Usability of Virtual Museums and the Diffusion of Cultural Heritage

Les prioritats per als museus canvien. La missió de la nova museologia és convertir els museus en llocs per a gaudir i aprendre, cosa que fa que hagin de dur a terme una gestió financera molt semblant a la d'una empresa social que competeixi en el sector del lleure. Amb el pas del temps, els museus han d'establir i aplicar els criteris necessaris per a la supervivència, aplanant el terreny perquè altres institucions públiques siguin més obertes en els seus esforços per comunicar i difondre el seu patrimoni. Ja podem començar a parlar d'algunes conclusions comunament acceptades sobre el comportament dels visitants, que són necessàries per a planificar exposicions futures que vegin l'aprenentatge com un procés constructiu, les col·leccions com a objectes amb significat i les mateixes exposicions com a mitjans de comunicació que haurien de transformar la manera de pensar de l'espectador i que estan al servei del mateix missatge. Sembla que internet representa un mitjà efectiu per a assolir aquests objectius, ja que és capaç (a) d'adaptar-se als interessos i les característiques intel·lectuals d'un públic divers; (b) de redescobrir els significats dels objectes i adquirir un reconeixement sociocultural del seu valor per mitjà del seu potencial interactiu, i (c) de fer ús d'elements atractius i estimulants perquè tothom en gaudeixi. Per a aquest propòsit, és bàsic fer-nos les preguntes següents: quins criteris ha de seguir un museu virtual per a optimar la difusió del seu patrimoni?; quins elements estimulen els usuaris a quedar-se en una pàgina web i fer visites virtuals que els siguin satisfactòries?; quin paper té la usabilitat de l'aplicació en tot això?

The effect of post-discharge educational intervention on patients in achieving objectives in modifiable risk factors six months after discharge following an episode of acute coronary syndrome, (CAM-2 Project): a randomized controlled trial

We investigated whether an intervention mainly consisting of a signed agreement between patient and physician on the objectives to be reached, improves reaching these secondary prevention objectives in modifiable cardiovascular risk factors six-months after discharge following an acute coronary syndrome. BACKGROUND: There is room to improve mid-term adherence to clinical guidelines' recommendations in coronary heart disease secondary prevention, specially non-pharmacological ones, often neglected. METHODS: In CAM-2, patients discharged after an acute coronary syndrome were randomly assigned to the intervention or the usual care group. The primary outcome was reaching therapeutic objectives in various secondary prevention variables: smoking, obesity, blood lipids, blood pressure control, exercise and taking of medication. RESULTS: 1757 patients were recruited in 64 hospitals and 1510 (762 in the intervention and 748 in the control group) attended the six-months follow-up visit. After adjustment for potentially important variables, there were, between the intervention and control group, differences in the mean reduction of body mass index (0.5 vs. 0.2; p < 0.001) and waist circumference (1.6 cm vs. 0.6 cm; p = 0.05), proportion of patients who exercise regularly and those with total cholesterol below 175 mg/dl (64.7% vs. 56.5%; p = 0.001). The reported intake of medications was high in both groups for all the drugs considered with no differences except for statins (98.1% vs. 95.9%; p = 0.029). CONCLUSIONS: At least in the short term, lifestyle changes among coronary heart disease patients are achievable by intensifying the responsibility of the patient himself by means of a simple and feasible intervention.

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

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