To exploit the full potential of advanced high-strength steels (AHSS), a more in-depth understanding of the complex micromechanical interaction of thin-film retained austenite (RA) and lath martensite is indispensable. Inspired by the medium-Mn steel microstructure, a three-dimensional micromechanical modeling approach is therefore proposed in the present work, embedding the thin RA films explicitly into the hierarchical lath martensite structure. This enables systematic studies of the effect of RA film thickness and volume fraction on the local stresses and strains as well as their partitioning within the microstructure. The investigations reveal that with shrinking RA volume fraction, both stress and especially strain heterogeneity in the thin-film RA intensifies. In the martensite blocks, stress and strain heterogeneity also intensifies, although stresses are generally more heterogeneously, and strains much more homogeneously, distributed than in RA. The results underline the key role of RA with thin-film morphology for further optimizing AHSS microstructures.

Multimodal mobility systems provide seamless travel by integrating different types of transportation modes. Most existing studies model service operations and users’ travel choices independently or iteratively and constrained with pre-defined multimodal travel options. The paper proposes a choice-based optimization approach that optimizes service operations with explicitly embedded travelers’ choices described by the multinomial logit (MNL) model. It allows the flexible combination of travel modes and routes in multimodal mobility systems. We propose a computationally efficient linearization method for transformed MNL constraints with bounded errors to solve the choice-based optimization model. The model is validated using a mobility on demand and public transport network by comparing it with a simulation sampling-based MNL linearization method. The results show that the mixed-integer formulation provides a high-quality solution in terms of both the estimated choice probability errors and computational speed. We also conduct an error analysis and a sensitivity analysis to explore the behavior of the proposed approach. The real-world case study in Stockholm further illustrates that the analytical formulation achieves a better system operation performance than the traditional iterative supply–demand updating optimization method. The choice-based optimization model and solution formulation are highly adaptable for operations decision support integrating stochastic travel choices in multimodal mobility systems.

Erosion-corrosion experiments were carried out a chromia-forming steel (316L) alumina-forming ferritic, austenitic and martensitic steels and coated 316L at 480-520 °C in liquid lead. Testing was done under low oxygen conditions (10-7-10-8 wt.% O) for times up to 1392 hours using a purpose-built Erosion Corrosion rig (ECO). It was found that uncoated 316L suffered from Ni dissolution to a depth of 140 µm and severe erosion-corrosion attack. After coating with alumina oxide via Detonation Gun (DG) and Pack Cementation (PC) methods, the 316L remained unaffected. The commercial alumina forming alloys containing multiple reactive elements, Kanthal EF 100, Alkrothal 14 and Kanthal APMT, performed well and were minimally affected by erosion-corrosion. However, Kanthal AF, which contains only the single reactive element Y, lost a similar amount of mass as the 316L sample. The experimental alumina forming austenitic alloy denoted AFA 3 showed very poor resistance to erosion-corrosion, suffering from severe mass loss and with signs of Ni dissolution to a depth of 25 µm. The experimental alumina-forming martensitic steel, AFM, on the other hand, remained unaffected by erosion-corrosion. Hydrodynamic simulations were carried out using ANSYS FLUENT to determine the relative velocity between the HLM and the samples, calculating the highest velocity to be 9.9 m/s. It also demonstrated a good qualitative alignment between the experimental result and the simulations. This indicates that the erosion damage originated from a combination of the turbulence created inside the ECO-rig and particle erosion.

The growing complexity and interconnectivity of modern systems have made the proactive identification and mitigation of vulnerabilities increasingly challenging. DTs, which offer dynamic and real-time virtual representations of physical systems, have emerged as a potential solution to enhance cybersecurity. However, several significant challenges exist, including the integration of DTs with existing cybersecurity frameworks and the effective use of DTs to detect and mitigate zero-day vulnerabilities. In this study, we conduct a systematic literature review to explore these challenges by addressing four key research questions. Our findings reveal critical challenges and open research problems associated with the implementation of DTs for cybersecurity. These insights are essential for advancing the security of complex systems, paving the way toward building resilient and secure cyber-infrastructures, enabling real-time assessment, effective remediation actions and cyber perception on emerging threats.

In the context of human-centric smart manufacturing, human-robot collaboration (HRC) systems leverage the strengths of both humans and machines to achieve more flexible and efficient manufacturing. In particular, estimating and monitoring human motion status determines when and how the robots cooperate. However, the presence of occlusion in industrial settings seriously affects the performance of human pose estimation (HPE). Using more sensors can alleviate the occlusion issue, but it may cause additional computational costs and lower workers' comfort. To address this issue, this work proposes a visual-inertial fusion-based method for HPE in HRC, aiming to achieve accurate and robust estimation while minimizing the influence on human motion. A part-specific cross-modal fusion mechanism is designed to integrate spatial information provided by a monocular camera and six Inertial Measurement Units (IMUs). A multi-scale temporal module is developed to model the motion dependence between frames at different granularities. Our approach achieves 34.9 mm Mean Per Joint Positional Error (MPJPE) on the TotalCapture dataset and 53.9 mm on the 3DPW dataset, outperforming state-of-the-art visual-inertial fusion-based methods. Tests on a synthetic-occlusion dataset further validate the occlusion robustness of our network. Quantitative and qualitative experiments on a real assembly case verified the superiority and potential of our approach in HRC. It is expected that this work can be a reference for human motion perception in occluded HRC scenarios.