A major concern in the recent past was the traditional static spectrum allocation which gave rise to spectrum underutilization and scarcity in wireless networks. In an attempt to solve this challenge, cognitive radios technology was proposed. It allows a spectrum to be accessed dynamically by Cognitive radio users or secondary users (SU). Dynamic access can efficiently be achieved by making necessary adjustment to some Medium access control (MAC) layer functionalities such as sensing and channel allocation. MAC protocols play a central role in scheduling sensing periods and channel allocation which ensure that the interference is reduced to a tolerable level. In order to improve the accuracy of sensing algorithm, necessary adjustments should be made at MAC layer. Sensing delays and errors are major challenges in the design of a more accurate spectrum sensing algorithm. This study focuses on designing a channel selection algorithm to efficiently utilize the spectrum. Channels are ordered and grouped to allow faster discovery of channel access opportunities. The ordering is based on descending order of channel’s idling probabilities. Grouping of channels ensured that channels are sensed simultaneously. These two techniques greatly reduce delays and maximized throughput of SU. Hence, Extended Generalized Predictive Channel Selection Algorithm, a proposed scheme has significantly performed better than its counterpart (Generalized Predictive Channel Selection Algorithm). Matlab simulation tool was used to simulate and plot the results of the proposed channel selection algorithm.

Background: The primary purpose of introducing the COVID-19 vaccine was to fight the pandemic. However, the vaccine was not well received worldwide. This challenge has threatened the effective implementation and roll-out of COVID-19 immunization campaigns. The challenge of vaccine hesitancy was reported to be more prevalent in rural areas due to various factors such as cultural beliefs, misinformation, poverty, lack of education, and distrust of vaccines. Yet there seems to be a scarcity of studies determining the prevalence of vaccine hesitancy in deep rural areas of Limpopo. Purpose: The study aimed to explore the prevalence of COVID-19 hesitancy among the rural black population in South Africa. Study Methods: A longitudinal quantitative study was conducted with data from the DIMAMO Health and Demographic Surveillance Site (HDSS) database for 2020 to 2022. A non-probability total sampling technique was used to select the respondents. Trained fieldworkers collected data using an electronic data capture questionnaire. A comparison of categorical variables was performed using Chi-Square in SPSS version 26 and the statistical significance was set at p < 0.05, with a 95% confidence interval to analyze the data. Results: The limited availability of vaccination sites in Limpopo Province, South Africa, was associated with a reduced certainty that the vaccine would be accepted, as reported in the current study. The prevalence of visiting traditional healers was significantly higher in non-vaccinated than vaccinated participants among the rural black population, indicating a different cultural belief among the rural black population that existed before and during the COVID-19 pandemic. Conclusion: The present study findings show diverse factors of concern associated with vaccination hesitancy for COVID-19 among rural black people. Lack of education, gender, not being diagnosed with COVID-19, not being a Christian, visiting traditional healers, vaccine mistrust, unknown side effects, and a lack of confidence in the vaccine itself. Conspiracy theories were factors that impacted vaccine acceptance among black people living in rural areas. The prevalence of visiting traditional healers was significantly higher in non-vaccinated than vaccinated participants. Therefore, the present study findings emphasize the need to collectively integrate and utilize the traditional healers in the South African healthcare system.

Background In March 2020 South Africa implemented strict non-pharmaceutical interventions (NPIs) to contain Covid-19. Over the subsequent five months NPIs were eased in stages according to national strategy. Covid-19 spread throughout the country heterogeneously, reaching rural areas by July and peaking in July-August. Data on the impact of NPI policies on social and economic wellbeing and access to healthcare is limited. We therefore analysed how rural residents of three South African provinces changed their behaviour during the first epidemic wave. Methods The South African Population Research Infrastructure Network (SAPRIN) nodes in Mpumalanga (Agincourt), KwaZulu-Natal (AHRI) and Limpopo (DIMAMO) provinces conducted longitudinal telephone surveys among randomly sampled households from rural and peri-urban surveillance populations every 2-3 weeks. Interviews included questions on: Covid-19 knowledge and behaviours; health and economic impact of NPIs; and mental health. Results 2262 households completed 10,966 interviews between April and August 2020. By August, self-reported satisfaction with Covid-19 knowledge had risen from 48% to 85% and facemask use to over 95%. As selected NPIs were eased mobility increased, and economic losses and anxiety and depression symptoms fell. When Covid-19 cases spiked at one node in July, movement dropped rapidly, and missed daily medication rates doubled. Economic concerns and mental health symptoms were lower in households receiving a greater number of government-funded old-age pensions. Conclusions South Africans reported complying with stringent Covid-19 NPIs despite the threat of substantial social, economic and health repercussions. Government-supported social welfare programmes appeared to buffer interruptions in income and healthcare access during local outbreaks. Epidemic control policies must be balanced against impacts on wellbeing in resource-limited settings and designed with parallel support systems where they threaten income and basic service access.

Background: Childhood obesity has become the most important and growing public health problem in the world. They add to public health challenges by increasing the burden of chronic non-communicable diseases. However, in spite of its importance, there is limited literature that evaluates the prevalence of obesity among rural adolescents in sub-Saharan Africa. We report the first study to present an insight into rural black overweight South African children and the physical characteristics and socio-economic status of the household head. A quantitative cross-sectional population study was conducted involving 51 selected primary villages within the DIMAMO surveillance area in the Capricorn District of Limpopo Province, South Africa. The study involved 294 adolescents, 154 girls and 140 boys, who were under the age of 18. Of these participants, 127 (43%) were within the normal weight range, and 167 (57%) were overweight. Gender made a significant difference, with more girls being overweight than boys. Adolescents who did not receive child grants and whose heads of household were 45–54 years old, poor, and overweight had a higher prevalence of obesity (p-value < 0.05). This study suggests that public health interventionists need to target both the heads of household and their children in hopes of reducing the prevalence of overweight and obese South African children. We further propose a better understanding of the causes of childhood overweight and obesity to guide policy development and implementation in rural settings.

In the past, it has been reported that the black South African population may have a cardio-protective lipid profile; however, this may no longer be the case with urbanization, industrialization, and the nutritional transition that occurred in South Africa. Although these transitions may be low in rural areas, one would expect this influence to be lower in the rural populations; however, they are not immune to these changes. Hence, the present study aimed to determine the association of serum lipid profiles and lipid ratios with kidney dysfunction. This cross-sectional retrospective study used the AWI-Gen 1 dataset. This study consisted of 1399 participants who took part in the AWI-Gen phase 1. Participants aged below 40 years, pregnant women, mentally disturbed and participants with incomplete information to answer the aims and objectives of this study were excluded in the analysis of this study. The data were analyzed using SPSS. In the present study, the prevalence of kidney dysfunction was 11.7%, with women having a significantly higher prevalence as compared to men. Women with kidney dysfunction had significantly higher TC, TG, TG/HDL-C, and TC/HDL-C compared to those without kidney dysfunction. However, in men, there was no association between the two groups. TC, and LDL/HDL-C were associated with kidney dysfunction in women only. TG/HDL-C was associated with kidney dysfunction in both women and men. Elevated TC, LDL/HDL-C, TC/HDL-C, and TG/HDL-C were the risk factors for kidney dysfunction, particularly in women. This suggests that TC, TC/HDL-C, and TG/HDL-C levels may be useful for risk stratification and a potential target to reduce the risk of developing kidney dysfunction, particularly in women. Upcoming longitudinal studies examining the causal connection between serum lipids, and lipid ratios with the risk of kidney dysfunction are necessary to fully understand the potential relationship between TG/HDL-C, TC, and TC/HDL-C levels and kidney dysfunction.

The automated data quality management system serves as a comprehensive solution developed to enhance the precision, dependability, and uniformity of data within data-driven organizations. Such systems play an important role in eliminating the shortcomings associated with manual data quality management, which is prevalent in health and demographic surveillance systems (HDSS). The ongoing difficulty of ensuring data quality through manual processing hinders the HDSS's capacity to optimize data quality effectively. To address this challenge, our study adopted design science methodologies to provide guidelines for the design and implementation of the automated data quality control system. The open source technologies (Pentaho data integration, R Studio, SQL, Windows task scheduler) were used to facilitate the automation and validation of the incoming and database resident data. The quality of data has vastly improved since the implementation of the proposed system. The findings suggest that the automated data quality control system exhibited superior performance compared to the manual methods, thereby minimizing errors and time-wasting efforts.