Large-scale renewable energy plants such as solar photovoltaic (PV) farms are vital to the global transition to a green energy economy. They reduce greenhouse gas emissions, mitigate climate change, and promote sustainable and resilient energy. However, large-scale solar PV farms need adequate planning and site selection for optimal performance. This study presents a geographic information system (GIS)-based multi-criteria decision-making (MCDM) framework utilizing the analytic hierarchy process (AHP) to identify optimal sites for utility-scale photovoltaic (PV) farms in Ikorodu, Lagos State, Nigeria. By integrating critical environmental, technical, economic, and social factors, the model evaluates land suitability for solar energy projects across the study area. The finding indicates that 68.77% of the land is unsuitable for development, with only 17.78% classified as highly suitable and 12.67% as moderately suitable. Marginally suitable and most appropriate areas are minimal, at 0.73% and 0.04%, respectively. This study provides a replicable approach for stakeholders and policymakers aiming to implement sustainable energy solutions, aligning with national renewable energy targets. Future research could integrate dynamic factors such as community engagement, land use changes, and evolving environmental policies to enhance decision-making models. This framework offers valuable insights into renewable energy planning and contributes to advancing Nigeria’s transition to sustainable energy systems.

Selecting the most preferred software for teaching power systems engineering at the undergraduate level is a complex problem in developing countries, and it requires making an informed decision by compromising on various criteria. This study proposes a multi-criteria framework to determine the most preferred software solution for instructing undergraduate power system modules using the Fuzzy-ARAS (additive ratio assessment) method and expert opinions. Twelve evaluation criteria were used to evaluate eight widely used software packages. A questionnaire was designed to capture views from professionals in academia and industry on the criteria weights and ranking of software options. Linguistic terms were used to represent the experts’ judgment, and weights were assigned to each criterion. The Fuzzy-ARAS multi-criteria decision approach was applied to obtain ratings for each software alternative. Based on the result, MATLAB emerged as the most preferred software for instructing power systems analysis, whereas MATPOWER (V 8.0) was rated as the least preferred choice. In addition, the Fuzzy-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach was used, producing a separate ranking; the most preferred software was MATPOWER, while the least preferred software was NEPLAN (V 360 10.5.1). A new coefficient that combines the findings of the two approaches was suggested to reconcile the ranks. The combined ranking aligns with the result of the Fuzzy-TOPSIS method by returning MATLAB as the most preferred, while the least preferred software was NEPLAN. This study significantly contributes to the choice of software for undergraduate power systems analysis instruction by providing direction to educators and institutions looking for software solutions to improve undergraduate power systems analysis education.

The role of restaurants, canteens, other forms of eatery and food service outlets are becoming very big part of most economies at different levels. However improvement in customer experience while making orders plays a very critical role in having a smooth and efficient menu ordering process. This article presents a novel hybrid approach that tackles the challenges associated with menu ordering especially in the post-covid era; both from software and hardware perspectives. First, a web-application - powered by ReactJS and GraphQL, which enables order request anywhere at any time was developed. In addition, is the implementation of hardware with mobility feature on trips to the restaurant using two Arduino microcontrollers (transmitter and receiver). The result allows the user to browse through a catalogue, check-in and out on a Thin-Film-Transistor (TFT) liquid-crystal display (LCD) and deliver a seamless experience to the customer.

This article seeks to understand the barriers to information and com- munications technology (ICT) utilisation among middle-level academics in Nigerian universities. It makes use of a cross-sectional survey and key informant interviews to interrogate the problem. A total of 1,325 middle- level lecturers drawn from 12 South-western Nigerian universities took part in the study. Because of the nature and character of the Nigerian state and the historical contexts of its higher education, the political economy approach was adopted along with ICT utilisation resistance theory to explain barriers to ICT utilisation in Nigerian universities. The article finds that there is a significant relationship between the availability, utilisation and quality of teaching in Nigerian universities. Specifically, it finds that in universities where lecturers had tablets they were 1.5 times more likely to deliver quality teaching. It also finds that in universities where lecturers used multimedia projectors, students were 2.7 times more likely to receive quality teaching. On the barriers to ICT utilisation, the article finds that lack of funding, lack of strong institutional policy and support infrastructure such as broadband internet connectivity and constant electricity supply are among the major constraints to ICT-based higher education. It therefore suggests the development of a comprehensive policy on ICT-based education and massive investment in university education in Nigeria.

These days, hardware devices and its associated activities are greatly impacted by threats amidst of various technologies. Hardware trojans are malicious modifications made to the circuitry of an integrated circuit, Exploiting such alterations and accessing the level of damage to devices is considered in this work. These trojans, when present in sensitive hardware system deployment, tends to have potential damage and infection to the system. This research builds a hardware trojan detector using machine learning techniques. The work uses a combination of logic testing and power side-channel analysis (SCA) coupled with machine learning for power traces. The model was trained, validated and tested using the acquired data, for 5 epochs. Preliminary logic tests were conducted on target hardware device as well as power SCA. The designed machine learning model was implemented using Arduino microcontroller and result showed that the hardware trojan detector identifies trojan chips with a reliable accuracy. The power consumption readings of the hardware characteristically start at 1035-1040mW and the power time-series data were simulated using DC power measurements mixed with additive white Gaussian noise (AWGN) with different standard deviations. The model achieves accuracy, precision and accurate recall values. Setting the threshold proba¬bility for the trojan class less than 0.5 however increases the recall, which is the most important metric for overall accuracy acheivement of over 95 percent after several epochs of training.