The reconfiguration problem consists of finding a new network topology with minimal power losses, while all the system constraints such as radial structure, lines power flow below capacity limits, node voltage magnitude within limits and all nodes connected are satisfied. This is a combinational optimization problem where the aim is to specify the final status of all switches (open/closed,) in a large-scale distribution system. Although there are a plenty of methods in the literature, but comprehensive analysis of bus and line failure has not been accomplished and just a limited version of failure has been studied. This paper presents a learning automaton-based algorithm for reconfiguration of large-scale distribution systems with assumption of probabilistic failure in both of the buses and lines. The main objective of the proposed algorithm is to minimize the power loss and voltage deviation, and also to maintain the distribution system in radial structure. To demonstrate the applicability of the proposed algorithm, it is tested on two standard IEEE sample systems and the obtained results are compared with other methods. Also, the numerical result indicates that the proposed method supplying power to the non-faulted areas with minimal power loss and maintains the radial structure of distribution system under abnormal conditions. Manuscript profile

Human resource management is a comprehensive system that tries to integrate all the tasks and departments in an organization using a single computer system that can meet the special needs of these departments. They help programmers to complete their web application quickly and with minimal involvement. With the increasing development of web applications and the use of unique features of HTML 5, many possibilities are available to web programmers. In this paper, four advanced and applied frameworks are introduced and compared with the best frameworks of the previous research. Then we proposed Multi-criteria decision-making based approach for selecting optimum framework in enterprise resource planning. To demonstrate the applicability of the proposed approach, it is tested on different category of originations. Results show that our proposed approach is more applicable in different companies Manuscript profile

With the emergence of smart cities vision, its large distributed applications such as intelligent transportation systems demand scalable low-latency trusted data exchange architecture with high storage and computational resources for storing the high-volume of IoT data and providing real-time services. In recent years, blockchain technology has gained extensive attention to fulfil the requirements of such highly distributed large systems. However, there are a number of technical challenges in the integration of blockchain and IoT applications. Firstly, Bitcoin blockchain with low scalability and throughput is not able to provide fast services. Secondly, there are limitations like constrained spaces for establishing big blockchain nodes storing a massive volume of data generated by numerous smart IoT devices or sensors inside the streets of cities. This paper argues that solving both issues in one large blockchain network is infeasible. Therefore, we prioritize this two weakness of blockchain in relation to such systems and propose two separate level of blockchain networks cooperating with each other asynchronously to address them. One network called Fast BlockChain (FBC) composed of multiple scalable sub-blockchain networks responsible for fast services. Another network, CityBC, supports the networks of FBC through the long-term storing of their data and providing their smart manager with knowledge for dynamic autonomous partitioning of them in order to decrease network-to-network communications and avoid wasting storage resources and network bandwidth. Furthermore, this paper evaluates the ideal size of sub-blockchain and then proposes a novel idea for an initial partitioning technique before using collected data by blockchain nodes for dynamic partition of network. Manuscript profile