Organ on Chip Technology to Model Cancer Growth and Metastasis

Organ on chip (OOC) has emerged as a major technological breakthrough and distinct model system revolutionizing biomedical research and drug discovery by recapitulating the crucial structural and functional complexity of human organs in vitro. OOC are rapidly emerging as powerful tools for oncology research. Indeed, Cancer on chip (COC) can ideally reproduce certain key aspects of the tumor microenvironment (TME), such as biochemical gradients and niche factors, dynamic cell–cell and cell–matrix interactions, and complex tissue structures composed of tumor and stromal cells. Here, we review the state of the art in COC models with a focus on the microphysiological systems that host multicellular 3D tissue engineering models and can help elucidate the complex biology of TME and cancer growth and progression. Finally, some examples of microengineered tumor models integrated with multi-organ microdevices to study disease progression in different tissues will be presented.

The Effect of Connecting Bridges on Vortex-induced Vibration of Skyscrapers

The shapes of slender skyscrapers are unfavourable for carrying horizontal loads. In this paper, we investigate the possibility of improving their structural behaviour by adding urban-scale networks of structural connections among the buildings. We focus on vibrations of skyscrapers in response to wind-induced vortex shedding. We develop a conceptual model of those structural networks composed of springs, dampers and point masses. The proposed model enables rapid numerical simulations involving large networks, which is not possible in the case of more detailed engineering models. The effect of connections, dilatation gaps, and network size are investigated for random collections of high-rise buildings, and triangular networks of horizontal bar connections among them. It is found that connections efficiently reduce vibrations in the network, especially for large network size. This study aims to be a first step towards uncovering the benefits of a novel form of urban development. A karcsú felhőkarcolók alakja kedvezőtlen a rájuk ható vízszintes terhek viselése szempontjából. Munkánkban a szerkezeti viselkedés javítási lehetőségeit vizsgáljuk az épületeket összekötő szerkezeti kapcsolatok városi léptékű hálózata segítségével. Vizsgálatunk középpontjában a szél által kiváltott örvényleválás okozta szerkezeti rezgések állnak. A rendszert rugókból, csillapítóelemekből és tömegpontokból álló koncepcionális modell segítségével írjuk le. Ez a megközelítésmód lehetővé teszi nagy hálózatok gyors numerikus szimulációját, amely részletesebb mérnöki modellek esetében nem lehetséges. Véletlenszerűen generált épületcsoportok, és vízszintes rúdszerű kapcsolatokból kialakított háromszögelt hálózatok esetén vizsgáljuk a kapcsolatoknak, a bennük kialakított dilatációs hézagoknak és a hálózat méretének a hatását. Eredményeink azt mutatják, hogy a kapcsolatok jelentősen csökkentik a hálózat rezgéseit, különösen nagy hálózati méret esetén. A tanulmány célja, hogy kezdeti lépéseket tegyünk egy újszerű városfejlesztési modell előnyeinek feltárására.

Prediction as a Basis for Skilled Reading: Insights from Natural Language Engineering Models

Reading is not an inborn human capability, and yet, English-speaking adults read with impressive speed. This study considered how predictions of upcoming words impact on this skilled behaviour. We used a powerful computer model from natural language engineering (GPT-2) to derive predictions of upcoming words in text passages. These predictions were highly accurate, and showed a tight relationship to fine-grained aspects of eye-movement behaviour when adults read those same passages, including whether to skip the next word and how long to spend on it. Strong predictions that did not materialise resulted in a prediction error cost on fixation durations. Our findings suggest that predictions for upcoming words can be made based on relatively superficial statistical information in reading, and that these predictions guide how our eyes interrogate text. This study is the first to demonstrate a relationship between the internal state of a modern natural language engineering model and eye-movement behaviour in reading, opening substantial new opportunities for language research and application.

Experimental analysis of radially resolved dynamic inflow effects due to pitch steps

The dynamic inflow effect denotes the unsteady aerodynamic response to fast changes in rotor loading due to a gradual adaption of the wake. This does lead to load overshoots. The objective of the paper was to increase the understanding of that effect based on pitch step experiments on a 1.8 m diameter model wind turbine, which are performed in the large open jet wind tunnel of ForWind – University of Oldenburg. The flow in the rotor plane is measured with a 2D laser Doppler anemometer, and the dynamic wake induction factor transients in axial and tangential direction are extracted. Further, integral load measurements with strain gauges and hot-wire measurements in the near and close far wake are performed. The results show a clear gradual decay of the axial induction factors after a pitch step, giving the first direct experimental evidence of dynamic inflow due to pitch steps. Two engineering models are fitted to the induction factor transients to further investigate the relevant time constants of the dynamic inflow process. The radial dependency of the axial induction time constants as well as the dependency on the pitch direction is discussed. It is confirmed that the nature of the dynamic inflow decay is better described by two rather than only one time constant. The dynamic changes in wake radius are connected to the radial dependency of the axial induction transients. In conclusion, the comparative discussion of inductions, wake deployment and loads facilitate an improved physical understanding of the dynamic inflow process for wind turbines. Furthermore, these measurements provide a new detailed validation case for dynamic inflow models and other types of simulations.

Modification of Variance-Based Sensitivity Indices for Stochastic Evaluation of Monitoring Measures

In complex engineering models, various uncertain parameters affect the computational results. Most of them can only be estimated or assumed quite generally. In such a context, measurements are interesting to determine the most decisive parameters accurately. While measurements can reduce parameters’ variance, structural monitoring might improve general assumptions on distributions and their characteristics. The decision on variables being measured often relies on experts’ practical experience. This paper introduces a method to stochastically estimate the potential benefits of measurements by modified sensitivity indices. They extend the established variance-based sensitivity indices originally suggested by Sobol’. They do not quantify the importance of a variable but the importance of its variance reduction. The numerical computation is presented and exemplified on a reference structure, a 50-year-old pre-stressed concrete bridge in Germany, where the prediction of the fatigue lifetime of the pre-stressing steel is of concern. Sensitivity evaluation yields six important parameters (e.g., shape of the S–N curve, temperature loads, creep, and shrinkage). However, taking into account individual monitoring measures and suited measurements identified by the modified sensitivity indices, creep and shrinkage, temperature loads, and the residual pre-strain of the tendons turn out to be most efficient. They grant the highest gains of accuracy with respect to the lifetime prediction.

Digital Solutions Suite: Big Data, Artificial Intelligence, and Digital Barrel

The concept of digital transformation is based on two principles: data driven—exploiting every bit of data source—and user focused. The objective is not only to consolidate data from multiple systems, but to apply an analytics approach to extract insights that are the product of the aggregation of multiple sources then present it to the user (field manager, production and surveillance engineer, region manager, and country) with criteria's of simplicity, specificity, novelty—and most importantly, clarity. The idea is to liberate the data across the whole upstream community and intended for production operations people by providing a one-stop production digital platform that taps into unstructured data and is transformed into structured to be used as input to engineering models and as a result provide data analytics and generate insights. There is three main key objectives: To have only one source of truth using cloud-based technology To incorporate artificial intelligence models to fill the data gaps of production and operations parameters such as pressure and temperature To incorporate multiple solutions for the upstream community that helps during the slow, medium, and fast loops of upstream operations. The new "way of working" helps multiple disciplines such as subsurface team, facilities, and operations, HSSE and business planning, combining business process management and technical workflows to generates insights and create value that impact the profit and losses (P&L) sheet of the operators. The "new ways of working" tackle values pillars such as production optimization, reduced unplanned deferment, cost avoidance, and improved process cycle efficiency. The use of big data and artificial intelligence algorithms are key to understand the production of the wells and fields, as well as anchoring on processing the data with automated engineering models, thus enabling better decision making including the span of time scale such as fast, medium, or slow loop actions.

Translator from Extended SysML to Physical Interaction and Signal Flow Simulation Platforms, Version 1.1

The design of complex systems often requires engineers from multiple disciplines (mechanical, electrical, production, and so on) to communicate with each other and exchange system design information. Systems engineering models are a cross-disciplinary foundation for this process, but are not well-integrated with specialized engineering information, leading to redundant and inconsistent system specifications. The software provided here translates system models in the Systems Modeling Language (SysML) to physical interaction and signal flow (also known as lumped-parameter, one-dimensional, or network) models on two simulation platforms used in many engineering domains.