Modelling a maintenance management framework based on PAS 55 standard

This article shows the process of modelling a reference maintenance management framework (MMF) that represents the general requirements of the asset management specification PAS 55. The modelled MMF is expressed using the standardized and publicly available Business Process Modelling (BPM) languages UML 2.1 (Unified Modelling Language) and BPMN 1.0 (BPM Notation). The features of these notations allow to easily integrate the modelled processes into the general information system of an organization and to create a flexible structure that can be quickly and even automatically adapted to new necessities. This article presents a brief review about the usage of UML in maintenance projects, general characteristics of PAS 55, modelling concepts and their applications in the project of modelling the MMF. The arguments underlying the methodology and the choice of UML and BPMN are exposed. The general architecture of the suggested MMF is described and modelled through diagrams elucidating the general operation of PAS 55. From this development is appreciated the operation structure of a software tool that can incorporate MIMOSA standards and that can be made suitable for e‐maintenance functions, as an alternative to the commercial systems. Finally, some conclusions about the modelled framework are presented. Copyright © 2010 John Wiley & Sons, Ltd.     

Using argument notation to engineer biological simulations with increased confidence

The application of computational and mathematical modelling to explore the mechanics of biological systems is becoming prevalent. To significantly impact biological research, notably in developing novel therapeutics, it is critical that the model adequately represents the captured system. Confidence in adopting in silico approaches can be improved by applying a structured argumentation approach, alongside model development and results analysis. We propose an approach based on argumentation from safety-critical systems engineering, where a system is subjected to a stringent analysis of compliance against identified criteria. We show its use in examining the biological information upon which a model is based, identifying model strengths, highlighting areas requiring additional biological experimentation and providing documentation to support model publication. We demonstrate our use of structured argumentation in the development of a model of lymphoid tissue formation, specifically Peyer''s Patches. The argumentation structure is captured using Artoo (www.york.ac.uk/ycil/software/artoo), our Web-based tool for constructing fitness-for-purpose arguments, using a notation based on the safety-critical goal structuring notation. We show how argumentation helps in making the design and structured analysis of a model transparent, capturing the reasoning behind the inclusion or exclusion of each biological feature and recording assumptions, as well as pointing to evidence supporting model-derived conclusions.     

Using Scrum and unified modelling language to analyze and design an automatic course scheduling system

This research used a case study methodology to examine large-scale software projects accomplished despite ambiguous customer requirements. This study adopted Scrum as the agile software development method and used unified modelling language (UML) diagrams to enhance design implementation documents and improve the software development process. This study presented how the case company explored a Scrum-based automatic course scheduling system for elementary and secondary schools. Through interviews, the case company incorporated customers’ requirements by using the corresponding UML diagrams, which helped the project team document the software development process and design the functions to satisfy customer demand. Then, the proposed method was introduced to the automatic elementary and secondary school course scheduling system project undertaken by the case company, verifying the feasibility of the proposed method. A few problems arose in the proposed software development process, and remedies were discussed. Software companies could use these results as a reference when implementing a large-scale software project with ambiguous customer requirements.     

Predictive constitutive modelling of arteries by deep learning

The constitutive modelling of soft biological tissues has rapidly gained attention over the last 20 years. Current constitutive models can describe the mechanical properties of arterial tissue. Predicting these properties from microstructural information, however, remains an elusive goal. To address this challenge, we are introducing a novel hybrid modelling framework that combines advanced theoretical concepts with deep learning. It uses data from mechanical tests, histological analysis and images from second-harmonic generation. In this first proof of concept study, our hybrid modelling framework is trained with data from 27 tissue samples only. Even such a small amount of data is sufficient to be able to predict the stress–stretch curves of tissue samples with a median coefficient of determination of R² = 0.97 from microstructural information, as long as one limits the scope to tissue samples whose mechanical properties remain in the range commonly encountered. This finding suggests that deep learning could have a transformative impact on the way we model the constitutive properties of soft biological tissues.     

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.     

A WWW-based integrated product development platform for sheet metal parts intelligent concurrent design and manufacturing

This paper presents a WWW (World Wide Web) based integrated product development platform for intelligent concurrent design and manufacturing of sheet metal parts. In order to achieve this platform, several major modules are discussed. These modules mainly include the structure of the WWW-based integrated product development platform, an information integration framework, a RTCAPP (real time computer aided process planning) module, a customer interface module, and a design/manufacturing knowledge-based module for supporting product design and manufacturing. This paper gives the structure of the information integration framework for concurrent design and manufacturing of sheet metal parts. An information integration framework, called 'step structure information framework' is proposed. The principles, called 'zero thickness and zero bend radius' are put forward, which can be used to abstract the geometric entities of sheet metal parts in order to facilitate product design and modelling. Finally, case studies are given.     

Modelling the initial phase of an epidemic using incidence and infection network data: 2009 H1N1 pandemic in Israel as a case study

This paper presents new computational and modelling tools for studying the dynamics of an epidemic in its initial stages that use both available incidence time series and data describing the population''s infection network structure. The work is motivated by data collected at the beginning of the H1N1 pandemic outbreak in Israel in the summer of 2009. We formulated a new discrete-time stochastic epidemic SIR (susceptible-infected-recovered) model that explicitly takes into account the disease''s specific generation-time distribution and the intrinsic demographic stochasticity inherent to the infection process. Moreover, in contrast with many other modelling approaches, the model allows direct analytical derivation of estimates for the effective reproductive number (R_e) and of their credible intervals, by maximum likelihood and Bayesian methods. The basic model can be extended to include age–class structure, and a maximum likelihood methodology allows us to estimate the model''s next-generation matrix by combining two types of data: (i) the incidence series of each age group, and (ii) infection network data that provide partial information of ‘who-infected-who’. Unlike other approaches for estimating the next-generation matrix, the method developed here does not require making a priori assumptions about the structure of the next-generation matrix. We show, using a simulation study, that even a relatively small amount of information about the infection network greatly improves the accuracy of estimation of the next-generation matrix. The method is applied in practice to estimate the next-generation matrix from the Israeli H1N1 pandemic data. The tools developed here should be of practical importance for future investigations of epidemics during their initial stages. However, they require the availability of data which represent a random sample of the real epidemic process. We discuss the conditions under which reporting rates may or may not influence our estimated quantities and the effects of bias.     

Identifying latent dynamic components in biological systems

In computational systems biology, the general aim is to derive regulatory models from multivariate readouts, thereby generating predictions for novel experiments. In the past, many such models have been formulated for different biological applications. The authors consider the scenario where a given model fails to predict a set of observations with acceptable accuracy and ask the question whether this is because of the model lacking important external regulations. Real‐world examples for such entities range from microRNAs to metabolic fluxes. To improve the prediction, they propose an algorithm to systematically extend the network by an additional latent dynamic variable which has an exogenous effect on the considered network. This variable''s time course and influence on the other species is estimated in a two‐step procedure involving spline approximation, maximum‐likelihood estimation and model selection. Simulation studies show that such a hidden influence can successfully be inferred. The method is also applied to a signalling pathway model where they analyse real data and obtain promising results. Furthermore, the technique can be employed to detect incomplete network structures.Inspec keywords: biology computing, RNA, splines (mathematics), maximum likelihood estimation, approximation theory, biochemistryOther keywords: latent dynamic components, biological systems, computational system biology, regulatory models, multivariate readouts, biological applications, external regulations, real‐world examples, microRNA, metabolic fluxes, latent dynamic variables, variable time course, two‐step procedure, spline approximation, maximum‐likelihood estimation, model selection, signalling pathway model, real data, incomplete network structures     

Modelling of elastic continua using a grillage of structural elements based on discrete element concepts

A framework is described for modelling an elastic continuum using a grillage of beam‐like structural elements derived from discrete element concepts. The beam element properties are derived in detail and implemented in a structural analysis code for validation against classical two‐dimensional plane elasticity solutions. The framework offers the possibility of modelling the onset and propagation of fracture in materials that are initially continuous, without the need for specialized elements or remeshing in the context of traditional finite elements. Copyright © 2001 John Wiley & Sons, Ltd.     

Modelling seasonal influenza: the role of weather and punctuated antigenic drift

Seasonal influenza appears as annual oscillations in temperate regions of the world, yet little is known as to what drives these annual outbreaks and what factors are responsible for their inter-annual variability. Recent studies suggest that weather variables, such as absolute humidity, are the key drivers of annual influenza outbreaks. The rapid, punctuated, antigenic evolution of the influenza virus is another major factor. We present a new framework for modelling seasonal influenza based on a discrete-time, age-of-infection, epidemic model, which allows the calculation of the model''s likelihood function in closed form. This framework may be used to perform model inference and parameter estimation rigorously. The modelling approach allows us to fit 11 years of Israeli influenza data, with the best models fitting the data with unusually high correlations in which r > 0.9. We show that using actual weather to modulate influenza transmission rate gives better results than using the inter-annual means of the weather variables, providing strong support for the role of weather in shaping the dynamics of influenza. This conclusion remains valid even when incorporating a more realistic depiction of the decay of immunity at the population level, which allows for discrete changes in immunity from year to year.     

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.     

Customized dimensional analysis conceptual modelling framework for design optimization—a case study on the cross-flow micro turbine model

Dimensional Analysis Conceptual Modelling (DACM) is a framework used for conceptual modelling and simulation in system and product designs. The framework is based on cause–effect analysis between variables and functions in a problem. This article presents an approach that mobilizes concepts from the DACM framework to assist solve high-dimensional expensive optimization problems with lower computational costs. The latter fundamentally utilizes theories and concepts from well-practised dimensional analysis, functional modelling and bond graphing. Statistical design-of-experiments theory is also utilized in the framework to measure impact levels of variables towards the objective. Simplifying as well as decomposing followed by optimization of expensive problems are the focuses of the article. To illustrate the approach, a case study on the performance optimization of a cross-flow micro hydro turbine is presented. The customized DACM framework assisted optimization approach converges faster and returns better results than the one without. A single-step simplification approach is employed in the case study and it returns a better average optimization result with about only one fifth of the function evaluations compared to optimization using the original model.     

Actant model of an extraction plant

Facing a growing complexity of industrial plants, we recognise the need for qualitative modelling methods capturing functional and causal complexity in a human-centred way. The present paper presents actant modelling as a functional modelling method rooted in linguistics and semiotics. Actant modelling combines actant models from linguistics with multilevel flow modelling (MFM). Thus the semantics of MFM functions is developed further and given an interpretation in terms of actant functions. The present challenge is to provide coherence between seemingly different categories of knowledge. Yet the gap between functional and causal modelling methods can be bridged. Actant modelling provides an open and provisional, but in no way exhaustive or final answer as to how teleological concepts like goals and functions relate to causal concepts. As the main focus of the paper an actant model of an extraction plant is presented. It is shown how the actant model merges functional and causal knowledge in a natural way.     

Undefining life's biochemistry: implications for abiogenesis

In the mid-twentieth century, multiple Nobel Prizes rewarded discoveries of a seemingly universal set of molecules and interactions that collectively defined the chemical basis for life. Twenty-first-century science knows that every detail of this Central Dogma of Molecular Biology can vary through either biological evolution, human engineering (synthetic biology) or both. Clearly the material, molecular basis of replicating, evolving entities can be different. There is far less clarity yet for what constitutes this set of possibilities. One approach to better understand the limits and scope of moving beyond life''s central dogma comes from those who study life''s origins. RNA, proteins and the genetic code that binds them each look like products of natural selection. This raises the question of what step(s) preceded these particular components? Answers here will clarify whether any discrete point in time or biochemical evolution will objectively merit the label of life''s origin, or whether life unfolds seamlessly from the non-living universe.     

Automated Test Case Generation from Requirements: A Systematic Literature Review

Software testing is an important and cost intensive activity in software development. The major contribution in cost is due to test case generations. Requirement-based testing is an approach in which test cases are derivative from requirements without considering the implementation’s internal structure. Requirement-based testing includes functional and nonfunctional requirements. The objective of this study is to explore the approaches that generate test cases from requirements. A systematic literature review based on two research questions and extensive quality assessment criteria includes studies. The study identifies 30 primary studies from 410 studies spanned from 2000 to 2018. The review’s finding shows that 53% of journal papers, 42% of conference papers, and 5% of book chapters’ address requirements-based testing. Most of the studies use UML, activity, and use case diagrams for test case generation from requirements. One of the significant lessons learned is that most software testing errors are traced back to errors in natural language requirements. A substantial amount of work focuses on UML diagrams for test case generations, which cannot capture all the system’s developed attributes. Furthermore, there is a lack of UML-based models that can generate test cases from natural language requirements by refining them in context. Coverage criteria indicate how efficiently the testing has been performed 12.37% of studies use requirements coverage, 20% of studies cover path coverage, and 17% study basic coverage.     

Explaining a complex living system: dynamics, multi-scaling and emergence

Complex living systems are difficult to understand. They obey the laws of physics and chemistry, but these basic laws do not explain their behaviour; each component part of a complex system participates in many different interactions and these interactions generate unforeseeable, emergent properties. For example, microscopic interactions between non-living molecules, at the macroscopic level, produce a living cell. Here we discuss how to explain such complexity in the format of a dynamic model that is mathematically precise, yet understandable. Precise, computer-aided modelling will make it easier to formulate novel experiments and attain understanding and control of key biological processes.     

A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18F-FMISO-PET

Glioblastoma multiforme (GBM) is a highly invasive primary brain tumour that has poor prognosis despite aggressive treatment. A hallmark of these tumours is diffuse invasion into the surrounding brain, necessitating a multi-modal treatment approach, including surgery, radiation and chemotherapy. We have previously demonstrated the ability of our model to predict radiographic response immediately following radiation therapy in individual GBM patients using a simplified geometry of the brain and theoretical radiation dose. Using only two pre-treatment magnetic resonance imaging scans, we calculate net rates of proliferation and invasion as well as radiation sensitivity for a patient''s disease. Here, we present the application of our clinically targeted modelling approach to a single glioblastoma patient as a demonstration of our method. We apply our model in the full three-dimensional architecture of the brain to quantify the effects of regional resistance to radiation owing to hypoxia in vivo determined by [¹⁸F]-fluoromisonidazole positron emission tomography (FMISO-PET) and the patient-specific three-dimensional radiation treatment plan. Incorporation of hypoxia into our model with FMISO-PET increases the model–data agreement by an order of magnitude. This improvement was robust to our definition of hypoxia or the degree of radiation resistance quantified with the FMISO-PET image and our computational model, respectively. This work demonstrates a useful application of patient-specific modelling in personalized medicine and how mathematical modelling has the potential to unify multi-modality imaging and radiation treatment planning.     

Manufacturing process analysis with support of workflow modelling and simulation

Process analysis is recognized as a major stage in business process reengineering that has developed over the last two decades. Manufacturing process analysis (MPA) is defined as performance analysis of the production process. A manufacturing process analysis framework is outlined with emphasis on linking a company's strategy to operational process. Two issues, namely process modelling and simulation based analysis, are investigated. A compound workflow model (CWM) is proposed to provide graphic presentation of the production process that can be easily understood. Also it can be used directly by simulation to study the impacts of scheduling policy and analyse the process performance. A two-stage simulation analysis method is provided to quantitatively and efficiently define cause-and-effect relations to identify drivers for improvement. The manufacturing environment, PSC (production planning, scheduling and control) factors and the process structure are three main concerns considered in the simulation. An example is discussed in the final part of the paper.