<div><p>Tissue Forge is an open-source interactive environment for particle-based physics, chemistry and biology modeling and simulation. Tissue Forge allows users to create, simulate and explore models and virtual experiments based on soft condensed matter physics at multiple scales, from the molecular to the multicellular, using a simple, consistent interface. While Tissue Forge is designed to simplify solving problems in complex subcellular, cellular and tissue biophysics, it supports applications ranging from classic molecular dynamics to agent-based multicellular systems with dynamic populations. Tissue Forge users can build and interact with models and simulations in real-time and change simulation details during execution, or execute simulations off-screen and/or remotely in high-performance computing environments. Tissue Forge provides a growing library of built-in model components along with support for user-specified models during the development and application of custom, agent-based models. Tissue Forge includes an extensive Python API for model and simulation specification via Python scripts, an IPython console and a Jupyter Notebook, as well as C and C++ APIs for integrated applications with other software tools. Tissue Forge supports installations on 64-bit Windows, Linux and MacOS systems and is available for local installation via conda.</p></div>

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Dörnchen, Calvin · Figshare · 2024

The source code provided are used in my bachelor thesis.

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Sarafraz, Ideh, Agahi, Hamed, Mahmoodzadeh, Azar · Figshare · 2024

CNN is considered an efficient tool in brain image segmentation. However, neonatal brain images require specific methods due to their nature and structural differences from adult brain images. Hence, it is necessary to determine the optimal structure and parameters for these models to achieve the desired results. In this article, an adaptive method for CNN automatic configuration for neonatal brain image segmentation is presented based on the encoder-decoder structure, in which the hyperparameters of this network, i.e., size, length, and width of the filter in each layer along with the type of pooling functions with a reinforcement learning approach and an LA model are determined. These LA models determine the optimal configuration for the CNN model by using DICE and ASD segmentation quality evaluation criteria, so that the segmentation quality can be maximized based on the goal criteria. The effectiveness of the proposed method has been evaluated using a database of infant MRI images and the results have been compared with previous methods. The results show that by using the proposed method, it is possible to segment NBI with higher quality and accuracy.

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Clément Bernard · Figshare · 2023

Source code for the visualisation plots of a benchmark of nine existing tools for the prediction of RNA 3D structures.

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Matthias Hannen, Vladimir Parashin, Cross Compiler et al. · 2016

Internals till 2005: Why change Architecture? • FE close to BE • common representation • structural complexity

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Otness, Karl, Gjoka, Arvi, Bruna, Joan et al. · Faculty Digital Archive (New York University Florence) · 2021

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ALBALWY, FAISAL · Figshare · 2022

Distributed software applications are one of the most important nowadays used applications. Rising demand has led to a rapid increase in the number and complexity of distributed software applications. Such applications are also more vulnerable to different types of attacks because of their distributed nature. Detecting and addressing attacks is an open issue concerning distributed software applications. This paper proposes a new mechanism that uses blockchain technology to devise a security testing mechanism to detect attacks on distributed software applications. The proposed mechanism can detect several categories of attacks like denial-of-service attacks, malware, and others. The process starts by creating a static blockchain that stores the software application sequence obtained using software testing techniques. This sequence information exposes weaknesses in the application code. When the application is executed, a dynamic blockchain helps create the static blockchain for recording the responses expected from the application. Every response should be validated using the proposed consensus mechanism associated with the static and dynamic blockchains. Valid responses indicate the absence of attacks, while invalid responses are indicative of attacks.

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Polepaka, Sanjeeva, R P, Ramkumar · Figshare · 2022

This repository contains the source code for the manuscript titled "<em>Comparison of image enhancement approaches and enhanced brain tumor localization through modified dynamic histogram equalization approach"</em>

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Xie, Pulin · Figshare · 2023

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Rahman, Irvanu · Figshare · 2024

Code for Lorenz Curve and Gini Index Calculation

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Vivekanandan Suresh Kumar, Varun, David Boulanger · OSF Preprints (OSF Preprints) · 2023

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Jacob Whitehill, Class Myclass · 2011

• Before studying the more complicated data structures, it is important to understand the relationship between a computer program and the memory on which it operates. • In the case of Java, this entails a discussion of the Java Virtual Machine (JVM). Wednesday, August 3, 2011“Real machines” • To appreciate “virtual machines”, let’s first consider how a source program is converted to an executable program on a “real machine”: • The programmer writes some source code. • He/she then compiles the source code into machine instructions that are specific to the particular hardware platform. Wednesday, August 3, 2011“Real machines” • If the programmer wants her program to run on 5 different hardware platforms, she needs to: • Write the source code only once (thank goodness!). • Compile the source code 5 times.

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