Hybrid Supramolecular and Colloidal Hydrogels that Bridge Multiple Length Scales

Hybrid nanocomposites were constructed based on colloidal nanofibrillar hydrogels with interpenetrating supramolecular hydrogels, displaying enhanced rheological yield strain and a synergistic improvement in storage modulus. The supramolecular hydrogel consists of naphthyl‐functionalized hydroxyethyl cellulose and a cationic polystyrene derivative decorated with methylviologen moieties, physically cross‐linked with cucurbit[8]uril macrocyclic hosts. Fast exchange kinetics within the supramolecular system are enabled by reversible cross‐linking through the binding of the naphthyl and viologen guests. The colloidal hydrogel consists of nanofibrillated cellulose that combines a mechanically strong nanofiber skeleton with a lateral fibrillar diameter of a few nanometers. The two networks interact through hydroxyethyl cellulose adsorption to the nanofibrillated cellulose surfaces. This work shows methods to bridge the length scales of molecular and colloidal hybrid hydrogels, resulting in synergy between reinforcement and dynamics.     

Normalized Augmented Inverse Probability Weighting with Neural Network Predictions

The estimation of average treatment effect (ATE) as a causal parameter is carried out in two steps, where in the first step, the treatment and outcome are modeled to incorporate the potential confounders, and in the second step, the predictions are inserted into the ATE estimators such as the augmented inverse probability weighting (AIPW) estimator. Due to the concerns regarding the non-linear or unknown relationships between confounders and the treatment and outcome, there has been interest in applying non-parametric methods such as machine learning (ML) algorithms instead. Some of the literature proposes to use two separate neural networks (NNs) where there is no regularization on the network’s parameters except the stochastic gradient descent (SGD) in the NN’s optimization. Our simulations indicate that the AIPW estimator suffers extensively if no regularization is utilized. We propose the normalization of AIPW (referred to as nAIPW) which can be helpful in some scenarios. nAIPW, provably, has the same properties as AIPW, that is, the double-robustness and orthogonality properties. Further, if the first-step algorithms converge fast enough, under regulatory conditions, nAIPW will be asymptotically normal. We also compare the performance of AIPW and nAIPW in terms of the bias and variance when small to moderate L1 regularization is imposed on the NNs.     

Self-Adaptive Genetic Algorithm for Clustering

Clustering is a hard combinatorial problem which has many applications in science and practice. Genetic algorithms (GAs) have turned out to be very effective in solving the clustering problem. However, GAs have many parameters, the optimal selection of which depends on the problem instance. We introduce a new self-adaptive GA that finds the parameter setup on-line during the execution of the algorithm. In this way, the algorithm is able to find the most suitable combination of the available components. The method is robust and achieves results comparable to or better than a carefully fine-tuned non-adaptive GA.     

On the geometry of the coefficient body (a 2 , a 3 ) for bounded univalent functions and some related coefficient problems

Journal d'Analyse Mathématique -