The increasing prevalence of high-speed trains necessitates robust analysis tools to ensure the safety and reliability of railway bridges. This paper presents a user-friendly software application designed for the dynamic analysis of railway bridges subjected to high-speed train loadings. Leveraging the semi-analytical modal method, the software offers a balanced approach that combines computational efficiency with high accuracy. Key features include an intuitive interface, rapid analysis capabilities, and reliable prediction of bridge responses, facilitating design optimization and maintenance planning. This software is poised to become an indispensable tool for structural engineers, researchers, and infrastructure planners.
FlowTransformer is a software framework tailored for building Machine Learning based Network Intrusion Detection Systems (NIDSs) leveraging transformer architectures known for their effectiveness in both NLP and more broadly for handling sequences of data. FlowTransformer is a flexible pipeline composed of a definable dataset definition, efficient preprocessing, and a flexible model construction, supporting different input-encodings, transformer models and classification heads. Furthermore, users can extend the framework by defining their own components. FlowTransformer’s contribution lies in its easy customisation, and ability to leverage transformers to enable enhanced long-term pattern detection, offering cybersecurity researchers and practitioners a valuable tool.
A quantum field lens coding simulator (QF-LCS) is presented on a high-level end-user application software run by CLI GUI with custom commands input by the user to process, analyze, validate QF-LC algorithm (QF-LCA) datasets in a QF-LC Python game. On the low-level system software, measurement data are acquired from quantum computers. The datasets contain these measurement data, processed and classified according to QF-LCA circuit design and steps determining system states and their prediction. This software, impacts advances made in applied sciences, statistics, law and physics, where data validation of samples including system simulation projecting and predicting events are achieved.
Despite its age, Fortran remains essential in many scientific fields. Ensuring code quality in long-term projects with evolving standards is critical, but few tools analyse Fortran, and they are not free. We present FortranAnalyser, a multi-platform, static analysis tool designed to enhance Fortran code quality. This paper outlines its development, features, and comparison with other tools. Additionally, we demonstrate its effectiveness through real-world applications, such as improving the Fortran code in a major global climate model.