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To track COVID-19 immunity efficiently in point-of-care (POC) settings, we present a paper-based multiplexed vertical flow immunoassay (xVFA) along with a custom-designed serodiagnostic algorithm. During the development and testing of our algorithm, we utilized serum samples from individuals who had received mRNA-COVID-19 vaccines, tracking their antibody levels before and after each vaccine dose. By categorizing these samples based on their IgM and IgG levels into three categories (i.e., protected, unprotected, and infected), we trained and blindly evaluated a neural network-based algorithm for its inference accuracy. Leveraging this serodiagnostic algorithm, our cost-effective, paper-based xVFA platform swiftly measured the IgG and IgM levels from serum samples, facilitating the accurate monitoring of COVID-19 immunity levels. With its simple operation, scalability, and cost-effectiveness, our xVFA technology offers accessible COVID-19 serology testing to classify patients' immunity status rapidly. 

Monitoring the level of COVID-19 immunity within populations is crucial for understanding the disease trend and guiding public health policies, as SARS-CoV-2 remains a threat. Serological testing is essential in assessing immunity by detecting antibodies, IgG, and IgM, the immune system's response developed against infection or after vaccination. To rapidly and cost-effectively monitor COVID-19 immunity levels, we developed a paper-based multiplexed vertical flow immunoassay (xVFA) which detects the IgG and IgM levels in less than 20 mins, aiming to monitor the COVID-19 immunity levels of individuals longitudinally and categorize immune levels into three groups: protected, unprotected, and infected. 

Biomimetic scale-covered substrates are architected meta-structures exhibiting fascinating emergent nonlinearities via the geometry of collective scales contacts. Despite much progress in understanding their elastic nonlinearity, their dissipative behavior arising from scales sliding is relatively uninvestigated in the dynamic regime. Recently discovered is the phenomena of viscous emergence, where dry Coulomb friction between scales can lead to apparent viscous damping behavior of the overall multi-material substrate. In contrast to this structural dissipation, material dissipation common in many polymers has never been considered, especially synergistically with geometrical factors. This aspect is addressed here, where material viscoelasticity is introduced via a simple Kelvin–Voigt model for brevity and clarity. The results contrast the two damping sources in these architectured systems: material viscoelasticity and geometrical frictional scales contact. It is discovered that although topically similar in effective damping, viscoelastic damping follows a different damping envelope than dry friction, including starkly different effects on damping symmetry and specific damping capacity. 

Accurate predictions of water temperature are the foundation for many decisions and regulations, with direct impacts on water quality, fishery yields, and power production. Building accurate broad-scale models for lake temperature prediction remains challenging in practice due to the variability in the data distribution across different lake systems monitored by static and time-series data. In this paper, to tackle the above challenges, we propose a novel machine learning based approach for integrating static and time-series data in deep recurrent models, which we call Invertibility-Aware-Long Short-Term Memory(IA-LSTM), and demonstrate its effectiveness in predicting lake temperature. Our proposed method integrates components of the Invertible Network and LSTM to better predict temperature profiles (forward modeling) and infer the static features (i.e., inverse modeling) that can eventually enhance the prediction when static variables are missing. We evaluate our method on predicting the temperature profile of 450 lakes in the Midwestern U.S. and report a relative improvement of 4\% to capture data heterogeneity and simultaneously outperform baseline predictions by 12\% when static features are unavailable.