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Top-Rank-K Frequent Itemset (or Pattern) Mining (FPM) is an important data mining task, where user decides on the number of top frequency ranks of patterns (itemsets) they want to mine from a transactional dataset. This problem does not require the minimum support threshold parameter that is typically used in FPM problems. Rather, the algorithms solving the Top-Rank-K FPM problem are fed with K , the number of frequency ranks of itemsets required, to compute the threshold internally. This paper presents two declarative approaches to tackle the Top-Rank-K Closed FPM problem. The first approach is Boolean Satisfiability-based (SAT-based) where we propose an effective encoding for the problem along with an efficient algorithm employing this encoding. The second approach is CP-based, that is, utilizes Constraint Programming technique, where a simple CP model is exploited in an innovative manner to mine the Top-Rank-K Closed FPM itemsets from transactional datasets. Both approaches are evaluated experimentally against other declarative and imperative algorithms. The proposed SAT-based approach significantly outperforms IM, another SAT-based approach, and outperforms the proposed CP-approach for sparse and moderate datasets, whereas the latter excels on dense datasets. An extensive study has been conducted to assess the

Energy demand has increased significantly in the recent years due to the emerging of new technologies and industries, in particular in the developing countries. This increase requires much more developed power grid system than the existing traditional ones. Smart grid (SG) offers a potential solution to this problem. Being one of the most needed and complex cyber-physical systems (CPS), SG has been addressed exhaustively by researchers, from different views and aspects. However, energy optimization yet needs much more studying and examination. Therefore, this chapter presents a comprehensive investigation and analysis of the state-of-the-art developments in SG as a CPS with emphasis on energy optimization techniques and challenges. It also surveys the main challenges facing the SG considering CPS factors and the remarkable accomplishments and techniques in addressing these challenges. In addition, the document contrasts between different techniques according to their efficiency, usage, and feasibility. Moreover, this work explores the most effective applications of the SG as a CPS.