Identifying Influential Bloggers: Time Does Matter

Blogs have recently become one of the most favored services on the Web. Many users maintain a blog and write posts to express their opinion, experience and knowledge about a product, an event and every subject of general or specific interest. More users visit blogs to read these posts and comment them. This "participatory journalism" of blogs has such an impact upon the masses that Keller and Berry argued that through blogging "one American in tens tells the other nine how to vote, where to eat and what to buy" \cite{keller1}. Therefore, a significant issue is how to identify such influential bloggers. This problem is very new and the relevant literature lacks sophisticated solutions, but most importantly these solutions have not taken into account temporal aspects for identifying influential bloggers, even though the time is the most critical aspect of the Blogosphere. This article investigates the issue of identifying influential bloggers by proposing two easily computed blogger ranking methods, which incorporate temporal aspects of the blogging activity. Each method is based on a specific metric to score the blogger's posts. The first metric, termed MEIBI, takes into consideration the number of the blog post's inlinks and its comments, along with the publication date of the post. The second metric, MEIBIX, is used to score a blog post according to the number and age of the blog post's inlinks and its comments. These methods are evaluated against the state-of-the-art influential blogger identification method utilizing data collected from a real-world community blog site. The obtained results attest that the new methods are able to better identify significant temporal patterns in the blogging behaviour

Indication and timing of soft tissue augmentation at maxillary and mandibular incisors in orthodontic patients. A systematic review

SUMMARYOBJECTIVE: To assess the indication and timing of soft tissue augmentation for prevention or treatment of gingival recession when a change in the inclination of the incisors is planned during orthodontic treatment. MATERIALS AND METHODS: Electronic database searches of literature were performed. The following electronic databases with no restrictions were searched: MEDLINE, EMBASE, Cochrane, and CENTRAL. Two authors performed data extraction independently using data collection forms. RESULTS: No randomized controlled trial was identified. Two studies of low-to-moderate level of evidence were included: one of prospective and retrospective data collection and one retrospective study. Both implemented a periodontal intervention before orthodontics. Thus, best timing of soft tissue augmentation could not be assessed. The limited available data from these studies appear to suggest that soft tissue augmentation of bucco-lingual gingival dimensions before orthodontics may yield satisfactory results with respect to the development or progression of gingival recessions. However, the strength of the available evidence is not adequate in order to change or suggest a possible treatment approach in the daily practice based on solid scientific evidence. CONCLUSIONS: Despite the clinical experience that soft tissue augmentation of bucco-lingual gingival dimensions before orthodontic treatment may be a clinically viable treatment option in patients considered at risk, this treatment approach is not based on solid scientific evidence. Moreover, the present data do not allow to draw conclusions on the best timing of soft tissue augmentation when a change in the inclination of the incisors is planned during orthodontic treatment and thus, there is a stringent need for randomized controlled trials to clarify these open issue

Social Clustering of Vehicles Based on Semi-Markov Processes

The full text version attached to this record is the authors final peer reviewed version. The publisher's final version of record can be found by following the DOI link.Vehicle clustering is a crucial network managementtask for vehicular networks in order to address the broadcaststorm problem, and also to cope with rapidly changing networktopology. Developing algorithms that createstable clustersis avery challenging procedure because of the highly dynamic movingpatterns of vehicles and the dense topology. Previous approachesto vehicle clustering have been based on either topology-agnosticfeatures, such as vehicle IDs, on hard to set parameters, orhave exploited very limited knowledge of vehicle trajectories.This article develops a pair of algorithms, namelySociologicalPattern Clustering (SPC), andRoute Stability Clustering (RSC),the latter being a specialization of the former that exploit, forthe first time in the relevant literature, the “social behavior”of vehicles, i.e. their tendency to share the same/similar routes.Both methods exploit the historic trajectories of vehiclesgatheredby road-side units located in each subnetwork of a city, anduse the recently introduced clustering primitive ofvirtual forces.The mobility, i.e. mobile patterns of each vehicle are modeledas semi-Markov processes. In order to assess the performanceof the proposed clustering algorithms, we performed a detailedexperimentation by simulation to compare its behavior withthat of high-performance state-of-the-art algorithms, namely, theLow-Id,DDVCandMPBCprotocols. The comparison involvedthe investigation of the impact of a range of parameters onthe performance of the protocols, including vehicle speed andtransmission range as well as the existence and strength of socialpatterns, for both urban and highway-like environments. Allthe received results attested to the superiority of the proposedalgorithms for creating stable and meaningful clusters