A Semi-Procedural Convolutional Material Prior

Lightweight material capture methods require a material prior, defining the subspace of plausible textures within the large space of unconstrained texel grids. Previous work has either used deep neural networks (trained on large synthetic material datasets) or procedural node graphs (constructed by expert artists) as such priors. In this paper, we propose a semi-procedural differentiable material prior that represents materials as a set of (typically procedural) grayscale noises and patterns that are processed by a sequence of lightweight learnable convolutional filter operations. We demonstrate that the restricted structure of this architecture acts as an inductive bias on the space of material appearances, allowing us to optimize the weights of the convolutions per-material, with no need for pre-training on a large dataset. Combined with a differentiable rendering step and a perceptual loss, we enable single-image tileable material capture comparable with state of the art. Our approach does not target the pixel-perfect recovery of the material, but rather uses noises and patterns as input to match the target appearance. To achieve this, it does not require complex procedural graphs, and has a much lower complexity, computational cost and storage cost. We also enable control over the results, through changing the provided patterns and using guide maps to push the material properties towards a user-driven objective.     

Can support by digital technologies stimulate intrapreneurial behaviour? The moderating role of management support for innovation and intrapreneurial self-efficacy

Drawing on theorising on digital technologies as external enablers of entrepreneurial activities and an interactionist perspective on corporate entrepreneurship, this article examines the relationship between digital technology support and employee intrapreneurial behaviour. We propose that management support for innovation as an organisational characteristic and intrapreneurial self-efficacy as an individual characteristic moderate this relationship. Findings from a metric conjoint experiment with 1360 decisions nested within 85 employees showed that support by social media, support by collaborative technologies, and support by intelligent decision support systems were significant predictors of employee intrapreneurial behaviour. However, the relative impact of support by these digital technologies varied with different levels of management support for innovation and intrapreneurial self-efficacy.     

Simulation Model for Digital Twins of Pneumatic Vacuum Ejectors

Increasing productivity, as well as flexibility, is required for the industrial production sector. To meet these challenges, concepts in the field of “Industry 4.0” are arising, such as the concept of Digital Twins. Vacuum handling systems are a widespread technology for material handling in industry and face the same challenges and opportunities. In this field, a key issue is the lack of Digital Twins containing behavior models for vacuum handling systems and their components in different applications and use cases. A novel concept for modeling and simulating the fluidic behavior of pneumatic vacuum ejectors as key components of vacuum handling systems is proposed. In order to increase the simulation accuracy, the concept can access instance-specific data of the used asset instead of object-specific data. The model and the data are part of the Digital Twins of pneumatic vacuum ejectors, which shall be able to be combined with other components to represent a Digital Twin of entire vacuum handling systems. The proposed model is validated in an experimental test setup and in an industrial application delivering sufficiently accurate results.     

Adaptive state observation of linear time-varying systems with delayed measurements and unknown parameters

In this article, we address the problem of adaptive state observation of linear time-varying systems with delayed measurements and unknown parameters. Our new developments extend the results reported in our recently works. The case with known parameters has been studied by many researchers. However in this article we show that the generalized parameter estimation-based observer design provides a very simple solution for the unknown parameter case. Moreover, when this observer design technique is combined with the dynamic regressor extension and mixing estimation procedure the estimated state and parameters converge in fixed-time imposing extremely weak excitation assumptions.     

Characterization of hemp fiber fire reaction

Integrating bio-resources in materials allows to reduce the environmental impact of the building industry. This study deals with fire-retardant treatments, alternative to boric acid and ammonium salts solutions, applied to hemp fibers for thermal insulation application. The aim is to limit the energy, sanitary and environmental impacts of the treatment, while optimizing the technical performances. A laboratory protocol evaluates the flame-retardant effect of the developed treatments. Treated fibers, including commercial treatments, are subjected to characterization tests: direct ignition small flame, thermal analysis (thermogravimetric analysis [TGA], differential thermal analysis [DTA]), pyrolysis-combustion flow calorimeter and cone calorimeter. Some of the methods have been adapted to be applied to fibrous materials. The obtained results orientate the formulation of a treatment and highlight the complementarity of the analysis methods. Coupling the results, the ConeTools's predictive model leads to the estimation of the reaction to fire class according to the Euroclass. Euroclass C appears accessible with a tailored treatment.     

Image-Based Flow Regime Recognition in Aerated Stirred Tanks Using Deep Transfer Learning

Monitoring of flow regimes in aerated stirred tanks is important to ensure energy efficiency and product quality. The use of deep learning models for the recognition of flow regimes shows promising results. However, such models require a large amount of data for training. The aim of this paper is to apply the deep transfer learning approach to address this challenge. We compare various pre-trained models with the differential learning rate and 2-step transfer learning approaches to analyse the resultant model performance. We also investigate the effect of the dataset size on the classification accuracy.     

Automated Evaluation of Biochemical Plant KPIs based on DEXPI Information

Implementation of data-based key performance indicators (KPIs) within existing plants usually requires a thorough manual analysis of the plant topology to define which sensors could provide reliable information for calculating the KPIs. In this publication, we propose an assistance system that automates this process. First the automatic linkage between plant structure and operational data is presented for the evaluation of the process efficiency. Next, a concept and prototypical implementation of the evaluation of predefined KPIs is described. The plant structure is described according to the DEPXI specification, while a rule-based framework was developed to depict the process-engineering knowledge. The proposed methods have been validated in an experimental setup. It could be shown that with standardized inputs, a generic extendable evaluation of the plant performance can be achieved.     

An Advanced ‘clickECM’ That Can be Modified by the Inverse-Electron-Demand Diels-Alder Reaction

The extracellular matrix (ECM) represents the natural environment of cells in tissue and therefore is a promising biomaterial in a variety of applications. Depending on the purpose, it is necessary to equip the ECM with specific addressable functional groups for further modification with bioactive molecules, for controllable cross-linking and/or covalent binding to surfaces. Metabolic glycoengineering (MGE) enables the specific modification of the ECM with such functional groups without affecting the native structure of the ECM. In a previous approach (S. M. Ruff, S. Keller, D. E. Wieland, V. Wittmann, G. E. M. Tovar, M. Bach, P. J. Kluger, Acta Biomater. 2017 , 52, 159–170), we demonstrated the modification of an ECM with azido groups, which can be addressed by bioorthogonal copper-catalyzed azide-alkyne cycloaddition (CuAAC). Here, we demonstrate the modification of an ECM with dienophiles (terminal alkenes, cyclopropene), which can be addressed by an inverse-electron-demand Diels-Alder (IEDDA) reaction. This reaction is cell friendly as there are no cytotoxic catalysts needed. We show the equipment of the ECM with a bioactive molecule (enzyme) and prove that the functional groups do not influence cellular behavior. Thus, this new material has great potential for use as a biomaterial, which can be individually modified in a wide range of applications.