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Most of the proposed methods for investigating the transient and short-term voltage (STV) stability have assumed that only one of the two phenomena exists, i.e. either transient instability or STV instability. While these two phenomena exist simultaneously with one of them being dominant, according to the operating point of the system and disturbance condition. A unified online prediction method is presented in this paper to distinguish between the stable and unstable conditions. In the proposed method, by employing the intelligent wide area measurement system (IWAMS), the frequency deviation and the active power imbalance are calculated by the generation buses, which are considered as the intelligent generation buses (IGB). By tracking the trajectories of the frequency deviation and the active power imbalance and checking some simple defined rules, early prediction of both transient and STV instability can be achieved. In order to evaluate the performance of the proposed algorithm, the method has been tested on single machine infinite bus (SMIB) system, single machine load bus (SMLB) system, IEEE 39-bus system and IEEE 118-bus system. According to the obtained results, the proposed algorithm has the overall accuracy of 99.5% under symmetrical and asymmetrical faults, PMU measurement error, different operating points and topological changes. [ABSTRACT FROM AUTHOR]

As social networks grow, they become more and more complex, and analyzing them becomes complicated. One way to reduce this complexity is to divide the network into subnets, which are also called communities. Dividing social networks into desirable communities can help analysts and experts to understand the behavior and function of the networks. Community detection in networks is a challenging topic in network science and various methods have been proposed for that. Modularity maximization is one of the state-of-the-art methods suggested for community detection. Modularity maximization is an NP-hard problem meaning that no polynomial-time algorithm exists that could solve the problem optimally unless P=NP. A group of approaches that could solve such problems is approximate algorithms. Identifying the influential nodes has many important applications in social networks. This technique could also be used in community detection. To maximize modularity, in this paper, we propose approximate algorithms based on identifying the influential nodes and their influence domain. We used the concept of scale-free networks to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods of community detection algorithms. [ABSTRACT FROM AUTHOR]

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The impact of artificial intelligence algorithms is widely felt on many different aspects of life, and because of the efficiency and speed of such algorithms, decisions can be taken as well as their implementation on behalf of humans in the field of criminal justice. Despite the progress that has been made, there are still great concerns on the issue of rapid mechanization of the justice sector in general, and the judiciary in particular. Therefore, discussion and analysis of the impact of such algorithms on the justice system in this paper is of high importance. Such discussions will be carried out through three chapters; while chapter one reviews the concept of algorithms and their importance in criminal justice, chapters two and three are devoted to discuss the use of artificial intelligence algorithms in pre-trail and during trail stages respectively. This work, however, ended with a conclusion that contained the main results of the research and recommendations

The impact of artificial intelligence algorithms is widely felt on many different aspects of life, and because of the efficiency and speed of such algorithms, decisions can be taken as well as their implementation on behalf of humans in the field of criminal justice. Despite the progress that has been made, there are still great concerns on the issue of rapid mechanization of the justice sector in general, and the judiciary in particular. Therefore, discussion and analysis of the impact of such algorithms on the justice system in this paper is of high importance. Such discussions will be carried out through three chapters; while chapter one reviews the concept of algorithms and their importance in criminal justice, chapters two and three are devoted to discuss the use of artificial intelligence algorithms in pre-trail and during trail stages respectively. This work, however, ended with a conclusion that contained the main results of the research and recommendations