Universiti Teknologi Malaysia

About: Universiti Teknologi Malaysia is a education organization based out in Johor Bahru, Malaysia. It is known for research contribution in the topics: Membrane & Control theory. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.

A practical approach to synthesize polyamide thin film nanocomposite (TFN) membranes with improved separation properties for water/wastewater treatment

Abstract: Thin film nanocomposite (TFN) membranes containing 0.05 or 0.10 w/v% surface-functionalized titanate nanotubes (TNTs) in a polyamide selective layer were synthesized via interfacial polymerization of piperazine (PIP) and trimesoyl chloride (TMC) monomers. Nanomaterials were dispersed into the monomer solution using two different approaches. In the first one, the functionalized TNTs were dispersed into the amine aqueous solution, while in the second approach the same nanomaterials were dispersed in TMC organic solution. The TFN membranes were characterized and compared with a control thin film composite (TFC) membrane to investigate the effect of nanofiller loadings and the fabrication approach on membrane properties. Results showed that introducing nanofillers into the organic phase was more effective to synthesize a TFN membrane of greater separation performance as the use of a rubber roller to remove aqueous solution from the substrate surface could cause the loss of a significant amount of nanofillers, which further affected the polyamide layer integrity. It was also found that the incorporation of a high nanofiller loading tended to interfere with interfacial polymerization and weaken the bonds between monomer blocks, resulting in poor polyamide–nanotube integrity. Compared to the TFC membrane, the TFN membrane made of 2% PIP and 0.15% TMC with 0.5% nanofiller incorporation could achieve greater water flux (7.5 vs. 5.4 L m−2 h−1 bar−1) and Na2SO4 rejection (96.4% vs. 86%) while exhibiting higher resistance against fouling by protein and dye.

Water resources management strategy for adaptation to droughts in China

Abstract: Water Scarcity and drought are recurrent phenomena in China. In the context of environmental change, an increasing tendency in drought frequency and severity is observed in China in recent years. Therefore, it is imperative to take necessary initiatives to reduce the impacts of drought. In this paper, an attempt is made to identify the best water management strategies to cope with droughts. For this objective the records of historical droughts and their impacts in China over the period of 1950–2009 are analyzed. It is observed that the drought affected area has increased nearly by 12 folds and the drought damaged area has increased by about 22 times in China in last 60 years. Over 87,000 reservoirs were built with a total water storage capacity of about 7,064 billion m3 to cope with droughts. However, this structural supply-based management strategy was not enough to meet the increasing water demand caused by rapid economic development and population growth. A typical relationship between socio-economic development and water resources management strategy to attain sustainability in water management is developed in this study. The relationship shows that the demand-side water management strategies can be the best option to meet the challenges posed by increased severity of drought, population growth, economic development and possible climate change. The concept is later verified through the analysis of changing pattern of water consumptions by different sectors in last 60 years.

Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation.

Abstract: Grouping and clustering alerts for intrusion detection based on the similarity of features is referred to as structurally base alert correlation and can discover a list of attack steps. Previous researchers selected different features and data sources manually based on their knowledge and experience, which lead to the less accurate identification of attack steps and inconsistent performance of clustering accuracy. Furthermore, the existing alert correlation systems deal with a huge amount of data that contains null values, incomplete information, and irrelevant features causing the analysis of the alerts to be tedious, time-consuming and error-prone. Therefore, this paper focuses on selecting accurate and significant features of alerts that are appropriate to represent the attack steps, thus, enhancing the structural-based alert correlation model. A two-tier feature selection method is proposed to obtain the significant features. The first tier aims at ranking the subset of features based on high information gain entropy in decreasing order. The‏ second tier extends additional features with a better discriminative ability than the initially ranked features. Performance analysis results show the significance of the selected features in terms of the clustering accuracy using 2000 DARPA intrusion detection scenario-specific dataset.