Biology, Fishery, Conservation and Management of Indian Ocean Tuna Fisheries

The focus of the study is to explore the recent trend of the world tuna fishery with special reference to the Indian Ocean tuna fisheries and its conservation and sustainable management. In the Indian Ocean, tuna catches have increased rapidly from about 179959 t in 1980 to about 832246 t in 1995. They have continued to increase up to 2005; the catch that year was 1201465 t, forming about 26% of the world catch. Since 2006 onwards there has been a decline in the volume of catches and in 2008 the catch was only 913625 t. The Principal species caught in the Indian Ocean are skipjack and yellowfin. Western Indian Ocean contributed 78.2% and eastern Indian Ocean 21.8% of the total tuna production from the Indian Ocean. The Indian Ocean stock is currently overfished and IOTC has made some recommendations for management regulations aimed at sustaining the tuna stock. Fishing operations can cause ecological impacts of different types: by catches, damage of the habitat, mortalities caused by lost or discarded gear, pollution, generation of marine debris, etc. Periodic reassessment of the tuna potential is also required with adequate inputs from exploratory surveys as well as commercial landings and this may prevent any unsustainable trends in the development of the tuna fishing industry in the Indian Ocean

Hierarchy of Scales in Language Dynamics

Methods and insights from statistical physics are finding an increasing variety of applications where one seeks to understand the emergent properties of a complex interacting system. One such area concerns the dynamics of language at a variety of levels of description, from the behaviour of individual agents learning simple artificial languages from each other, up to changes in the structure of languages shared by large groups of speakers over historical timescales. In this Colloquium, we survey a hierarchy of scales at which language and linguistic behaviour can be described, along with the main progress in understanding that has been made at each of them − much of which has come from the statistical physics community. We argue that future developments may arise by linking the different levels of the hierarchy together in a more coherent fashion, in particular where this allows more effective use of rich empirical data sets

Multivariate Bayesian classification of tongue movement ear pressure signals based on the wavelet packet transform

Tongue movement ear pressure signals have been used to generate controlling commands in human-machine interfaces. The objective of this study is to classify the controlled movement relating to an intended action from interfering signals that can be experienced. These interfering signals include but are not limited to, speech, coughing and drinking. Thus data was collected for six types of controlled movement and the various interfering signals, when subjects spoke, coughed or drank. The signal processing involves detection, segmentation, feature extraction and selection, and classification of tongue motions. The segmented signals were initially transformed into the wavelet packet domain, allowing for various features to be extracted based on statistical properties of the wavelet coefficients. These are then used as input into a Bayesian classifier under multivariate Gaussian assumptions. The average classification performance for identifying controlled movements and interfering tongue signals achieved 98% and 93.5% respectively. Thus the classification of tongue movement ear pressure signals based on the wavelet packet transform is robust. The application of this Bayesian classification strategy significantly reduces the interference of controlling commands when considered within a human-machine interface system operating in a challenging environment.<br/