Solid–liquid structural break-up in M2 tool steel for semi-solid metal processing

The success of semi-solid metal processing mostly depends on the formation of suitable starting microstructure, which must consist of solid metal spheroids in a liquid matrix. Various methods of obtaining this structure have been established; they include recrystallisation and partial melting (RAP), strain-induced melt-activated (SIMA), or simple mechanical stirring, to name a few. These methods, as widely discussed, have mostly been applied with light alloys, mainly aluminium based. This article discusses solid–liquid structural break-up in M2 tool steel subjected to a direct re-melting procedure from the as-annealed condition. The role of carbide dissolution in the grain boundary liquation of the steel is described. This leads to the production of near spheroidal solid grains in a liquid matrix, a microstructure suitable for the thixoforming process. Microstructural examination revealed that carbide particles contained in bands at 1220 °C slowly disappeared with temperature. At 1300 °C, the solid grains seemed to be free from carbides. Most of the carbides had now re-precipitated at the grain boundaries. Thixoforming carried out at 1340 and 1360 °C revealed the thixotropic properties of the semi-solid metal slurries. The results indicate a widening of the range of potential routes to thixoformable microstructures.     

Extraction and physicochemical properties of low free fatty acid crude palm oil

Response surface methodology was applied to optimize the pre-treatment of oil palm (Elaeis guineensis) fruit spikelets before oil extraction. The treatment applied was drying at different times and temperatures. The dried spikelets were then subjected to mechanical processes and crude oil was extracted. A central composite design was employed to study the responses, namely percentage of free fatty acids (FFA) and oil yield, and the optimum conditions for minimum FFA and maximum oil yield were identified from their respective contour plots. It was concluded that the pre-treatment should be carried out for 12.8 h at 66.8 °C. Under the optimum conditions, the corresponding response values for FFA and oil yield were 1% and 33.6%, respectively. Some of the physicochemical properties of the extracted oil were then determined. The low free fatty acid crude palm oil exhibited good physicochemical properties and could be useful for industrial applications.     

A hybrid approach for learning concept hierarchy from Malay text using artificial immune network

A concept hierarchy is an integral part of an ontology but it is expensive and time consuming to build. Motivated by this, many unsupervised learning methods have been proposed to (semi-) automatically develop a concept hierarchy. A significant work is the Guided Agglomerative Hierarchical Clustering (GAHC) which relies on linguistic patterns (i.e., hypernyms) to guide the clustering process. However, GAHC still relies on contextual features to build the concept hierarchy, thus data sparsity still remains an issue in GAHC. Artificial Immune Systems are known for robustness, noise tolerance and adaptability. Thus, an extension to the GAHC is proposed by hybridizing it with Artificial Immune Network (aiNet) which we call Guided Clustering and aiNet for Learning Concept Hierarchy (GCAINY). In this paper, we have tested GCAINY using two parameter settings. The first parameter setting is obtained from the literature as a baseline parameter setting and second is by automatic parameter tuning using Particle Swarm Optimization (PSO). The effectiveness of the GCAINY is evaluated on three data sets. For further validations, a comparison between GCAINY and GAHC has been conducted and with statistical tests showing that GCAINY increases the quality of the induced concept hierarchy. The results reveal that the parameters value found by using PSO significantly produce better concept hierarchy than the vanilla parameter. Thus it can be concluded that the proposed approach has greater ability to be used in the field of ontology learning.     

The application of ridge polynomial neural network to multi-step ahead financial time series prediction

Motivated by the slow learning properties of multilayer perceptrons (MLPs) which utilize computationally intensive training algorithms, such as the backpropagation learning algorithm, and can get trapped in local minima, this work deals with ridge polynomial neural networks (RPNN), which maintain fast learning properties and powerful mapping capabilities of single layer high order neural networks. The RPNN is constructed from a number of increasing orders of Pi–Sigma units, which are used to capture the underlying patterns in financial time series signals and to predict future trends in the financial market. In particular, this paper systematically investigates a method of pre-processing the financial signals in order to reduce the influence of their trends. The performance of the networks is benchmarked against the performance of MLPs, functional link neural networks (FLNN), and Pi–Sigma neural networks (PSNN). Simulation results clearly demonstrate that RPNNs generate higher profit returns with fast convergence on various noisy financial signals.     

Development of Pistachio (Pistacia vera L.) Spread

Pistachio nut (Pistacia vera L.) is one of the most delicious and nutritious nuts in the world. Pistachio spreads were developed using pistachio paste as the main component, icing sugar, soy protein isolate (SPI), and red palm oil (RPO), at different ratios. The highest mean scores of all the sensory attributes were depicted by spreads that were made without addition of SPI. It was found that the work of shear was 0 to 11.0 kg s for an acceptable spread. Sensory spreadability, overall texture, spreadability, and overall acceptability were negatively correlated (R > 0.83) with the work of shear of spreads. The findings indicated that the presence of RPO had a direct effect on the viscoelastic behavior of the pistachio spreads. The a values, which are related to the green color of the pistachio product ranged from 1.7 to 3.9 for spread without addition of RPO, and 4.0 to 5.3 in the presence of RPO. Practical Application The development of pistachio spread would potentially increase the food uses of pistachio and introduce consumers with a healthier snack food.     

Epoxidized natural rubber–alumina nanoparticle composites: Optimization of mixer parameters via response surface methodology

Epoxidized natural rubber–alumina nanoparticle composites were prepared by melt compounding with an internal mixer for a constant filler loading of 10 phr. Mixer parameters such as the mixing temperature, mixing time, and rotor speed were screened and optimized with response surface methodology to maximize the impact strength. The parameters were selected as three independent variables and the impact strength (J/m) was selected as the response in a screening factor study. The mixing temperature and its interaction terms were identified as insignificant factors with a P value greater than 0.0500. The optimum calculated values of the tested variables (rotor speed and mixing time) for the maximum impact strength were found to be a rotor speed of 60 rpm and a mixing time of 6 min with a predicted impact strength of 208.88 J/m. These predicted optimum parameters were tested in real experiments. The final impact strength was found to be close to the predicted value of 215.84 J/m, with only a 3.33% deviation. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2010