Abstract The importance of social media such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly recognized. During crisis situations, rapid and effective response actions by emergency services are critical to assure the safety of the public. In this paper, we propose a conceptual representation for crisis-related tweet classification. In order to classify a stream of tweets related to the incident, the crisis-related terms in each tweet are represented as conceptual entities such as event entities, category indicator entities, information type entities, URL entities, and user entities. For tweet classification, we have compared support vector machines and deep learning model which combines class activation mapping with one-shot learning in convolutional neural networks. Experimental results on TREC 2018 Incident Streams test collection show significant improvement over the baseline system.

ABSTRACT Design candidates obtained from optimization techniques may have meaningful information, which provides not only the best solution, but also a relationship between object functions and design variables. In particular, trade-off studies for optimum airfoil shape design involving various objectives and design variables require the effective analysis tool to take into account a complexity between objectives and design variables. In this study, for the multiple-conflicting objectives that need to be simultaneously fulfilled, the real-coded Adaptive Range Multi-Objective Genetic Algorithm code, which represents the global and stochastic multi-objective evolutionary algorithm, was developed for an airfoil shape design. Furthermore, the PARSEC method reflecting geometrical properties of airfoil is adopted to generate airfoil shapes. In addition, the Self-Organizing Maps, based on the neural network, are used to visualize trade-offs of a relationship between the objective function space and the design variable space obtained by evolutionary computation. The Self-Organizing Maps that can be considered as data mining of the engineering design generate clusters of object functions and design variables as an essential role of trade-off studies. The aerodynamic data for all candidate airfoils is obtained through Computational Fluid Dynamics. Lastly, the relationship between the maximum lift coefficient and maximum lift-to-drag ratio as object functions and 12 airfoil design parameters based on the PARSEC method is investigated using the Self-Organizing Maps method.

Purpose To assess the clinical utility of the prostate-specific antigen mass ratio (PSA-MR), a newly developed PSA derivative, simply defined as the (i) PSA density (PSA-D) multiplied by the plasma volume or (ii) total PSA amount in circulation per prostate volume, for predicting prostate cancer (PCa) among men undergoing repeated prostate biopsy (PBx). Materials and Methods Patients (n = 286), who underwent a repeated PBx, were analyzed. The various parameters associated with PCa detection were noted in each patient. PSA-MR was also calculated. Results PCa was detected in 63 (22.0%) of 286 patients. PSA-MR was the independent predictor in the univariate- and multivariate logistic regression analyses (OR = 3.448, p = 0.001 and OR = 13.430, p = 0.033, respectively). A nomogram that incorporated PSA-MR was considered a useful tool (predictive accuracy: 79.2%, 95% CI: 0.726-0.858, p < 0.001). Furthermore, a nomogram that incorporated PSA-MR would have avoided 59.6% of unnecessary repeated PBx. The predictive accuracy of PSA-MR was also superior to that of PSA or PSA-D (p = 0.013 and 0.009, respectively). Conclusions PSA-MR was an independent predictor, and its consideration would have avoided 59.6% of unnecessary repeated PBx for PCa detection. PSA-MR was also superior than PSA or PSA-D. Our results support the use of PSA-MR to facilitate counseling with patients after a negative initial PBx, and use of PSA-MR might reduce further unnecessary biopsies.