University of South Africa

Smallholder farmers in most of the rural areas in African countries rear non-descript village chickens for petty cash, food provision and for performing rituals. Village chicken production systems are regarded as low input- low output because the chickens receive minimum care and produce average to less eggs and meat. The chickens receive minimal biosecurity and are often left to scavenge for feed and thus exposes them to potential vector parasites that can transmit parasites such as haemoparasites. Haemosporidian parasites (Haemosporidia, Apicomplexa) are blood parasites infecting avian species, especially chickens. They are transmitted by blood sucking vectors such as biting midges, mosquitoes, black flies and louse flies. Infections are mild to severe causing reproduction, production and health losses such as decreased fertility, reduced body weight and egg production, anaemia and inflammation of vital organs such as the liver and spleen. Haemoparasites infections in chickens can be lowered through controlling vector parasites and the use of antimalarial drugs on exotic chicken breeds. The aim of this review is to characterize the avian haemosporidian parasites affecting non-descript village chickens in Africa, describing their morphology, life cycle, pathogenicity, control and prevention measures.

As a result of the emergence of the internet, semantic web and social media, netizens have been able to create digital footprints on scholarly, commercial or non-scholarly platforms. However, it remains a mystery as to what happens to the data created by ordinary users of digital platforms, whether they create text, visuals, sounds or multimedia files. Despite the privatization and commoditization of digital spaces by informational capitalism, austerity policies across the globe have led to deterioration and diminishing of physical public spaces and infrastructure for providing services. This article seeks to explore the concept of data capitalism amidst rising surveillance capitalism and suggests ways through which libraries can protect the interests of their users against data capitalism

This study examines the nexus between ICT diffusion, financial development, industrialization, and economic growth using a novel panel VAR approach in the generalized method of moments (GMM) estimation. Different proxies were used to measure the aforementioned variables, diverging from the commonly used measures in prior literature. Based on panel data covering 45 countries from 2000 to 2018, the empirical results suggest that there is bidirectional causality between ICT diffusion and economic growth, financial development and industrialization, financial development and economic growth, as well as industrialization and economic growth. The findings further provide evidence that financial development , levels of industrialization, and economic growth are not significant or positive predictors of ICT diffusion. The study's implications for policy are profound, suggesting that SSA governments should adopt a holistic approach to economic policy development, integrating ICT, financial, and industrial policies to harness these interdependencies effectively.

Plant detritus is abundant in grasslands but decomposes slowly and is relatively nutrient‐poor, whereas animal carcasses are labile and nutrient‐rich. Recent studies have demonstrated that labile nutrients from carcasses can significantly alter the long‐term soil microbial function at an ecosystem scale. However, there is a paucity of knowledge on the functional and structural response and temporal scale of soil microbiomes beneath large herbivore carcasses. This study compared microbiome functions and structures of soil beneath Connochaetes taurinus (hereafter ‘wildebeest’) carcasses at various postmortem intervals of decomposition to matched control samples over 18 months. Microbial functions were compared by their community‐level physiological profiles determined by sole‐carbon substrate utilisation and structures by metagenomic sequences using 16S rRNA gene markers. Overall metabolism and metabolic diversity remained increased and functionally dissimilar to control soils throughout the experimental period, with successive sole‐carbon substrate utilisation observed. Conversely, diversity was initially reduced and structurally dissimilar from the control soil but recovered within the experimental period. The study contributes to the knowledge of carcass decomposition by investigating the long‐term soil microbiome dynamics resulting from large herbivore carcasses decomposing in a mesic grassland. Microbial functional succession and ecologically relevant bacterial biomarkers of soil beneath the decomposing carcasses were identified for various postmortem intervals.

Birds are often used as ecological indicators because they are widely distributed across diverse habitats and display distinct behavioural responses to environmental changes. The Endangered Grey Crowned Crane Balearica regulorum is regarded as a flagship species of Africa’s wetland and grassland habitats, both of which are undergoing substantial transformation to alternative land uses. The delayed reproductive strategies and habitat specialisation of this crane species makes them more vulnerable to extinction, but this risk is further compounded by data paucity. We employed traditional and contemporary survey methods to collect breeding metrics to calculate stage transition probabilities (i.e. egg–hatchling, hatchling–juvenile) and to identify possible macro-environmental factors that either promote or hinder their reproductive output in a key agricultural area in KwaZulu-Natal, South Africa. We found that Grey Crowned Cranes have a low hatching rate of 38.4% (95% confidence interval 29.3–48.4%) and show that this low hatching rate is exacerbated under high rainfall intensity. Multivariate analyses and multi-model inference revealed that successful nest-sites were generally associated with larger open water-bodies, greater distances from shore, and increased proximity to secondary roads, buildings, and natural grasslands. Although increased agricultural activities might promote greater foraging opportunities, the overall breeding outcomes of this species were poor in this key agricultural region. Our findings stress the urgent need for further fine-scale data collection and monitoring activities to better inform conservation strategies for this species. We also encourage future studies to focus on aspects affecting Grey Crowned Crane breeding in regions where proximity to human activities is inevitable.

This research successfully synthesized semiconductive magnesioferrite (MgFe2O4) nanomaterials using a green chemistry method that utilizes the natural extract of Moringa olefeira serving as both a reducing and oxidizing agent. The optical characteristics and crystalline structure of the MgFe2O4 nanomaterials were analysed using photoluminescence, diffuse reflectance spectroscopy, and X-ray diffraction. Additionally, Fourier transform infrared spectroscopy provided valuable insights into the chemical bonding and composition. High-resolution transmission electron microscopy was employed to obtain extensive information on crystalline size and distribution. Furthermore, the electrochemical properties were assessed through cyclic voltammetry and electrochemical impedance spectroscopy, revealing an excellent voltametric response and pseudo-capacitive behaviour associated with faradaic reactions, as well as outstanding conductivity linked to the unique charge transport mechanisms present in the MgFe2O4 structure. The effectiveness of the MgFe2O4 nanomaterials in the photodegradation of methylene blue from aqueous solutions was evaluated under visible light irradiation. Photocatalytic experiments measured the influence of various parameters, including catalyst loading, dye concentration, and pH. The MgFe2O4 nanomaterials exhibited impressive photocatalytic degradation efficiency, achieving an 81% degradation rate at pH 5.0 within 120 min. Kinetic studies indicated that the degradation process adhered to a pseudo-first-order model, with a rate constant of 0.01533 min⁻¹, signifying a rapid reaction under optimal conditions. This study provides a thorough understanding of the electrochemical properties and enhanced photocatalytic capabilities of MgFe2O4 nanomaterials, thereby advancing green nanotechnology for environmental remediation.

South Africa has seen a surge in child offending. Child offenders commit violent crimes such as armed robbery, housebreaking, rape and murder. Conversely, not all child offenders commit violent crimes. Many child offenders are detained for minor charges such as shoplifting, theft and possession of illegal substances. Most of these children face numerous levels of adversity, including poverty, dysfunctional households and limited parental involvement. Responses to child criminal behaviour accentuate rehabilitation through measures such as diversion. Narrative accounts of children in conflict with the law who underwent mentorship programmes, as a diversion initiative, are scarce and underrepresented. Through a qualitative inquiry, 13 children who completed the National Youth Development Outreach (NYDO) Centre’s Mentoring Diversion Programme were interviewed and data were analysed thematically. Findings provided insight into the participants’ background and context, the mentor–mentee relationship, responsibility, effectiveness of the programme, and aftercare support. This paper contributes to scientific research and is conducive to curtailing child offending.

South Africa’s climate studies generally focus on coarser provincial levels, which aid policy recommendations, but have limited application at the farm level. District level climate studies are essential for farmer participation in climate change mitigation strategies and management. Our study aimed to investigate historical climate data for trends and their influence on maize yields at the magisterial level. Six sites were selected from three major maize-producing provinces in South Africa: Mpumalanga, Northwest, and Free State. Magisterial districts in each province were selected from different Köppen-Geiger climate zones. The climate variables assessed by the Mann–Kendall trend test included maximum or minimum temperature, rainfall, number of extreme high-temperature days, rainfall onset and cessation from 1986 to 2016. The average maximum temperatures were observed to have significant upward trends in most locations, except for Schweizer-Reneke and Bethlehem. The fastest rate of change was observed at Klerksdorp (0.1 °C per 30 years of study), while the Schweizer-Reneke district was the slowest (0.05 °C per 30 years of study). No significant changes were observed in rainfall onset, cessation, or total rainfall in Schweizer-Reneke, Standerton, and Bethlehem, which are scattered across the different provinces. The other districts in each province showed significant changes in these parameters. Rainfall accounted for the significant variation in maize yields over the study period, explaining between 18 and 40% of the variation in the North West, and between 1 and 17% in the Free State. These findings highlight the importance of understanding location-specific changes at a finer scale, which can help farming communities adjust agronomic practices and adapt to local climate shifts.

Background Global health agencies advocate that no mother should die while giving life, more so from preventable causes. However, there are persistently high maternal mortalities in various regions with a current global maternal mortality ratio of 211/100,000 live births. This study sought to investigate the causes and determinants of maternal mortality. Materials and Methods A four-year retrospective, cross-sectional study was conducted in three tertiary hospitals within Migori county in Kenya. Data were extracted from 101 maternal mortality records from January 1, 2016 to December 31, 2019. Results Leading complications were hemorrhage 34.70%, eclampsia 20.80%, and sepsis 15.80%. Mothers who were unmonitored using partograph, had reactive HIV status, were in the postpartum period, were referred from periphery facilities, and low socioeconomic levels were most vulnerable. Conclusions Improvement in healthcare systems to enable optimal care to mothers diagnosed with leading complications and socioeconomically empowering women in Migori county is urgently needed.

This study maps the evolution of research themes on datafication, analyzing trends, key authors, interdisciplinary collaborations, and emerging topics from 1994 to 2023. The analysis reveals a notable increase in publication volume, particularly from 2014 onwards, reflecting advancements in digital technologies and heightened interest in data-driven research. A significant surge occurred during the COVID-19 pandemic, with 26.10% of total publications in 2022 and 30.52% in 2023 alone. Thematic clusters identified through keyword mapping include Social Media and Privacy, Artificial Intelligence and Machine Learning, Human Dimensions, and Infrastructure and Trust, highlighting diverse research foci. Emerging discussions on data justice and inequality reflect growing attention to the ethical and socio-political implications of datafication. The study also examines the types of documents and subject areas, revealing the dominance of peer-reviewed journal articles (71.41%) and a strong representation of social sciences (46.93%), computer science (14.75%), and arts and humanities (11.57%). Interdisciplinary connections underscore the broad impact of datafication across technology, healthcare, education, and media studies. This research offers insights into the dynamic nature of datafication, pointing to the need for further interdisciplinary collaboration, especially in addressing societal and ethical concerns such as data governance and digital inequality. Future research directions should focus on the human dimensions of datafication, data literacy, and the development of robust data governance frameworks to mitigate potential inequalities and power imbalances in a rapidly data-driven world.

Background Despite the adverse health outcomes associated with longer duration diarrhea (LDD), there are currently no clinical decision tools for timely identification and better management of children with increased risk. This study utilizes machine learning (ML) to derive and validate a predictive model for LDD among children presenting with diarrhea to health facilities. Methods LDD was defined as a diarrhea episode lasting ≥ 7 days. We used 7 ML algorithms to build prognostic models for the prediction of LDD among children < 5 years using de-identified data from Vaccine Impact on Diarrhea in Africa study (N = 1,482) in model development and data from Enterics for Global Health Shigella study (N = 682) in temporal validation of the champion model. Features included demographic, medical history and clinical examination data collected at enrolment in both studies. We conducted split-sampling and employed K-fold cross-validation with over-sampling technique in the model development. Moreover, critical predictors of LDD and their impact on prediction were obtained using an explainable model agnostic approach. The champion model was determined based on the area under the curve (AUC) metric. Model calibrations were assessed using Brier, Spiegelhalter’s z-test and its accompanying p-value. Results There was a significant difference in prevalence of LDD between the development and temporal validation cohorts (478 [32.3%] vs 69 [10.1%]; p < 0.001). The following variables were associated with LDD in decreasing order: pre-enrolment diarrhea days (55.1%), modified Vesikari score(18.2%), age group (10.7%), vomit days (8.8%), respiratory rate (6.5%), vomiting (6.4%), vomit frequency (6.2%), rotavirus vaccination (6.1%), skin pinch (2.4%) and stool frequency (2.4%). While all models showed good prediction capability, the random forest model achieved the best performance (AUC [95% Confidence Interval]: 83.0 [78.6–87.5] and 71.0 [62.5–79.4]) on the development and temporal validation datasets, respectively. While the random forest model showed slight deviations from perfect calibration, these deviations were not statistically significant (Brier score = 0.17, Spiegelhalter p-value = 0.219). Conclusions Our study suggests ML derived algorithms could be used to rapidly identify children at increased risk of LDD. Integrating ML derived models into clinical decision-making may allow clinicians to target these children with closer observation and enhanced management.

The growing global population has intensified concerns about food security, making it essential to produce crops sustainably to meet increasing demands without harming the environment. In this regard, biological control agents (BCAs) have recently gained more attention owing to their potential to manage fungal diseases of crops, particularly in the Solanaceae family. The proper use of selected BCAs such as Trichoderma spp., Bacillus spp., Pseudomonas fluorescens, Beauveria bassiana, and Gliocladium spp. has several benefits for Solanaceae crops. This review aims to summarize the effectiveness of various biological control strategies for fungal diseases in Solanaceae crops. We also provide basic knowledge on BCAs along with suggestions for further research to reduce the severity of these destructive diseases.

We examine the effect of governance quality on sustainable development in Africa. We focus on 48 African countries for the period of 2010 to 2022 using the Generalized Method of Moments framework to analyze the data. We measured sustainable development through three key variables: sustainable economic development, sustainable social development and sustainable environmental development. The findings of this study provides a strong evidence that governance quality plays a critical role in promoting sustainable development. Thus, the empirical evidence in this study largely proves a strong and robust link between the governance quality and sustainable development in Africa.