Miscibility of oligomeric poly(dimethyl siloxanes) with long-chain unbranched alkanes: 2. Unsymmetrical cloud point curves

Previously obtained cloud point curves for mixtures of oligomeric poly(dimethyl siloxanes) (weight fraction W₁) with oligomeric polyethylenes have been supplemented by the use of samples of higher molecular weight. Initial flat regions at low-medium W₁ as well as a slight shoulder at extremely high W₁ are now considered to be due to crystallization, since the temperatures at which they occur are the melting points of the oligoethylenes. The actual cloud point curves were of an unsymmetrical dome shape, the form of which could not be simulated by using a concentration-independent interaction parameter g in the Flory-Huggins equation. However, spinodals calculated by assuming a quadratic dependence of g on composition were of the same shape as the experimental cloud point curves. The critical compositions calculated on this basis were also close to the experimental ones.     

Effect of Addition of Ni metal catalyst onto the Co and Fe supported catalysts for the formation of carbon nanotubes

Production of novel porous material is a major target in current material science research due to its wide applications. As carbon nanotube (CNTs) is a one dimensional hollow structure it is also one of the promising materials in applications ranging from electronics to hydrogen storage medium. Catalytic chemical vapor deposition (CCVD) is a method whereby CNTs can be produced in large amount. Thus, in this work, we have synthesized CNTs via pyrolysis of acetylene using various supported transition-metal catalysts in a fixed-bed reactor. Scanning electron microscope (SEM) and transmission electron microscope (TEM) were used to investigate the CNTs structure. The structures of nanotubes formed by acetylene pyrolysis were dependent on the catalysts used. It was found that alumina supported Ni/Fe catalyst inhibited the formation of CNTs growth while alumina supported Ni/Co catalyst gave high density of CNTs. However, nanotubes grown over alumina supported Ni/Fe catalyst were less dense due to the deactivation of the catalyst at the early stage of the pyrolysis process.     

Asymmetric Sharpless epoxidation of 13S‐hydroxy‐ 9Z, 11E‐octadecadienoic acid (13S‐HODE)

The asymmetric Sharpless epoxidation of methyl 13S‐hydroxy‐9Z, 11E‐octadeca‐dienoate (13S‐HODE, 1 ) with tert‐butyl hydroperoxide (TBHP) catalysed by titanium tetraisopropoxide {Ti(ⁱOPr)₄} in the presence of L(+)‐diisopropyl tartrate (L‐DIPT) gave methyl 13S‐hydroxy‐11S, 12S‐epoxy‐9Z‐octadecenoate 2 (erythro isomer) in 84% diastereomeric excess (de). The epoxidation of 1 with TBHP catalysed by Ti(ⁱOPr)₄ in the presence of D(‐)‐DIPT yielded methyl 13S‐hydroxy‐11RR12R‐epoxy‐9Z‐octadecenoate (threo isomer) 3 in 76% de.     

Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

The prerequisite for new versatile grippers is the capability to locate and perceive protests in their surroundings. It is realized that automated controllers are profoundly nonlinear frameworks, and a faultless numerical model is hard to get, in this way making it troublesome to control utilizing tried and true procedure. Here, a design of an adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict optimal inputs displacement of the gripper according to experimental tests and shapes of grasping objects. Instead of minimizing the observed training error, SVR _poly and SVR _rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach compared to other soft computing methodology.     

Graft Copolymerization of Acrylamide onto Oil Palm Empty Fruit Bunch (OPEFB) Fiber

Grafting of acrylamide (AAm) onto oil palm empty fruits bunch fiber using hydrogen peroxide as initiator and methyl acrylate as comonomer was investigated. The amount of comonomer needed to make grafting of acrylamide possible was determined. The percentage of poly(acrylamide) and the comonomer in the final graft copolymer was estimated by elemental analysis. Results obtained indicated that methyl acrylate facilitated the incorporation of acrylamide monomer onto OPEFB. The reactivity ratios for both monomers were determined by using Fineman–Ross plot. The effects of reaction temperature and period as well as amount of the initiator, solvent, monomer and comonomer on the percentage of grafting at fixed amount of comonomer (11 mmol) were studied. Maximum percentage of grafting was achieved when the amount of initiator and solvent 3.98×10⁻³ mol and 50 mL respectively. The optimum reaction temperature was 50 ^○C and the reaction period was 90 min. Highest percentage of grafting was 232% when 25.6 mmol of acrylamide was used under these optimum conditions. The presence of functional group in the grafted polymer is characterized by infrared spectroscopy and the surface morphology is observed by scanning electron microscopy. Thermoanalytic investigation on OPEFB and OPEFB-g-PAAM were carried out to evaluate the thermal stability and respective activation energy of the materials.     

Using a virtual environment to assess cognition in the elderly

Early diagnosis of Alzheimer’s disease (AD) is essential if treatments are to be administered at an earlier point in time before neurons degenerate to a stage beyond repair. In order for early detection to occur tools used to detect the disorder must be sensitive to the earliest of cognitive impairments. Virtual reality technology offers opportunities to provide products which attempt to mimic daily life situations, as much as is possible, within the computational environment. This may be useful for the detection of cognitive difficulties. We develop a virtual simulation designed to assess visuospatial memory in order to investigate cognitive function in a group of healthy elderly participants and those with a mild cognitive impairment (MCI). Participants were required to guide themselves along a virtual path to reach a virtual destination which they were required to remember. The preliminary results indicate that this virtual simulation has the potential to be used for detection of early AD since significant correlations of scores on the virtual environment with existing neuropsychological tests were found. Furthermore, the test discriminated between healthy elderly participants and those with a MCI.     

A survey on video content rating: taxonomy,challenges and open issues

Multimedia Tools and Applications - Rating a video based on its content is one of the most important solutions to classify videos for audience age groups. In this regard, Film content rating and TV...     

Color image segmentation using adaptive unsupervised clustering approach

This paper presents the Region Splitting and Merging-Fuzzy C-means Hybrid Algorithm (RFHA), an adaptive unsupervised clustering approach for color image segmentation, which is important in image analysis and in understanding pattern recognition and computer vision field. Histogram thresholding technique is applied in the formation of all possible cells, used to split the image into multiple homogeneous regions. The merging technique is applied to merge perceptually close homogeneous regions and obtain better initialization for the Fuzzy C-means clustering approach. Experimental results have demonstrated that the proposed scheme could obtain promising segmentation results, with 12% average improvement in clustering quality and 63% reduction in classification error compared with other existing segmentation approaches.     

Self-generation of reward by logarithmic transformation of multiple sensor evaluations

Artificial Life and Robotics - Although the design of the reward function in reinforcement learning is important, it is difficult to design a system that can adapt to a variety of environments and...     

LPP solution schemes for use with face recognition

Locality preserving projection (LPP) is a manifold learning method widely used in pattern recognition and computer vision. The face recognition application of LPP is known to suffer from a number of problems including the small sample size (SSS) problem, the fact that it might produce statistically identical transform results for neighboring samples, and that its classification performance seems to be heavily influenced by its parameters. In this paper, we propose three novel solution schemes for LPP. Experimental results also show that the proposed LPP solution scheme is able to classify much more accurately than conventional LPP and to obtain a classification performance that is only little influenced by the definition of neighbor samples.