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The thermoresponsive properties of poloxamine (tetra-branch PEO-PPO block copolymer) hydrogels are related to several variables. Of particular interest to this study were the molecular weight of the polymer, the molar ratio between PEO and PPO blocks, and the concentration of the aqueous solution. Accurately controlling the thermoresponsive behaviors of the polymer is critical to the application of such materials; therefore, the structure–property relationship of tetra-branch PEO-PPO block copolymer was studied by synthesis via anionic ring-opening polymerization (AROP). The structure–property relationships were studied by measuring the thermoresponsive behavior via differential scanning calorimetry (DSC) and developing an empirical model which statistically fit the collected data. This empirical model was then used for designing poloxamines that have critical micellization temperatures (CMT) between room temperature and physiological temperature. The model was validated with three polymers that targeted a CMT of 308 K (35°C). The empirical model showed great success in guiding the synthesis of poloxamines showing a temperature difference of less than 3 K between the predicted and the observed CMTs. This study showed a great potential of using an empirical model to set synthesis parameters to control the properties of the polymer products. 

Abstract Computer model calibration typically operates by fine-tuning parameter values in a computer model so that the model output faithfully predicts reality. By using performance targets in place of observed data, we show that calibration techniques can be repurposed for solving multi-objective design problems. Our approach allows us to consider all relevant sources of uncertainty as an integral part of the design process. We demonstrate our proposed approach through both simulation and fine-tuning material design settings to meet performance targets for a wind turbine blade. 

Abstract Calibration of computer models and the use of those design models are two activities traditionally carried out separately. This paper generalizes existing Bayesian inverse analysis approaches for computer model calibration to present a methodology combining calibration and design in a unified Bayesian framework. This provides a computationally efficient means to undertake both tasks while quantifying all relevant sources of uncertainty. Specifically, compared with the traditional approach of design using parameter estimates from previously completed model calibration, this generalized framework inherently includes uncertainty from the calibration process in the design procedure. We demonstrate our approach to the design of a vibration isolation system. We also demonstrate how, when adaptive sampling of the phenomenon of interest is possible, the proposed framework may select new sampling locations using both available real observations and the computer model. This is especially useful when a misspecified model fails to reflect that the calibration parameter is functionally dependent upon the design inputs to be optimized. 

This paper studies the “age of information” (AoI) in a multi-source status update system where N active sources each send updates of their time-varying process to a monitor through a server with packet delivery errors. We analyze the average AoI for stationary randomized and round-robin scheduling policies. For both of these scheduling policies, we further analyze the effect of packet retransmission policies, i.e., retransmission without re- sampling, retransmission with resampling, or no retransmission, when errors occur. Expressions for the average AoI are derived for each case. It is shown that the round-robin schedule policy in conjunction with retransmission with resampling when errors occur achieves the lowest average AoI among the considered cases. For stationary randomized schedules with equiprobable source selection, it is further shown that the average AoI gap to round-robin schedules with the same packet management policy scales as O(N). Finally, for stationary randomized policies, the optimal source selection probabilities that minimize a weighted sum average AoI metric are derived. 

This paper considers the problem of tracking and predicting dynamical processes with model switching. The classical approach to this problem has been to use an interacting multiple model (IMM) which uses multiple Kalman filters and an auxiliary system to estimate the posterior probability of each model given the observations. More recently, data-driven approaches such as recurrent neural networks (RNNs) have been used for tracking and prediction in a variety of settings. An advantage of data-driven approaches like the RNN is that they can be trained to provide good performance even when the underlying dynamic models are unknown. This paper studies the use of temporal convolutional networks (TCNs) in this setting since TCNs are also data-driven but have certain structural advantages over RNNs. Numerical simulations demonstrate that a TCN matches or exceeds the performance of an IMM and other classical tracking methods in two specific settings with model switching: (i) a Gilbert-Elliott burst noise communication channel that switches between two different modes, each modeled as a linear system, and (ii) a maneuvering target tracking scenario where the target switches between a linear constant velocity mode and a nonlinear coordinated turn mode. In particular, the results show that the TCN tends to identify a mode switch as fast or faster than an IMM and that, in some cases, the TCN can perform almost as well as an omniscient Kalman filter with perfect knowledge of the current mode of the dynamical system.