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The use of digital twins to represent a product or process digitally is trending in many engineering disciplines. This term has also been recently introduced in the medical field. In arthroscopic surgery education, the paradigm shift from apprenticeship to simulation training has driven the need for better simulators, and the current focus is on improving simulators with respect to computational efficiency and system accuracy. However, expanding surgical simulations towards digital twins has not yet been explored. This paper introduces the digital twin concept for arthroscopic surgery, and explores its potential in light of the existing scientific literature. Thus, a systematic review was conducted to summarize and analyze the literature with respect to fast and robust design of an arthroscopic digital twin using patient-specific information, and methods for interactive surgical soft tissue simulation. The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol with three reliable scientific search engines: IEEE Xplore, ScienceDirect and PubMed. Eighty papers were included in the review, and the extracted data included modeling methods, tissue types, constitutive behavior, computational efficiency or accuracy, hardware configuration, haptic device description, software tools, and system architectures. Considering the review, a novel macro-level conceptual arthroscopic digital twin system is presented, and the applicability of the review findings for the identified subsystems are discussed. The proposed system integrates patient-specific images, diagnostic data, intraoperative sensor data, and surgical practice as inputs, and conceptually enables surgical skills training, preoperative planning, and a database of virtual surgeries.

In handling complex power system simulation tasks, semi-analytical solution (SAS) methods have proven to be numerically robust and computationally efficient. They provide a competitive alternative to traditional numerical approaches. Still, there is inadequate power system simulation software, especially the open-source tools, that implements this technology. This paper introduces PowerSAS.m, an open-source toolbox that closes this gap by providing SAS baseline simulation options for power system steady-state and dynamic simulations. At its core, it implements a novel SAS method and encloses various heuristics and simulation techniques to ensure enhanced computational performance. In case studies, we verify PowerSAS.m in benchmarking comparisons and demonstrate its functionalities in grid analysis scenarios.

Computer simulation has been an effective experimental method to study the mechanism of thrombosis. As a computational model is the core of the simulation, a hybrid computational model which focuses on improving the modelling of thrombus growth is proposed in this paper. In the model, the process of thrombus growth is implemented by using Level Set Method that remains the particle morphology while converting particles into continuum materials. Furthermore, the model is constructed on a novel Adaptive Mesh Refinement strategy that enhances the efficiency of calculation and data storage. The proposed model is used in the simulation experiments of platelet thrombus formation in stenotic arteries to gained the influences of stenosis and blood flow velocity for thrombosis which are consistent with in vivo experiments. The simulations show that compared to refining the grid containing thrombus uniformly, the local level set based adaptive refinement strategy performs wall in reducing cell data.