Green manufacturing is increasingly becoming popular, especially in lubricant manufacturing, as more environmentally friendly substitutes for mineral base oil and synthetic additives are being found among plant extracts and progress in methodologies for extraction and synthesis is being made. It has been observed that some of the important performance characteristics need enhancement, of which nanoparticle addition has been noted as one of the effective solutions. However, the concentration of the addictive that would optimised the performance characteristics of interest remains a contending area of research. The research was out to find how the concentration of green synthesized aluminum oxide nanoparticles in nano lubricants formed from selected vegetable oils influences friction and wear. A bottom-up green synthesis approach was adopted to synthesize aluminum oxide (Al₂O₃) from aluminum nitrate (Al(NO₃)₃) precursor in the presence of a plant-based reducing agent—Ipomoea pes-caprae. The synthesized Al₂O₃ nanoparticles were characterized using TEM and XRD and found to be mostly of spherical shape of sizes 44.73 nm. Al₂O₃ nanoparticles at different concentrations—0.1 wt%, 0.3 wt%, 0.5 wt%, 0.7 wt%, and 1.0 wt%—were used as additives to castor, jatropha, and palm kernel oils to formulate nano lubricants and tested alternately on a ball-on-aluminum (SAE 332) and low-carbon steel Disc Tribometer. All the vegetable-based oil nano lubricants showed a significant decrease in the coefficient of friction (CoF) and wear rate with Ball-on-(aluminum SAE 332) disc tribometer up to 0.5wt% of the nanoparticle: the best performances (e_COF = 92.29; e_WR = 79.53) came from Al₂O₃-castor oil nano lubricant and Al₂O₃-palm kernel oil; afterwards, they started to increase. However, the performance indices displayed irregular behaviour for both COF and Wear Rate (WR) when tested on a ball-on-low-carbon steel Disc Tribometer.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

The undeniable importance of migrants’ remittances to the welfare of developing countries was again demonstrated during the COVID-19 pandemic. This has therefore led to a significant shift in attention to the relevance of remittances and has likewise spurred research interest in factors that motivate the inflows of remittances. However, in spite of the increasing recognition of the roles of digital technology in the macroeconomic performance of developed and developing economies alike, empirical analysis of its possible impacts on remittance inflows has not been well explored in the literature. Therefore, pooling the annual data of 35 sub-Saharan African (SSA) countries from 2011 to 2020, this study investigates the nexus between digital technology and remittance inflows within the generalized method of moments (GMM) framework. Using two measures of digital technology infrastructure—internet usage and mobile cellular subscription—the study finds a positive relationship between digital technology and remittances inflow. In addition, the findings indicate that the magnitude of the effect is relatively higher for internet usage. The study thus shows that the increased rate of remittance mobilization constitutes a significant pathway through which digital technology impacts the economies of the SSA region. Moreover, it offers further insight on the importance of digital technology in the socioeconomic development of developing countries. From a policy standpoint, governments and policymakers in SSA countries should intensify efforts to promote the diffusion and penetration of digital infrastructure.

An experiment was carried out to investigate the effect of different organic nutrient solutions and day of harvest on growth parameters, biomass and chemical composition of hydroponically grown sorghum red fodder. The experiment was a 3 × 2 factorial design comprising of 3 nutrient solutions (cattle, poultry and rabbit) and 2 harvesting regimes (8th and 10th day). Cattle, poultry and rabbit dungs were collected fresh and processed into nutrient solutions. Sorghum red seeds were treated, planted on trays, and irrigated twice per day with organic nutrient solution according to the treatments. Growth parameters which were investigated included fodder mat thickness, seedling height, leaf length and width, number of leaves, fresh and dry matter yield; and proximate composition. The results showed that sorghum red fodder irrigated with cattle manure nutrient solution (NS) harvested at 10 days was higher in all, except one (fodder mat thickness) of the growth parameters considered. The crude protein (CP) was highest and similar (P > 0.05) for Poultry NS harvested at 8 and 10 days, and Cattle NS at 10 days (13.13%, 12.67%, and 12.69% respectively). The ash content also favored Cattle NS at 10 days. Cattle NS at 10 days harvest was significantly (P < 0.05) the highest (7.00%), but comparable (P > 0.05) with Rabbit NS at 10 days for NDF. Fresh and DM yields were highest for Cattle harvested at 10 and 8 days respectively. The study recommends Cattle NS as hydroponic organic NS for sorghum red as it enhances fresh and dry matter yields, and nutritive values.