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The World Health Organization estimated that 8 million adults between 15 and 49 years old acquired syphilis globally in 2022. China CDC reported that there were 530 116 cases of syphilis in mainland China in 2023. Since syphilis is a sexually transmitted disease and age structure of the host population plays a crucial role, in this series of two papers we develop an age-structured model with four infection stages (primary, secondary, latent and tertiary) to study the transmission dynamics of syphilis. In part I (Wuet al. 2025Proc. R. Soc. A481: 20240218 (doi:10.1098/rspa.2024.0218)), we investigated the well-posedness of the model and studied stability of the steady states. In part II, first, we consider the optimal control of the age-structured model. Second, utilizing the Markov Chain Monte Carlo method, we calibrate the reported syphilis data in China by using a demographic model. Finally, we apply the relevant simulation results to numerically simulate the age-structured model. Our results indicate that (i) for the syphilis demographic model, the basic reproduction number ${\mathcal{R}}_0\approx 2.4876$with CI (95%) (1.6823, 3.1434); (ii) tertiary stage infection is more severe in the elderly population; (iii) reducing the number of secondary and latent stage syphilis individuals can effectively reduce the total number of infected populations. 

Abstract Global Navigation Satellite System (GNSS) is pervasively used in position, navigation, and timing (PNT) applications. As a consequence, important assets have become vulnerable to intentional attacks on GNSS, where of particular relevance is spoofing transmissions that aim at superseding legitimate signals with forged ones in order to control a receiver’s PNT computations. Detecting such attacks is therefore crucial, and this article proposes to employ an algorithm based on deep learning to achieve the task. A data-driven classifier is considered that has two components: a deep learning model that leverages parallelization to reduce its computational complexity and a clustering algorithm that estimates the number and parameters of the spoofing signals. Based on the experimental results, it can be concluded that the proposed scheme exhibits superior performance compared to the existing solutions, especially under moderate-to-high signal-to-noise ratios. 

We consider the problem of resource provisioning for real-time cyber-physical applications in an open system environment where there does not exist a global resource scheduler that has complete knowledge of the real-time performance requirements of each individual application that shares the resources with the other applications. Regularity-based Resource Partition (RRP) model is an effective strategy to hierarchically partition and assign various resource slices among such applications. However, previous work on RRP model only discusses uniform resource environment, where resources are implicitly assumed to be synchronized and clocked at the same frequency. The challenge is that a task utilizing multiple resources may experience unexpected delays in non-uniform environments, where resources are clocked at different frequencies. This paper extends the RRP model to non-uniform multi-resource open system environments to tackle this problem. It first introduces a novel composite resource partition abstraction and then proposes algorithms to construct and reconfigure the composite resource partitions. Specifically, theAcyclic Regular Composite Resource Partition Scheduling (ARCRP-S)algorithm constructs regular composite resource partitions and theAcyclic Regular Composite Resource Partition Dynamic Reconfiguration (ARCRP-DR)algorithm reconfigures the composite resource partitions in the run time upon requests of partition configuration changes. Our experimental results show that compared with state-of-the-art methods, ARCRP-S can prevent unexpected resource supply shortfall and improve the schedulability up to 50%. On the other hand, ARCRP-DR can guarantee the resource supply during the reconfiguration with moderate computational overhead.