Effect of prey concentrations and feed training on production of Hoplias lacerdae juvenile

The aim of this work was to investigate the s of daily prey concentration during the first 15 days of active feeding of Hoplias lacerdae larvae, and the juvenile size on the feed training. In the first phase, the larvae received five Artemia nauplii concentrations (P). In the second phase, the juveniles from each treatment were trained to accept formulated diet. Superior growth was related to higher initial daily prey concentrations (900 and 1100 nauplii larvae-1). During feed training, the growth tendency was similar to that verified in the first phase. The lowest values of specific growth rate (SGR) were registered after the introduction of the semi-moist diet used in the feed training. However, the values of SGR recovered along the experiment and similar rates were found among the treatments. Survival, mortality and cannibalism were similar in the different treatments at the end of both phases. It can be concluded that: the prey concentration affects growth of H. lacerdae during the first 15 days of active feeding, and feed training can be initialized with juveniles of about 16 mm of total length.

Produção de juvenis de Pseudoplatystoma coruscans (Agassiz, 1829) com dietas vivas e formuladas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Treinamento alimentar de pintado Pseudoplatystoma coruscans (Agassiz, 1829): sobrevivência, crescimento e aspectos econômicos

Pós-graduação em Aquicultura - FCAV

Proactive communication management beats hostile media exposure : training for multi-cultural community leaders in living with mass media

Proactive communication management instead of mortification in the glare of hostile media attention became the theme of a four-day training program for multi-cultural community leaders, the object of this research. The program in Brisbane from December 2009 through to February this year was conducted under auspices of a Community Media Link grant program shared by Griffith University and the Queensland Ethnic Communities Council, together with Journalism academics from the Queensland University of Technology. Twenty-eight participants from 23 organisations took part, with a team of nine facilitators from the host organisations, and guest presenters from the news media. This paper reviews the process, taking into account: its objectives, to empower participants by showing how Australian media operate and introducing participants to journalists; pedagogical thrust, where overview talks, with role play seminars with guest presenters from the media, were combined with practice in interviews and writing for media; and outcomes, assessed on the basis of participants’ responses. The research methodology is qualitative, in that the study is based on discussions to review the planning and experience of sessions, and anonymous, informal feed-back questionnaires distributed to the participants. Background literature on multiculturalism and community media was referred to in the study. The findings indicate positive outcomes for participants from this approach to protection of persons unversed in living in the Australian “mediatised” environment. Most affirmed that the “production side” perspective of the exercise had informed and motivated them effectively, such that henceforth they would venture far more into media management, in their community leadership roles.

Novel algorithm for online voltage stability assessment based on feed forward neural network

This paper presents an artificial feed forward neural network (FFNN) approach for the assessment of power system voltage stability. A novel approach based on the input-output relation between real and reactive power, as well as voltage vectors for generators and load buses is used to train the neural net (NN). The input properties of the feed forward network are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The neural network is trained for the L-index output as the target vector for each of the system loads. Two separate trained NN, corresponding to normal loading and contingency, are investigated on the 367 node practical power system network. The performance of the trained artificial neural network (ANN) is also investigated on the system under various voltage stability assessment conditions. As compared to the computationally intensive benchmark conventional software, near accurate results in the value of L-index and thus the voltage profile were obtained. Proposed algorithm is fast, robust and accurate and can be used online for predicting the L-indices of all the power system buses. The proposed ANN approach is also shown to be effective and computationally feasible in voltage stability assessment as well as potential enhancements within an overall energy management system in order to determining local and global stability indices

Tracking the states of a nonlinear system in the weight-space of a feed-forward neural network

Nonlinear, non-stationary signals are commonly found in a variety of disciplines such as biology, medicine, geology and financial modeling. The complexity (e.g. nonlinearity and non-stationarity) of such signals and their low signal to noise ratios often make it a challenging task to use them in critical applications. In this paper we propose a new neural network based technique to address those problems. We show that a feed forward, multi-layered neural network can conveniently capture the states of a nonlinear system in its connection weight-space, after a process of supervised training. The performance of the proposed method is investigated via computer simulations.

Regularization and complexity control in feed-forward networks

In this paper we consider four alternative approaches to complexity control in feed-forward networks based respectively on architecture selection, regularization, early stopping, and training with noise. We show that there are close similarities between these approaches and we argue that, for most practical applications, the technique of regularization should be the method of choice.

Topographic mappings and feed-forward neural networks

This thesis is a study of the generation of topographic mappings - dimension reducing transformations of data that preserve some element of geometric structure - with feed-forward neural networks. As an alternative to established methods, a transformational variant of Sammon's method is proposed, where the projection is effected by a radial basis function neural network. This approach is related to the statistical field of multidimensional scaling, and from that the concept of a 'subjective metric' is defined, which permits the exploitation of additional prior knowledge concerning the data in the mapping process. This then enables the generation of more appropriate feature spaces for the purposes of enhanced visualisation or subsequent classification. A comparison with established methods for feature extraction is given for data taken from the 1992 Research Assessment Exercise for higher educational institutions in the United Kingdom. This is a difficult high-dimensional dataset, and illustrates well the benefit of the new topographic technique. A generalisation of the proposed model is considered for implementation of the classical multidimensional scaling (¸mds}) routine. This is related to Oja's principal subspace neural network, whose learning rule is shown to descend the error surface of the proposed ¸mds model. Some of the technical issues concerning the design and training of topographic neural networks are investigated. It is shown that neural network models can be less sensitive to entrapment in the sub-optimal global minima that badly affect the standard Sammon algorithm, and tend to exhibit good generalisation as a result of implicit weight decay in the training process. It is further argued that for ideal structure retention, the network transformation should be perfectly smooth for all inter-data directions in input space. Finally, there is a critique of optimisation techniques for topographic mappings, and a new training algorithm is proposed. A convergence proof is given, and the method is shown to produce lower-error mappings more rapidly than previous algorithms.

Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting

Extreme learning machine (ELM) is originally proposed for single- hidden layer feed-forward neural networks (SLFN). From the functional equivalence of fuzzy logic systems and SLFN, the fuzzy logic systems can be interpreted as a special case of SLFN under some mild conditions. Hence the fuzzy logic systems can be trained using SLFN's learning algorithms. Considering the same equivalence, ELM is utilized here to train interval type-2 fuzzy logic systems (IT2FLSs). Based on the working principle of the ELM, the parameters of the antecedent of IT2FLSs are randomly generated while the consequent part of IT2FLSs is optimized using Moore-Penrose generalized inverse of ELM. Application of the developed model to electricity load forecasting is another novelty of the research work. Experimental results shows better forecasting performance of the proposed model over the two frequently used forecasting models.

A Best Practice Process for Fleet Safety Training

This paper outlines a process for fleet safety training based on research and management development programmes undertaken at the University of Huddersfield in the UK (www.hud.ac.uk/sas/trans/transnews.htm) and CARRS-Q in Australia (www.carrsq.qut.edu.au/staff/Murray.jsp) over the past 10 years.