Journal articles on the topic 'EJS. Easy Java Simulations'

Neste trabalho é apresentada a ferramenta de software Easy Java Simulations-Ejs. Além de citar suas principais características e potencialidades na produção de simulações-Applets dirigidas ao ensino de Física, desenvolvem-se duas aplicações de modelagem em atividades de ensino: um sistema massa-mola e a solução numérica da Equação Schroedinger independente do tempo.

The objective of this research was to develop a force and motion simulation based on the open-source Easy Java Simulation. The process of computer simulation development was done following the ADDIE model. Based on the Analysis and Design phases, the Development phase used the open-source Easy Java Simulation (EJS) to develop a computer simulation with physics content that was relevant to the subtopic. Computing and communication technology continue to make an increasing impact on all aspects of education. EJS is a powerful didactic resource that gives us the ability to focus our students’ attention on the principles of physics. Using EJS, a computer simulation was created through which the motion of a particle under the action of a specific force can be studied. The implementation phase is implemented the computer simulation in the teaching and learning process. To describe the improvements in the students’ understanding of the force and motion concepts, we used a t-test to evaluate each of the four phases. These results indicated that the use of the computer simulation could improve students’ force and motion conceptual competence regarding Newton's second law of motion.

This paper describes design and implementation of remote lab with industrial robot accessible through the web based on Moodle portal, Easy Java Simulations (EJS) and Arduino Sw & Hw. The main purpose of this lab is to improve study, training and programming knowledge in industrial and service robotics for students, teachers of secondary vocational schools and company workers that deal with problems that arise on real robotic workplaces. This lab allows the user to work from their homes and operates with industrial robot at real workplace. Such remote lab can also enable users to use expensive lab equipment, which is not usually available to all persons. Practical example of application of the lab with industrial robot on Department of Robotics, Technical University of Kosice, Slovakia is presented.

The aim of the present article is to analyze the relation of physical computing with the computational thinking dimensions and the transdisciplinary approach of STEM epistemology in inquiry-based learning environments, when the methodology of the computational experiment is implemented. We argue that computational science and computational experiment can be applied in connection with STEM epistemology, when physical computing activities are embedded in the curriculum for Higher Education students. In order to implement this connection, we present software applications that combine algorithms and physical computing. We believe that engaging students through their existing STEM courses in physical computing - in the form of the computational experiment methodology- is a strategy that is much more likely to succeed in increasing the interest and appeal of STEM epistemology. Different learning modules were designed, which covered the combination of easy java simulations (Ejs) with Arduino and Raspberry pi.

For leakage current safety, Residual Current Device (RCD) has been well known. The purpose of this work is to make the employment of low price components to measure residual current feasible and the Residual Current Device (RCD) must to be taken into account because it is a well known device for leakage current safety. For this purpose, experiments employing the Helmholtz Coil Configuration were performed with the different current flow. Furthermore, the residual current was formulated and simulated through the software Easy Java Simulation (EJS). The results showed that it is possible to move the magnet into different angles using leakage current with linear gradient as low as 0.382 degree/mA. Finally, it was proposed a way to increase the sensitivity and to reduce the hysteresis phenomenon.

En este artículo se presenta el análisis, diseño y construcción de un simulador virtual, que representa gráficamente el comportamiento de un sistema no lineal como el del péndulo invertido rotacional. Esta interfaz se presenta por medio de un Applet de Java que permite a los usuarios hacer la variación de los parámetros del modelo, formando un puente entre los conceptos teóricos y los comportamientos reales del proceso. Del lado del servidor se utiliza Matlab/Simulink como motor de cálculo numérico dada su facilidad para construir modelos no lineales mediante diagramas de bloques. La interfaz de usuario ha sido diseñada mediante la herramienta de software gratuito Easy Java Simulations, que permite crear aplicaciones gráficas con alto grado de interactivi-dad como interfaces con objetos en 3D. Esta herramienta es de gran ayuda para la enseñanza del control automático.

Purpose Computer simulations improve the knowledge of physical models and are widely used in teaching and research. Key aspects are to understand their solutions and to make interactive changes to the models, observing their effects in real-time. The drawback of creating interactive simulations of physical models is the high level of programming expertise required. The purpose of this study is to facilitate this task. Design/methodology/approach Java is the perfect language for this task; it yields high-quality graphics and is widely spread in the scientific community. Because many important physical models are described by means of partial differential equations (PDEs), the combination of Java with FreeFem++, a C++ PDE solver based on the finite element method, is considered. Findings In this study, a Java library is introduced to numerically solve PDE equations via a run-time connection with FreeFem++. The solution is encapsulated into Java objects that are ready to be used in different programming tasks. The library also includes new Java visualization elements for solutions and meshes in the context of the Open Source Physics project library. Together, the connection features and the visualization elements facilitate the creation of Java simulations by programming researchers. For those with less programming capabilities, this work has been included into Easy Java Simulations, a tool to further ease the creation of interactive simulations. Originality/value The present study approach allows simulating models given PDEs. The equations are solved either in local or in remote mode (e.g. by a network accessible to a high-performance computer) and visualized locally, providing a high degree of interactivity to the end user.

Whether they need Chinese language flash cards, steps and music to a Scottish country dance, physics simulations, or any one of hundreds of other teaching tools, educators and researchers can find them at Apple's Educational Object Economy (EOE) project (http://trp.research.apple.com).The EOE is a complementary online community based around the creation, sharing, and use of teaching resources that incorporate Java applets for web-based learning. Java applets are small, easy-to-use programs written in Sun Microsystem's Java programming language. Using virtually any computer equipped with a standard web browser, educators can access hundreds of teaching and curriculum development tools for use in the classroom, for research, or in other educational endeavors.

In order to disseminate and encourage the use of remote experiments, their creation and deployment need to be simplified. This work presents a method to easily develop remote experiments interfaces in Javascript and to quickly embed them in Moodle. This solution requires the use of Easy Java/Javascript Simulations for the development of the interfaces and the EJSApp Moodle plugin to deploy them in the web platform. The proven flexibility of such solution has fostered the integration of two new experiments and also the easy adaptation of an already existing one, opening new remote labs flexibility to educational and/or training activities.

<p>En este trabajo presentamos un nuevo laboratorio virtual, Difract, desarrollado con Easy Java Simulations para su uso en cursos de Óptica como una herramienta informática para la modelización matemática de las propiedades de difracción de redes fractales 1D y 2D. Este laboratorio virtual permite a los estudiantes analizar rápida y fácilmente la influencia en el patrón de difracción de Fraunhofer de los diferentes parámetros de construcción de la red fractal. Como ejemplo de aplicación se ha considerado el conjunto fractal de Cantor.</p>

Desenvolvemos um hiperdocumento construído com software livre visando apoiar o ensino e a aprendizagem de conceitos de eletromagnetismo. A pesquisa envolveu a elaboração, produção e validação de um Hiperdocumento contendo simulações interativas produzidas com o software EASY JAVA SIMULATIONS, tendo por base as concepções de aprendizagem de David Ausubel. Consideramos ainda os princípios que caracterizam a hipermídia enquanto linguagem que possibilita o acesso não-linear à informação e a apresentação desta com a utilização dos recursos gráficos, sonoros, interativos e de animação do computador, e suas implicações para as práticas de ensino. Identificamos evidências de que a diversidade de elementos de mídia auxiliou os alunos na compreensão dos conceitos e interpretação dos fenômenos. Observamos que o hiperdocumento estruturado nas concepções de aprendizagem de Ausubel auxiliou o desenvolvimento de subsunçores para ancorar a aprendizagem, tornando os alunos participantes ativos na aquisição de informações e construção de novos conhecimentos.

To date, discrete event stochastic simulations of large scale biological reaction systems are extremely compute-intensive and time-consuming. Besides, it has been widely accepted that spatial factor plays a critical role in the dynamics of most biological reaction systems. The NSM (the Next Sub-Volume Method), a spatial variation of the Gillespie’s stochastic simulation algorithm (SSA), has been proposed for spatially stochastic simulation of those systems. While being able to explore high degree of parallelism in systems, NSM is inherently sequential, which still suffers from the problem of low simulation speed. Fine-grained parallel execution is an elegant way to speed up sequential simulations. Thus, based on the discrete event simulation framework JAMES II, we design and implement a PDES (Parallel Discrete Event Simulation) TW (time warp) simulator to enable the fine-grained parallel execution of spatial stochastic simulations of biological reaction systems using the ANSM (the Abstract NSM), a parallel variation of the NSM. The simulation results of classical Lotka-Volterra biological reaction system show that our time warp simulator obtains remarkable parallel speed-up against sequential execution of the NSM.I.IntroductionThe goal of Systems biology is to obtain system-level investigations of the structure and behavior of biological reaction systems by integrating biology with system theory, mathematics and computer science [1][3], since the isolated knowledge of parts can not explain the dynamics of a whole system. As the complement of “wet-lab” experiments, stochastic simulation, being called the “dry-computational” experiment, plays a more and more important role in computing systems biology [2]. Among many methods explored in systems biology, discrete event stochastic simulation is of greatly importance [4][5][6], since a great number of researches have present that stochasticity or “noise” have a crucial effect on the dynamics of small population biological reaction systems [4][7]. Furthermore, recent research shows that the stochasticity is not only important in biological reaction systems with small population but also in some moderate/large population systems [7].To date, Gillespie’s SSA [8] is widely considered to be the most accurate way to capture the dynamics of biological reaction systems instead of traditional mathematical method [5][9]. However, SSA-based stochastic simulation is confronted with two main challenges: Firstly, this type of simulation is extremely time-consuming, since when the types of species and the number of reactions in the biological system are large, SSA requires a huge amount of steps to sample these reactions; Secondly, the assumption that the systems are spatially homogeneous or well-stirred is hardly met in most real biological systems and spatial factors play a key role in the behaviors of most real biological systems [19][20][21][22][23][24]. The next sub-volume method (NSM) [18], presents us an elegant way to access the special problem via domain partition. To our disappointment, sequential stochastic simulation with the NSM is still very time-consuming, and additionally introduced diffusion among neighbor sub-volumes makes things worse. Whereas, the NSM explores a very high degree of parallelism among sub-volumes, and parallelization has been widely accepted as the most meaningful way to tackle the performance bottleneck of sequential simulations [26][27]. Thus, adapting parallel discrete event simulation (PDES) techniques to discrete event stochastic simulation would be particularly promising. Although there are a few attempts have been conducted [29][30][31], research in this filed is still in its infancy and many issues are in need of further discussion. The next section of the paper presents the background and related work in this domain. In section III, we give the details of design and implementation of model interfaces of LP paradigm and the time warp simulator based on the discrete event simulation framework JAMES II; the benchmark model and experiment results are shown in Section IV; in the last section, we conclude the paper with some future work.II. Background and Related WorkA. Parallel Discrete Event Simulation (PDES)The notion Logical Process (LP) is introduced to PDES as the abstract of the physical process [26], where a system consisting of many physical processes is usually modeled by a set of LP. LP is regarded as the smallest unit that can be executed in PDES and each LP holds a sub-partition of the whole system’s state variables as its private ones. When a LP processes an event, it can only modify the state variables of its own. If one LP needs to modify one of its neighbors’ state variables, it has to schedule an event to the target neighbor. That is to say event message exchanging is the only way that LPs interact with each other. Because of the data dependences or interactions among LPs, synchronization protocols have to be introduced to PDES to guarantee the so-called local causality constraint (LCC) [26]. By now, there are a larger number of synchronization algorithms have been proposed, e.g. the null-message [26], the time warp (TW) [32], breath time warp (BTW) [33] and etc. According to whether can events of LPs be processed optimistically, they are generally divided into two types: conservative algorithms and optimistic algorithms. However, Dematté and Mazza have theoretically pointed out the disadvantages of pure conservative parallel simulation for biochemical reaction systems [31]. B. NSM and ANSM The NSM is a spatial variation of Gillespie’ SSA, which integrates the direct method (DM) [8] with the next reaction method (NRM) [25]. The NSM presents us a pretty good way to tackle the aspect of space in biological systems by partitioning a spatially inhomogeneous system into many much more smaller “homogeneous” ones, which can be simulated by SSA separately. However, the NSM is inherently combined with the sequential semantics, and all sub-volumes share one common data structure for events or messages. Thus, directly parallelization of the NSM may be confronted with the so-called boundary problem and high costs of synchronously accessing the common data structure [29]. In order to obtain higher efficiency of parallel simulation, parallelization of NSM has to firstly free the NSM from the sequential semantics and secondly partition the shared data structure into many “parallel” ones. One of these is the abstract next sub-volume method (ANSM) [30]. In the ANSM, each sub-volume is modeled by a logical process (LP) based on the LP paradigm of PDES, where each LP held its own event queue and state variables (see Fig. 1). In addition, the so-called retraction mechanism was introduced in the ANSM too (see algorithm 1). Besides, based on the ANSM, Wang etc. [30] have experimentally tested the performance of several PDES algorithms in the platform called YH-SUPE [27]. However, their platform is designed for general simulation applications, thus it would sacrifice some performance for being not able to take into account the characteristics of biological reaction systems. Using the similar ideas of the ANSM, Dematté and Mazza have designed and realized an optimistic simulator. However, they processed events in time-stepped manner, which would lose a specific degree of precisions compared with the discrete event manner, and it is very hard to transfer a time-stepped simulation to a discrete event one. In addition, Jeschke etc.[29] have designed and implemented a dynamic time-window simulator to execution the NSM in parallel on the grid computing environment, however, they paid main attention on the analysis of communication costs and determining a better size of the time-window.Fig. 1: the variations from SSA to NSM and from NSM to ANSMC. JAMES II JAMES II is an open source discrete event simulation experiment framework developed by the University of Rostock in Germany. It focuses on high flexibility and scalability [11][13]. Based on the plug-in scheme [12], each function of JAMES II is defined as a specific plug-in type, and all plug-in types and plug-ins are declared in XML-files [13]. Combined with the factory method pattern JAMES II innovatively split up the model and simulator, which makes JAMES II is very flexible to add and reuse both of models and simulators. In addition, JAMES II supports various types of modelling formalisms, e.g. cellular automata, discrete event system specification (DEVS), SpacePi, StochasticPi and etc.[14]. Besides, a well-defined simulator selection mechanism is designed and developed in JAMES II, which can not only automatically choose the proper simulators according to the modeling formalism but also pick out a specific simulator from a serious of simulators supporting the same modeling formalism according to the user settings [15].III. The Model Interface and SimulatorAs we have mentioned in section II (part C), model and simulator are split up into two separate parts. Thus, in this section, we introduce the designation and implementation of model interface of LP paradigm and more importantly the time warp simulator.A. The Mod Interface of LP ParadigmJAMES II provides abstract model interfaces for different modeling formalism, based on which Wang etc. have designed and implemented model interface of LP paradigm[16]. However, this interface is not scalable well for parallel and distributed simulation of larger scale systems. In our implementation, we accommodate the interface to the situation of parallel and distributed situations. Firstly, the neighbor LP’s reference is replaced by its name in LP’s neighbor queue, because it is improper even dangerous that a local LP hold the references of other LPs in remote memory space. In addition, (pseudo-)random number plays a crucial role to obtain valid and meaningful results in stochastic simulations. However, it is still a very challenge work to find a good random number generator (RNG) [34]. Thus, in order to focus on our problems, we introduce one of the uniform RNGs of JAMES II to this model interface, where each LP holds a private RNG so that random number streams of different LPs can be independent stochastically. B. The Time Warp SimulatorBased on the simulator interface provided by JAMES II, we design and implement the time warp simulator, which contains the (master-)simulator, (LP-)simulator. The simulator works strictly as master/worker(s) paradigm for fine-grained parallel and distributed stochastic simulations. Communication costs are crucial to the performance of a fine-grained parallel and distributed simulation. Based on the Java remote method invocation (RMI) mechanism, P2P (peer-to-peer) communication is implemented among all (master-and LP-)simulators, where a simulator holds all the proxies of targeted ones that work on remote workers. One of the advantages of this communication approach is that PDES codes can be transferred to various hardwire environment, such as Clusters, Grids and distributed computing environment, with only a little modification; The other is that RMI mechanism is easy to realized and independent to any other non-Java libraries. Since the straggler event problem, states have to be saved to rollback events that are pre-processed optimistically. Each time being modified, the state is cloned to a queue by Java clone mechanism. Problem of this copy state saving approach is that it would cause loads of memory space. However, the problem can be made up by a condign GVT calculating mechanism. GVT reduction scheme also has a significant impact on the performance of parallel simulators, since it marks the highest time boundary of events that can be committed so that memories of fossils (processed events and states) less than GVT can be reallocated. GVT calculating is a very knotty for the notorious simultaneous reporting problem and transient messages problem. According to our problem, another GVT algorithm, called Twice Notification (TN-GVT) (see algorithm 2), is contributed to this already rich repository instead of implementing one of GVT algorithms in reference [26] and [28].This algorithm looks like the synchronous algorithm described in reference [26] (pp. 114), however, they are essentially different from each other. This algorithm has never stopped the simulators from processing events when GVT reduction, while algorithm in reference [26] blocks all simulators for GVT calculating. As for the transient message problem, it can be neglect in our implementation, because RMI based remote communication approach is synchronized, that means a simulator will not go on its processing until the remote the massage get to its destination. And because of this, the high-costs message acknowledgement, prevalent over many classical asynchronous GVT algorithms, is not needed anymore too, which should be constructive to the whole performance of the time warp simulator.IV. Benchmark Model and Experiment ResultsA. The Lotka-Volterra Predator-prey SystemIn our experiment, the spatial version of Lotka-Volterra predator-prey system is introduced as the benchmark model (see Fig. 2). We choose the system for two considerations: 1) this system is a classical experimental model that has been used in many related researches [8][30][31], so it is credible and the simulation results are comparable; 2) it is simple but helpful enough to test the issues we are interested in. The space of predator-prey System is partitioned into a2D NXNgrid, whereNdenotes the edge size of the grid. Initially the population of the Grass, Preys and Predators are set to 1000 in each single sub-volume (LP). In Fig. 2,r1,r2,r3stand for the reaction constants of the reaction 1, 2 and 3 respectively. We usedGrass,dPreyanddPredatorto stand for the diffusion rate of Grass, Prey and Predator separately. Being similar to reference [8], we also take the assumption that the population of the grass remains stable, and thusdGrassis set to zero.R1:Grass + Prey ->2Prey(1)R2:Predator +Prey -> 2Predator(2)R3:Predator -> NULL(3)r1=0.01; r2=0.01; r3=10(4)dGrass=0.0;dPrey=2.5;dPredato=5.0(5)Fig. 2: predator-prey systemB. Experiment ResultsThe simulation runs have been executed on a Linux Cluster with 40 computing nodes. Each computing node is equipped with two 64bit 2.53 GHz Intel Xeon QuadCore Processors with 24GB RAM, and nodes are interconnected with Gigabit Ethernet connection. The operating system is Kylin Server 3.5, with kernel 2.6.18. Experiments have been conducted on the benchmark model of different size of mode to investigate the execution time and speedup of the time warp simulator. As shown in Fig. 3, the execution time of simulation on single processor with 8 cores is compared. The result shows that it will take more wall clock time to simulate much larger scale systems for the same simulation time. This testifies the fact that larger scale systems will leads to more events in the same time interval. More importantly, the blue line shows that the sequential simulation performance declines very fast when the mode scale becomes large. The bottleneck of sequential simulator is due to the costs of accessing a long event queue to choose the next events. Besides, from the comparison between group 1 and group 2 in this experiment, we could also conclude that high diffusion rate increased the simulation time greatly both in sequential and parallel simulations. This is because LP paradigm has to split diffusion into two processes (diffusion (in) and diffusion (out) event) for two interactive LPs involved in diffusion and high diffusion rate will lead to high proportional of diffusion to reaction. In the second step shown in Fig. 4, the relationship between the speedups from time warp of two different model sizes and the number of work cores involved are demonstrated. The speedup is calculated against the sequential execution of the spatial reaction-diffusion systems model with the same model size and parameters using NSM.Fig. 4 shows the comparison of speedup of time warp on a64X64grid and a100X100grid. In the case of a64X64grid, under the condition that only one node is used, the lowest speedup (a little bigger than 1) is achieved when two cores involved, and the highest speedup (about 6) is achieved when 8 cores involved. The influence of the number of cores used in parallel simulation is investigated. In most cases, large number of cores could bring in considerable improvements in the performance of parallel simulation. Also, compared with the two results in Fig. 4, the simulation of larger model achieves better speedup. Combined with time tests (Fig. 3), we find that sequential simulator’s performance declines sharply when the model scale becomes very large, which makes the time warp simulator get better speed-up correspondingly.Fig. 3: Execution time (wall clock time) of Seq. and time warp with respect to different model sizes (N=32, 64, 100, and 128) and model parameters based on single computing node with 8 cores. Results of the test are grouped by the diffusion rates (Group 1: Sequential 1 and Time Warp 1. dPrey=2.5, dPredator=5.0; Group 2: dPrey=0.25, dPredator=0.5, Sequential 2 and Time Warp 2).Fig. 4: Speedup of time warp with respect to the number of work cores and the model size (N=64 and 100). Work cores are chose from one computing node. Diffusion rates are dPrey=2.5, dPredator=5.0 and dGrass=0.0.V. Conclusion and Future WorkIn this paper, a time warp simulator based on the discrete event simulation framework JAMES II is designed and implemented for fine-grained parallel and distributed discrete event spatial stochastic simulation of biological reaction systems. Several challenges have been overcome, such as state saving, roll back and especially GVT reduction in parallel execution of simulations. The Lotka-Volterra Predator-Prey system is chosen as the benchmark model to test the performance of our time warp simulator and the best experiment results show that it can obtain about 6 times of speed-up against the sequential simulation. The domain this paper concerns with is in the infancy, many interesting issues are worthy of further investigated, e.g. there are many excellent PDES optimistic synchronization algorithms (e.g. the BTW) as well. Next step, we would like to fill some of them into JAMES II. 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Neste trabalho desenvolvemos um hiperdocumento construído com software livre com a finalidade de apoiar o ensino e a aprendizagem de conceitos de Eletromagnetismo no Ensino Médio. A pesquisa envolveu a elaboração, produção e validação de um hiperdocumento que contém simulações interativas produzidas com o software EASY JAVA SIMULATIONS, fundamentadas nas concepções de aprendizagem significativa de David Ausubel. Foram considerados também os princípios fundamentais que caracterizam a hipermídia enquanto linguagem que permite o acesso não-linear à informação e a apresentação desta com a utilização dos recursos gráficos, sonoros, interativos e de animação do computador. Buscamos ainda destacar as implicações do uso desses recursos instrucionais para as práticas de ensino, na medida em que eles podem oferecer expressivas contribuições para as atividades docentes e para a aprendizagem dos estudantes. Nesse sentido, encontramos evidências de que a diversidade de elementos de mídia auxiliou os alunos na compreensão dos conceitos físicos, na fixação do conteúdo e interpretação dos fenômenos eletromagnéticos abordados. Observamos ainda que o hiperdocumento estruturado nas concepções de aprendizagem significativa de Ausubel ajudou no desenvolvimento de subsunçores para apoiar a aprendizagem, tornando esses alunos participantes ativos na aquisição de informações e construção de novos conhecimentos.

<p style="margin: 0cm 0cm 0pt; line-height: normal;"> </p><p style="margin: 0cm 0cm 0pt; line-height: normal;"><span style="font-family: 'Times New Roman','serif'; font-size: 8pt;" lang="ES-TRAD"><br /></span></p><p style="margin: 0cm 0cm 0pt; line-height: normal;"><span style="font-family: 'Times New Roman','serif'; font-size: 8pt;" lang="ES-TRAD">En este trabajo de investigación se utilizaron, sistemas de adquisición, técnicas computacionales y procesamiento de datos, para modelar fenómenos físicos que nos permitieron introducir al estudiante en la experimentación, la resolución de problemas, el análisis e interpretación de resultados obtenidos por modelos computacionales. Para la elaboración de este proyecto se empleó Labview plataforma de programación y diseño propia para el proceso computacional y análisis de la información. También se utilizaron herramientas de visualización para las de simulaciones que garanticen la conexión vía Web entre los sistemas físicos reales y los simulados, para ésto se utilizó Easy Java Simulations, y Modellus. Para la toma y adquisición de datos se emplearon foto detectores, un sistema de acople mecánico que son leídos por una tarjeta de adquisición y serán almacenados en una computadora personal. Con este trabajo de investigación se espera: 1. Favorecer el aprendizaje significativo, desarrollando habilidades en los estudiantes para la solución de problemas. 2. Mejorar la comprensión e interpretación de los conceptos desarrollados en el aula y laboratorio. 3. Trabajar con modelos computacionales donde el estudiante pueda tomar decisiones en determinados sucesos, o poder modificar las variables con el objetivo de comprobar las hipótesis vistas en el aula de clase.</span></p>

Se implementa el Easy Java Simulations (EJS) como instrumento de apoyo en el proceso de enseñanza aprendizaje de los estudiantes de análisis matemático. Inicialmente se identifican las variables involucradas en el proceso de enseñanza - aprendizaje que desarrollan los estudiantes de la materia de análisis matemático, así como las dificultades que estos tienen, para con ello estructurar una guía de uso del EJS. La propuesta desarrollada permite obtener una aproximación teórica – práctica, identificando aquellos elementos que pueden ser de utilidad y aporte tanto para el docente como para el estudiante en el proceso de enseñanza aprendizaje de la matemática a través del EJS. Para culminar se comprueba que existen diferencias significativas entre un grupo de control y un grupo de intervención, al cual se implementó EJS, en el proceso de enseñanza aprendizaje de análisis matemático. Se concluye un efecto positivo en el rendimiento académico.

AbstractWith the decline of Moore’s law and the ever increasing availability of cheap massively parallel hardware, it becomes more and more important to embrace parallel programming methods to implement Agent-Based Simulations (ABS). This has been acknowledged in the field a while ago and numerous research on distributed parallel ABS exists, focusing primarily on Parallel Discrete Event Simulation as the underlying mechanism. However, these concepts and tools are inherently difficult to master and apply and often an excess in case implementers simply want to parallelise their own, custom agent-based model implementation. However, with the established programming languages in the field, Python, Java and C++, it is not easy to address the complexities of parallel programming due to unrestricted side effects and the intricacies of low-level locking semantics. Therefore, in this paper we propose the use of a lock-free approach to parallel ABS using Software Transactional Memory (STM) in conjunction with the pure functional programming language Haskell, which in combination, removes some of the problems and complexities of parallel implementations in imperative approaches. We present two case studies, in which we compare the performance of lock-based and lock-free STM implementations in two different well known Agent-Based Models, where we investigate both the scaling performance under increasing number of CPU cores and the scaling performance under increasing number of agents. We show that the lock-free STM implementations consistently outperform the lock-based ones and scale much better to increasing number of CPU cores both on local hardware and on Amazon EC. Further, by utilizing the pure functional language Haskell we gain the benefits of immutable data and lack of unrestricted side effects guaranteed at compile-time, making validation easier and leading to increased confidence in the correctness of an implementation, something of fundamental importance and benefit in parallel programming in general and scientific computing like ABS in particular.