A computational framework for thermal coupling in hybrid fire simulation

In structural fire engineering, it is crucial to estimate the global structural behavior in a realistic scheme. This necessity arises from the reason that the single element testing doesn’t represent the global behavior of the structure correctly due to the possible load redistribution into alternative load paths and change of static systems in case of global fire. Therefore, hybrid simulation method can be accounted as a key method, which fulfills the possibility of study of the global structural behavior in structure with coupling the numerical simulation and experimental testing. In this method, the numerical simulation procedure of the whole structure is coupled and controlled with the outcomes of the experiment performed on a single part of the structure, which is critical or difficult to study numerically. So far, several attempts have been made to study hybrid fire simulation. There, however, exist severe shortcomings in so-far research: - the correct consideration of the stiffness and material properties for the heated element and their degradation during fire exposure, - retaining the compatibility and the equilibrium between the substructures, - the automatic real-time interaction between the substructures and also - realistic consideration of the thermal coupling between substructures with regard to the transfer of the heat from fire exposed component to adjacent elements. In hybrid fire simulation, the thermo-mechanical coupling can be studied realistically, when the heat exposed to the single compartment, its transfer to the adjacent substructures and the effect of two latter on the mechanical response of the structure is considered. In the current paper, this purpose is studied on a steel structure benchmark with two different approaches: sequentially-coupled thermal-stress analysis and fully-coupled thermal-stress analysis. Here, the mathematical and mechanical aspects of each approach and their difference regarding the response of the structure will be investigated. Also, their application in the hybrid fire simulation and the importance of the real-time issue in these approaches are outlined. In this paper, the numerical model of the intended benchmark which interacts automatically with another numerical model, representing the experimental substructure exposed to fire is studied. Therefore, the implementation of hybrid fire simulation and different aspects of the thermal coupling including the existence of heat transfer and mechanical and thermal properties will be discussed

Consolidated fire testing – a framework for thermomechanical modelling

Consolidated testing facilitates the investigation of the global behavior of structures subjected to fire and therefore may become increasingly important in structural fire engineering. In order to develop a consolidated testing procedure that meets the requirements arising from structural fire engineering and considers thermal strains, thermal creep effects as well as strength and stiffness degradation, a consolidated testing benchmark problem is elaborated. The benchmark problem allows to perform coupled experimental and numerical tests that can be verified by pure physical testing. Furthermore, a framework for a consolidated test setup is developed, including a tangent stiffness update algorithm. Two preliminary tests at ambient temperature show the eligibility of the consolidated testing framework and are presented in this paper

Class reconstruction driven adversarial domain adaptation for hyperspectral image classification

We address the problem of cross-domain classification of hyperspectral image (HSI) pairs under the notion of unsupervised domain adaptation (UDA). The UDA problem aims at classifying the test samples of a target domain by exploiting the labeled training samples from a related but different source domain. In this respect, the use of adversarial training driven domain classifiers is popular which seeks to learn a shared feature space for both the domains. However, such a formalism apparently fails to ensure the (i) discriminativeness, and (ii) non-redundancy of the learned space. In general, the feature space learned by domain classifier does not convey any meaningful insight regarding the data. On the other hand, we are interested in constraining the space which is deemed to be simultaneously discriminative and reconstructive at the class-scale. In particular, the reconstructive constraint enables the learning of category-specific meaningful feature abstractions and UDA in such a latent space is expected to better associate the domains. On the other hand, we consider an orthogonality constraint to ensure non-redundancy of the learned space. Experimental results obtained on benchmark HSI datasets (Botswana and Pavia) confirm the efficacy of the proposal approach

Revealing cytotoxic substructures in molecules using deep learning

In drug development, late stage toxicity issues of a compound are the main cause of failure in clinical trials. In silico methods are therefore of high importance to guide the early design process to reduce time, costs and animal testing. Technical advances and the ever growing amount of available toxicity data enabled machine learning, especially neural networks, to impact the field of predictive toxicology. In this study, cytotoxicity prediction, one of the earliest handles in drug discovery, is investigated using a deep learning approach trained on a highly consistent in-house data set of over 34,000 compounds with a share of less than 5% of cytotoxic molecules. The model reached a balanced accuracy of over 70%, similar to previously reported studies using Random Forest. Albeit yielding good results, neural networks are often described as a black box lacking deeper mechanistic understanding of the underlying model. To overcome this absence of interpretability, a Deep Taylor Decomposition method is investigated to identify substructures that may be responsible for the cytotoxic effects, the so-called toxicophores. Furthermore, this study introduces cytotoxicity maps which provide a visual structural interpretation of the relevance of these substructures. Using this approach could be helpful in drug development to predict the potential toxicity of a compound as well as to generate new insights into the toxic mechanism. Moreover, it could also help to de-risk and optimize compounds

High Precision Surface Structuring with Ultra-Short Laser Pulses and Synchronized Mechanical Axes

For surface and 3D structuring in a 2.5D process, ultra-short pulsed laser systems are mostly used in combination with mechanical axes, whereas the mechanical axes can include electrical motor as well as beam deflecting systems like a galvo scanner. The motion of the axes is synchronized with the clock of the laser pulses, by a modification of the electronic axes control. This work shows the scalability of the ablation process up to MHz-regime in relation to surface quality and ablation efficiency, drilling of thin foils without any heat accumulation and deforming problems of the foil. Furthermore the transfer of the machining strategy from a synchronized galvo scanner to a rotating cylinder setup is shown

Ethynylogous Amides and Urethanes

Vinylogous amides have proved useful as starting compounds for the preparation of polycyclic conjugated, nonbenzenoid π-electron systems [¹]

Invasive floating water weeds – killing life and commerce

Weeds by definition are plants that grow in the wrong place. When their seeds or other plant parts are transported to other regions where their natural enemies are absent, they can multiply unhindered. Indigenous plants, especially those that are adapted for invading disturbed areas, can also become weeds. The first category is a particularly good target for classical biological control. Insects, mites and micro-organisms that feed on them are imported from their original area and released against the new invader. Against indigenous plants however, biological control is far less promising. By the end of 1980s, many of the water bodies in West Africa were invaded by alien plant species considered to be among the world’s worst aquatic weeds: water hyacinth Eichhornia crassipes, water lettuce Pistia stratiotes, and water fern Salvinia molesta. They were accidentally or deliberately introduced as ornamentals or for use in aquariums from their native range South America to many parts of the world where they have become invasive