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The properties of supramolecular hydrogels of cationic phenylalanine derivatives are modified by multicomponent formulation with anionic amino acids. 

Abstract Controlled modulation of electronic and magnetic properties in stimuli‐responsive materials provides valuable insights for the design of magnetoelectric or multiferroic devices. This paper demonstrates the modulation of electrical and magnetic properties of a semiconductive, paramagnetic metal−organic framework (MOF) Cu₃(C₆O₆)₂with small gaseous molecules, NH₃, H₂S, and NO. This study merges chemiresistive and magnetic tests to reveal that the MOF undergoes simultaneous changes in electrical conductance and magnetization that are uniquely modulated by each gas. The features of response, including direction, magnitude, and kinetics, are modulated by the physicochemical properties of the gaseous molecules. This study advances the design of multifunctional materials capable of undergoing simultaneous changes in electrical and magnetic properties in response to chemical stimuli. 

Stratospheric dynamics are strongly affected by the absorption/emission of radiation in the Earth’s atmosphere and Rossby waves that propagate upward from the troposphere, perturbing the zonal flow. Reduced order models of stratospheric wave–zonal interactions, which parameterize these effects, have been used to study interannual variability in stratospheric zonal winds and sudden stratospheric warming (SSW) events. These models are most sensitive to two main parameters: Λ, forcing the mean radiative zonal wind gradient, and h, a perturbation parameter representing the effect of Rossby waves. We take one such reduced order model with 20 years of ECMWF atmospheric reanalysis data and estimate Λ and h using both a particle filter and an ensemble smoother to investigate if the highly-simplified model can accurately reproduce the averaged reanalysis data and which parameter properties may be required to do so. We find that by allowing additional complexity via an unparameterized Λ(t), the model output can closely match the reanalysis data while maintaining behavior consistent with the dynamical properties of the reduced-order model. Furthermore, our analysis shows physical signatures in the parameter estimates around known SSW events. This work provides a data-driven examination of these important parameters representing fundamental stratospheric processes through the lens and tractability of a reduced order model, shown to be physically representative of the relevant atmospheric dynamics. 

Controlled modulation of electronic and magnetic properties in stimuli‐responsive materials provides valuable insights for the design of magnetoelectric or multiferroic devices. This paper demonstrates the modulation of electrical and magnetic properties of a semiconductive, paramagnetic metal−organic framework Cu3(C6O6)2 with small gaseous molecules, NH3, H2S, and NO. This study merges chemiresistive and magnetic tests to reveal that the MOF undergoes simultaneous changes in electrical conductance and magnetization that are uniquely modulated by each gas. The features of response, including direction, magnitude, and kinetics, are modulated by the physicochemical properties of the gaseous molecules. This study advances the design of multifunctional materials capable of undergoing simultaneous changes in electrical and magnetic properties in response to chemical stimuli. 

Rate-induced tipping occurs when a ramp parameter changes rapidly enough to cause the system to tip between co-existing, attracting states. We show that the addition of noise to the system can cause it to tip well below the critical rate at which rate-induced tipping would occur. Moreover, it does so with significantly increased probability over the noise acting alone. We achieve this by finding a global minimizer in a canonical problem of the Freidlin–Wentzell action functional of large deviation theory that represents the most probable path for tipping. This is realized as a heteroclinic connection for the Euler–Lagrange system associated with the Freidlin–Wentzell action and we find it exists for all rates less than or equal to the critical rate. Its role as the most probable path is corroborated by direct Monte Carlo simulations. 

Determining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information to locate hydrogen bonds to identify SSEs. When some spatial atomic details are missing, locating SSEs becomes a hinder. To address the problem, when some atomic information is missing, three approaches for classifying SSE types using Cα atoms in protein chains were developed: (1) a mathematical approach, (2) a deep learning approach, and (3) an ensemble of five machine learning models. The proposed methods were compared against each other and with a state-of-the-art approach, PCASSO.