scholarly journals Towards Fabrication Information Modeling (FIM): Four Case Models to Derive Designs informed by Multi-Scale Trans-Disciplinary Data

2015 ◽  
Vol 1800 ◽  
Author(s):  
Jorge Duro-Royo ◽  
Neri Oxman
Keyword(s):  
Digital Fabrication ◽  
Biological Data ◽  
Environmental Data ◽  
Information Modeling ◽  
Functional Constraints ◽  
Integrate Form ◽  
Form Generation ◽  
Multi Scale ◽  
Standard Design ◽  
Material Computation

ABSTRACTDespite recent advancements in digital fabrication and manufacturing, limitations associated with computational tools are preventing further progress in the design of non-standard architectures. This paper sets the stage for a new theoretical framework and an applied approach for the design and fabrication of geometrically and materially complex functional designs coined Fabrication Information Modeling (FIM). We demonstrate systems designed to integrate form generation, digital fabrication, and material computation starting from the physical and arriving at the virtual environment. The paper reviews four computational strategies for the design of custom systems through multi-scale trans-disciplinary data, which are classified and ordered by the level of overlap between the modeling media and the fabrication media: (1) the first model takes as input biological data and outputs 3D printed digital materials organized according to functional constraints; (2) the second model takes as input geometry and environmental data and outputs robotically wound fibers organized according to functional constraints; (3) the third model takes as input material and environmental data and outputs CNC deposited pastes organized according to functional constraints; (4) the forth model takes as input biological, material and environmental data and outputs robotically deposited polymers organized according to functional constraints. The analysis of these models will demonstrate the FIM approach and point towards its value to designers who seek to inform their work through multi-scale transdisciplinary data, a capability that is currently missing from standard design-to-fabrication workflows.

scholarly journals Autonomous Surface and Underwater Vehicles as Effective Ecosystem Monitoring and Research Platforms in the Arctic—The Glider Project

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6752
Author(s):  
Lionel Camus ◽  
Hector Andrade ◽  
Ana Sofia Aniceto ◽  
Magnus Aune ◽  
Kanchana Bandara ◽  
...  
Keyword(s):  
Underwater Vehicles ◽  
Biological Data ◽  
Environmental Data ◽  
The Arctic ◽  
Spatiotemporal Resolution ◽  
Ecosystem Monitoring ◽  
High Biological Activity ◽  
Ocean Observing ◽  
Ocean Observing Systems ◽  
Observing Systems

Effective ocean management requires integrated and sustainable ocean observing systems enabling us to map and understand ecosystem properties and the effects of human activities. Autonomous subsurface and surface vehicles, here collectively referred to as “gliders”, are part of such ocean observing systems providing high spatiotemporal resolution. In this paper, we present some of the results achieved through the project “Unmanned ocean vehicles, a flexible and cost-efficient offshore monitoring and data management approach—GLIDER”. In this project, three autonomous surface and underwater vehicles were deployed along the Lofoten–Vesterålen (LoVe) shelf-slope-oceanic system, in Arctic Norway. The aim of this effort was to test whether gliders equipped with novel sensors could effectively perform ecosystem surveys by recording physical, biogeochemical, and biological data simultaneously. From March to September 2018, a period of high biological activity in the area, the gliders were able to record a set of environmental parameters, including temperature, salinity, and oxygen, map the spatiotemporal distribution of zooplankton, and record cetacean vocalizations and anthropogenic noise. A subset of these parameters was effectively employed in near-real-time data assimilative ocean circulation models, improving their local predictive skills. The results presented here demonstrate that autonomous gliders can be effective long-term, remote, noninvasive ecosystem monitoring and research platforms capable of operating in high-latitude marine ecosystems. Accordingly, these platforms can record high-quality baseline environmental data in areas where extractive activities are planned and provide much-needed information for operational and management purposes.

Direct Building Manufacturing of Homes with Digital Fabrication

2012 ◽  
pp. 71-82 ◽  
Author(s):  
Lawrence Sass
Keyword(s):  
Digital Fabrication ◽  
Information Modeling ◽  
3D Cad ◽  
The Core ◽  
Construction Techniques ◽  
Building Information ◽  
Cad Cam ◽  
Cad Models ◽  
Paper Documentation ◽  
Small Building

Architecture, engineering, and construction industries maintain a long standing desire to enhance design communication through various forms of 3D CAD modeling. In spite the introduction of Building Information Modeling (BIM), designers and builders expect varying amounts of communication loss once construction has started due to indirect construction techniques or hand based methods to manufacture buildings. This is especially true for houses and small structures, buildings that makeup the core of villages and suburbs. Unfortunately, paper documentation and reading 3D CAD models on screen continue the trend of indirect production defined in most manufacturing industries as error. The emerging application of CAD/CAM within design and construction industries provides hope for elevated communication between design and building. With CAD/CAM, it is possible to manufacture buildings of all types and sizes directly from CAD files similar to mass produced artifacts, thus reducing complexity in communication between parties. This chapter is presentation of one process of direct manufacturing from CAD and the emerging possibilities for small building production using digital fabrication. The chapter will focus on houses to illustrate the potential of direct manufacturing of buildings from CAD data.

scholarly journals Bioregions in Marine Environments: Combining Biological and Environmental Data for Management and Scientific Understanding

BioScience ◽  
2019 ◽  
Vol 70 (1) ◽  
pp. 48-59 ◽  
Author(s):  
Skipton N C Woolley ◽  
Scott D Foster ◽  
Nicholas J Bax ◽  
Jock C Currie ◽  
Daniel C Dunn ◽  
...  
Keyword(s):  
Natural Resources ◽  
Biodiversity Conservation ◽  
Statistical Models ◽  
Spatial Representation ◽  
Spatial Scales ◽  
Scientific Understanding ◽  
Biological Data ◽  
Environmental Data ◽  
Marine Environments ◽  
Multiple Spatial Scales

Abstract Bioregions are important tools for understanding and managing natural resources. Bioregions should describe locations of relatively homogenous assemblages of species occur, enabling managers to better regulate activities that might affect these assemblages. Many existing bioregionalization approaches, which rely on expert-derived, Delphic comparisons or environmental surrogates, do not explicitly include observed biological data in such analyses. We highlight that, for bioregionalizations to be useful and reliable for systems scientists and managers, the bioregionalizations need to be based on biological data; to include an easily understood assessment of uncertainty, preferably in a spatial format matching the bioregions; and to be scientifically transparent and reproducible. Statistical models provide a scientifically robust, transparent, and interpretable approach for ensuring that bioregions are formed on the basis of observed biological and physical data. Using statistically derived bioregions provides a repeatable framework for the spatial representation of biodiversity at multiple spatial scales. This results in better-informed management decisions and biodiversity conservation outcomes.

scholarly journals Living organisms and sedimentary remains from high mountain lakes in the Alps

2021 ◽  
Author(s):  
Aldo Marchetto ◽  
Angela Boggero ◽  
Diego Fontaneto ◽  
Andrea Lami ◽  
André F. Lotter ◽  
...  
Keyword(s):  
Aquatic Organisms ◽  
Mountain Lakes ◽  
Biological Data ◽  
Environmental Data ◽  
High Mountain ◽  
High Mountain Lakes ◽  
Data Set ◽  
Lake Bottom ◽  
The Alps ◽  
Living Organisms

We publish a data set of environmental and biological data collected in 2000 during the ice-free period in high mountain lakes located above the local timberline in the Alps, in Italy, Switzerland and Austria. Environmental data include coordinates, geographical attributes and detailed information on vegetation, bedrock and land use in lake catchments. Chemical analyses of a sample for each lake collected at the lake surface in Summer 2000 are also reported. Biological data include phytoplankton (floating algae and cyanobacteria), zooplankton (floating animals), macroinvertebrates (aquatic organisms visible to the naked eye living in contact with sediments on lake bottom), benthic diatoms. Diatoms, cladocera and chironomids remains and algal and bacterial pigments were also analysed in lake sediments.

scholarly journals Do androids dream of electric sorghum?: Predicting Phenotypes from Multi-Scale Genomic and Environmental Data using Neural Networks and Knowledge Graphs

2020 ◽  
Author(s):  
Ryan Bartelme ◽  
Michael Behrisch ◽  
Emily Jean Cain ◽  
Remco Chang ◽  
Ishita Debnath ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Scale Effects ◽  
Environmental Data ◽  
Phenotypic Data ◽  
Multi Scale ◽  
Data Schema ◽  
The Face ◽  
Disparate Data ◽  
Knowledge Graphs

The interplay between an organism's genes, its environment, and the expressed phenotype is dynamic. These interactions within ecosystems are shaped by non-linear multi-scale effects that are difficult to disentangle into discrete components. In the face of anthropogenic climate chance, it is critical to understand environmental and genotypic influences on plant phenotypes and phenophase transitions. However, it is difficult to integrate and interoperate between these datasets. Advances in the fields of ontologies, unsupervised learning, and genomics may overcome the disparate data schema. Here we present a framework to better link phenotypes, environments, and genotypes of plant species across ecosystem scales. This approach utilizing phenotypic data, knowledge graphing, and deep learning, serves as the groundwork for a new scientific sub-discipline: “Computational Ecogenomics”

scholarly journals Geospatial web services for limnological data: a case study of sensor observation service for ecological observations

2014 ◽  
Vol II-4 ◽  
pp. 9-14 ◽  
Author(s):  
C. Arias Muñoz ◽  
A. Oggioni ◽  
M. A. Brovelli
Keyword(s):  
Web Services ◽  
Spatial Data ◽  
Drainage Basin ◽  
Biological Species ◽  
Biological Data ◽  
Environmental Data ◽  
Ecological Data ◽  
Essential Components ◽  
Geospatial Web Services

The present work aims at designing and implementing a spatial data infrastructure for storing and sharing ecological data through geospatial web services. As case study, we concentrated on limnological data coming from the drainage basin of Lake Maggiore in the Northern of Italy. In order to establish the infrastructure, we started with two basic questions: (1) What type of data is the ecological dataset? (2) Which are the geospatial web services standards most suitable to store and share ecological data? In this paper we describe the possibilities for sharing ecological data using geospatial web services and the difficulties that can be encountered in this task. In order to test actual technological solutions, we use real data of a limnological published study.We concluded that limnological data can be considered observational data, composed by biological (species) data and environmental data, and it can be modeled using Observation and Measurement (O&M) specification. With the actual web service implementation the geospatial web services that could potentially be used to publish limnological data are Sensor Observation Services (SOS) and Web Feature Services (WFS). SOS holds the essential components to represent time series observations, while WFS is a simple model that requires profiling. Both, SOS and WFS are not perfectly suitable to publish biological data, so other alternatives must be considered, as linked data.

The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling

2020 ◽  
Vol 20 (14) ◽  
pp. 1357-1374 ◽  
Author(s):  
Valeria V. Kleandrova ◽  
Alejandro Speck-Planche
Keyword(s):  
Drug Discovery ◽  
Structural Information ◽  
Biological Data ◽  
Heterogeneous Databases ◽  
Biomedical Sciences ◽  
Multi Scale ◽  
Scale Modeling ◽  
Multi Scale Modeling

Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.

Direct Building Manufacturing of Homes with Digital Fabrication

2012 ◽  
pp. 1231-1242
Author(s):  
Lawrence Sass
Keyword(s):  
Digital Fabrication ◽  
Information Modeling ◽  
3D Cad ◽  
The Core ◽  
Construction Techniques ◽  
Building Information ◽  
Cad Cam ◽  
Cad Models ◽  
Paper Documentation ◽  
Small Building

Architecture, engineering, and construction industries maintain a long standing desire to enhance design communication through various forms of 3D CAD modeling. In spite the introduction of Building Information Modeling (BIM), designers and builders expect varying amounts of communication loss once construction has started due to indirect construction techniques or hand based methods to manufacture buildings. This is especially true for houses and small structures, buildings that makeup the core of villages and suburbs. Unfortunately, paper documentation and reading 3D CAD models on screen continue the trend of indirect production defined in most manufacturing industries as error. The emerging application of CAD/CAM within design and construction industries provides hope for elevated communication between design and building. With CAD/CAM, it is possible to manufacture buildings of all types and sizes directly from CAD files similar to mass produced artifacts, thus reducing complexity in communication between parties. This chapter is presentation of one process of direct manufacturing from CAD and the emerging possibilities for small building production using digital fabrication. The chapter will focus on houses to illustrate the potential of direct manufacturing of buildings from CAD data.

scholarly journals The North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project – Part 2: Environmental driver data

2014 ◽  
Vol 7 (6) ◽  
pp. 2875-2893 ◽  
Author(s):  
Y. Wei ◽  
S. Liu ◽  
D. N. Huntzinger ◽  
A. M. Michalak ◽  
N. Viovy ◽  
...  
Keyword(s):  
North American ◽  
Lessons Learned ◽  
Environmental Data ◽  
Data Sets ◽  
Model Intercomparison ◽  
Standard Format ◽  
Oak Ridge ◽  
Multi Scale ◽  
Intercomparison Project ◽  
Environmental Driver

Abstract. Ecosystems are important and dynamic components of the global carbon cycle, and terrestrial biospheric models (TBMs) are crucial tools in further understanding of how terrestrial carbon is stored and exchanged with the atmosphere across a variety of spatial and temporal scales. Improving TBM skills, and quantifying and reducing their estimation uncertainties, pose significant challenges. The Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal multi-scale and multi-model intercomparison effort set up to tackle these challenges. The MsTMIP protocol prescribes standardized environmental driver data that are shared among model teams to facilitate model–model and model–observation comparisons. This paper describes the global and North American environmental driver data sets prepared for the MsTMIP activity to both support their use in MsTMIP and make these data, along with the processes used in selecting/processing these data, accessible to a broader audience. Based on project needs and lessons learned from past model intercomparison activities, we compiled climate, atmospheric CO2 concentrations, nitrogen deposition, land use and land cover change (LULCC), C3 / C4 grasses fractions, major crops, phenology and soil data into a standard format for global (0.5° × 0.5° resolution) and regional (North American: 0.25° × 0.25° resolution) simulations. In order to meet the needs of MsTMIP, improvements were made to several of the original environmental data sets, by improving the quality, and/or changing their spatial and temporal coverage, and resolution. The resulting standardized model driver data sets are being used by over 20 different models participating in MsTMIP. The data are archived at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, http://daac.ornl.gov) to provide long-term data management and distribution.

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