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Even with strong-column-weak-beam design requirements, story mechanisms have been observed in Moment Resisting Frames (MRF), resulting in concentrated drift demands that can result in severe structural damage to drift-sensitive components. Frame-Spine systems can redistribute demands with building height, but near-elastic higher-mode effects tend to contribute to floor accelerations, affecting damage to acceleration-sensitive nonstructural components. To mitigate this tradeoff, Force-Limiting Connections (FLCs) have been proposed to reduce accelerations through yielding components between the Frame and Spine, thereby limiting the magnitude of the forces. This study examines the sizing and placement of FLCs in a four-story Frame-Spine system using stochastic simulations. The T-shape yielding element dimensions in the FLC were modeled as random variables at each floor, and Monte Carlo simulations were used to explore their effect on drifts and accelerations. Results show the dominant role of the first-story FLC on balancing drifts and accelerations, while upper-story devices offered limited benefit. Design recommendations are provided to constrain first-story yielding element dimensions within effective bounds that reduce peak accelerations relative to the baseline Frame-Spine configuration. 

Emerging satellite networks integrated with terrestrial and aerial systems form a key part of next-generation infrastructures supporting the Internet of Everything (IoE). This chapter outlines the current status of PQC-based authentication in integrated Space-Aerial-Terrestrial Networks (SATIN), highlighting the technical challenges in achieving quantum-resilient security within constrained and complex environments. While quantum computing necessitates migration to post-quantum cryptography (PQC), existing standards often demand resources that are unsuited for SATIN’s limited hardware and fragile links. We analyze leading NIST PQC signature and key encapsulation schemes in the SATIN context, evaluating trade-offs in computational cost, signature size, and protocol compatibility. Emerging directions, including broader algorithm evaluations, advanced protocol integrations (e.g., EMSS and NIST-PQC with terrestrial backbone, PQ group key management), and some alternative PQ technologies are discussed. Addressing these challenges requires advanced simulation and experimental frameworks to enable scalable, practical, and quantum-resilient secure communications in future integrated networks. 

Microplastics are widespread in the environment, including in the bodies of freshwater fish, with their concentrations influenced by factors such as proximity to point sources, such as wastewater treatment plants (WWTPs), and trophic level. However, few studies have simultaneously assessed the combined effects of these factors on microplastic abundance in urban stream fish. To do so, we measured microplastics and assessed trophic level via N stable isotope (δ15N) content in 6 species of small-bodied fishes (species = Lepomis macrochirus Rafinesque, 1819, Neogobius melanostomus [Pallas, 1814], Fundulus notatus [Rafinesque, 1820], Pimephales notatus [Rafinesque, 1820], Notemigonus crysoleucas [Mitchill, 1814], and Dorosoma cepedianum [Lesueur, 1818]) collected upstream, at, and downstream of a large WWTP in Chicago, Illinois, USA. Additionally, we analyzed stomach contents for 2 of these species (L. macrochirus and N. melanostomus). Four of the six species exhibited δ15N enrichment at and downstream of the WWTP, indicating prolonged residence times at the study sites (i.e., several weeks). Stomach contents of the 2 species measured supported this pattern, showing high chironomid consumption at the WWTP and variable stomach contents elsewhere. For microplastics, 1 species had higher concentrations near the WWTP, but microplastic concentrations did not differ among locations in the other 5 species. We found no evidence of a relationship between δ15N enrichment and microplastic concentration. Overall, the stable isotope and stomach content results suggest a strong relationship of WWTP effluent with fish diet but not with microplastic concentrations in fish. The results suggest that microplastic concentrations in fish is are shaped by interacting, species-specific factors including behavior (i.e., movement and foraging) and physiology (i.e., egestion rates and feeding mechanisms), in addition to proximity to point sources. Our study highlights the complexity of microplastic infiltration into food webs and underscores the need for further research to disentangle the drivers of microplastic accumulation in aquatic organisms. 

Wildfires pose an escalating risk to communities and infrastructure, especially in regions undergoing increased fuel dryness and temperature extremes driven by climate change, as well as continued expansion into the wildland-urban interface (WUI). Probabilistic wildfire risk assessment provides a rigorous means of quantifying potential impacts, but its application is often hindered by the high computational cost of working with hundreds of thousands of complex wildfire scenarios. This study introduces a novel scenario reduction framework tailored to the unique characteristics of wildfire hazards, which often lack standard intensity metrics and exhibit highly nonlinear, spatially distributed behavior. The proposed framework selects a subset of scenarios that best represent the spatial and statistical diversity of the full dataset, thereby greatly reducing computational costs while accounting for uncertainties. This is achieved by mapping complex wildfire scenarios into a high-dimensional feature space, enabling similarity assessments based on spatial consequence patterns rather than standard intensity metrics. A k-medoids clustering approach is then used to identify a representative subset of scenarios, while an active-learning-based outlier selection procedure incorporates rare but high-impact events without inflating computational demands. The framework was first demonstrated using a simple illustrative example to show how its performance responds to different data characteristics. To further demonstrate the practicality of the framework, it was used for wildfire risk assessment in Spokane County, Washington, where the full dataset (1000 scenarios) was reduced to 41 representative scenarios while preserving the spatial patterns of burn probability and building damage with high fidelity. The results demonstrated that the framework significantly improves computational efficiency and accuracy compared to traditional scenario reduction methods, offering a scalable and flexible tool for probabilistic wildfire risk assessment.