scholarly journals Use of Descriptive Statistical Indicators for Aggregating Environmental Data in Multi-Scale European Databases

10.5772/38696 ◽  
2012 ◽  
Author(s):  
Panos Panagos ◽  
Yusuf Yigini ◽  
Luca Montanarell
Keyword(s):  
Environmental Data ◽  
Multi Scale ◽  
Statistical Indicators

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 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 Construction and Use of the Regional Input-Output Model with Environmental and Economic Development Blocks

2020 ◽  
Vol 20 (3) ◽  
pp. 27-46
Author(s):  
Аlexander О. Baranov ◽  
Victor N. Pavlov ◽  
Tatiana O. Tagaeva ◽  
Yuliia M. Slepenkova
Keyword(s):  
Mathematical Modeling ◽  
Environmental Economics ◽  
Statistical Data ◽  
Environmental Issues ◽  
Environmental Data ◽  
State University ◽  
Input Output ◽  
The 1960S ◽  
Statistical Indicators ◽  
Made In

The paper analyzes environmental-economics models developed by foreign and Russian scientists. The first attempts to combine both economic and environmental issues in mathematical modeling were made in the 1960s, with most of them been theoretical due to lack of necessary data. With the development of modeling approaches, following on Wassily Leontief’s models, an environmental block has been included into input-output models. However, most of the existing models can hardly be applied to practice due to lack of statistical data and the absence of inter-industry approach. Even today the main restricting factor for these models to be used in practice is still limited availability of information, including not only economic but also environmental data, which is especially critical for regional researches, as the number of statistical indicators for the regions is much lower than at the macro level. The article provides mathematical description of the input-output model with an environmental block. The model is developed at the Institute of Economics and Industrial Engineering of the Siberian Branch of the RAS in collaboration with the Novosibirsk State University. The model can be used for regional researches, given the input-output tables for the region is available.

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.

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

2013 ◽  
Vol 6 (4) ◽  
pp. 5375-5422 ◽  
Author(s):  
Y. Wei ◽  
S. Liu ◽  
D. N. Huntzinger ◽  
A. M. Michalak ◽  
N. Viovy ◽  
...  
Keyword(s):  
North American ◽  
National Laboratory ◽  
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 model 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, 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° x 0.5° resolution) and regional (North American, 0.25° x 0.25° resolution) simulations. In order to meet the needs of MsTMIP, improvements were made to several of the original environmental data sets, by changing the quality, the spatial and temporal coverage, resolution, or a combination of these. The resulting standardized model driver data sets are being used by over 20 different models participating 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.

Design and Implementation of Mobile Monitoring System for Environmental Monitoring

2013 ◽  
Vol 295-298 ◽  
pp. 933-939 ◽  
Author(s):  
Zhen Feng Shao ◽  
Cheng Jing ◽  
Jia Chen ◽  
Lin Ding ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Environmental Monitoring ◽  
Spatial Data ◽  
Disaster Response ◽  
High Efficiency ◽  
Environmental Parameters ◽  
Quantitative Model ◽  
Environmental Data ◽  
Environment Monitoring ◽  
Multi Scale ◽  
Design And Implementation

Environmental monitoring is increasingly playing a significant role in such aspects as environment protection, emergency disaster response and rescue, and macro decision-making etc. However, the intrinsic characteristics of complexity and spatial-temporal diversity, multi-scale features and heterogeneity brought from various means of data acquisition make the integration of multi-source data with high-efficiency becomes an international challenge nowadays. In this paper, the design and implementation of a vehicle-borne platform based on Internet of Things for environmental monitoring has been achieved. And then, by merging and matching environmental data and spatial data, more intensive multi-source environmental parameters and information can be obtained to act as meaningful supplementation of fixed environment monitoring stations. The research of this paper is conductive to the transition of environment monitoring from static methods to dynamic methods and from data-based empirical model to sensor network-based quantitative model. As a result, current environment monitoring will become more timely, dynamic, integrated and intelligent.

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