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Natural disasters frequently inflict severe damage to the built environment, which demands a rapid, reliable, and cost-effective damage assessment for emergency response. However, traditional methods for post-disaster damage assessment often rely on static, labor-intensive data collection strategies that can be prohibitively expensive and struggle to adapt to dynamic post-disaster conditions. In this study, we propose a cost-aware Bayesian optimization framework combined with level-set estimation that continuously guides autonomous data collectors, e.g., an unmanned aerial vehicle (UAV), toward the most informative regions. By dynamically updating damage estimates across different geographic zones, our approach systematically reduces uncertainty while minimizing operational costs. The proposed framework is first validated using a controlled synthetic toy study, demonstrating the agent’s ability to efficiently trace damage boundaries, recover the underlying damage map, and rapidly reduce predictive uncertainty. Furthermore, the approach is evaluated using high-fidelity disaster data generated by the Regional Resilience Determination (R2D) software. The results of the algorithm provide accurate and timely damage estimates that support an informed and fast emergency response. 

Natural disasters frequently inflict severe damage to the built environment, which demands a rapid, reliable, and cost-effective damage assessment for emergency response. However, traditional methods for post-disaster damage assessment often rely on static, labor-intensive data collection strategies that can be prohibitively expensive and struggle to adapt to dynamic post-disaster conditions. In this study, we propose a cost-aware Bayesian optimization framework combined with level-set estimation that continuously guides autonomous data collectors, e.g., an unmanned aerial vehicle (UAV), toward the most informative regions. By dynamically updating damage estimates across different geographic zones, our approach systematically reduces uncertainty while minimizing operational costs. The proposed framework is first validated using a controlled synthetic toy study, demon- strating the agent’s ability to efficiently trace damage boundaries, recover the underlying damage map, and rapidly reduce predictive uncertainty. Furthermore, the approach is evaluated using high-fidelity disaster data generated by the Regional Resilience Determination (R2D) software. The results of the algorithm provide accurate and timely damage estimates that support an informed and fast emergency response. 

Constraining proximity-based drugs, such as proteolysis targeting chimeras (PROTACs), into their bioactive conformation can significantly impact their selectivity and potency. However, traditional methods for achieving this often involve complex and time-consuming synthetic procedures. Here, we introduced an alternative approach by demonstrating DNA-templated spatially controlled PROTACs (DTACs), which leverage the programmability of nucleic acid-based self-assembly for efficient synthesis and offer precise control over inhibitors’ spacing and orientation. The resulting constructs revealed distance- and orientation-dependent selectivity and degradation potency for the Cyclin D1–CDK4/6 protein complex in cancer cells. Notably, the optimal construct DTAC-V1 demonstrated unprecedented synchronous degradation of the entire Cyclin D1–CDK4/6 complex, leading to robust G1-phase cell cycle arrest and effective inhibition of cancer cell proliferation. Furthermore, in a xenograft mouse model, DTAC-V1 exhibited potent therapeutic efficacy by effectively degrading Cyclin D1–CDK4/6 and suppressing tumor growth, underscoring its potential as an anticancer agent. Overall, our findings demonstrate the feasibility of DTAC as a rapid, scalable, and modular platform for the spatial control of functional inhibitors for optimal effectiveness, making it a promising method for proximity-based therapeutics.