TY - CHAP A1 - Ambellan, Felix A1 - Lamecker, Hans A1 - von Tycowicz, Christoph A1 - Zachow, Stefan ED - Rea, Paul M. T1 - Statistical Shape Models - Understanding and Mastering Variation in Anatomy T2 - Biomedical Visualisation N2 - In our chapter we are describing how to reconstruct three-dimensional anatomy from medical image data and how to build Statistical 3D Shape Models out of many such reconstructions yielding a new kind of anatomy that not only allows quantitative analysis of anatomical variation but also a visual exploration and educational visualization. Future digital anatomy atlases will not only show a static (average) anatomy but also its normal or pathological variation in three or even four dimensions, hence, illustrating growth and/or disease progression. Statistical Shape Models (SSMs) are geometric models that describe a collection of semantically similar objects in a very compact way. SSMs represent an average shape of many three-dimensional objects as well as their variation in shape. The creation of SSMs requires a correspondence mapping, which can be achieved e.g. by parameterization with a respective sampling. If a corresponding parameterization over all shapes can be established, variation between individual shape characteristics can be mathematically investigated. We will explain what Statistical Shape Models are and how they are constructed. Extensions of Statistical Shape Models will be motivated for articulated coupled structures. In addition to shape also the appearance of objects will be integrated into the concept. Appearance is a visual feature independent of shape that depends on observers or imaging techniques. Typical appearances are for instance the color and intensity of a visual surface of an object under particular lighting conditions, or measurements of material properties with computed tomography (CT) or magnetic resonance imaging (MRI). A combination of (articulated) statistical shape models with statistical models of appearance lead to articulated Statistical Shape and Appearance Models (a-SSAMs).After giving various examples of SSMs for human organs, skeletal structures, faces, and bodies, we will shortly describe clinical applications where such models have been successfully employed. Statistical Shape Models are the foundation for the analysis of anatomical cohort data, where characteristic shapes are correlated to demographic or epidemiologic data. SSMs consisting of several thousands of objects offer, in combination with statistical methods ormachine learning techniques, the possibility to identify characteristic clusters, thus being the foundation for advanced diagnostic disease scoring. Y1 - 2019 SN - 978-3-030-19384-3 SN - 978-3-030-19385-0 U6 - https://doi.org/10.1007/978-3-030-19385-0_5 VL - 3 IS - 1156 SP - 67 EP - 84 PB - Springer Nature Switzerland AG ET - 1 ER - TY - CHAP A1 - Gotzes, Uwe A1 - Buchholz, Annika A1 - Kallrath, Josef A1 - Lindner, Niels A1 - Koch, Thorsten T1 - Flexible Pooling Pattern Design with Integer Programming T2 - Theory, Algorithms and Experiments in Applied Optimization. In Honor of the 70th Birthday of Panos Pardalos N2 - Sample pooling has the potential to significantly enhance large-scale screening procedures, especially in scenarios like the COVID-19 pandemic, where rapid and widespread PCR testing has been crucial. Efficient strategies are essential to increase the testing capacity, i.e., the number of tests that can be processed within a given timeframe. Non-adaptive pooling strategies can further streamline the testing process by reducing the required testing rounds. In contrast to adaptive strategies, where subsequent tests depend on prior results, non-adaptive pooling processes all samples in a single round, eliminating the need for sequential retesting and reducing delays. This paper presents a highly flexible method based on integer programming to design optimized pooling patterns suitable for various applications, including medical diagnostics and quality control in industrial production. Using coronavirus testing as a case study, we formulate and solve optimization and satisfiability models that compute efficient pool designs. Our optimized pooling does not only increase testing capacity, but also accelerates the testing process and reduces overall costs. The proposed method is adaptable and can be seamlessly integrated into automated testing systems. Y1 - 2025 VL - 226 PB - Springer ER - TY - CHAP A1 - Diekerhof, M. A1 - Monti, A. A1 - Lebedeva, E. A1 - Tkaczyk, A. H. A1 - Yüksel-Ergün, I. A1 - Zittel, J. A1 - Escudero, L. F. A1 - Soroudi, A. A1 - Helmberg, C. A1 - Kanovíc, Ž. A1 - Petkovic, M. A1 - Lacalandra, F. A1 - Frangioni, A. A1 - Lee, J. A1 - De Filippo, A. A1 - Lombardi, M. A1 - Milano, M. A1 - Ezran, P. A1 - Haddad, Y. T1 - Production and Demand Management T2 - Mathematical Optimization for Efficient and Robust Energy Networks N2 - Demand Side Management (DSM) is usually considered as a process of energy consumption shifting from peak hours to off-peak times. DSM does not always reduce total energy consumption, but it helps to meet energy demand and supply. For example, it balances variable generation from renewables (such as solar and wind) when energy demand differs from renewable generation. Y1 - 2020 SN - 978-3-030-57442-0 U6 - https://doi.org/https://doi.org/10.1007/978-3-030-57442-0_1 VL - 4 PB - Springer ER - TY - CHAP A1 - Schwarz, R. A1 - Lacalandra, F. A1 - Schewe, L. A1 - Bettinelli, A. A1 - Vigo, D. A1 - Bischi, A. A1 - Parriani, T. A1 - Martelli, E. A1 - Vuik, K. A1 - Lenz, R. A1 - Madsen, H. A1 - Blanco, I. A1 - Guericke, D. A1 - Yüksel-Ergün, I. A1 - Zittel, J. T1 - Network and Storage T2 - Mathematical Optimization for Efficient and Robust Energy Networks N2 - Natural gas is considered by many to be the most important energy source for the future. The objectives of energy commodities strategic problems can be mainly related to natural gas and deal with the definition of the “optimal” gas pipelines design which includes a number of related sub problems such as: Gas stations (compression) location and Gas storage locations, as well as compression station design and optimal operation. Y1 - 2020 SN - 978-3-030-57442-0 U6 - https://doi.org/https://doi.org/10.1007/978-3-030-57442-0_6 VL - 4 PB - Springer ER - TY - CHAP A1 - Pedersen, Jaap A1 - Ljubić, Ivana ED - Pardalos, Panos M. ED - Prokopyev, Oleg A. T1 - Prize-Collecting Steiner Tree Problem and its Variants T2 - Encyclopedia of Optimization Y1 - 2024 U6 - https://doi.org/10.1007/978-3-030-54621-2_869-1 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Hoppmann-Baum, Kai A1 - Mexi, Gioni A1 - Burdakov, Oleg A1 - Casselgren, Carl Johan A1 - Koch, Thorsten ED - Hebrard, Emmanuel ED - Musliu, Nysret T1 - Minimum Cycle Partition with Length Requirements T2 - Integration of Constraint Programming, Artificial Intelligence, and Operations Research N2 - In this article we introduce a Minimum Cycle Partition Problem with Length Requirements (CPLR). This generalization of the Travelling Salesman Problem (TSP) originates from routing Unmanned Aerial Vehicles (UAVs). Apart from nonnegative edge weights, CPLR has an individual critical weight value associated with each vertex. A cycle partition, i.e., a vertex disjoint cycle cover, is regarded as a feasible solution if the length of each cycle, which is the sum of the weights of its edges, is not greater than the critical weight of each of its vertices. The goal is to find a feasible partition, which minimizes the number of cycles. In this article, a heuristic algorithm is presented together with a Mixed Integer Programming (MIP) formulation of CPLR. We furthermore introduce a conflict graph, whose cliques yield valid constraints for the MIP model. Finally, we report on computational experiments conducted on TSPLIB-based test instances. Y1 - 2020 SN - 978-3-030-58941-7 U6 - https://doi.org/10.1007/978-3-030-58942-4_18 VL - 12296 SP - 273 EP - 282 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Benner, Peter A1 - Grundel, Sara A1 - Himpe, Christian A1 - Huck, Christoph A1 - Streubel, Tom A1 - Tischendorf, Caren T1 - Gas Network Benchmark Models T2 - Applications of Differential-Algebraic Equations: Examples and Benchmarks N2 - The simulation of gas transportation networks becomes increasingly more important as its use-cases broaden to more complex applications. Classically, the purpose of the gas network was the transportation of predominantly natural gas from a supplier to the consumer for long-term scheduled volumes. With the rise of renewable energy sources, gas-fired power plants are often chosen to compensate for the fluctuating nature of the renewables, due to their on-demand power generation capability. Such an only short-term plannable supply and demand setting requires sophisticated simulations of the gas network prior to the dispatch to ensure the supply of all customers for a range of possible scenarios and to prevent damages to the gas network. In this work we describe the modeling of gas networks and present benchmark systems to test implementations and compare new or extended models. Y1 - 2019 SN - 978-3-030-03718-5 U6 - https://doi.org/10.1007/11221_2018_5 SP - 171 EP - 197 PB - Springer International Publishing ER - TY - CHAP A1 - Gamrath, Gerald A1 - Berthold, Timo A1 - Heinz, Stefan A1 - Winkler, Michael T1 - Structure-Based Primal Heuristics for Mixed Integer Programming T2 - Optimization in the Real World N2 - Primal heuristics play an important role in the solving of mixed integer programs (MIPs). They help to reach optimality faster and provide good feasible solutions early in the solving process. In this paper, we present two new primal heuristics which take into account global structures available within MIP solvers to construct feasible solutions at the beginning of the solving process. These heuristics follow a large neighborhood search (LNS) approach and use global structures to define a neighborhood that is with high probability significantly easier to process while (hopefully) still containing good feasible solutions. The definition of the neighborhood is done by iteratively fixing variables and propagating these fixings. Thereby, fixings are determined based on the predicted impact they have on the subsequent domain propagation. The neighborhood is solved as a sub-MIP and solutions are transferred back to the original problem. Our computational experiments on standard MIP test sets show that the proposed heuristics find solutions for about every third instance and therewith help to improve the average solving time. Y1 - 2015 SN - 978-4-431-55419-6 U6 - https://doi.org/10.1007/978-4-431-55420-2_3 VL - 13 SP - 37 EP - 53 PB - Springer Japan ER - TY - CHAP A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Statistical Shape Modeling of Musculoskeletal Structures and Its Applications T2 - Computational Radiology for Orthopaedic Interventions N2 - Statistical shape models (SSM) describe the shape variability contained in a given population. They are able to describe large populations of complex shapes with few degrees of freedom. This makes them a useful tool for a variety of tasks that arise in computer-aided madicine. In this chapter we are going to explain the basic methodology of SSMs and present a variety of examples, where SSMs have been successfully applied. Y1 - 2016 SN - 978-3-319-23481-6 U6 - https://doi.org/10.1007/978-3-319-23482-3 VL - 23 SP - 1 EP - 23 PB - Springer ER - TY - CHAP A1 - Anteghini, Marco A1 - Martins Dos Santos, Vitor T1 - Computational Approaches for Peroxisomal Protein Localization T2 - Peroxisomes N2 - Computational approaches are practical when investigating putative peroxisomal proteins and for sub-peroxisomal protein localization in unknown protein sequences. Nowadays, advancements in computational methods and Machine Learning (ML) can be used to hasten the discovery of novel peroxisomal proteins and can be combined with more established computational methodologies. Here, we explain and list some of the most used tools and methodologies for novel peroxisomal protein detection and localization. Y1 - 2023 SN - 978-1-0716-3047-1 U6 - https://doi.org/10.1007/978-1-0716-3048-8_29 VL - 2643 SP - 405 EP - 411 PB - Humana, New York ER -