Eswatini College of Technology, also referred to a ECOT, is a state-owned college in Eswatini. It offers courses in engineering, science and education. Dr. Grace Mdluli is the current principal of the college. In 2011, the college collaborated with Vaal University of Technology in South Africa..
Aluminium alloy composites are promising for automotive applications due to their significant properties, including lightweight, enhanced strength, and improved corrosion resistance. However, it found that consequences such as porosity, uneven particle distribution, and agglomerated structure reduce the mechanical behaviour of composites. This research addresses a research gap and enriches the overall properties of the aluminium alloy (AA2024) composite, integrated with 10 wt
The present research aims to reduce casting defects and enrich the functional properties of magnesium alloy (AZ91E) composites with hybrid reinforcements, including silicon carbide nanoparticles (SiC) and carbon nanotube (CNT) fibers, through a liquid stir casting process associated with vacuum die casting. The final composite contained 5 vol
Poly (Thiophene) (PTh) is familiar for optoelectronic applications due to strong absorption, better conductivity, and high charge carrier mobility. However, it found low photoluminescence efficiency and variations in electron carrier mobility due to reduced photoluminescence. The motto of current research is to overcome the above drawback and enrich the optoelectronic behaviour of PTh featuring 30, 40, 50, and 60 nm of zinc oxide (ZnO) nanoparticles (20 nm) via the electrochemical deposition method. This structure consists of 100 nm indium tin oxide (ITO), ZnO electron transport layer (ETL), ZnO/PTh active emission layer, PTh hole transport layer (HTL), and 80 nm silver top electrode. The influences of ZnO quantum dot layer thickness and silver top electrode on optical, electrical, stability lifetime, and X-ray diffraction behaviour of PTh quantum dot are evaluated. The structural analyses confirmed improved structural integrity, and the stability evaluations showed enhanced operational lifetime. The 50 nm ZnO:PTh active layer found better functional characteristics like reduced wavelength, enhanced electron voltage, higher photoluminescence efficiency, superior electron mobility, higher current density, and superior lifespan (life cycle), which are 3
Perovskite solar cells (PSCs) are famous for their remarkable efficiency and promising potential for low-cost fabrication, which is configured with a fluorine-doped tin oxide (FTO) base window layer and methylammonium lead iodide (CH3NH3PbI3) absorption layer found better electrical performance. However, optical loss due to moderate optical properties and degradation is the main challenge for FTO-configured perovskite solar cells. This research intends to enhance the optical properties and electrical properties of hybrid perovskite solar cells featuring 10, 20, 30, and 40 nm of tin oxide (SnO2) electron transport layer (ETL), which is formed through the atomic layer deposition technique. Final perovskite solar cells consist of FTO/SnO2/CH3NH3PbI3/poly(triarylamine) (PTAA)/fullerene (PCBM)/copper (Cu) layers. However, the role of SnO2 ETL thickness in influencing the efficiency and stability of PSCs remains an active area of investigation. The findings underscore the potential of tailored ETL architectures to elevate the overall performance and environmental robustness of PSCs. The 40 nm SnO2 layer, optimized for the highest power conversion efficiency (PCE) of 24.0
The rising demand for electric vehicles highlights the global push for green transportation. Predicting energy demand is crucial for managing electricity. This study presents a federated learning model using a deep stacked autoencoder-based long short-term memory for electric vehicles energy demand prediction. Initially, electric vehicles network nodes are simulated, and local training occurs at charging stations. Power grids are then upgraded, and model aggregation happens at the power grid. The enhanced training process iteratively predicts energy demand at charging stations using the deep stacked autoencoder-based long short-term memory. Here, the merging of deep stacked autoencoder and long short-term memory designs a deep stacked autoencoder-based long short-term memory. The deep stacked autoencoder and long short-term memory are combined using a fractional concept. Then, local updates and server accumulation are adjusted based on the average technique. The proposed model is analyzed using the metrics resource average, loss function, mean squared error, root mean squared error, false positive rate, mean average precision, computational efficiency, memory usage, and run time and obtained a value of 0.302, 0.090, 0.082, 0.287, 0.068, 0.921, 0.717, 2.684, and 5.139, respectively. The proposed model helps to reduce the cost associated with energy production, distribution, and maintenance by optimizing charging schedules and energy use.
