SEARCH FOR GRAVITATIONALLY REDSHIFTED 2.2 MEV LINE FROM 4U 1820-30

We have analyzed 1.7 Ms of the INTEGRAL data of the Low Mass X-Ray Binary (LMXB) 4U 1820-30 and searched for the redshifted 2.2 MeV neutron capture gamma-ray line. This source is unique in that it is thought to be accreting pure Helium and might be a powerful 2.2 MeV line source. If detected, this line would strongly constrain the neutron star equation of state, motivating this search. The line is expected to be redshifted to 1.30-1.72 MeV so we scanned the 1-2 MeV region. Although we failed to detect the redshifted 2.2 MeV line, mainly due to the intense background noise to which INTEGRAL is exposed, we placed upper limits on the source’s flux for different line widths. We plan to do analysis on the rest of the data (over 8 Ms) in the future

Evaluation of Noise Performance of Multi-Lane Highways in the State of Qatar

In recent years, a growing number of people are complaining to transportation authorities for the traffic noise as a result of increased traffic volume on the highways. Therefore, transportation authorities are actively pursuing ways to reduce the traffic noise. In recent years, quieter pavement has become one of the cost-effective ways of sustainable highway noise reduction. However, for design and construction of quieter pavements, proper and accurate noise measurement is of paramount importance for evaluating the noise level of existing pavement as well as evaluating noise level of newly constructed pavements. In this study, the variation of noise level among different lanes of existing multi-lane highways in the State of Qatar was examined. Tire-pavement noise was measured by using onboard sound intensity (OBSI) technique. OBSI test results showed that highest noise level was observed at outer lane of multilane highways. This is probably due to the pavement distress caused by the heavy vehicle travelling at the outer lane of multi-lane highways.This paper was made possible by the NPRP grant (NPRP 7-110-2-056) from the Qatar National Research Fund (a member of Qatar Foundation)Scopu

Two Phase Description Logic Reasoning for Efficient Information Retrieval

Description Logics are used more and more frequently for knowledge representation, creating an increasing demand for efficient automated DL reasoning. However, the existing implementations are inefficient in the presence of large amounts of data. This paper summarizes the results in transforming DL axioms to a set of function-free clauses of first-order logic which can be used for efficient, query oriented data reasoning. The described method has been implemented in a module of the DLog reasoner openly available on SourceForge to download

OWL-POLAR : semantic policies for agent reasoning

The original publication is available at www.springerlink.comPostprin

Using ontological contexts to assess the relevance of statements in ontology evolution

Ontology evolution tools often propose new ontological changes in the form of statements. While different methods exist to check the quality of such statements to be added to the ontology (e.g., in terms of consistency and impact), their relevance is usually left to the user to assess. Relevance in this context is a notion of how well the statement fits in the target ontology. We present an approach to automatically assess such relevance. It is acknowledged in cognitive science and other research areas that a piece of information flowing between two entities is relevant if there is an agreement on the context used between the entities. In our approach, we derive the context of a statement from online ontologies in which it is used, and study how this context matches with the target ontology. We identify relevance patterns that give an indication of rele- vance when the statement context and the target ontology fulfill specific conditions. We validate our approach through an experiment in three dif- ferent domains, and show how our pattern-based technique outperforms a naive overlap-based approach

OntoMathPROOntoMath^{PRO} Ontology: A Linked Data Hub for Mathematics

In this paper, we present an ontology of mathematical knowledge concepts that covers a wide range of the fields of mathematics and introduces a balanced representation between comprehensive and sensible models. We demonstrate the applications of this representation in information extraction, semantic search, and education. We argue that the ontology can be a core of future integration of math-aware data sets in the Web of Data and, therefore, provide mappings onto relevant datasets, such as DBpedia and ScienceWISE.Comment: 15 pages, 6 images, 1 table, Knowledge Engineering and the Semantic Web - 5th International Conferenc

Get my pizza right: Repairing missing is-a relations in ALC ontologies (extended version)

With the increased use of ontologies in semantically-enabled applications, the issue of debugging defects in ontologies has become increasingly important. These defects can lead to wrong or incomplete results for the applications. Debugging consists of the phases of detection and repairing. In this paper we focus on the repairing phase of a particular kind of defects, i.e. the missing relations in the is-a hierarchy. Previous work has dealt with the case of taxonomies. In this work we extend the scope to deal with ALC ontologies that can be represented using acyclic terminologies. We present algorithms and discuss a system

A Semantic Model for Enhancing Network Services Management and Auditing

The road toward ubiquity, heterogeneity and virtualization of network services and resources urges for a formal and systematic approach to network management tasks. In particular, the semantic characterization and modeling of services provided to users assume an essential role in fostering autonomic service management, service negotiation and auditing. This paper is centered on the definition of an ontology for multiservice IP networks which intends to address multiple service management goals, namely: (i) to foster client and service provider interoperability; (ii) to manage network service contracts, facilitating the dynamic negotiation between clients and ISPs; (iii) to access and query SLA/SLSs data on an individual or aggregated basis to assist service provisioning in the network; and (iv) to sustain service monitoring and auditing. In order to take full advantage of the proposed semantic model, a service model API is provided to allow service management platforms to access the ontological contents. This ontological development also takes advantage of SWRL to discover new knowledge, enriching the possibilities of systems described using this support

Improving the Surface Quality and Mechanical Properties of Selective Laser Sintered PA2200 Components by the Vibratory Surface Finishing Process

This paper attempts to improve the physical and mechanical properties of selective laser sintered polyamide PA2200 components through a vibratory surface finishing process by inducing severe plastic deformation at the outer surface layers. The industrial target of additive manufacturing components is to obtain structures having surface roughness, hardness, and other mechanical properties equivalent to or better than those produced conventionally. Compared to the as-built SLS PA2200 samples, vibratory surface finishing treated specimens exhibited a smooth surface microstructure and more favorable roughness, hardness, and tensile strength. Also, the duration of the vibratory surface finishing process showed a further improvement in the surface roughness and hardness of the SLS samples. Compared to the asbuilt state, the roughness and hardness of the surface-treated samples improved by almost 90% and 15%, respectively. Consequently, microstructural analysis indicates that lower surface roughness and enhanced surface hardness is a crucial factor in influencing the overall tensile strength of SLS-PA2200 components. We consider that the combination of VSF and SLS processes can successfully handle a wide range of potential applications. This study also highlights the efficiency and applicability of the vibratory surface finishing process to other additive manufacturing processes and materials

Predicting the understandability of OWL inferences

In this paper, we describe a method for predicting the understandability level of inferences with OWL. Specifically, we present a model for measuring the understandability of a multiple-step inference based on the measurement of the understandability of individual inference steps. We also present an evaluation study which confirms that our model works relatively well for two-step inferences with OWL. This model has been applied in our research on generating accessible explanations for an entailment of OWL ontologies, to determine the most understandable inference among alternatives, from which the final explanation is generated