Patients with SSc-SLE are younger at diagnosis, more frequently have PAH, and less frequently have cutaneous manifestations of SSc. They should be monitored for ILD, renal crisis, and digital ulcers.

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Cloud computing has become a popular topic in the research community because of its ability to transform computer software, platforms, and infrastructure as a service. However, cloud computing literature currently lacks user studies despite the fact that users play a crucial role in the success and failure of emerging technologies. This paper presents a study aimed at investigating users’ acceptance of cloud computing in Saudi Arabia. As a baseline, it utilizes the Technology Acceptance Model (TAM) along with five additional factors believed to affect users’ acceptance of new technology in the region in order to achieve the study goals. These factors are gender, age, education level, job domain, and nationality. The results demonstrated a high level of acceptance of cloud computing and a valid TAM in its standard form. The results also indicated that age, education, job domain, and nationality have a significant effect on users’ attitudes toward the adoption of cloud computing. However, no difference was found in the attitude toward the adoption of cloud computing between male and female employees.

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In the field of computing, combinatorics, and related areas, researchers have formulated several techniques for the Minimum Dominating Set of Queens Problem (MDSQP) pertaining to the typical chessboard based puzzles. However, literature shows that limited research has been carried out to solve the MDSQP using bioinspired algorithms. To fill this gap, this paper proposes a simple and effective solution based on genetic algorithms to solve this classical problem. We report results which demonstrate that near optimal solutions have been determined by the GA for different board sizes ranging from 8 × 8 to 11 × 11.

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The traveling thief problem (TTP) is a benchmark problem that consists of two well-known problems, the traveling salesman problem (TSP) and the knapsack problem (KP). It was defined to imitate complex real-world applications that comprise different interdependent sub-problems. Various approaches were proposed in the literature to solve such a problem. These approaches mostly focus on local search algorithms, heuristics methods and evolutionary approaches. In addition, some of these approaches concentrated on solving the problem by considering each sub-problem independently. Thus far, limited approaches were proposed to solve the problem using swarm intelligence. In this article, the authors introduce a modified artificial bees colony (ABC) algorithm that addresses the TTP in an interdependent manner. The performance of this approach was compared with various recent approaches in the literature using different benchmark instances. The obtained results demonstrated that it is competitive with the state-of-the-art approaches, especially on small and medium instances.

Ontologies recently have become a topic of interest in computer science since they are seen as a semantic support to explicit and enrich data-models as well as to ensure interoperability of data. Moreover, supporting ontology adaptation becomes essential and extremely important, mainly when using ontologies in changing environments. An important issue when dealing with ontology adaptation is the management of several versions. Ontology versioning is a complex and multifaceted problem as it should take into account change management, versions storage and access, consistency issues, etc. The purpose of this paper is to propose an approach and tool for ontology adaptation and versioning. A series of techniques are proposed to 'safely' evolve a given ontology and produce a new consistent version. The ontology versions are ordered in a graph according to their relevance. The relevance is computed based on four criteria: conceptualisation, usage frequency, abstraction and completeness. The techniques to carry out the versioning process are implemented in the Consistology tool, which has been developed to assist users in expressing adaptation requirements and managing ontology versions.