ACC: First Time Guide
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AtomicChargeCalculator (ACC) is a tool that calculates, visualizes, and analyzes atomic charges in biomacromolecules and drug-like molecules. It allows users to upload molecules in supported formats, select an empirical method and parameters to calculate charges, and then examine the results including atomic and residue charges, statistics, and 3D visualizations of the charged structures. The guide walks users through the three step process of uploading molecules, setting up the calculation, and analyzing the output results.
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Title:
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Instructor:
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luigi.vanfretti@gmail.com
Abstract:
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Bio:
Luigi Vanfretti (SMIEEE’14) obtained the M.Sc. and Ph.D. degrees in electric power engineering at Rensselaer Polytechnic Institute, Troy, NY, USA, in 2007 and 2009, respectively.
He was with KTH Royal Institute of Technology, Stockholm, Sweden, as Assistant 2010-2013), and Associate Professor (Tenured) and Docent (2013-2017/August); where he lead the SmarTS Lab and research group. He also worked at Statnett SF, the Norwegian electric power transmission system operator, as consultant (2011 - 2012), and Special Advisor in R&D (2013 - 2016).
He joined Rensselaer Polytechnic Institute in August 2017, to continue to develop his research at ALSETLab: http://alsetlab.com
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- Immunotherapeutics aim to activate the body's immune system to fight disease by triggering or mimicking immune responses. They include cytokines, monoclonal antibodies, antibody conjugates, and antibody-directed enzyme prodrug therapy.
- Monoclonal antibodies can directly induce cell death or block growth receptors. They also recruit immune cells through antibody-dependent cytotoxicity and complement-dependent cytotoxicity.
- Early monoclonal antibodies were murine but caused human anti-mouse antibody responses. Newer techniques generate chimeric antibodies by combining mouse and human portions or fully human antibodies from libraries or transgenic mice to reduce this immune response.
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Monoclonal antibodies can be produced in specialized cells through a technique now popularly known as hybridoma technology.
Hybridoma technology was discovered in 1975 by two scientists, G. Kohler and C. Milstein, were awarded Noble prize for physiology and medicine in 1984.
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Similar to ACC: First Time Guide
Hands-on-OpenIPSL.org using OpenModelica!
Title:
Hands-on-OpenIPSL.org using OpenModelica!
Instructor:
Luigi Vanfretti, PhD - RPI
luigi.vanfretti@gmail.com
Abstract:
The Modelica language, being standardized and equation-based, has proven valuable for the for model exchange, simulation and even for model validation applications in actual power systems. These important features have been now recognized by the European Network of Transmission System Operators, which have adopted the Modelica language for dynamic model exchange in the Common Grid Model Exchange Standard (v2.5, Annex F).
Following previous FP7 project results, within the ITEA 3 openCPS project, the presenters have continued the efforts of using the Modelica language for power system modeling and simulation, by developing and maintaining the OpenIPSL library: http://openipsl.org
This tutorial will follow the seminar introducing OpenIPSL.org and give you hands-on-experience on using the library using the OpenModelica modeling and simulation environment.
It is assumed that you have very little experience with OpenModelica and the Modelica language, so detailed instructions are provided.
You will need to bring your computer with OpenModelica installed, see the following link for a .pdf information on installation: https://goo.gl/oLAFv4
You will be working with three examples. In the first example, you will work setting up a power system from scratch and performing simulations using OpenModelica and the OpenIPSL. The second example consists on performing linear analysis using OMNotebook, and implementing a power system stabilizer for the model of example one. Finally, in the third example, you will perform simulations of a typical IEEE 9-Bus power systems and perform a simple analysis of results.
Bio:
Luigi Vanfretti (SMIEEE’14) obtained the M.Sc. and Ph.D. degrees in electric power engineering at Rensselaer Polytechnic Institute, Troy, NY, USA, in 2007 and 2009, respectively.
He was with KTH Royal Institute of Technology, Stockholm, Sweden, as Assistant 2010-2013), and Associate Professor (Tenured) and Docent (2013-2017/August); where he lead the SmarTS Lab and research group. He also worked at Statnett SF, the Norwegian electric power transmission system operator, as consultant (2011 - 2012), and Special Advisor in R&D (2013 - 2016).
He joined Rensselaer Polytechnic Institute in August 2017, to continue to develop his research at ALSETLab: http://alsetlab.com
His research interests are in the area of synchrophasor technology applications; and cyber-physical power system modeling, simulation, stability and control.
Machine learning systems based on xgBoost and MLP neural network applied in s...
This document summarizes a study that uses machine learning to estimate the internal impedance of lithium-ion battery packs from satellite systems. The study uses data from batteries tested under various conditions. Two machine learning models are developed - a multi-layer perceptron neural network and an xgBoost gradient tree boosting model. These models are trained on selected battery characteristics and tested to estimate impedance. The xgBoost and neural network models are then compared based on their root mean square error to determine which provides more accurate impedance estimates.
Machine Learning Systems Based on xgBoost and MLP Neural Network Applied in S...
In this work, the internal impedance of the lithium-ion battery pack (important measure of the degradation level of the batteries) is estimated by means of machine learning systems
based on supervised learning techniques MLP - Multi Layer Perceptron - neural network and xgBoost - Gradient Tree Boosting. Therefore, characteristics of the electric power system, in which
the battery pack is inserted, are extracted and used in the construction of supervised models
through the application of two different techniques based on Gradient Tree Boosting and Multi
Layer Perceptron neural network. Finally, with the application of statistical validation techniques,
the accuracy of both models are calculated and used for the comparison between them and the
feasibility analysis regarding the use of such models in real systems.
Perceptron in ANN
Inroduction to Perceptron and how it is used in Machine Learning and Artificial Neural Network.
This presentation is prepared by Zaid Al-husseini, as a lectur for third stage of undergraduate students in Softwrae department - faculity of IT - University of Babylon, Iraq.
It is publicly availabe for the beginners to learn in theory and mathmatically how the Perceptron is working.
Notice: the slides are not detailed. And need a teacher to explain them deeply.
Machine Learning Systems Based on xgBoost and MLP Neural Network Applied in S...
In this work, the internal impedance of the lithium-ion battery pack (important measure
of the degradation level of the batteries) is estimated by means of machine learning systems
based on supervised learning techniques MLP - Multi Layer Perceptron - neural network and xgBoost
- Gradient Tree Boosting. Therefore, characteristics of the electric power system, in which
the battery pack is inserted, are extracted and used in the construction of supervised models
through the application of two different techniques based on Gradient Tree Boosting and Multi
Layer Perceptron neural network. Finally, with the application of statistical validation techniques,
the accuracy of both models are calculated and used for the comparison between them and the
feasibility analysis regarding the use of such models in real systems.
Machine Learning Systems Based on xgBoost and MLP Neural Network Applied in S...
In this work, the internal impedance of the lithium-ion battery pack (important measure of the degradation level of the batteries) is estimated by means of machine learning systems based on supervised learning techniques MLP - Multi Layer Perceptron - neural network and xgBoost - Gradient Tree Boosting. Therefore, characteristics of the electric power system, in which the battery pack is inserted, are extracted and used in the construction of supervised models through the application of two different techniques based on Gradient Tree Boosting and Multi Layer Perceptron neural network. Finally, with the application of statistical validation techniques, the accuracy of both models are calculated and used for the comparison between them and the feasibility analysis regarding the use of such models in real systems
Machine learning systems based on xgBoost and MLP neural network applied in s...
In this work, the internal impedance of the lithium-ion battery pack (important measure of the degradation level of the batteries) is estimated by means of machine learning systems based on supervised learning techniques MLP - Multi Layer Perceptron - neural network and xgBoost - Gradient Tree Boosting. Therefore, characteristics of the electric power system, in which the battery pack is inserted, are extracted and used in the construction of supervised models through the application of two different techniques based on Gradient Tree Boosting and Multi Layer Perceptron neural network. Finally, with the application of statistical validation techniques, the accuracy of both models are calculated and used for the comparison between them and the feasibility analysis regarding the use of such models in real systems.
eQUMEC Catalog
The document describes eQUMEC, a software for simulating lithium-ion batteries that is under development by QUMEC Company. eQUMEC uses multiscale computational algorithms to simulate ion diffusion, stress evolution, temperature changes, and other variables. It allows designing battery components in 3D and simulating charging and discharging to calculate performance metrics. Future versions will expand capabilities to other battery technologies and incorporate quantum mechanics for materials design.
ARCHIVED: new version available - METASPACE Step by Step guide
These slides provide a guide to using the METASPACE platform for annotating metabolites in high resolving power imaging mass spectrometry datasets. It describes
* the science behind molecular annotation
* how to use our web application to upload, browse, interpret and export annotations from the platform.
Proteomics and metabolomics
This document provides information about a presentation on proteomics and metabolomics given by a group of students from the Department of Botany at Shah Abdul Latif University in Khairpur, Sindh. The presentation covers three topics: 1) 2D PAGE (Polyacramyl Gel Electrophoresis) which is used to separate proteins based on their isoelectric point and molecular weight, 2) Mass spectroscopy which uses electric and magnetic fields to separate samples based on molecular weight and can identify isotopes, and 3) Protein arrays. The document includes details on each technique presented by various group members.
Artificial Neural Network for machine learning
An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain's neural networks. It consists of interconnected nodes, often referred to as neurons or units, organized in layers. These layers typically include an input layer, one or more hidden layers, and an output layer.
Rehabilitation of nablus electrical network by adding a new connection point
This document summarizes a project to rehabilitate the electrical network in Nablus, Palestine. The project aims to study the performance of the existing network and add a new connection point to improve reliability and reduce costs. A load flow analysis of the network will be performed using ETAP software to analyze the effects of adding the new connection point and improving the power factor and voltage level. The analysis will explore technical and economic feasibility of adjustments to optimize performance of the eastern region network in Nablus.
Lect24-Efficient test suite mgt - IV.pptx
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Report pv module performance
The document describes the development of MATLAB software to analyze and predict photovoltaic (PV) module performance. Indoor and outdoor measurements were taken on three crystalline silicon PV modules to characterize their electrical behavior under different irradiance and temperature conditions. MATLAB programs were created to process the measurement data, plot the results, and write outputs. The software was then used to analyze the modules and predict their energy production when installed in different locations using a PV web tool.
Bio Linux
Vigyaan is a live Linux environment based on Knoppix that provides bioinformatics tools for tasks like sequence alignment, structure visualization, molecular dynamics simulations, and more. It contains demos of tools like Artemis, ClustalX, Ghemical, GROMACS, Jmol, Open Babel, NJPlot, PSI3, PyMOL, Rasmol, Raster3D, and TINKER. PSI3 is a program for ab initio quantum chemistry calculations that can perform Hartree-Fock, coupled cluster, and configuration interaction methods with basis sets of up to 32,768 functions.
Jacob Kleine undergrad. Thesis
This thesis examines calculations of binding free energies between cucurbit[7]uril and small ligands using end-state molecular modeling methods. The author conducted molecular dynamics simulations using AMBER with implicit solvent models to calculate binding free energies, which were then compared to experimental values from the SAMPL4 challenge. Various error metrics including R2 correlation, root mean squared error, and linear regression slope were used to evaluate the results. The author found some improvement over previous methods, suggesting further refinement of conformational sampling could enhance the accuracy of binding free energy predictions.
Hpc Visualization with X3D (Michail Karpov)
The document describes two modes of data analysis for workload rendering on server clusters:
1. General system analysis uses a molecular metaphor to visualize task load across cluster nodes over time, allowing users to identify bottlenecks.
2. Detailed task analysis uses a greenhouse metaphor to test individual tasks under varying hardware/software parameters and identify bottlenecks like CPU, memory, or disk limitations.
Both modes aim to optimize workload distribution and resource usage across clusters.
MAXIMUM POWER POINT TRACKING WITH ARTIFICIAL NEURAL NET WORK
Fossil fuels’ rapid depletion and need to protect the environment has left us to think upon alternatives and solutions to curb the excess use of conventional sources and shift focus on the renewable energy. In this paper we have designed a prototype model inclusive of techniques that support the need to harness the solar energy.
The Basic of Molecular Dynamics Simulation
Molecular dynamics simulation is a computational method that analyzes the physical movements of atoms and molecules over time. It works by calculating the acceleration, position, and velocity of atoms in a system using Newton's laws of motion. The forces between atoms are determined from interatomic potential functions, and initial atom velocities are assigned randomly based on temperature using the Boltzmann distribution. The simulation is iterated in small time steps to track how atom positions, velocities, and accelerations change over time. This provides insights into molecular structure, function, and interactions at the atomic scale.
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in this we are talking about the pv system and mppt technology which enhance the capability of solar
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Machine learning systems based on xgBoost and MLP neural network applied in s...
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ACC: First Time Guide
- 1. AtomicChargeCalculator - ACC First Time User Guide DO: calculate, visualize and analyze WHAT: atomic charges which respond to changes in molecular conformation and chemical environment WHERE: biomacromolecules and drug-like molecules.
- 2. Calculate atomic charges in 3 easy steps Help... Help... 2
- 3. Upload your protonated molecule in a supported format: pdb, pqr, mol2, mol... Upload multiple molecules as .zip Help... Help... Help... Step 1 – Upload molecule(s) 3
- 4. Once you chose the input file with molecule(s), click Upload. Step 1 – Upload molecule(s) 4 Help... Help... Help...
- 5. Help... Once you upload your molecule(s), ACC tries to prepare a default setup, so you can start your calculation immediately... Step 2 – Setup 5 Start calculation using default setup … or check and adjust the setup
- 6. Help... A default setup may not be available, or may not be optimal for your calculation. Check and adjust each section of the setup... Step 2 – Setup 6 Check and adjust the setup
- 7. Help... Check the total molecular charge (default=0) Check the messages (missing H, suboptimal formatting...) Total molecular charge Step 2 – Setup 7
- 8. Help... EEM is an empirical approach used to calculate atomic charges. Click Show sets to list all EEM parameter sets available in ACC. EEM parameters Step 2 – Setup 8
- 9. Help...Step 2 – Setup For biomacromolecules, ACC provides time and memory efficient implementations of EEM. Click Add to pick a suitable method. Efficiency 9
- 10. Help...Step 3 – Start Keep the default settings or make your adjustments, then start the calculation 10
- 11. … try any of the multiple interactive guides ... check the tool tips … consult the Wiki manual Got lost during setup? 11
- 12. Download structure files with charges (mol2, pqr...), statistics. Unique URL available for later access Retrieve results 12 URL Help...
- 13. Alternatively, for each molecule you can examine the results directly in your browser... Help... URL Retrieve results 13
- 14. A job is defined by its unique setup (total molecular charge, set of EEM parameters, computation method), and produces a unique set of charges. Help... Examine results for each molecule - Summary Molecule Input and computation warnings Basic info about the computation 14
- 15. The charges can be inspected at two levels of resolution: - atomic charges - residue charges (sum of atomic charges per residue) Help... Examine results for each molecule – Raw Data Molecule Unique set of charges, resulted from a job or read in from the input file 15
- 16. Interesting statistics available for atomic and residue charges Help... Examine results for each molecule – Analyze Molecule The charge on the Mg ions does not vary much 16
- 17. Help... Examine results for each molecule – Compare Molecule Comparison between sets of charges resulted from different jobs, or read from the input file 17
- 18. 3D model built using: - atomic positions, colored by atomic charges or charge differences - residue positions, colored by residue charges or charge differences Help... Examine results for each molecule – 3D Model Molecule Several visualization options to help interpret the results 18
- 19. … try any of the multiple interactive guides ... check the tool tips … consult the Wiki manual Need help with interpreting the results? 19
- 20. Help... Help... Try the setup examples, with detailed explanations... Don't know where to start? 20
- 21. … or view the setup and results for a few interesting computations... Help... Don't know where to start? 21 Help... Help...