This paper presents a method that utilizes graph theory and state modelling algorithms to perform automatic complexity analysis of the architecture of cyber-physical systems (CPS). It describes cyber physical systems risk assessment (CPSRA), a tool to provide automatic decision support for enhancing the overall resilience of CPS architectures often used in critical infrastructures. CPRSA is built to enhance industrial risk assessment and improve the resilience of CPS architecture against malicious attacks on the cyber domain that can affect industrial processes, which is critical in a distributed cyber environment. Such attacks often compromise execution states on physical components and lead to hazards or even disasters through plant malfunction. CPSRA is tested against a real-world testbed model of a large SCADA system that is infused with real-world CVE vulnerabilities in some of its components. The tool creates an isomorphic graph of the CPS process model and uses graph algorithms and network analytics on the model to test cyber-attacks and evaluate attack resilience aspects. The tool’s output is then used to pinpoint high-complexity components in terms of influence on the overall CPS architecture and suggest mitigation points for security measure implementation while considering every potential subattack path and subliminal path on the model’s attack graph. The paper complements standardized assessment reports and contributes to automatic architecture assessment for critical infrastructure environments and can be used as the basis to model dependencies and threat propagation in larger digital twins, a need outlined in major NIST publications concerning the security of industrial systems that was previously done manually, without automatic insight into state and vulnerability influences.

A decision support system (DSS) was developed that outputs suggestions for socket-rectification actions to the prosthetist, aiming at improving the fitness of transfemoral prosthetic socket design and reducing the time needed for the final socket design. For this purpose, the DSS employs a fuzzy-logic inference engine (IE) which combines a set of rectification rules with pressure measurements generated by sensors embedded in the socket, for deciding the rectification actions. The latter is then processed by an algorithm that receives, manipulates and modifies a 3D digital socket model as a triangle mesh formatted inside an STL file. The DSS results were validated and tested in an FEA simulation environment, by simulating and comparing the donning process among a good-fitting socket, a loose socket (poor-fit) and several rectified sockets produced by the proposed DSS. The simulation results indicate that volume reduction improves the pressure distribution over the stump. However, as the intensity of socket rectification increases, i.e., as volume reduction increases, high pressures appear in other parts of the socket which generate discomfort. Therefore, a trade-off is required between the amount of rectification and the balance of the pressure distributions experienced at the stump.

Lower limb prostheses offer a solution to restore the ambulation and self-esteem of amputees. One key component is the prosthetic socket that serves as the interface between prosthetic device and amputee stump and thereby has a wide range of impacts on efficient fitting, appropriate load transmission, operational stability, and control. For the design and optimization of a prosthetic socket, an understanding of the actual intra-socket operational conditions becomes therefore necessary. This is however a difficult task due to the inherent complexity and restricted observability of socket operation. In this study, an innovative mechatronics-twin framework that integrates advanced biomechanical models and simulations with physical prototyping and dynamic operation testing for effective exploration of operational behaviors of prosthetic sockets with amputees is proposed. Within this framework, a specific Stewart manipulator is developed to enable dynamic operation testing, in particular for a well-managed generation of dynamic intra-socket loads and behaviors that are otherwise difficult to observe or realize with the real amputees. A combination of deep learning and Bayesian Inference algorithms is then employed for analyzing the intra-socket load conditions and revealing possible anomalous.