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[Supplementary Data] PowerModel-AI: A First On-the-fly Machine-Learning Predictor for AC Power Flow Solutions.
<h1><strong>Abstract</strong></h1> <p>The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to autonomously build models while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce <em>PowerModel-AI</em>, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures the load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model's performance is sub-optimal, based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using <em>PowerModel.jl</em>, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.</p> <div> <h1><strong>Overview</strong></h1> <p>This dataset, provided as supplementary material for the above-referenced study, includes a comprehensive collection of images (plots) from the study’s analyses, along with Jupyter notebooks containing Python scripts used for the training, validation, and testing phases of PowerModel-AI. Additionally, it includes all training and external test data used in this work, generated via LANL-based open-source power flow solver, PowerModels.jl.</p> <p>The primary objective of this dataset is to ensure full reproducibility of the study’s analyses and facilitate critical examination by the scientific community, thereby maximizing the overall impact of the work.</p> <h1>Directory Structure</h1> <p>The hierarchy of folders and file organization of the dataset is illustrated in the chart below. The directory contains a README.md file which contains the information provided here and a requirements.txt that contains all libraries necessary to run the python scripts or Jupyter notebooks in this directory. A brief description of the folders and what they contain are provided below: </p> </div> <div> <strong>.</strong></div> <div>├── <strong>Models/</strong></div> <div>│ ├── _PM_AI_Models/</div> <div>│ │ ├── Type1/</div> <div>│ │ ├── Type2/</div> <div>│ │ └── Type3/</div> <div>│ │</div> <div>│ ├── _PM_AI_Module/</div> <div>│ │ ├── __init__.py</div> <div>│ │ └── PM_Methods.py</div> <div>│ │</div> <div>│ ├── _PM_JL_Data/</div> <div>│ ├── C1_Model/</div> <div>│ ├── C2_Model/</div> <div>│ ├── C3_Model/</div> <div>│ ├── M1_Model/</div> <div>│ ├── M2_Model/ </div> <div>│ └── M3_Model/</div> <div>│</div> <div>├── <strong>NodeSensitivityAnalysis/</strong></div> <div>│ ├── Sensitivity_Plots/</div> <div>│ ├── Sensitivity_PM_JL_Data/</div> <div>│ ├── get_sensitivity_PMJL_data.py</div> <div>│ └── NodeSensitivityAnalysis.ipynb</div> <div>│</div> <div>├── <strong>PlotsForOnTheFlyAnalysis/</strong></div> <div>│ ├── BaseModel_A/</div> <div>│ ├── BaseModel_B/ </div> <div>│ └── BaseModel_C/</div> <div>│ </div> <div>├── <strong>README.md</strong></div> <div>└── <strong>requirements.txt</strong></div> <p> </p> <h2>Files Description </h2> <h3><strong>1. </strong><strong>Models/</strong></h3> <p><strong><em>_PM_AI_Models/</em></strong> contains the ML models discussed in the manuscript. Type1, Type2, and Type3 refers to the C and M models with numbers "1", "2," and "3". Each Type folder contains individual subfolders for the synthetic grids discussed.</p> <p><strong> <em>_PM_AI_Module/</em></strong> contains a python script that has all the functions used in model training and analysis. It is imported in the Jupyter notebooks in the C and M subfolders in this directory.</p> <p><strong><em>_PM_JL_Data/</em> </strong>contains the following subfolders:</p> <p>a) <strong> </strong><em>_</em><strong><em><strong>G</strong>eneratePowerModelData/</em></strong> has a python script (<u>get_PowerModelJLData.py)</u> that is used to parse <u>PowerModels.jl</u> to compute AC power flow solutions for different power demand configurations. It also contains a subfolder, <strong><em>BusData_MATLAB/</em></strong>, that has all the synthetic grids used in the study and in MATLAB format.</p> <p>b) It also contains other subfolders (not shown in the chart above) that contains AC power flow solutions generated using LANL’s PowerModels.jl, for each synthetic grids and other grid-related data.</p> <p>The folders starting with C and M are the control and candidate models (more details in manuscript). Each folder contains Jupyter notebooks (for each power grid) that has python algorithms used in model training and testing, as well as functions to analyze and plot the prediction performance of the models. It accesses (if already trained) or stores (if newly trained) the models in the <strong><em>_PM_AI_Models/</em></strong> directory. Additionally, they contain 2 subfolders (not shown in the chart above): <strong>AbsoluteErrorPlots/</strong> and <strong>PredictionPlots/</strong> were the results (plots) from the analyses are stored. Some of these results are shown in the manuscript (<em>Figures 4</em>,<em> 5</em>, <em>6</em>, and <em>7</em>).</p> <h3><strong>2. </strong><strong>NodeSensitivityAnalysis/</strong></h3> <p>This folder contains the tools used for the node dependency analysis in Section 3.1.1 of the manuscript.</p> <p>It contains a python script called get_sensitivity_PMJL_data.py, which has similar operation like the <u>get_PowerModelJLData.py</u> script but only compute changes for one bus at a time (see details in manuscript). There is also a Jupyter notebook called <u>NodeSensitivityAnalysis.ipynb </u>that analyzes the bus node dependencies and produces the plots that are shown in Figure 3 in the manuscript. It contains 2 additional sub-folders:</p> <p>a) <strong> <em>Sensitivity_PM_JL_Data/</em></strong> where PowerModels.jl generated AC power flow solution data are stored, and</p> <p>b) <em><strong>Sensitivity_Plots/</strong> </em>where the results from <u>NodeSensitivityAnalysis.ipynb</u> are stored.</p> <h3><strong>3. </strong><strong>PlotsForOnTheFlyAnalysis/</strong></h3> <p>This directory contains only the results for the discussion in Section 3.2 in the manuscript, which is the on-the-fly implementation of PowerModel-AI. The on-the-fly algorithm will be provided and distributed separately in the PowerModel-AI package, which will be publicly available through LANL’s <a href="https://github.com/lanl-ansi" target="_blank" rel="noopener">The Advanced Network Science Initiative</a> (Github). It contains 3 subfolders with similar names but ends with "A", "B," and "C" which correspond to the designations shown and discussed in <em>Figure 8</em> of the manuscript.</p> <h1>Summary</h1> <h3><strong> </strong><strong>Models/</strong></h3> <p><strong>1. _PM_AI_Models/: </strong></p> <p>Contains the machine learning models discussed in the manuscript. The Type1, Type2, and Type3 folders refer to C and M models labeled "1", "2," and "3". Each type folder includes subfolders for the corresponding synthetic grids analyzed.</p> <div><strong>2. _PM_AI_Module/: </strong> </div> <div>Contains Python scripts for model training and analysis. PM_Methods.py is the script that defines functions for model training and analysis, which are imported in the Jupyter notebooks in the C and M subfolders. </div> <div> </div> <div><strong>3. _PM_JL_Data/: </strong></div> <div>-_GeneratePowerModelData/: Contains a Python script (get_PowerModelJLData.py) used to parse PowerModels.jl and compute AC power flow solutions for various power demand configurations. This folder also includes BusData_MATLAB/, which holds the synthetic grid data in MATLAB format. </div> <div>- Other Subfolders: Contain PowerModels.jl AC power flow solutions for each synthetic grid, as well as related data.</div> <div> </div> <div><strong>4. C1_Model/, C2_Model/, C3_Model/, M1_Model/, M2_Model/, M3_Model/: </strong></div> <div>These folders contain the control and candidate models (refer to manuscript details). Each folder includes pre-run Jupyter notebooks for model training and analysis, as well as two subfolders: </div> <div> - <em>AbsoluteErrorPlots/</em>: Contains saved analysis results for each grid.</div> <div> <strong> </strong>- <em>PredictionPlots/</em>: Stores model prediction results. </div> <div>Some results are shown in <em>Figures 4, 5, 6,</em> and <em>7</em> of the manuscript and can be reproduced using the notebooks.</div> <h3>NodeSensitivityAnalysis/</h3> <div><strong>1. Node Dependency Analysis Tools: </strong> </div> <div> This folder contains scripts and data used for the node dependency analysis in Section 3.1.1 of the manuscript.</div> <div> </div> <div><strong>2. Scripts: </strong></div> <div> - get_sensitivity_PMJL_data.py: Computes power flow changes for individual buses (refer to the manuscript for details). </div> <div> - NodeSensitivityAnalysis.ipynb: Analyzes dependencies and generates plots for Figure 3 in the manuscript. </div> <div> </div> <div><strong>3. Subfolders:</strong> </div> <div> - Sensitivity_PM_JL_Data/: Stores PowerModels.jl data for sensitivity analysis. </div> <div> - Sensitivity_Plots/: Contains the generated results from the Jupyter notebook.</div> <h3>PlotsForOnTheFlyAnalysis/</h3> <div><strong>On-the-Fly Learning Results: </strong>Contains the results for the on-the-fly learning analysis discussed in Section 3.2 of the manuscript. The subfolders BaseModel_A/, BaseModel_B/, and BaseModel_C/ correspond to the designations in <em>Figure 8</em> of the manuscript. These subfolders contain plots generated during analysis.</div> <h3>Additional Files</h3> <div>- README.md: This file.</div> <div>- requirements.txt: Contains a list of necessary Python libraries required to run the scripts and Jupyter notebooks.</div> <h3>Notes:</h3> <div>- The PowerModel-AI code is publicly available on GitHub.</div> <div>- The supplementary materials include additional Jupyter notebooks and results for each power grid analysis. </div> <div>- The generated results and predictions in this repository are consistent with those discussed in the manuscript.</div>
Tetramethylammonium–acetate solution molecular dynamics simulations
<p><span>Simulation data used for the publication: "Ion pairing in aqueous tetramethylammonium–acetate solutions by neutron scattering and molecular dynamics simulations".</span></p> <p><span>GROMACS FFMD simulations:</span></p> <ul> <li> <p><span>SI-charmm-full</span></p> </li> <li> <p><span>SI-charmm-ecc85</span></p> </li> <li> <p><span>SI-charmm-ecc75</span></p> </li> <li> <p><span>SI-charmm-lowCH</span></p> </li> <li> <p><span>SI-charmm-lowCO</span></p> </li> <li> <p><span>SI-charmm-highCO</span></p> </li> <li> <p><span>SI-amber-full</span></p> </li> <li> <p><span>SI-amber-ecc85</span></p> </li> <li> <p><span>SI-amber-ecc75</span></p> </li> <li> <p><span>SI-FFMD-for-AIMD-1tma-1acet</span></p> </li> </ul> <p><span>CP2K AIMD simulation: </span></p> <ul> <li> <p><span>SI-AIMD</span></p> </li> </ul>
Agri-Environmental Communication Interaction Strategic Link to Food Security Solution
<p>Developing countries face challenges of food production (food insecurity) and malnutrition on daily basis and this mainly affects women and children who are culprits of hunger and death. Kenya faces the worst scenario over food deficits and malnutrition. Meeting sustainable food requirements as it is in developed countries becomes an uphill task. Kenya imports more food grains such as maize, rice, sugar and wheat among others. Nearly 40% of our food stores contain imported foods. What could be the reason for this shortage of food? Information captured by local farmers indicates the scientific language used in training and instructing farmers is foreign and scientific hence difficult to conceptualize and interpret to a local environmental language that is simpleto understand and implement by farmers. Farmers therefore end up not doing the right things. This essential agri-environmental communication technique between farmers and instructional language complexity contributes significantly to poor agricultural output. The farmers' claim has been thinly covered in research studies regardingthe food production system. This is the reason for carrying out this research. This paper seeks to address the claim that agri-environmental communication interaction can be key to enhancing food production and nutrition as well as improving human health. The objective was to find out the causes of low food production dueto lack of agri-environmental communication interaction; To assess the effect of food insecurity and malnutrition on human health due to agri-environmental communication interactions and to make recommendations towards enhancing food production and nutrition in improving human health in Kenya through agri-environmental communication interaction. The research study and data was collected from secondary sources, media observations and focus group discussions from the forty-seven counties in Kenya. The findings revealed that, food production is very low due to poor communication in scientific agricultural terminologies interpretation inlocal language for effective use by farmers. The recommendation is that, government and stakeholders should take action and improve on effective agri- environmental communication interaction in order to solve food insecurity and malnutrition. The scientific language used in instructions product labels should include local languages.</p>
FIGURES 1–2 in A simple solution for accurate measuring of long and contorted ovipositors in pinned specimens of Hymenoptera
FIGURES 1–2. Demonstration of the proposed method for ovipositor straightening. The wasp used is an old museum specimen of Megischus acutus LePeletier et Serville (Hymenoptera, Stephanidae). 1, Specimen as originally found in a museum drawer. 2, Final stage of straightening (see text for details). Legends: ov, ovipositor; vv, ovipositor sheaths.
Nanoscale zero-valent iron loaded vermiform expanded graphite for the removal of Cr (VI) from aqueous solution
<p><span>Cr (VI)</span><span> is indispensable in industrial manufacturing, and its extensive use leads to severe heavy-metal pollution in the water environment around people, posing a </span><span>great</span><span> danger to physical health and living environment of multitudinous organisms. Expanded graphite (EG) is considered as a typical material for adsorption, while nanoscale zero-valent iron (nZVI) can be applied to degrade and sedimentate various organic or inorganic pollutants. In this study, a simultaneous collaboration of EG and nZVI is carried out, with the investigation on the influence of different test conditions for adsorption performances. These findings demonstrate that nZVI@EG manifests favorable adsorptive performance on the removal of hexavalent chromium efficiently. nZVI, acted as an electron donor, is supposed to reduce Cr(VI) to Cr(III), turning itself into iron oxide or hydroxide. The whole process is an exothermic reaction, accompanying chemical reduction and physical adsorption. And Cr(III) is fastened on the appearance by deposition of chromium hydroxide or ferrochromium complex precipitation, which greatly reduces the total chromium content in the aqueous solution. Herein, as a new composite adsorbent, nZVI@EG shows promising prospects of practical applications in water contamination and environmental remediation.</span></p>
FIGURE 7 in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 7. Prochaetoderma gilrowei (publication Figs 16, 17): A, holotype; B, paratype. C–K, sclerites from: C, oral shield; D, dorsal to oral shield; E, anterium; F, ventral anterior trunk; G, lateral anterior trunk; H, dorsal mid-trunk; I, shank; J, fringe; K, knob. Scales in mm.
FIGURE 9 in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 9. Spathoderma quadratum (publication Figs 21, 22): A, holotype. B–J, sclerites from: B, oral shield; C, dorsal to oral shield; D, E, anterium; F, anterior trunk; G, ventral mid-trunk; H, dorsal mid-trunk, I (note change of scale), shank and fringe; J, knob. Scales in mm.
FIGURE 5 in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 5. Claviderma mexicanum (publication Figs 11, 12): A, holotype. B–I, sclerites from: B, oral shield; C, dorsal to oral shield; D, anterium; E, ventral anterium; F, ventral mid-trunk; G, dorsal mid-trunk; H, shank; I, knob. Scales in mm.
FIGURE 4 in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 4. Claviderma crassum (publication Figs 9, 10): A, B, paratypes; B, juvenile. C–M, sclerites from: C, oral shield; D, dorsal to oral shield; E, anterium; F, ventral anterior trunk; G, dorsal anterior trunk; H, dorsal mid-trunk; I, J, shank; K, L, knob; M, fringe. Scales in mm.
FIGURE 3 in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 3. Claviderma compactum (publication Figs 7, 8): A, holotype, B, juvenile paratype. C–M, sclerites from: C, oral shield; D, dorsal to oral shield; E, anterium; F, trunk beneath anteroventral bulge; G, H, ventral trunk posterior to bulge; I, J, dorsal anterior and mid-trunk; K, shank; L, fringe; M, knob. Scales in mm.
FIGURE 23 in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 23. Sclerites of Spathoderma n. spp. paratypes (SEM). A, B. Sp. bulbosum (DGoMB, S38/3): A, frontal view of anterior trunk sclerite; B, faint striations in A enlarged. C, D. Sp. quadratum (DGoMB, S36/3): C, anterium sclerites as they appear in situ; D, frontal view of anterium sclerite showing waist and striations on base.
FIGURE 2 in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 2. Claviderma amplum (publication Figs 5, 6): A, paratype. B–J, sclerites from: B, oral shield; C, dorsal to oral shield; D, lateral and ventral anterium; E, ventral trunk; F, G, anterior and lateral mid-trunk; H, shank; I, fringe; J, knob. Scales in mm.
FIGURE 22. Spathoderma quadratum n in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 22. Spathoderma quadratum n. sp., sclerites from paratype (DGoMB, S36/3). A. Oral shield. B. Dorsal to oral shield. C, D. Anterium. E. Anterior trunk. F. Ventral mid-trunk. G. Dorsal mid-trunk. H. Shank. I. Knob. J. Fringe. K. Lateral view of trunk sclerite, blade bent outwards (to right) from blade.
FIGURE 21. Spathoderma quadratum n in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 21. Spathoderma quadratum n. sp., Gulf of Mexico. A, E. Holotype (DGoMB, S36/1). B–D. Paratypes (DGoMB, S36/3). B. Oral shield sclerites in situ. C. Jaw. D. Radula tooth and central plate. E. Lateral mid-trunk sclerites.
FIGURE 1 in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 1. Chevroderma cuspidatum (publication Figs 2, 3): A, holotype; B, juvenile paratype. C–J, sclerites from: C, oral shield; D, dorsal to oral shield; E, anterium; F, G, ventral anterium and ventral trunk; H, dorsal trunk; I, shank; J, knob and fringe. Scales in mm.
FIGURE 20. Spathoderma bulbosum n in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 20. Spathoderma bulbosum n. sp., sclerites from same paratype as Fig. 19B. A. Oral shield. B. Dorsal to oral shield. C. Anterium. D. Ventral anterior trunk. E. Lateral anterior trunk. F. Ventral mid-trunk. G. Dorsal mid-trunk. H. Shank. I, J. Knob, short and long types. K. Lateral view of trunk sclerite, blade bent outward (to right) from base.
FIGURE 18 in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 18. Sclerites of n. spp., western Atlantic Prochaetodermatidae (SEM). A–C. From paratype of Niteomica captainkiddae (DGoMB, S36/2). A. Mid-trunk sclerites as they appear in situ. B. Frontal view of posterior trunk sclerite showing blade bent outward from the body. C. Anterior trunk sclerites showing sharp ridges on blade. D–F. From paratype of Prochaetoderma gilrowei, type locality. D, F. Frontal view of mid-trunk sclerite showing ridges on blade and fine striations on base just below waist. E. Frontal view of mid-trunk sclerite showing blade curved towards body (cf. straight sclerite in Fig. 17J).
FIGURE 16. Prochaetoderma gilrowei n in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 16. Prochaetoderma gilrowei n. sp., Gulf of Mexico. A, E. Holotype (DGoMB, S42/1). B–D, F, G. Paratypes from type locality. A, B, C. Entire. D. Oral shield sclerites in situ. E. Mid-trunk sclerites. F. Jaw. G. Radula tooth and central plate.
FIGURE 15. Niteomica captainkiddae n in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 15. Niteomica captainkiddae n. sp., sclerites from same paratype as Figure 14B. A. Oral shield. B. Dorsal to oral shield. C. Dorsal anterium. D. Lateral and ventral anterium. E. Ventral anterior trunk. F. Dorsal anterior trunk. G. Ventral mid-trunk. H, I. Dorsal mid-trunk. J. Shank. K. Knob. L. Fringe. M. Lateral view of trunk sclerite showing blade bent outward at waist from base, but both blade and base curved toward body.
FIGURE 14. Niteomica captainkiddae n in NMR solution structures of KAP-1PHD finger-bromodomain
FIGURE 14. Niteomica captainkiddae n. sp., Gulf of Mexico. A, G. Holotype (NGoMCS sample 3043). B–F. Paratypes (C is juvenile) (NGoMCS 5436). A, B, C. Entire. D. Oral shield sclerites in situ. E. Jaw. F. Radula tooth and central plate. G. Mid-trunk sclerites.
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