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.

A family of fitness landscapes modeled through gene regulatory networks

Over 100 years, Fitness landscapes have been a powerful metaphor for understanding the evolution of biological systems. These landscapes describe how genotypes are connected to each other and are related according to relative fitness. Despite the high dimensionality of such real-world landscapes, empirical studies are often limited in their ability to quantify the fitness of different genotypes beyond point mutations, while theoretical works attempt statistical/mechanistic models to reason the overall landscape structure. However, most classical fitness landscape models overlook an instinctive constraint that genotypes leading to the same phenotype almost certainly share the same fitness value, since the information of genotype-phenotype mapping is rarely incorporated. Here, we investigate fitness landscape models through the lens of Gene Regulatory Networks (GRNs), where the regulatory products are computed from multiple genes and collectively treated as the phenotypes. With the assumption that regulatory mediators/products exhibit binary states, we prove topographical features of GRN fitness landscape models such as accessibility and connectivity insensitive to the choice of the fitness function. Furthermore, using graph theory, we deduce a mesoscopic structure underlying GRN fitness landscape models that retains necessary information for evolutionary dynamics with minimal complexity. We also propose an algorithm to construct such a mesoscopic backbone which is more efficient than the brute-force approach. Combined, this work provides mathematical implications for fitness landscape models with high-dimensional genotype-phenotype mapping, yielding the potential to elucidate empirical landscapes and their resulting evolutionary processes in a manner complementary to existing computational studies.

Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis

Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated—whether as a subtype of depression, or as a distinct disorder altogethe—interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics.

Geological evolution of the Mississippi River into the Anthropocene

The Mississippi River maintains commercial and societal networks of the USA along its >3700 km length. It has accumulated a fluvial sedimentary succession over 80 million years. Through the last 11,700 years of the Holocene Epoch, the wild river shaped the landscape, models of which have become classic in geological studies of ancient river strata. Studies of the river were led by the need to develop infrastructure and to search for hydrocarbons, through which, these models have become quite sophisticated. However, whilst the models demonstrate how the wild river behaves, a monumental shift in fundamental controls on the entire fluvial system, broadly coinciding with the proposed mid-20th century onset of the Anthropocene Epoch, has generated new geological patterns that are becoming globally ubiquitous, and which the Mississippi River typifies. As such, whilst classic Holocene river models may be compared to human-modified systems such as the Lower Mississippi River (and others worldwide), locally the models may now only directly apply to its fossilized components preserved in the sub-surface. Such river models need adapting to better understand the present dynamics, and future evolution of these landscapes.

Understanding and Modeling Forest Disturbance Interactions at the Landscape Level

Disturbances, both natural and anthropogenic, affect the configuration, composition, and function of forested ecosystems. Complex system behaviors emerge from the interactions between disturbance regimes, the vegetation response to those disturbances, and their interplay with multiple drivers (climate, topography, land use, etc.) across spatial and temporal scales. Here, we summarize conceptual advances and empirical approaches to disturbance interaction investigation, and used those insights to evaluate and categorize 146 landscape modeling studies emerging from a systematic review of the literature published since 2010. Recent conceptual advances include formal disaggregation of disturbances into their constituent components, embedding disturbance processes into system dynamics, and clarifying terminology for interaction factors, types, and ecosystem responses. Empirical studies investigating disturbance interactions now span a wide range of approaches, including (most recently) advanced statistical methods applied to an expanding set of spatial and temporal datasets. Concurrent development in spatially-explicit landscape models, informed by these empirical insights, integrate the interactions among natural and anthropogenic disturbances by coupling these processes to account for disturbance stochasticity, disturbance within and across scales, and non-linear landscape responses to climate change. Still, trade-offs between model elegance and complexity remain. We developed an index for the degree of process integration (i.e., balance of static vs. dynamic components) within a given disturbance agent and applied it to the studies from our systematic review. Contemporary model applications in this line of research have applied a wide range process integration, depending on the specific question, but also limited in part by data and knowledge. Non-linear “threshold” behavior and cross-scaled interactions remain a frontier in temperate, boreal, and alpine regions of North America and Europe, while even simplistic studies are lacking from other regions of the globe (e.g., subtropical and tropical biomes). Understanding and planning for uncertainty in system behavior—including disturbance interactions—is paramount at a time of accelerated anthropogenic change. While progress in landscape modeling studies in this area is evident, work remains to increase model transparency and confidence, especially for understudied regions and processes. Moving forward, a multi-dimensional approach is recommended to address the uncertainties of complex human-ecological dynamics.

Psychophysiological Response According to the Greenness Index of Subway Station Space

This study proposed a plan for implementing a pleasant and healthy indoor landscape in subway station space. To this end, it established a 3D landscape model of the subway interior by reviewing previous studies on indoor landscape and the greenness index of indoor spaces. Moreover, it investigated and analyzed psychophysiological responses of users to environmental indoor landscape design in subway station space. Subway stations were classified as underground subway stations and ground subway stations according to the presence of natural light inflow. The greenness index of indoor spaces was also divided into four types of 0%, 10%, 15%, and 20%. Through this process, eight 3D landscape models of the subway interior were implemented. In addition, this study investigated psychophysiological responses of 60 male and female adults in their 20s and 30s using the models implemented. The investigation result was analyzed based on a frequency analysis, the χ2 test, T-test, one-way analysis of variance, and multidimensional scaling, which were performed in SPSS Statistics 25. The results of this study can be summarized as follows. First, physiological responses of research subjects were analyzed based on their prefrontal α wave asymmetric values. The analytic result showed that the environment where interior landscape was adopted produced more positive effects than the environment where interior landscape was not adopted. Second, psychological responses of research subjects were examined based on their greenness index preference, awareness of interior landscape area, attention restoration effect, and space images. The analytic result indicated that, among eight 3D landscape models of the subway interior, they preferred the model with the greenness index of 15% for underground subway stations. In addition, they preferred the model with the greenness index of 10% the most for ground subway stations.

Virtual Reality Design and Realization of Interactive Garden Landscape

It is very important to study and explore the application of virtual reality technology in landscape garden design, especially in the current environment of triple network integration and Internet of Things construction, to promote and facilitate the rapid development of digital landscape garden design in China. In this paper, we study the implementation method of virtual landscape gardening system and establish a virtual environment based on the ancient city of Yangcheng. On the computer platform, we study and realize a virtual roaming system of medium complexity with more complete roaming functions. Using the Quest3D software platform, a desktop-type virtual garden simulation system was developed, focusing on the virtual reality modeling technology method and virtual system implementation. The experimental results show that the GPU-accelerated drawing method based on GLSL can significantly improve the drawing frame rate of 3D garden landscape vegetation scenes with a small amount of scene data and has a certain feasibility. Based on the OpenSceneGraph (OSG) graphics rendering engine, the visualization of various types of 3D landscape models is realized, and the spatial layout of various types of landscape with parametric control is realized through digital vector layers, which flexibly manage and organize various garden elements and reasonably organize the spatial topological relationship between various types of landscape in 3D space. By integrating cross-platform ArcGISEngine components, the basic data of garden scenes including terrain data and vector data are managed. Through scene view cropping and hierarchical detail modeling technologies, the drawing efficiency and rendering real time of the garden landscape are improved. It realizes interactive 3D scene browsing and provides a six-degree-of-freedom all-round display of the overall landscape.

Quantifying cell transitions in C. elegans with data-fitted landscape models

Increasing interest has emerged in new mathematical approaches that simplify the study of complex differentiation processes by formalizing Waddington’s landscape metaphor. However, a rational method to build these landscape models remains an open problem. Here we study vulval development in C. elegans by developing a framework based on Catastrophe Theory (CT) and approximate Bayesian computation (ABC) to build data-fitted landscape models. We first identify the candidate qualitative landscapes, and then use CT to build the simplest model consistent with the data, which we quantitatively fit using ABC. The resulting model suggests that the underlying mechanism is a quantifiable two-step decision controlled by EGF and Notch-Delta signals, where a non-vulval/vulval decision is followed by a bistable transition to the two vulval states. This new model fits a broad set of data and makes several novel predictions.

Landscape Models of Complex Change

This chapter reviews recent advances in the NK modeling literature conceptualizing organizational change and innovation as a search over a complex landscape. It discusses both strengths and limitations of this perspective and delineates potential for future research directions. The key argument is that the NK model in its traditional form may be exhausting the theoretical insights that it can provide to the field. However, substantial modifications and extensions of the NK model or new classes of landscape models may provide fresh perspectives. Specifically, we consider the modeling efforts that endogenize the landscape construction as the next frontier in this literature. We also discuss several recent studies that incorporate various extensions of the NK model and allow for agent-driven changes to the landscape.