Design Pattern

The paper deals with some aspects of designing and using deterministic and stochastic simulators for military trainings. These aspects are divided into three groups: (1) connected with experiences of authors concerning the usage of deterministic and stochastic models in simulators for military trainings; (2) connected with designing simulators for military trainings using simulation design patterns, (3) connected with a set of tools, which enables fast preparation of a game scenario, calibration of simulation models and evaluation of simulation results.

Both quantitative and qualitative methods are being increasingly used to investigate the learning dynamics that take place within CSCL environments. Since such practices are a crucial aspect of the CSCL field, Design Patterns (DPs) can be used for capitalizing on experience and sharing know-how among practitioners. This paper describes three DP instances that have been developed and fine tuned by a community of practice consisting of researchers, instructional designers and tutors with the aim of supporting monitoring and evaluation of CSCL interactions. The DP solutions are based on a set of indicators, some of quantitative and some of qualitative nature, as well as on the methods to gauge these indicators, starting from data tracked by the e-learning system. The process of development of these DPs is described and examples of use are reported and discussed in order to advance the instructional design field and inform the development of CSCL systems. The proposed DPs belong to a Language, aimed at describing tracking problems in different types of e-learning systems and at capturing in their solutions the know-how developed by communities of experts in the different fields.

The finite element method is an important, widely used numerical technique for solving partial differential equations. This technique utilizes basis functions for approximating the geometry and the variation of the solution field over finite regions, or elements, of the domain. These basis functions are generally formed by combinations of polynomials. In the past, the polynomial order of the basis has been low-typically of linear and quadratic order. However, in recent years so-called p and hp methods have been developed, which may elevate the order of the basis to arbitrary levels with the aim of accelerating the convergence of the numerical solution. The increasing complexity of numerical basis functions poses a significant challenge to visualization systems. In the past, such systems have been loosely coupled to simulation packages, exchanging data via file transfer, and internally reimplementing the basis functions in order to perform interpolation and implement visualization algorithms. However, as the basis functions become more complex and, in some cases, proprietary in nature, it becomes increasingly difficult if not impossible to reimplement them within the visualization system. Further, most visualization systems typically process linear primitives, in part to take advantage of graphics hardware and, in part, due to the inherent simplicity of the resulting algorithms. Thus, visualization of higher-order finite elements requires tessellating the basis to produce data compatible with existing visualization systems. In this paper, we describe adaptive methods that automatically tessellate complex finite element basis functions using a flexible and extensible software framework. These methods employ a recursive, edge-based subdivision algorithm driven by a set of error metrics including geometric error, solution error, and error in image space. Further, we describe advanced pretessellation techniques that guarantees capture of the critical points of the polynomial basis. The framework has been designed using the adaptor design pattern, meaning that the visualization system need not reimplement basis functions, rather it communicates with the simulation package via simple programmatic queries. We demonstrate our method on several examples, and have implemented the framework in the open-source visualization system VTK.

The Super Iterator pattern, like the standard Iterator pattern, traverses an unknown data structure without exposing that structure. With the standard Iterator pattern, clients must create a different iterator for each new structure, and the object returned must be of the specific type stored in the structure, even when they share a common super class. With the Super Iterator pattern, the object returned is of the common super class, and the iterator itself need not be altered when adding a new subtype with custom data structures. The client, however, must change two lines of code to load and instantiate the new subclass.

Building proper reference ontologies is a hard task. There are a number of methods and tools that traditionally have been used to support this task. These include the use of foundational theories, the reuse of domain and core ontologies, the adoption of development methods, as well as the support of proper software tools. In this context, an approach that has gained increasing attention in recent years is the systematic application of ontology patterns. However, a pattern-based approach to ontology engineering requires: the existence of a set of suitable patterns that can be reused in the construction of new ontologies; a proper methodological support for eliciting these patterns, as well as for applying them in the construction of these new models. The goal of this paper is twofold: (i) firstly, we present an approach for deriving conceptual ontology patterns from ontologies. These patterns are derived from ontologies of different generality levels, ranging from foundational to domain ontologies; (ii) secondly, we present guidelines that describe how these patterns can be applied in combination for building reference domain ontologies in a reuse-oriented process. In summary, this paper is about the construction of ontology patterns from on-tologies, as well as the construction of ontologies from ontology patterns.