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Background Digital volume correlation (DVC) of in-situ X-ray micro-computed tomography (μCT) images provides a powerful means to quantify internal deformation and damage characteristics of particulate composite materials under mechanical loading. However, accurately tracking structural evolution becomes more challenging when the material undergoes large deformations accompanied by crack formation and growth. Objective This study aims to improve the accuracy and robustness ofDVCanalysis in heavily damaged particulate composites by implementing a backward incremental DVC approach. Methods The internal response of a mock plastic-bonded explosive (PBX) composite was examined. The composite was fabricated by embedding IDOX crystals, 75–150 μm in size, within a polyurethane-based binder, Estane. A cylindrical specimen was subjected to unconfined compression, during which sequential μCT scans were acquired. In the backward DVC approach, correlations are performed in reverse, from the most deformed state back to the undeformed configuration through intermediate steps, using the conventional DVC framework. This method enhances displacement tracking fidelity in regions with severe cracking and interfacial failure. Results The backward incremental DVC approach provides improved resolution of displacement and deformation fields near crack-affected regions compared with conventional DVC methods. It enables detailed observation of interface delamination between grains and binder associated with crack initiation and coalescence. Conclusion The results demonstrate that the backward incremental DVC technique effectively characterizes complex deformation mechanisms in damaged particulate composites and provides valuable experimental data for validating high-fidelity numerical simulations that resolve grain-scale interactions. 

Integration of third-party SDKs are essential in the development of mobile apps. However, the rise of in-app privacy threat against mobile SDKs — called cross-library data harvesting (XLDH), targets social media/platform SDKs (called social SDKs) that handles rich user data. Given the widespread integration of social SDKs in mobile apps, XLDH presents a significant privacy risk, as well as raising pressing concerns regarding legal compliance for app developers, social media/platform stakeholders, and policymakers. The emerging XLDH threat, coupled with the increasing demand for privacy and compliance in line with societal expectations, introduces unique challenges that cannot be addressed by existing protection methods against privacy threats or malicious code on mobile platforms. In response to the XLDH threats, in our study, we generalize and define the concept of privacypreserving social SDKs and their in-app usage, characterize fundamental challenges for combating the XLDH threat and ensuring privacy in design and utilization of social SDKs. We introduce a practical, clean-slate design and end-to-end systems, called PESP, to facilitate privacy-preserving social SDKs. Our thorough evaluation demonstrates its satisfactory effectiveness, performance overhead and practicability for widespread adoption.