A semantic interoperability approach to health‐care data: Resolving data‐level conflicts

The integration of data from various electronic health record (EHR) systems presents a critical conflict in the sharing and exchanging of patient information across a diverse group of health‐oriented organizations. Patient health records in each system are annotated with ontologies utilizing different health‐care standards, creating ontology conflicts both at the schema and data levels. In this study, we introduce the concept of semantic ontology mapping for the facilitation and interoperability of heterogeneous EHR systems. This approach proposes a means of detecting and resolving the data‐level conflicts that generally exist in the ontology mapping process. We have extended the semantic bridge ontology in support of ontology mapping at the data level and generated the required mapping rules to reconcile data from different ontological sources into a canonical format. As a result, linked‐patient data are generated and made available in a semantic query engine to facilitate user queries of patient data across heterogeneous EHR systems.     

Semantic Ontology Mapping for Interoperability of Learning Resource Systems using a rule-based reasoning approach

There is an increasing demand for sharing learning resources between existing learning resource systems to support reusability, exchangeability, and adaptability. The learning resources need to be annotated with ontologies into learning objects that use different metadata standards. These ontologies have introduced the problems of semantic and structural heterogeneity. This research proposes a Semantic Ontology Mapping for Interoperability of Learning Resource Systems. To enable semantic ontology mapping, this research proposes conflict detection and resolution techniques for both semantic and structural conflicts. The Semantic Bridge Ontology has been proposed as a core component for generating mapping rules to reconcile terms defined in local ontologies into terms defined in the target common ontology. This work defines the reasoning rules to classify related learning objects to enhance the powerful deductive reasoning capabilities of the system. As a consequence, ontology-based learning object metadata are generated and used by the semantic query engine to facilitate user queries of learning objects across heterogeneous learning resource systems.     

Modified rubber seed oil based polyurethane foams

In this work, rubber seed oil (RSO) was extracted with cyclohexane at room temperature for 24 h. The yield of RSO was about 40 %. The RSO was purified by using sodium hydroxide to eliminate some free fatty acids. Then the RSO was modified by simultaneous epoxidation and hydroxylation. Hydroxylated RSO (HRSO) had the hydroxylation extents 120 and 230 mgKOH/g with 2 h and 4 h reaction times, respectively. The chemical structures of the oils were studied by FT-IR and ¹H-NMR, and they were characterized also in other ways. Polyurethane foam samples based on the HRSOs were successfully prepared, and their physico-mechanical and thermal properties were also studied, with varied NCO index and water content in the formulations. The PUF230 had better thermal stability and compressive strength but lower density than the PUF120. Both the NCO index and the water content had large effects on density, cell size, and compressive strength of the polyurethane foams.     

Improvement of the stopband spurious window for a dual-mode dielectric resonator filter by new coupling technique

This paper presents a new design technique for two configurations of a dual-mode filter using a dielectric loaded cavity operating at 1.95 GHz. The dielectric puck in the proposed filter was floated in the middle of a cylindrical metal cavity with short circuits on the side walls and holes through the ceramic from the side face. These holes allowed the insertion of coupling probes which did not affect the unloaded quality factor or the spurious window. The inline structure fourth pole Chebyshev dual mode filter offers a 870 MHz suppression window while the planar configuration was 710 MHz from the resonance frequency and the operation bandwidth of 54 MHz. The volume reduction ratio was eight times larger compared with a coaxial filter of the same Q factor.     

Determination of the appropriate parameters for K‐means clustering using selection of region clusters based on density DBSCAN (SRCD‐DBSCAN)

K‐means clustering can be highly accurate when the number of clusters and the initial cluster centre are appropriate. An inappropriate determination of the number of clusters or the initial cluster centre decreases the accuracy of K‐means clustering. However, determining these values is problematic. To solve these problems, we used density‐based spatial clustering of application with noise (DBSCAN) because it does not require a predetermined number of clusters; however, it has some significant drawbacks. Using DBSCAN with high‐dimensional data and data with potentially different densities decreases the accuracy to some degree. Therefore, the objective of this research is to improve the efficiency of DBSCAN through a selection of region clusters based on density DBSCAN to automatically find the appropriate number of clusters and initial cluster centres for K‐means clustering. In the proposed method, DBSCAN is used to perform clustering and to select the appropriate clusters by considering the density of each cluster. Subsequently, the appropriate region data are chosen from the selected clusters. The experimental results yield the appropriate number of clusters and the appropriate initial cluster centres for K‐means clustering. In addition, the results of the selection of region clusters based on density DBSCAN method are more accurate than those obtained by traditional methods, including DBSCAN and K‐means and related methods such as Partitioning‐based DBSCAN (PDBSCAN) and PDBSCAN by applying the Ant Clustering Algorithm DBSCAN (PACA‐DBSCAN).     

Association rule hiding in risk management for retail supply chain collaboration

Association rule hiding is an efficient solution that helps enterprises avoid the risk caused by sensitive knowledge leakage when sharing data in their collaborations. This study examines how data sharing has the potential to create risk for enterprises in retail supply chain collaboration and proposes a new algorithm to remove sensitive knowledge from the released database based on the intersection lattice of frequent itemsets. The proposed algorithm specifies the victim item such that the modification of this item causes the least impact on frequent itemsets and the non-sensitive association rule. In the experiment described in this paper, this algorithm is used in risk avoidance for a retailer sharing data in retail supply chain collaboration. The results indicate that our approach is applicable in a real context and outperforms previous mechanisms.