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The purpose of this paper is to develop an algorithm and software for determining the size of a line-start permanent magnet synchronous motor (LSPMSMs) based on its optimization.

The software consists of an optimization procedure that cooperates with a FEM model to provide the desired behavior of the motor under consideration. The proposed improved version of the genetic algorithm has modifications enabling efficient optimization of LSPMSMs. The objective function consists of three important functional parameters describing the designed machine. The 2-D field-circuit mathematical model of the dynamics operation of the LSPMSMs consists of transient electromagnetic field equations, equations describing electric windings and mechanical motion equations. The model has been developed in the ANSYS Maxwell environment.

In this proposed approach, the set of design variables contains the variables describing the stator and rotor structure. The improved procedure of the optimization algorithm makes it possible to find an optimal motor structure with correct synchronization properties. The proposed modifications make the optimization procedure faster and more

This proposed approach can be successfully applied to solve the design problems of LSPMSMs.

The purpose of the study is to analyse municipal solid waste (MSW) disposed of in Jimeta-Yola metropolis for landfill gas (LFG), methane and project viability potential.

The data was collected daily from landfills for four weeks. About 7,329.55 Mg/year of waste was analysed. These waste were separated into bio-degradable components i.e. paper and textile (263.66 Mg), non-food organic (681.45 Mg), wood and straw (189.50 Mg) and food and kitchen waste (1797.20 Mg). Non-degradable components include plastics, polythene bags, metals, sand, stones, cans etc. (4397.73 Mg). The component's characteristics such as a number of samples, weight, volume, landfill age etc. were measured. The waste, methane (CH4) and energy potential were also analysed using LFG energy cost model.

The landfills received 15 Gg/year of MSW and emit 0.31 Gg/year of LFG having CH4 content of 82.95 Mg in 2016. These can produce 33.78 GWh of heat energy equivalent to 10.14 GWh of electricity analytically. Therefore, between 2016 and 2022, about 2.24 Gg CH4 and 5201.32 MWh of electricity were wasted. Henceforth, proper management of these waste substances can produce 186.4 Gg CH4 which will generate 432.52 GWh of electricity. The most economically viable project is an electricity project generating 418 kW/year at a sale price of $1.14/kWh (58.38/kWh) and a payback period of 11 years.

Raw LFG collected can be used in heating brick kilns, boilers, furnaces and greenhouses. When treated, the LFG can produce renewable natural gas (RNG), which is used in energy generation and various domestic, vehicle and industrial applications.

The analytical energy generation can provide gross revenue of ₦19.46bn at an average of ₦192.71million/year. Using Landfill Gas Emissions Model (LandGEM) model, the gross and net revenue will be $0.42m and $0.28m yearly, respectively. The project can provide jobs and economic boost to the immediate community through associated ripple effect.

The research is a pre-feasibility study for LFG to gas or electricity projects in Jimeta-Yola. The study contributed to the body of knowledge as a source of literature for further studies locally and globally.

Since the onset of the COVID-19 pandemic in China, student engagement in online learning has been a critical issue for all educational institutions. The university student engagement inventory (USEI) is the most used scale for assessing the construct of student engagement. The present study aimed to evaluate the psychometric properties of the USEI among 1504 Chinese university students who completed a survey through an online platform between December 2020 and January 2021.

In this cross-sectional study, content validity, construct validity and reliability of the scale were assessed.

The results supported the three-factor model with acceptable goodness of fit (χ² (71) = 369.717, p = 0.13, χ²/df = 5.207, comparative fit index (CFI) = 0.967, normed fit index (NFI) = 0.960, Tucker–Lewis index (TLI) = 0.958, standardized root mean square residual (SRMR) = 0.030, root mean square error of approximation (RMSEA) (90% CI) = 0.053 [0.049, 0.057]), good internal consistency and construct reliability (Cronbach's alpha and omega coefficient >0.70) and strong convergent validity. Also, the measurement invariance was confirmed across gender.

This study showed that the 3-factor structure of USEI with Chinese university students had good construct validity, internal consistency and reliability. It could help measure student engagement in online learning in China.