Polymerization and Collision in High Concentrations for Brownian Coagulation

Aggregation always occurs in industrial processes with fractal-like particles, especially in dense systems (the volume fraction, ϕ>1%). However, the classic aggregation theory, established by Smoluchowski in 1917, cannot sufficiently simulate the particle dynamics in dense systems, particularly those of generat ed fractal-like particles. In this article, the Langevin dynamic was applied to study the collision rate of aggregations as well as the structure of aggregates affected by different volume fractions. It is shown that the collision rate of highly concentrated particles is progressively higher than that of a dilute concentration, and the SPSD (self-preserving size distribution) is approached (σg,n≥1.5). With the increase in volume fraction, ϕ, the SPSD broadens, and the geometric standard is 1.54, 1.98, and 2.73 at ϕ= 0.1, 0.2, and 0.3. When the volume fraction, ϕ, is higher, the radius of gyration is smaller with the same cluster size (number-based), which means the particle agglomerations are in a tighter coagulation. The fractal-like property Df is in the range of 1.60–2.0 in a high-concentration system. Knowing the details of the collision progress in a high-concentration system can be useful for calculating the dynamics of coagulating fractal-like particles in the industrial process.

Mesoscale Simulation of Bacterial Chromosome and Cytoplasmic Nanoparticles in Confinement

In this study we investigated, using a simple polymer model of bacterial chromosome, the subdiffusive behaviors of both cytoplasmic particles and various loci in different cell wall confinements. Non-Gaussian subdiffusion of cytoplasmic particles as well as loci were obtained in our Langevin dynamic simulations, which agrees with fluorescence microscope observations. The effects of cytoplasmic particle size, locus position, confinement geometry, and density on motions of particles and loci were examined systematically. It is demonstrated that the cytoplasmic subdiffusion can largely be attributed to the mechanical properties of bacterial chromosomes rather than the viscoelasticity of cytoplasm. Due to the randomly positioned bacterial chromosome segments, the surrounding environment for both particle and loci is heterogeneous. Therefore, the exponent characterizing the subdiffusion of cytoplasmic particle/loci as well as Laplace displacement distributions of particle/loci can be reproduced by this simple model. Nevertheless, this bacterial chromosome model cannot explain the different responses of cytoplasmic particles and loci to external compression exerted on the bacterial cell wall, which suggests that the nonequilibrium activity, e.g., metabolic reactions, play an important role in cytoplasmic subdiffusion.

Joint Topical Word Embedding for Detecting Drift in Social Media Text

Social media texts like tweets and blogs are collaboratively created by human interaction. Fast change in trends leads to topic drift in the social media text. This drift is usually associated with words and hashtags. However, geotags play an important part in determining topic distribution with location context. Rate of change in the distribution of words, hashtags and geotags cannot be considered as uniform and must be handled accordingly. This paper builds a topic model that associates topic with a mixture of distributions of words, hashtags and geotags. Stochastic gradient Langevin dynamic model with varying mini-batch sizes is used to capture the changes due to the asynchronous distribution of words and tags. Topical word embedding with co-occurrence and location contexts are specified as hashtag context vector and geotag context vector respectively. These two vectors are jointly learned to yield topical word embedding vectors related to tags context. Topical word embeddings over time conditioned on hashtags and geotags predict, location-based topical variations effectively. When evaluated with Chennai and UK geolocated Twitter data, the proposed joint topical word embedding model enhanced by the social tags context, outperforms other methods.

Joint Topical Word Embedding for Detecting Drift in Social Media Text

Social media texts like tweets and blogs are collaboratively created by human interaction. Fast change in trends leads to topic drift in the social media text. This drift is usually associated with words and hashtags. However, geotags play an important part in determining topic distribution with location context. Rate of change in the distribution of words, hashtags and geotags cannot be considered as uniform and must be handled accordingly. This paper builds a topic model that associates topic with a mixture of distributions of words, hashtags and geotags. Stochastic gradient Langevin dynamic model with varying mini-batch sizes is used to capture the changes due to the asynchronous distribution of words and tags. Topical word embedding with co-occurrence and location contexts are specified as hashtag context vector and geotag context vector respectively. These two vectors are jointly learned to yield topical word embedding vectors related to tags context. Topical word embeddings over time conditioned on hashtags and geotags predict, location-based topical variations effectively. When evaluated with Chennai and UK geolocated Twitter data, the proposed joint topical word embedding model enhanced by the social tags context, outperforms other methods.

Joint Topical Word Embedding for Detecting Drift in Social Media Text

Social media texts like tweets and blogs are collaboratively created by human interaction. Fast change in trends leads to topic drift in the social media text. This drift is usually associated with words and hashtags. However, geotags play an important part in determining topic distribution with location context. Rate of change in the distribution of words, hashtags and geotags cannot be considered as uniform and must be handled accordingly. This paper builds a topic model that associates topic with a mixture of distributions of words, hashtags and geotags. Stochastic gradient Langevin dynamic model with varying mini-batch sizes is used to capture the changes due to the asynchronous distribution of words and tags. Topical word embedding with co-occurrence and location contexts are specified as hashtag context vector and geotag context vector respectively. These two vectors are jointly learned to yield topical word embedding vectors related to tags context. Topical word embeddings over time conditioned on hashtags and geotags predict, location-based topical variations effectively. When evaluated with Chennai and UK geolocated Twitter data, the proposed joint topical word embedding model enhanced by the social tags context, outperforms other methods.

Dynamics Simulation Studies of Solvation Effect on the Trans-Xylomollin Conformation

The present work describes the solvation effect on the trans-xylomollin conformation. We have studied the trans-xylomollin conformations with the distance restraints using simulation calculations. Distance Restraint Molecular Dynamic (DR-MD) and Distance Restraint Langevin Dynamic (DR-LD) simulations of the trans-xylomollin were performed with an efficient program. The geometries, interaction energies, bonds, angles, and the Van der Waals (VDW) interactions were carried out in solution and gas phases. This comparative study shows that the trans-xylomollin acquires low total energy in solution using DR-MD method and stable conformation under the AMBER field. This molecule reaches its high stable conformation state in solution environment. The solvation effect is more important with DR-MD simulations. Our results are goods and in agreement with the used force field.