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OBJECTIVES: To study the long-term outcomes of rotational atherectomy (RA) for chronic total occlusion (CTO) percutaneous coronary intervention (PCI). BACKGROUND: There is little evidence on the incidence, procedural results and long-term outcomes of RA for CTO PCI. METHODS: This registry included data from consecutive patients undergoing CTO PCI at four specialized centers. Major adverse cardiac events (MACE: cardiac death, target-vessel myocardial infarction and ischemia-driven target-vessel revascularization) on follow-up were the primary endpoint. RESULTS: A total of 1003 patients were included. Of these, 35 (3.5%) required RA. As compared with Conventional PCI, RA patients were older (68.9 +/- 9.5 vs. 64.6 +/- 10.7 years, P = 0.02), had higher prevalence of diabetes (58% vs. 37%, P = 0.01) and of a J-CTO score >= 2 (80% vs. 58%, P = 0.009), driven by severe calcification. Antegrade wire escalation was used more frequently in RA (74% vs. 53%, P = 0.08). RA was performed for balloon failure-to-cross in 51% and failure-to-expand in 49%. One burr was utilized in 86%. The 1.25-mm burr was the largest burr used in 43%. Slow flow/no-reflow was observed in 17%. No other serious RA-related complications were observed. Procedural success was 77% vs. 89% (P = 0.04) in RA vs. Conventional PCI. After a mean follow-up of 658 +/- 412 days, MACE rates were similar between groups (15% vs. 13%, P = 0.70). CONCLUSIONS: The use of RA in CTO PCI was safe, despite a worse patient risk profile and higher procedural complexity, as compared with conventional techniques. Although procedural success was lower in the RA group, there were no differences in long-term clinical outcomes between groups. (C) 2016 Wiley Periodicals, Inc.

© Springer International Publishing AG 2017. As the number of connected devices is exponentially growing, the IoT community is investigating potential ways of overcoming the resulting heterogeneity to enable device compatibility, interoperability and integration. The Semantic Web technologies, frequently used to address these issues, have been employed to develop a number of ontological frameworks, aiming to provide a common vocabulary of terms for the IoT domain. Defined in Web Ontology Language – a language based on the Description Logics, and thus equipped with the ‘off-the-shelf’ support for formal reasoning – these ontologies, however, seem to neglect the built-in automated reasoning capabilities. Accordingly, this paper discusses the possibility of leveraging this idle potential for automated analysis in the context of defining and enforcing policies for the IoT. As a first step towards a proof of concept, the paper focuses on a simple use case and, using the existing IoT-Lite ontology, demonstrates different types of semantic classification to enable policy enforcement. As a result, it becomes possible to detect a critical situation, when a dangerous temperature threshold has been exceeded. With the proposed approach, IoT practitioners are offered an already existing, reliable and optimised policy enforcement mechanism. Moreover, they are also expected to benefit from support for policy governance, separation of concerns, a declarative approach to knowledge engineering, and an extensible architecture.

© Springer International Publishing AG 2017. The ubiquitous Internet of Things is underpinned by the recent advancements in the wireless networking technology, which enabled connecting previously scattered devices into the global network. IoT engineers, however, are required to handle current limitations and find the right balance between data transferring range, throughput, and power consumption of wireless IoT devices. As a result, existing IoT systems, based on collecting data from a distributed network of edge devices, are limited by the amount of data they are able to transfer over the network. This means that some sort of data fusion mechanism has to be introduced, which would be responsible for filtering raw data before sending them further to a next node through the network. As a potential way of implementing such a mechanism, this paper proposes utilising Complex Event Processing and introduces a hierarchical distributed architecture for enabling data fusion at various levels.

© Springer International Publishing AG 2017. The increasing number of smart interactive devices connected to the network opens new business opportunities for digital content and advertisement providers, interested in reaching out to new customer audiences. To this end, they employ various device discovery and data collection techniques to gather user- and device-specific information in order to build a user profile and deliver targeted content accordingly. However, the extreme (and constantly growing) number of smart devices, dynamically connecting to and disconnecting from a network in the IoT scenario, renders existing routing techniques, such as multicasting and broadcasting, unscalable, especially when using the IPv6 128-bit addresses. Moreover, these existing solutions can hardly provide information about technical capabilities of end devices. To address this limitation, this paper discusses the potential of implementing the IoT device discovery for device-specific content delivery, based on device properties, such as screen size and resolution, network connectivity, presence of speakers, supported languages, etc., and presents an approach to enable property-based access to IoT nodes using Bloom filters. The proposed approach demonstrates space- and network-efficient characteristics, as well as provides an opportunity to perform device discovery at various granularity levels.