Reference Points Based on Dynamic Optimisation: A Versatil Algorithm for Mixed Fishery Management with Bio-economic Agestructured Models

Single-species management objectives may not be consistent within mixed fisheries. They may lead species to unsafe situations, promote discarding of over-quota and/or misreporting of catches. We provide an algorithm for characterising bio-economic reference points for a mixed fishery as the steady-state solution of a dynamic optimal management problem. The optimisation problem takes into account: i) that species are fishing simultaneously in unselective fishing operations and ii)intertemporal discounting and fleet costs to relate reference points to discounted economic profits along optimal trajectories. We illustrate how the algorithm can be implemented by applying it to the European Northern Stock of Hake (Merluccius merluccius), where fleets also capture Northern megrim (Lepidorhombus whiffiagonis) and Northern anglerfish (Lophius piscatorius and Lophius budegassa). We find that optimal mixed management leads to a target reference point that is quite similar to the 2/3 of the Fmsy single-species (hake) target. Mixed management is superior to singlespecies management because it leads the fishery to higher discounted profits with higher long-term SSB for all species. We calculate that the losses due to the use of the Fmsy single-species (hake) target in this mixed fishery account for 11.4% of total discounted profits.

Study of an object recognition algorithm

This project introduces an improvement of the vision capacity of the robot Robotino operating under ROS platform. A method for recognizing object class using binary features has been developed. The proposed method performs a binary classification of the descriptors of each training image to characterize the appearance of the object class. It presents the use of the binary descriptor based on the difference of gray intensity of the pixels in the image. It shows that binary features are suitable to represent object class in spite of the low resolution and the weak information concerning details of the object in the image. It also introduces the use of a boosting method (Adaboost) of feature selection al- lowing to eliminate redundancies and noise in order to improve the performance of the classifier. Finally, a kernel classifier SVM (Support Vector Machine) is trained with the available database and applied for predictions on new images. One possible future work is to establish a visual servo-control that is to say the reac- tion of the robot to the detection of the object.

Genetics of Type III Bartter Syndrome in Spain, Proposed Diagnostic Algorithm

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Molecular diagnosis of distal renal tubular acidosis in Tunisian patients: proposed algorithm for Northern Africa populations for the ATP6V1B1, ATP6V0A4 and SCL4A1 genes

Background: Primary distal renal tubular acidosis (dRTA) caused by mutations in the genes that codify for the H+ -ATPase pump subunits is a heterogeneous disease with a poor phenotype-genotype correlation. Up to now, large cohorts of dRTA Tunisian patients have not been analyzed, and molecular defects may differ from those described in other ethnicities. We aim to identify molecular defects present in the ATP6V1B1, ATP6V0A4 and SLC4A1 genes in a Tunisian cohort, according to the following algorithm: first, ATP6V1B1 gene analysis in dRTA patients with sensorineural hearing loss (SNHL) or unknown hearing status. Afterwards, ATP6V0A4 gene study in dRTA patients with normal hearing, and in those without any structural mutation in the ATP6V1B1 gene despite presenting SNHL. Finally, analysis of the SLC4A1 gene in those patients with a negative result for the previous studies. Methods: 25 children (19 boys) with dRTA from 20 families of Tunisian origin were studied. DNAs were extracted by the standard phenol/chloroform method. Molecular analysis was performed by PCR amplification and direct sequencing. Results: In the index cases, ATP6V1B1 gene screening resulted in a mutation detection rate of 81.25%, which increased up to 95% after ATP6V0A4 gene analysis. Three ATP6V1B1 mutations were observed: one frameshift mutation (c.1155dupC; p.Ile386fs), in exon 12; a G to C single nucleotide substitution, on the acceptor splicing site (c.175-1G > C; p.?) in intron 2, and one novel missense mutation (c. 1102G > A; p. Glu368Lys), in exon 11. We also report four mutations in the ATP6V0A4 gene: one single nucleotide deletion in exon 13 (c.1221delG; p. Met408Cysfs* 10); the nonsense c.16C > T; p.Arg6*, in exon 3; and the missense changes c.1739 T > C; p.Met580Thr, in exon 17 and c.2035G > T; p.Asp679Tyr, in exon 19. Conclusion: Molecular diagnosis of ATP6V1B1 and ATP6V0A4 genes was performed in a large Tunisian cohort with dRTA. We identified three different ATP6V1B1 and four different ATP6V0A4 mutations in 25 Tunisian children. One of them, c.1102G > A; p.Glu368Lys in the ATP6V1B1 gene, had not previously been described. Among deaf since childhood patients, 75% had the ATP6V1B1 gene c. 1155dupC mutation in homozygosis. Based on the results, we propose a new diagnostic strategy to facilitate the genetic testing in North Africans with dRTA and SNHL.

Some experiments on solving multistage stochastic mixed 0-1 programs with time stochastic dominance constraints

In this work we extend to the multistage case two recent risk averse measures for two-stage stochastic programs based on first- and second-order stochastic dominance constraints induced by mixed-integer linear recourse. Additionally, we consider Time Stochastic Dominance (TSD) along a given horizon. Given the dimensions of medium-sized problems augmented by the new variables and constraints required by those risk measures, it is unrealistic to solve the problem up to optimality by plain use of MIP solvers in a reasonable computing time, at least. Instead of it, decomposition algorithms of some type should be used. We present an extension of our Branch-and-Fix Coordination algorithm, so named BFC-TSD, where a special treatment is given to cross scenario group constraints that link variables from different scenario groups. A broad computational experience is presented by comparing the risk neutral approach and the tested risk averse strategies. The performance of the new version of the BFC algorithm versus the plain use of a state-of-the-artMIP solver is also reported.

Application of Entropy and Fractal Dimension Analyses to the Pattern Recognition of Contaminated Fish Responses in Aquaculture

The objective of the work was to develop a non-invasive methodology for image acquisition, processing and nonlinear trajectory analysis of the collective fish response to a stochastic event. Object detection and motion estimation were performed by an optical flow algorithm in order to detect moving fish and simultaneously eliminate background, noise and artifacts. The Entropy and the Fractal Dimension (FD) of the trajectory followed by the centroids of the groups of fish were calculated using Shannon and permutation Entropy and the Katz, Higuchi and Katz-Castiglioni's FD algorithms respectively. The methodology was tested on three case groups of European sea bass (Dicentrarchus labrax), two of which were similar (C1 control and C2 tagged fish) and very different from the third (C3, tagged fish submerged in methylmercury contaminated water). The results indicate that Shannon entropy and Katz-Castiglioni were the most sensitive algorithms and proved to be promising tools for the non-invasive identification and quantification of differences in fish responses. In conclusion, we believe that this methodology has the potential to be embedded in online/real time architecture for contaminant monitoring programs in the aquaculture industry.

A Data Dropout Compensation Algorithm Based on the Iterative Learning Control Methodology for Discrete-Time Systems

This paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input signals vector to its corresponding model at the sampling instants and the sensor measures the output signals vector. The iterative learning law is processed in a controller located far away of the models so the control signals vector has to be transmitted from the controller to the actuators through transmission channels. Such a law uses the measurements of each model to generate the input vector to be applied to its subsequent model so the measurements of the models have to be transmitted from the sensors to the controller. All transmissions are subject to failures which are described as a binary sequence taking value 1 or 0. A compensation dropout technique is used to replace the lost data in the transmission processes. The convergence to zero of the errors between the output signals vector and a reference one is achieved as the number of models tends to infinity.