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In recent years, stingless bee honey has gained popularity due to its health benefits and commercial potential. There is a worrying tendency of bee colonies leaving its hives suddenly, and a specific research has been done to monitor the insect's habits. This project proposed a testbed for a stingless beehive monitoring system capable of capturing sensory data that provides an accurate indication of the bee's health and activity. This project utilises a raspberry pi gateway as a LoRa packet forwarder and four Arduino end nodes to collect sensor data from stingless beehives. The outcome showed that all planned functionalities had been accomplished, and these characteristics are expected to provide sufficient information to the beekeeper to take appropriate action to avert colony collapse. Future studies will focus on the modelling and correlation of measured environmental data in order to forecast bee colony loss.

Installing capacitors in a large unbalanced electrical distribution system will indeed improves the performance of the system in terms of its voltage profile and real power loss stability. However, determining the suitable locations for capacitors installation with an appropriate sizing in an unbalanced electrical distribution system involves an intricate process. This impediment can be resolved by implementing an optimal capacitors placement and sizing. The proposed technique is a highly nonlinear optimization problem which requires discrete and multi-dimensional control variables of capacitor locations and sizes. This paper proposed a new artificial intelligence approach used to reduce the total line real power loss and total real power consumption while maintaining the voltage profile along the feeders. It was done by integrating the circuitry schematic diagram of an unbalanced electrical distribution system modeled in SIMULINK® software with the computational programming based differential evolution particle swarm optimization (DEPSO) for optimal capacitors placement and sizing developed under the MATLAB® software. In this study, pre-selection of the capacitor locations can be considered as the first stage of the proposed concept and it is commenced prior to the optimization process performed by the DEPSO algorithm considered as the second stage of the proposed concept. A modified IEEE 13-bus unbalanced radial distribution system is used verify effectiveness of the proposed technique in solving the problem. The results will be discussed notably through comparative studies on the objective function of total cost and performance of the DEPSO technique.

The optimal capacitor placement problem involves determination on the type, number, location and size of capacitors to be placed in a distribution system for the attainment of energy efficiency. The main objectives of the capacitors placement and sizing are to reduce total line loss and energy consumption while satisfying the operational constraints. This paper presents the optimal capacitor placement and sizing problems solved by using the Ant Colony Optimization (ACO) technique with the integrated of circuitry unbalanced electrical distribution modeled in SIMULINK®MATLAB®software. The proposed technique was tested on a modified IEEE 13-bus unbalanced radial distribution system and the results revealed that the proposed technique has the merit in achieving optimal solution for addressing the problems.