Seasonal variations in gross biochemical composition, percent edibility, and condition index of the clam Ruditapes Decussatus cultivated in the Ria Formosa (South Portugal)

The grooved carpet shell clam, Ruditapes decussatus (L. 1758), is one of the most popular and profitable molluscs exploited in rearing plots in the Mediterranean. However, annual catch has been declining steadily since the early nineties. In order to understand the seasonality of its nutritional value, thus providing an improved basis for economical valuation of the resource, gross biochemical composition, percentage edibility and condition index were investigated during a year with monthly periodicity in a commercially exploited population of the clam Ruditapes decussatus in the Ria Formosa, a temperate mesotidal coastal lagoon located in the south of Portugal. Our results show that total and non-protein nitrogen co-varied during the year, resulting in a protein content that peaked in the warmest months. Although complementary in summer, carbohydrate and lipid contents showed irregular annual trends. The observed seasonality was comparable to that shown by studies elsewhere at similar latitudes, and are underpinned by the reproductive cycle of the species. Our results show the clams to be at their prime nutritional value at the beginning of summer, when protein content peaks.

Adaptive generalized predictive control algorithm implemented over an heterogeneous parallel architecture

In this paper a parallel implementation of an Adaprtive Generalized Predictive Control (AGPC) algorithm is presented. Since the AGPC algorithm needs to be fed with knowledge of the plant transfer function, the parallelization of a standard Recursive Least Squares (RLS) estimator and a GPC predictor is discussed here.

Adaptive generalized predictive control algorithm implemented over a DSP network

In this paper a parallel implementation of an Adaprtive Generalized Predictive Control (AGPC) algorithm is presented. Since the AGPC algorithm needs to be fed with knowledge of the plant transfer function, the parallelization of a standard Recursive Least Squares (RLS) estimator and a GPC predictor is discussed here.

Implementation of an daptive generalized predictive control algorithm over an heterogeneous parallel architecture

In this paper a parallel implementation of an Adaprtive Generalized Predictive Control (AGPC) algorithm is presented. Since the AGPC algorithm needs to be fed with knowledge of the plant transfer function, the parallelization of a standard Recursive Least Squares (RLS) estimator and a GPC predictor is discussed here.

Parallel implementation of an adaptive generalized predictive control algorithm

The Adaptive Generalized Predictive Control (AGPC) algorithm can be speeded up using parallel processing. Since the AGPC algorithm needs to be fed with the knowledge of the plant transfer function, the parallelization of a standard Recursive Least Squares (RLS) estimator and a GPC predictor is discussed here.

Parallel implementation of an adaptive generalized predictive control algorithm

The Adaptive Generalized Predictive Control (GPC) algorithm can be speeded up using parallel processing. Since the GPC algorithm needs to be fed with knowledge of the plant transfer function, the parallelization of a standard Recursive Least Squares (RLS) estimator and a GPC predictor is discussed here.