Software Impacts最新文献

For fibre-reinforced composites, most of their mechanical properties is tied to the fibre scale. Thus, imaging-based characterisation demands resolving fibres to characterise these materials accurately. However, high resolutions limit the field of view and lead to lengthy acquisition times. Emerging non-destructive imaging technologies and algorithms now accurately provide fibre orientations without detecting individual fibres. Studies show that voxel sizes up to fifteen times the fibre diameter are feasible, still allowing accurate tensile modulus predictions. Our presented software incorporates sub-voxel fibre orientation distributions using ultra-low-resolution three-dimensional X-ray tomography data in a numerical model, providing an effective method for characterising these materials.

This article describes a software suite designed for systematic and automatic literature reviews. Actually, we used these tools to help us build our systematic literature review on our research topic: biometric authentication using PPG (photoplethysmography). However, our tools can be easily applied (and have been applied) to other fields, whether scientific (state of the art) or technological (technology watch). Our software suite is made of two softwares. The first one helps to extract, merge and filter all the references in the pdf file versions of the papers. The second one helps to make statistical analysis on the dataset created for the SLR.

Accurate multiphysics simulations of particle-formed materials are required in various engineering applications. The Discrete Element Method (DEM) stands out for its ability to capture the complex behavior of such materials. However, the multitude of particle interaction models and parameters involved pose challenges for analysts and researchers. DEMLab addresses this issue as an open-source MATLAB software that offers a user-friendly environment for exploring DEM models in small to medium-scale problems. With a modular and extensible code, it facilitates immediate implementations for testing different formulations or developing new ones. This paper details the software capabilities and how it impacts academic activities.

Understanding the function of sleep and its associated neural rhythms is an important goal in neuroscience. While many theoretical models of neural dynamics during sleep exist, few include the effects of neuromodulators on sleep oscillations and describe transitions between sleep and wake states or different sleep stages. Here, we started with a C++-based thalamocortical network model that describes characteristic thalamic and cortical oscillations specific to sleep. This model, which includes a biophysically realistic description of intrinsic and synaptic channels, allows for testing the effects of different neuromodulators, intrinsic cell properties, and synaptic connectivity on neural dynamics during sleep. We present a complete reimplementation of this previously-published sleep model in the standardized NEURON/Python framework, making it more accessible to the wider scientific community.

The BlockSupply project is a pioneering initiative that seeks to redefine supply chain management by leveraging blockchain technology to ensure real-time monitoring of product movements and enhance transparency, security, and traceability. The software offers key functionalities such as creating a private blockchain network, integrating tracking sensors for real-time data collection, secure recording of product data, user authentication, authorization management, and alerts for abnormal events. It is developed using technologies like Web3.js, React.js, Solidity, Ganache, and GitHub, which are integrated into the project’s architecture to create an efficient and secure blockchain-based supply chain solution. Furthermore, the project’s impact is expected to be significant, contributing to scientific advancements in secure and efficient supply chain operations and reshaping the landscape of logistics traceability. As the software gains traction in real-world scenarios, its transformative influence on data reliability, security, and operational efficiency in supply chain research is poised to be showcased in publications.

General Data Protection Regulation (GDPR) is compulsory for processing personally identifiable data (PID) across Europe after 2018. Specifically when sharing research data, we cannot process PID without a legal basis defined by GDPR. To facilitate this, we present a scalable and interoperable automated Contract Compliance Verification (CCV) tool that enables GDPR-compliant contract management and data sharing. With the implementation of two scenarios in smart cities and insurance domains, we show how CCV is helpful to process PID and make data collection and integration (e.g., from crowdsourcing) easier.

Triply Periodic Minimal Surfaces (TPMS) are parametrically generated lattices with topology-driven properties. They are emerging as a significant innovation, finding wide-ranging applications. This paper introduces LattGen, a software solution that addresses the complexity of designing these surfaces. This tool allows users to easily customize their desired surfaces’ size, volume fraction, grading, and hybridization. LattGen also allows for the import of arbitrary domains and the export to standard computer-aided design platforms for additive manufacturing. This design platform automates the creation of porous structures, making them a valuable tool in various scientific, engineering, and creative fields.

An intrusion detection system (IDS) is critical in protecting organizations from cyber threats. The susceptibility of Machine Learning and Deep Learning-based IDSs against adversarial attacks arises from malicious actors’ deliberate construction of adversarial samples. This study proposes a Python-based open-source code repository named IDS-Anta with a robust defense​ mechanism to identify adversarial attacks without compromising IDS performance. It uses Multi-Armed Bandits with Thomson Sampling, Ant Colony Optimization (ACO), and adversarial attack generation methods and is validated using three public benchmark datasets. This code repository can be readily applied and replicated on IDS datasets against adversarial attacks.

This paper introduces ASReview Makita, a tool designed to enhance the efficiency and reproducibility of simulation studies in systematic reviews. Makita streamlines the setup of large-scale simulation studies by automating workflow generation, repository preparation, and script execution. It employs Jinja and Python templates to create a structured, reproducible environment, aiding both novice and expert researchers. Makita’s flexibility allows for customization to specific research needs, ensuring a repeatable research process. This tool represents an advancement in the field of systematic review automation, offering a practical solution to the challenges of managing complex simulation studies.

The Luria–Delbrück fluctuation assay is an essential experiment in calculating mutation rates, especially in genetic and mutation research. Its reliability and accuracy have made it the go-to method for numerous researchers. In this article, we provide an R-code that statistically analyzes the assay results more easily and offers the most challenging code for calculating 95% confidence intervals based on the gold standard method “Ma–Sandri–Sarkar Maximum Likelihood.” Recently, the maximization of the likelihood function through optimization functions in R can be a challenging task. The recursive format of the likelihood function is known to cause memory stack issues. Our findings indicate that utilizing a non-recursive version of the function can increase the tractability of the maximization process. With this code, future scientists can unlock valuable statistical insights related to the biological mechanisms that drive genetic variation and can, therefore, contribute to developing novel therapeutic interventions and innovative solutions to various biological and medical challenges.