Preliminary observations on the effect of sudden changes of temperature on survival of young Matrinxã (Brycon cephalus) under laboratory conditions

Matrinxã is a very promising amazonian fish for fish culture in Brazil. This study is aimed at determining the approximate tolerated temperature range in this species. Groups of ten young matrinxã specimens (15.1±0.8 cm average length and 58.3±10.3 g average weight) were subjected to 9 different temperatures for 24 hours without previous acclimation. Fish were transferred from an initial temperature of 27ºC to those ranging from 12 to 39ºC at 3ºC intervals. Both 12ºC and 39ºC temperatures were lethal for this species with 100% mortality rate. Following 2 minutes of exposure to 39ºC fish changed behavior, showing an increase in opercular movements and erratic swimming; mortality reached 100% after 18 minutes. At 12ºC, fish lost equilibrium immediately after exposure and started swimming erratically; after only 4 minutes fish became lethargic and remained immobile on the bottom of the tank. Total mortality was only evident following 24 hours. At 15ºC matrinxã lost equilibrium after 5 to 6 minutes of exposure but mortality was only 20% after 24 hours. Fish tolerated well temperatures ranging from 18 to 36ºC with 100% survival after 24 hours. This preliminary study suggests that temperatures between 18 and 36ºC are the approximate range normally tolerated by this species, although survival at other temperatures may be increased by gradually acclimating fish to the more severe increases or decreases in temperature. In addition, it indicates that matrinxã may be cultivated over a wide geographical area.

Thermal conditions affecting heat transfer in FDM/FFE: a contribution towards the numerical modelling of the process

The performance of parts produced by Free Form Extrusion (FFE), an increasingly popular additive manufacturing technique, depends mainly on their dimensional accuracy, surface quality and mechanical performance. These attributes are strongly influenced by the evolution of the filament temperature and deformation during deposition and solidification. Consequently, the availability of adequate process modelling software would offer a powerful tool to support efficient process set-up and optimisation. This work examines the contribution to the overall heat transfer of various thermal phenomena developing during the manufacturing sequence, including convection and radiation with the environment, conduction with support and between adjacent filaments, radiation between adjacent filaments and convection with entrapped air. The magnitude of the mechanical deformation is also studied. Once this exercise is completed, it is possible to select the material properties, process variables and thermal phenomena that should be taken in for effective numerical modelling of FFE.

Weld lines in extrusion: Understanding the role of the flow conditions

This work reports the implemen tation and verification of a new so lver in OpenFOAM® open source computational library, able to cope w ith integral viscoelastic models based on the integral upper-convected Maxwell model. The code is verified through the comparison of its predictions with anal ytical solutions and numerical results obtained with the differential upper-convected Maxwell model

Gamification e game-based learning: uma abordagem lúdica à aprendizagem

Relatório de estágio de mestrado em Ensino de Informática

O ensino em grupo de instrumentos musicais: um estudo de caso múltiplo em Portugal e no Brasil

Tese de Doutoramento em Estudos da Criança (área de especialização em Educação Musical)

Cross-language influences on new second language vocabulary acquisition: the effect of learning method

Dissertação de mestrado integrado em Psicologia

Das intenções às iniciativas: contributos para a integração das tecnologias de informação e comunicação nas práticas de ensino/aprendizagem na Universidade de Cabo Verde

Tese de Doutoramento em Ciências da Educação (área de especialização em Tecnologia Educativa).

Adult learning and education policies in Germany, Portugal and Sweden: An analysis of national reports to CONFINTEA VI

The chapter presents a theoretical proposal of three analytical models of Adult Learning and Education (ALE) policies. Some analytical categories and the corresponding dimensions are organised according to the ALE rationale which is typical of each social policy model. Historical, cultural and educational features are mentioned in connexion with the different policy models and its interpretative capacity to making sense of policies and practices implemented in Germany, Portugal and Sweden. !e analysis includes the states of the art and the official representations of ALE produced by the respective national authorities through national reports which were presented to CONFINTEA VI (2009).

Using students’ learning style to create effective learning groups in MCSCL environments

Students have different ways for learning and processing information. Some students prefer learning through seeing while others prefer learning through listening; some students prefer doing activities while other prefer reflecting.Some students reason logically, while others reason intuitively, etc. Identifying the learning style of each student, and providing learning content based on these styles represents a good method to enhance the learning quality. However, there are no efforts onhow to detect the students’ learning styles in mobile computer supported collaborative learning (MCSCL) environments. We present in this paper new ways for automatically detecting the learning styles of students in MCSCL environments based on the learning style model of Felder-Silverman. The identified learning styles of students could be then stored and used at anytime toassign each one of them to his/her appropriate learning group.

Literacy practices in adult learning biographies: possibilities and constraints

[Extrat] The answer to the social and economic challenges that it is assumed literacy (or its lack) puts to developed countries deeply concerns public policies of governments namely those of the OECD area. In the last decades, these concerns gave origin to several and diverse monitoring devices, initiatives and programmes for reading (mainly) development, putting a strong stress on education. UNESCO (2006, p. 6), for instance, assumes that the literacy challenge can only be met raising the quality of primary and secondary education and intensifying programmes explicitly oriented towards youth and adult literacy. (...)

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Learning Robotics for youngsters - the RoboParty experience

Series: "Advances in intelligent systems and computing , ISSN 2194-5357, vol. 417"

Um modelo para a gestão do conhecimento organizacional no contexto dos serviços partilhados com recurso à utilização do e-Learning

Tese de Doutoramento em Tecnologias e Sistemas de Informação

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.