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5,573 results for “optimization”
Dataset for "Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation"
<p>This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 187223) in association with the recent publication entitled “Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation”. This work aims to study the capacitive response of a resonating capacitive device coated with phase changing material (jojoba oil) as it melts when crossing its melting temperature. Several configuration were simulated with different electrode spacing, oil volume and encapsulation thickness and the induced changes in capacitance were tested experimentaly. An eco-friendly implementation of the optimized spiral resonating devices was tested wirelessly over a custom made near field antenna and the frequency of resonance was measured as the oil melted over the structure, irreversibly changing its resonance frequency. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.</p>
Conductive PANI/acrylate composites for DLP: Optimization of HDODA crosslinker
<p>This data set corresponds to the analyses carried out in the following article: Arias-Ferreiro, G., Ares-Pernas, A., Dopico-García, M. S., Lasagabaster-Latorre, A., & Abad, M. J. (2020). <br>Photocured conductive PANI/acrylate composites for digital light processing. Influence of HDODA crosslinker in rheological and physicochemical properties. <br>European Polymer Journal, 136, 109887. </p>
Dataset on Physics-Based Indicators for Optimizing Phase Change Material Effectiveness in Building Design
<p>This research dataset includes the results as well as the EnrgyPlus models developed to investigate and validate newly proposed indicators to quantify the effectiveness of phase change materials in buildings.</p>
Dataset of optimal cement-based panels enhanced with microencapsulated phase change material for EnergyPlus simulations
<p>This dataset includes:<br> - A series of EnergyPlus models of the BESTEST - Case 900 - from ANSI/ASHRAE Standard 140-2011 for the original (Baseline_Case900) and three enhanced designs (Opt-1, Opt-2, Opt-5) by using a cement-based panel containing microencapsulated phase change material.<br> - All the optimal solutions (parameters and corresponding performance) in XLSX format, which were obtained for two multiobjective optimization studies of the thermophysical properties (ParetoFront_CaseA and ParetoFront_CaseB).<br> - The typical meteorological year (TMY) of Sofia city employed to obtain the results, which is freely provided by Climate.One.Building.Org repository (https://climate.onebuilding.org/)</p>
Surrogate-based optimization using an artificial neural network for a parameter identification in a 3D marine ecosystem model
<p><strong>Abstract:</strong></p> <p>Parameter identification for marine ecosystem models is important for the assessment and validation of marine ecosystem models against observational data. The surrogate-based optimization (SBO) is a computationally efficient method to optimize complex models. SBO replaces the computationally expensive (high-fidelity) model by a surrogate constructed from a less accurate but computationally cheaper (low-fidelity) model in combination with an appropriate correction approach, which improves the accuracy of the low-fidelity model. To construct a computationally cheap low-fidelity model, we tested three different approaches to compute an approximation of the annually periodic solution (i.e., a steady annual cycle) of a marine ecosystem model: firstly, a reduced number of spin-up iterations (several decades instead of millennia), secondly, an artificial neural network (ANN) approximating the steady annual cycle and, finally, a combination of the both approaches. Except for the low-fidelity model using only the ANN, the SBO yielded a solution close to the target and reduced the computational effort significantly. If an ANN approximating appropriately a marine ecosystem model is available, the SBO using this ANN as low-fidelity model presents a promising and computational efficient method for the validation.</p> <p> </p> <p><strong>Content:</strong></p> <ul> <li>SQLite database including the data of the different optimization runs</li> <li>Structure and weights of the used artificial neural network</li> <li>Tracer concentrations obtain from the high-fidelity model for the different optimization runs</li> </ul>
Phase characteristic optimization of resonant MEMS environmental sensors (Data)
<p>Origin projects and figures used for the article "Phase characteristic optimization of resonant MEMS environmental sensors", published in the proceedings of Sensoren und Messsysteme 2018, 19. ITG/GMA-Fachtagung; 26.06.2018 to 27.06.2018; Nürnberg, Germany.</p>
Dataset for evaluation of unrealistic optimism in time of pandemic COVID-19 on a Polish sample
<p>This dataset contains the data used in a project called "Unrealistic optimism in the eye of the storm. Positive bias towards the consequences of COVID-19 during the second and third waves of the pandemic. ". The project concerns the occurrence of a cognitive bias - unrealistic optimism - with regard to contracting the coronavirus. The following information are attached to the dataset: codebooks with variables' names; analysis codes to replicate our results; supplementary materials with the description of procedures. </p>
Supplementary codes and datasets for "Modular-topology optimization of structures and mechanisms with free material design and clustering"
<p>This repository supports Tyburec, M., Doškář, M., Zeman, J., & Kružík, M. (2022). Modular-topology optimization of structures and mechanisms with free material design and clustering. <em>Computer Methods in Applied Mechanics and Engineering</em>, <em>395</em>, 114977. <a href="https://doi.org/10.1016/j.cma.2022.114977">https://doi.org/10.1016/j.cma.2022.114977</a> (first published as preprint <a href="http://arxiv.org/abs/2111.10439">2111.10439</a> at arXiv.org).</p> <p>This repository contains:</p> <ol> <li>MATLAB source codes for <em>(modular) free material optimisation</em> and <em>hierarchical stiffness clustering</em> (folder <code>./mFMO/</code>)</li> <li>C++ source codes for <em>modular topology optimization</em> (folder <code>./MTO/</code>)</li> <li>Input/output data of the test suite (folder <code>./data/</code>)</li> </ol> <p><strong>1. Data flow</strong></p> <p>The test suite considered in the manuscript covers 4 problems:</p> <ol> <li>Messerschmitt-Bölkow-Blohm beam (labelled as <code>mbb</code>)</li> <li>Inverter compliant mechanism (labelled as <code>inv</code>)</li> <li>Gripper compliant mechanism (labelled as <code>grip</code>)</li> <li>Reusable design of both compliant mechanisms (labelled as <code>invgrip</code>)</li> </ol> <p>Each problem in the dataset is stored within a separate subfolder named according to the labels mentioned above. The final level of subdirectories <code>{X}color</code> comprises of the results for problems with <code>X</code> denoting the number of edge codes considered for each edge direction during the clustering (<code>0color</code> stands for a non-modular design and <code>1color</code> represents the design based on Periodic Unit Cell).</p> <p>Each of the folders contains outputs of the modular free material optimisation in the following form:</p> <ul> <li><code>{label}{X}.mat</code></li> <li><code>{label}{X}.til</code></li> <li><code>{label}{X}.tset</code></li> <li><code>{label}{X}guess.mat</code></li> </ul> <p>Files <code>*.til</code>, <code>*.tset</code>, and <code>*guess.mat</code> are then converted into a JSON input file for the modular topology optimization code with generator scripts which can be found in <code>./MTO/scripts</code> folder. Note that each of the problems in the test suite has its own generator script <code>generate_modular_problem_{MBB,inverter,gripper,inverterAndGripper}.mat</code>. The generator scripts make a directory named according to the key <code>MTO_{n}_kernelSensitivity</code>, where <code>n</code> denotes the resolution of each module (i.e. the number of nodes along one direction). The directory also contains the outputs of the modular topology optimisation in the form of the initial and the final state of the optimization in <code>VTK</code> files and visualisation of the final state in <code>SVG</code> files. The log file <code>log.txt</code> stores the optimized objective and progress of the value along with stopping criteria quantities during iterations.</p> <p><strong>2. Running codes</strong></p> <p><strong>2.1 Modular free material optimisation</strong></p> <p>MATLAB scripts and functions for (modular) Free Material Optimization (FMO) are contained in the <code>mFMO</code> data folder. The codes have been tested with MATLAB R2019b. To run the codes the user is required to install the <a href="http://www.penopt.com">PENNON optimizer</a>. A free academic license is provided by its authors on request.</p> <p>Input files for individual problems are defined in the <code>mFMO/problems</code> folder and are launched with the <code>runproblem(problemName, numClusters)</code>, where <code>problemName</code> refers to the file in the <code>mFMO/problems</code> folder without the file extension and <code>numClusters</code> denotes the maximum number of color codes in Wang tiling formalism.</p> <p>If successful, the optimization produces output files in <code>mFMO/fmo_fig/{label}/{X}colors/{T}/</code>:</p> <ul> <li><code>{label}{X}.mat</code> (contains clustering and tiling information)</li> <li><code>{label}{X}_tmp.mat</code> (contains results of non-modular FMO)</li> <li><code>{label}{X}.til</code> (the assembly plan)</li> <li><code>{label}{X}.tset</code> (Wang tile set)</li> <li><code>{label}{X}guess.mat</code> (guess for TO)</li> </ul> <p>where <code>T</code> is the optimization time stamp.</p> <p><strong>2.2 Modular topology optimisation</strong></p> <p>All results were obtained with version <code>v1.1.2</code>, which is also provided in the folder <code>MTO</code>, and linked Intel® oneAPI Math Kernel Library and the incorporated PARDISO sparse solver. For the recent development of the code see the open git repository at <a href="https://gitlab.com/MartinDoskar/modular-topology-optimization">https://gitlab.com/MartinDoskar/modular-topology-optimization</a>. The repository also contains a detailed description of input parameters and code design.</p> <p>Modular topology optimisation code uses CMake for the cross-platform build automation. For instance, under Linux, the whole code can be compiled in the standard five steps:</p> <pre><code>cd ./MTO mkdir build cd ./build cmake -DCMAKE_BUILD_TYPE=Release .. make </code></pre> <p>All executables are automatically stored in <code>./MTO/bin/</code> folder. Individual problems can be optimized by parsing the JSON files obtained from the generator scripts as an argument to the MTO.Application binary, e.g.,</p> <pre><code>./MTO/bin/MTO.Application.exe path_to_data/mbb/2color/MTO_100_kernelSensitivity/input_modular_mbb_2colours_100.json </code></pre> <p><strong>Acknowledgement</strong></p> <p>The related research and code development was supported by the <a href="https://gacr.cz/en/">Czech Science Foundation</a>, project No. 19-26143X.</p>
Multi-scale simulations for optimizing cancer treatment
<p>The dataset comprises the output of several simulations of a model of tumor growth with different parameter values (10.1101/2021.12.17.473136). The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction.</p> <p>The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to 425 different simulations launched and automatically tagged as Interesting/Non-Interesting based on the effect of the parameters on the simulation (see README file). Each simulation was tagged by them with the following parameters (in that order):</p> <ul> <li>oxygen_necrotic, oxygen_critical</li> <li>oxygen_no_proliferation</li> <li>oxygen_reference</li> <li>initial_uptake_rate</li> <li>protein_threshold</li> <li>secretion_rate</li> <li>oxygen_concentration</li> <li>tnf_concentration</li> </ul> <p>Details on how these files are built can be found in the <strong>Biological Use Case</strong> output format file (<a href="https://zenodo.org/record/3921049">https://zenodo.org/record/3921049</a>). </p>
Satellite monthly surface chlorophyll-a concentration, particulate backscattering, Secchi Disk depth, Mixed Layer Depth, Sea Surface Temperature at 25 km resolution optimally interpolated for the North Atlantic Ocean (1998-2018)
<p>Satellite monthly records of surface chlorophyll-a concentration (CHL), particulate backscattering at 443nm (bbp), Secchi Disk depth (zsd), Mixed Layer Depth (MLD), Sea Surface Temperature (SST) at 25 km resolution optimally interpolated via Multivariate Singular Spectrum Analysis (MSSA) for the North Atlantic Ocean for the period 1998-2018. This dataset has been used for the article "Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations" Leonelli et al. 2022, where details of interpolation method are fully explained.</p>
Shape Optimization of Thermoacoustic Systems Using a Two-Dimensional Adjoint Helmholtz Solver
<p>rijke stands for Rijke tube and tswc stands for turbulent swirl combustor.</p> <p>mesh_*.xml contains the mesh in xml format.</p> <p>p_dir_*.xml and p_adj_*.xml contain the direct and adjoint fields in xml format.</p> <p>omega_*.txt contains the eigenvalue.</p> <p>my_dict_*.pickle contains the control points and the shape derivatives as byte streams.</p> <p>The dataset also contains the pvd (ParaView Data) files for the amplitude and phase of the direct and adjoint fields.</p>
DIRECTLib - a library of global optimization problems for DIRECT-type methods
<p><strong>DIRECTLib - a library of a box and generally-constrained global optimization problems for DIRECT-type methods</strong></p> <p>In this library, we present an extended collection of a box and generally constrained global optimization test problems (in MATLAB format) typically used in benchmarking various DIRECT-type [1] methods in the relevant literature (see, e.g., [2-6] and references given therein).</p> <p>File: <strong>WCGO_Test_results.xlsx </strong>contains<strong> </strong>experimental results presented in: <a href="https://arxiv.org/abs/2109.14912">https://arxiv.org/abs/2109.14912</a></p> <p><strong>References</strong></p> <ol> <li>Jones, D. R., Perttunen, C. D. and Stuckman, B. E. (1993) ‘Lipschitzian optimization without the Lipschitz constant’, <em>Journal of Optimization Theory and Applications</em>, 79(1), pp. 157–181. <strong>doi</strong><strong>: 10.1007/BF00941892</strong>.</li> <li> <p>R. Paulavičius, J. Žilinskas. (2014) Simplicial Global Optimization, SpringerBriefs in Optimization, Springer New York, New York, NY. <strong>doi:10.1007/978-1-4614-9093-7</strong></p> </li> <li> <p>L. Stripinis, R. Paulavičius, J. Žilinskas. (2018) Improved scheme for selection of potentially optimal hyper-rectangles in DIRECT, Optimization Letters 12 (7) 1699–1712. <strong>doi:10.1007/s11590-017-1228-4</strong></p> </li> <li> <p>L. Stripinis, R. Paulavičius, J. Žilinskas. (2019) Penalty functions and two-step selection procedure based DIRECT-type algorithm for constrained global optimization, Structural and Multidisciplinary Optimization 59 (6) 2155–2175. <strong>doi:10.1007/s00158-018-2181-2</strong>.</p> </li> <li> <p>L. Stripinis, J. Žilinskas, L. G. Casado, R. Paulavičius (2021) On MATLAB experience in accelerating DIRECT-GLce algorithm for constrained global optimization through dynamic data structures and parallelization. <em>Applied Mathematics and Computation</em>, <a href="https://doi.org/10.1016/j.amc.2020.125596">DOI: 10.1016/j.amc.2020.125596</a></p> </li> <li> <p>L. Stripinis, R. Paulavičius (2021) A new DIRECT-GLh algorithm for global optimization with hidden constraints. <em>Optimization Letters</em>, 15, p. 1865-1884, <a href="https://doi.org/10.1007/s11590-021-01726-z">DOI: 10.1007/s11590-021-01726-z</a></p> </li> </ol>
Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination
<p>Submissions for the Tractostorm 2 Project [1] from our collaborators (raters) are available for new analysis.<br> Contains regions of interest (ROIs) as well as resulting bundles. Segmentations were performed with MI-Brain [2] (<a href="https://github.com/imeka/mi-brain">MI-Brain</a>)</p> <p>Initial data is the same as in the initial <a href="https://zenodo.org/record/2547025#.YRV2S3VKiUk">Tractostorm Project</a> [3]<br> Contains the data as sent to collaborators and the written document containing the dissection protocol in detail.</p> <p>[1] Rheault, Francois, et al. "Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination." <em>Human Brain Mapping</em> (2022).<br> [2] Rheault, Francois, et al. "MI-Brain, a software to handle tractograms and perform interactive virtual dissection." <em>Proceedings of the ISMRM Diffusion study group workshop, Lisbon</em>. 2016.<br> [3] Rheault, Francois, et al. "Tractostorm: The what, why, and how of tractography dissection reproducibility." <em>Human brain mapping</em> 41.7 (2020): 1859-1874.</p> <p>Data Organization:<br> The 5 HCP subjects were duplicated 4 times each.<br> 193441 -> A111, B218, C317, D418<br> 219231 -> A127, B228, C320, D426<br> 286650 -> A136, B237, C338, D436<br> 486759 -> A149, B246, C344, D443<br> 615441 -> A156, B252, C359, D450<br> <br> Bundles can be segmented automatically using the <a href="https://github.com/scilus/scilpy">scilpy</a> toolbox.<br> scil_filter_tractogram.py ${INPUT} ${OUTPUT} ${OPTIONS}</p> <ul> <li>${INPUT} would be the whole brain tractogram of an HCP subject in data_to_segment.zip</li> <li>${OUTPUT} would be the bundle filename (preferably .trk format)</li> <li>${OPTIONS} would be the sequence of ROIs to apply, one for each bundle. <ul> <li><strong>CC</strong>: '--drawn_roi CENTRAL_CC.nii.gz any include --drawn_roi LOWER_AXIAL_LIM.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any exclude --drawn_roi PRE_C_L.nii.gz any exclude --drawn_roi POST_C_R.nii.gz any exclude --drawn_roi PRE_C_R.nii.gz any exclude'</li> <li><strong>AF_L</strong>: '--drawn_roi CENTRAL_CS_L.nii.gz any include --drawn_roi MEDIAL_SAGITTAL_LIM.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any include --drawn_roi PRE_C_L.nii.gz any include --drawn_roi TEMPORAL_ENTRY.nii.gz any include --drawn_roi TEMPORAL_STEM.nii.gz any exclude'</li> <li><strong>PYT_L</strong>: '--drawn_roi IC_L.nii.gz any include --drawn_roi MO_L.nii.gz any include --drawn_roi MB_L.nii.gz any include --drawn_roi MO_L_NOT.nii.gz any exclude --drawn_roi MID_SAGITTAL_PLANE.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any exclude --drawn_roi PRE_C_L.nii.gz any exclude'</li> </ul> </li> </ul>
Butcher's tableaux of the optimized explicit Runge-Kutta schemes for high-order collocated discontinuous Galerkin methods for compressible fluid dynamics
<p>This folder contains the Butcher's tableaux of the optimized explicit Runge-Kutta schemes for high-order collocated discontinuous Galerkin methods for compressible fluid dynamics presented in Al Jahdali et al., "Optimized explicit Runge--Kutta schemes for high-order collocated discontinuous Galerkin methods for compressible fluid dynamics," Computers & Mathematics with Applications, 2022.</p> <p>Specifically,</p> <p><a href="https://zenodo.org/api/files/754318a5-0881-4252-9059-086da4607b49/Butcher_coefficients_ADV.txt">Butcher_coefficients_ADV.txt</a> contains the Butcher's tableaux of the explicit Runge-Kutta schemes optimized using the spectra of the 2D advection equation.</p> <p><a href="https://zenodo.org/api/files/754318a5-0881-4252-9059-086da4607b49/Butcher_coefficients_IEV.txt">Butcher_coefficients_IEV.txt</a> contains the Butcher's tableaux of the explicit Runge-Kutta schemes optimized using the spectra of the isentropic vortex propagation for the compressible Euler equations.</p> <p> </p> <p> </p> <p> </p>
EPTGODD-WHU: Ensemble Precipitation and Temperature from CMIP6 GCMs optimized by OLS-DT-DNN methods integration (1850-2100)
<p>This monthly global climate dataset EPTGODD-WHU (precipitation and mean temperature variables with grid size of 0.5°×0.5°) was ensembled from 16 selected CMIP6 GCMs. The published dataset was optimized by OLS (Ordinary Linear Square)-DT (Decision Tree)-DNN (Deep Neural Network) methods integration. The CF (Climate and Forecast) v1.6 was employed as the guideline for NetCDF4 format. The periods of temperature files can be divided into historical (1850-1900) and future (2015-2100) periods. For precipitation, this product provides future (2015-2100) period. Three future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) were selected for both variables. The units of this dataset are degrees Celsius and mm/month for temperature and precipitation, respectively. Each NetCDF4 file in this dataset includes three dimensions (time, latitude (-89.75°N to 89.75°N) and longitude (-179.75°E to 179.75°E)).</p>
Optimal elevated agrivoltaic system design and key performance indicators across Europe based on three crop light levels
<p>Optimal elevated (stilted) agrivoltaic system design (PV coverage ratio) is given on a European gridded level (25km grid and NUTS3 regions) based on three light levels: shade-loving crops (daily light integral (DLI) of 12 mol/m²day), shade-tolerant crops (DLI of 12 mol/m²day) and shade-intolerant crops (DLI of 25 mol/m²day)</p> <p>Estimations of other performance indicators are given: power capacity (kWp/ha), energy production (MWh/ha), levelized cost of electricity (€/MWh) and land equivalent ratio (LER -).</p> <p>The assumptions and methodology of this dataset can be found in the article "Geospatial assessment of elevated agrivoltaics on arable land in Europe to highlight the implications on design, land use and economic level."</p> <p>Interactive maps can be found on https://iiw.kuleuven.be/apps/agrivoltaics/maps.html</p>
Source code and simulation results for the computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators
<p><strong>Summary</strong></p> <p>Data and source code relate to the article "Computation of eigenfrequency sensitivities using Riesz projections for<br> efficient optimization of nanophotonic resonators" [<a href="https://doi.org/10.1038/s42005-022-00977-1">1</a>]. It combines direct differentiation of scattering problems with a contour integral method [<a href="https://doi.org/10.1016/j.jcp.2020.109678">2</a>] to compute eigenfrequency sensitivities. An optimization is used to demonstrate the relevance of the method.</p> <p><strong>Structure</strong></p> <p>The most important elements of this publication are the MATLAB scripts 'sensitivities.m' and 'optimization.m', which can be used to reproduce the most important results of the paper. The directories <strong>code</strong>, <strong>scattering</strong> and <strong>results </strong>contain the software RPExpand [<a href="https://doi.org/10.1016/j.softx.2021.100763">3</a>], input files for JCMsuite [<a href="https://doi.org/10.1002/pssb.200743192">4</a>] and results produced with the scripts, respectively. Furthermore, the latter contains the subfolder <strong>tabulated,</strong> which contains text files tabulating data presented in Figures 2 and 4 of the paper. Eventually, the function 'code/observation.m' evaluates the target for the optimization.</p> <p><strong>Additional Information</strong></p> <p>The applicaton is based on an example from the literature [<a href="https://doi.org/10.1126/science.aaz3985">5</a>]. Using apriori knowledge about the eigenmode of interest, we chose the scalar observable, as defined in Section B of the paper, to be the component of the electric field normal to the plane defining the solid of revolution.</p> <p>The convergence studies are based on the discrete, circular contour <span>\(\tilde{C} = \big\{ c_n~|~ c_n=r_0 e^{2\pi i n/8}, n \in \{0,1,...,7\}\big\}\)</span> with center <span>\(\omega_0 = 2 \pi c/(1600~\mathrm{nm})\)</span> and radius <span>\(r_0 = \omega_0\times10^{-2}\)</span>. For finite element degrees <span>\(d\)</span> higher than 5, the error saturates. For this reason, the differences between results for <span>\(d=5\)</span> and <span>\(d = 6\)</span> may depend on the hardware architecture.</p> <p>A larger radius <span>\(r = 4\times10^{13}\)</span> has been chosen for the optimization to include information from poles located further away from the frequency of interest. The target function <span>\(t(p_1,\dots,p_5) = -q_n \left(1 - \frac{(\omega_n-\omega_0)^2}{r^2} \right)\)</span>is minimized. The first factor is the negative <em>Q-</em>Factor and the second factor ensures that the target is zero at the boundary. If no eigenfrequency <span>\(\omega_n\)</span> is located inside the contour, the target is set to zero. For the purpose of this data publication some numerical parameters have been improved. This resulted in a faster convergence of the optimization.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite (version 5.2.0 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>. </p> <p><strong>References</strong></p> <p>[1] Felix Binkowski, Fridtjof Betz, Martin Hammerschmidt, Philipp-Immanuel Schneider, Lin Zschiedrich, Sven Burger, Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators, Communications Physics <strong>5</strong>, 202 (2022), https://doi.org/10.1038/s42005-022-00977-1</p> <p>[2] Felix Binkowski, Lin Zschiedrich, Sven Burger, A Riesz-projection-based method for nonlinear eigenvalue problems, Journal of Computational Physics <strong>419</strong>, 109678 (2020), https://doi.org/10.1016/j.jcp.2020.109678</p> <p>[3] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763</p> <p>[4] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192</p> <p>[5] Kirill Koshelev, Sergey Kruk, Elizaveta Melik-Gaykazyan, Jae-Hyuck Choi, Andrey Bogdanov, Hong-Gyu Park, Yuri Kivshar, Subwavelength dielectric resonators for nonlinear nanophotonics, Science <strong>367</strong>, 288 (2020), http://dx.doi.org/%2010.1126/science.aaz3985</p>
Dataset of multi-objective optimization results for a new latent energy storage approach in buildings based on several phase change materials with different melting temperatures
<p>This dataset comprises the multi-objective optimization results obtained for a new latent energy storage approach based on several phase change materials (PCMs) with different melting temperatures in buildings. The results were obtained for a small office building in eight climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe).</p> <p>The dataset contains:</p> <p>- The EnergyPlus baseline models employed as a case study for each climate.</p> <p>- The Pareto fronts obtained after the multi-objective optimization in each climate.</p> <p>- The EnergyPlus models for the best designs achieved on the Pareto fronts in terms of annual total load reductions.</p>
Raw data for the plot in the article entitled "On the electrophoretic deposition of Bi2Te3 nanoparticles through electrolyte optimization and substrate design"
<p>raw data of transport presented in Fig1a of the open access article with the following details:</p> <p>On the electrophoretic deposition of Bi2Te3nanoparticles through electrolyte optimization and substrate design</p> <p><a href="https://www.sciencedirect.com/journal/colloids-and-surfaces-a-physicochemical-and-engineering-aspects">Colloids and Surfaces A: Physicochemical and Engineering Aspects</a></p> <p><a href="https://www.sciencedirect.com/journal/colloids-and-surfaces-a-physicochemical-and-engineering-aspects/vol/649/suppl/C">Volume 649</a>, 20 September 2022, 129537</p> <p><a href="https://doi.org/10.1016/j.colsurfa.2022.129537">https://doi.org/10.1016/j.colsurfa.2022.129537</a></p>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Machine Breakdown Event
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> an announcement of a machine breakdown event was simulated on Friday at 6:00, describing that machine MAQ119 could breakdown at any moment, detected using a predictive maintenance system. Accordingly, the proposed scheduler imposed a machine available frames constraint, during its event, of 0 usable frames, thus removing the machine from production. The proposed genetic algorithm was executed for 1 hour at period 769 (Friday at 7:00) until the remainder of the schedule’s time window. Also, the predefined optimization weights were 1 for total cost and 0 for machine occupancy deviation.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Machine_Breakdown_Optimization - JSON input data for the machine breakdown event</li> <li>Output_JSON_Machine_Breakdown_Optimization - JSON output data for the machine breakdown event</li> <li>Output_Statistics_Machine_Breakdown_Optimization - Excel output machine breakdown event statistics</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.