One significant importance of street vending in South Africa is its role in providing livelihoods and economic opportunities, especially for marginalized and vulnerable populations. However, Street vendors, particularly those selling agricultural commodities, face numerous challenges. Street vending in Moletjie Mmotong is a vital source of income and employment, offering affordable goods and services, including food, clothing, and household items. One potential solution is online selling, but there is limited knowledge about it in the informal sector. This study aims to analyze the factors affecting street vendors’ willingness to sell fruits and vegetables online in Moletjie Mmotong under Polokwane Municipality. Data was collected from 60 street vendors using a questionnaire and simple random sampling. Descriptive statistics identified and described the socio-economic characteristics of the vendors, while a binary logistic regression model analyzed the factors influencing their willingness to sell online. The study found that age, education level, gender, household size, and access to online selling information significantly influenced their willingness to sell online. The findings highlight the potential benefits of online selling for street vendors, such as increased sales and a broader customer base. The study recommends that governments provide training and workshops on online selling, develop educational programs, distribute educational materials, and create marketing strategies to support street vendors in transitioning to online platforms.

To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.

Global energy agencies and commissions report a sharp increase in energy demand based on commercial, industrial, and residential activities. At this point, we need energy-efficient and high-performance systems to maintain a sustainable environment. More than 30% of the generated electricity has been consumed by HVAC-R units, and heat exchangers are the main components affecting the overall performance. This study combines experimental measurements, numerical investigations, and ANN-aided optimization studies to determine the optimal operating conditions of an industrial shell and tube heat exchanger system. The cold/hot stream temperature level is varied between 10 ℃ and 50 ℃ during the experiments and numerical investigations. Furthermore, the flow rates are altered in a range of 50–500 L/h to investigate the thermal and hydraulic performance under laminar and turbulent regime conditions. The experimental and numerical results indicate that U-tube bundles dominantly affect the total pumping power; therefore, the energy consumption experienced at the cold side is about ten times greater the one at the hot side. Once the required data sets are gathered via the experiments and numerical investigations, ANN-aided stochastic optimization algorithms detected the C10H50 scenario as the optimal operating case when the cold and hot stream flow rates are at 100 L/h and 500 L/h, respectively.

This study investigated the level of satisfaction among consumers of special tea (Monsonia burkeana) in the Capricorn District Municipality, Limpopo Province, South Africa. It sought to identify the factors that influenced this satisfaction. A total of 225 respondents were selected using snowball sampling, and primary data were collected through structured questionnaires. Descriptive statistics were used to analyse consumer profiles and satisfaction levels, while multinomial logistic regression determined the factors influencing satisfaction across four categories: “Not satisfied at all”, “Satisfied”, “Not sure”, and “Highly satisfied”. The results revealed an average respondent age of 29.95 years and an average annual tea consumption of 4.684 uses, with over 50% of both male and female respondents expressing satisfaction. Regression analysis indicated that market access, cultural influences, income level, and the person introducing the tea significantly influenced dissatisfaction relative to high satisfaction. The income level was the only significant factor distinguishing “Satisfied” from “Highly satisfied”. Gender, age, marital status, and employment type were significant predictors for “Not sure” compared to “Highly satisfied”. These findings highlight the importance of developing the medicinal plant market, promoting cultural education, and implementing sustainable cultivation and conservation practices for Monsonia burkeana. Efforts to improve market access and address income disparities are also necessary to enhance consumer satisfaction and ensure the tea’s continued availability and cultural relevance.