Synchrophasors are time-synchronized electrical measurements that represent both the magnitude and phase angle of the electrical sinusoids. Synchrophasors are measured by fast time-stamped devices called phasor measurement units (PMUs) to constitute the basis of realtime monitoring and control actions in the electric grid. Due to its enhanced situational awareness capabilities, many applications of PMUs are presented in the literature in the past decades. This paper presents a comprehensive summary of synchrophasor technology, its architecture, optimal placement techniques and its applications in electric power transmission and distribution systems. These applications include wide-area situational awareness and monitoring, state estimation, fault location and protective relaying, islanding detection etc. This review also covers some of the existing challenges in its implementation and its potential applications.

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Cancer is a major public health problem across the globe due to which millions of deaths occur every year. In the United States, prostate cancer is the second leading cause of cancer-related deaths in men. The major causes of prostate cancer include increasing age, family history, diet, sexual behavior, and geographic location. Early detection of prostate cancer can effectively reduce the mortality rate. In the past, researchers have adopted various multimodal feature extracting strategies to extract diverse and comprehensive quantitative imaging features and employed machine learning methods to detect prostate cancer. However, existing techniques lack detailed analysis of the magnitude of relationship among different individual discriminatory features, which is very important to understand the dynamics of the disease. In this study, we extracted diverse morphological features to summarize the imaging profile of patients of prostate cancer imaging database and employed Bayesian network analysis approach to quantify the association between different features and the strength of the association. The features and the association between the features were, respectively, modeled as the nodes and the edges of the network. The strength of association between the nodes was computed using Pearson's correlation, mutual Information and Kullback-Liebler methods. The strongest associations were found between multiple features: (Area → Equidiameter), (Area → Circulatory 2), (Circulatory 1→ (Elongatedness), (Circulatory 1→ Entropy), (Circulatory 1→ Max. Radius), and (Min. Radius → Eccentricity). Moreover, interaction impact among nodes and node force was also computed. This analysis will help in finding the features that are more dominant to establish the relationship and can further increase the detection performance.

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