A simple method for estimating growth parameters from multiple length-frequency data in presence of continuous recruitment

The extended recruitment season for short-lived species such as prawns biases the estimation of growth parameters from length-frequency data when conventional methods are used. We propose a simple method for overcoming this bias given a time series of length-frequency data. The difficulties arising from extended recruitment are eliminated by predicting the growth of the succeeding samples and the length increments of the recruits in previous samples. This method requires that some maximum size at recruitment can be specified. The advantages of this multiple length-frequency method are: it is simple to use; it requires only three parameters; no specific distributions need to be assumed; and the actual seasonal recruitment pattern does not have to be specified. We illustrate the new method with length-frequency data on the tiger prawn Penaeus esculentus from the north-western Gulf of Carpentaria, Australia.

Continuous after-the-fact leakage-resilient eCK-secure key exchange

Security models for two-party authenticated key exchange (AKE) protocols have developed over time to capture the security of AKE protocols even when the adversary learns certain secret values. Increased granularity of security can be modelled by considering partial leakage of secrets in the manner of models for leakage-resilient cryptography, designed to capture side-channel attacks. In this work, we use the strongest known partial-leakage-based security model for key exchange protocols, namely continuous after-the-fact leakage eCK (CAFL-eCK) model. We resolve an open problem by constructing the first concrete two-pass leakage-resilient key exchange protocol that is secure in the CAFL-eCK model.