Testing Delivery Systems in Transnational virtual learning: the vocational management training for the european tourism industry (VocMat) case study

This article discusses the lessons learned from developing and delivering the Vocational Management Training for the European Tourism Industry (VocMat) online training programme, which was aimed at providing flexible, online distance learning for the European tourism industry. The programme was designed to address managers ‘need for flexible, senior management level training which they could access at a time and place which fitted in with their working and non-work commitments. The authors present two main approaches to using the Virtual Learning Environment, the feedback from the participants, and the implications of online Technology in extending tourism training opportunities

Using problem-based learning for introducing producer theory and market structure in intermediate microeconomics

This paper shows how instructors can use the problem‐based learning method to introduce producer theory and market structure in intermediate microeconomics courses. The paper proposes a framework where different decision problems are presented to students, who are asked to imagine that they are the managers of a firm who need to solve a problem in a particular business setting. In this setting, the instructors’ role isto provide both guidance to facilitate student learning and content knowledge on a just‐in‐time basis

Viability study of a multiplex diagnostic platform for Chagas disease

A new multiplex assay platform was evaluated to detect Trypanosoma cruzi infection using the recombinant antigens CRA, FRA, CRAFRA fusion and parasite lysate. The antigens presented different sensitivity and specificity in a singleplex test when compared to a serial dilution of two pools comprising 10 positive serum samples and one pool of 10 negative samples. The recombinant protein CRA presented lower sensitivity (55%) in contrast to the 100% specificity and sensitivity of FRA, CRAFRA and T. cruzi lysate. These antigens also showed good results in a duplex test and the duplex test with CRAFRA/T. cruzi lysate showed better performance with 100% specificity and sensitivity, as well as a lower cut-off value in comparison to the other duplex test, FRA/T. cruzi lysate. Hence, when the antigens were used in duplex format, both tests showed decreased cut-off values and no interference between different bead sets, resulting in increasing sensitivity and specificity. The results of these multiplex tests show that they could be an alternative to singleplex detection for Chagas disease, and also indicate the necessity of using multiplex diagnostic tools to increase the sensitivity and specificity for diagnostic tests. Emerging data from the T. cruzi genome and from its ORFeome project will also allow the identification of new antigens for this disease detection application.

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Policy gradient based Reinforcement Learning for real autonomous underwater cable tracking

This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task

The oldest Triassic platform margin reef from the Alpine - Carpathian region (Aggtelek, NE Hungary): platform evolution, reefal biota and biostratigraphic framework

The 1:10,000 scale mapping of the southern part of the Aggtelek Plateau (Western Carpathians, Silica Nappe, NE Hungary) and the study of five sections revealed two Middle Triassic reef bodies. In the late Pelsonian the uniform Steinalm Platform was drowned and dissected due to the Reifling Event. A connection with the open sea was established, indicated by the appearance of gladigondolellid conodonts from the early Illyrian. Basins and highs were formed. In the NW part of the studied area lower - middle? Illyrian basinal carbonates were followed by a platform margin reef (early? - middle Illyrian; reef stage 1) developed on a morphological high. This is the oldest known Triassic platform margin reef within the Alpine-Carpathian region. The reef association is dominated by sphinctozoans and microproblematics. The fossils are characteristic of the Wetterstein - type reef communities. Differently from this in the SE part of the studied region a basin existed from the late Pelsonian until the early Ladinian. During the late Illyrian - early Ladinian, the reef prograded to the SE, and reef stage 2 was established. Meanwhile, on the NW part of the platform a lagoon was formed behind the reef. Based on our palaeontological study the stratigraphic range of Colospongia catenulata, Follicatena cautica, Solenolmia manon manon, Vesicocaulis oenipontanus must be extended down to the middle Illyrian. Synsedimentary tectonics were detected in the 1. Binodosus Subzone, 2. Trinodosus Zone - the most part of the Reitzi Zone, 3. Avisianum Subzone.

Visuo-motor and cognitive procedural learning in children with basal ganglia pathology.

We investigated procedural learning in 18 children with basal ganglia (BG) lesions or dysfunctions of various aetiologies, using a visuo-motor learning test, the Serial Reaction Time (SRT) task, and a cognitive learning test, the Probabilistic Classification Learning (PCL) task. We compared patients with early (<1 year old, n=9), later onset (>6 years old, n=7) or progressive disorder (idiopathic dystonia, n=2). All patients showed deficits in both visuo-motor and cognitive domains, except those with idiopathic dystonia, who displayed preserved classification learning skills. Impairments seem to be independent from the age of onset of pathology. As far as we know, this study is the first to investigate motor and cognitive procedural learning in children with BG damage. Procedural impairments were documented whatever the aetiology of the BG damage/dysfunction and time of pathology onset, thus supporting the claim of very early skill learning development and lack of plasticity in case of damage.