Equilibrium and Dynamic Surface Tension Behavior in Colloidal Unimolecular Polymers (CUP)

Studies of the interfacial behavior of pure aqueous nanoparticles have been limited due tothe difficulty of making contaminant-free nanoparticles while also providing narrow size distribu-tion. Colloidal unimolecular polymers (CUPs) are a new type of single-chain nanoparticle with a particle size ranging from 3 to 9 nm, which can be produced free of surfactants and volatile organic contents (VOCs). CUP particles of different sizes and surface charges were made. The surface tension behavior of these CUP particles in water was studied using a maximum bubble pressure tensiometer. The equilibrium surface tension decreased with increasing concentration and the number of charges present on the surface of the CUP particles influences the magnitude of the interfacial behavior. The effect of electrostatic repulsion between the particles on the surface tension was related. At higher concentrations, surface charge condensation started to dominate the surface tension behavior. The dynamic surface tension of CUP particles shows the influence of the diffusion of the particles to the interface on the relaxation time. The relaxation time of the CUP polymer was 0.401 s, which is closer to the diffusion-based relaxation time of 0.133s for SDS (sodium dodecyl sulfate)

Equilibrium and Dynamic Surface Tension Behavior in Colloidal Unimolecular Polymers (CUP)

Studies of the interfacial behavior of pure aqueous nanoparticles have been limited due tothe difficulty of making contaminant-free nanoparticles while also providing narrow size distribu-tion. Colloidal unimolecular polymers (CUPs) are a new type of single-chain nanoparticle with a particle size ranging from 3 to 9 nm, which can be produced free of surfactants and volatile organic contents (VOCs). CUP particles of different sizes and surface charges were made. The surface tension behavior of these CUP particles in water was studied using a maximum bubble pressure tensiometer. The equilibrium surface tension decreased with increasing concentration and the number of charges present on the surface of the CUP particles influences the magnitude of the interfacial behavior. The effect of electrostatic repulsion between the particles on the surface tension was related. At higher concentrations, surface charge condensation started to dominate the surface tension behavior. The dynamic surface tension of CUP particles shows the influence of the diffusion of the particles to the interface on the relaxation time. The relaxation time of the CUP polymer was 0.401 s, which is closer to the diffusion-based relaxation time of 0.133s for SDS (sodium dodecyl sulfate)

EFFICIENT RELIABILITY AND UNCERTAINTY ASSESSMENT ON LIFELINE NETWORKS USING THE SURVIVAL SIGNATURE

Lifeline networks, such as water distribution and transportation networks, are the backbone of our societies, and the study of their reliability of them is required. In this paper, a survival signature-based reliability analysis method is proposed to analyse the complex networks. It allows to consider all the characters of the network instead of just analysing the most critical path. What is more, the survival signature separates the system structure from its failure distributions, and it only needs to be calculated once, which makes it efficient to analyse complex networks. However, due to lack of data, there often exists imprecision within the network failure time distribution parameters and hence the survival signature. An efficient algorithm which bases on the reduced ordered binary decision diagrams (BDD) data structure for the computation of survival signatures is presented. Numerical example shows the applicability of the approaches

Relying on critical articulators to estimate vocal tract spectra in an articulatory-acoustic database

We present a new phone-dependent feature weighting scheme that can be used to map articulatory configurations (e.g. EMA) onto vocal tract spectra (e.g. MFCC) through table lookup. The approach consists of assigning feature weights according to a feature's ability to predict the acoustic distance between frames. Since an articulator's predictive accuracy is phone-dependent (e.g., lip location is a better predictor for bilabial sounds than for palatal sounds), a unique weight vector is found for each phone. Inspection of the weights reveals a correspondence with the expected critical articulators for many phones. The proposed method reduces overall cepstral error by 6\% when compared to a uniform weighting scheme. Vowels show the greatest benefit, though improvements occur for 80\% of the tested phones

Defining the Collapse Point in Colloidal Unimolecular Polymer (CUP) Formation

Colloidal unimolecular polymer (CUP) particles were made using polymers with different ratios of hydrophobic and hydrophilic monomers via a self-organization process known as water reduction. The water-reduction process and the collapse of the polymer chain to form a CUP were tracked using viscosity measurements as a function of composition. A vibration viscometer, which allowed for viscosity measurement as the water was being added during the water-reduction process, was utilized. The protocol was optimized and tested for factors such as temperature control, loss of material, measurement stability while stirring, and changes in the solution volume with the addition of water. The resulting viscosity curve provided the composition of Tetrahydrofuran (THF)/water mixture that triggers the collapse of a polymer chain into a particle. Hansen as well as dielectric parameters were related to the polymer composition and percentage v/v of THF/water mixture at the collapse point. It was observed that the collapse of the polymer chain occurred when the water/THF composition was at a water volume of between 53.8 to 59.3% in the solvent mixture

Fluorescent and photo-oxidizing TimeSTAMP tags track protein fates in light and electron microscopy.

Protein synthesis is highly regulated throughout nervous system development, plasticity and regeneration. However, tracking the distributions of specific new protein species has not been possible in living neurons or at the ultrastructural level. Previously we created TimeSTAMP epitope tags, drug-controlled tags for immunohistochemical detection of specific new proteins synthesized at defined times. Here we extend TimeSTAMP to label new protein copies by fluorescence or photo-oxidation. Live microscopy of a fluorescent TimeSTAMP tag reveals that copies of the synaptic protein PSD95 are synthesized in response to local activation of growth factor and neurotransmitter receptors, and preferentially localize to stimulated synapses in rat neurons. Electron microscopy of a photo-oxidizing TimeSTAMP tag reveals new PSD95 at developing dendritic structures of immature neurons and at synapses in differentiated neurons. These results demonstrate the versatility of the TimeSTAMP approach for visualizing newly synthesized proteins in neurons

Supporting Energy-Based Learning With An Ising Machine Substrate: A Case Study on RBM

Nature apparently does a lot of computation constantly. If we can harness some of that computation at an appropriate level, we can potentially perform certain type of computation (much) faster and more efficiently than we can do with a von Neumann computer. Indeed, many powerful algorithms are inspired by nature and are thus prime candidates for nature-based computation. One particular branch of this effort that has seen some recent rapid advances is Ising machines. Some Ising machines are already showing better performance and energy efficiency for optimization problems. Through design iterations and co-evolution between hardware and algorithm, we expect more benefits from nature-based computing systems. In this paper, we make a case for an augmented Ising machine suitable for both training and inference using an energy-based machine learning algorithm. We show that with a small change, the Ising substrate accelerate key parts of the algorithm and achieve non-trivial speedup and efficiency gain. With a more substantial change, we can turn the machine into a self-sufficient gradient follower to virtually complete training entirely in hardware. This can bring about 29x speedup and about 1000x reduction in energy compared to a Tensor Processing Unit (TPU) host