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Past earthquakes demonstrated the high vulnerability of industrial facilities equipped with complex process technologies leading to serious damage of the process equipment and multiple and simultaneous release of hazardous substances in industrial facilities. Nevertheless, the design of industrial plants is inadequately described in recent codes and guidelines, as they do not consider the dynamic interaction between the structure and the installations and thus the effect of seismic response of the installations on the response of the structure and vice versa. The current code-based approach for the seismic design of industrial facilities is considered not enough for ensure proper safety conditions against exceptional event entailing loss of content and related consequences. Accordingly, SPIF project (Seismic Performance of Multi-Component Systems in Special Risk Industrial Facilities) was proposed within the framework of the European H2020 - SERA funding scheme (Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe). The objective of the SPIF project is the investigation of the seismic behaviour of a representative industrial structure equipped with complex process technology by means of shaking table tests. The test structure is a three-story moment resisting steel frame with vertical and horizontal vessels and cabinets, arranged on the three levels and connected by pipes. The dynamic behaviour of the test structure and of its relative several installations is investigated. Furthermore, both process components and primary structure interactions are considered and analyzed. Several PGA-scaled artificial ground motions are applied to study the seismic response at different levels. After each test, dynamic identification measurements are carried out to characterize the system condition. The contribution presents the experimental setup of the investigated structure and installations, selected measurement data and describes the obtained damage. Furthermore, important findings for the definition of performance limits, the effectiveness of floor response spectra in industrial facilities will be presented and discussed.
Because of simple construction process, high energy efficiency, significant fire resistance and excellent sound isolation, masonry infilled reinforced concrete (RC) frame structures are very popular in most of the countries in the world, as well as in seismic active areas. However, many RC frame structures with masonry infills were seriously damaged during earthquake events, as the traditional infills are generally constructed with direct contact to the RC frame which brings undesirable infill/frame interaction. This interaction leads to the activation of the equivalent diagonal strut in the infill panel, due to the RC frame deformation, and combined with seismically induced loads perpendicular to the infill panel often causes total collapses of the masonry infills and heavy damages to the RC frames. This fact was the motivation for developing different approaches for improving the behaviour of masonry infills, where infill isolation (decoupling) from the frame has been more intensively studied in the last decade. In-plane isolation of the infill wall reduces infill activation, but causes the need for additional measures to restrain out-of-plane movements. This can be provided by installing steel anchors, as proposed by some researchers. Within the framework of European research project INSYSME (Innovative Systems for Earthquake Resistant Masonry Enclosures in Reinforced Concrete Buildings) the system based on a use of elastomers for in-plane decoupling and steel anchors for out-of-plane restrain was tested. This constructive solution was tested and deeply investigated during the experimental campaign where traditional and decoupled masonry infilled RC frames with anchors were subjected to separate and combined in-plane and out-of-plane loading. Based on a detailed evaluation and comparison of the test results, the performance and effectiveness of the developed system are illustrated.
Planning the layout and operation of a technical system is a common task
for an engineer. Typically, the workflow is divided into consecutive stages: First,
the engineer designs the layout of the system, with the help of his experience or of
heuristic methods. Secondly, he finds a control strategy which is often optimized
by simulation. This usually results in a good operating of an unquestioned sys-
tem topology. In contrast, we apply Operations Research (OR) methods to find a
cost-optimal solution for both stages simultaneously via mixed integer program-
ming (MILP). Technical Operations Research (TOR) allows one to find a provable
global optimal solution within the model formulation. However, the modeling error
due to the abstraction of physical reality remains unknown. We address this ubiq-
uitous problem of OR methods by comparing our computational results with mea-
surements in a test rig. For a practical test case we compute a topology and control
strategy via MILP and verify that the objectives are met up to a deviation of 8.7%.
In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions.
We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.
Messenger apps like WhatsApp or Telegram are an integral part of daily communication. Besides the various positive effects, those services extend the operating range of criminals. Open trading groups with many thousand participants emerged on Telegram. Law enforcement agencies monitor suspicious users in such chat rooms. This research shows that text analysis, based on natural language processing, facilitates this through a meaningful domain overview and detailed investigations. We crawled a corpus from such self-proclaimed black markets and annotated five attribute types products, money, payment methods, user names, and locations. Based on each message a user sends, we extract and group these attributes to build profiles. Then, we build features to cluster the profiles. Pretrained word vectors yield better unsupervised clustering results than current
state-of-the-art transformer models. The result is a semantically meaningful high-level overview of the user landscape of black market chatrooms. Additionally, the extracted structured information serves as a foundation for further data exploration, for example, the most active users or preferred payment methods.
Purpose
To assess potential cognitive deficits under the influence of static magnetic fields at various field strengths some studies already exist. These studies were not focused on attention as the most vulnerable cognitive function. Additionally, mostly no magnetic resonance imaging (MRI) sequences were performed.
Materials and Methods
In all, 25 right-handed men were enrolled in this study. All subjects underwent one MRI examination of 63 minutes at 1.5 T and one at 7 T within an interval of 10 to 30 days. The order of the examinations was randomized. Subjects were referred to six standardized neuropsychological tests strictly focused on attention immediately before and after each MRI examination. Differences in neuropsychological variables between the timepoints before and after each MRI examination were assessed and P-values were calculated
Results
Only six subtests revealed significant differences between pre- and post-MRI. In these tests the subjects achieved better results in post-MRI testing than in pre-MRI testing (P = 0.013–0.032). The other tests revealed no significant results.
Conclusion
The improvement in post-MRI testing is only explicable as a result of learning effects. MRI examinations, even in ultrahigh-field scanners, do not seem to have any persisting influence on the attention networks of human cognition immediately after exposure.