Evaluation of quality of shark livers using bio-chemical properties and organoleptic score sheet

Shark livers are considered as an important raw material providing a quality fish oil. It has been reported to aid white — blood-cell production and act as an active ingredient in hemorrhoid treatments. It is also reported that liver oil as a good supplement of vitamin A and poly-unsaturated fatty acids which are important to the development of brain cells in human. Freshness of livers is very important to extract better quality oil. In Sri Lanka, the annual shark production amounts to 8000t, however the quality of livers collected from landing sites has not being measured yet. Present study was conducted to evaluate the quality of silky (Charcarninus fakiformis) shark livers available in Negombo and Beruwala landing sites in the West Coast of Sri Lanka and also to study the relationship between organoleptic and bio-chemical correlation on freshness of shark livers. Liver samples which were collected from landing sites in the West coast of Sri Lanka, were evaluated for external and internal colour, texture and odour. Total volatile nitrogen (TVN), pH value, free fatty acid (FFA%) and peroxide (PV) values of livers were also determined to assess quality. According to the organoleptic scoring system 4.3% of liver samples were categorized as best in quality while 30.4%, 56.5% and 8.7% rated as good, medium and poor in quality respectively at the Negombo and Beruwala landing sites. Bio-chemical analysis showed that the better quality livers had the highest score for sensory evaluation and low values for TVN, FFA and peroxide value while low quality livers gave low score for sensory evaluation and high TVN, FFA, peroxide values. Correlation coefficient of organoleptic scores against total volatile nitrogen value, pH value, free fatty acid % and peroxide value of shark livers were determined by statistical analysis. Organoleptic score of shark livers was found to be highly.

Learning the dynamics of the external world: Brain inspired learning for robotic applications

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has become exceptionally well adapted to learning to deal not only with the complex dynamics of our own limbs but also with novel dynamics in the external world. While learning of these dynamics includes learning the complex time-varying forces at the end of limbs through the updating of internal models, it must also include learning the appropriate mechanical impedance in order to stabilize both the limb and any objects contacted in the environment. This article reviews the field of human learning by examining recent experimental evidence about adaptation to novel unstable dynamics and explores how this knowledge about the brain and neuro-muscular system can expand the learning capabilities of robotics and prosthetics. © 2006.

Biologically-inspired object recognition system with features from complex wavelets

In this paper, a novel cortex-inspired feed-forward hierarchical object recognition system based on complex wavelets is proposed and tested. Complex wavelets contain three key properties for object representation: shift invariance, which enables the extraction of stable local features; good directional selectivity, which simplifies the determination of image orientations; and limited redundancy, which allows for efficient signal analysis using the multi-resolution decomposition offered by complex wavelets. In this paper, we propose a complete cortex-inspired object recognition system based on complex wavelets. We find that the implementation of the HMAX model for object recognition in [1, 2] is rather over-complete and includes too much redundant information and processing. We have optimized the structure of the model to make it more efficient. Specifically, we have used the Caltech 5 standard dataset to compare with Serre's model in [2] (which employs Gabor filter bands). Results demonstrate that the complex wavelet model achieves a speed improvement of about 4 times over the Serre model and gives comparable recognition performance. © 2011 IEEE.

Nature-inspired microfluidic propulsion using magnetic actuation.

In this work we mimic the efficient propulsion mechanism of natural cilia by magnetically actuating thin films in a cyclic but non-reciprocating manner. By simultaneously solving the elastodynamic, magnetostatic, and fluid mechanics equations, we show that the amount of fluid propelled is proportional to the area swept by the cilia. By using the intricate interplay between film magnetization and applied field we are able to generate a pronounced asymmetry and associated flow. We delineate the functional response of the system in terms of three dimensionless parameters that capture the relative contribution of elastic, inertial, viscous, and magnetic forces.

Adhesive properties of gecko-inspired mimetic via micropatterned carbon nanotube forests

The adhesive properties of the gecko foot have inspired designs of advanced micropatterned surfaces with increased contact areas. We have fabricated micropatterned pillars of vertically aligned carbon nanotube forests with a range of pillar diameters, heights, and spacings (or pitch). We used nanoindentation to measure their elastic and orthogonal adhesion properties and derive their scaling behavior. The patterning of nanotube forests into pillar arrays allows a reduction of the effective modulus from 10 to 15 MPa to 0.1-1 MPa which is useful for developing maximum conformal adhesion. © 2012 American Chemical Society.