An empirical examination of application frameworks success based on technology acceptance model

Framework-based development is currently regarded as one of the most promising software development approaches when it comes to improvements in lead time, productivity and quality. However, many frameworks and projects based on frameworks still report failures, which indicate that there are problems related to both frameworks technology and frameworks usage. The objective of our research was to examine the major drivers that have an impact on a framework’s acceptance and a framework’s success. We used the technology acceptance model (TAM) and Seddon’s information systems success model as our underlying theory. Data collected from an online survey of 389 active framework users was used to test hypothesized models. Data analysis was performed via structural equation modeling. Our findings support the post-adoption version of TAM and the relationship between continuous use and the successful use of systems, where more use also means more net benefits. We found that the successful use of frameworks is mainly dependent on two factors: continuous framework usage intention and the perceived usefulness of the framework. Several practical and theoretical implications can be foreseen including advances in framework development guidelines and insight into the relationship between the acceptance and success of frameworks.     

A scalable algorithm for extraction and clustering of event-related pictures

The event detection problem, which is closely related to clustering, has gained a lot of attentions within event detection for textual documents. However, although image clustering is a problem that has been treated extensively in both Content-Based Image Retrieval (CBIR) and Text-Based Image Retrieval (TBIR) systems, event detection within image management is a relatively new area. Having this in mind, we propose a novel approach for event extraction and clustering of images, taking into account textual annotations, time and geographical positions. Our goal is to develop a clustering method based on the fact that an image may belong to an event cluster. Here, we stress the necessity of having an event clustering and cluster extraction algorithm that are both scalable and allow online applications. To achieve this, we extend a well-known clustering algorithm called Suffix Tree Clustering (STC), originally developed to cluster text documents using document snippets. The idea is that we consider an image along with its annotation as a document. Further, we extend it to also include time and geographical position so that we can capture the contextual information from each image during the clustering process. This has appeared to be particularly useful on images gathered from online photo-sharing applications such as Flickr. Hence, our STC-based approach is aimed at dealing with the challenges induced by capturing contextual information from Flickr images and extracting related events. We evaluate our algorithm using different annotated datasets mainly gathered from Flickr. As part of this evaluation we investigate the effects of using different parameters, such as time and space granularities, and compare these effects. In addition, we evaluate the performance of our algorithm with respect to mining events from image collections. Our experimental results clearly demonstrate the effectiveness of our STC-based algorithm in extracting and clustering events.     

Application of Optimal Homotopy Asymptotic Method for solving nonlinear equations arising in heat transfer

We consider one of the newest analytical methods, the Optimal Homotopy Asymptotic Method (OHAM), to solve nonlinear equations arising in heat transfer. Two specific applications are considered: cooling of a lumped system with variable specific heat and the temperature distribution equation in a thick rectangular fin radiation to free space. Results obtained by OHAM, which does not need small parameters are compared with numerical results and a very good agreement was found. This method provides us with a convenient way to control the convergence of approximation series and adjust convergence regions when necessary. The results reveal that the proposed method is explicit, effective and easy to use.     

Nonlinear dynamic analysis of an electrical machine rotor–bearing system by the optimal homotopy perturbation method

In this paper an analytical technique, called the optimal homotopy perturbation method (OHPM), is employed to study the nonlinear behaviour of an electrical machine modelled as a rotor supported by two journal bearings with nonlinear suspension. The dynamics of the rotor centre and bearing centre are studied and the spatial displacements in the horizontal and vertical directions are obtained. It is shown that the main strength of the OHPM is its fast convergence, since after only one iteration we obtain very accurate results for a complicated nonlinear problem, which proves that this method is very efficient in practice.     

Model-driven engineering of process control software – beyond device-centric abstractions

This paper presents a new, two-level, model-driven engineering approach to industrial process control software. The first level (infrastructure engineering) is concerned with the following: the definition of the development process and guidelines, the definition of a domain-specific modeling language, the specification of the model transformations, and the development of a tool suite. This tool suite enables modeling of the process control software and the automatic code generation for programmable logic controllers. In the second level (application engineering), the process control software is engineered using the results of the infrastructure level. The approach is demonstrated on excerpts from an industrial project.     

Liquid phase equilibrium of phenol extraction from bio-oil produced by biomass pyrolysis using thermodynamic models

Utilization of biomass as a new and renewable energy source is being actively conducted by various parties. One of the technologies for utilizing or converting biomass as an energy source is pyrolysis, to convert biomass into a more valuable product which is bio-oil. Bio-oil is a condensed liquid from the vapor phase of biomass pyrolysis such as coconut shells and coffee shells. Biomass composition consisting of hemicellulose, cellulose, and lignin will oxidize to phenol which is the main content in bio-oil. The total phenolic compounds contained in bio-oil are 47.03% (coconut shell) and 45% (coffee shell). The content of phenol compounds in corrosive bio-oils still quite high, the use of this bio-oil directly will cause various difficulties in the combustion system due to high viscosity, low calorific value, corrosivity, and instability. Phenol compounds have some benefits as one of the compounds for floor cleaners and disinfectants which are contained in bio-oil.The correlation between experimental data and calculations shows that the UNIQUAC Functional-group Activity Coefficients (UNIFAC) equilibrium model can be used to predict the liquid–liquid equilibrium in the phenol extraction process of the coconut shell pyrolysis bio-oil. While the Non-Random Two Liquid (NRTL) equilibrium model can be used to predict liquid–liquid equilibrium in the extraction process of phenol from bio-oil pyrolysis of coffee shells.     

CAGISTrans: Providing Adaptable Transactional Support for Cooperative Work – an Extended Treatment

The theme of this paper is on transactional support for cooperative work environments, focusing on data sharing – i.e., providing suitable mechanisms to manage concurrent access to shared resources. The subject is not new per se. In fact, in terms of transaction models and frameworks, several solutions already exist. Still, there are some problems that are not solved. Among these are the problems that result from the dynamic and heterogeneous nature of cooperative work. Our solution is to provide transactional support that not only can be tailored to suit different situations, but can also be modified following changes in the actual environment while the work is being performed – i.e., adaptable transactional support. As part of this, we have identified and extracted the beneficial features from existing models and attempted to extend these to form a transactional framework, called CAGISTrans. This is a framework for the specification of transaction models suiting specific applications. To handle dynamic environments we propose a new way of organizing the elements of a transaction model to allow runtime refinement. In addition, we have developed a transaction management system, built on the middleware principle, to allow interoperability and database independence. Thus this addresses the problems induced by the heterogeneous nature of cooperative environments.     

Applying temporal dependence to detect changes in streaming data

Detection of changes in streaming data is an important mining task, with a wide range of real-life applications. Numerous algorithms have been proposed to efficiently detect changes in streaming data. However, the limitation of existing algorithms is that they assume that data are generated independently. In particular, temporal dependencies of data in a stream are still not thoroughly studied. Motivated by this, in this work we propose a new efficient method to detect changes in streaming data by exploring the temporal dependencies of data in the stream. As part of this, we introduce a new statistical model called the Candidate Change Point (CCP) model, with which the main idea is to compute the probabilities of finding change points in the stream. The computed probabilities are used to generate a distribution, which is, in turn, used in statistical hypothesis tests to determine the candidate changes. We use the CCP model to develop a new algorithm called Candidate Change Point Detector (CCPD), which detects change points in linear time, and is thus applicable for real-time applications. Our extensive experimental evaluation demonstrates the efficiency and the feasibility of our approach.