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 & Adsorption. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.

Flexible microfluidic cloth-based analytical devices using a low-cost wax patterning technique

Abstract: This paper describes the fabrication of microfluidic cloth-based analytical devices (μCADs) using a simple wax patterning method on cotton cloth for performing colorimetric bioassays. Commercial cotton cloth fabric is proposed as a new inexpensive, lightweight, and flexible platform for fabricating two- (2D) and three-dimensional (3D) microfluidic systems. We demonstrated that the wicking property of the cotton microfluidic channel can be improved by scouring in soda ash (Na2CO3) solution which will remove the natural surface wax and expose the underlying texture of the cellulose fiber. After this treatment, we fabricated narrow hydrophilic channels with hydrophobic barriers made from patterned wax to define the 2D microfluidic devices. The designed pattern is carved on wax-impregnated paper, and subsequently transferred to attached cotton cloth by heat treatment. To further obtain 3D microfluidic devices having multiple layers of pattern, a single layer of wax patterned cloth can be folded along a predefined folding line and subsequently pressed using mechanical force. All the fabrication steps are simple and low cost since no special equipment is required. Diagnostic application of cloth-based devices is shown by the development of simple devices that wick and distribute microvolumes of simulated body fluids along the hydrophilic channels into reaction zones to react with analytical reagents. Colorimetric detection of bovine serum albumin (BSA) in artificial urine is carried out by direct visual observation of bromophenol blue (BPB) colour change in the reaction zones. Finally, we show the flexibility of the novel microfluidic platform by conducting a similar reaction in a bent pinned μCAD.

Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility

Abstract: The main objective of the present study was to produce a novel ensemble data mining technique that involves an adaptive neuro-fuzzy inference system (ANFIS) optimized by Shuffled Frog Leaping Algorithm (SFLA) and Particle Swarm Optimization (PSO) for spatial modeling of landslide susceptibility Step-wise Assessment Ratio Analysis (SWARA) was utilized for the evaluation of the relation between landslides and landslide-related factors providing ANFIS with the necessary weighting values The developed methods were applied in Langao County, Shaanxi Province, China Eighteen factors were selected based on the experience gained from studying landslide phenomena, the local geo-environmental conditions as well as the availability of data, namely; elevation, slope aspect, slope angle, profile curvature, plan curvature, sediment transport index, stream power index, topographic wetness index, land use, normalized difference vegetation index, rainfall, lithology, distance to faults, fault density, distance to roads, road density, distance to rivers and river density A total of 288 landslides were identified after analyzing previous technical surveys, airborne imagery and conducting field surveys Also, 288 non-landslide areas were identified with the usage of Google Earth imagery and the analysis of a digital elevation model The two datasets were merged and later divided into two subsets, training and testing, based on a random selection scheme The produced landslide susceptibility maps were evaluated by the receiving operating characteristic and the area under the success and predictive rate curves (AUC) The results showed that AUC based on the training and testing dataset was similar and equal to 089 However, the processing time during the training and implementation phase was considerable different SWARA-ANFIS-PSO appeared six times faster in respect to the processing time achieved by SWARA-ANFIS-SFLA The proposed novel approach, which combines expert knowledge, neuro-fuzzy inference systems and evolutionary algorithms, can be applied for land use planning and spatial modeling of landslide susceptibility