Single‐wall carbon nanotubes dispersion behavior and its effects on the morphological and mechanical properties of the electrospun nanofibers

The dispersion behavior of single‐walled carbon nanotube (SWCNT) has important effects on morphological and mechanical properties of SWCNT composite nanofibers. The relationship of the dispersion conditions with morphological and mechanical characteristics for SWCNT / polyacrylonitrile (PAN) / polyvinylpyrrolidone (PVP) composite nanofibers have been examined. The SEM and TEM analyses of the nanofibers revealed that the deformation in the nanofiber structures increases with increasing concentration of SWCNTs. Tensile results showed that only 2 wt% SWCNT loading to the electrospun composite nanofibers gave rise to 10‐fold and 3‐fold increase in the tensile modulus and tenacity of nanofiber layers, respectively. Essentially, high mechanical properties and uniform morphology of the composite nanofibers were found at SWCNT concentration of ∼2 wt% due to their stable and individual dispersion. POLYM. COMPOS., 33:1951–1959, 2012. © 2012 Society of Plastics Engineers     

The relation between the state indices and the characteristic features of undrained behaviour of silty sand

The characteristic features of a series of isotropically and K₀-consolidated undrained triaxial compression behaviour of a silty sand were investigated for a range of initial void ratio ($e)$ and mean effective confining stress ($p^{\prime }$). The silty sand used in this study contained about 10% natural fines. The critical state line (CSL) of K₀-consolidated specimens, K₀U was slightly lower than the CSL for isotropically consolidated specimens, CIU. The respective CSLs for K₀U and CIU were used to define state indices, such as state parameter ($\psi )$, state index ($I_s$), state pressure index ($I_p$) and modified state parameter (${\psi }_m$), within critical state soil mechanics (CSSM) framework. It was found that each state index exhibit a unique relation with liquefaction potential, irrespective of consolidation type, however different relationships were observed between state indices and the stress ratio at the triggering of liquefaction, ${\eta }_{\mathit{IS}}$ or the liquefaction resistance, $q_{\mathit{IS}}$. The correlation of characteristic features of undrained shearing (i.e., liquefaction potential, ${\eta }_{\mathit{IS}}$ and $q_{\mathit{IS}}$) and drained shearing (maximum rate of volume change, ${\left(d{\epsilon }_v/d{\epsilon }_a\right)}_{\mathit{m}\mathit{a}\mathit{x}}$) with state indices were compared statistically in terms of root mean square deviations (RMSD). All characteristic features of undrained shearing generally showed the best correlation with $I_p$ in term of RMSDs, however $\psi$ showed comparatively wider scatter for the specimens showing flow and limited flow behaviour.     

Content-based image retrieval with relevance feedback using random walks

In this paper, we propose a novel approach to content-based image retrieval with relevance feedback, which is based on the random walker algorithm introduced in the context of interactive image segmentation. The idea is to treat the relevant and non-relevant images labeled by the user at every feedback round as “seed” nodes for the random walker problem. The ranking score for each unlabeled image is computed as the probability that a random walker starting from that image will reach a relevant seed before encountering a non-relevant one. Our method is easy to implement, parameter-free and scales well to large datasets. Extensive experiments on different real datasets with several image similarity measures show the superiority of our method over different recent approaches.     

Understanding cassava varietal preferences through pairwise ranking of gari-eba and fufu prepared by local farmer–processors

Within communities in Osun and Imo States of Nigeria, farmer–processors grew and processed a diverse set of improved and landrace cassava varieties into the locally popular foods, gari, eba and fufu. Local and 15 main varieties were grown in a ‘mother and baby trials’ design in each state. Mother trials with three replications were processed by farmer–processors renown in their community for their processing skills. Baby trials were managed and processed by other farmer–processors. The objective was to identify food quality criteria to inform demand-led breeding to benefit users, especially women, given their key roles in processing. Farmer–processors evaluated the overall quality of fresh roots and derived food products through pairwise comparisons. Improved varieties had higher fresh and dry root yield. Overall, landraces ranked first for quality of gari and eba, but several improved varieties were also appreciated for good quality. Landraces in Osun had higher gari yield and a higher swelling power compared to improved varieties. Colour (browning), bulk density, swelling power, solubility and water absorption capacity were the criteria most related to food product ranking by farmer–processors. Evaluation of varieties under farmer–processors’ conditions is crucial for providing guidance to breeders on critical selection criteria.     

Modeling and optimization of electrospun PAN nanofiber diameter using response surface methodology and artificial neural networks

Response surface methodology (RSM) based on a three‐level, three‐variable Box‐Benkhen design (BBD), and artificial neural network (ANN) techniques were compared for modeling the average diameter of electrospun polyacrylonitrile (PAN) nanofibers. The multilayer perceptron (MLP) neural networks were trained by the sets of input–output patterns using a scaled conjugate gradient backpropagation algorithm. The three important electrospinning factors were studied including polymer concentration (w/v%), applied voltage (kV) and the nozzle‐collector distance (cm). The predicted fiber diameters were in agreement with the experimental results in both ANN and RSM techniques. High‐regression coefficient between the variables and the response (R² = 0.998) indicates excellent evaluation of experimental data by second‐order polynomial regression model. The R² value was 0.990, which indicates that the ANN model was shows good fitting with experimental data. Moreover, the RSM model shows much lower absolute percentage error than the ANN model. Therefore, the obtained results indicate that the performance of RSM was better than ANN. The RSM model predicted the 118 nm value of the finest nanofiber diameter at conditions of 10 w/v% polymer concentration, 12 cm of nozzle‐collector distance, and 12 kV of the applied voltage. The predicted value (118 nm) showed only 2.5%, difference with experimental results in which 121 nm at the same setting were observed. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Effect of Curing Stress and Period on the Mechanical Properties of Cement-Mixed Sand

Cement mixing is one of the popular ground improvement technologies in geotechnical engineering practice. In order to effectively and confidently design cement-mixed soil structures for specific purposes, its stress-strain behavior needs to be well understood. Though there have been many studies on cement-mixed soils using different types of soils, their behaviors have not been generalized yet. As is the case with concrete materials, the hydration of cement in cement-mixed soil continues with time, thereby improving the strength and deformation characteristics of cement-mixed soil over time. In the field, the cementation bonds are formed under stress in case of in-situ soil. However, in the usual testing techniques, cementation bonds under stress has not been a point of consideration in most of the previous studies. This has led to an underestimation of the stress-strain behavior of cement-mixed soil. On the other hand, soils are subjected to confining stress during loading which has also some effect on the strength and deformation characteristics of soil which has not been considered yet in the case of cement-mixed sand. This study investigates the effect of curing stress and period on the strength and deformation characteristics of cement-mixed sand. The effect of confining stress in the triaxial test is also investigated in another series of specimens. A series of consolidated drained (CD) triaxial compression (TC) tests were done along with the small strain cyclic loading and bender element tests during monotonic loading to determine the small strain Young's modulus (E_v) and shear modulus (G_vh) respectively. The effect of the curing period is significant in the peak strength, stiffness, E_v, G_vh and also in the post peak regime. The curing stress also has a significant effect on the peak strength, E_v and G_vh. The confining stress has an effect on the peak strength, stiffness and in the post peak regime. However, the effect is small compared to clean sand.     

High mobility orthogonal frequency division multiple access channel estimation using basis expansion model

Owing to the loss of subcarrier orthogonalities in high-speed applications, the use of conventional frequency-domain-based channel estimation in high mobility orthogonal frequency division multiple access (OFDMA) systems such as mobile WiMax may give rise to an unacceptable high channel estimation error floor. To alleviate this problem, the authors develop some basis expansion model (BEM)-based estimation schemes for the OFDMA uplink. Specifically, the authors express the time-varying channel as a superposition of a small number of complex exponential basis functions spanning the entire Doppler range, and then formulate least square (LS) and linear minimum mean square error (LMMSE) algorithms to estimate the basis coefficients for two different types of pilot patterns. The authors also derive the respective Cramer-Rao lower bounds for these estimators. It has been shown that the time domain BEM using a pilot scheme where pilots are placed over time axis will give better performance under a high Doppler scenario. Lastly, using simulation results, the proposed algorithms have been found to have better estimation accuracy over current frequency domain estimation techniques. This is in addition to the advantage that the proposed algorithms have in general a lower computational complexity.     

The impact of design on improved learning in virtual worlds: an experimental study

Multimedia Tools and Applications - Virtual worlds are the most advanced form of virtual environments, which offer one of the best platforms for serving various domains. They are, especially, well...     

Using and comparing different decision tree classification techniques for mining ICDDR,B Hospital Surveillance data

In this research we have used decision tree induction algorithm on Hospital Surveillance data to classify admitted patients according to their critical condition. Three class labels, low, medium and high, are used to distinguish the criticality of the admitted patients. Several decision tree models are developed, evaluated, and compared with different performance metrics. Finally an efficient classifier is developed to classify records and make decision/predictions on some input parameters. The models developed in this research could be helpful during epidemic when huge number of patients arrive daily. Due to rush of duty doctors and scarcity of required number of physicians, it is hard to diagnose every patient. Any computer application could be helpful to diagnose and measure the criticality of the newly arrived patient with the help of the historical data kept in the surveillance database. The application would ask few questions on physical condition and on history of disease of the patient and accordingly determines the critical condition of the patient as low, medium or high.