Landscaper: A Modeling System for 3D Printing Scale Models of Landscapes

Landscape models of geospatial regions provide an intuitive mechanism for exploring complex geospatial information. However, the methods currently used to create these scale models require a large amount of resources, which restricts the availability of these models to a limited number of popular public places, such as museums and airports. In this paper, we have proposed a system for creating these physical models using an affordable 3D printer in order to make the creation of these models more widely accessible. Our system retrieves GIS relevant to creating a physical model of a geospatial region and then addresses the two major limitations of affordable 3D printers, namely the limited number of materials and available printing volume. This is accomplished by separating features into distinct extruded layers and splitting large models into smaller pieces, allowing us to employ different methods for the visualization of different geospatial features, like vegetation and residential areas, in a 3D printing context. We confirm the functionality of our system by printing two large physical models of relatively complex landscape regions.

Inspecting the Solution Space of Genome-Scale Metabolic Models

Genome-scale metabolic models are frequently used in computational biology. They offer an integrative view on the metabolic network of an organism without the need to know kinetic information in detail. However, the huge solution space which comes with the analysis of genome-scale models by using, e.g., Flux Balance Analysis (FBA) poses a problem, since it is hard to thoroughly investigate and often only an arbitrarily selected individual flux distribution is discussed as an outcome of FBA. Here, we introduce a new approach to inspect the solution space and we compare it with other approaches, namely Flux Variability Analysis (FVA) and CoPE-FBA, using several different genome-scale models of lactic acid bacteria. We examine the extent to which different types of experimental data limit the solution space and how the robustness of the system increases as a result. We find that our new approach to inspect the solution space is a good complementary method that offers additional insights into the variance of biological phenotypes and can help to prevent wrong conclusions in the analysis of FBA results.

Catching Up on Multilevel Modeling

This review focuses on the use of multilevel models in psychology and other social sciences. We target readers who are catching up on current best practices and sources of controversy in the specification of multilevel models. We first describe common use cases for clustered, longitudinal, and cross-classified designs, as well as their combinations. Using examples from both clustered and longitudinal designs, we then address issues of centering for observed predictor variables: its use in creating interpretable fixed and random effects of predictors, its relationship to endogeneity problems (correlations between predictors and model error terms), and its translation into multivariate multilevel models (using latent-centering within multilevel structural equation models). Finally, we describe novel extensions—mixed-effects location–scale models—designed for predicting differential amounts of variability.

A Multi-Scale Numerical Model for Investigation of Flame Dynamics in a Thermal Flow Reversal Reactor

In this paper, the flame dynamics in a thermal flow reversal reactor are studied using a multi-scale model. The challenges of the multi-scale models lie in the data exchanges between different scale models and the capture of the flame movement of the filtered combustion by the pore-scale model. Through the multi-scale method, the computational region of the porous media is divided into the inlet preheating zone, reaction zone, and outlet exhaust zone. The three models corresponding to the three zones are calculated by volume average method, pore-scale method, and volume average method respectively. Temperature distribution is used as data for real-time exchange. The results show that the multi-scale model can save computation time when compared with the pore-scale model. Compared with the volumetric average model, the multi-scale model can capture the flame front and predict the flame propagation more accurately. The flame propagation velocity increases and the flame thickness decreases with the increase of inlet flow rates and mixture concentration. In addition, the peak value of the initial temperature field and the width of the high-temperature zone also affect the flame propagation velocity and flame thickness.

Geotechnical characterization and modelling of the “Fallen Lands” phenomenon in the amazon environment

In many rivers of the Amazon, mainly along the Amazon River, the phenomenon of "fallen land" occurs at the time of the rivers' ebb, causing the erosion of river banks, resulting in major social and economic problems. The influence of saturation/percolation of soil layers on the shear resistance of the affected massifs is unknown. Thus, the main objective of the characterization and geotechnical modelling of these slopes is to determine the shear strength of the affected massifs. For this purpose, deformed and undeformed samples were collected from the various layers of the soil massif in a location where the phenomenon was under development. These samples were characterized physically, chemically, and mechanically. Subsequently, the massifs were recreated in reduced scale models, observing the critical conditions in which the disaster occurred, and qualitatively comparing them with slope stability computational models. The results showed that the layers are classified as silty and sandy soils, with a small fraction of clay, and that there are three types of movements associated with the fallen soils: the fall, overturning, and rotational landslide, all actively influenced by the effects of the river's ebb, saturation/percolation, and texture.

The use of «native» wavelet transform for determining lateral density variation of the Volgo-Uralian subcraton

In the last two decades in conjunction with the development of satellite gravimetry, the techniques of regional-scale inverse and forward gravity modeling started to be more actively incorporated in the construction of crustal and lithospheric scale models. Such regional models are usually built as a set of layers and bodies with constant densities. This approach often leads to a certain difference between the initially used measured gravity field and a gravity field that is produced by the model. One of the examples of this kind of models is a recent lithospheric model of the Volgo-Uralian subcraton. In the current study, we are applying the method of «native» wavelet transform to the residual gravity anomaly for defining the possible lateral density variations within the lithospheric layers of Volgo-Uralia. Keywords: wavelet transform; gravity field inversion; forward gravity modeling; Volgo-Uralian subcraton; satellite gravimetry.