Effect of the Slag Composition on the Process Behavior,Energy Consumption,and Nonmetallic Inclusions during Electroslag Remelting

Electroslag remelting (ESR) is an important process to produce high-quality tool steels. The slag composition has a strong effect on the remelting behavior, particularly on energy consumption and the removal of nonmetallic inclusions (NMI). The latter aspect is strongly related to chemical reactions between the slag and the metal and determines the necessary composition of the slag. Also, the electrical conductivity of the slag is determined by the slag composition, and a high resistivity is desirable. The effect of different slag compositions with 0%–60% CaF₂ and a corresponding wide range of electrical conductivities is investigated regarding slag movement, slag surface temperature, and slag skin thickness, as well as their impact on chemical reactions and the removal of NMI. Therefore, a laboratory-scale ESR unit and the plastic mold steel X40Cr14 are used for the experimental trials. The results show a strong impact on the remelting behavior as well as on the specific energy consumption ranging from ≈900 to over 1700 kWh h⁻¹. The findings from the chemical analysis and detection of NMI indicate that a similar metallurgical behavior is feasible, leading to comparable amounts of dominantly Al₂O₃–MgO-type inclusions with some variation due to different activities in the slag.     

Second-Generation CD73 Inhibitors Based on a 4,6-Biaryl-2-thiopyridine Scaffold

Various series of 4,6-biaryl-2-thiopyridine derivatives were synthesized and evaluated as potential ecto-5′-nucleotidase (CD73) inhibitors. Two synthetic routes were explored and the coupling of 4,6-disubstituted 3-cyano-2-chloro-pyridines with selected thiols allowed us to explore the structural diversity. Somehow divergent results were obtained in biological assays on CD73 inhibition using either the purified recombinant protein or cell-based assays, highlighting the difficulty to target protein-protein interface on proteins existing as soluble and membrane-bound forms. Among the 18 new derivatives obtained, three derivatives incorporating morpholino substituents on the 4,6-biaryl-2-thiopyridine core were shown to be able to reverse the adenosine-mediated immune suppression on human T cells. The higher blockade efficiency was observed for 2-((3-cyano-4,6-bis(4-morpholinophenyl)pyridin-2-yl)thio)-N-(isoxazol-3-yl)acetamide (with total reversion at 100 μM) and methyl 2-((3-cyano-4,6-bis(4-morpholinophenyl)pyridin-2-yl)thio)acetate (with partial reversion at 10 μM). Thus, this series of compounds illustrates a new chemotype of CD73 allosteric inhibitors.     

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.     

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.     

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.     

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.     

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.     

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.