Software Impacts最新文献

The software ‘Particle Droplet Population Balance Simulation’ (Particle-Droplet-PBS) simulates the time of particle-shell formation and particle growth in burning spray droplets. Application examples are provided to demonstrate how the code can be used to incorporate a variety of particles, precursor chemicals and solvents. The software solves the population balance equations for particle coagulation and nucleation, while applying an adaptive grid method requiring only two cells per droplet. Thus, the computation time is drastically reduced. This enables users without access to high-performance work stations to run droplet simulations in a timely manner, making it a valuable tool for research and process engineering.

René Thom’s work on topological instabilities applied new methods to questions of dynamical stability that traditionally belonged to the domain of dynamical systems theorists. Topological instability focuses on universal properties of bifurcations in systems where multiple equilibria form and disappear as a function of system parameters. However, the complete mathematical description is quite abstract and the analysis benefits from graphical intuitions. Here we provide the code, in the form of a Mathematica notebook, used in our recent Games and Economic Behaviour paper (Harriset al., 2023). It illustrates our main results providing the intuition necessary to explore the bifurcations in the formal proofs.

Classification of sentences is a challenging problem in the field of natural language processing. We present a prototype for classifying the pairs of sentences using their syntactic and semantic relations. The sentences are classified into three classes — entailment, contradiction and neutral. The grammatical relations of the sentences are extracted and these relations are represented as word embeddings using global vector, Glove. The word to word semantics is found using Wordnet semantic relations. The prototype is based on statistical measures which was implemented using Python 3.6.9 version.

Over conventional statistical models, machine learning mechanisms are establishing themselves as a potential area for modeling and forecasting complex time series. Because it can integrate several forecasting methodologies’ capabilities, hybrid time series models are fundamental in data science. Here, we present a Python script that builds a combined architecture of the ARIMA-LSTM model with random forest technique to generate a high-accuracy prediction. This script is a step-by-step process to create a statistical and then machine learning model through statistical assumption.

The article presents an open-access code, written in CUDA® and C++ programming language, applicable for generating computational models of nanostructured surface coatings deposited by electrodeposition. The code uses the Schrödinger equation, energy potentials, and electrochemistry as a theoretical basis to determine the deposition and electrodeposition energies, allowing the prediction of the formation and growth of these coatings. Likewise, the parameter variation enabled within the code provides for determining the main electrodeposition parameters (voltage, current, concentration, and residence time) for experimental depositions. The code can be easily implemented for any metallic coating-substrate arrangement where the filler material is nanomaterials.

The paper introduces ACS-IoT, an Anomaly Classification System for IoT networks, structured as a two-tiered framework. In the first, it employs a decision tree classifier for anomaly detection. In the second, a CNN-BiLSTM model is utilized for more profound analysis and classification of anomaly types. To address data imbalance, SMOTE is used, and feature selection is enhanced with PSO. The approach showcases strong practical applicability in real-world industrial settings, achieving an accuracy of 88%, precision of 89%, recall of 88%, and F1-score of 88% for multi-class classification, surpassing other machine learning approaches by at least 6% in all metrics.

SPARC is an accurate, efficient, and scalable real-space electronic structure code for performing ab initio Kohn–Sham density functional theory calculations. Version 2.0.0 of the software provides increased efficiency, and includes spin–orbit coupling, dispersion interactions, and advanced semilocal as well as hybrid exchange–correlation functionals, where it outperforms state-of-the-art planewave codes by an order of magnitude and more, with increasing advantages as the number of processors is increased. These new features further expand the range of physical applications amenable to first principles investigation.

This paper presents a scalable framework for modeling floc evolution and flocculation kinetics in water treatment. Unlike the existing methods that subjects Non-intrusive Dynamic Image Analysis (NiDIA) data to complex mathematical concepts, the proposed software devised a scaling concept for NiDIA data and designed an effective algorithm with the capability to predict varying floc lengths and the underlying kinetics under a broad flocculation conditions ($\text{G}f$ and $\text{T}f$). Technically, the designed machine-intelligence framework (MI-NiDIA) involves data preprocessing, automatic parameter selection, validation and prediction of floc length evolution with metrics. For instance, MI-NiDIA-MLP recorded $R^2$ of 0.95–1.0 for varying floc length at $\text{G}f60s^{-1}$.

Raster Forge is a Python library and graphical user interface for raster data manipulation and analysis. The tool is focused on remote sensing applications, particularly in wildfire management. It allows users to import, visualize, and process raster layers for tasks such as image compositing or topographical analysis. For wildfire management, it generates fuel maps using predefined models. Its impact extends from disaster management to hydrological modeling, agriculture, and environmental monitoring. Raster Forge can be a valuable asset for geoscientists and researchers who rely on raster data analysis, enhancing geospatial data processing and visualization across various disciplines.

This paper presents MR-LEAP (Mixed-Reality Learning Environment for Aspirational Programmers), a framework developed for learning programming through Mixed Reality and gamification mechanics. MR-LEAP’s architecture is designed to facilitate the understanding of basic programming concepts while allowing the gradual incorporation of more complex concepts. The framework provides a simple visual level editor. MR-LEAP is supported by the Mixed Reality Toolkit framework to promote portability to new Mixed Reality devices. Our goal is to facilitate programming education using Mixed Reality technology. MR-LEAP has already been used in both research and educational.