An even finer level of granularity than the developer level is representation level processmodelling; that is, modelling a development process at the level of actions or activities that relate to or manipulate elements of a representation scheme. Fine-grain processmodelling at this level is concerned with providing links between development process knowledge and representation knowledge which may then be used to produce a specification for a particular problem domain (fig. 2). A process model at the representation level is useful for the individual developer because it may be used to provide g u i d a n c e expressed in a language the developer understands best - the language he or she is using! Thus, guidance can take the form of a recommendation on what to do next in order to advance the specification process, or advice on handling existing specification inconsistencies. This is in contrast with processes that are “unaware” of the representation schemes they manipulate and therefore treat them as coarse- grain “vanilla” objects with no internal structure or semantics.
UML consists of 13 diagram types with use case and activity diagrams perhaps most applicable to processmodelling. Use-case diagrams describe a sequence of interactions between a system and external actors (person, system, or device) with a use case a discrete, stand-alone activity that an actor can perform to achieve an outcome. A scenario is a specific instance (typically classified as normal or alternative scenarios) providing a high-level visual representation of user requirements and logical scenarios. Basic use-case notation illustrates actors as stick figures, use cases as ovals, and the system boundary as a box border. Use case diagrams are relatively simple, but are supported by extensive written descriptions of system behaviour (preconditions, post-conditions, normal and alternative course, exceptions and business rules). Activity diagrams provide a dynamic view of a system by depicting flow from one activity to another (similar to flowcharts) showing decision points and alternative courses. Basic notation consists of start and end points (filled in circles), activities (rounded rectangle), and decision points (diamonds).
Various modelling approaches have been used to specify eBusiness systems. These range from statecharts [Harel et al., 1987], which specify transitions within a standalone sys- tem, to workflows which coordinate multiple business activities [WfMC], to Web services standards which build the interfaces among communicating parties across the Web [W3C] [WSDL, 2002], and finally to the business processmodelling languages which orchestrate participants to successfully do business with each other [BPEL, 2003] [Cabrera et al., 2003] [Cabrera et al., 2002]. However, these approaches yield complicated specifications when facing dynamically changing business requirements. In particular, the effort of dealing with autonomy and system abnormalities grows rapidly as the requirements change and grow. Below we describe the limitations of some existing models.
The next challenges in the area of processmodelling related to generalisation are related to the design of systems able to perform on demand mapping, which includes generalisation, but also the collection of user requirements, data integra- tion and automatic cartographic design (to style the resulting map). As prototypes of these systems emerge, we know that they will be hindered by performance issues. This is due to the fact that the lack of predefined sequence of action will be overcome by complex strategies to build the sequence, possibly including expen- sive trial and error strategies. More research in optimisation techniques will there- fore be required (machine learning, parallel computing, streaming based pro- cessing, etc.)
An obvious approach to enforcing the change con- trol procedures is to model them with a process mod- elling language and to guide the process by enacting the model. This requires development tools to be in- tegrated into the process environment in a way that tools cannot be abused to circumvent the process that has been modelled. Moreover, tool integration, at least partially, has to be ne-grained so that the process model can react to the execution of individualtool com- mands, rather than complete tool sessions. The reason is that there are usually documents, such as Booch dia- grams, whose components represent other documents. Inter-document consistency constraints require the cre- ation, modication or deletion of documents whenever components are created, modied or deleted. When- ever a new class icon is created in a Booch diagram, for instance, a corresponding class interface denition document has to be created. Since documents are gen- erally considered as rst class processmodelling con- cepts, the process model should at least be notied about document creation, if not be responsible for the creation itself. Therefore, a tool command for the cre- ation of a component in one document that represents another document has to be integrated with the pro- cess model.
The contribution of this paper is an account of the experience that we gained when we applied the SLANG processmodelling formalism and the SPADE environment to the modelling and enactment of a software process in an industrial setting, namely at British Airways (BA). BA is a large software developer in the UK, with some 2,000 IT sta. To increase productivity and quality, BA have founded a group called Infrastructure who is in charge of maintaining the design, implementation and documentation of reusable C++ class libraries. SLANG was used to capture, model and improve the class library development and maintenance process. The process was not only modelled, but also supported with a customised PSEE, the British Airways SEE. This PSEE integrates the SPADE process engine with tools for the development of Booch class diagrams, C++ class interface denitions, C++ class implementations and class documentations. These tools were generated with the GENESIS tool construction tool- set [Emmerich et al., 1997a]. The integration of tools and process model was done in a way that facilitates process guidance at a ner level of granularity than tool invocation.
for research purposes to educational and other non-profit institutions for a small processing fee (contact Prof. Gail E. Kaiser, Columbia University, Department of Computer Science, 500 West 120th Street, New York, NY 10027, USA, email@example.com). All criticisms of the overall approach set aside, Marvel is an interesting product providing a clear and well constructed demonstration of software processmodelling techniques. It is a seminal contribution to the field and we would recommend it to any software engineering research group seeking to gain
proaches. This is due to GP modelling a dis- tribution with continuous support, which is inap- propriate for modelling discrete counts. Chang- ing the model from a GP to a better fitting to the modelling temporal count data LGCP gives a big improvement, even when a point estimate of the prediction is considered (MSE). The 0 baseline is very strong, since many rumours have compara- tively little discussion in the second hour of their lifespan relative to the first hour. Incorporating in- formation about other rumours helps outperform this method. ICM, TXT and ICM+TXT multi- task learning approaches achieve the best scores and significantly outperform all baselines. TXT turns out to be a good approach to multi-task learn- ing and outperforms ICM. In Figure 1a we show an example rumour frequency profile for the ex- trapolation setting. TXT makes a lower error than LGCP and LGCPICM, both of which underesti- mate the counts in the second hour.
CD is also an emerging profession and the values of community practitioners from a range of backgrounds are a further source of values and agendas in the implementation of RCD. The principles expounded by various authors listed in Table 2 not only demonstrate differing levels of detail, but a complex and differing language drawn from different disciplines for the same or similar concepts. While the principles in each column reflect those in the others, participation is the only term common across them all. Nelson and Prilleltensky (2005) encourage community practitioners from a community psychology background to uphold the values underpinned by the psychological orientation of individuals in community/their environment, whereas the principles detailed for the CD profession are drawn from a sociological orientation of understanding how society works. Under the banner of CD, Ife (2002) details 26 principles grouped as relating to the ecological, social justice, ‘bottom-up’, process and global concerns underpinning CD. These also overlap with each other as seen in, for example, a ‘community determined pace of development’ being a natural consequence of organic development. Cheers et al (2007) more succinctly lists nine principles which overlap and encompass the detail of Ife’s work. Bhattacharyya (2004) summarises the values of CD under the two pursuits of solidarity and agency, describing CD as the development of solidarity and agency through the three key principles of self-help, felt needs and participation. He further suggests that without these underpinning principles, an activity cannot be considered as CD. Principles from this perspective are thus a defining aspect of CD. In all cases, principles and values are acknowledged as paramount. This highlights the significance of values as part of the complex dynamics in RCD. It is therefore important to recognise and manage these within CD research. This issue is addressed later when positioning the current study in CD theory.
The results also indicate that the posterior probability that the inter- specific parameter is contained in the model is 1, an indication of decisive posterior support for this parameter. In addition, the model with the high- est posterior support is model 6 which contained two interaction parameters; the intraspecific interaction parameter for Astroloma xerophyllum and the interspecific interaction parameter between the two species. A possible ex- planation for the attraction between the two species is that the Astroloma xerophyllum species benefits from being in close proximity to the extensive proteoid roots of the Banksia species which modify the surrounding soil con- ditions facilitating nutrient uptake. In addition, Astroloma xerophyllum has been reported to exist in symbiotic associations or mycorrhizas with ericoidal fungi [Bell and Pate, 1996, Read, 1995]. The clustering of individuals of this species may be due to associations between more than one Astroloma xero- phyllum plant with the same ericoidal fungus, the presence of which improves the efficiency of nutrient uptake by the associated plant (see Section 1.2.1). Studies aimed at modelling the spatial positions of ericoidal fungi and As- troloma xerophyllum would help clarify these clustered patterns. Further analyses aimed at investigating the nature of the interspecific interactions (whether or not they are asymmetric) would shed light on the underlying factors which give rise to the spatial distribution of the two species. This would necessitate the use of asymmetric point processes.
ENergy is one of the most important factors to global prosperity. The dependence on fossil fuels as primary energy source has led to global climate change, environmental degradation, and human health problems. In the year 2040, the world as predicted by the United Nations will have 9–10 billion people and they must be provided with energy and materials . Moreover, the recent rise in oil and natural gas prices may drive the current economy toward alternative energy sources such as biogas. Anaerobic digestion is the most widely used method of organic waste disposal due to its high performance in volume reduction and stabilization and the production of biogas that makes the process profitable. However, biological hydrolysis, which is the rate-limiting step for the anaerobic degradation  has to be improved to enhance the overall process performance and to reduce the associated cost. Several mechanical, thermal, chemical, or biological pre-treatment methods have been considered to improve hydrolysis and anaerobic digestion performance. These pre-treatments result in the ‗lysis‘ or disintegration of cells [3, 4] and release of intracellular matter that becomes more accessible to anaerobic micro-organisms , thus improving anaerobic digestion .
information browse, search, subscriptions. The middle layer is called application logic layer that reflects the interact logic among person, activities and information. Its specific functions include collaborative process management, information sharing and reuse, integration with existing systems. The bottom layer is data storage layer, whose main role is to change product data into knowledge wealth. Its specific
Data mining concepts have huge scope in insurance industry because it handles rich data sources. Till recent years many insurance industries suffered to handle obese databases. The evolution of Data mining discipline has turned the burden of handling database into a vital asset for the insurance industry. Data mining yields predictive models with which insurance industry can handle policy issuance, claims management, fraud detection, database segmentation, identify target customers, process optimization, new product development, and marketing strategies.
In order to improve the efficiency of inspections and usability of inspection data we set out to enable infrastructure owners to define the site/structure specific inspection programme. All infrastructure components are unique be it through the structural type, form of use soil conditions, environmental pollution, etc. Luckily, designers have a very good idea about early years performance so we can rely on them to deliver structures that will last. As it stands the inspection regimes do not acknowledge that, in early years, current inspection techniques have very low deterioration detection likelihood. That is why we consider implementation of stochastic processmodelling.
This study outlines two problems regarding parametric CAD system issues which are difficulties in learning curve and less flexibility in the design process. These kinds of problems lead to the lack of proficient worker that generate product with quality and dragging product development time consumption respectively.
In the authors opinion, it is not commonplace that the simulator is run at a large number of param- eter combinations. This problem mainly arises in a high dimensional parameter space where most of the parameters actively and significantly effect the output. Here even a space-filled design will result in a large number of simulator runs and a sparse GP approximation is applicable. Sparse GPs are more useful in a Bayesian optimisation [4, 5] or Bayesian history matching setting [6, 7]. Both methods often require predictions from the emulator for a large number of parameter combinations in order to accurately assess the output space for optimal solutions. The moderate computational saving in the prediction per test point means that a better exploration of the space can be performed. This becomes more important in a sequential design process for adding additional simulator runs to the optimisation, as used in an entropy search or information gain approach . These methods often predict based on a set grid size for the parameter space; reducing the computational load for prediction means a finer grid can be set. Due to the approximate nature of sparse GPs their use is not always needed or favourable for creating emulators. The approximation introduces a nugget term that cannot be fixed as it is a cou- pling between a noise parameter for the data and an estimation of the error introduced by a low rank approximation. This means that deterministic predictions at know simulator outputs are not possible, as is the case with the full GP emulator. This has to be considered when the code uncertainty affects the results of additional processes, as is the case with Bayesian optimisation and Bayesian history matching.
The process to construct a neural network consists of two stages. In the first stage, the software design and activation functions of neurons are set up. In the second stage, weighing coefficients of neuron correlations are sorted out. The second stage is also called network training. The advantageous implementation of neural networks is an ability to come to terms with the changes in the chemical process through the retraining or readjustment of the network.
In this paper, it clearly shows the set up procedure for performing a springback analysis for Radioss. The part shape and stress and strain states at the end of a simple draw forming operation are the inputs to setup. Appropriate material and section properties are assigned to the blank component. Fixture constraints are applied to the part to eliminate rigid body modes. Springback is encountered in all forming process but it is most easily recognized and studied in bending. The radius of curvature before release of load R is smaller than the radius after release of the load R. But the bending allowance is same before and after bending. So