron Morphology on Cortical Network Architecture (2022)
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    Publication Date: 2023-11-06
    Description: The neurons in the cerebral cortex are not randomly interconnected. This specificity in wiring can result from synapse formation mechanisms that connect neurons depending on their electrical activity and genetically defined identity. Here, we report that the morphological properties of the neurons provide an additional prominent source by which wiring specificity emerges in cortical networks. This morphologically determined wiring specificity reflects similarities between the neurons’ axo-dendritic projections patterns, the packing density and cellular diversity of the neuropil. The higher these three factors are the more recurrent is the topology of the network. Conversely, the lower these factors are the more feedforward is the network’s topology. These principles predict the empirically observed occurrences of clusters of synapses, cell type-specific connectivity patterns, and nonrandom network motifs. Thus, we demonstrate that wiring specificity emerges in the cerebral cortex at subcellular, cellular and network scales from the specific morphological properties of its neuronal constituents.
    Language: English
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  • 118
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    On Gradient Formulas in an Algorithm for the Logarithm of the Sasaki Metric (2022)
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    Publication Date: 2023-11-06
    Description: The Sasaki metric is the canonical metric on the tangent bundle TM of a Riemannian manifold M. It is highly useful for data analysis in TM (e.g., when one is interested in the statistics of a set of geodesics in M). To this end, computing the Riemannian logarithm is often necessary, and an iterative algorithm was proposed by Muralidharan and Fletcher. In this note, we derive approximation formulas of the energy gradients in their algorithm that we use with success.
    Language: English
    Type: reportzib , doc-type:preprint
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  • 119
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    A Stratification Matrix Viewer for Analysis of Neural Network Data (2022)
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    Publication Date: 2023-11-06
    Description: The analysis of brain networks is central to neurobiological research. In this context the following tasks often arise: (1) understand the cellular composition of a reconstructed neural tissue volume to determine the nodes of the brain network; (2) quantify connectivity features statistically; and (3) compare these to predictions of mathematical models. We present a framework for interactive, visually supported accomplishment of these tasks. Its central component, the stratification matrix viewer, allows users to visualize the distribution of cellular and/or connectional properties of neurons at different levels of aggregation. We demonstrate its use in four case studies analyzing neural network data from the rat barrel cortex and human temporal cortex.
    Language: English
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  • 120
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    Boosting quantum optics experiments with Bayesian optimization (2022)
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    Publication Date: 2023-11-07
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 121
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    Triangle meshes of shadow-recieving surfaces of ancient sundials (2022)
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    Publication Date: 2023-11-06
    Description: This repository contains triangle meshes of the shadow-recieving surfaces of 13 ancient sundials; three of them are from Greece and 10 from Italy. The meshes are in correspondence.
    Language: English
    Type: researchdata , doc-type:ResearchData
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  • 122
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    Non-stationary Transition Path Theory with applications to tipping and agent-based models (2022)
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    Publication Date: 2023-11-09
    Description: The interesting dynamical regimes in agent-based models (ABMs) of social dynamics are the transient dynamics leading to metastable or absorbing states, and the transition paths between metastable states possibly caused by external influences. In this thesis, we are particularly interested in the pathways of rare and critical transitions such as the tipping of the public opinion in a population or the forming of social movements. For a detailed quantitative analysis of these transition paths, we consider the agent-based models as Markov chains and employ Transition Path Theory. Since ABMs are usually not considered in stationarity and possibly even forced, we generalize Transition Path Theory to time-dependent dynamics, for example on finite-time intervals or with periodically varying transition probabilities. We also specifically consider the case of dynamics with absorbing states and show how the transitions prior to absorption can be studied. These generalizations can also be useful in other application domains such as for studying tipping in climate models or transitions in molecular models with external stimuli. Another obstacle when analysing the dynamics of agent-based models is the large number of agents resulting in a high-dimensional state space for the model. However, the emergent dynamics of the ABM usually has significantly fewer degrees of freedom and many symmetries enabling a model reduction. On the example of two stationary ABMs we demonstrate how a long model simulation can be employed to find a lower-dimensional parametrization of the state space using a manifold learning algorithm called Diffusion Maps. In the considered models, agents adapt their binary behaviour to the local neighbourhood. When the interaction network consists of several densely connected communities, the dynamics result in a largely coherent behaviour in each community. The low-dimensional structure of the state space is therefore a hypercube. The corners represent metastable states with coherent agent behaviour in each group and the edges correspond to transition paths where agents in a community change their behaviour through a chain reaction. Finally, we can apply Transition Path Theory to the effective dynamics in the reduced space to reveal, for example, the dominant transition paths or the agents that are most indicative of an impending tipping event.
    Language: English
    Type: doctoralthesis , doc-type:doctoralThesis
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  • 123
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    Bi-invariant Dissimilarity Measures for Sample Distributions in Lie Groups (2022)
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    Publication Date: 2024-02-21
    Description: Data sets sampled in Lie groups are widespread, and as with multivariate data, it is important for many applications to assess the differences between the sets in terms of their distributions. Indices for this task are usually derived by considering the Lie group as a Riemannian manifold. Then, however, compatibility with the group operation is guaranteed only if a bi-invariant metric exists, which is not the case for most non-compact and non-commutative groups. We show here that if one considers an affine connection structure instead, one obtains bi-invariant generalizations of well-known dissimilarity measures: a Hotelling $T^2$ statistic, Bhattacharyya distance and Hellinger distance. Each of the dissimilarity measures matches its multivariate counterpart for Euclidean data and is translation-invariant, so that biases, e.g., through an arbitrary choice of reference, are avoided. We further derive non-parametric two-sample tests that are bi-invariant and consistent. We demonstrate the potential of these dissimilarity measures by performing group tests on data of knee configurations and epidemiological shape data. Significant differences are revealed in both cases.
    Language: English
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  • 124
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    FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients (2022)
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    Publication Date: 2024-01-12
    Description: We present FrankWolfe.jl, an open-source implementation of several popular Frank–Wolfe and conditional gradients variants for first-order constrained optimization. The package is designed with flexibility and high performance in mind, allowing for easy extension and relying on few assumptions regarding the user-provided functions. It supports Julia’s unique multiple dispatch feature, and it interfaces smoothly with generic linear optimization formulations using MathOptInterface.jl.
    Language: English
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  • 125
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    Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings (2022)
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    Publication Date: 2024-01-12
    Language: English
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  • 126
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    Qualitative Euclidean embedding of Disjoint Sets of Points (2022)
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    Publication Date: 2024-01-12
    Description: We consider two disjoint sets of points with a distance metric, or a proximity function, associated with each set. If each set can be separately embedded into separate Euclidean spaces, then we provide sufficient conditions for the two sets to be jointly embedded in one Euclidean space. In this joint Euclidean embedding, the distances between the points are generated by a specific relation-preserving function. Consequently, the mutual distances between two points of the same set are specific qualitative transformations of their mutual distances in their original space; the pairwise distances between the points of different sets can be constructed from an arbitrary proximity function (might require scaling).
    Language: English
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  • 127
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    Modelling the acclimation capacity of coral reefs to a warming ocean (2022)
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    Publication Date: 2024-01-12
    Description: The symbiotic relationship between corals and photosynthetic algae is the foundation of coral reef ecosystems. This relationship breaks down, leading to coral death, when sea temperature exceeds the thermal tolerance of the coral-algae complex. While acclimation via phenotypic plasticity at the organismal level is an important mechanism for corals to cope with global warming, community-based shifts in response to acclimating capacities may give valuable indications about the future of corals at a regional scale. Reliable regional-scale predictions, however, are hampered by uncertainties on the speed with which coral communities will be able to acclimate. Here we present a trait-based, acclimation dynamics model, which we use in combination with observational data, to provide a first, crude estimate of the speed of coral acclimation at the community level and to investigate the effects of different global warming scenarios on three iconic reef ecosystems of the tropics: Great Barrier Reef, South East Asia, and Caribbean. The model predicts that coral acclimation may confer some level of protection by delaying the decline of some reefs such as the Great Barrier Reef. However, the current rates of acclimation will not be sufficient to rescue corals from global warming. Based on our estimates of coral acclimation capacities, the model results suggest substantial declines in coral abundances in all three regions, ranging from 12% to 55%, depending on the region and on the climate change scenario considered. Our results highlight the importance and urgency of precise assessments and quantitative estimates, for example through laboratory experiments, of the natural acclimation capacity of corals and of the speed with which corals may be able to acclimate to global warming.
    Language: English
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  • 128
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    Fourier-Cattaneo equation: stochastic origin, variational formulation, and asymptotic limits (2022)
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    Publication Date: 2024-01-12
    Description: We introduce a variational structure for the Fourier-Cattaneo (FC) system which is a second-order hyperbolic system. This variational structure is inspired by the large-deviation rate functional for the Kac process which is closely linked to the FC system. Using this variational formulation we introduce appropriate solution concepts for the FC equation and prove an a priori estimate which connects this variational structure to an appropriate Lyapunov function and Fisher information, the so-called FIR inequality. Finally, we use this formulation and estimate to study the diffusive and hyperbolic limits for the FC system.
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  • 129
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    The Open Virtual Mirror Framework for Enfacement Illusions - Enhancing the Sense of Agency With Avatars That Imitate Facial Expressions (2022)
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    Publication Date: 2024-01-12
    Language: German
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  • 130
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    A Discrete-Continuous Algorithm for Globally Optimal Free Flight Trajectory Optimization (2022)
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    Publication Date: 2024-01-12
    Description: We present an efficient algorithm that finds a globally optimal solution to the 2D Free Flight Trajectory Optimization Problem (aka Zermelo Navigation Problem) up to arbitrary precision in finite time. The algorithm combines a discrete and a continuous optimization phase. In the discrete phase, a set of candidate paths that densely covers the trajectory space is created on a directed auxiliary graph. Then Yen’s algorithm provides a promising set of discrete candidate paths which subsequently undergo a locally convergent refinement stage. Provided that the auxiliary graph is sufficiently dense, the method finds a path that lies within the convex domain around the global minimizer. From this starting point, the second stage will converge rapidly to the optimum. The density of the auxiliary graph depends solely on the wind field, and not on the accuracy of the solution, such that the method inherits the superior asymptotic convergence properties of the optimal control stage.
    Language: English
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  • 131
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    Cutting Plane Selection with Analytic Centers and Multiregression (2022)
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    Publication Date: 2024-01-12
    Description: Cutting planes are a crucial component of state-of-the-art mixed-integer programming solvers, with the choice of which subset of cuts to add being vital for solver performance. We propose new distance-based measures to qualify the value of a cut by quantifying the extent to which it separates relevant parts of the relaxed feasible set. For this purpose, we use the analytic centers of the relaxation polytope or of its optimal face, as well as alternative optimal solutions of the linear programming relaxation. We assess the impact of the choice of distance measure on root node performance and throughout the whole branch-and-bound tree, comparing our measures against those prevalent in the literature. Finally, by a multi-output regression, we predict the relative performance of each measure, using static features readily available before the separation process. Our results indicate that analytic center-based methods help to significantly reduce the number of branch-and-bound nodes needed to explore the search space and that our multiregression approach can further improve on any individual method.
    Language: English
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  • 132
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    Efficient adaptivity for simulating cardiac electrophysiology with spectral deferred correction methods (2022)
    Details
    Publication Date: 2023-12-18
    Description: The locality of solution features in cardiac electrophysiology simulations calls for adaptive methods. Due to the overhead incurred by established mesh refinement and coarsening, however, such approaches failed in accelerating the computations. Here we investigate a different route to spatial adaptivity that is based on nested subset selection for algebraic degrees of freedom in spectral deferred correction methods. This combination of algebraic adaptivity and iterative solvers for higher order collocation time stepping realizes a multirate integration with minimal overhead. This leads to moderate but significant speedups in both monodomain and cell-by-cell models of cardiac excitation, as demonstrated at four numerical examples.
    Language: English
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  • 133
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    Ehrenfest dynamics implemented in the all-electron package exciting (2022)
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    Publication Date: 2023-12-18
    Description: Ehrenfest dynamics combined with real-time time-dependent density functional theory has proven to be a reliable tool to study non-adiabatic molecular dynamics with a reasonable computational cost. Among other possibilities, it allows for assessing in real time electronic excitations generated by ultra-fast laser pulses, as e.g., in pump–probe spectroscopy, and their coupling to the nuclear vibrations even beyond the linear regime. In this work, we present its implementation in the all-electron full-potential package exciting. Three cases are presented as examples: diamond and cubic boron nitride (BN) relaxed after an initial lattice distortion, and cubic BN exposed to a laser pulse. Comparison with the Octopus code exhibits good agreement.
    Language: English
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  • 134
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    Network-induced Unit Commitment - A model class for investment and production portfolio planning for multi-energy systems (2022)
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    Publication Date: 2023-12-19
    Description: In light of the energy transition production planning of future decarbonized energy systems lead to very large and complex optimization problems. A widely used modeling paradigm for modeling and solving such problems is mathematical programming. While there are various scientific energy system models and modeling tools, most of them do not provide the necessary level of detail or the modeling flexibility to be applicable for industrial usage. Industrial modeling tools, on the other hand, provide a high level of detail and modeling flexibility. However, those models often exhibit a size and complexity that restricts their scope to a time horizon of several months, severely complicating long-term planning. As a remedy, we propose a model class that is detailed enough for real-world usage but still compact enough for long-term planning. The model class is based on a generalized unit commitment problem on a network with investment decisions. The focus lies on the topological dependency of different energy production and transportation units.
    Language: English
    Type: reportzib , doc-type:preprint
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  • 135
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    Article's Scientific Prestige: Measuring the Impact of Individual Articles in the Web of Science (2022)
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    Publication Date: 2023-12-19
    Description: We performed a citation analysis on the Web of Science publications consisting of more than 63 million articles and 1.45 billion citations on 254 subjects from 1981 to 2020. We proposed the Article’s Scientific Prestige (ASP) metric and compared this metric to number of citations (#Cit) and journal grade in measuring the scientific impact of individual articles in the large-scale hierarchical and multi-disciplined citation network. In contrast to #Cit, ASP, that is computed based on the eigenvector centrality, considers both direct and indirect citations, and provides steady-state evaluation cross different disciplines. We found that ASP and #Cit are not aligned for most articles, with a growing mismatch amongst the less cited articles. While both metrics are reliable for evaluating the prestige of articles such as Nobel Prize winning articles, ASP tends to provide more persuasive rankings than #Cit when the articles are not highly cited. The journal grade, that is eventually determined by a few highly cited articles, is unable to properly reflect the scientific impact of individual articles. The number of references and coauthors are less relevant to scientific impact, but subjects do make a difference.
    Language: English
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  • 136
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    PCL insufficient patients with increased translational and rotational passive knee joint laxity have no increased range of anterior–posterior and rotational tibiofemoral motion during level walking (2022)
    Details
    Publication Date: 2024-01-16
    Language: English
    Type: article , doc-type:article
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  • 137
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    Coding for secrecy in remote state estimation with an adversary (2022)
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    Publication Date: 2024-01-23
    Description: We study the problem of remote state estimation in the presence of an eavesdropper. A sensor transmits state information over a packet-dropping link to a legitimate user. This information is randomly overheard by an eavesdropper. To reduce information leakage to the eavesdropper, previous studies have shown that by encoding the estimate with the acknowledgments (Acks), perfect secrecy can be achieved. However, this strategy greatly relies on the accuracy of the Acks and may easily fail if the Acks are compromised by cyberattacks. In this article, we tackle this issue by proposing to switch between sending an encoded state and sending the plain state to stay resilient against fake Acks. Specifically, we assume the Acks to be randomly attacked and derive recursive expressions for the minimum-mean-squared error estimates and error covariance matrices at the legitimate user and at the eavesdropper. Based upon this, we propose a transmission policy that depends on the probability of synchronization. We formulate a partially observable Markov decision process to model the evolution of the synchronization status and derive associated optimal transmission policies. Numerical examples are provided to verify the theoretical results.
    Language: English
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  • 138
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    Feedback Loops in Opinion Dynamics of Agent-Based Models with Multiplicative Noise (2022)
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    Publication Date: 2024-01-24
    Description: We introduce an agent-based model for co-evolving opinions and social dynamics, under the influence of multiplicative noise. In this model, every agent is characterized by a position in a social space and a continuous opinion state variable. Agents’ movements are governed by the positions and opinions of other agents and similarly, the opinion dynamics are influenced by agents’ spatial proximity and their opinion similarity. Using numerical simulations and formal analyses, we study this feedback loop between opinion dynamics and the mobility of agents in a social space. We investigate the behaviour of this ABM in different regimes and explore the influence of various factors on the appearance of emerging phenomena such as group formation and opinion consensus. We study the empirical distribution, and, in the limit of infinite number of agents, we derive a corresponding reduced model given by a partial differential equation (PDE). Finally, using numerical examples, we show that a resulting PDE model is a good approximation of the original ABM.
    Language: English
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  • 139
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    Understanding the Romanization Spreading on Historical Interregional Networks in Northern Tunisia (2022)
    Details
    Publication Date: 2024-01-24
    Description: Spreading processes are important drivers of change in social systems. To understand the mechanisms of spreading it is fundamental to have information about the underlying contact network and the dynamical parameters of the process. However, in many real-wold examples, this information is not known and needs to be inferred from data. State-of-the-art spreading inference methods have mostly been applied to modern social systems, as they rely on availability of very detailed data. In this paper we study the inference challenges for historical spreading processes, for which only very fragmented information is available. To cope with this problem, we extend existing network models by formulating a model on a mesoscale with temporal spreading rate. Furthermore, we formulate the respective parameter inference problem for the extended model. We apply our approach to the romanization process of Northern Tunisia, a scarce dataset, and study properties of the inferred time-evolving interregional networks. As a result, we show that (1) optimal solutions consist of very different network structures and spreading rate functions; and that (2) these diverse solutions produce very similar spreading patterns. Finally, we discuss how inferred dominant interregional connections are related to available archaeological traces. Historical networks resulting from our approach can help understanding complex processes of cultural change in ancient times.
    Language: English
    Type: reportzib , doc-type:preprint
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  • 140
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    Understanding the Romanization Spreading on Historical Interregional Networks in Northern Tunisia (2022)
    Details
    Publication Date: 2024-01-24
    Description: Spreading processes are important drivers of change in social systems. To understand the mechanisms of spreading it is fundamental to have information about the underlying contact network and the dynamical parameters of the process. However, in many real-wold examples, this information is not known and needs to be inferred from data. State-of-the-art spreading inference methods have mostly been applied to modern social systems, as they rely on availability of very detailed data. In this paper we study the inference challenges for historical spreading processes, for which only very fragmented information is available. To cope with this problem, we extend existing network models by formulating a model on a mesoscale with temporal spreading rate. Furthermore, we formulate the respective parameter inference problem for the extended model. We apply our approach to the romanization process of Northern Tunisia, a scarce dataset, and study properties of the inferred time-evolving interregional networks. As a result, we show that (1) optimal solutions consist of very different network structures and spreading rate functions; and that (2) these diverse solutions produce very similar spreading patterns. Finally, we discuss how inferred dominant interregional connections are related to available archaeological traces. Historical networks resulting from our approach can help understanding complex processes of cultural change in ancient times.
    Language: English
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  • 141
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    Periodic Timetabling with Integrated Track Choice for Railway Construction Sites (2022)
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    Publication Date: 2024-01-29
    Description: We propose a mixed-integer linear programming model to generate and optimize periodic timetables with integrated track choice in the context of railway construction sites. When a section of a railway network becomes unavailable, the nearby areas are typically operated close to their capacity limits, and hence carefully modeling headways and allowing flexible routings becomes vital. We therefore discuss first how to integrate headway constraints into the Periodic Event Scheduling Problem (PESP) that do not only prevent overtaking, but also guarantee conflict-free timetables in general and particularly inside stations. Secondly, we introduce a turn-sensitive event-activity network, which is able to integrate routing alternatives for turnarounds at stations, e.g., turning at a platform vs. at a pocket track for metro-like systems. We propose several model formulations to include track choice, and finally evaluate them on six real construction site scenarios on the S-Bahn Berlin network.
    Language: English
    Type: reportzib , doc-type:preprint
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  • 142
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    a framework for generalized Benders' decomposition and its applications to multilevel optimization (2022)
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    Publication Date: 2024-01-31
    Language: English
    Type: article , doc-type:article
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  • 143
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    block-activated algorithms for multicomponent fully nonsmooth minimization (2022)
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    Publication Date: 2024-01-31
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 144
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    Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models (2022)
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    Publication Date: 2024-01-31
    Language: English
    Type: article , doc-type:article
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  • 145
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    signal recovery from inconsistent nonlinear observations (2022)
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    Publication Date: 2024-01-31
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 146
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    a variational inequality model for the construction of signals from inconsistent nonlinear equations (2022)
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    Publication Date: 2024-01-31
    Language: English
    Type: article , doc-type:article
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  • 147
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    The Machine Learning for Combinatorial Optimization Competition (ML4CO): results and insights (2022)
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    Publication Date: 2024-01-31
    Language: English
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  • 148
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    Secondary Structure Transitions for a Family of Amyloidogenic, Antimircobial Uperin 3 Peptides in Contact with Sodium Dodecyl Sulfate (2022)
    Details
    Publication Date: 2024-02-09
    Language: English
    Type: article , doc-type:article
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  • 149
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    Price Optimal Routing in Public Transportation (2022)
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    Publication Date: 2024-02-16
    Description: We consider the price-optimal earliest arrival problem in public transit (POEAP) in which we aim to calculate the Pareto-front of journeys with respect to ticket price and arrival time in a public transportation network. Public transit fare structures are often a combination of various fare strategies such as, e.g., distance-based fares, zone-based fares or flat fares. The rules that determine the actual ticket price are often very complex. Accordingly, fare structures are notoriously difficult to model as it is in general not sufficient to simply assign costs to arcs in a routing graph. Research into POEAP is scarce and usually either relies on heuristics or only considers restrictive fare models that are too limited to cover the full scope of most real-world applications. We therefore introduce conditional fare networks (CFNs), the first framework for representing a large number of real-world fare structures. We show that by relaxing label domination criteria, CFNs can be used as a building block in label-setting multi-objective shortest path algorithms. By the nature of their extensive modeling capabilities, optimizing over CFNs is NP-hard. However, we demonstrate that adapting the multi-criteria RAPTOR (MCRAP) algorithm for CFNs yields an algorithm capable of solving POEAP to optimality in less than 400 ms on average on a real-world data set. By restricting the size of the Pareto-set, running times are further reduced to below 10 ms.
    Language: English
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  • 150
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    Generalized Nash equilibrium problems with partial differential operators: theory, algorithms, and risk aversion (2022)
    Details
    Publication Date: 2024-02-27
    Language: English
    Type: incollection , doc-type:Other
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  • 151
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    Algorithm configuration problem (2022)
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    Publication Date: 2024-02-27
    Language: English
    Type: incollection , doc-type:Other
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  • 152
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    Accelerated Riemannian optimization: Handling constraints with a prox to bound geometric penalties (2022)
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    Publication Date: 2024-02-27
    Language: English
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  • 153
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    Optimizing a low-dimensional convex function over a high-dimensional cube (2022)
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    Publication Date: 2024-02-27
    Language: English
    Type: article , doc-type:article
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  • 154
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    Learning Reduced Models for Large-Scale Agent-Based Systems (2022)
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    Publication Date: 2024-03-18
    Description: Modeling social systems and studying their dynamical behavior plays an important role in many fields of research. Agent-based modeling provides a high degree of detail into artificial societies by describing the model from the perspective of the agents. The interactions of agents, often characterized by simple rules, lead to complex, time-evolving patterns. Their understanding is of great importance, e.g., for predicting and influencing epidemics. Analysis and simulation, however, often becomes prohibitively time-consuming when the number of agents or the time scale of interest is large. Therefore, this thesis is devoted to learn significantly reduced models of large-scale agent-based systems from simulation data. We show how data-driven methods based on transfer operators can be used to find reduced models represented by ordinary or stochastic differential equations that describe the dynamical behavior of larger groups or entire populations and thus enable the analysis and prediction of agent-based systems. To this end, we first present an extension of EDMD (extended dynamic mode decomposition) called gEDMD to approximate the Koopman generator from data. This method can be used to compute eigenfunctions, eigenvalues, and modes of the generator, as well as for system identification and model reduction of both deterministic and non-deterministic dynamical systems. Secondly, we analyze the long-term behavior of certain agent-based models and their pathwise approximations by stochastic differential equations for large numbers of agents using transfer operators. We show that, under certain conditions, the transfer operator approach connects the pathwise approximations on finite time scales with methods for describing the behavior on possibly exponentially long time scales. As a consequence, we can use the finite-time, pathwise approximations to characterize metastable behavior on long time scales using transfer operators. This can significantly reduce the computational cost. The third part addresses the data-driven model reduction since in many cases no analytical limit models are known or existent. We show how the Koopman operator theory can be used to infer the governing equations of agent-based systems directly from simulation data. Using benchmark problems, we demonstrate that for sufficiently large population sizes the data-driven models agree well with analytical limit equations and, moreover, that the reduced models allow predictions even in cases far from the limit or when no limit equations are known. Lastly, we demonstrate the potential of the presented approach. We present an ansatz for the multi-objective optimization of agent-based systems with the help of data-driven surrogate models based on the Koopman generator. In particular, when limit models are unknown or non-existent, this approach makes multi-objective optimization problems solvable that would otherwise be computationally infeasible due to very expensive objective functions.
    Language: English
    Type: doctoralthesis , doc-type:doctoralThesis
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  • 155
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    Accurate reduced models for the pH oscillations in the urea-urease reaction confined to giant lipid vesicles (2022)
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    Publication Date: 2024-03-21
    Description: Our theoretical study concerns an urea-urease-based pH oscillator confined to giant lipid vesicles. Under suitable conditions, differential transport of urea and hydrogen ion across the unilamellar vesicle membrane periodically resets the pH clock that switches the system from acid to basic, resulting in self-sustained oscillations. We analyse the structure of the limit cycle, which controls the dynamics for giant vesicles and dominates the strongly stochastic oscillations in small vesicles of submicrometer size. To this end, we derive reduced models, amenable to analytic treatments, and show that the accuracy of predictions, including the period of oscillations, is highly sensitive to the choice of the reduction scheme. In particular, we suggest an accurate two-variable model and show its equivalence to a three-variable model that admits an interpretation in terms of a chemical reaction network. The accurate description of a single pH oscillator appears crucial for rationalizing experiments and understanding communication of vesicles and synchronization of rhythms.
    Language: English
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  • 156
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    A Kendall Shape Space Approach to 3D Shape Estimation from 2D Landmarks -- Source Code and Data (2022)
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    Publication Date: 2024-04-05
    Description: Source code and novel dataset of basking shark head skeletons facilitating the reproduction of the results presented in 'A Kendall Shape Space Approach to 3D Shape Estimation from 2D Landmarks' - ECCV 2022.
    Language: English
    Type: software , doc-type:Other
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  • 157
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    A Kendall Shape Space Approach to 3D Shape Estimation from 2D Landmarks (2022)
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    Publication Date: 2024-04-05
    Description: 3D shapes provide substantially more information than 2D images. However, the acquisition of 3D shapes is sometimes very difficult or even impossible in comparison with acquiring 2D images, making it necessary to derive the 3D shape from 2D images. Although this is, in general, a mathematically ill-posed problem, it might be solved by constraining the problem formulation using prior information. Here, we present a new approach based on Kendall’s shape space to reconstruct 3D shapes from single monocular 2D images. The work is motivated by an application to study the feeding behavior of the basking shark, an endangered species whose massive size and mobility render 3D shape data nearly impossible to obtain, hampering understanding of their feeding behaviors and ecology. 2D images of these animals in feeding position, however, are readily available. We compare our approach with state-of-the-art shape-based approaches both on human stick models and on shark head skeletons. Using a small set of training shapes, we show that the Kendall shape space approach is substantially more robust than previous methods and always results in plausible shapes. This is essential for the motivating application in which specimens are rare and therefore only few training shapes are available.
    Language: English
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  • 158
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    IOSIG: Declarative I/O-Stream Properties Using Pragmas (2022)
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    Publication Date: 2024-04-05
    Language: English
    Type: article , doc-type:article
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  • 159
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    Estimating 3D Shape of the Head Skeleton of Basking Sharks Using Annotated Landmarks on a 2D Image (2022)
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    Publication Date: 2024-04-05
    Description: Basking sharks are thought to be one of the most efficient filter-feeding fish in terms of the throughput of water filtered through their gills. Details about the underlying morphology of their branchial region have not been studied due to various challenges in acquiring real-world data. The present thesis aims to facilitate this, by developing a mathematical shape model which constructs the 3D structure of the head skeleton of a basking shark using annotated landmarks on a single 2D image. This is an ill-posed problem as estimating the depth of a 3D object from a single 2D view is, in general, not possible. To reduce this ambiguity, we create a set of pre-defined training shapes in 3D from CT scans of basking sharks. First, the damaged structures of the sharks in the scans are corrected via solving a set of optimization problems, before using them as accurate 3D representations of the object. Then, two approaches are employed for the 2D-to-3D shape fitting problem–an Active Shape Model approach and a Kendall’s Shape Space approach. The former represents a shape as a point on a high-dimensional Euclidean space, whereas the latter represents a shape as an equivalence class of points in this Euclidean space. Kendall’s shape space approach is a novel technique that has not yet been applied in this context, and a comprehensive comparison of the two approaches suggests this approach to be superior for the problem at hand. This can be credited to an improved interpolation of the training shapes.
    Description: Riesenhaie zählen zu den effizientesten Filtrierern hinsichtlich des durch die Kiemen gefilterten Wasservolumens. Die Kiemenregion dieser Tiere besitzt eine markante Morphologie, die jedoch bisher nicht umfassend erforscht werden konnte, da es schwierig ist, reale Daten dieser Tiere zu erheben. Die vorliegende Arbeit zielt darauf ab, dies durch die Entwicklung eines mathematischen Formmodels zu ermöglichen, das es erlaubt, die 3D-Struktur des Schädelskeletts anhand von Landmarken, die auf einem 2D-Bild platziert werden, zu rekonstruieren. Die hierzu benötigte Tiefenbestimmung der Landmarken aus einer 2D-Projektion ist ein unterbestimmtes Problem. Wir lösen dies durch die Hinzunahme von Trainingsformen, welche wir aus CT-Scans von Riesenhaien gewinnen. Der Zustand der tomografierten Exemplare erfordert jedoch einen vorhergehenden Korrekturschritt, den wir mit Hilfe eines Optimierungsansatzes lösen, bevor die extrahierten Strukturen als 3D-Trainingsformen dienen können. Um die 3D-Struktur des Schädelskelettes aus 2D-Landmarken zu rekonstruieren, vergleichen wir zwei Ansätze – den sogenannten Active-Shape-Model (ASM)-Ansatz und einen Ansatz basierend auf Kendalls Formenraum. Während eine Form des ASM-Ansatzes durch einen Punkt in einem hochdimensionalen Euklidischen Raum repräsentiert ist, repräsentiert eine Form im Kendall-Formenraum eine Äquivalenzklasse von Punkten des Euklidischen Raumes. Die Anwendung des Kendall-Formenraumes für das beschriebene Problem ist neu und ein umfassender Vergleich der Methoden hat ergeben, dass dieser Ansatz für die spezielle Anwendung zu besseren Ergebnissen führt. Wir führen dies auf die überlegene Interpolation der Trainingsformen in diesem Raum zurück.
    Language: English
    Type: masterthesis , doc-type:masterThesis
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  • 160
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    Mathematical Optimization for Analyzing and Forecasting Nonlinear Network Time Series (2022)
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    Publication Date: 2024-04-26
    Description: This work presents an innovative short to mid-term forecasting model that analyzes nonlinear complex spatial and temporal dynamics in energy networks under demand and supply balance constraints using Network Nonlinear Time Series (TS) and Mathematical Programming (MP) approach. We address three challenges simultaneously, namely, the adjacency matrix is unknown; the total amount in the network has to be balanced; dependence is unnecessarily linear. We use a nonparametric approach to handle the nonlinearity and estimate the adjacency matrix under the sparsity assumption. The estimation is conducted with the Mathematical Optimisation method. We illustrate the accuracy and effectiveness of the model on the example of the natural gas transmission network of one of the largest transmission system operators (TSOs) in Germany, Open Grid Europe. The obtained results show that, especially for shorter forecasting horizons, the proposed method outperforms all considered benchmark models, improving the average nMAPE for 5.1% and average RMSE for 79.6% compared to the second-best model. The model is capable of capturing the nonlinear dependencies in the complex spatial-temporal network dynamics and benefits from both sparsity assumption and the demand and supply balance constraint.
    Language: English
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  • 161
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    Faster Algorithms for Steiner Tree and related Problems: From Theory to Practice (2022)
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    Publication Date: 2024-04-26
    Language: English
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  • 162
    facet.materialart.
    Unknown
    Day and time aggregations for optimal design of energy supply systems with storage units by a hierarchical MILP method (2022)
    Details
    Publication Date: 2024-04-26
    Description: Mixed-integer linear programming (MILP) methods have been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, some strategies have been proposed to enhance the computation efficiency furthermore. As one of the strategies, a method of reducing model by time aggregation has been proposed to search design candidates efficiently in the relaxed optimal design problem at the upper level. In this paper, the hierarchical MILP method with the strategies is extendedly applied to the optimal design of energy supply systems with storage units. Especially, the method of reducing model is extended by aggregating both representative days and sampling times separately in consideration of the characteristics of energy storage units. A case study is conducted on the optimal design of a cogeneration system with a thermal storage tank. Through the study, it turns out the hierarchical MILP method is effective to derive the optimal solutions in short computation times. It also turns out that the model reduction with day and time aggregations is effective to shorten the computation times furthermore when the number of candidates for equipment capacities is relatively small.
    Language: English
    Type: article , doc-type:article
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  • 163
    facet.materialart.
    Unknown
    Evaluation of robustness in multiple performance criteria for designing energy supply systems based on a mixed-integer linear model (2022)
    Details
    Publication Date: 2024-04-26
    Description: In designing energy supply systems, it is important to consider the uncertainty in energy demands, evaluate the robustness in some performance criteria, and heighten the robustness. A robust optimal design method has been previously proposed to maximize the robustness in a single performance criterion against the uncertainty in energy demands based on a mixed-integer linear model. In this paper, as a preliminary step toward multiobjective robust optimal design, a method of evaluating robustness in multiple performance criteria against the uncertainty in energy demands is proposed based on a mixed-integer linear model. The problems of evaluating the robustness in the performance criteria are formulated as bilevel mixed-integer linear programming (MILP) ones. They are solved by evaluating lower and upper bounds for the maximum regrets in the performance criteria alternately and repeatedly with the aid of a solution method based on reformulation and decomposition. In addition, for the purpose of applying the proposed method to practical problems, a hierarchical MILP method is used to efficiently solve some MILP problems in the solution process. Through a case study on a cogeneration system, the robustness in the annual total cost and primary energy consumption is evaluated and its trade-off relationship is clarified.
    Language: English
    Type: article , doc-type:article
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  • 164
    facet.materialart.
    Unknown
    Modeling and Forecasting Gas Network Flows with Multivariate Time Series and Mathematical Programming Approach (2022)
    Details
    Publication Date: 2024-04-26
    Description: With annual consumption of approx. 95 billion cubic meters and similar amounts of gas just transshipped through Germany to other EU states, Germany’s gas transport system plays a vital role in European energy supply. The complex, more than 40,000 km long high-pressure transmission network is controlled by several transmission system operators (TSOs) whose main task is to provide security of supply in a cost-efficient way. Given the slow speed of gas flows through the gas transmission network pipelines, it has been an essential task for the gas network operators to enhance the forecast tools to build an accurate and effective gas flow prediction model for the whole network. By incorporating the recent progress in mathematical programming and time series modeling, we aim to model natural gas network and predict gas in- and out-flows at multiple supply and demand nodes for different forecasting horizons. Our model is able to describe the dynamics in the network by detecting the key nodes, which may help to build an optimal management strategy for transmission system operators.
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 165
    facet.materialart.
    Unknown
    Evaluation of robustness in multiple performance criteria for designing energy supply systems based on a mixed-integer linear model (2022)
    Details
    Publication Date: 2024-04-26
    Description: In designing energy supply systems, it is important to consider the uncertainty in energy demands, evaluate the robustness in some performance criteria, and heighten the robustness. A robust optimal design method has been previously proposed to maximize the robustness in a single performance criterion against the uncertainty in energy demands based on a mixed-integer linear model. In this paper, as a preliminary step toward multiobjective robust optimal design, a method of evaluating robustness in multiple performance criteria against the uncertainty in energy demands is proposed based on a mixed-integer linear model. The problems of evaluating the robustness in the performance criteria are formulated as bilevel mixed-integer linear programming (MILP) ones. They are solved by evaluating lower and upper bounds for the maximum regrets in the performance criteria alternately and repeatedly with the aid of a solution method based on reformulation and decomposition. In addition, for the purpose of applying the proposed method to practical problems, a hierarchical MILP method is used to efficiently solve some MILP problems in the solution process. Through a case study on a cogeneration system, the robustness in the annual total cost and primary energy consumption is evaluated and its trade-off relationship is clarified. As a result, it turns out how the values of these performance criteria are close to the optimal ones in relation to the uncertainty in energy demands. This work will be an important step toward developing a multiobjective robust optimal design method.
    Language: English
    Type: article , doc-type:article
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  • 166
    facet.materialart.
    Unknown
    時間集約によるエネルギー供給システムの 近似最適設計解の導出および評価 (階層的最適化を援用した近似最適設計) (2022)
    Details
    Publication Date: 2024-04-26
    Description: For the purpose of attaining the highest performance of energy supply systems, it is important to design the systems optimally in consideration of their operational strategies for seasonal and hourly variations in energy demands. An ap- proach to efficiently solve such an optimal design problem with a large number of periods for variations in energy de- mands is to derive an approximate optimal design solution by aggregating periods with a clustering method. However, such an approach does not provide any information on the accuracy for the optimal value of the objective function. The purpose of this paper is to propose a time aggregation approach for deriving suitable aprroximate optimal design solutions and evaluting their values of the objective function accurately. This time aggregation approach is realized by combining a robust optimal design method under uncertain energy demands and a hierarchical approach for solving large scale optimal design problems. A case study is conducted for a cogeneration system with a practical configuration, and it turns out that the proposed approach enables one to evaluate effective upper and lower bounds for the optimal value of the objective function as compared with those obtained by a conventional approach.
    Language: Japanese
    Type: article , doc-type:article
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  • 167
    facet.materialart.
    Unknown
    時間集約によるエネルギー供給システムの 近似最適設計解の導出および評価 (階層的最適化を援用した設計解の評価) (2022)
    Details
    Publication Date: 2024-04-26
    Description: For the purpose of attaining the highest performance of energy supply systems, it is important to design the systems optimally in consideration of their operational strategies for seasonal and hourly variations in energy demands. An ap- proach to solve such an optimal design problem with a large number of periods efficiently is to derive an approximate optimal design solution by aggregating periods with a clustering method. However, such an approach does not provide any information on the accuracy for the optimal value of the objective function. The purpose of this paper is to provide a time aggregation method for deriving aprroximate optimal design solutions and evaluting their values of the objective function. Especially, a method of evaluating design solutions is presented here using both methods of evaluating the robustness under uncertain energy demands and solving optimal design problems by a hierarchical approach. A case study is conducted for a cogeneration system with a practical configuration, and it turns out that the proposed approach enables one to evaluate effective lower bounds for the optimal value of the objective function as compared with those obtained by a conventional approach.
    Language: Japanese
    Type: article , doc-type:article
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  • 168
    facet.materialart.
    Unknown
    Day and time aggregations for optimal design of energy supply systems with storage units by a hierarchical MILP method (2022)
    Details
    Publication Date: 2024-04-26
    Description: Mixed-integer linear programming (MILP) methods have been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, some strategies have been proposed to enhance the computation efficiency furthermore. As one of the strategies, a method of reducing model by time aggregation has been proposed to search design candidates efficiently in the relaxed optimal design problem at the upper level. In this paper, the hierarchical MILP method with the strategies is extendedly applied to the optimal design of energy supply systems with storage units. Especially, the method of reducing model is extended by aggregating both representative days and sampling times separately in consideration of the characteristics of energy storage units. A case study is conducted on the optimal design of a cogeneration system with a thermal storage tank. Through the study, it turns out the hierarchical MILP method is effective to derive the optimal solutions in short computation times. It also turns out that the model reduction with day and time aggregations is effec- tive to shorten the computation times furthermore when the number of candidates for equipment capacities is relatively small.
    Language: English
    Type: article , doc-type:article
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  • 169
    facet.materialart.
    Unknown
    Viscosity and porosity effects on tangential-discontinuity surface stability in 3D Compressible Media (2022)
    Details
    Publication Date: 2024-04-26
    Description: The stability of a flow in porous media relates to the velocity rate of injecting and withdrawing natural gases inside porous storage. We thus aim to analyze the stability of flows in porous media to accelerate the energy transition process. This research examines a flow model of a tangential--velocity discontinuity with porosity and viscosity changes in a three-dimensional (3D) compressible medium because of a co-existence of different gases in a storage. The fluids are assumed to move in a relative motion where the plane y=0 is a tangential-velocity discontinuity surface. We obtain that the critical value of the Mach number to stabilize a tangential discontinuity surface of flows via porous media is smaller than the one of flows in a plane. The critical value of the Mach number M to stabilize a discontinuity surface of the 3D flow is different by a factor |cosθ| compared to the two-dimensional (2D) flow. Here, θ is the angle between velocity and wavenumber vectors. Our results also show that the flow model with viscosity and porosity effects is stable faster than those without these terms. Our analysis is done for both infinite and finite flows. The effect of solid walls along the flow direction could suppress the instability, i.e., the tangential-discontinuity surface is stabilized faster
    Language: English
    Type: article , doc-type:article
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  • 170
    facet.materialart.
    Unknown
    Koopman-based spectral clustering of directed and time-evolving graphs (2022)
    Details
    Publication Date: 2024-05-16
    Description: While spectral clustering algorithms for undirected graphs are well established and have been successfully applied to unsupervised machine learning problems ranging from image segmentation and genome sequencing to signal processing and social network analysis, clustering directed graphs remains notoriously difficult. Two of the main challenges are that the eigenvalues and eigenvectors of graph Laplacians associated with directed graphs are in general complex-valued and that there is no universally accepted definition of clusters in directed graphs. We first exploit relationships between the graph Laplacian and transfer operators and in particular between clusters in undirected graphs and metastable sets in stochastic dynamical systems and then use a generalization of the notion of metastability to derive clustering algorithms for directed and time-evolving graphs. The resulting clusters can be interpreted as coherent sets, which play an important role in the analysis of transport and mixing processes in fluid flows.
    Language: English
    Type: article , doc-type:article
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  • 171
    facet.materialart.
    Unknown
    Polyp-Cavity Segmentation of Cold-Water Corals guided by Ambient Occlusion and Ambient Curvature (2022)
    Details
    Publication Date: 2024-05-30
    Description: The segmentation of cavities in three-dimensional images of arbitrary objects is a difficult problem since the cavities are usually connected to the outside of the object without any difference in image intensity. Hence, the information whether a voxel belongs to a cavity or the outside needs to be derived from the ambient space. If a voxel is enclosed by object material, it is very likely that this voxel belongs to a cavity. However, there are dense structures where a voxel might still belong to the outside even though it is surrounded to a large degree by the object. This is, for example, the case for coral colonies. Therefore, additional information needs to be considered to distinguish between those cases. In this paper, we introduce the notion of ambient curvature, present an efficient way to compute it, and use it to segment coral polyp cavities by integrating it into the ambient occlusion framework. Moreover, we combine the ambient curvature with other ambient information in a Gaussian mixture model, trained from a few user scribbles, resulting in a significantly improved cavity segmentation. We showcase the superiority of our approach using four coral colonies of very different morphological types. While in this paper we restrict ourselves to coral data, we believe that the concept of ambient curvature is also useful for other data. Furthermore, our approach is not restricted to curvature but can be easily extended to exploit any properties given on an object's surface, thereby adjusting it to specific applications.
    Language: English
    Type: article , doc-type:article
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  • 172
    facet.materialart.
    Unknown
    Optimal discrete pipe sizing for tree-shaped CO2 networks (2022)
    Details
    Publication Date: 2024-05-31
    Description: While energy-intensive industries like the steel industry plan to switch to renewable energy sources, other industries, such as the cement industry, have to rely on carbon capture storage and utilization technologies to reduce the inevitable carbon dioxide (CO2) emissions of their production processes. In this context, we investigate the problem of finding optimal pipeline diameters from a discrete set of diameters for a tree-shaped network transporting captured CO2 from multiple sources to a single sink. The general problem of optimizing arc capacities in potential-based fluid networks is a challenging mixed-integer nonlinear program. Additionally, the behaviour of CO2 is highly sensitive and nonlinear regarding temperature and pressure changes. We propose an iterative algorithm splitting the problem into two parts: a) the pipe-sizing problem under a fixed supply scenario and temperature distribution and b) the thermophysical modelling including mixing effects, the Joule-Thomson effect, and heat exchange with the surrounding environment. We show the effectiveness of our approach by applying our algorithm to a real-world network planning problem for a CO2 network in Western Germany.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
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  • 173
    facet.materialart.
    Unknown
    A probabilistic framework for particle-based reaction–diffusion dynamics using classical Fock space representations (2022)
    Details
    Publication Date: 2024-06-10
    Language: English
    Type: article , doc-type:article
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  • 174
    facet.materialart.
    Unknown
    Discovering collective variable dynamics of agent-based models (2022)
    Details
    Publication Date: 2024-06-13
    Description: Analytical approximations of the macroscopic behavior of agent-based models (e.g. via mean-field theory) often introduce a significant error, especially in the transient phase. For an example model called continuous-time noisy voter model, we use two data-driven approaches to learn the evolution of collective variables instead. The first approach utilizes the SINDy method to approximate the macroscopic dynamics without prior knowledge, but has proven itself to be not particularly robust. The second approach employs an informed learning strategy which includes knowledge about the agent-based model. Both approaches exhibit a considerably smaller error than the conventional analytical approximation.
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 175
    Book
    Book
    Terraform : up and running : writing infrastructure as code (2022)
    Sebastopol ; Boston :O'Reilly Media,
    Details
    Title: Terraform : up and running : writing infrastructure as code
    Author: Brikman, Yevgeniy
    Edition: 3rd edition
    Publisher: Sebastopol ; Boston :O'Reilly Media,
    Year of publication: 2022
    Pages: 459 Seiten
    ISBN: 978-1-0981-1674-3
    Type of Medium: Book
    Language: English
    Keywords: Cloud computing ; Operating systems (Computers) ; Software
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  • 176
    Book
    Book
    PostgreSQL : Praxisbuch für Administratoren und Entwickler (2022)
    München :Hanser,
    Details
    Title: PostgreSQL : Praxisbuch für Administratoren und Entwickler
    Author: Fröhlich, Lutz
    Publisher: München :Hanser,
    Year of publication: 2022
    Pages: XI, 585 Seiten : , Illustrationen, Diagramme
    ISBN: 978-3-446-46929-7
    Type of Medium: Book
    Language: German
    URL: Inhaltsverzeichnis
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  • 177
    Book
    Book
    ¬The¬ future of enriched, linked, open and filtered metadata : making sense of IFLA LRM, RDA, linked data and BIBFRAME (2022)
    London :Facet Publishing,
    Details
    Title: ¬The¬ future of enriched, linked, open and filtered metadata : making sense of IFLA LRM, RDA, linked data and BIBFRAME
    Author: Alemu, Getaneh
    Publisher: London :Facet Publishing,
    Year of publication: 2022
    Pages: 222 Seiten
    ISBN: 978-1-78330-492-9 , 978-1-78330-494-3
    Type of Medium: Book
    Language: English
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  • 178
    Online Resource
    Online Resource
    ¬The¬ rise of AI : implications and applications of artificial intelligence in academic libraries (2022)
    Chicago :Association of College and Research Libraries,
    Details
    Title: ¬The¬ rise of AI : implications and applications of artificial intelligence in academic libraries
    Author: Hervieux, Sandy
    Contributer: Wheatley, Amanda
    Publisher: Chicago :Association of College and Research Libraries,
    Year of publication: 2022
    Pages: 1 Online-Ressource (XII, 207 Seiten)
    ISBN: 978-0-8389-3760-0
    Type of Medium: Online Resource
    Language: English
    URL: https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=3202823&site=ehost-live
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  • 179
    Book
    Book
    ¬The¬ Routledge companion to digital humanities and art history / (2022)
    New York ; London :Routledge, Taylor & Francis Group,
    Details
    Title: ¬The¬ Routledge companion to digital humanities and art history /
    Contributer: Brown, Kathryn
    Publisher: New York ; London :Routledge, Taylor & Francis Group,
    Year of publication: 2022
    Pages: 524 Seiten
    ISBN: 978-1-032-33639-8
    Type of Medium: Book
    Language: English
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  • 180
    Book
    Book
    Advanced reduced order methods and applications in computational fluid dynamics /; 27 (2022)
    Philadelphia :SIAM, Society for Industrial and Applied Mathematics,
    Details
    Title: Advanced reduced order methods and applications in computational fluid dynamics /; 27
    Author: Rozza, Gianluigi
    Contributer: Stabile, Giovanni , Ballarin, Francesco
    Publisher: Philadelphia :SIAM, Society for Industrial and Applied Mathematics,
    Year of publication: 2022
    Pages: 466 Seiten
    Series Statement: Computational science & engineering 27
    ISBN: 978-1-61197-725-7
    Type of Medium: Book
    Language: English
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  • 181
    Book
    Book
    Deep learning with Python / (2022)
    Shelter Island, NY :Manning Publications,
    Details
    Title: Deep learning with Python /
    Author: Chollet, François
    Edition: Second edition
    Publisher: Shelter Island, NY :Manning Publications,
    Year of publication: 2022
    Pages: 400 Seiten
    ISBN: 978-1-61729-686-4
    Type of Medium: Book
    Language: English
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  • 182
    Book
    Book
    Networks of Care : Politiken des (Er)haltens und (Ent)sorgens (2022)
    Berlin :nGbK,
    Details
    Title: Networks of Care : Politiken des (Er)haltens und (Ent)sorgens
    Author: Schäffler, Anna
    Contributer: Schäfer, Friederike , Buurman, Nanne , Neue Gesellschaft für Bildende Kunst / Arbeitsgruppe Networks of Care
    Edition: 1. Auflage
    Publisher: Berlin :nGbK,
    Year of publication: 2022
    Pages: 178 Seiten
    ISBN: 978-3-938515-95-2
    Type of Medium: Book
    Language: German , English
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  • 183
    Book
    Book
    Discovering modern C++ : an intensive course for scientists, engineers, and programmers (2022)
    Details
    Title: Discovering modern C++ : an intensive course for scientists, engineers, and programmers
    Author: Gottschling, Peter
    Edition: 2nd edition
    Year of publication: 2022
    Pages: 576 Seiten
    Series Statement: C++ in-depth series
    ISBN: 978-0-13-667764-2
    Type of Medium: Book
    Language: English
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  • 184
    Book
    Book
    Schreibenlassen : Texte zur Literatur im Digitalen (2022)
    Berlin :August Verlag,
    Details
    Title: Schreibenlassen : Texte zur Literatur im Digitalen
    Author: Bajohr, Hannes
    Edition: Erste Auflage
    Publisher: Berlin :August Verlag,
    Year of publication: 2022
    Pages: 223 Seiten
    ISBN: 9783751890021
    Type of Medium: Book
    Language: German
    URL: https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=3319136&site=ehost-live
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  • 185
    Book
    Book
    GWB : Vergaberecht Kommentar (2022)
    Köln :Bundesanzeiger,
    Details
    Title: GWB : Vergaberecht Kommentar
    Contributer: Müller-Wrede, Malte , Amelung, Steffen
    Edition: 2. aktual. und erw. Aufl.
    Publisher: Köln :Bundesanzeiger,
    Year of publication: 2022
    Pages: LIX, 1443 Seiten
    ISBN: 978-3-8462-1093-2
    Type of Medium: Book
    Language: German
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  • 186
    Book
    Book
    Probabilistic Machine Learning : An Introduction (2022)
    Cambridge :MIT Press,
    Details
    Title: Probabilistic Machine Learning : An Introduction
    Author: Murphy, Kevin
    Publisher: Cambridge :MIT Press,
    Year of publication: 2022
    Pages: 944 Seiten
    ISBN: 978-0-262-04682-4
    Type of Medium: Book
    Language: German
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  • 187
    Book
    Book
    Introduction to nanophotonics / (2022)
    Oxford :Oxford University Press,
    Details
    Title: Introduction to nanophotonics /
    Author: Benisty, Henri
    Contributer: Greffet, Jean-Jacques , Lalanne, Philippe
    Publisher: Oxford :Oxford University Press,
    Year of publication: 2022
    Pages: xiv, 655 Seiten
    ISBN: 978-0-19-878613-9
    Type of Medium: Book
    Language: English
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  • 188
    Book
    Book
    Praxisbuch Infografik : Informationen visualisieren, Daten präzise darstellen, gängige Fehler vermeiden (2022)
    Frechen :mitp,
    Details
    Title: Praxisbuch Infografik : Informationen visualisieren, Daten präzise darstellen, gängige Fehler vermeiden
    Author: Fichtel, Stefan
    Edition: 1. Auflage
    Publisher: Frechen :mitp,
    Year of publication: 2022
    Pages: 276 Seiten : , Illustrationen, Diagramme
    ISBN: 978-3-7475-0443-7
    Type of Medium: Book
    Language: German
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  • 189
    Book
    Book
    ¬Die¬ Automatisierung des Schreibens & Gegenprogramme der Literatur / (2022)
    Berlin :Matthes & Seitz Berlin,
    Details
    Title: ¬Die¬ Automatisierung des Schreibens & Gegenprogramme der Literatur /
    Author: Schönthaler, Philipp
    Edition: Erste Auflage
    Publisher: Berlin :Matthes & Seitz Berlin,
    Year of publication: 2022
    Pages: 575 Seiten
    ISBN: 978-3-7518-0341-0 , 9783751803427
    Type of Medium: Book
    Language: German
    URL: https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=3145923&site=ehost-live
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  • 190
    Book
    Book
    Schematron : a language for validating XML (2022)
    XML Press,
    Details
    Title: Schematron : a language for validating XML
    Author: Siegel, Erik
    Publisher: XML Press,
    Year of publication: 2022
    Pages: 272 SeitenXML Press
    ISBN: 978-1-937434-80-9
    Type of Medium: Book
    Language: English
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  • 191
    Book
    Book
    Handelsgesetzbuch : mit Einführungsgesetz, Publizitätsgesetz und Handeslssregisterverordnung; 5002 : Beck-Texte im dtv (2022)
    Details
    Title: Handelsgesetzbuch : mit Einführungsgesetz, Publizitätsgesetz und Handeslssregisterverordnung; 5002 : Beck-Texte im dtv
    Contributer: Fleischer, Holger , Deutschland [Normerlassende Gebietskörperschaft]
    Edition: 68., überarbeitete Auflage
    Year of publication: 2022
    Pages: 371 Seiten
    Series Statement: dtv ; 5002 5002 : Beck-Texte im dtv
    ISBN: 978-3-423-53175-7
    Type of Medium: Book
    Language: German
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  • 192
    Book
    Book
    99 + 1 Warm-ups für den digitalen Raum : Wie du räumliche Distanzen überwindest, Menschen verbindest und für frische Energie sorgst! (2022)
    Berlin :BoD – Books on Demand,
    Details
    Title: 99 + 1 Warm-ups für den digitalen Raum : Wie du räumliche Distanzen überwindest, Menschen verbindest und für frische Energie sorgst!
    Author: Tonhauser, Pauline
    Publisher: Berlin :BoD – Books on Demand,
    Year of publication: 2022
    Pages: 310 Seiten
    Type of Medium: Book
    Language: German
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  • 193
    Electronic Resource
    Electronic Resource
    THE PATHOGENESIS OF HELICOBACTER PYLORIĐ??INDUCED GASTRO-DUODENAL DISEASES (2006)
    Palo Alto, Calif. : Annual Reviews
    Annual Review of Pathology: Mechanisms of Disease (2006) 1 (2006), S. 63-96 
    Details
    ISSN: 1553-4006
    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
    Topics: Medicine
    Notes: Helicobacter pylori is the main cause of peptic ulceration, distal gastric adenocarcinoma, and gastric lymphoma. Only 15% of those colonized develop disease, and pathogenesis depends upon strain virulence, host genetic susceptibility, and environmental cofactors. Virulence factors include the cag pathogenicity island, which induces proinflammatory, pro-proliferative epithelial cell signaling; the cytotoxin VacA, which causes epithelial damage; and an adhesin, BabA. Host genetic polymorphisms that lead to high-level pro-inflammatory cytokine release in response to infection increase cancer risk. Pathogenesis is dependent upon inflammation, a Th-1 acquired immune response and hormonal changes including hypergastrinaemia. Antral-predominant inflammation leads to increased acid production from the uninflamed corpus and predisposes to duodenal ulceration; corpus-predominant gastritis leads to hypochlorhydria and predisposes to gastric ulceration and adenocarcinoma. Falling prevalence of H. pylori in developed countries has led to a falling incidence of associated diseases. However, whether there are disadvantages of an H. pylori-free stomach, for example increased risk of esosphageal adenocarcinoma, remains unclear.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1146/annurev.pathol.1.110304.100125
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  • 194
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    GENETICS OF SOFT TISSUE TUMORS (2006)
    Palo Alto, Calif. : Annual Reviews
    Annual Review of Pathology: Mechanisms of Disease (2006) 1 (2006), S. 435-466 
    Details
    ISSN: 1553-4006
    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
    Topics: Medicine
    Notes: Sarcomas form a highly diverse group of rare tumors that are derived from connective tissue. More than 100 different malignant and benign soft tissue neoplasms can be recognized by histologic examination. Few diagnostic markers exist, and the cell of origin for many soft tissue tumors is unknown. The accurate diagnosis of many of these tumors therefore remains a challenge. The study of sarcomas has yielded many insights that can be applied to other neoplasms such as carcinoma. For example, the success of the treatment of gastrointestinal stromal tumor with Imatinib has led to an increased effort to find targeted therapies for other malignancies. Here we describe the known molecular changes in a number of sarcomas and focus on novel scientific approaches that can be expected to lead to improved diagnosis, prognostication, and therapy of sarcoma.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1146/annurev.pathol.1.110304.100052
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  • 195
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    Electronic Resource
    FLUID MECHANICAL ASPECTS OF THE GAS-LIFT TECHNIQUE (2006)
    Palo Alto, Calif. : Annual Reviews
    Annual Review of Fluid Mechanics 38 (2006), S. 225-249 
    Details
    ISSN: 0066-4189
    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Notes: The gas-lift technique comprises the injection of gas bubbles in vertical oil wells to increase production. It is based on a reduction of the tubing gravitational pressure gradient. Several fluid-flow phenomena influencing such vertical gas-liquid flows are discussed. These effects include the radial distribution of void fraction and of gas and liquid velocity, flow regime changes, and system stability problems. Associated consequences for gas-lift performance and related optimization approaches are also discussed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1146/annurev.fluid.38.061505.093942
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  • 196
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    Electronic Resource
    PASSIVE AND ACTIVE FLOW CONTROL BY SWIMMING FISHES AND MAMMALS (2006)
    Palo Alto, Calif. : Annual Reviews
    Annual Review of Fluid Mechanics 38 (2006), S. 193-224 
    Details
    ISSN: 0066-4189
    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Notes: What mechanisms of flow control do animals use to enhance hydrodynamic performance? Animals are capable of manipulating flow around the body and appendages both passively and actively. Passive mechanisms rely on structural and morphological components of the body (i.e., humpback whale tubercles, riblets). Active flow control mechanisms use appendage or body musculature to directly generate wake flow structures or stiffen fins against external hydrodynamic loads. Fish can actively control fin curvature, displacement, and area. The vortex wake shed by the tail differs between eel-like fishes and fishes with a discrete narrowing of the body in front of the tail, and three-dimensional effects may play a major role in determining wake structure in most fishes.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1146/annurev.fluid.38.050304.092201
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  • 197
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    Electronic Resource
    LONG NONLINEAR INTERNAL WAVES (2006)
    Palo Alto, Calif. : Annual Reviews
    Annual Review of Fluid Mechanics 38 (2006), S. 395-425 
    Details
    ISSN: 0066-4189
    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Notes: Over the past four decades, the combination of in situ and remote sensing observations has demonstrated that long nonlinear internal solitary-like waves are ubiquitous features of coastal oceans. The following provides an overview of the properties of steady internal solitary waves and the transient processes of wave generation and evolution, primarily from the point of view of weakly nonlinear theory, of which the Korteweg-de Vries equation is the most frequently used example. However, the oceanographically important processes of wave instability and breaking, generally inaccessible with these models, are also discussed. Furthermore, observations often show strongly nonlinear waves whose properties can only be explained with fully nonlinear models.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1146/annurev.fluid.38.050304.092129
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  • 198
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    Electronic Resource
    ELECTROKINETIC FLOW AND DISPERSION IN CAPILLARY ELECTROPHORESIS (2006)
    Palo Alto, Calif. : Annual Reviews
    Annual Review of Fluid Mechanics 38 (2006), S. 309-338 
    Details
    ISSN: 0066-4189
    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Notes: Electrophoretic separation of a mixture of chemical species is a fundamental technique of great usefulness in biology, health care, and forensics. In capillary electrophoresis (which has evolved from its predecessor, slab-gel electrophoresis), the sample migrates through a single microcapillary instead of through the network of pores in a gel. A fundamental design problem is to minimize dispersion in the separation direction. Molecular diffusion is inevitable and sets a theoretical limit on the best separation that can be achieved. But in practice, there are a number of effects arising out of the interplay between fluid flow, chemistry, thermal effects, and electric fields that result in enhanced dispersion. This paper reviews the subject of fluid flow in such capillary microchannels and examines the various causes of enhanced dispersion that limit the efficiency of separation.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1146/annurev.fluid.38.050304.092053
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  • 199
    Electronic Resource
    Electronic Resource
    AERODYNAMICS OF RACE CARS (2006)
    Palo Alto, Calif. : Annual Reviews
    Annual Review of Fluid Mechanics 38 (2006), S. 27-63 
    Details
    ISSN: 0066-4189
    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Notes: Race car performance depends on elements such as the engine, tires, suspension, road, aerodynamics, and of course the driver. In recent years, however, vehicle aerodynamics gained increased attention, mainly due to the utilization of the negative lift (downforce) principle, yielding several important performance improvements. This review briefly explains the significance of the aerodynamic downforce and how it improves race car performance. After this short introduction various methods to generate downforce such as inverted wings, diffusers, and vortex generators are discussed. Due to the complex geometry of these vehicles, the aerodynamic interaction between the various body components is significant, resulting in vortex flows and lifting surface shapes unlike traditional airplane wings. Typical design tools such as wind tunnel testing, computational fluid dynamics, and track testing, and their relevance to race car development, are discussed as well. In spite of the tremendous progress of these design tools (due to better instrumentation, communication, and computational power), the fluid dynamic phenomenon is still highly nonlinear, and predicting the effect of a particular modification is not always trouble free. Several examples covering a wide range of vehicle shapes (e.g., from stock cars to open-wheel race cars) are presented to demonstrate this nonlinear nature of the flow field.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1146/annurev.fluid.38.050304.092016
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  • 200
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    Electronic Resource
    LARGE-EDDY SIMULATION OF TURBULENT COMBUSTION (2006)
    Palo Alto, Calif. : Annual Reviews
    Annual Review of Fluid Mechanics 38 (2006), S. 453-482 
    Details
    ISSN: 0066-4189
    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Notes: Large-eddy simulation (LES) of turbulent combustion is a relatively new research field. Much research has been carried out over the past years, but to realize the full predictive potential of combustion LES, many fundamental questions still have to be addressed, and common practices of LES of nonreacting flows revisited. The focus of the present review is to highlight the fundamental differences between Reynolds-averaged Navier-Stokes (RANS) and LES combustion models for nonpremixed and premixed turbulent combustion, to identify some of the open questions and modeling issues for LES, and to provide future perspectives.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1146/annurev.fluid.38.050304.092133
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