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The research focuses on an approach for the development of an automated workflow in the field of fire prevention for the validation of BIM models regarding “Subjected Activities” (according to Italian law - Presidential Decree 151/2011), with reference to the contents of the Horizontal Technical Regulation of the Fire Prevention Code. In fact, since 2019, the Italian Fire Department, through the Fire Digital Check project, is seeking to digitize the Fire Prevention Code in order to allow professionals to integrate their BIM models with the objectives required by the fire regulations. The workflow proposed involves three distinct processes, integrating them within a single workflow: study of the technical regulations and transposition into the digital environment using proven methods such as RASE, Tx3 and TIO, information modelling of a case study and implementation of validation algorithms using Solibri software. One of the main points of the proposed process is to use an open and interoperable format such as that offered by the IFC schema for the information exchange between the different software involved

The DoD has a critical need to evaluate energy consumption of existing facilities. To address this need Rapid Energy Modeling (REM) methodology was deployed. The workflow captures and information on operations and geometry of a building, generates a 3D building model and then simulates energy use patterns and reports on energy conservation measures. REM was applied to a sample of 23 DoD buildings. The simulated and actual building energy data was analyzed by energy type (electricity and natural gas) and energy use intensity (EUI).

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results.

This thesis uses conceptual modeling and discrete event simulation (DES) to assess warehousing efficiency for Marine Forces Storage Center (MFSC), Marine Corps Logistics Command. In April 2018, MFSC took possession of a new warehouse aboard Marine Corps Logistics Base-Albany to consolidate multiple warehousing operations into one warehouse facility to store, issue, receive, and account for military equipment. In August 2018, MFSC began the consolidation process and started moving all military equipment into one single facility. Today, MFSC conducts warehousing in a 57,000-square-foot storage facility, significantly smaller than the 120,000-square-foot facility it operated in previously. First, simulation event graphs illustrate the specific warehouse workflow behaviors and procedures. Simkit, a Java-based simulation package, is used to build a DES to represent the warehouse workflow processes, compute statistical outputs, and assess the warehousing performances based on different input values. This study analyzes the warehouse throughput using different statistical analysis methods, such as simulation experiments and predictive modeling, to reveal the input-output relationships that promote optimal warehouse performance. The warehouse workflow simulation model serves as a proof of concept and verification tool to promote workflow optimization, identify and mitigate bottlenecks in the workflow processes, and assist data driven decision-making to improve operating procedures.

 Fuze 07 - Hard Surface Modeling Workflow with Maya 2017

Reconnaissance missions are not only one of the vital modes of intelligence gathering methods, they are one of the most important contributors of military intelligence as well. They show the battlefield as it is to the commander. A simplified reconnaissance cycle includes the arrival of reconnaissance requests, planning of reconnaissance flights, flying the mission and exploitation of the films or images, and then dissemination of the intelligence reports. The reconnaissance cycle is modeled for four different scenarios (peace and war as situations, RF-4 and F-16 as configurations). There are two points of view regarding this cycle. The first is the reconnaissance requesters' view: they want to know the estimated time it would take for a request to be answered, based on the resources and other factors, before an actual request was made. The second is the reconnaissance squadron commanders' perspective: they want to respond to as many reconnaissance requests as possible. For that reason, they want to know and revise the ideal numbers of personnel and equipment. Analysis includes regression models and partition trees. When results are considered, we see that there is no common rule to determine which factors (either decision or noise) are the key determinants for each scenario. But we noticed that noise factors have much more impact on several measures of effectiveness than decision factors in each model.

Department of Defense (DoD) energy success is measured against mandated goals for energy reduction and sustainable facility management. In order to make consistent and well-informed decisions across its entire portfolio of buildings, DoD has a critical need for a consistent, scalable approach to evaluating energy consumption of existing facilities, to compare tradeoffs between energy conservation measures, and to identify facilities that are in greatest need of improvement. In the last several years, it has become increasingly evident that existing methods of simulating and estimating energy use in buildings require highly trained engineers to spend significant time constructing energy analysis simulations. Shortcomings of past approaches included laborintensive data inputs, the need for subject matter experts to operate the modeling systems, and the inability to model the DoD building inventory in a timely or cost effective way. Autodesk began looking at ways to combine various data collection methods, best practices and software tools to address this problem, and the idea of Rapid Energy Modeling (REM) was conceived. Overall, the goal of the demonstration was to evaluate REM workflows and performance by comparing simulated to actual building energy consumption and investigate the scalability of REM workflows for the DoD. This project demonstrated that the REM workflow quickly captures and utilizes information on operations, geometry, orientation, weather, and materials, generating Three-Dimensional (3D) Building Information Models (BIM) guided by satellite views of building footprints and simulating energy use patterns. In addition, the project demonstrated the application of simulated Energy Conservation Measures (ECM) on a subset population of buildings to understand effective ways to reduce their energy consumption.

Department of Defense (DoD) energy success is measured against mandated goals for energy reduction and sustainable facility management. In order to make consistent and well-informed decisions across its entire portfolio of buildings, DoD has a critical need for a consistent, scalable approach to evaluating energy consumption of existing facilities, to compare tradeoffs between energy conservation measures, and to identify facilities that are in greatest need of improvement. In the last several years, it has become increasingly evident that existing methods of simulating and estimating energy use in buildings require highly trained engineers to spend significant time constructing energy analysis simulations. Shortcomings of past approaches included laborintensive data inputs, the need for subject matter experts to operate the modeling systems, and the inability to model the DoD building inventory in a timely or cost effective way. Autodesk began looking at ways to combine various data collection methods, best practices and software tools to address this problem, and the idea of Rapid Energy Modeling (REM) was conceived. Overall, the goal of the demonstration was to evaluate REM workflows and performance by comparing simulated to actual building energy consumption and investigate the scalability of REM workflows for the DoD. This project demonstrated that the REM workflow quickly captures and utilizes information on operations, geometry, orientation, weather, and materials, generating Three-Dimensional (3D) Building Information Models (BIM) guided by satellite views of building footprints and simulating energy use patterns. In addition, the project demonstrated the application of simulated Energy Conservation Measures (ECM) on a subset population of buildings to understand effective ways to reduce their energy consumption.

With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Fortunately, material information management systems and physics-based multiscale modeling methods have kept pace with the growing user demands. Herein, recent efforts to establish workflow for and demonstrate a unique set of web application tools for linking NASA GRC's Integrated Computational Materials Engineering (ICME) Granta MI database schema and NASA GRC's Integrated multiscale Micromechanics Analysis Code (ImMAC) software toolset are presented. The goal is to enable seamless coupling between both test data and simulation data, which is captured and tracked automatically within Granta MI®, with full model pedigree information. These tools, and this type of linkage, are foundational to realizing the full potential of ICME, in which materials processing, microstructure, properties, and performance are coupled to enable application-driven design and optimization of materials and structures.

Reconnaissance missions are not only one of the vital modes of intelligence-gathering methods; they are one of the most important contributors of military intelligence as well. They show the battlefield as it is to the commander. A simplified reconnaissance cycle includes the arrival of reconnaissance requests, planning of reconnaissance flights, flying the mission and exploitation of the films or images, and then dissemination of the intelligence reports. The reconnaissance cycle is modeled for four different scenarios (peace and war as situations, RF-4 and F-16 as configurations). There are two points of view regarding this cycle. The first is the reconnaissance requesters' view: they want to know the estimated time it would take for a request to be answered, based on the resources and other factors, before an actual request was made. The second is the reconnaissance squadron commanders' perspective: they want to respond to as many reconnaissance requests as possible. For that reason, they want to know and revise the ideal numbers of personnel and equipment. For the purpose of answering these questions, satisfying these requests, and having a better understanding about the reconnaissance cycle, Reconnaissance Squadron Workflow is modeled, experimented and analyzed in this thesis. Analysis includes regression models and partition trees. When results are considered, we see that there is no common rule to determine which factors (either decision or noise) are the key determinants for each scenario. But we noticed that noise factors have much more impact on several measures of effectiveness than decision factors in each model. Some of these noise factors could be controllable, including aircraft, camera and pod defect probabilities and their repair times. Therefore, some precautionary measures should be taken to reduce these defect probabilities and repair times. Specifically, in the RF-4 configuration models, pilot filming error is a significant factor, which shows that training of the pilots cannot be ignored. When the F-16 models are considered, we see that data link defect probability is a significant factor too. This suggests that special precautions should be taken to keep this capability working.

This article is from Frontiers in Plant Science , volume 4 . Abstract During the last decade genome sequencing has experienced a rapid technological development resulting in numerous sequencing projects and applications in life science. In plant molecular biology, the availability of sequence data on whole genomes has enabled the reconstruction of metabolic networks. Enzymatic reactions are predicted by the sequence information. Pathways arise due to the participation of chemical compounds as substrates and products in these reactions. Although several of these comprehensive networks have been reconstructed for the genetic model plant Arabidopsis thaliana, the integration of experimental data is still challenging. Particularly the analysis of subcellular organization of plant cells limits the understanding of regulatory instances in these metabolic networks in vivo. In this study, we develop an approach for the functional integration of experimental high-throughput data into such large-scale networks. We present a subcellular metabolic network model comprising 524 metabolic intermediates and 548 metabolic interactions derived from a total of 2769 reactions. We demonstrate how to link the metabolite covariance matrix of different Arabidopsis thaliana accessions with the subcellular metabolic network model for the inverse calculation of the biochemical Jacobian, finally resulting in the calculation of a matrix which satisfies a Lyaponov equation. In this way, different strategies of metabolite compartmentation and involved reactions were identified in the accessions when exposed to low temperature.

This article is from Frontiers in Plant Science , volume 4 . Abstract During the last decade genome sequencing has experienced a rapid technological development resulting in numerous sequencing projects and applications in life science. In plant molecular biology, the availability of sequence data on whole genomes has enabled the reconstruction of metabolic networks. Enzymatic reactions are predicted by the sequence information. Pathways arise due to the participation of chemical compounds as substrates and products in these reactions. Although several of these comprehensive networks have been reconstructed for the genetic model plant Arabidopsis thaliana, the integration of experimental data is still challenging. Particularly the analysis of subcellular organization of plant cells limits the understanding of regulatory instances in these metabolic networks in vivo. In this study, we develop an approach for the functional integration of experimental high-throughput data into such large-scale networks. We present a subcellular metabolic network model comprising 524 metabolic intermediates and 548 metabolic interactions derived from a total of 2769 reactions. We demonstrate how to link the metabolite covariance matrix of different Arabidopsis thaliana accessions with the subcellular metabolic network model for the inverse calculation of the biochemical Jacobian, finally resulting in the calculation of a matrix which satisfies a Lyaponov equation. In this way, different strategies of metabolite compartmentation and involved reactions were identified in the accessions when exposed to low temperature.