Biocharts: a visual formalism for complex biological systems

We address one of the central issues in devising languages, methods and tools for the modelling and analysis of complex biological systems, that of linking high-level (e.g. intercellular) information with lower-level (e.g. intracellular) information. Adequate ways of dealing with this issue are crucial for understanding biological networks and pathways, which typically contain huge amounts of data that continue to grow as our knowledge and understanding of a system increases. Trying to comprehend such data using the standard methods currently in use is often virtually impossible. We propose a two-tier compound visual language, which we call Biocharts, that is geared towards building fully executable models of biological systems. One of the main goals of our approach is to enable biologists to actively participate in the computational modelling effort, in a natural way. The high-level part of our language is a version of statecharts, which have been shown to be extremely successful in software and systems engineering. The statecharts can be combined with any appropriately well-defined language (preferably a diagrammatic one) for specifying the low-level dynamics of the pathways and networks. We illustrate the language and our general modelling approach using the well-studied process of bacterial chemotaxis.     

WebML modelling in UML

In recent years, we have witnessed how the Web Engineering community has started using the standard unified modelling language (UML) notation, techniques and supporting tools for modelling Web systems, which has led to the adaptation to UML of several existing modelling languages, notations and development processes. This interest for being MOF and UML-compliant arises from the increasing need to interoperate with other notations and tools, and to exchange data and models, thus facilitating reuse. WebML, like any other domain-specific language, allows one to express in a precise and natural way the concepts and mechanisms of its domain of reference. However, it cannot fully interoperate with other notations, nor be integrated with other model-based tools. As a solution to these requirements, a UML 2.0 profile for WebML which allows WebML models to be used in conjunction with other notations and modelling tools has been described. The paper also evaluates UML 2.0 as a platform for Web modelling and identifies some key requirements for making this version of the standard more usable.     

Towards automated verification of layered graph transformation specifications

Graph transformation systems have recently become more and more popular as a general formal modelling language. It is a suitable formalism for modelling different systems like distributed and complex systems. However, modelling must be complemented with proper analysis capabilities to let the user understand how designed models behave and whether stated requirements are fulfilled and model checking has proven to be a viable solution for this purpose. The authors propose an efficient solution for model checking attributed typed and layered graph transformation systems. Layered graph transformation systems are a powerful formalism to formally model different systems like hierarchical systems. In our proposal, AGG layered graph transformation specifications are translated to Bandera intermediate representation (BIR) - the input language of a Bogor model checker - and then Bogor verifies the model against some interesting properties defined by combining LTL (linear temporal logic) and special graph rules. The experimental results are encouraging and show that in most cases our proposal improves existing approaches, in terms of both performance and expressiveness.     

Modelling biological behaviours with the unified modelling language: an immunological case study and critique

We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties observed in the real world. We demonstrate how the UML can be successfully applied within our framework, and provide a critique of UML''s ability to capture concepts fundamental to immunology and biology more generally. We show how specialized, well-explained diagrams with less formal semantics can be used where no suitable UML formalism exists. We highlight UML''s lack of expressive ability concerning cyclic feedbacks in cellular networks, and the compounding concurrency arising from huge numbers of stochastic, interacting agents. To compensate for this, we propose several additional relationships for expressing these concepts in UML''s activity diagram. We also demonstrate the ambiguous nature of class diagrams when applied to complex biology, and question their utility in modelling such dynamic systems. Models created through our framework are non-executable, and expressly free of simulation implementation concerns. They are a valuable complement and precursor to simulation specifications and implementations, focusing purely on thoroughly exploring the biology, recording hypotheses and assumptions, and serve as a communication medium detailing exactly how a simulation relates to the real biology.     

Theory for classifying equivalences of unified modelling language activity diagrams

Despite the fact that there has been a wide adoption of unified modelling language activity diagrams (UML ADs) for software development, research focusing on the equivalence notions of UML ADs is scarce. To address this area of concern, the author presents a sound theoretical foundation for UML ADs. Through the use of these formal definitions of UML ADs, the author propounds a method which classifies various types of equivalences of UML ADs in a systematic way. The proposed classification, which is the core result of our work, provides a framework that enables the study of the properties and inter-relationships of the equivalences.     

Classification of model transformation techniques used in UML-based Web engineering

Software development techniques are continuously evolving with the goal of solving the main problems that still affect the building and maintenance of software systems: time, costs and error-proneness. Model-driven engineering (MDE) approaches aim to reduce at least some of these problems providing techniques for the construction of models and the specification of transformation rules, tool support and automatic generation of code and documentation. The method of resolution of MDE is to first build models, which are independent of the platform, transforming them in later stages to technological-dependent models, and to achieve automatic model and code generation based on transformation rules. Web engineering is a domain where model-driven approaches can be used to address evolution and adaptation of Web software to continuously emerging new platforms and changes in technologies. We present an overview of the development process of the Unified Modelling Language (UML)-based Web engineering (UWE) defined as an MDE approach. The main characteristic of UWE is the use of standards including the UML, XML metadata interchange as model exchange, meta-object facility for metamodelling, model-driven architecture and the transformation language query-view-transformation. We focus on the model transformation aspects of the UWE process.     

基于UML和SystemC的嵌入式系统集成开发方法的研究

针对嵌入式系统的特点，提出了一种基于UML和SystemC的集成开发方法。在这种方法中，UML用于嵌入式系统的分析与设计，SystemC用于建立软硬件协同仿真模型。UML模型到C 语言自然而直观的映射提供了连接系统分析阶段和实现阶段的桥梁，而通过SystemC模型的执行可以对系统的高层分析、抽象和定义提供尽可能早的验证。     

Developing manufacturing ontologies for knowledge reuse in distributed manufacturing environment

The use of ontologies for knowledge sharing and distributed collaboration has been widely recognised in the knowledge modelling community, but the lack of a systematic and constructive methodology for developing manufacturing ontologies has impeded their wide usage for knowledge reuse in distributed manufacturing environments. This paper presents a constructive, two-level knowledge modelling approach to systematically develop manufacturing ontologies using both software engineering and Semantic Web paradigms. The UML/OCL (Unified Modeling Language/Object Constraint Language)-based object modelling is used first to serve as a graphical and structured basis for conceptual communication between domain experts and knowledge engineers. The OWL/SWRL (Web Ontology Language/Semantic Web Rule Language)-based ontology modelling then extends the UML/OCL-based object models with added semantics using a progressive, semantics-oriented knowledge acquisition method. An illustrative example for manufacturing ontology development in the manufacturing industry for producing electronic connectors is used to demonstrate the practicality of the proposed approach.     

Voxelization and fabrication of freeform models

We present a volume graphics system for the generation of freeform models and synthesis of complex objects. A unique non-uniform rational B-spline (NURBS) volume representation and its voxelization algorithm are proposed. The forward difference technique is employed to speed up the voxelization process while the desired topological connectivity is preserved. For synthesis of our models and other three-dimensional objects, such as computed tomography images, voxel-based cutting tools are developed for the AutoCAD user interface. Volume rendering is applied to visualize the intermediate and final models. An isosurface is then extracted over the synthesized model and exported to a rapid prototyping machine as an STL file for fabrication. The approach taken in this system has been proven robust and efficient in modelling and fabrication of complex freeform models.     

A deep-learning based citation count prediction model with paper metadata semantic features

Predicting the impact of academic papers can help scholars quickly identify the high-quality papers in the field. How to develop efficient predictive model for evaluating potential papers has attracted increasing attention in academia. Many studies have shown that early citations contribute to improving the performance of predicting the long-term impact of a paper. Besides early citations, some bibliometric features and altmetric features have also been explored for predicting the impact of academic papers. Furthermore, paper metadata text such as title, abstract and keyword contains valuable information which has effect on its citation count. However, present studies ignore the semantic information contained in the metadata text. In this paper, we propose a novel citation prediction model based on paper metadata text to predict the long-term citation count, and the core of our model is to obtain the semantic information from the metadata text. We use deep learning techniques to encode the metadata text, and then further extract high-level semantic features for learning the citation prediction task. We also integrate early citations for improving the prediction performance of the model. We show that our proposed model outperforms the state-of-the-art models in predicting the long-term citation count of the papers, and metadata semantic features are effective for improving the accuracy of the citation prediction models.

     

A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

Clinical Study and automatic diagnosis of electrocardiogram (ECG) data always remain a challenge in diagnosing cardiovascular activities. The analysis of ECG data relies on various factors like morphological features, classification techniques, methods or models used to diagnose and its performance improvement. Another crucial factor in the methodology is how to train the model for each patient. Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy. This paper proposes an adaptive approach to identify performance improvement in building a training model that analyze global training methodology against an individual training methodology and identifying a gap between them. We provide our investigation and comparative study on these methods and model with standard classification techniques with basic morphological features and Heart Rate Variability (HRV) that may aid real time application. This approach helps in analyzing and evaluating the performance of different techniques and can suggests adoption of a best model identification with efficient technique and efficient attribute set for real-time systems.

     

An Efficient Hybrid Model for Arabic Text Recognition

In recent years, Deep Learning models have become indispensable in several fields such as computer vision, automatic object recognition, and automatic natural language processing. The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field, especially for the Arabic language, which, compared to other languages, has a dearth of published works. In this work, we presented an efficient and new system for offline Arabic handwritten text recognition. Our new approach is based on the combination of a Convolutional Neural Network (CNN) and a Bidirectional Long-Term Memory (BLSTM) followed by a Connectionist Temporal Classification layer (CTC). Moreover, during the training phase of the model, we introduce an algorithm of data augmentation to increase the quality of data. Our proposed approach can recognize Arabic handwritten texts without the need to segment the characters, thus overcoming several problems related to this point. To train and test (evaluate) our approach, we used two Arabic handwritten text recognition databases, which are IFN/ENIT and KHATT. The Experimental results show that our new approach, compared to other methods in the literature, gives better results.     

Modelling competence-based virtual organisations using the unified enterprise competence modelling language

Virtual organisations are non-conventional structures aiming at sharing the same goals, developing common practices or creating wealth in a collaborative manner. Accordingly, competence issues are important topics to be addressed in order to ensure coherence between the different objectives of entities taking part in a virtual organisation to guarantee an efficient use of available resources. This paper introduces the principles for modelling competence-based virtual organisations by using a uni?ed enterprise competence modelling language (UECML). Our proposed modelling approach is inspired from previous related works mainly in competence-based enterprise modelling. The language provides a neutral interface to virtual organisation modelling based on competences, taking into account the several roles and entities participating in a virtual organisation. Therefore, providing constructs covering processes, resources, competences, and virtual organisation (VO) entities. A case study of the Swiss virtual network Virtuelle Fabrik with regards to the project Abfallhai is presented and discussed, showing how the developed modelling language is adapted to model virtual organisations.     

Batch sizes optimisation by means of queueing network decomposition and genetic algorithm

Batch sizes have a considerable impact on the performance of a manufacturing process. Determining optimal values for batch sizes helps to reduce inventories/costs and lead times. The deterministic nature of the available batch size optimisation models reduces the practical value of the obtained solutions. Other models focus only on critical parts of the system (e.g., the bottleneck). In this paper, we present an approach that overcomes important limitations of such simplified solutions. We describe a combination of queueing network analysis and a genetic algorithm that allows us to take into account the real characteristics of the system when benefiting from an efficient optimisation mechanism. We are able to demonstrate that the application of our approach on a real-sized problem with 49 products allows us to obtain a solution (values for batch sizes) with less than 4% relative deviation of the cycle time from the exact minimal value.     

Linear models for 1-group two-dimensional guillotine cutting problems

In this study we present integer linear and non-linear models to generate 1-group constrained and unconstrained two-dimensional guillotine cutting patterns, including exact and non-exact cases. These patterns appear in different cutting processes as, for example, in the furniture industry. The models are useful for research and development of more effective solution methods, exploring particular structures, model decomposition, model relaxations, etc. They are also helpful for the performance evaluation of heuristic methods, since they allow (at least for problems of moderate size) an estimation of the optimality gap of heuristic solutions. To demonstrate the effectiveness of the proposed models, we compare them with models of the literature by solving a number of examples randomly generated and an actual example derived from a furniture company. Such results were produced using a well-known commercial software (the modelling language GAMS and the solver CPLEX) and they show that the computational efforts required to solve the models can be very different.     

Models for automated storage and retrieval systems: a literature review

Automated Storage and Retrieval Systems (AS/RS) are warehousing systems that use mechanised devices to accomplish the repetitive tasks of storing and retrieving parts in racks. Since these systems represent a significant investment and considerable operating costs, their use must be as efficient as possible. AS/RS performance is the result of the interaction of many complex and stochastic subsystems. This reality creates a need for robust and efficient evaluation models. This article complements previous surveys on AS/RS by focusing on the particular research question addressed by each work and the associated assumptions used for the various models designed for evaluating AS/RS. Dynamic models based on simulation dominate the most recent literature; however, static approaches based on travel-time modelling have strongly contributed to the study of AS/RS. This review includes dynamic – simulation-based – models, but considers also steady-state (travel-time-based) models. We believe that this review may be of great help to researchers and industrial users in their search for the best modelling approach for a specific problem.     

Allocation of variance reduction targets under the influence of supplier interaction

A common quality improvement strategy used by manufacturers is to periodically allocate quality improvement targets among their suppliers. We propose a formal modelling and optimization approach for assessing quality improvement targets for suppliers. In this approach it is understood that a manufacturer's quality improvement results from reductions in supplier process variances, which occurs only through investments in learning. A constrained nonlinear optimization model is developed for determining an optimal allocation of variance reduction target that minimizes expected total cost, where the relationship between performance measures and the set of design parameters is generally represented by second-order polynomial functions. An example in the fabrication of a tyre tread compound is used both to demonstrate the implementation of our proposed models as well as to provide an empirical comparison of optimal learning rates for different functional relationships between the performance measures and the set of design parameters.     

Thermomechanical finite element simulations of selective electron beam melting processes: performance considerations

The present contribution is concerned with the macroscopic modelling of the selective electron beam melting process by using the finite element method. The modelling and simulation of the selective electron beam melting process involves various challenges: complex material behaviour, phase changes, thermomechanical coupling, high temperature gradients, different time and length scales etc. The present contribution focuses on performance considerations of solution approaches for thermomechanically coupled problems, i.e. the monolithic and the adiabatic split approach. The material model is restricted to nonlinear thermoelasticity with temperature-dependent material parameters. As a numerical example a straight scanning path is simulated, the predicted temperatures and stresses are analysed and the performance of the two algorithms is compared. The adiabatic split approach turned out to be much more efficient for linear thermomechanical problems, i.e. the solution time is three times less than with the monolithic approach. For nonlinear problems, stability issues necessitated the use of the Euler backward integration scheme, and therefore, the adiabatic split approach required small time steps for reasonable accuracy. Thus, for nonlinear problems and in combination with the Euler backward integration scheme, the monolithic solver turned out to be more efficient.