PySPH is an open-source, Python-based, framework for particle methods in general and Smoothed Particle Hydrodynamics (SPH) in particular. PySPH allows a user to define a complete SPH simulation using pure Python. High-performance code is generated from this high-level Python code and executed on either multiple cores, or on GPUs, seamlessly. It also supports distributed execution using MPI. PySPH supports a wide variety of SPH schemes and formulations. These include, incompressible and compressible fluid flow, elastic dynamics, rigid body dynamics, shallow water equations, and other problems. PySPH supports a variety of boundary conditions including mirror, periodic, solid wall, and inlet/outlet boundary conditions. The package is written to facilitate reuse and reproducibility. This article discusses the overall design of PySPH and demonstrates many of its features. Several example results are shown to demonstrate the range of features that PySPH provides.

Compyle allows users to execute a restricted subset of Python on a variety of HPC platforms. It is an embedded domain-specific language (eDSL) for parallel computing. It currently supports multi-core execution using Cython, and OpenCL and CUDA for GPU devices. Users write code in a restricted subset of Python that is automatically transpiled to high-performance Cython or C. Compyle also provides a few very general purpose and useful parallel algorithms that allow users to write code once and have them run on a variety of HPC platforms.In this article, we show how to implement a simple two-dimensional molecular dynamics (MD) simulation package in pure Python using Compyle. The result is a fully parallel program that is relatively easy to implement and solves a non-trivial problem. The code transparently executes on multi-core CPUs and GPGPUs allowing simulations with millions of particles. A 3D MD code is also provided and compares very favorably with a well known, open source, molecular dynamics package.

Counterfeit products have become a major issue in the global market, causing significant economic losses to businesses and health and safety risks to consumers. Blockchain technology has the potential to address this problem by creating a secure and immutable record of the origin and movement of products. This paper explores the use of blockchain technology for identifying counterfeit products. We examine the characteristics of blockchain technology, its benefits, and limitations. We also discuss the current approaches used for product identification and how blockchain technology can be used to enhance these methods. We present a case study on how blockchain technology can be implemented to identify counterfeit pharmaceuticals, one of the most critical areas of concern. The results show that blockchain technology can be a useful tool in identifying counterfeit products, as it provides an immutable record of product provenance, enhances traceability, and reduces the risk of fraud.

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