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The mollified (discrete) uniform distribution and its applications
No full textThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).The mollified uniform distribution is rediscovered, which constitutes a “soft” version of the continuous uniform distribution. Important stochastic properties are presented and used to demonstrate potential fields of applications. For example, it constitutes a model covering platykurtic, mesokurtic, and leptokurtic shapes. Its cumulative distribution function may also serve as the soft-clipping response function for defining generalized linear models with approximately linear dependence. Furthermore, it might be considered for teaching, as an appealing example for the convolution of random variables. Finally, a discrete type of mollified uniform distribution is briefly discussed as well.Vo
China research, politics and expertise in Germany
No full textDieses Werk steht unter der Lizenz Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International (https://creativecommons.org/licenses/by-sa/4.0/).Vo
Dispatching rules for a two-stage hybrid flow shop scheduling with no inter-stage waiting time
No full textThis computational study investigates a production scheduling problem for a two-stage hybrid flow shop (HFS) with parallel machines at least at one stage and with zero inter-stage waiting policies between process steps. This scenario is important in industries such as steel production and chemical processing, where cooling time between process steps must be avoided. In this paper, we propose eight dispatching rules that are applied to instances of up to 200 jobs, and benchmark them based on various performance metrics that demonstrate the effectiveness of the proposed heuristic approaches. The dispatching rules, such as Shortest Task Time (STT) and Shortest Processing Time (SPT) are combined with machine assignment rules, such as First Available Machine (FAM) and Minimum Idle Time (MIT), and optimized for makespan and total completion time in the no-wait HFS. Furthermore, for our computational study, we investigate two sequencing approaches - stage-oriented decomposition (A1) and reduction to a flow shop problem (A2) - in the benchmark of this computational study.SMU
Convergence of a continuous Galerkin method for the Biot-Allard poroelasticity system
No full textThis work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).We study a space-time finite element method for a system of poromechanics with memory effects that are modeled by a convolution integral. In the literature, the system is referred to as the Biot-Allard model. We recast the model as a first-order system in time, where the memory effects are transformed into an auxiliary differential equation. This allows for a computationally efficient numerical scheme. The system is discretized by continuous Galerkin methods in time and equal-order finite element methods in space. An optimal order error estimate is proved for the norm of the first-order energy of the unknowns of the system. The estimate is confirmed by numerical experiments.SMU
An unsupervised learning approach to predict the deterioration of aging bridges using inspection data
No full textA common approach for developing degradation models for aging bridges involves fitting a stochastic process, such as a Markov or semi‐Markov chain, to condition data collected from visual inspections and stored within Bridge Management Systems. However, variations in environmental, structural, and operational factors result in different aging rates among bridges. Consequently, identifying groups of bridges exhibiting similar deterioration patterns and developing tailored deterioration models for each group can reduce the uncertainty in remaining useful life estimations and optimize the allocation of maintenance resources. This article presents an unsupervised learning approach to identify bridge populations with homogeneous degradation rates. The SNOB algorithm is applied to cluster bridge sojourn times across predefined degradation levels utilizing Weibull Mixture Models. Three distinct groups of bridges are identified, here referred to as fragile, normal, and robust bridges, each one characterized by a different degradation rate. For each group, a deterioration model based on a semi‐Markov process is developed, capturing the evolution of bridge conditions within the cluster. The proposed approach is applied to condition data from the US National Bridge Inventory (NBI) and the results are discussed by emphasizing a possible correlation between the identified clusters and climate conditions of bridge locations.Vo
Erwachsenenerziehung durch Sachbücher?
No full textDieser Artikel ist unter der Creative Commons Namensnennung 4.0 International Lizenz veröffentlicht (https://creativecommons.org/licenses/by/4.0/deed.de).Vo
Breaking free: Decoupling forced systems with Laplace neural networks
No full textThis chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).Forecasting the behaviour of industrial robots, power grids or pandemics under changing external inputs requires accurate dynamical models that can adapt to varying signals and capture long-term effects such as delays or memory. While recent neural approaches address some of these challenges individually, their reliance on computationally intensive solvers and their black-box nature limit their practical utility. In this work, we propose Laplace-Net, a decoupled, solver-free neural framework for learning forced and delay-aware dynamical systems. It uses the Laplace transform to (i) bypass computationally intensive solvers, (ii) enable the learning of delays and memory effects and (iii) decompose each system into interpretable control-theoretic components. Laplace-Net also enhances transferability, as its modular structure allows for targeted re-training of individual components to new system setups or environments. Experimental results on eight benchmark datasets–including linear, nonlinear and delayed systems–demonstrate the method’s improved accuracy and robustness compared to state-of-the-art approaches, particularly in handling complex and previously unseen inputs.Vo