Condition monitoring and rating of bridge components in a rail or road network by using SHM systems within SRP

The safety and performance of bridges could be monitored and evaluated by Structural Health Monitoring (SHM) systems. These systems try to identify and locate the damages in a structure and estimate their severities. Current SHM systems are applied to a single bridge, and they have not been used to monitor the structural condition of a network of bridges. This paper propose a new method which will be used in Synthetic Rating Procedures (SRP) developed by the authors of this paper and utilizes SHM systems for monitoring and evaluating the condition of a network of bridges. Synthetic rating procedures are used to assess the condition of a network of bridges and identify their ratings. As an additional part of the SRP, the method proposed in this paper can continuously monitor the behaviour of a network of bridges and therefore it can assist to prevent the sudden collapses of bridges or the disruptions to their serviceability. The method could be an important part of a bridge management system (BMS) for managers and engineers who work on condition assessment of a network of bridges.

An accurate rating aggregation method for generating item reputation

Many websites presently provide the facility for users to rate items quality based on user opinion. These ratings are used later to produce item reputation scores. The majority of websites apply the mean method to aggregate user ratings. This method is very simple and is not considered as an accurate aggregator. Many methods have been proposed to make aggregators produce more accurate reputation scores. In the majority of proposed methods the authors use extra information about the rating providers or about the context (e.g. time) in which the rating was given. However, this information is not available all the time. In such cases these methods produce reputation scores using the mean method or other alternative simple methods. In this paper, we propose a novel reputation model that generates more accurate item reputation scores based on collected ratings only. Our proposed model embeds statistical data, previously disregarded, of a given rating dataset in order to enhance the accuracy of the generated reputation scores. In more detail, we use the Beta distribution to produce weights for ratings and aggregate ratings using the weighted mean method. Experiments show that the proposed model exhibits performance superior to that of current state-of-the-art models.

Validity of the OMNI rating of perceived exertion scale for children and adolescents with cerebral palsy

Aim This study evaluated the validity of the OMNI Walk/Run Rating of Perceived Exertion (OMNI-RPE) scores with heart rate and oxygen consumption (VO2) for children and adolescents with cerebral palsy (CP). Method Children and adolescents with CP, aged 6 to 18 years and Gross Motor Function Classification System (GMFCS) levels I to III completed a physical activity protocol with seven trials ranging in intensity from sedentary to moderate-to-vigorous. VO2 and heart rate were recorded during the physical activity trials using a portable indirect calorimeter and heart rate monitor. Participants reported OMNI-RPE scores for each trial. Concurrent validity was assessed by calculating the average within-subject correlation between OMNI-RPE ratings and the two physiological indices. Results For the correlational analyses, 48 participants (22 males, 26 females; age 12y 6mo, SD 3y 4mo) had valid bivariate data for VO2 and OMNI-RPE, while 40 participants (21 males, 19 females; age 12y 5mo, SD 2y 9mo) had valid bivariate data for heart rate and OMNI-RPE. VO2 (r=0.80; 95% CI 0.66–0.88) and heart rate (r=0.83; 95% CI 0.70–0.91) were moderately to highly correlated to OMNI-RPE scores. No difference was found for the correlation of physiological data and OMNI-RPE scores across the three GMFCS levels. The OMNI-RPE scores increased significantly in a dose-response manner (F6,258=116.1, p<0.001) as exercise intensity increased from sedentary to moderate-to-vigorous. Interpretation OMNI-RPE is a clinically feasible option to monitor exercise intensity in ambulatory children and adolescents with CP.

Reputation model based on rating data and application in recommender systems

This thesis introduced two novel reputation models to generate accurate item reputation scores using ratings data and the statistics of the dataset. It also presented an innovative method that incorporates reputation awareness in recommender systems by employing voting system methods to produce more accurate top-N item recommendations. Additionally, this thesis introduced a personalisation method for generating reputation scores based on users' interests, where a single item can have different reputation scores for different users. The personalised reputation scores are then used in the proposed reputation-aware recommender systems to enhance the recommendation quality.

Relative ranking: A biased rating

Reviewers' ratings have become one of the most influential parameters when making a decision to purchase or rent the products or services from the online vendors. Star Rating system is the de-facto standard for rating a product. It is regarded as one of the most visually appealing rating systems that directly interact with the consumers; helping them find products they will like to purchase as well as register their views on the product. It offers visual advantage to pick the popular or most rated product. Any system that is not as appealing as star system will have a chance of rejection by online business community. This paper argues that, the visual advantage is not enough to declare star rating system as a triumphant, the success of a ranking system should be measured by how effectively the system helps customers make decisions that they, retrospectively, consider correct. This paper argues and suggests a novel approach of Relative Ranking within the boundaries of star rating system to overcome a few inherent disadvantages the former system comes with. © Springer Science+Business Media B.V. 2010.