Financial assistance for purchase of large fishing vessels

The Government of Sri Lanka is improving off-shore and deep-shore fishing by introducing more sophisticated and larger vessels into the Sri Lanka waters, together with the offer of subsidies, tax holidays and other concessions to those who seek to operate such fishing vessels. As a first step, some thirty 38 foot class fishing vessels are to be introduced. The requisite variety and quantity of fishing gear is supplied together with the vessels, and operators will be given further assistance to either export or market their catches locally by provision of insulated fish transport vans, jeeps with trailers, crushed ice plants, fish storage cabinets and adequate spares for at least the next few years. A description is given of the credit terms offered for the purchase of such vessels, pre-shipment finance, and loans and hire purchase facilities.

Supporting excavations in clay - From analysis to decision-making

Finite Element Analysis (FEA) is used to calibrate a decision-making tool based on an extension of the Mobilized Strength Design (MSD) method which permits the designer an extremely simple method of predicting ground displacements during construction. This newly extended MSD approach accommodates a number of issues which are important in underground construction between in-situ walls, including: alternative base heave mechanisms suitable either for wide excavations in relatively shallow soft clay strata, or narrow excavations in relatively deep soft strata; the influence of support system stiffness in relation to the sequence of propping of the wall; and the capability of dealing with stratified ground. These developments should make it possible for a design engineer to take informed decisions on the relationship between prop spacing and ground movements, or the influence of wall stiffness, or on the need for and influence of a jet-grouted base slab, for example, without having to conduct project-specific FEA. © 2009 Taylor & Francis Group.

Decision tree-based context clustering based on cross validation and hierarchical priors

The standard, ad-hoc stopping criteria used in decision tree-based context clustering are known to be sub-optimal and require parameters to be tuned. This paper proposes a new approach for decision tree-based context clustering based on cross validation and hierarchical priors. Combination of cross validation and hierarchical priors within decision tree-based context clustering offers better model selection and more robust parameter estimation than conventional approaches, with no tuning parameters. Experimental results on HMM-based speech synthesis show that the proposed approach achieved significant improvements in naturalness of synthesized speech over the conventional approaches. © 2011 IEEE.