Energy systems modelling and design are a critical aspect of planning and development among researchers, electricity planners, infrastructure developers, utilities, decision-makers, and other relevant stakeholders. However, to achieve a sustainable energy supply, the energy planning approach needs to integrate some key dimensions. Importantly, these dimensions are necessary to guide the simulation and evaluation. It is against this backdrop that this paper focuses on the simulation and analysis approaches for sustainable planning, design, and development of microgrids based on clean energy resources. The paper first provides a comprehensive review of the existing simulation tools and approaches used for designing energy generation technologies. It then discusses and compares the traditional strategies and the emerging trends in energy systems simulation based on the software employed, the type of problem to be solved, input parameters provided, and the expected output. The paper introduces a practical simulation framework for sustainable energy planning, which is based on the social-technical-economic-environmental-policy (STEEP) model. The STEEP represents a holistic sustainability model that considers the key energy systems planning dimensions compared to the traditional techno-economic model used in several existing simulation tools and analyses. The paper provides insights into data-driven analysis and energy modelling software development applications.

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The door is an essential part of any structure that provides access and security of lives and properties. The manual operation of a door could be cumbersome and laborious when the traffic volume is high. Also, it has been observed that doors could serve as a medium of spreading the deadly coronavirus disease 2019 (COVID-19) infection. Therefore, a prototype automatic sliding door that plays a crucial role in curbing the spread of this infectious diseases has been designed and implemented in this paper. The design of the prototype sliding door is in two parts namely; the structural part and the automation part. The structural design of the door was achieved using the Microsoft Visio 2016 while the design of the automation system was achieved using express printed circuit board. The implementation of the structural part was achieved using 1 inch particle board while the implementation of the automation system was based on the components like the active infrared sensor, resistors (10 kΩ), capacitor (1000 µF), transistors (TIP41 Q8, BC548 Q7), LED indicators, press button switch, pulley system, drive belt, stepper motor (IP65), and ATMEGA 8 microcontroller. The result of the tests carried out on the door showed that the prototype automatic sliding door was characterized by average opening time, closing time, delay time, and optimal sensing range of 3.10 s, 3.05 s, 5.72 s, and 23.5 cm, respectively. It can therefore be concluded from this work that the prototype automatic sliding door is effective in overriding the manual operation of the door.

Machine learning has been an effective tool to connect networks of enormous information for predicting personality. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in most research efforts. This research modeled user personality based on set of features extracted from the Facebook data using Map-Reduce Back Propagation Neural Network (MRBPNN). The performance of the MRBPNN classification model was evaluated in terms of five basic personality dimensions: Extraversion (EXT), Agreeableness (AGR), Conscientiousness (CON), Neuroticism (NEU), and Openness to Experience (OPN) using True positive, False Positive, accuracy, precision and F-measure as metrics at the threshold value of 0.32. The experimental results reveal that MRBPNN model has accuracy of 91.40%, 93.89%, 91.33%, 90.43% and 89.13% CON, OPN, EXT, NEU and AGR respectively for personality recognition which is more computationally efficient than Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). Therefore, personality recognition based on MRBPNN would produce a reliable prediction system for various personality traits with data having a very large instance.  Keywords— Machine learning, Facebook, MRBPNN, Personality Recognition, Neuroticism, Agreeableness.

This study designs, simulates and assesses the performance of a Self-Adaptive Partitioned Particle Swarm Optimization (SAP-PSO) routing model in a MANET. The model automatically groups nodes into partitions and obtains the local best for each partition. The local best for each partition communicates with each other to form the global best. The model was simulated and benchmarked with the Traditional PSO (T-PSO) and the Ant Colony Optimization (ACO) using global best and computational time as performance metrics. Simulation results showed that the T-PSO and SAP-PSO does not have any significant difference in performance when there is no intermediate node on the network. The T-PSO outperformed both ACO and SAP-PSO models when intermediate nodes on the network were few. When a large number of intermediate nodes are present on the network, the proposed SAP-PSO performed better than PSO and ACO. This makes SAP-PSO a better routing optimization when large numbers of intermediate nodes are on the network and the search space is complex.