Operatives training programmes in India

During the last few years great changes have taken place in the fishing industry as a result of which production of fish in the world has increased enormously. From an insignificant trade employing tools and methods of primitive nature, fishing in many countries has become an important industry utilizing complex modern vessels equipped with electronic equipment and operating in the high seas with highly mechanized fishing gear.

Effect of some environmental factors on the acute toxicity of deltamethrin to common carp: a laboratory study under aerobic condition

The toxicity of xenobiotic in aquatic ecosystems is influenced by many factors such as ambient temperature, water hardness, pond soil type, etc. In the present study, it was observed that air temperature, water hardness and soil sediment have profound influence on the toxicity of deltamethrin to common carp fry (ay. length 3.5 ± 0.5 cm, ay. weight 0.58 ± 0.25 g); 96h LC(sub)50 values for common carp at 38.07 ± 2.20°C maximum and 27.86 ± 1.22°C minimum air temperature in soft and very hard water were 0.102 and 0.495 µg lˉ¹, respectively. This value had increased significantly to 2.37 and 3.02 µg at 30.55 ± 1.21°C maximum and 26.04 ± 0.61°C minimum air temperature, respectively. When sediment was included, 96h LC(sub)50 at 38.07°C maximum temperature in very hard water was 1.808 µg 1ˉ¹ and this had increased to 8.073 µg 1ˉ¹ when tested at 30.55°C maximum temperature. Due to the 7.5°C increase in maximum and 1.7°C in minimum temperature, toxicity increased significantly. Lower toxicity in very hard water in comparison to soft water may be due to the lower solubility of deltarnethrin and high level of calcium. Adsorption reaction of deltamethrin with clay, humus, FeOOH, MnOOH and particulate organic carbon, and complexation reaction with dissolved organic carbon were responsible for the lowered toxicity in the experiment with sediment. Exposure time had no significant effect on acute toxicity of deltamethrin.

Competitive training in hidden Markov models

The use of hidden Markov models is placed in a connectionist framework, and an alternative approach to improving their ability to discriminate between classes is described. Using a network style of training, a measure of discrimination based on the a posteriori probability of state occupation is proposed, and the theory for its optimization using error back-propagation and gradient ascent is presented. The method is shown to be numerically well behaved, and results are presented which demonstrate that when using a simple threshold test on the probability of state occupation, the proposed optimization scheme leads to improved recognition performance.

MMI training for continuous phoneme recognition on the TIMIT database

This paper reports our experiences with a phoneme recognition system for the TIMIT database which uses multiple mixture continuous density monophone HMMs trained using MMI. A comprehensive set of results are presented comparing the ML and MMI training criteria for both diagonal and full covariance models. These results using simple monophone HMMs show clear performance gains achieved by MMI training, and are comparable to the best reported by others including those which use context-dependent models. In addition, the paper discusses a number of performance and implementation issues which are crucial to successful MMI training.