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575 results for “Modeling Codes”

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zenodo36/100

Data and codes: Speech-recognition in landlide predictive modelling

This is the data and codes for the manuscript "Speech-recognition in landlide predictive modelling"

opencc-by-4.0Jun 2023View details →

zenodo36/100

Computer Code and Data - Determination of server location in emergency care systems: an index proposal using Data Envelopment Analysis and the Hypercube Queuing Model

Computer code and data related to the research project "Determination of server location in emergency care systems: an index proposal using Data Envelopment Analysis and the Hypercube Queuing Model".

opencc-by-4.0Jul 2023View details →

zenodo36/100

Modeling Attack Resistant Strong PUF Exploiting Stagewise Obfuscated Interconnections With Improved Reliability [DATASET and Source Code]

Thanks for your interest in our work! In order to facilitate your assessment and replication, we provides the dataset and source codes (verilog/python model/matlab) of our work (OIPUF) here.  By the way, our latest work (SOI PUF and cSOI PUF) published in IEEE TIFS (2024) is based on OIPUF.  <blockquote> If you have any questions, please feel free to contact with us: <a href="mailto:chongyaoxu@126.com">chongyaoxu@126.com</a> / <a href="mailto:mklaw@um.edu.mo">mklaw@um.edu.mo</a> Full text about OIPUF can be downloaded from <a href="https://ieeexplore.ieee.org/document/10103139">https://ieeexplore.ieee.org/document/10103139</a> Full text about SOI PUF and cSOI PUF can be downloaded from <a href="https://ieeexplore.ieee.org/document/10458688">https://ieeexplore.ieee.org/document/10458688</a> Source code and FPGA project of SOI PUF and cSOI PUF can be download from <a href="https://github.com/yg99992/SOI_PUF">https://github.com/yg99992/SOI_PUF</a>.  </blockquote>   Matlab code <code>matlab/Generate_OI_block.m</code> This is a matlab manuscript used for generating the verilog code of random OI block. <code>matlab/OIPUF_64x4_placement.m</code> This is a matlab function used for generating XDC file for constraining the placement of (64,4)-OI block <code>matlab/OIPUF_64x8_placement.m</code> This is a matlab function used for generating XDC file for constraining the placement of (64,8)-OI block <code>matlab/OIPUF_placement_example.m</code> An example manuscript used for demonstrating the usage of OIPUF_64x4_placement.m and OIPUF_64x8_placement.m   Python code <code>python/puf_models.py</code> The python models of XOR PUFs and OIPUFs, which can be used to generate CRPs. for example: <pre><code>from puf_models import oi_puf # generate a (64,4)-OIPUF and further use the generated OIPUF to generate 1M CRPs crps, puf_instance = oi_puf.gen_CRPs_PUF(64, 4, 1_000_000)   </code></pre>   <code>python/attack_pypuf.py</code> A manuscript used to conduct to ANN attack on XOR PUF and OIPUF ('pypuf' package should be installed correctly).   Verilog code <code>verilog/OIPUF_64_4/</code> All the verilog files of (64, 4)-OIPUF <code>verilog/OIPUF_64_8/</code> All the verilog files of (64, 8)-OIPUF   CRP datasets extracted from FPGA It consists of 13 CRP files (All the CRPs are extracted from FPGA): <code>FPGA_CRPs/FPGA3_CHAL_100M.csv</code> The 100 million 64-bit challenges <code>FPGA_CRPs/FPGA3_k4_PUF0.csv</code> The 100 million 1-bit responses extracted from (64,4)-OIPUF0 <code>FPGA_CRPs/FPGA3_k4_PUF1.csv</code> The 100 million 1-bit responses extracted from (64,4)-OIPUF1 <code>FPGA_CRPs/FPGA3_k4_PUF2.csv</code> The 100 million 1-bit responses extracted from (64,4)-OIPUF2 <code>FPGA_CRPs/FPGA3_k4_PUF3.csv</code> The 100 million 1-bit responses extracted from (64,4)-OIPUF3 <code>FPGA_CRPs/FPGA3_k4_PUF4.csv</code> The 100 million 1-bit responses extracted from (64,4)-OIPUF4 <code>FPGA_CRPs/FPGA3_k4_PUF5.csv</code> The 100 million 1-bit responses extracted from (64,4)-OIPUF5 <code>FPGA_CRPs/FPGA3_k8_PUF0.csv</code> The 100 million 1-bit responses extracted from (64,8)-OIPUF0 <code>FPGA_CRPs/FPGA3_k8_PUF1.csv</code> The 100 million 1-bit responses extracted from (64,8)-OIPUF1 <code>FPGA_CRPs/FPGA3_k8_PUF2.csv</code> The 100 million 1-bit responses extracted from (64,8)-OIPUF2 <code>FPGA_CRPs/FPGA3_k8_PUF3.csv</code> The 100 million 1-bit responses extracted from (64,8)-OIPUF3 <code>FPGA_CRPs/FPGA3_k8_PUF4.csv</code> The 100 million 1-bit responses extracted from (64,8)-OIPUF4 <code>FPGA_CRPs/FPGA3_k8_PUF5.csv</code> The 100 million 1-bit responses extracted from (64,8)-OIPUF5

opencc-by-4.0Aug 2023View details →

dryad36/100

Codes for: A numerical model supports the evolutionary advantage of recombination plasticity in shifting environments

Numerous empirical studies have witnessed an increase in meiotic recombination rate in response to physiological stress imposed by unfavorable environmental conditions. Thus, inherited plasticity in recombination rate is hypothesized to be evolutionarily advantageous in changing environments. Previous theoretical models proceeded from the assumption that organisms increase their recombination rate when the environment becomes more stressful and demonstrated the evolutionary advantage of such a form of plasticity. Here, we numerically explore a complementary scenario – when the plastic increase in recombination rate is triggered by the environmental shifts. Specifically, we assume increased recombination in individuals developing in a different environment than their parents and optionally, also in offspring of such individuals. We show that such shift-inducible recombination is always superior when the optimal constant recombination implies an intermediate rate. Moreover, under certain conditions, plastic recombination may appear beneficial also when the optimal constant recombination is either zero or free. The advantage of plastic recombination was better predicted by the range of the population's mean fitness over the period of environmental fluctuations, compared to the geometric mean fitness. These results hold for both panmixia and partial selfing, with faster dynamics of recombination modifier alleles under selfing. We think that recombination plasticity can be acquired under the control of environmentally responsive mechanisms such as chromatin epigenetics remodeling.

opencc-zeroAug 2023View details →

zenodo36/100

Code: Decarbonization Employment and Energy Systems (DEERS) Model

The Decarbonization Employment and Energy Systems (DEERS) model is a data-driven framework for estimating labor market pathways of large-scale, low-carbon energy-supply infrastructure development. The DEERS model is designed as a tool to inform regional and national workforce and infrastructure planning and policy-making in the U.S. The model simulates the distribution of labor effects over time and across economic sectors, resource sectors, occupations, and geography for multi-decadal energy-supply system transition scenarios. The model is used to estimate employment demand and wages, as well as experience, education, and training requirements, across domestic energy supply chains. We also incorporate time-variant factors, such as labor productivity and wage inflation, which are especially important in the context of emerging labor markets and long-term transitions. The DEERS model is adaptable to different energy system contexts and readily coupled with regional and downscaled macro-energy system modeling outputs. It can also be used to explore modifiable workforce and infrastructure planning and policy decisions, such as high road labor policies, siting domestic manufacturing facilities, creating just transition funds, and changing fossil fuel exports over time.

opencc-by-4.0Aug 2023View details →

zenodo36/100

Data and Code Supplement for "A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models"

Code and Data Supplement for "A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models" =========================================================== This directory contains the data and scripts used to create the plots from our publication as well as the source code modifications necessary to run this test case within the CESM and MPAS models. Generating Plots --------------- The `netcdf` directory contains the nominal half-degree runs necessary to generate nearly all of the plots from the paper. The one plot which is not reproducible from these data is the volume-integrated Eddy Kinetic Energy in the Spectral Element model. Storing high-resolution 4D wind fields requires a prohibitive amount of space. These data can be provided by the corresponding author, O.K. Hughes (owhughes@umich.edu). However, because this is several hundred GB of data I would strongly recommend generating these high-resolution runs yourself on your local system if you need them. Using 288 Intel Skylake cores (that is, 8 nodes each with two 18C processors) ran on the order of an hour. In order to generate the plots from the paper, you need only install NCL and then run run.bash. Instructions for installing NCL can be found in the `run.bash` script. Source Code Modifications ---------------- CESM The `src` subdirectory contains the files `user_nl_cam` and `ic_baroclinic.F90`. Create a case using `--compset=FKESSLER` and `--run-unsupported` options when running `create_newcase`. If your case is located at `${CASE_DIR}`, then from within the directory containing this README, run `cp user_nl_cam ${CASE_DIR}/user_nl_cam`, and then run `cp ic_baroclinic.F90 ${CASE_DIR}/SourceMods/src.cam/`. Then build and run the model using the usual workflow. MPAS The MPAS code was run using a branch of the MPAS model provided by the model developers to the authors. While the source code modifications are provided in the `src` directory, I would strongly recommend contacting the corresponding author if you wish to run this test case in the MPAS codebase.

opencc-by-4.0Oct 2022View details →

zenodo36/100

Datasets and code for "Multi-site transfer function approach for real-time modeling of the ground electric field induced by laterally-nonuniform ionospheric source" by Kruglyakov et al. (2023)

<ol> <li>Archive calculate_weights_for_rt.tgz contains the code for calculation of weights used for computation of electric fields based on multi-site transfer function approach following Kruglyakov et al. (2023). The code is written in Fortran 2003 and the only external dependency is LAPACK/BLAS -compatible library, for example OpenBLAS from https://www.openblas.net. See READ.ME for details.</li> <li>Files GICs*.dat contain observed and modelled geomagnetically induced currents (GICs) at Mäntsälä compressor station in southern Finland (60.6 N, 25.2 E) (https://space.fmi.fi/gic/) for three events (in 2000, 2001, and 2003).</li> <li>Files E_x*. E_y* contain corresponding components of measured (detrended and downsampled from 1s to 10s) and modeled electric fields at sites M02 and M05 from 05:15 to 06:15 UT, 11 Sep 2005.</li> <li>Files MS_TF*.dat contain multi-site transfer functions for different sets of IMAGE magnetometers (based on the data availability during the simulated events) in the frequency domain and the corresponding weights for calculation of electric field in the time domain.</li> <li>File E_to_GICs_W.dat contains coefficients for computation of GICs at Mäntsälä station from electric fields at 18 sites used in the simulation. See Equation (15) of Kruglyakov et al. (2023) for details.</li> </ol>

opencc-by-4.0Oct 2023View details →

dryad36/100

Data and model code from: Tracing growth patterns in cod (Gadus morhua L.) using bioenergetic modelling

Understanding individual growth in commercially exploited fish populations is key to successful stock assessment and informed ecosystem-based fisheries management. Traditionally, growth rates in marine fish are estimated using otolith age-reading in combination with age-length relationships from field samples, or tag-recapture field experiments. However, for some species, otolith-based approaches have been proven unreliable, and tag-recapture experiments suffer from high working effort and costs as well as low recapture rates. An important alternative approach for estimating fish growth is represented by bioenergetic modelling, which, in addition to pure growth estimation, can provide valuable insights into the processes leading to temporal growth changes resulting from environmental and related behavioural changes. We here developed an individual-based bioenergetic model for Western Baltic cod (Gadus morhua), traditionally a commercially important fish species that however collapsed recently and likely suffers from climate change effects. Western Baltic cod is an ideal case study for bioenergetic modelling because of recently gained in-situ process knowledge on spatial distribution and feeding behaviour based on highly resolved data on stomachs and fish distribution. Additionally, physiological processes such as gastric evacuation, consumption, net-conversion efficiency, and metabolic rates have been well studied for cod in laboratory experiments. Our model reliably reproduced seasonal growth patterns observed in the field. Importantly, our bioenergetic modelling approach implementing depth-use patterns and food intake allowed us to explain the potentially detrimental effect summer heat periods have on growth of Western Baltic cod that likely will increasingly occur in the future. Hence our model simulations highlighted a potential mechanism of how warming due to climate change affects the growth of a key species that may apply for similar environments elsewhere. Here we provide access to the individual-based bioenergetic growth model which is set up to model the growth of cod in ages 2 to 4 (Gadus morhua L.) in the Belt Sea (western part of the Western Baltic Sea) on a daily basis within one year. The model incorporates contemporary in-situ process knowledge on food intake and seasonal- and temperate-related spatial distribution of cod and allows us to identify seasonal growth patterns. The model is written in the statistical and programming environment R.

opencc-zeroOct 2023View details →

zenodo36/100

Calibration of a pedestrian ingress model based on CCTV surveillance data using machine learning methods: data and code

The package includes a dataset of trajectories obtained from a real-time pedestrian traffic detector at the vaccination centre and the code needed to validate the analyses described in the paper entitled Calibration of a pedestrian ingress model based on CCTV surveillance data using machine learning methods. Version 1.0.1 is a patch that corrects path to data.

opencc-by-4.0Sep 2023View details →

dryad36/100