Pattern-based Scheme Research Articles

Improving Scalability and Reward of Utility-Driven Self-Healing for Large Dynamic Architectures

Self-adaptation can be realized in various ways. Rule-based approaches prescribe the adaptation to be executed if the system or environment satisfies certain conditions. They result in scalable solutions but often with merely satisfying adaptation decisions. In contrast, utility-driven approaches determine optimal decisions by using an often costly optimization, which typically does not scale for large problems. We propose a rule-based and utility-driven adaptation scheme that achieves the benefits of both directions such that the adaptation decisions are optimal, whereas the computation scales by avoiding an expensive optimization. We use this adaptation scheme for architecture-based self-healing of large software systems. For this purpose, we define the utility for large dynamic architectures of such systems based on patterns that define issues the self-healing must address. Moreover, we use pattern-based adaptation rules to resolve these issues. Using a pattern-based scheme to define the utility and adaptation rules allows us to compute the impact of each rule application on the overall utility and to realize an incremental and efficient utility-driven self-healing. In addition to formally analyzing the computational effort and optimality of the proposed scheme, we thoroughly demonstrate its scalability and optimality in terms of reward in comparative experiments with a static rule-based approach as a baseline and a utility-driven approach using a constraint solver. These experiments are based on different failure profiles derived from real-world failure logs. We also investigate the impact of different failure profile characteristics on the scalability and reward to evaluate the robustness of the different approaches.

The Napkin-Ring Sign Indicates Advanced Atherosclerotic Lesions in Coronary CT Angiography

This study sought to determine the accuracy of plaque pattern assessment by coronary computed tomography angiography (CCTA) to differentiate between early and advanced atherosclerotic lesions as defined by histology. A ringlike attenuation pattern of coronary atherosclerotic plaques termed as napkin-ring sign (NRS) was described in CCTA of patients who had acute coronary syndrome. All procedures were performed in accordance with local and federal regulations and the Declaration of Helsinki. Approval of the local ethics committees was obtained. We investigated 21 coronary arteries of 7 donor hearts. Overall, 611 histological sections were obtained and coregistered with CCTA images. The CCTA cross sections were read in random order for conventional plaque categories (noncalcified [NCP], mixed [MP], calcified [CP]) and plaque patterns (homogenous, heterogeneous with no napkin-ring sign [non-NRS], and heterogeneous with NRS). No plaque was detected in 134 (21.9%), NCP in 254 (41.6%), MP in 191 (31.3%), and CP in 32 (5.2%) CCTA cross sections. The NCP and MP were further classified into homogenous plaques (n = 207, 46.5%), non-NRS plaques (n = 200, 44.9%), and NRS plaques (n = 38, 8.6%). The specificities of NCP and MP to identify advanced lesions were moderate (57.9%, 95% confidence interval [CI]: 50.1% to 65.6%, and 72.1%, 95% CI: 64.7% to 79.4%, respectively), which were similar to the homogenous and heterogeneous plaques (62.6%, 95% CI: 54.8% to 70.3%, and 67.3%, 95% CI: 58.6% to 76.1%, respectively). In contrast, the specificity of the NRS to identify advanced lesions was excellent (98.9%, 95% CI: 97.6% to 100%). The diagnostic performance of the pattern-based scheme to identify advanced lesions was significantly better than that of the conventional plaque scheme (area under the curve: 0.761 vs. 0.678, respectively; p = 0.001). The assessment of the plaque pattern improves diagnostic accuracy of CCTA to identify advanced atherosclerotic lesions. The CCTA finding of NRS has a high specificity and high positive predictive value for the presence of advanced lesions.

Challenges in the classification of atrial fibrillation

The incidence and prevalence of atrial fibrillation (AF) are increasing worldwide. AF is of public health importance because it accounts for substantial morbidity, mortality, and health-care costs. AF may be transient initially, but many patients have progressive disease marked by increasing frequency and duration of episodes. Various classification schemes for AF have been proposed, although current guidelines are based on temporal rhythm-based patterns. We discuss existing schemes for the classification of AF, focusing on the advantages and limitations of the pattern-based scheme, in the context of new knowledge about AF pathophysiology, AF patterns, and clinical outcomes. Furthermore, we address gaps in knowledge that present opportunities to re-examine the current pattern-based classification of AF. A future classification scheme should ideally combine elements such as the risk of stroke, an assessment of symptoms, and the degree of impairment of the atrial substrate.

Distributed Deployment Schemes for Mobile Wireless Sensor Networks to Ensure Multilevel Coverage

One of the research issues in wireless sensor networks (WSNs) is how to efficiently deploy sensors to cover an area. In this paper, we solve the k-coverage sensor deployment problem to achieve multi-level coverage of an area I. We consider two sub-problems: k-coverage placement and distributed dispatch problems. The placement problem asks how to determine the minimum number of sensors required and their locations in I to guarantee that I is k-covered and the network is connected; the dispatch problem asks how to schedule mobile sensors to move to the designated locations according to the result computed by the placement strategy such that the energy consumption due to movement is minimized. Our solutions to the placement problem consider both the binary and probabilistic sensing models, and allow an arbitrary relationship between the communication distance and sensing distance of sensors. For the dispatch problem, we propose a competition-based and a pattern-based schemes. The former allows mobile sensors to bid for their closest locations, while the latter allows sensors to derive the target locations on their own. Our proposed schemes are efficient in terms of the number of sensors required and are distributed in nature. Simulation results are presented to verify their effectiveness.