Bauhaus University, Weimar

About: Bauhaus University, Weimar is a education organization based out in Weimar, Thüringen, Germany. It is known for research contribution in the topics: Finite element method & Isogeometric analysis. The organization has 1421 authors who have published 2998 publications receiving 104454 citations. The organization is also known as: Bauhaus-Universität Weimar & Hochschule für Architektur und Bauwesen.

A cell-based smoothed finite element method for kinematic limit analysis

Abstract: This paper presents a new numerical procedure for kinematic limit analysis problems, which incorporates the cell-based smoothed finite element method with second-order cone programming. The application of a strain smoothing technique to the standard displacement finite element both rules out volumetric locking and also results in an efficient method that can provide accurate solutions with minimal computational effort. The non-smooth optimization problem is formulated as a problem of minimizing a sum of Euclidean norms, ensuring that the resulting optimization problem can be solved by an efficient second-order cone programming algorithm. Plane stress and plane strain problems governed by the von Mises criterion are considered, but extensions to problems with other yield criteria having a similar conic quadratic form or 3D problems can be envisaged.

Boron–graphdiyne: a superstretchable semiconductor with low thermal conductivity and ultrahigh capacity for Li, Na and Ca ion storage

Abstract: Most recently, boron–graphdiyne, a π-conjugated two-dimensional (2D) structure made from a merely sp carbon skeleton connected with boron atoms was successfully experimentally realized through a bottom-up synthetic strategy. Motivated by this exciting experimental advance, we conducted density functional theory (DFT) and classical molecular dynamics simulations to study the mechanical, thermal conductivity and stability, electronic and optical properties of single-layer B-graphdiyne. We particularly analyzed the application of this novel 2D material as an anode for Li, Na, Mg and Ca ion storage. Uniaxial tensile simulation results reveal that B-graphdiyne owing to its porous structure and flexibility can yield superstretchability. The single-layer B-graphdiyne was found to exhibit a semiconducting electronic character, with a narrow band-gap of 1.15 eV based on the HSE06 prediction. It was confirmed that mechanical straining can be employed to further tune the optical absorbance and electronic band-gap of B-graphdiyne. Ab initio molecular dynamics results reveal that B-graphdiyne can withstand high temperatures, like 2500 K. The thermal conductivity of suspended single-layer B-graphdiyne was predicted to be very low, ∼2.5 W mK−1 at room temperature. Our first-principles results reveal the outstanding prospect of B-graphdiyne as an anode material with ultrahigh charge capacities of 808 mA h g−1, 5174 mA hg−1 and 3557 mA h g−1 for Na, Ca and Li ion storage, respectively. The comprehensive insight provided by this investigation highlights the outstanding physics of B-graphdiyne nanomembranes, and suggests them as highly promising candidates for the design of novel stretchable nanoelectronics and energy storage devices.

Determinants of motion picture box office and profitability: an interrelationship approach

Abstract: Introduction Producing and marketing motion pictures is notoriously risky, with only three out of ten movies breaking even and one becoming profitable at the box office. Extending knowledge on the factors that influence a movie’s box-office and on the interrelations between these factors can be seen as major contribution to aid in lowering the number of failures in the motion picture industry. The major aim of this study is to distinguish direct and indirect effects between potential success drivers and motion picture success by understanding the interrelationships among different determinants of movie success.

An efficient optimization approach for designing machine learning models based on genetic algorithm

Abstract: Machine learning (ML) methods have shown powerful performance in different application Nonetheless, designing ML models remains a challenge and requires further research as most procedures adopt a trial and error strategy In this study, we present a methodology to optimize the architecture and the feature configurations of ML models considering a supervised learning process The proposed approach employs genetic algorithm (GA)-based integer-valued optimization for two ML models, namely deep neural networks (DNN) and adaptive neuro-fuzzy inference system (ANFIS) The selected variables in the DNN optimization problems are the number of hidden layers, their number of neurons and their activation function, while the type and the number of membership functions are the design variables in the ANFIS optimization problem The mean squared error (MSE) between the predictions and the target outputs is minimized as the optimization fitness function The proposed scheme is validated through a case study of computational material design We apply the method to predict the fracture energy of polymer/nanoparticles composites (PNCs) with a database gathered from the literature The optimized DNN model shows superior prediction accuracy compared to the classical one-hidden layer network Also, it outperforms ANFIS with significantly lower number of generations in GA The proposed method can be easily extended to optimize similar architecture properties of ML models in various complex systems

A case study on automated safety compliance checking to assist fall protection design and planning in building information models

Abstract: Worldwide occupational safety statistics show that the construction industry in many countries experiences one of the highest accident rates of all industry sectors. Falls remain a major concern as they contribute to very serious injuries or even fatalities on construction projects around the world. Since the standards and rules for protective safety equipment vary by country, the growing numbers of internationally operating companies are in need of tools that allow ubiquitous understanding and planning of safety regardless of the country where they operate. The problem is examined using a customizable automatic safety rule-checking platform for building information models. The applied rule-based checking algorithms are designed to be add-ons to existing building information modelling (BIM) software and can check models for safety hazards early in the design and planning process. Once hazards have been identified preventative safety equipment can be designed, estimated, and included in the construction sc...