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High-quality strategic planning of autonomous mobility-on-demand (AMOD) systems is critical for the success of the subsequent phases of AMOD system implementation. To assist in strategic AMOD planning, we propose a dynamic and flexible flow-based model of an AMOD system. The proposed model is computationally fast while capturing the state transitions of two coordinated flows (i.e. co-flows): the AMOD service fleet vehicles and AMOD customers. Capturing important quantity dynamics and conservations through a system of ordinary differential equations, the model can economically respond to a large number and a wide range of scenario-testing requests. The paper illustrates the model efficacy through a basic example and a more realistic case study. The case study envisions replacing Manhattan's existing taxi service with a hypothetical AMOD system. The results show that even a simple co-flow model can robustly predict the systemwide AMOD dynamics and support the strategic planning of AMOD systems. 

We propose using surface and aerial shared autonomous electric vehicles (SAEVs) to improve the resilience of infrastructure and communities, or SAEV-R. In disruptive events, SAEVs can be temporarily deployed to evacuate and rescue at-risk populations, provide essential supplies and services to vulnerable households, and transport repair crews and equipment. We present a modeling framework for feasibility analysis and strategic planning associated with deploying SAEVs for disaster relief. The framework guides our examination of three scenarios: a hurricane-induced power outage, a pandemic-affected vulnerable population, and earthquake-damaged infrastructure. The results demonstrate the flexibility of the proposed framework and showcase the potential and versatility of SAEV-R systems to improve resilience. 

Impaired glucose metabolism in diabetes causes severe acute and long‐term complications, making real‐time detection of blood glucose indispensable for diabetic patients. Existing continuous glucose monitoring systems are unsuitable for long‐term clinical glycemic management due to poor long‐term stability. Polymer dot (Pdot) glucose transducers are implantable optical nanosensors that exhibit excellent brightness, sensitivity, selectivity, and biocompatibility. Here, it is shown that hydrogen peroxide—a product of glucose oxidation in Pdot glucose sensors—degrades sensor performance via photobleaching, reduces glucose oxidase activity, and generates cytotoxicity. By adding catalase to a glucose oxidase‐based Pdot sensor to create an enzymatic cascade, the hydrogen peroxide product of glucose oxidation is rapidly decomposed by catalase, preventing its accumulation and improving the sensor's photostability, enzymatic activity, and biocompatibility. Thus, a next‐generation Pdot glucose transducer with a multienzyme reaction system (Pdot–GOx/CAT) that provides excellent sensing characteristics as well as greater detection system stability is presented. Pdot glucose transducers that incorporate this enzymatic cascade to eliminate hydrogen peroxide will possess greater long‐term stability for improved continuous glucose monitoring in diabetic patients.