In this study, different approaches including addition of disulfide bridges, saturation of surface charges, and decrease of segmental flexibility have been used to increase thermostability of firefly luciferase. A disulfide bridge is introduced into Photinus pyralis firefly luciferase to make two separate mutant enzymes. One of the designed mutant (A103C/S121C) showed remarkable thermal stability, its specific activity decreased, whereas the A296C/A326C mutant showed tremendous thermal stability and 7.3-fold increase of specific activity. Analysis of enzyme according to residues B-factors shows that its C-terminal is much more flexible than its N-terminal. Two mutations in the most flexible region of luciferase were designed. Analysis shows that D476N mutation doesn' t have any significant effect but D474K mutation destabilized protein. On the other hand, flexibility analysis using dynamic quenching and limited proteolysis demonstrates that D474K mutation became more flexible than wild type. Protein engineering studies have shown that thermostable proteins have a higher frequency of Arg, especially in exposed states. To further elucidate the arginine saturation effects on stability of firefly luciferase, some of hydrophobic solvent-exposed residues in luciferase are changed to arginine. All of these residues are located at the external loops of L. turkestanicus luciferase.Introduction of double mutation (-Q35R/I232R) and triple mutation (- Q35R/I232R/I182R) were reserved specific activity of enzyme while its stability was enlarged and its flexibility was declined. Structural and functional properties of the mutants were investigated using different spectroscopic methods. It seems a clear relationship between stability and segmental flexibility in firefly luciferase controls its activity under different conditions.

The sharp upper bounds and the sharp lower bounds of the largest eigenvalues l1, the least eigenvalue ln, the second largest eigenvalue l2, the spread and the separator among all firefly graphs on n vertices are determined.

Luciferase is the key enzyme that catalyzes the oxygenation of luciferin, generating energy-rich peroxidic intermediates, emitting a photon of visible light. Inositol 1, 4, 5- trisphosphate (IP3) plays a vital role in the control of a large number of cellular processes.Alteration in its content has been observed in metabolic disorders. Hence, development of high-throughput screening systems to monitor temporal changes of IP3 is essential for screening of new potential therapeutic compounds. Toward a sensitive, rapid and simple method to measuring IP3, we describe the development and application of a luciferasefragment complementation strategy, which converts the ligand-induced conformational changes to light. In order to create a good performance sensor for IP3 monitoring, IP3 binding domain (IP3BD) of IP3R that consist of all the specific binding site of IP3 was selected as a ligand binding domain. The NLuc of firefly luciferase is fused to the Nterminus of IP3BD whiles the CLuc of luciferase is attached to the C-terminus of IP3BD (NLuc-IP3BD-CLuc). A flexible peptide linker (G4S) 2 was inserted between the fragmented luciferase and IP3BD to assist in proper protein folding and luciferase complementation. According to the results, the screening time was very fast and maximum response was obtained up to 11-fold higher than background. Moreover, the designed biosensor was able to monitor release of IP3 upon induction by different inducer like Bradykinin and ATP. The current biosensor not only provides a specific IP3 detector in vitro, but also facilitates to monitoring of the response of IP3 in living organisms.

Maintenance scheduling of power generators is of particular importance for power grids from both economic and reliability aspects. In this paper, the discrete firefly algorithm is implemented for maintenance scheduling of generators. This project utilizes the combination of the discrete firefly algorithm, as the main tool, and heuristic methods, as the secondary tool, for constructing initial solutions and searching the solution space. The paper investigates a case study consisting of a 32-generator problem formulated as a mixed-integer problem. The performance of the proposed method is compared with those in the literature and its strengths and weaknesses are discussed. The obtained results showed that performance of the proposed algorithm is not highly affected by its parameters’ values and is capable of providing multiple efficient maintenance schedules in a desirable time.

In this paper, we have investigated a new spectral Quasi-Newton (QN) algorithm. New search directions of the proposed algorithm increase its stability and increase the arrival to the optimum solution with a lowest cost value and our numerical applications on the standard firefly Algorithm (FA)and the new proposed algorithm are powerful as in meta-heuristic field. Our new proposed algorithm has quite common uses in several sciences and engineering problems. Finally, our numerical results show that the proposed technique is the best and its accuracy higher than the accuracy of the standard FA. These numerical results are compared using statistical analysis to evaluate the efficiency and the robustness of new proposed algorithm.

In the last decade, online shopping has played a vital role in the customers' approach to purchase different products, providing convenience to the shops and many benefits for the economy. E-commerce is widely used for digital media products such as movies, images, and software. Thus, the recommendation systems are of great importance, especially in the today's hectic world, which searches for the content that would be interesting to an individual. This research proposes a new two-step recommender system based on the demographic data and user ratings on the public MovieLens datasets. In the first step, clustering on the training dataset is performed based on the demographic data, grouping customers in homogeneous clusters. The clustering includes a hybrid firefly Algorithm (FA) and K-means approach. Due to the FA's ability to avoid trapping into the local optima, which resolves K-means' main pitfall, the combination of these two techniques leads to a much better performance. In the next step, for each cluster, two recommender systems are proposed based on K-Nearest Neighbor (KNN) and Naï, ve Bayesian Classification. The results obtained are evaluated based on many internal and external measures like the Davies-Bouldin index, precision, accuracy, recall, and F-measure. The results obtained show the effectiveness of the K-means/FA/KNN compared with the other extant models.

In data grid, using reservation is accepted to provide scheduling and service quality.Users need to have an access to the stored data in geographical environment, which can be solved by using replication, and an action taken to reach certainty. As a result, users are directed toward the nearest version to access information. The most important point is to know in which sites and distributed system the produced versions are located. By selecting a suitable place for versions, the versions having performance, efficiency and lower access time are used. In this study, an efficient method is presented to select the best place for those versions created in data grid by using the users’ firefly algorithm which is compared with two algorithms. Results show that firefly algorithm has better performance than others.