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Data-driven approaches are increasingly popular for identifying dynamical systems due to improved accuracy and availability of sensor data. However, relying solely on data for identification does not guarantee that the identified systems will maintain their physical properties or that the predicted models will generalize well. In this paper, we propose a novel method for data-driven system identification by integrating a neural network as the first-order derivative of the learned dynamics in a Taylor series instead of learning the dynamical function directly. In addition, for dynamical systems with known monotonic properties, our approach can ensure monotonicity by constraining the neural network derivative to be non-positive or non-negative to the respective inputs, resulting in Monotonic Taylor Neural Networks (MTNN). Such constraints are enforced by either a specialized neural network architecture or regularization in the loss function for training. The proposed method demonstrates better performance compared to methods without the physics-based monotonicity constraints when tested on experimental data from an HVAC system and a temperature control testbed. Furthermore, MTNN shows good performance in a control application of a model predictive controller for a nonlinear MIMO system, illustrating the practical application of our method. 

This study employs physics-informed neural networks (PINNs) to reconstruct multiple flow fields in a transient natural convection system solely based on instantaneous temperature data at an arbitrary moment. Transient convection problems present reconstruction challenges due to the temporal variability of fields across different flow phases. In general, large reconstruction errors are observed during the incipient phase, while the quasi-steady phase exhibits relatively smaller errors, reduced by a factor of 2–4. We hypothesize that reconstruction errors vary across different flow phases due to the changing solution space of a PINN, inferred from the temporal gradients of the fields. Furthermore, we find that reconstruction errors tend to accumulate in regions where the spatial gradients are smaller than the order of 10−6, likely due to the vanishing gradient phenomenon. In convection phenomena, field variations often manifest across multiple scales in space. However, PINN-based reconstruction tends to preserve larger-scale variations, while smaller-scale variations become less pronounced due to the vanishing gradient problem. To mitigate the errors associated with vanishing gradients, we introduce a multi-scale approach that determines scaling constants for the PINN inputs and reformulates inputs across multiple scales. This approach improves the maximum and mean errors by 72.2% and 6.4%, respectively. Our research provides insight into the behavior of PINNs when applied to transient convection problems with large solution space and field variations across multiple scales. 

Escherichia coli expresses surface appendages including fimbriae, flagella, and curli, at various levels in response to environmental conditions and external stimuli. Previous studies have revealed an interplay between expression of fimbriae and flagella in several E. coli strains, but how this regulation between fimbrial and flagellar expression affects adhesion to interfaces is incompletely understood. Here, we investigate how the concurrent expression of fimbriae and flagella by engineered strains of E. coli MG1655 affects their adhesion at liquid–solid and liquid–liquid interfaces. We tune fimbrial and flagellar expression on the cell surface through plasmid-based inducible expression of the fim operon and fliC-flhDC genes. We show that increased fimbrial expression increases interfacial adhesion as well as bacteria-driven actuation of micron-sized objects. Co-expression of flagella in fimbriated bacteria, however, does not greatly affect either of these properties. Together, these results suggest that interfacial adhesion as well as motion actuated by adherent bacteria can be altered by controlling the expression of surface appendages. 

Iron- and reactive oxygen species (ROS)-dependent ferroptosis occurs in plant cells. Ca²⁺acts as a conserved key mediator to control plant immune responses. Here, we report a novel role of cytoplasmic Ca²⁺influx regulating ferroptotic cell death in rice immunity using pharmacological approaches. High Ca²⁺influx triggered iron-dependent ROS accumulation, lipid peroxidation, and subsequent hypersensitive response (HR) cell death in rice (Oryza sativa). DuringMagnaporthe oryzaeinfection, 14 different Ca²⁺influx regulators altered Ca²⁺, ROS and Fe²⁺accumulation,glutathione reductase(GR) expression, glutathione (GSH) depletion and lipid peroxidation, leading to ferroptotic cell death in rice. High Ca²⁺levels inhibited the reduction of glutathione isulphide (GSSG) to GSHin vitro. Ca²⁺chelation by ethylene glycol-bis (2-aminoethylether)-N, N, N’, N’-tetra-acetic acid (EGTA) suppressed apoplastic Ca²⁺influx in rice leaf sheaths during infection. Blocking apoplastic Ca²⁺influx into the cytoplasm by Ca²⁺chelation effectively suppressed Ca²⁺-mediated iron-dependent ROS accumulation and ferroptotic cell death. By contrast, acibenzolar-S-methyl (ASM), a plant defense activator, significantly enhanced Ca²⁺influx, as well as ROS and iron accumulation to trigger ferroptotic cell death in rice. The cytoplasmic Ca²⁺influx through calcium-permeable cation channels, including the putative resistosomes, could mediate iron- and ROS-dependent ferroptotic cell death under reducedGRexpression levels in rice immune responses.