Frequent pandemics of infectious diseases have had devastating global consequences. A central epidemiological metric for guiding effective public health interventions is the basic reproduction number, R0. Although mathematical models are commonly used to characterize transmission dynamics and derive R0, accurately computing this quantity remains challenging when models incorporate multiple transmission routes, time-varying parameters driven by seasonal or behavioral changes, or mobility-induced diffusion processes. In this work, we develop a unified framework—Math-model Informed Neural Network (MINN)—that integrates machine learning with mechanistic disease models to estimate R0. Our approach leverages a neural network representation of the linearized infection subsystem, allowing for the computation of R0 without explicitly solving the system. We apply MINN to spatiotemporally homogeneous, spatially heterogeneous, temporally homogeneous, and fully spatiotemporally heterogeneous models of infectious diseases, including those with direct, environmental, and vector-borne transmission. Across all scenarios, MINN accurately estimates R0 and closely recovers previously established analytical or numerical benchmarks. This AI-augmented modeling framework provides a generalizable and efficient approach for estimating basic reproduction numbers, with potential utility in informing timely and effective pandemic-prevention policies.
One of the central problems throughout the history of solid-state sintering science has been the optimization of heating regimes to achieve the highest possible density with minimal grain growth. This problem is especially important for sintering of nanostructured materials. The approach proposed in this work is based on a direct analysis of coupled equations of continuum sintering theory. A mathematically rigorous framework based on optimal control theory is developed to determine the form of the thermal cycle that minimizes grain growth for a prescribed sintering time, with the accuracy of the result limited only by the precision of the underlying phenomenological sintering equations. The modeling results predict an optimal heating schedule in which the temperature gradually increases during densification in proportion to grain growth, compensating for the corresponding decrease in the densification rate. These theoretical predictions are compared with experimental data obtained during the sintering of 3Y-TZP zirconia.
Collagen is a structural extracellular protein that contributes to the anisotropic elastic behavior of the right ventricle (RV). It is established that collagen is significantly altered in pulmonary hypertension (PH), including overall content, degree of cross-linking, and fiber alignment. The RV is viscoelastic, though, and how collagen contributes to the overall tissue viscoelastic behavior with PH development remains unknown. Therefore, the aim of this study was to investigate the role of collagen in the anisotropic viscoelasticity of the RV free wall (RVFW) during PH progression. Equibiaxial stress relaxation tests were performed in healthy and PH rat RVFW before and after the enzymatic breakdown of collagen via collagenase. A Fung-type quasilinear viscoelastic constitutive model was then applied to further delineate the changes in RVFW mechanics. We found that collagen degradation significantly reduced RVFW viscosity and elasticity, with stronger effects in the diseased group. The reduction in RVFW elastic behavior was stronger in the longitudinal direction than the circumferential, while the reduction in viscosity was similar between directions in both groups. Constitutive modeling results showed weakened RVFW stiffness after collagenase treatment in both groups, but distinct responses of relaxation strength coefficients between the healthy and diseased groups. In the longitudinal direction, the relaxation strength coefficients were positively correlated with the viscosity to elasticity ratio measured from experimental data in both groups. Our findings demonstrate distinct collagen deposition and morphology between healthy and diseased RVFW, suggesting that RV fibrosis results in unique regulation of RVFW mechanics.
Ecosystems are experiencing changes in multiple environmental factors, and these changes have the potential to influence species both directly and indirectly via altered species interactions. We conducted a factorial field manipulation of CO2 (reducing pH) and temperature in twenty tide pools over a six-month growing season on the coast of Alaska, USA. Our manipulations resulted in unexpected increases in community evenness due to reduced abundance of dominant mobile herbivores, which reflected relationships with basal species. The climate factor most strongly related to population changes differed by species, and we observed greater changes upon exposure to single rather than multiple environmental factors. Total taxon richness did not differ between treatments over the time scale and magnitude of our experimental manipulations. These findings highlight the importance of long-term experiments in natural, dynamic ecosystems to account for environmental variation, community interactions, and behavioral responses of mobile species.
Claims regarding the implications of employee cannabis use vary considerably even though little research explores cannabis as it relates to the modern workplace. Drawing from substance use theories and the epidemiology literature, we develop a conceptual model that suggests after-work cannabis use is a mechanism to help employees protect cognitive resources in the evening, allowing them to stay engaged at work the next day. In a daily diary study of cannabis users and non-users, we find no link between after-work cannabis use and next-day executive functioning, cognitive engagement or daily task performance. A deeper dive into the literature led to a contextualized model including both within- and between-person moderators that we tested in a second daily diary study of cannabis users. We specifically predict moderated indirect effects from after-work cannabis use to cognitive engagement at work the following day. Results from this second study indicate after-work cannabis use interacts with daily workload and a between-person performance-related cannabis use motive to predict next-day cognitive engagement. Daily cognitive engagement is subsequently related to changes in daily task performance. Collectively, results suggest that theories on employee substance use should be updated to better account for the daily effects (or non-effects) of after-work cannabis use on important workplace outcomes.
