Surface modification of seawater reverse osmosis (SWRO) membrane using methyl methacrylate-hydroxy poly(oxyethylene) methacrylate (MMA-HPOEM) comb-polymer and its performance

Comb-like amphiphilic copolymer, methyl methacrylate-hydroxy poly(oxyethylene) methacrylate (MMA-HPOEM), was synthesized by free radical polymerization and then applied to a seawater reverse osmosis (SWRO) membrane to introduce hydrophilicity and fouling resistance. The amphiphilic copolymer preparation was verified using nuclear magnetic resonance, and the deposition of the copolymer on the membrane using dip-coating method was confirmed using X-ray photoelectron spectroscopy. The surface charge that resulted from the amphiphilic copolymer application was analyzed using zeta-potential analysis. Cross-flow fouling test studies showed that the MMA-HPOEM coating on the membrane improved the fouling resistance to bovine serum albumin, Escherichia coli, and seawater. Although the initial flux of the modified membrane was lower than that of the virgin membrane due to the additional hydraulic resistance, the rate of flux decline slowed after the modification and compensated for the initial flux decline within several days.close7

Design and Implementation of Dynamic Process Management for Grid-enabled MPICH

Abstract. This paper proposes the design and impementation of MPI Rejoin() for MPICH-G2, which is not specified in the original MPI. The ‘rejoin ’ operation allows the restored process to rejoin the existing group by updating the corresponding entry of the channel table with the new physical address. This is the primitive of fault tolerance or task migration for message passing processes. The ‘rejoin ’ events are informed by hierarchical process managers. We have analyzed communication mechanism of MPICH-G2 and mesured the ‘rejoin ’ cost with NPB application(LU).

An Idle Compute Cycle Prediction Service for Computational Grids

Abstract. The utilization of idle compute cycles has been known as most promising and cost-effective way to build a large scale high performance computing system, but not widely used because of the lack of effective idleness prediction techniques. In this paper, we argue PCs at university computer labs have a great potential for the utilization of idle CPU cycles, and propose two techniques for predicting idle cycles of those PCs: heuristic and statistical. Based on these techniques, we present the design and implementation of an idle compute cycle prediction service for computational grids. Our experimental results show that the utilization of idle compute cycles is a viable approach to cost-effective large scale computational grids.

Acoustic Scene Classification Based on a Large-margin Factorized CNN

In this paper, we present an acoustic scene classification framework based on a large-margin factorized convolutional neural network (CNN). We adopt the factorized CNN to learn the patterns in the time-frequency domain by factorizing the 2D kernel into two separate 1D kernels. The factorized kernel leads to learn the main component of two patterns: the long-term ambient and short-term event sounds which are the key patterns of the audio scene classification. In training our model, we consider the loss function based on the triplet sampling such that the same audio scene samples from different environments are minimized, and simultaneously the different audio scene samples are maximized. With this loss function, the samples from the same audio scene are clustered independently of the environment, and thus we can get the classifier with better generalization ability in an unseen environment. We evaluated our audio scene classification framework using the dataset of the DCASE challenge 2019 task1A. Experimental results show that the proposed algorithm improves the performance of the baseline network and reduces the number of parameters to one third. Furthermore, the performance gain is higher on unseen data, and it shows that the proposed algorithm has better generalization ability.454

Weakly Labeled Sound Event Detection using Tri-training and Adversarial Learning

This paper considers a semi-supervised learning framework for weakly labeled polyphonic sound event detection problems for the DCASE 2019 challenge's task4 by combining both the tri-training and adversarial learning. The goal of the task4 is to detect onsets and offsets of multiple sound events in a single audio clip. The entire dataset consists of the synthetic data with a strong label (sound event labels with boundaries) and real data with weakly labeled (sound event labels) and unlabeled dataset. Given this dataset, we apply the tri-training where two different classifiers are used to obtain pseudo labels on the weakly labeled and unlabeled dataset, and the final classifier is trained using the strongly labeled dataset and weakly/unlabeled dataset with pseudo labels. Also, we apply the adversarial learning to reduce the domain gap between the real and synthetic dataset. We evaluated our learning framework using the validation set of the task4 dataset, and in the experiments, our learning framework shows a considerable performance improvement over the baseline model.18418

Enhancing the anti-fouling property of the SWRO membrane through the surface coating with the styrene-PEGA copolymer

Polyamide-based reverse osmosis membranes have been used as a wastewater treatment process in recent years. However, natural organic materials present in the filtration medium cause severe membrane fouling problem, which makes the system less competitive. It is well known that membrane fouling can be influenced by the surface property of the membrane surface morphology, chemical composition, surface charge, etc. To introduce hydrophilic materials on the membrane is one of the promising modification methods to mitigate membrane fouling. In this study, we investigated the effect of amphiphilic comb polymer coating layer on anti-fouling property of seawater reverse osmosis (SWRO) membranes. Styrene PEGA amphiphilic copolymer was synthesized by a free radical solution polymerization method. The chemical structure and properties of the synthesized styrene PEGA copolymer were determined by Fourier transform-infrared spectroscopy (FT-IR), atomic force microscopy (AFM), and xi-potential. Obtained copolymer was coated on the membrane surface via a simple dipping method. The performance of the coated membrane was evaluated in a cross flow mode. The anti-fouling property of the surface coated membrane was investigated using model foulant solution filtration. Bovine serum albumin was used as a model foulant. The modified membranes were less fouled than pristine RO membranes, and recovered 95% of its initial flux after hydraulic cleaning.close0