Event Driven Programming in Java

Event driven programming is a way of writing a program that works by responding to things happening (rather than executing a preplanned series of tasks). It is most often used to manage more advanced user interactions, such as GUI programs. In this session we look at how event driven programming works in Java GUIs, as both an introduction to events (using MouseListeners), and also to the way that GUI programs are constructed.

Java path setting batch file

This is a batch file written to help students on ECS' Programming 1 course (COMP1202) using iSolutions machines which have the JDK, but do not add it to the PATH variable, making compilation from the command line difficult. It attempts to find the JDK directory and add it to the Windows PATH. The code is as follows: @SET JAVA_HOME=C:\Program Files\Java @FOR /F %%G IN ('DIR /B "%JAVA_HOME%\JDK*"') DO @SET JDK_HOME=%JAVA_HOME%\%%G @SET PATH=%JDK_HOME%\bin;%PATH% @javac -version @echo. @echo %JDK_HOME%\bin successfully added to Windows PATH @echo. @echo Now type 'javac'. @echo. @echo. @echo. @CMD

Educación interactiva y Java.

El lenguaje Java, implementado a través de 'applets', son las herramientas naturales para elaborar contenidos interactivos, independientes de plataforma y accesibles por internet. Nuestra aportación consiste en la presentación de ejemplos de 'applets' creados en torno a los contenidos de tres asignaturas de la ESO. Introducen el proyecto para escribir con el mismo formato, para las asignaturas de mecánica de la carrera de Ciencias Físicas.

La interacción con applets Java para el aprendizaje de las matemáticas.

Resumen tomado de la publicación

La Ingeniería Lingüística con Java.

Resumen basado en el de la publicación

Los microciberjuegos y el aprendizaje de las Ciencias Sociales : el mundo java.

Resumen tomado de la publicación

Aplicaciones Android y creatividad musical en primaria

Resumen en inglés. Resumen basado en el de la publicación

Motivar y aprender con el móvil creando una aplicación para Android, mediante una metodología lúdica, constructivista y social

Resumen basado en el de la publicación.

gvSIG Mini y Phone Cache

gvSIG Mini es una aplicación open-source de usuario final cliente móvil de Infraestructura de Datos Espaciales IDEs con licencia GNU/ GPL, diseñada para teléfonos móviles Java y Android que permite la visualización y navegación sobre cartografía digital estructurada en tiles procedente de servicios web OGC como WMS(-C) y de servicios como OpenStreetMap (OSM), Yahoo Maps, Maps Bing, así como el almacenamiento en caché para reducir al mínimo el ancho de banda. gvSIG Mini puede acceder a servicios geoespaciales como NameFinder, para la búsqueda de puntos de interés y YOURS (Yet Another OpenStreetMap Routing Service) para el cálculo de rutas y la renderización de la información vectorial el lado del cliente. Por otra parte, gvSIG Mini también ofrece servicio de localización GPS. La versión de gvSIG Mini para Android, posee algunas características adicionales como son el soporte de localización Android o el uso del lacelerómetro para centrado. Esta versión también hace uso de servicios como son la predicción del tiempo o TweetMe que permite compartir una localización utilizando el popular servicio social Twitter. gvSIG Mini es una aplicación que puede ser descargada y usada libremente, convirtiéndose en una plataforma para el desarrollo de nuevas soluciones y aplicaciones en el campo de Location Based Services (LBS). gvSIG Mini ha sido desarrollado por Prodevelop, S.L. No es un proyecto oficial de gvSIG, pero se une a la familia a través del catálogo de extensiones no oficiales de gvSIG. Phone Cache es una extensión que funciona sobre gvSIG 1.1.2 que permite generar una caché, para poder utilizar gvSIG Mini para Java en modo desconectado

Glob3 Mobile: hacia un SIG 3D para entornos Apple-iOS, Android y WebGL

El trabajo describe el proyecto de desarrollo de un SIG 3D de código abierto para dispositivos móviles (Apple-iOS y Android) y para navegadores web con tecnología WebGL. En la fase actual, nos centraremos en el diseño e implementación del globo virtual, como elemento esencial que da soporte al SIG 3D y de una IDE que permite la programación de nuevas funcionalidades al globo. Dentro de los objetivos de diseño del globo virtual tenemos (i) simplicidad, con código estructurado que facilita la portabilidad y con una API de código abierto sencilla, (ii) eficiencia, tomando en cuenta los recursos hardware de los dispositivos móviles más extendidos en el mercado, (ii) usabilidad, implementando una navegación intuitiva mediante gestos para la interacción en pantalla y (iv) escalabilidad, gracias a una API desarrollada, se permite aumentar de las prestaciones mediante el desarrollo de scripts y podrán ser ejecutados tanto dentro del navegador web como de forma nativa en las plataformas móviles. Ante un panorama de clara proliferación de aplicaciones para móviles, Glob3 Mobile pretende ser una apuesta fuerte que llegue a convertirse en un SIG 3D de código abierto que abarque variadas aplicaciones sectoriales, algunas ya en marcha

Grid Remote Execution (G-Rex) of Climate Models

G-Rex is light-weight Java middleware that allows scientific applications deployed on remote computer systems to be launched and controlled as if they are running on the user's own computer. G-Rex is particularly suited to ocean and climate modelling applications because output from the model is transferred back to the user while the run is in progress, which prevents the accumulation of large amounts of data on the remote cluster. The G-Rex server is a RESTful Web application that runs inside a servlet container on the remote system, and the client component is a Java command line program that can easily be incorporated into existing scientific work-flow scripts. The NEMO and POLCOMS ocean models have been deployed as G-Rex services in the NERC Cluster Grid, and G-Rex is the core grid middleware in the GCEP and GCOMS e-science projects.

Running Climate Models On The NERC Cluster Grid Using G-Rex

Compute grids are used widely in many areas of environmental science, but there has been limited uptake of grid computing by the climate modelling community, partly because the characteristics of many climate models make them difficult to use with popular grid middleware systems. In particular, climate models usually produce large volumes of output data, and running them usually involves complicated workflows implemented as shell scripts. For example, NEMO (Smith et al. 2008) is a state-of-the-art ocean model that is used currently for operational ocean forecasting in France, and will soon be used in the UK for both ocean forecasting and climate modelling. On a typical modern cluster, a particular one year global ocean simulation at 1-degree resolution takes about three hours when running on 40 processors, and produces roughly 20 GB of output as 50000 separate files. 50-year simulations are common, during which the model is resubmitted as a new job after each year. Running NEMO relies on a set of complicated shell scripts and command utilities for data pre-processing and post-processing prior to job resubmission. Grid Remote Execution (G-Rex) is a pure Java grid middleware system that allows scientific applications to be deployed as Web services on remote computer systems, and then launched and controlled as if they are running on the user's own computer. Although G-Rex is general purpose middleware it has two key features that make it particularly suitable for remote execution of climate models: (1) Output from the model is transferred back to the user while the run is in progress to prevent it from accumulating on the remote system and to allow the user to monitor the model; (2) The client component is a command-line program that can easily be incorporated into existing model work-flow scripts. G-Rex has a REST (Fielding, 2000) architectural style, which allows client programs to be very simple and lightweight and allows users to interact with model runs using only a basic HTTP client (such as a Web browser or the curl utility) if they wish. This design also allows for new client interfaces to be developed in other programming languages with relatively little effort. The G-Rex server is a standard Web application that runs inside a servlet container such as Apache Tomcat and is therefore easy to install and maintain by system administrators. G-Rex is employed as the middleware for the NERC1 Cluster Grid, a small grid of HPC2 clusters belonging to collaborating NERC research institutes. Currently the NEMO (Smith et al. 2008) and POLCOMS (Holt et al, 2008) ocean models are installed, and there are plans to install the Hadley Centre’s HadCM3 model for use in the decadal climate prediction project GCEP (Haines et al., 2008). The science projects involving NEMO on the Grid have a particular focus on data assimilation (Smith et al. 2008), a technique that involves constraining model simulations with observations. The POLCOMS model will play an important part in the GCOMS project (Holt et al, 2008), which aims to simulate the world’s coastal oceans. A typical use of G-Rex by a scientist to run a climate model on the NERC Cluster Grid proceeds as follows :(1) The scientist prepares input files on his or her local machine. (2) Using information provided by the Grid’s Ganglia3 monitoring system, the scientist selects an appropriate compute resource. (3) The scientist runs the relevant workflow script on his or her local machine. This is unmodified except that calls to run the model (e.g. with “mpirun”) are simply replaced with calls to "GRexRun" (4) The G-Rex middleware automatically handles the uploading of input files to the remote resource, and the downloading of output files back to the user, including their deletion from the remote system, during the run. (5) The scientist monitors the output files, using familiar analysis and visualization tools on his or her own local machine. G-Rex is well suited to climate modelling because it addresses many of the middleware usability issues that have led to limited uptake of grid computing by climate scientists. It is a lightweight, low-impact and easy-to-install solution that is currently designed for use in relatively small grids such as the NERC Cluster Grid. A current topic of research is the use of G-Rex as an easy-to-use front-end to larger-scale Grid resources such as the UK National Grid service.

Prediction of global and local model quality in CASP8 using the ModFOLD server

The development of effective methods for predicting the quality of three-dimensional (3D) models is fundamentally important for the success of tertiary structure (TS) prediction strategies. Since CASP7, the Quality Assessment (QA) category has existed to gauge the ability of various model quality assessment programs (MQAPs) at predicting the relative quality of individual 3D models. For the CASP8 experiment, automated predictions were submitted in the QA category using two methods from the ModFOLD server-ModFOLD version 1.1 and ModFOLDclust. ModFOLD version 1.1 is a single-model machine learning based method, which was used for automated predictions of global model quality (QMODE1). ModFOLDclust is a simple clustering based method, which was used for automated predictions of both global and local quality (QMODE2). In addition, manual predictions of model quality were made using ModFOLD version 2.0-an experimental method that combines the scores from ModFOLDclust and ModFOLD v1.1. Predictions from the ModFOLDclust method were the most successful of the three in terms of the global model quality, whilst the ModFOLD v1.1 method was comparable in performance to other single-model based methods. In addition, the ModFOLDclust method performed well at predicting the per-residue, or local, model quality scores. Predictions of the per-residue errors in our own 3D models, selected using the ModFOLD v2.0 method, were also the most accurate compared with those from other methods. All of the MQAPs described are publicly accessible via the ModFOLD server at: http://www.reading.ac.uk/bioinf/ModFOLD/. The methods are also freely available to download from: http://www.reading.ac.uk/bioinf/downloads/.

The ModFOLD server for the quality assessment of protein structural models

The reliable assessment of the quality of protein structural models is fundamental to the progress of structural bioinformatics. The ModFOLD server provides access to two accurate techniques for the global and local prediction of the quality of 3D models of proteins. Firstly ModFOLD, which is a fast Model Quality Assessment Program (MQAP) used for the global assessment of either single or multiple models. Secondly ModFOLDclust, which is a more intensive method that carries out clustering of multiple models and provides per-residue local quality assessment.