Earnings disparities in South Africa, and specifically the Eastern Cape region are influenced by a complex interplay of historical, socio-economic, and demographic factors. Despite significant progress since the end of apartheid, persistent disparities in earnings continue to raise questions about the effectiveness of policies aimed at reducing inequality and promoting equitable social system. Individual-level dataset from the 2021 South African general household survey were subjected to exploratory analysis, while Heckman selection model was used to investigate the determinants of earnings disparities in the study area. The results showed that majority of the population are not working for a wage, commission or salary, which also pointed to the gravity of unemployment situation in the area of study. Most of the working population (both male and female) are lowest earners (R ≤ 10,000), and this also cuts across all age-group categories. Majority of working population have no formal education, are drop out, or have less than grade-12 certificate, and very few working populations with higher education status were found in the moderate and relatively high earnings categories. While many of the working population are engaged in the informal sector, those in the formal sector are in the lowest earners group. Compared to any other race, the Black African group constituted the majority of non-wage earners, and most in this group were found in the lowest earners group. Some of the working population who were beneficiaries of social grants and medical aids scheme were found in the lowest, low, and moderate earnings categories. The findings significantly isolated the earnings-effect of age, marital status, gender, race, education, geographic indicators, employment sector, and index of health conditions and disabilities. The study recommends interventions addressing racial, gender, and geographic wage gaps, while also emphasizing the importance of equitable access to education, health infrastructure, and skills development.

Identify and diagnosis of homogenous units and separating them and eventually planning separately for each unit are considered the most principled way to manage units of forests and creating these trustable maps of forest’s types, plays important role in making optimum decisions for managing forest ecosystems in wide areas. Field method of circulation forest and Parcel explore to determine type of forest require to spend cost and much time. In recent years, providing these maps by using digital classification of remote sensing’s data has been noticed. The important tip to create these units is scale of map. To manage more accurate, it needs larger scale and more accurate maps. Purpose of this research is comparing observed classification of methods to recognize and determine type of forest by using data of Land Cover of Modis satellite with 1 kilometer resolution and on images of OLI sensor of LANDSAT satellite with 30 kilometers resolution by using vegetation indicators and also timely PCA and to create larger scale, better and more accurate resolution maps of homogenous units of forest. Eventually by using of verification, the best method was obtained to classify forest in Golestan province’s forest located on north-east of country.

This research study explores the addition of chromium (Cr⁶⁺) ions as a nucleating agent in the alumino-silicate-glass (ASG) system (i.e., Al₂O₃-SiO₂-MgO-B₂O₃-K₂O-F). The important feature of this study is the induction of nucleation/crystallization in the base glass matrix on addition of Cr⁶⁺ content under annealing heat treatment (600 ± 10 °C) only. The melt-quenched glass is found to be amorphous, which in the presence of Cr⁶⁺ ions became crystalline with a predominant crystalline phase, Spinel (MgCr₂O₄). Microstructural experiment revealed the development of 200–500 nm crystallite particles in Cr⁶⁺-doped glass-ceramic matrix, and such type microstructure governed the mechanical properties. The machinability of the Cr-doped glass-ceramic was thereby higher compared to base alumino-silicate glass (ASG). From the nano-indentation experiment, the Young’s modulus was estimated 25(±10) GPa for base glass and increased to 894(±21) GPa for Cr-doped glass ceramics. Similarly, the microhardness for the base glass was 0.6(±0.5) GPa (nano-indentation measurements) and 3.63(±0.18) GPa (micro-indentation measurements). And that found increased to 8.4(±2.3) (nano-indentation measurements) and 3.94(±0.20) GPa (micro-indentation measurements) for Cr-containing glass ceramic.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

This work shows the results of the biosynthesis of silver nanoparticles using the microalga Chlorella sp, using growth media with different concentrations of glycerol, between 5%–20%, and different light and temperature conditions. The synthesis of nanoparticles was studied using supernatants and pellets from autotrophic, heterotrophic and mixotrophic cultures of the microalga. The presence of nanoparticles was verified by ultraviolet-visible spectroscopy and the samples showing the highest concentration of nanoparticles were characterized by scanning electron microscopy. The mixotrophic growth conditions favored the excretion of exopolymers that enhanced the reduction of silver and thus the formation of nanoparticles. The nanoparticles obtained presented predominantly ellipsoidal shape with dimensions of 108 nm × 156 nm and 87 nm × 123 nm for the reductions carried out with the supernatants of the mixotrophic cultures with 5% and 10% glycerol, respectively.