Interaction of guided light in rib polymer waveguides with dielectrophoretically controlled nanoparticles

This work demonstrates an optofluidic system, where dielectrophoretically controlled suspended nanoparticles are used to manipulate the properties of an optical waveguide. This optofluidic device is composed of a multimode polymeric rib waveguide and a microfluidic channel as its upper cladding. This channel integrates dielectrophoretic (DEP) microelectrodes and is infiltrated with suspended silica and tungsten trioxide nanoparticles. By applying electrical signals with various intensities and frequencies to the DEP microelectrodes, the nanoparticles can be concentrated close to the waveguide surface significantly altering the optical properties in this region. Depending on the particle refractive indices, concentrations, positions and dimensions, the light remains confined or is scattered into the surrounding media in the microfluidic channel.     

A state-of the-art survey of TOPSIS applications

Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors’ nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.     

Production of calcium nitrate crystals via membrane distillation crystallization using polyvinylidene fluoride/sorbitan trioleate membranes

As an alternative to the energy-intensive evaporation-crystallization method, membrane distillation crystallization (MDC) was applied for the first time to obtain calcium nitrate crystals from its aqueous solution. Calcium nitrate solution was obtained through the reaction between calcium carbonate and nitric acid, and then it was concentrated in the membrane distillation (MD) process and further crystallized. The MD step was conducted using hydrophobic polyvinylidene fluoride (PVDF)/sorbitan trioleate (Span 85) membranes. Span 85 was incorporated into the membrane structure in various concentrations to improve the hydrophobicity of membranes, and the resultant membranes were characterized via different methods. In addition, the resultant calcium nitrate crystals were characterized by X-ray fluorescence (XRF) spectroscopy. The MDC results showed that the optimum amount of Span 85 in the polymeric solution was 4%, which led to the formation of a membrane with higher porosity (67.2%) and water contact angle (95.7°) compared to the neat PVDF membrane. The mentioned membrane exhibited the highest water flux in the MD process compared to the other membranes, and also it produced the highest amount of crystals due to its remarkably better performance in the MD step in terms of feed concentration.     

Calculation of maximum surface settlement induced by EPB shield tunnelling and introducing most effective parameter

This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon.Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-EPB method,this research has considered the tunnel's geometric,strength,and operational factors as the dependent variables.At first,multiple regression(MR) method was used to propose equations based on various parameters.The results indicated the dependency of surface settlement on many parameters so that the interactions among different parameters make it impossible to use MR method as it leads to equations of poor accuracy.As such,adaptive neuro-fuzzy inference system(ANFIS),was used to evaluate its capabilities in terms of predicting surface settlement.Among generated ANFIS models,the model with all input parameters considered produced the best prediction,so as its associated R~2 in the test phase was obtained to be 0.957.The equations and models in which operational factors were taken into consideration gave better prediction results indicating larger relative effect of such factors.For sensitivity analysis of ANFIS model,cosine amplitude method(CAM) was employed; among other dependent variables,fill factor of grouting(n) and grouting pressure(P) were identified as the most affecting parameters.     

Hand pose aware multimodal isolated sign language recognition

Multimedia Tools and Applications - Isolated hand sign language recognition from video is a challenging research area in computer vision. Some of the most important challenges in this area include...     

Dielectrophoretically assembled particles: feasibility for optofluidic systems

This work presents the dielectrophoretic manipulation of sub-micron particles suspended in water and the investigation of their optical responses using a microfluidic system. The particles are made of silica and have different diameters of 600, 450, and 250 nm. Experiments show a very interesting feature of the curved microelectrodes, in which the particles are pushed toward or away from the microchannel centerline depending on their levitation heights, which is further analyzed by numerical simulations. In doing so, applying an AC signal of 12 V_p–p and 5 MHz across the microelectrodes along with a flow rate of 1 μl/min within the microchannel leads to the formation of a tunable band of particles along the centerline. Experiments show that the 250 nm particles guide the longitudinal light along the microchannel due to their small scattering. This arrangement is employed to study the feasibility of developing an optofluidic system, which can be potentially used for the formation of particles-core/liquid-cladding optical waveguides.     

Using genetic programming for context-sensitive feature scoring in classification problems

Feature scoring is an avenue to feature selection that provides a measure of usefulness for the individual features of a classification task. Features are ranked based on their scores and selection is performed by choosing a small group of high-ranked features. Most existing feature scoring/ranking methods focus on the relevance of a single feature to the class labels regardless of the role of other features (context-insensitive). The paper proposes a genetic programming (GP)-based method to see how a set of features can contribute towards discriminating different classes. The features receive score in the context of other features participating in a GP program. The scoring mechanism is based on the frequency of appearance of each feature in a collection of GP programs and the fitness of those programs. Our results show that the proposed feature ranking method can detect important features of a problem. A variety of different classifiers restricted to just a few of these high-ranked features work well. The proposed scoring-ranking mechanism can also shrink the search space of size O(2 ⁿ ) of subsets of features to a search space of size O(n) in which there are points that are very likely to improve the classification performance.     

FQL‐RED: an adaptive scalable schema for active queue management

In a potentially congested network, random early detection (RED) active queue management (AQM) proved effective in improving throughput and average queuing delay. The main disadvantage of RED is its sensitive parameters that are impossible to estimate perfectly and adjust manually because of the dynamic nature of the network. For this reason, RED performs differently during different phases of a scenario and there is no guarantee that it will have optimal performance. Giving adaptability to RED has been the subject of broad research studies ever since RED was proposed. After a substantial study of AQM schemes and presenting a novel categorization for so‐called modern approaches utilizing artificial intelligence tools to improve AQM, this paper proposes an algorithm enhancing RED as an add‐on patch that makes minimal changes to the original RED. Being built on the basis of AQM schemes like ARED and Fuzzy‐RED, this algorithm inherits adaptability and is able to adjust RED inaccurate parameters regarding network traffic status, trying to optimize throughput and average queuing delay in a scenario. This algorithm is a Q‐learning method enhanced with a fuzzy inference system to provide RED with self‐adaptation and improved performance as a result. Given the name of FQL‐RED, this algorithm outperformed RED, ARED, and Fuzzy‐RED, as the OPNET simulations show. Copyright © 2010 John Wiley & Sons, Ltd.     

Effect of long-chain monoacrylate on the residual monomer content, swelling and thermomechanical properties of SAP hydrogels

Residual monomer is an important factor, particularly in hygienic materials such as superabsorbent polymer (SAP) hydrogels. Recently, we reported different approaches to minimizing residual monomer content in SAPs. In this paper, the effect of a long-chain monomer, poly(ethylene glycol) methylether methacrylate (PEG.MEMA), on the residual monomer content of SAP networks of partially neutralized acrylic acid–PEG.MEMA is investigated. The aim of using PEG.MEMA in SAP synthesis was to reduce the glass transition temperature (T _g) of SAP. As the temperature that is conventionally used to dry SAP (70–110 °C) is lower than the T _g of ordinary SAPs, the polymer is in the glassy state during the heating stage. It was assumed that converting SAP from the glassy state to the rubbery state during drying would facilitate the removal of acrylic acid monomer (AA) from the gel, thus reducing the residual monomer content. The results showed that the use of PEG.MEMA led to a reduction in residual AA when the drying temperature was 100 °C. The residual AA was decreased from 169 to 95 ppm when the drying time was increased from 3 to 15 hours at 100 °C. This positive effect of PEG.MEMA on the level of unwanted residual AA became insignificant at a higher drying temperature (140 °C). The effects of PEG.MEMA content on the thermal and mechanical properties (in the dried state) and the rheological properties (in the water-swollen state) of the SAP hydrogels were also investigated. The swelling capacity and rate was studied in relation to the PEG.MEMA content. It was found that a high level of PEG.MEMA restricted both the absorption capacity and the rate of water absorption.