GSRC, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria.
Community Coordinated Modeling Center, Greenbelt, Maryland, United States of America.
PLoS One. 2023 Oct 5;18(10):e0292217. doi: 10.1371/journal.pone.0292217. eCollection 2023.
Complex systems such as the global climate, biological organisms, civilisation, technical or social networks exhibit diverse behaviours at various temporal and spatial scales, often characterized by nonlinearity, feedback loops, and emergence. These systems can be characterized by physical quantities such as entropy, information, chaoticity or fractality rather than classical quantities such as time, velocity, energy or temperature. The drawback of these complexity quantities is that their definitions are not always mathematically exact and computational algorithms provide estimates rather than exact values. Typically, evaluations can be cumbersome, necessitating specialized tools. We are therefore introducing ComsystanJ, a novel and user-friendly software suite, providing a comprehensive set of plugins for complex systems analysis, without the need for prior programming knowledge. It is platform independent, end-user friendly and extensible. ComsystanJ combines already known algorithms and newer methods for generalizable analysis of 1D signals, 2D images and 3D volume data including the generation of data sets such as signals and images for testing purposes. It is based on the framework of the open-source image processing software Fiji and ImageJ2. ComsystanJ plugins are macro recordable and are maintained as open-source software. ComsystanJ includes effective surrogate analysis in all dimensions to validate the features calculated by the different algorithms. Future enhancements of the project will include the implementation of parallel computing for image stacks and volumes and the integration of artificial intelligence methods to improve feature recognition and parameter calculation.
复杂系统，如全球气候、生物有机体、文明、技术或社交网络，在各种时间和空间尺度上表现出多样的行为，通常具有非线性、反馈回路和涌现等特征。这些系统可以用熵、信息、混沌或分形等物理量来描述，而不是用时间、速度、能量或温度等经典物理量。这些复杂性物理量的缺点是，它们的定义并不总是数学上精确的，计算算法只能提供估计值而不是精确值。通常，这些评估方法繁琐，需要专门的工具。因此，我们引入了 ComsystanJ，这是一款新颖且用户友好的软件套件，为复杂系统分析提供了一套全面的插件，而无需事先具备编程知识。它具有平台独立性、面向终端用户的友好性和可扩展性。ComsystanJ 结合了已经成熟的算法和较新的方法，可用于 1D 信号、2D 图像和 3D 体积数据的通用分析，包括生成用于测试目的的数据集，如信号和图像。它基于开源图像处理软件 Fiji 和 ImageJ2 的框架。ComsystanJ 插件可记录宏并作为开源软件维护。ComsystanJ 在所有维度上都包含有效的替代分析，以验证不同算法计算得出的特征。该项目的未来增强将包括为图像堆栈和体积实施并行计算，并集成人工智能方法，以提高特征识别和参数计算的效率。
