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The definition of modeling languages is a key‐prerequisite for model‐driven engineering. In this respect, Domain‐Specific Modeling Languages (DSMLs) defined from scratch in terms of metamodels and the extension of Unified Modeling Language (UML) by profiles are the proposed options. For interoperability reasons, however, the need arises to bridge modeling languages originally defined as DSMLs to UML. Therefore, the paper aims to propose a semi‐automatic approach for bridging DSMLs and UML by employing model‐driven techniques.

The paper discusses problems of the ad hoc integration of DSMLs and UML and from this discussion a systematic and semi‐automatic integration approach consisting of two phases is derived. In the first phase, the correspondences between the modeling concepts of the DSML and UML are defined manually. In the second phase, these correspondences are used for automatically producing UML profiles to represent the domain‐specific modeling concepts in UML and model transformations for transforming DSML models to UML models and vice versa. The paper presents the ideas within a case study for bridging ComputerAssociate's DSML of the AllFusion Gen CASE tool with IBM's Rational Software Modeler for UML.

The ad hoc definition of UML profiles and model transformations for achieving interoperability is typically a tedious and error‐prone task. By employing a semi‐automatic approach one gains several advantages. First, the integrator only has to deal with the correspondences between the DSML and UML on a conceptual level. Second, all repetitive integration tasks are automated by using model transformations. Third, well‐defined guidelines support the systematic and comprehensible integration.

The paper focuses on the integrating direction DSMLs to UML, but not on how to derive a DSML defined in terms of a metamodel from a UML profile.

Although, DSMLs defined as metamodels and UML profiles are frequently applied in practice, only few attempts have been made to provide interoperability between these two worlds. The contribution of this paper is to integrate the so far competing worlds of DSMLs and UML by proposing a semi‐automatic approach, which allows exchanging models between these two worlds without loss of information.

A comprehensive and computationally efficient modeling strategy for the rapid and accurate simulation of implanted impurity distribution profiles in single‐crystal silicon has been developed. This modeling strategy exploits the advantages of both Monte Carlo simulation and semi‐empirical models by combining the two approaches in a complementary manner. The dual Pearson semi‐empirical model is used to accurately and efficiently model the dose and implant angle dependence of impurity profiles as well as the dependence on energy. This new comprehensive model allows convenient and accurate simulation of implanted boron distribution profiles in single‐crystal silicon as a function of dose, tilt angle, and rotation angle, in addition to ion energy, and it has been demonstrated by implementation in the process simulation code SUPREM III.

The purpose of this paper is to study the integration between this technology and barrel finishing (BF) operation to improve part surface quality. Fused deposition modeling (FDM) processes have limitation in term of accuracy and surface finishing. Hence, post-processing operations are needed. A theoretical and experimental investigations have been carried out.

A geometrical model of the profile under the action of machining is proposed. The model takes into account FDM formulation and allows to predict the surface morphology achievable by BF. The MR needed in the model is obtained by a particular profilometer methodology, based on the alignment of Firestone–Abbot (F–A) curves. The experimental performed on a suitable geometry validated geometrical model. Profilometer and dimensional measurements have been used to assess the output of the coupled technologies in terms of surface roughness and accuracy.

The coupling of FDM and BF has been assessed and characterized in terms of obtained part surfaces and dimension evolution. Deposition angle strongly affects the BF removal speed and alters nominal dimensions of part. The geometric profile model gave interesting information about profile morphology and machining mechanism; moreover, the height prevision allows to estimate BF working time to accomplish part requirements.

The prediction of the geometric profile as a function of FDM fabrication parameters is a powerful tool which permits to investigate surface properties such as mechanical coupling or tribological aspects. The coupling of BF and FDM has been assessed and now optimization of this process can be performed just evaluating effects of parameters.

This research has been focused to an industrial application, and results can be used in a computer-aided manufacturing. The prevision of surface obtainable by this integration is a tool to find the part optimum orientation to accomplish the drawing requirements. Both the experimental findings and the model can guide operator toward a proper process improvement, thus reducing or eliminating expensive trial and error phase in the post-processing operation of FDM prototypes.

In this paper, a novel model has been presented. It allows to know in advance profile morphology achievable by a specific surface of a FDM part after a determined BF working time. A particular application of FA curves gives the MR values.

Ubiquitous web applications (UWA) are a new type of web applications which are accessed in various contexts, i.e. through different devices, by users with various interests, at anytime from anyplace around the globe. For such full‐fledged, complex software systems, a methodologically sound engineering approach in terms of model‐driven engineering (MDE) is crucial. Several modeling approaches have already been proposed that capture the ubiquitous nature of web applications, each of them having different origins, pursuing different goals and providing a pantheon of concepts. This paper aims to give an in‐depth comparison of seven modeling approaches supporting the development of UWAs.

This methodology is conducted by applying a detailed set of evaluation criteria and by demonstrating its applicability on basis of an exemplary tourism web application. In particular, five commonly found ubiquitous scenarios are investigated, thus providing initial insight into the modeling concepts of each approach as well as to facilitate their comparability.

The results gained indicate that many modeling approaches lack a proper MDE foundation in terms of meta‐models and tool support. The proposed modeling mechanisms for ubiquity are often limited, since they neither cover all relevant context factors in an explicit, self‐contained, and extensible way, nor allow for a wide spectrum of extensible adaptation operations. The provided modeling concepts frequently do not allow dealing with all different parts of a web application in terms of its content, hypertext, and presentation levels as well as their structural and behavioral features. Finally, current modeling approaches do not reflect the crosscutting nature of ubiquity but rather intermingle context and adaptation issues with the core parts of a web application, thus hampering maintainability and extensibility.

Different from other surveys in the area of modeling web applications, this paper specifically considers modeling concepts for their ubiquitous nature, together with an investigation of available support for MDD in a comprehensive way, using a well‐defined as well as fine‐grained catalogue of more than 30 evaluation criteria.

This paper describes a modeling strategy and the model development for predicting ion implanted impurity distributions in single‐crystal silicon. Both a computationally efficient semi‐empirical model and a physically‐based, more computationally intense Monte Carlo model have been developed for boron distributions in silicon. The resulting models account very well for the detailed profile dependence on implant dose, tilt angle, and rotation angle in addition to energy.

The purpose of this study is to facilitate the task of finding appropriate information to read about, and searching for people who are in the same field of interest. Knowing that more people keep up with new streaming information on Twitter micro-blogging service. With the immense number of micro-posts shared via the follower/followee network graph, Twitter users find themselves in front of millions of tweets, which makes the task crucial.

In this paper, a long short–term memory (LSTM) model that relies on the latent Dirichlet allocation (LDA) output vector for followee recommendation, the LDA model applied as a topic modeling strategy is proposed.

This study trains the model using a real-life data set extracted based on Twitter follower/followee architecture. It confirms the effectiveness and scalability of the proposed approach. The approach improves the state-of-the-art models average-LSTM and time-LSTM.

This study improves mainly the existing followee recommendation systems. Because, unlike previous studies, it applied a non-hand-crafted method which is the LSTM neural network with LDA model for topics extraction. The main limitation of this study is the cold-start users cannot be treated, also some active fake accounts may not be detected.

The aim of this approach is to assist users seeking appropriate information to read about, by choosing appropriate profiles to follow.

This approach consolidates the social relationship between users in a microblogging platform by suggesting like-minded people to each other. Thus, finding users with the same interests will be easy without spending a lot of time seeking relevant users.

Instead of classic recommendation models, the paper provides an efficient neural network searching method to make it easier to find appropriate users to follow. Therefore, affording an effective followee recommendation system.

Technology and knowledge have become the buzzwords of the new millennium. Technological changes and demanding customers are creating a more knowledge intensive, turbulent, complex and uncertain environment. Organizations, which are able to continually build faster and cheaper new strategic assets than their competitors, create long-term competitive advantages. Thus, the growth of companies is directly associated with innovativeness and technological development, especially for small organizations that are more vulnerable to dynamic changes in market place. Organizations need a strategic framework that can help them to achieve the goal of technology development and competitiveness. The purpose of this paper is to develop such strategic framework for small organizations for their technology development and, hence, survival in marketplace.

Options field methodology, options profile methodology, analytic hierarchy process (AHP) and fuzzy set theory are utilized to generate various options and profiles to propose a conceptual framework for technology development.

The results from the study showed that “mixed approach,” “strategic simulation approach” and the “regulatory environment approach,” in this order, emerged as the top three important options for the strategic technological development of small manufacturing enterprises.

This result can provide an original and more accurate implementation pathway toward technological innovative development in emerging economies. The proposed framework can provide valuable guidelines and recommendations to practicing managers and analysts for policy development to promote innovative and technological developments.

The purpose of this paper is to help high frequency circuit designers understand how to choose the best permittivity value for a laminate material for accurate modeling.

In this paper, experimental measurements of the performance of simple circuits are compared to various mathematical and software models.

Higher permittivity values were obtained using samples with bonded copper foil compared to samples etched free of foil. These higher values yielded better agreement between measured and modelled performance using current automated design software. High profile foil on thin laminates was found to increase the surface impedance of the conductor and change the propagation constant and apparent permittivity of the laminate by 15 percent or more. It was also demonstrated that, under some circumstances, the anisotropy of the substrate could result in differences in measured and modelled performance.

Only a limited number of circuit laminate materials were closely examined. Future work should include a wider variety of laminates.

The paper details the magnitude of the effects of test method, conductor profile and substrate anisotropy on the values of a material's permittivity best suited for circuit design purposes.

Expert profiling plays an important role in expert finding for collaborative innovation in research social networking platforms. Dynamic changes in scientific knowledge have posed significant challenges on expert profiling. Current approaches mostly rely on knowledge of other experts, contents of static web pages or their behavior and thus overlook the insight of big social data generated through crowdsourcing in research social networks and scientific data sources. In light of this deficiency, this research proposes a big data-based approach that harnesses collective intelligence of crowd in (research) social networking platforms and scientific databases for expert profiling.

A big data analytics approach which uses crowdsourcing is designed and developed for expert profiling. The proposed approach interconnects big data sources covering publication data, project data and data from social networks (i.e. posts, updates and endorsements collected through the crowdsourcing). Large volume of structured data representing scientific knowledge is available in Web of Science, Scopus, CNKI and ACM digital library; they are considered as publication data in this research context. Project data are located at the databases hosted by funding agencies. The authors follow the Map-Reduce strategy to extract real-time data from all these sources. Two main steps, features mining and profile consolidation (the details of which are outlined in the manuscript), are followed to generate comprehensive user profiles. The major tasks included in features mining are processing of big data sources to extract representational features of profiles, entity-profile generation and social-profile generation through crowd-opinion mining. At the profile consolidation, two profiles, namely, entity-profile and social-profile, are conflated.

(1) The integration of crowdsourcing techniques with big research data analytics has improved high graded relevance of the constructed profiles. (2) A system to construct experts’ profiles based on proposed methods has been incorporated into an operational system called ScholarMate (www.scholarmate.com).

One shortcoming is currently we have conducted experiments using sampling strategy. In the future we will perform controlled experiments of large scale and field tests to validate and comprehensively evaluate our design artifacts.

The business implication of this research work is that the developed methods and the system can be applied to streamline human capital management in organizations.

The proposed approach interconnects opinions of crowds on one’s expertise with corresponding expertise demonstrated in scientific knowledge bases to construct comprehensive profiles. This is a novel approach which alleviates problems associated with existing methods. The authors’ team has developed an expert profiling system operational in ScholarMate research social network (www.scholarmate.com), which is a professional research social network that connects people to research with the aim of “innovating smarter” and was launched in 2007.

The purpose of this paper is to propose a method for simulating the profile of part edges as a result of the FDM process. Deviations from nominal edge shape are predicted as a function of the layer thickness and three characteristic angles depending on part geometry and build orientation.

Typical patterns of edge profiles were observed on sample FDM parts and interpreted as the effects of possible toolpath generation strategies. An algorithm was developed to generate edge profiles consistent with the patterns expected for any combination of input variables.

Experimental tests confirmed that the simulation procedure can correctly predict basic geometric properties of edge profiles such as frequency, amplitude and shape of periodic asperities.

The algorithm takes into account only a subset of the error causes recognized in previous studies. Additional causes could be integrated in the simulation to improve the estimation of geometric errors.

Edge simulation may help avoid process choices that result in aesthetic and functional defects on FDM parts.

Compared to the statistical estimation of geometric errors, graphical simulation allows a more detailed characterization of edge quality and a better diagnosis of error causes.