The effect of a tin-containing fluoride mouth rinse on the bond between resin composite and erosively demineralised dentin

OBJECTIVES To evaluate the effect of a tin-containing fluoride (Sn/F) mouth rinse on microtensile bond strength (μTBS) between resin composite and erosively demineralised dentin. MATERIALS AND METHODS Dentin of 120 human molars was erosively demineralised using a 10-day cyclic de- and remineralisation model. For 40 molars, the model comprised erosive demineralisation only; for another 40, the model included treatment with a NaF solution; and for yet another 40, the model included treatment with a Sn/F mouth rinse. In half of these molars (n = 20), the demineralised organic matrix was continuously removed by collagenase. Silicon carbide paper-ground, non-erosively demineralised molars served as control (n = 20). Subsequently, μTBS of Clearfil SE/Filtek Z250 to the dentin was measured, and failure mode was determined. Additionally, surfaces were evaluated using SEM and EDX. RESULTS Compared to the non-erosively demineralised control, erosive demineralisation resulted in significantly lower μTBS regardless of the removal of demineralised organic matrix. Treatment with NaF increased μTBS, but the level of μTBS obtained by the non-erosively demineralised control was only reached when the demineralised organic matrix had been removed. The Sn/F mouth rinse together with removal of demineralised organic matrix led to significantly higher µTBS than did the non-erosively demineralised control. The Sn/F mouth rinse yielded higher μTBS than did the NaF solution. CONCLUSIONS Treatment of erosively demineralised dentin with a NaF solution or a Sn/F mouth rinse increased the bond strength of resin composite. CLINICAL RELEVANCE Bond strength of resin composite to eroded dentin was not negatively influenced by treatment with a tin-containing fluoride mouth rinse.

Service level agreements-driven management of distributed applications in cloud computing environments

Advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing workload conditions, such as number of connected users, application performance might suffer, leading to violations of Service Level Agreements (SLA) and possible inefficient use of hardware resources. Combining dynamic application requirements with the increased use of virtualised computing resources creates a challenging resource Management context for application and cloud-infrastructure owners. In such complex environments, business entities use SLAs as a means for specifying quantitative and qualitative requirements of services. There are several challenges in running distributed enterprise applications in cloud environments, ranging from the instantiation of service VMs in the correct order using an adequate quantity of computing resources, to adapting the number of running services in response to varying external loads, such as number of users. The application owner is interested in finding the optimum amount of computing and network resources to use for ensuring that the performance requirements of all her/his applications are met. She/he is also interested in appropriately scaling the distributed services so that application performance guarantees are maintained even under dynamic workload conditions. Similarly, the infrastructure Providers are interested in optimally provisioning the virtual resources onto the available physical infrastructure so that her/his operational costs are minimized, while maximizing the performance of tenants’ applications. Motivated by the complexities associated with the management and scaling of distributed applications, while satisfying multiple objectives (related to both consumers and providers of cloud resources), this thesis proposes a cloud resource management platform able to dynamically provision and coordinate the various lifecycle actions on both virtual and physical cloud resources using semantically enriched SLAs. The system focuses on dynamic sizing (scaling) of virtual infrastructures composed of virtual machines (VM) bounded application services. We describe several algorithms for adapting the number of VMs allocated to the distributed application in response to changing workload conditions, based on SLA-defined performance guarantees. We also present a framework for dynamic composition of scaling rules for distributed service, which used benchmark-generated application Monitoring traces. We show how these scaling rules can be combined and included into semantic SLAs for controlling allocation of services. We also provide a detailed description of the multi-objective infrastructure resource allocation problem and various approaches to satisfying this problem. We present a resource management system based on a genetic algorithm, which performs allocation of virtual resources, while considering the optimization of multiple criteria. We prove that our approach significantly outperforms reactive VM-scaling algorithms as well as heuristic-based VM-allocation approaches.