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70 results found

Segmentation of lipid nanoparticles from cryogenic electron microscopy images

Data provided by  National Institute of Standards and Technology

Lipid nanoparticles (LNPs) were prepared as described (https://doi.org/10.1038/s42003-021-02441-2) using the lipids DLin-KC2-DMA, DSPC, cholesterol, and PEG-DMG2000 at mol ratios of 50:10:38.5:1.5. Four sample types were prepared: LNPs in the presence and absence of RNA, and with LNPs ejected into pH 4 and pH 7.4 buffer after microfluidic assembly. To prepare samples for imaging, 3 ?L of LNP formulation was applied to holey carbon grids (Quantifoil, R3.5/1, 200 mesh copper).

Tags: Lipid Nanoparticle,LNP,cryogenic electron microscopy,CryoEM,machine learning,ai,mRNA,

Modified: 2025-04-06

Software for Evaluating Convolutional Generative Adversarial Networks with Classical Random Process Noise Models

Data provided by  National Institute of Standards and Technology

This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models.

Tags: time series,machine learning,band-limited noise,power law noise,shot noise,impulsive noise,colored noise,fractional Gaussian noise,fractional Brownian motion,

Modified: 2025-04-06

Cost Assessment Tool for Sustainable Manufacturing (CATS)

Data provided by  National Institute of Standards and Technology

This tool uses techniques from ASTM E3200 for evaluating manufacturing investments from the perspective of environmentally sustainable manufacturing by pairing economic methods of investment analysis with environmental aspect of manufacturing. The economic techniques used include net present value, internal rate of return, payback period, and hurdle rate. These four techniques are deterministic, meaning that they deal with known values that are certain. The tool also conducts a sensitivity analysis using Monte Carlo techniques.

Tags: investment analysis,manufacturing,net present value,internal rate of return,environmental impact,sustainability,

Modified: 2025-04-06

SolDet: Solitonic feature detection package

Data provided by  National Institute of Standards and Technology

SolDet is an object-oriented package for solitonic feature detection in absorption images of Bose-Einstein condensate. with wider use for cold atom image analysis. Featured with classifier, object detector, and Mexican hat metric methods. Technical details are explained in https://arxiv.org/abs/2111.04881.

Tags: soliton,machine learning,python package,

Modified: 2025-04-06

Theory aware Machine Learning (TaML)

Data provided by  National Institute of Standards and Technology

A code repository and accompanying data for incorporating imperfect theory into machine learning for improved prediction and explainability. Specifically, it focuses on the case study of the dimensions of a polymer chain in different solvent qualities. Jupyter Notebooks for quickly testing concepts and reproducing figures, as well as source code that computes the mean squared error as a function of dataset size for various machine learning models are included.For additional details on the data, please refer to the README.md associated with the data.

Tags: polymers,machine learning,transfer learning,theory,

Modified: 2025-04-06

Dark solitons in BECs dataset 2.0

Data provided by  National Institute of Standards and Technology

Atomic Bose-Einstein condensates (BECs) are widely investigated systems that exhibit quantum phenomena on a macroscopic scale. For example, they can be manipulated to contain solitonic excitations including conventional solitons, vortices, and many more. Broadly speaking, solitonic excitations are solitary waves that retain their size and shape and often propagate at a constant speed. They are present in many systems, at scales ranging from microscopic, to terrestrial and even astronomical.

Tags: machine learning,Bose-Einstein condensates,dark solitons,

Modified: 2025-04-06

Entropy Source Validation Client

Data provided by  National Institute of Standards and Technology

This tool is a Python client that can interact with the NIST Entropy Source Validation Test System.

Tags: python,Entropy Source Validation Test System,NIST,CMVP,ESV Client,

Modified: 2025-04-06

Dataset for paper Y. Ma, S. Mosleh and J. Coder, "Analyzing 5G NR-U and WiGig Coexistence with Multiple-Beam Directional LBT," 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), 2022, pp. 272-275, doi: 10.1109/CCNC49033.2022.9700690

Data provided by  National Institute of Standards and Technology

This project produces synthetic datasets of spectrum sharing simulation results (I/Q data, metadata, and KPIs).

Tags: wireless coexistence,Spectrum sharing,machine learning,wireless communications and networks,4G,5G,6G,Wi-Fi,

Modified: 2025-04-06

Python tools for measuring filament defects in embedded 3D printing

Data provided by  National Institute of Standards and Technology

In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. This repository includes Python code for analyzing and managing images and videos of the printing process during extrusion of single filaments. The zip file contains the state of the code when the associated paper was submitted. The link to the GitHub page goes to version 1.0.0, which is the same as the code attached here. From there, you can also access the current state of the code.Associated with: L. Friedrich, R. Gunther, J.

Tags: python,digital image analysis,computer vision,3D printing,additive manufacturing,openCV,

Modified: 2025-04-06

QFlow 2.0: Quantum dot data for machine learning

Data provided by  National Institute of Standards and Technology

Using a modified Thomas-Fermi approximation, we model a reference semiconductor system comprising a quasi-1D nanowire with a series of five depletion gates whose voltages determine the number of quantum dots (QDs), the charges on each of the QDs, as well as the conductance through the wire. The original dataset, QFlow lite, consists of 1 001 idealized simulated measurements with gate configurations sampling over different realizations of the same type of device.

Tags: machine learning,quantum dots,simulated data,

Modified: 2025-04-06