Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations

Urban cellular automata (CA) models are broadly used in quantitative analyses and predictions of urban land-use dynamics. However, most urban CA developed with neighborhood rules consider only a small neighborhood scope under a specific spatial resolution. Here, we quantify neighborhood effects in a relatively large cellular space and analyze their role in the performance of an urban land use model. The extracted neighborhood rules were integrated into a commonly used logistic regression urban CA model (Logistic-CA), resulting in a large neighborhood urban land use model (Logistic-LNCA). Land-use simulations with both models were evaluated with urban expansion data in Xiamen City, China. Simulations with the Logistic-LNCA model raised the accuracies of built-up land by 3.0%–3.9% in two simulation periods compared with the Logistic-CA model with a 3 × 3 kernel. Parameter sensitivity analysis indicated that there was an optimal large window size in cellular space and a corresponding optimal parameter configuration.     

Mechanism optimization based on reaction rate rules

Accurate chemistry models form the backbone of detailed computational fluid dynamics (CFD) tools used for simulating complex combustion devices. Combustion chemistry is often very complex and chemical mechanisms generally involve more than one hundred species and one thousand reactions. In the derivation of these large chemical mechanisms, typically a large number of reactions appears, for which rate data are not available from experiment or theory. Rate data for these reactions are then often assigned using so-called reaction classes. This method categorizes all possible fuel-specific reactions as classes of reactions with prescribed rules for the rate constants. This ensures consistency in the chemical mechanism. In rate parameter optimizations found in the published literature, rate constants of single elementary reactions are usually systematically optimized to achieve good agreement between model performance and experimental measurements. However, it is not kinetically reasonable to modify the rate parameters of single reactions, because this will violate consistency of rate parameters of kinetically similar reactions. In this work, the rate rules, that determine the rates for reaction classes are calibrated instead of the rates of single elementary reactions leading to a chemically more consistent model optimization. This is demonstrated by optimizing an n-pentane combustion mechanism. The rate rules are studied with respect to reaction classes, abstracting species, broken C–H bonds, and ring strain energy barriers. Furthermore, the uncertainties of the rate rules and model predictions are minimized and the pressure dependence of reaction classes dominating low temperature oxidation is optimized.     

How to design effective priority rules: Example of simple assembly line balancing

Priority rule-based methods (PRBMs) rely on problem-specific knowledge to construct good solutions in a very short time. They can be used as stand-alone procedures or can be integrated into (partial) enumeration procedures, like branch and bound or dynamic programming, and heuristic solution methods. PRBMs are especially important for solving NP-hard optimization problems.In this paper, we formulate general design principles on how to construct good-performing PRBMs, based on a thorough computational investigation. Our principles allow to construct effective PRBMs already ad hoc, i.e. without time-consuming data mining algorithms. We conduct our analysis on the example of the NP-hard Simple Assembly Line Balancing Problem (SALBP), on which with small modifications most situations in the planning of assembly lines are based. We also provide a cross-validation of our results and illustrate the application of the formulated principles.     

Gauss–Galerkin quadrature rules for quadratic and cubic spline spaces and their application to isogeometric analysis

We introduce Gaussian quadrature rules for spline spaces that are frequently used in Galerkin discretizations to build mass and stiffness matrices. By definition, these spaces are of even degrees. The optimal quadrature rules we recently derived (Bartoň and Calo, 2016) act on spaces of the smallest odd degrees and, therefore, are still slightly sub-optimal. In this work, we derive optimal rules directly for even-degree spaces and therefore further improve our recent result. We use optimal quadrature rules for spaces over two elements as elementary building blocks and use recursively the homotopy continuation concept described in Bartoň and Calo (2016) to derive optimal rules for arbitrary admissible numbers of elements. We demonstrate the proposed methodology on relevant examples, where we derive optimal rules for various even-degree spline spaces. We also discuss convergence of our rules to their asymptotic counterparts, these are the analogues of the midpoint rule of Hughes et al. (2010), that are exact and optimal for infinite domains.     

Numerical investigation of lateral earth pressure for unreinforced masonry walls

For the design of unreinforced masonry walls under lateral earth pressure according to DIN EN 1996‐3 [1], the active earth pressure is used, which is less than the earth pressure at rest. For the consideration of active earth pressure, a sufficient deflection of the wall is needed. It is unknown whether the deflections in reality are large enough to justify a reduction of the active earth pressure. Therefore a numerical model has been developed which considers the load‐bearing behaviour of masonry walls, with several boundary conditions being considered to estimate the effective earth pressure.     

Web考试系统自动组卷策略的设计与实现

组卷策略是Web考试系统的重点和难点,合理设置试题库和组卷参数以及合适的组卷算法是关键,论文介绍了一种基于随机抽取法＋分类分段法进行组卷的设计思想及其实现过程。     

Shear resistance of reinforced masonry beams with and without additional concrete or prestress

This article investigates the transferability of the Simplified Modified Compression Field Theory (SMCFT) [2], which is known in reinforced concrete design and included in the fib Model Code for Concrete Structures 2010 (Volume 3) [1], to reinforced or prestressed masonry beams (RM beams) with or without an additional layer of concrete. The investigation for this work is the obsolete shear design concept that has been used until now for reinforced masonry under shear loading, which does not adequately reflect the actual load‐bearing behaviour of significant areas of masonry. The fundamentals of the SMCFT are explained and the transferability of the theory to RM beams is examined, taking into account in particular the different material properties of masonry compared to reinforced concrete. A first approach for future application is represented by the equations presented here for the determination of the shear force capacity of RM beams. The verification is performed through a comparison of the shear resistances determined experimentally (exp.) and by calculation (calc.).     

Lean rules extraction methodology for lean PSS design via key performance indicators monitoring

Lean PSS (Product-Service System) design comprises a promising strategy for delivering sustainable PSS offerings, considering several well-established lean practices. However, automated ways to apply lean practices and more specifically lean rules in industry are limited. This work proposes a methodology for improving the leanness of PSS design, by combining real-time KPI monitoring with lean principles and practices. Through a correlation of typical wastes with the metrics used in the calculation of KPIs, the Total Leanness Index (TLI) of the procedures is defined. Based on automatically identified trade-off values for TLI, lean rules are extracted to improve the performance of PSS lifecycle phases. The proposed lean rules extraction methodology (LeanREM) is validated through a case study of power waste reduction and the concurrently maintenance time decrease in a mould-making company.     

GPU accelerated pivoting rules for the simplex algorithm

Simplex type algorithms perform successive pivoting operations (or iterations) in order to reach the optimal solution. The choice of the pivot element at each iteration is one of the most critical step in simplex type algorithms. The flexibility of the entering and leaving variable selection allows to develop various pivoting rules. In this paper, we have proposed some of the most well-known pivoting rules for the revised simplex algorithm on a CPU–GPU computing environment. All pivoting rules have been implemented in MATLAB and CUDA. Computational results on randomly generated optimal dense linear programs and on a set of benchmark problems (Netlib-optimal, Kennington, Netlib-infeasible, Mészáros) are also presented. These results showed that the proposed GPU implementations of the pivoting rules outperform the corresponding CPU implementations.