College of Agriculture, Engineering and Science

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A study on the atmospheric and environmental impacts of aerosol, cloud and precipitation interaction.
(2022) Yakubu, Abdulaziz Tunde.; Chetty, Naven.
Understanding the mechanisms and processes of aerosol-cloud-precipitation interactions (ACPI) is essential in the determination of the specific role of aerosols in modulating extreme weather events and climate change in the long run. Atmospheric aerosols are mainly of various types and are emitted from differing sources. Considering they commonly exist in the heterogeneous forms in most environments, they significantly influence the incoming solar energy and the general perturbation of the clouds depending on their constituents. Thus, a systemic identification and characterisation of these particles are essential for proper representation in climate models. To better understand the process of climate change, this research explores the climate diversity of South Africa to examine aerosol sources and types concerning the atmospheric aerosol suspension over the region and their role in clouds and precipitation formation. The study further provided answers to the cause of extreme precipitation events, including drought and occasional flooding experienced over the region. Also, an insightful explanation of the process of ACPI is provided in the context of climate change. Furthermore, the research found that the effective radiative forcing (RF) over South Africa as monitored in Cape Town and Pretoria is negative (i.e., cooling effect) and provided an analysis of the cause. Similarly, the validation of some satellite datasets from MISR (Multiangle Imaging Spectroradiometer) and MODIS (Moderate Resolution Imaging Spectroradiometer) instruments against AERONET (Aerosol Robotic Network) is conducted over the region. Although a significant level of agreement is observed for the two instruments, intense improvements are needed, especially regarding measurements over water surfaces. Finally, the study demonstrated the proficiency of effective rainfall prediction from satellite instrument cloud datasets using machine learning algorithms.
Adsorption of pharmaceuticals by nano-molecularly imprinted polymers (nano-MIPs) from wastewater: kinetics, isotherms, and thermodynamics studies.
(2024) Nxumalo, Nonhlazeko Loveday.; Mahlambi, Precious Nokwethemba.; Mahlambi, Mphilisi.; Mngadi, Sihle.; Chokwe, Tlou.
It has been reported that pharmaceuticals are not entirely removed or broken down during the wastewater treatment process, allowing them to escape into effluent water. This stems from the pharmaceuticals widespread use and the inefficient wastewater treatment methods. Therefore, the objective of this study was to develop more effective methods for removing pharmaceuticals from wastewater systems. Adsorption-based pharmaceutical removal is one of the most promising approaches because it is easily incorporated into current water treatment systems. The first part of this work reports on literature studies for recent advancements in the adsorption process involving the incorporation of an artificial molecularly imprinted polymer (MIP), that is an effective molecular receptor that can selectively recognize and remove pollutants. In magnetic solid-phase extraction, dispersive solid-phase microextraction, and solid-phase extraction, MIPs can be used as a selective adsorbent for analyte cleanup and preconcentration. Moreover, MIPs can be produced by combining nanoparticles to develop composite nanomaterials (nanoMIPs). In comparison to conventional bulk adsorbents, the enhanced selective adsorption capacity and kinetics are attributed to the large surface area per unit volume and specific functionality of nanomaterials. Nonetheless, some significant barriers to the application of nanomaterials are their dispersive qualities, difficulty in cycling, and secondary pollution from the loss of adsorbent during treatment. Another way to use nanoparticles for detectability enhancement is to modify the molecularly imprinted polymers chemical or physical characteristics. The nanoparticles' embedding in the MIP enhances the material's surface area or gives the adsorbent new features. This study describes a method for creating reusable, economical, and effective polymer-based silver nanoparticles-adsorbents. Notably, silver nanoparticles have a wide range of applications due to their unique properties which include their large surface area, shape and size. Plant-mediated synthesis plays a significant role in their synthesis. Remarkably, the synthesis of silver nanoparticles from plant extracts is inexpensive, easily scalable, and harmless for the environment. Plant extracts can be used to produce nanoparticles with controlled sizes and shapes. The molecular imprinting technique was used to create species-specific functionalities like carboxylic acid (-COOH) on a polymer surface. MIPs offer several advantages, including large surface area, targeted functionalities for high reactivity, and the ability to minimise nanoparticles from leaking into the surrounding environment when MIP-based adsorbents are being handled. To further comprehend the behaviour of adsorbents and the adsorption process, kinetics, thermodynamics, and isotherm models were explored. The second part of the work involved synthesizing the MIPs for efficient and selective removal of pharmaceuticals from specific groups. All target compounds were employed as multiple templates in a bulk polymerization process carried out at 70 °C to synthesize MIPs. Additional reagents utilized in the synthesis included toluene as a porogenic solvent, ethylene glycol dimethacrylate as cross-linker, 1,1'-azobis-(cyclohexane carbonitrile) as an initiator and 2- vinyl pyridine as functional monomer, respectively. The synthesis of a non-imprinted polymer (NIP) was conducted without templates, using reaction conditions similar to those of MIP. Furthermore, following the synthesis, the polymers were characterized using X-ray diffraction, thermogravimetric analysis, differential scanning calorimetry, Fourier transform infrared spectroscopy, and scanning electron microscopy. Liquid chromatography-mass spectrometry (LC-MS) was successfully used to develop an analytical method for detection and quantification of the target pharmaceuticals. The method yielded quantification limits of 0.42 to 0.75 mg L-1 and detection limits of 0.14 to 0.25 mg L-1 for the target pharmaceuticals. The synthesized polymer attained maximum matrix-matched adsorption capacities of 3.89, 4.97 and 3.40 mg g-1 for sulfamethoxazole, nevirapine and ibuprofen, respectively within 10 minutes. Competitive adsorption of the target pharmaceuticals demonstrated a link between adsorption and the pharmaceuticals pKa, log Kow, and molecular size. Studies on batch adsorption and kinetics revealed that the binding of pharmaceuticals to the MIP particles suited the pseudo-second order kinetics, leading to various interactions through chemisorption. The data also fitted well in Langmuir isotherm which meant that the target pharmaceuticals adsorption occurred on the homogeneous binding sites of the MIP. Furthermore, the thermodynamic data demonstrated the adsorption process's endothermic and spontaneous nature. Notably, the synthesized MIP was highly selective and its application in environmental studies led to the development of a less expensive analytical method. Moreover, the MIP particles that had been generated were recovered to be reusable up to five cycles with removal efficiency >90%. The third part involved incorporating silver nanoparticles (AgNPs) into MIPs using ibuprofen, nevirapine, and sulfamethoxazole as templates. In this part, starch (St) and macadamia nutshells (MCD) were employed in the synthesis of AgNPs as reducing and stabilizing agents. Following that, each of these AgNPs was incorporated with MIP, and the most effective combination was identified through comparison. The synthesized adsorbents were further optimized for the adsorptive removal of selected target pharmaceuticals. The % removal efficiencies were greater than 70%, indicating that the adsorbents are suitable for use in water treatment processes. The material's adsorption mechanisms and performance were examined through the application of various kinetics and isotherm models. Both the St and MCD-AgNPs experimental data fit to Freundlich and Langmuir adsorption isotherms. However, based on the somewhat higher correlation coefficients, the Langmuir isotherm model provided a better fit. The St/MCD nanoMIPs best suited the Freundlich model, indicating that the adsorption occurred on the multilayer heterogeneous surface. Further, both the St/MCD nanoMIP adsorbents underwent spontaneous, endothermic adsorption, as demonstrated by the thermodynamic data, whereas the behaviour of the kinetics was effectively anticipated by pseudo-second order model, which suggested adsorption through chemisorption. Accordingly, large internal surface area, greater loading capacity, thermal stability, and reusability were among the advantageous properties of the nanoMIPs adsorbent materials. Moreover, both adsorbents showed improved qualities and were highly selective and effective in removing the selected pharmaceuticals in wastewater. As a result, during the course of five adsorption/desorption cycles, the St/MCD nanoMIPs show a removal efficiency of more than 90%. As a result, they demonstrated proficiency in efficient application. The fourth part involved the incorporation of MIP with Platanus acerifolia and Moringa oleifera silver nanoparticles. Using plants to synthesize AgNPs is a more cost-effective and lowmaintenance method; in contrast, using other organisms requires a particular medium and a specific amount of time. Therefore, the leaves of both the platanus acerifolia (PL) and moringa oleifera (MO) served as stabilizing and reducing agents during the synthesis of AgNPs. Each optimized parameter that could influence the adsorption potential, such as temperature, adsorbate concentration, pH, adsorbent dose, and contact time, was examined in relation to the removal effectiveness of the MO/PL nanoMIP adsorbents. These evaluated parameters were optimum at pH 7, concentration of 0.2 mg/L and contact time of 10 minutes for both MO and PL-nanoMIPs, mass dosage of 30 mg and 20 mg, and temperature of 40 and 30 °C for MO and PL-nanoMIP, respectively. Further, the maximum removal efficiencies obtained at these optimum conditions were >97% for both MO-nanoMIP and PL-nanoMIP. The adsorption experimental data for both MO/PL-AgNPs and MO/PL-nanoMIPs nano-adsorbents fitted with the linear Langmuir model which suggests that the binding took place on the homogenous monolayer surface. Additionally, compared to MO/PL-AgNPs, the MO/PL-nanoMIPs adsorption capacities for the target pharmaceuticals were higher, suggesting that the nanoMIPs larger surface areas contribute to their enhanced adsorption capacity. The linear pseudo-second order kinetic model best fitted on MO/PL-nanoMIPs which implied adsorption through chemisorption, whereas the thermodynamic data demonstrated that the adsorption process was endothermic and spontaneous. Moreover, the values of ΔH° for the MO/AgNPs were less than 40 kJ/mol and more than 40 kJ/mol for the MO/PL-nanoMIPs. This therefore confirmed that the MO/AgNPs was dominated by physical adsorption whereas the MP/PL-nanoMIPs was dominated by chemical adsorption. The MO/PL-nanoMIPs confirmed the high efficiency for the removal of target pharmaceuticals in wastewater. Upon recycling the adsorbents for five cycles, it was noted that the MO-nanoMIP adsorbent was effective continued to remove 86.7- 88.8% and 97-98% for PL-nanoMIP even in the fifth cycle. Indeed, the removal of sulfamethoxazole, nevirapine, and ibuprofen by nanoMIP adsorbents has demonstrated the importance of the surface area, structural stability, pore size and the electrostatic interactions brought about by the charges on the nanoMIPs surface. Consequently, among the investigated nanoMIP adsorbents, PL-nanoMIP demonstrated strong adsorption capacities for the targeted pharmaceuticals due to it large surface area and narrow size distribution as compared to the other nanoMIP adsorbents. The usability of plant leaves as a reducing and capping agent for nanoparticles as well as the recycling of nanoMIPs has the potential to transform waste that is no longer useful into valuable pollutants adsorbents. This would solve the problem of waste disposal and have beneficial impacts on the environment pollution and the economy. Notably, the nanoMIPs synthesized in this study are highly selective, reusable adsorbents that are cost effective and environmentally friendly. In contrast, as a substitute for more costly synthetic materials, these nanoMIPs are a promising material for the removal of different classes of pharmaceuticals in wastewater treatment plants and they can possibly be applied on a large scale.
Analysis of discrete time competing risks data with missing failure causes and cured subjects.
(2023) Ndlovu, Bonginkosi Duncan.; Zewotir, Temesgen Tenaw.; Melesse, Sileshi Fanta.
This thesis is motivated by the limitations of the existing discrete time competing risks models vis-a-vis the treatment of data that comes with missing failure causes or a sizableproportions of cured subjects. The discrete time models that have been suggested to date (Davis and Lawrance, 1989; Tutz and Schmid, 2016; Ambrogi et al., 2009; Lee et al., 2018) are cause-specific-hazard denominated. Clearly, this fact summarily disqualifies these models from consideration if data comes with missing failure causes. It is also a well documented fact that naive application of the cause-specific-hazards to data that has a sizable proportion of cured subjects may produce downward biased estimates for these quantities. The existing models can be considered within the multiple imputation framework (Rubin, 1987) for handling missing failure causes, but the prospects of scaling them up for handling cured subjects are minimal, if not nil. In this thesis we address these issues concerning the treatment of missing failure causes and cured subjects in discrete time settings. Towards that end, we focus on the mixture model (Larson and Dinse, 1985) and the vertical model (Nicolaie et al., 2010) because these models possess certain properties which dovetail with the objectives of this thesis. The mixture model has been upgraded into a model that can handle cured subjects. Nicolaie et al. (2015) have demonstrated that the vertical model can also handle missing failure causes as is. Nicolaie et al. (2018) have also extended the vertical model to deal with cured subjects. Our strategy in this thesis is to exploit both the mixture model and the vertical model as a launching pad to advance discrete time models for handling data that comes with missing failure causes or cured subjects.
Application of ELECTRE algorithms in ontology selection.
(2022) Sooklall, Ameeth.; Fonou-Dombeu, Jean Vincent.
The field of artificial intelligence (AI) is expanding at a rapid pace. Ontology and the field of ontological engineering is an invaluable component of AI, as it provides AI the ability to capture and express complex knowledge and data in a form that encourages computation, inference, reasoning, and dissemination. Accordingly, the research and applications of ontology is becoming increasingly widespread in recent years. However, due to the complexity involved with ontological engineering, it is encouraged that users reuse existing ontologies as opposed to creating ontologies de novo. This in itself has a huge disadvantage as the task of selecting appropriate ontologies for reuse is complex as engineers and users may find it difficult to analyse and comprehend ontologies. It is therefore crucial that techniques and methods be developed in order to reduce the complexity of ontology selection for reuse. Essentially, ontology selection is a Multi-Criteria Decision-Making (MCDM) problem, as there are multiple ontologies to choose from whilst considering multiple criteria. However, there has been little usage of MCDM methods in solving the problem of selecting ontologies for reuse. Therefore, in order to tackle this problem, this study looks to a prominent branch of MCDM, known as the ELimination Et. Choix Traduisant la RÉalite (ELECTRE). ELECTRE is a family of decision-making algorithms that model and provide decision support for complex decisions comprising many alternatives with many characteristics or attributes. The ELECTRE algorithms are extremely powerful and they have been applied successfully in a myriad of domains, however, they have only been studied to a minimal degree with regards to ontology ranking and selection. In this study the ELECTRE algorithms were applied to aid in the selection of ontologies for reuse, particularly, three applications of ELECTRE were studied. The first application focused on ranking ontologies according to their complexity metrics. The ELECTRE I, II, III, and IV models were applied to rank a dataset of 200 ontologies from the BioPortal Repository, with 13 complexity metrics used as attributes. Secondly, the ELECTRE Tri model was applied to classify the 200 ontologies into three classes according to their complexity metrics. A preference-disaggregation approach was taken, and a genetic algorithm was designed to infer the thresholds and parameters for the ELECTRE Tri model. In the third application a novel ELECTRE model was developed, named ZPLTS-ELECTRE II, where the concept of Z-Probabilistic Linguistic Term Set (ZPLTS) was combined with the traditional ELECTRE II algorithm. The ZPLTS-ELECTRE II model enables multiple decision-makers to evaluate ontologies (group decision-making), as well as the ability to use natural language to provide their evaluations. The model was applied to rank 9 ontologies according to five complexity metrics and five qualitative usability metrics. The results of all three applications were analysed, compared, and contrasted, in order to understand the applicability and effectiveness of the ELECTRE algorithms for the task of selecting ontologies for reuse. These results constitute interesting perspectives and insights for the selection and reuse of ontologies.
Assessment of antiretroviral drugs uptake by vegetables from contaminated soil and their adsorption by exfoliated graphite in river and wastewater.
(2022) Kunene, Philisiwe Nganaki.; Mahlambi, Precious Nokwethemba.
This study was directed toward vegetable uptake of the commonly used antiretroviral drugs (ARVDs), abacavir, nevirapine, and efavirenz. Antiretroviral drugs are used to treat the human immune-deficiency virus (HIV). South Africa (SA) is one of the countries with a high number of infected people on ARV therapy, therefore, the ARVDs are anticipated to be existing at high concentrations in the South African environment than in other countries worldwide. In recent years, the presence of ARVDs in the environment has drawn attention; hence studies have reported their presence in aquatic environments while very few studies have been conducted on their uptake using vegetables. This work was therefore based on the optimization and application of sensitive, simple, cost-effective, and robust techniques for quantifying ARVDs in vegetables. Based on this information, ultrasonic extraction (UE) and microwave-assisted extraction (MAE) were used to isolate target compounds from vegetable samples to the aqueous phase. Dispersive liquid-liquid microextraction (DLLME) and solid-phase extraction (SPE) were utilized to preconcentration and clean up the extracts from UE and MAE, respectively. A liquid chromatography photodiode array detector (LC-PDA) was utilized to detect and quantify the extracted compounds. The UE with and without DLLME cleanup were compared with each other, also, MAE with and without SPE cleanup were compared with each other. The methods comparison was done in terms of their detection (LOD) and quantification limits (LOQ), extraction efficiencies (%Recovery), relative standard deviations (%RSD), and concentrations of ARVDs found in vegetable samples. In comparison of UE and ultrasonic-assisted dispersive liquid-liquid microextraction (UADLLME), the LOD and LOQ obtained ranged between 0.0081 - 0.015 μg/kg and 0.027 - 0.049 μg/kg for UE and 0.0028 -0.0051 μg/kg and 0.0094 - 0.017 μg/kg for UADLLME respectively. High recoveries ranging from 93 to 113% in UE and 85 to 103% in UADLLME with less than 10% RSD in both procedures were obtained. These results indicated that UADLLME is more sensitive than the UE method, although they are both accurate and precise. The UE can be recommended for routine analysis as UADLLME showed the inability to extract analytes from root vegetables. The optimized UE and UADLLME methods were applied to extract ARVDs from vegetables bought from local fruit and veggie supermarket. Vegetables were categorized as root (carrot, potato, and sweet potatoes), leaf (cabbage and lettuce), and fruit (green paper, butternut, and tomato). The target ARVDs were quantified in most samples with concentrations up to 8.18 μg/kg. The concentrations obtained were slightly high in UADLLME than in UE as a result of its high sensitivity. Efavirenz was the most dominant drug, while the potato was the most contaminated vegetable. In the comparison of MAE and MAESPE, the obtained LOD and LOQ ranged from 0.020 to 0.032 μg/kg and 0.068 to 0.109 μg/kg for MAE and 0.019 to 0.066 μg/L and 0.065 to 0.22 μg/L for MAE-SPE. The obtained recoveries ranged from 85 to 103% for MAE and from 82 to 98 % for MAE-SPE, respectively, and the RSDs were all less than 6%. These results showed that both methods have comparable sensitivity; however, the recoveries values for MAE were slightly higher than those obtained in MAE-SPE, which signals MAE’s high accuracy. The optimized MAE and MAE-SPE methods were applied to remove ARVDs in the root (potatoes, onions, and beetroot), leaf (lettuce, and spinach), and fruit (green paper, cucumber, and eggplant) vegetables bought from local fruit and veggie supermarket. The obtained ARVDs concentration range was 1.48 ± 0.5 - 27.9 ± 1.2 μg/kg. The MAE-SPE resulted in low concentration compared to MAE without cleanup. Beetroot exhibited high concentrations of the target ARVDs, while nevirapine was found to have high concentration and as a dominant compound. The results obtained revealed that the vegetables from the studied area are contaminted with ARVDs, which could indicate their possible irrigation with wastewater effluent or the use of sludge as biosolids in the agricultural areas. This is a concern as it leads to unintentional consumption by consumers which could lead to drug resistance by the human body or have human health effects. The study was then expanded by conducting the phytoremediation approach to investigate the uptake of abacavir, nevirapine, and efavirenz by beetroot, spinach, and tomato from the contaminated soil. The three selected vegetable plants were planted and irrigated with ARVDs spiked (at 2000 and 5000 μg/L) water over a period of three months. The optimized UE and LC-PDA methods were used to extract and quantify the selected ARVDs from the target vegetables and soil. The obtained results showed that the studied vegetables have the potential to take up abacavir, nevirapine, and efavirenz from contaminated soil, be absorbed by the root, and translocate to the aerial part of the plants. Abacavir was found at high concentrations to a maximum of 40.21 μg/kg in the root, 18.43 μg/kg in the stem, and 6.77 μg/kg in the soil, while efavirenz was the highest concentrations, up to 35.44 μg/kg in leaves and 8.86 μg/kg in fruits. Spinach root accumulated more ARVDs than beetroot and tomato. The bio-accumulation factor ranged from 2.0-14 μg/kg in beetroot, 3.6 - 15 μg/kg in spinach, and 6 – 10 μg/kg in tomato. The root concentration factor range was 0.047 – 17.6 μg/kg; 0.34-5.9 μg/kg, and 0.14-2.82 μg/kg in beetroot, spinach, and tomato, respectively. The translocation factor range obtained was 0.40 – 38 μg/kg, 0.08 – 19 μg/kg, and 0.14 – 49 μg/kg in beetroot, spinach, and tomato, respectively. However, the accumulation of ARVDs in all studied plants showed that they could be used in phytoremediation. The results obtained in the phytoremediation approach revealed that the utilization of the contaminated water has an influence on the presence ARVDs in vegetables; hence this work also focused on evaluating the exfoliated graphite adsorption of ARVDs in water. Natural graphite was intercalated with acids and exfoliated with thermal shock to obtain the exfoliated graphite. The scanning electron microscopy images showed that the exfoliated graphite had increased c-axis distance between the layers with accordion-like structure which were confirmed by the lower density of exfoliated graphite material (0.0068 gmL-1) compared to the natural graphite (0.54 g mL-1). Fourier Transformed Infrared Spectroscopy results showed the C=C in natural and exfoliated graphite at 1635 cm-1 stretching. The phenolic, alcoholic, and carboxylic groups were observed from 1000 to 1700 cm-1 for the intercalated and exfoliated graphite. The Energy-dispersive X-ray results further confirmed these results, which showed carbon and oxygen peaks in the intercalated and exfoliated graphite spectrum, whereas natural graphite showed only a carbon peak. Raman spectroscopy results showed that the material’s crystallinity was not affected by the intercalation and exfoliation processes as observed from the ratios of the G and D peaks and the G' and D'. Natural, intercalated and exfoliated graphite contained the D, G, D', and G' peaks at about 1350 cm-1, 1570 cm-1, 2440 cm-1, and 2720 cm-1, respectively. The exfoliated graphite material showed the characteristic of a hexagonal phase graphitic structure by (002) and (110) reflections in the X-ray diffraction results. The exfoliated graphite adsorption method was optimized based on the pH of a solution, adsorbent dosage, and adsorption time prior to application to water samples. The optimum pH solution, adsorbent dosage, and adsorption time were 7, 30 mg, 0.01 μg/L, and 30 minutes respectively. The kinetics and isotherm studies were conducted to assess the model that best fit and explain the experimental data obtained. The kinetic model and adsorption isotherm studies showed that the experimental data fit well pseudo-second-order kinetics and is well explained by Freundlich’s adsorption isotherm. The maximum adsorption capacity of the exfoliated graphite (EG) for ARVDs ranges between 1.660-197.0, 1.660-232.5, and 1.650-237.7 mg/g for abacavir, nevirapine, and efavirenz, respectively. These results showed that under proper operating conditions, the EG adsorbent could potentially be applied as a water purifying tool for the removal of ARVDs pollutants.
Bayesian spatio-temporal and joint modelling of malaria and anaemia among Nigerian children aged under five years, including estimation of the effects of risk factors = I-Bayesian spatio-temporal kanye nemodelingi ehlanganisa umalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia, ezinganeni zaseNigeria ezineminyaka engaphansi kwemihlanu kuhlanganisa nokulinganiselwa kwemithelela yezimo eziyingozi.
(2023) Ibeji, Jecinta Ugochukwu.; Mwambi, Henry Godwell.; Iddrisu, Abdul-Karim.
Childhood mortality and morbidity in Nigeria have been linked to malaria and anaemia. This thesis focused on exploring the risk factors and the complexity of the relationship between malaria and anaemia in under 5 Nigerian children. Data from the 2010 and 2015 Nigeria Malaria Indicator Survey conducted by Demographic Health Survey were used. In 2010, the prevalence of malaria and anaemia was 48% and 72%, respectively, while in 2015, 27% and 68% were the respective prevalences of malaria and anaemia diseases. Machine learning-based exploratory classification methods were used to explain the relationship and patterns between the independent variables and the two dependent variables, namely malaria and anaemia. Decisions made by the public health body are centered on the administrative units (i.e., states) within the country. Therefore, the development of disease mapping and a brief overview of limiting assumptions and ways of tackling them was explained. Consequently, malaria and anaemia spatial variation for 2010 and 2015 was analyzed with the inclusion of their respective risk factors. A separate multivariate hierarchical Bayesian logistic model for each disease was adopted to investigate the spatial pattern of malaria and anaemia and adjust for the risk factors associated with each disease. Furthermore, a multilevel model analysis was applied to independently investigate the spatio-temporal distribution of malaria and anaemia. A joint model was further adopted to check for the relationship between malaria and anaemia and their common risk factors and relax the nonlinearity assumption. In the 2010 data, type of place of residence, mother’s highest educational level, source of drinking water, type of toilet facility, child’s sex, main floor material, and households that have electricity, radio, television, and water were significantly associated with malaria and anaemia. While in the 2015 data, the type of place of residence, source of drinking water, type of toilet facility, households with radio, main roof material, wealth index, child’s sex, and mother’s highest educational level had a significant relationship with malaria and anaemia. The results from this study can guide policymakers to tailor-make effective interventions to reduce or prevent malaria and anaemia diseases. This will help adequately distribute limited state health system resources, such as personnel, funds and facilities within the country. Iqoqa. Ukugula kanye nokushona kwezingane eNigeria kuyamene nomalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia. Lolu cwaningo lugxile ekuhloleni izimo eziyingozi kanye nobunkimbinkimbi bobudlelwano obukhona phakathi komalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia, ezinganeni zaseNigeria ezineminyaka engaphansi kwemihlanu. Kwasetshenziswa imininingo yesaveyi eyenziwa ye-Nigeria Malaria Indicator Survey ngo-2010 nango-2015 eyenziwa yi-Demographic Health Survey. Ngo-2010 ukusabalala komalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia, kwakungama-48% kanye nama-72% ngokulandelana, kanti ngo-2015 kwakungama-27% kanye nama-68% ngokulandelana ukusabalala kwezifo zomalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia. Kwasetshenziswa izindlela zokulinganisa ngokuhlwaya ezisebenzisa imishini ukuchaza ubudlelwano phakathi kwamavariyebuli azimele kanye namavariyebuli amabili angazimele, okungumalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia. Izinqumo ezathathwa yisigungu sezempilo yomphakathi ziqondene namayunithi okuphatha (izifunda) ezweni. Ngakho-ke, kwachazwa indlela okuthuthukiswe ngayo ukuvezwa kwezindawo okusabalele kuzo izifo kanye nohlaka olufingqiwe lwezimo eziyizithiyo kanye nezindlela zokubhekana nazo. Ekugcineni kwahlaziywa izingakwehlukana lomalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia, ngokwezindawo ngo-2010 kanye no-2015. Kulokhu kwahlanganiswa nezimo eziyingozi zakho ngokulandelana kwazo. Kwaqokwa imodeli ehlukile yohlaziyongxube oluyi-hierarchical Bayesian logistic model kuleso naleso sifo ukuhlola iphethini yezindawo okutholakala kuzo umalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia, bese ilungiselwa izimo eziyingozi ezihlobene nesifo ngasinye. Okunye futhi, kwasetshenziswa imodeli yohlaziyo enamazinga ayingxube ukuhlola ngendlela ezimele ukusabalala ngokwendawo nesikhathi komalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia. Imodeli eyinhlanganisela yasetshenziselwa ukuhlunga ubudlelwano obukhona phakathi komalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia, kanye nezimo ezivamile eziyingozi, bese kuthanjiswa ukucabangela ukuthi kunobudlelwano obuqondile phakathi kwamavariyebuli azimele nalawo angazimele. Emininingweni yango-2010 uhlobo lwendawo yokuhlala, izinga lemfundo kamama, umthombo wamanzi okuphuza, uhlobo lwendlu yangasese, ubulili bengane, izinto okwakhiwe ngazo isiyilo, kanti nemindeni enogesi, iwayilense/umsakazo, umabonakude, kanye namanzi yayihlobene kakhulu nomalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia. Kanti imininingo yango-2015 yaveza ukuthi ubudlelwano obukhulu phakathi komalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia, buhambisana nohlobo lwendawo yokuhlala, umthombo wamanzi okuphuza,uhlobo lwendlu yangasese, imindeni enomsakazo, izinto okwakhiwe ngazo uphahla, isigaba ngokwezomnotho, ubulili bengane, kanye nebanga lemfundo kamama. Imiphumela yalolu cwaningo ingaba usizo ukucaba indlela kulabo abasezikhundleni zokwenza izinqubomgomo ukuze bakwazi ukungenelela ngezindlela ezisebenza kahle ekunciphiseni noma ekunqandeni/ekuvikeleni izifo zomalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia. Lokhu kuzosiza ekusabalaliseni izinsiza ezifana nabasebenzi, izimali kanye nezikhungo zezinsiza ohlelweni lukahulumeni kwezempilo.
Comparison of extraction methods efficiency for the extraction of polycyclic aromatic hydrocarbons and phenolics in water matrices, sludge and sediment: sources of origin and ecological risk assessment.
(2023) Ndwabu, Sinayo.; Mahlambi, Precious Nokwethemba.; Malungana, Mncedisi.
Polycyclic aromatic hydrocarbons (PAHs) and phenolic compounds (PCs) are persistent and environmentally toxic compounds. This study therefore aimed to determine the levels of both PAHs and PCs in river water, wastewater, sludge and sediment samples. The evaluation of their origin source and ecological risk was also determined. The status of both these contaminants in South African environment is still not fully investigated, which is a gap this study intended to fill together with previous studies that have been carried-out. The PAHs and PCs were extracted using different extraction methods which include a solid phase extraction (SPE) and dispersive liquid-liquid micro-extraction (DLLME) in water matrices. The microwave assisted extraction (MAE) and Ultrasonication (UE) coupled with either filtering (F) or F + SPE as a clean-up technique was used for extraction of solid samples. The analytes extracted form water or sediment were determined using GC-MS. The PAH %recoveries obtained under optimum conditions in liquid samples were determined to be 72.1 - 118% for SPE and 70.7 – 88.4% for DLLME while the LOD and LOQ were 5.00 – 18.0 ng/L and 10.0 – 44.0 ng/L for SPE while they were 6.00 – 20.0 ng/L and 11.0 – 63.0 ng/L for DLLME. The recovery test for PAHs in solid samples gave a range of 93.7% - 121% for UE and 79.6% - 122% for MAE while the LOD and LOQ ranged from 0.0250 μg/kg to 1.21 μg/kg & 0.0800 μg/kg to 3.54 μg/kg for MAE and from 0.0840 μg/kg to 0.215 μg/kg & 0.0190 μg/kg to 0.642 μg/kg for UE respectively. The LOD and LOQs for PCs in both water and solid matrices were 0.01 – 2.00 μg/L and 0.02 – 6.07 μg/L for SPE, 0.05 – 1.20 μg/kg and 0.17 – 3.17 μg/kg for MAE and 0.09 – 1.33 μg/kg and 0.26 - 3.54 μg/kg for UE correspondingly, their %recovery test gave ranges of 75.2 – 112% (SPE), 80.9 – 110% (MAE) and 79.3 – 119% (UE).The optimization and validation of these methods indicated that they can be used for the extraction of PAHs or PCs in liquid samples, however, SPE when compared to DLLME showed to be more accurate and sensitive. Moreover, in solid samples the clean-up method was a deciding factor, with F + SPE cleaned samples giving higher concentrations of both PCs and PAHs than the filtered ones in both MAE and UE. The concentrations of PAHs ranged from nd (not detected) to 1046 ng/L in river water and nd to 778 ng/L in wastewater samples with naphthalene showing dominance over all other PAHs in both water matrices. The PC concentrations at 4.12 to 1134 μg/L for wastewater and nd to 98.0 μg/L for river water were high but still within the maximum allowable limit except for 2.4-DCP (2.4 dichlorophenol) at Wdv4. The concentrations obtained from F + SPE cleaned samples were higher for both PAHs and PCs with a range from 95.96 to 926.0 μg/kg and 1.30 to 310 μg/kg compared to concentrations from filtered only samples at 21.61 to 380.6 μg/kg and 0.90 to 266 μg/kg respectively. Pyrene showed dominance over all other PAHs in both sludge and sediments while 2.4-DCP and PCP dominated the sludge and sediment samples respectively. PAHs were determined to be of petrogenic (water matrices) and pyrolytic (solid samples) origin and on average posed low (water matrices) and a medium to high (solid matrices) ecological risk. The ILCRderm values at 4.98 x 10-1 and 2.62 x 10-1 (DahA) and 5.92 x 10-2 and 5.34 x 10-2 (PCP) were highfor adults compared to that of children at 1.92 x 10-1 and 1.01 x 10-1 (DahA) and 1.39 x 10-2 and 1.26 x 10-2 (PCP) for both sediment and sludge samples respectively. The low values of ILCRderm for children indicates that the have a high risk exposure even at low concentrations of the contaminants. The findings of this study showed that both areas (uMsunduzi river and Darvill wastewater works (WWW) of interest are polluted with PAHs and PCs therefore, more regulations such as the National Environmental Management: Waste Act (NEMWA) are needed to ensure environmental, human and animal safety.
Construction of functional and robust cobalt phthalocyanines modified electrodes for the electrocatalytic detection of metal-based and pharmaceutically derived pollutants.
(2024) Moodley, Danica.; Booysen, Irvin Noel.; Mambanda, Allen.
Water pollution has become a detrimental global concern in a world that continues to grow through industrialisation, population, and demand in sales from agricultural and pharmaceutical industries. It is therefore imperative for innovative methods of continuous water monitoring to be implemented to avoid the harsh effects that pollution poses to human, animal and environmental preservation. Advances from traditional analytical methods have been made to combat associated drawbacks such as tedious sample preparation, high maintenance costs, and lack of mobility. Electrochemical sensors can be used for the analysis of a vast range of water pollutants while offering on-site, simple analysis and inexpensive fabrication. Metallophthalocyanines have been utilised extensively as electrode modifiers due to their excellent redox properties and stability which can be fine-tuned by alteration of the metal centre and substituents. In addition, thes3e alterations improve selectivity, solubility and immobilisation onto electrode substrates. This research is aimed at the application of gold electrodes modified with CoPc-cou nanoconjugates and CoPc-cou electrospun nanofibers (ENFs) for the electrocatalytic detection of pollutants, paraquat and mercury, in real water samples. Experimental chapter one explores the optimization and application of a gold-modified electrode, CoPc-cou-f-MWCNTs/3-HT|Au, for the electrocatalytic detection of a water pollutant, paraquat (PQ). It was fabricated via a sequential modification procedure entailing the formation of self-assembled monolayers (SAMs) of a nanocomposite comprising of a coumarin tetra-substituted cobalt phthalocyanine (CoPc-cou) and carboxylic acid functionalized multiwalled carbon nanotubes (f-MWCNTs). This was followed by the in-situ immobilization of poly(3-hexylthiophene) ([3-HT]n) through electropolymerisation to render the chemically modified electrode (CME). Subsequently, the CME illustrated enhanced sensitivity towards PQ compared to the bare or CoPc-cou-f-MWCNTs modified electrodes. The CoPc-cou-f-MWCNTs/3-HT|Au electrode displayed a linear PQ detection range of 0.193 – 1000 μM with a limit of detection (LOD) and limit of quantification (LOQ) of 0.193 μM and 0.584 μM, respectively. Comparison between calibration curves for the modified electrode and HPLC-MS illustrates that the former method has a lower but comparable calibration sensitivity for PQ. In addition, this CME could electrocatalytically distinguish PQ within a real water sample collected from the Durban lagoon. Furthermore, the direct recovery of PQ in the lagoon water by the modified Au electrode was found to be 86%, which is lower than the calculated value of 97% obtained by HPLC-MS after rigorous solid-phase microextraction of the analyte. However, the lower percentage recovery could be rationalized by the interference studies. In experimental chapter two fabricated electrospun nanofibers containing CoPc-cou, polyaniline (PANI) and poly-vinyl alcohol (PVA) were used to modify a gold substrate which was subsequently immobilised using a 5% Nafion solution affording the CoPc-cou-ENFs-Nf|Au modified electrode. Comparison of the chemically modified electrode with the bare and other modified electrodes under optimised conditions displayed superior detection of mercury (Hg(II)) attaining a linear range of 10 – 3000 μM and an LOD and LOQ of 0.14 μM and 0.46 μM, respectively. This can be attributed to the affinity between Hg(II) and the mercaptocoumarin substituent (Hg-S) as well as the higher surface area occupied by the ENFs resulting in an increased number of active sites. Furthermore, the chemically modified electrode exhibit selectivity and sensitivity in an interference sample containing multiple heavy metals (Pb2+, Cd2+ and Hg2+). A good percentage recovery of 96% was attained when the CoPc-cou-ENFs-Nf|Au electrode was applied to a real water sample which was comparable to a percentage recovery of 98% which was attained using the ICP-OES to analyse the same water samples.
Deep learning for brain tumor segmentation and survival prediction.
(2024) Magadza, Tirivangani Batanai Hendrix Takura.; Viriri, Serestina.
A brain tumor is an abnormal growth of cells in the brain that multiplies uncontrolled. The death of people due to brain tumors has increased over the past few decades. Early diagnosis of brain tumors is essential in improving treatment possibilities and increasing the survival rate of patients. The life expectancy of patients with glioblastoma multiforme (GBM), the most malignant glioma, using the current standard of care is, on average, 14 months after diagnosis despite aggressive surgery, radiation, and chemotherapies. Despite considerable efforts in brain tumor segmentation research, patient diagnosis remains poor. Accurate segmentation of pathological regions may significantly impact treatment decisions, planning, and outcome monitoring. However, the large spatial and structural variability among brain tumors makes automatic segmentation a challenging problem, leaving brain tumor segmentation an open challenge that warrants further research endeavors. While several methods automatically segment brain tumors, deep learning methods are becoming widespread in medical imaging due to their resounding performance. However, the boost in performance comes at the cost of high computational complexity. Therefore, to improve the adoption rate of computer-assisted diagnosis in clinical setups, especially in developing countries, there is a need for more computational and memoryefficient models. In this research, using a few computational resources, we explore various techniques to develop deep learning models accurately for segmenting the different glioma sub-regions, namely the enhancing tumor, the tumor core, and the whole tumor. We quantitatively evaluate the performance of our proposed models against the state-of-the-art methods using magnetic resolution imaging (MRI) datasets provided by the Brain Tumor Segmentation (BraTS) Challenge. Lastly, we use segmentation labels produced by the segmentation task and MRI multimodal data to extract appropriate imaging/radiomic features to train a deep learning model for overall patient survival prediction.
Deep learning framework for speech emotion classification.
(2024) Akinpelu, Samson Adebisi.; Viriri, Serestina.
A robust deep learning-based approach for the recognition and classification of speech emotion is proposed in this research work. Emotion recognition and classification occupy a conspicuous position in human-computer interaction (HCI) and by extension, determine the reasons and justification for human action. Emotion plays a critical role in decision-making as well. Distinguishing among various emotions (angry, sad, happy, neutral, disgust, fear, and surprise) that exist from speech signals has however been a long-term challenge. There have been some limitations associated with existing deep learning techniques as a result of the complexity of features from human speech (sequential data) which consists of insufficient label datasets, Noise and Environmental Factors, Cross-cultural and Linguistic Differences, Speakers’ Variability and Temporal Dynamics. There is also a heavy reliance on huge parameter tunning, especially for millions of parameters before the model can learn the expected emotional features necessary for classification emotion, which often results in computational complexity, over-fitting, and poor generalization. This thesis presents an innovative deep learning framework-based approach for the recognition and classification of speech emotions. The deep learning techniques currently in use for speech-emotion classification are exhaustively and analytically reviewed in this thesis. This research models various approaches and architectures based on deep learning to build a framework that is dependable and efficient for classifying emotions from speech signals. This research proposes a deep transfer learning model that addresses the shortcomings of inadequate training datasets for the classification of speech emotions. The research also models advanced deep transfer learning in conjunction with a feature selection algorithm to obtain more accurate results regarding the classification of speech emotion. Speech emotion classification is further enhanced by combining the regularized feature selection (RFS) techniques and attention-based networks for the classification of speech emotion with a significant improvement in the emotion recognition results. The problem of misclassification of emotion is alleviated through the selection of salient features that are relevant to emotion classification from speech signals. By combining regularized feature selection with attention-based mechanisms, the model can better understand emotional complexities and outperform conventional ML model emotion detection algorithms. The proposed approach is very resilient to background noise and cultural differences, which makes it suitable for real-world applications. Having investigated the reasons behind the enormous computing resources required for many deep learning based methods, the research proposed a lightweight deep learning approach that can be deployed on low-memory devices for speech emotion classification. A redesigned VGGNet with an overall model size of 7.94MB is utilized, combined with the best-performing classifier (Random Forest). Extensive experiments and comparisons with other deep learning models (DenseNet, MobileNet, InceptionNet, and ResNet) over three publicly available speech emotion datasets show that the proposed lightweight model improves the performance of emotion classification with minimal parameter size. The research further devises a new method that minimizes computational complexity using a vision transformer (ViT) network for speech emotion classification. The ViT model’s capabilities allow the mel-spectrogram input to be fed into the model, allowing for the capturing of spatial dependencies and high-level features from speech signals that are suitable indicators of emotional states. Finally, the research proposes a novel transformer model that is based on shift-window for efficient classification of speech emotion on bi-lingual datasets. Because this method promotes feature reuse, it needs fewer parameters and works well with smaller datasets. The proposed model was evaluated using over 3000 speech emotion samples from the publicly available TESS, EMODB, EMOVO, and bilingual TESS-EMOVO datasets. The results showed 98.0%, 98.7%, and 97.0% accuracy, F1-Score, and precision, respectively, across the 7 classes of emotion.
Derivatised phenanthroline transition metal chelates : targeted chemotherapeutic agents = I-Derivatised phenanthroline transition metal chelates: i-targeted chemotherapeutic agents
(2024) Hunter, Leigh André.; Akerman, Matthew Piers.
The derivatisation of 1,10-phenanthroline at the 2-position afforded two classes of compounds with two different bridging groups in this study. The first group comprised two amide-bridged tetradentate N4-donor ligands and were chelated to copper(II), nickel(II) and palladium(II). The ligand chelation occurred with concomitant deprotonation of the amide N-H, resulting in a monoanionic ligand and monocationic complexes when coordinated to the divalent metal ions. The ligands N-(quinolin-8-yl)-1,10-phenanthroline-2-carboxamide, HL1, and N-(pyridin-2-ylmethyl)-1,10-phenanthroline-2-carboxamide, HL2, were characterised by NMR, IR and UV/vis spectroscopy as well as mass spectrometry. The second class of compounds were imine-bridged copper(II) chelates. These chelates were synthesised via a templating condensation reaction between various salicylaldehyde derivates and 1,10-phenanthrolin-2-ylmethanaminium chloride, yielding eight additional copper(II) chelates. The metal chelates were characterised by IR, UV/vis and EPR spectroscopy, and mass spectrometry. HL1, [Cu(L4)(NO3)] and [Cu(L7)](NO3) were further studied by X-ray diffraction. The copper(II) chelates exhibit two different solid-state structures with the nitrate counter ion coordinated to the metal centre in [Cu(L4)(NO3)], but in the outer coordination sphere for [Cu(L7)](NO3). The paramagnetic copper(II) chelates were studied with EPR spectroscopy, which confirmed the square planar coordination geometries of these chelates in solution. The metal chelates were designed to be chemotherapeutic agents, exerting their cytotoxicity through DNA intercalation and, for the copper(II) chelates, DNA cleavage through the catalytic production of ROS. The ability of the copper(II) chelates to catalyse the production of hydroxyl radical in situ in the presence of ascorbic acid and hydrogen peroxide was studied via a hydroxyl radical assay using Rhodamine B as an analogue for the aromatic DNA bases. Competitive binding studies determined the affinity of the metal chelates towards ct-DNA, [Cu(L1)](PF6) has the highest binding constant: 5.91 × 106 M-1. DFT calculations were performed on the ligands and metal chelates to determine the geometry-optimised structures, vibrational frequencies, 1H and 13C NMR chemical shifts and electronic transitions. The B3LYP/6-311G (d,p) level of theory was used for the ligands, copper(II) and nickel(II) chelates and the B3LYP/LanL2DZ level of theory for the palladium(II) chelates. The TD-DFT method was used for the energy calculations. The experimental and calculated results were compared where possible, and a reasonable correlation was found. The cytotoxicity of five amide-based chelates was evaluated against four human cancer cell lines, namely A549, TK-10, HT29 and U251, using an MTT assay. The screened chelates exhibited favourable anticancer activity with the mean IC50 values against the four cancer cell lines ranging from ca. 12 to 35 μM. Importantly, it was found that the combination of the copper(II) ion and the ligand was essential for enhanced cytotoxicity. The complex [Cu(L1)](PF6) was identified as the lead drug candidate based on the high DNA affinity and cytotoxicity. This compound was most cytotoxic towards the glioblastoma cell line U251 with an IC50 value of 7.59 μM. The imine-based chelates were screened against three human cancer cell lines: MDA-MB, HELA, and SHSY5Y, and a healthy human cell line, HEK293. The selectivity index of these chelates for neoplastic versus the healthy cell line was calculated. The imine-based chelates showed a high selectivity towards the triple-negative breast cancer MDA-MB, an order of magnitude more toxic to the tumour cell than the healthy one. This selectivity index is significantly improved over that of cisplatin. A gel mobility shift assay investigated the interactions between the copper(II) chelates and plasmid DNA. The in vivo biodistribution of [Cu(L1)](PF6) was determined using the copper-64 radiolabelled analogue of [Cu(L1)]Cl and microPET-CT scanning. The initial biodistribution studies suggested that the complex has good serum stability and showed that there was no significant accumulation in any organs. The subsequent study involved a xenograft model using the A549 cell line and showed significant uptake and retention of the complex in the tumour. The cytotoxicity of the chelate when synthesised with the non-radioactive isotopes of copper and the uptake of the radiolabelled equivalent in a tumour model suggest that this complex could have application as a “theranostic agent”. Iqoqa. Ukukhishwa kwe-1,10-phenanthroline endaweni ye-2 kunikeze amakilasi amabili enhlanganisela namaqembu amabili ahlukene ukuhlanganisa kulolu cwaningo. Iqembu lokuqala lalihlanganisa ama-amide-bridged tetradentate N4-donor ligands futhi ayenziwe ngethusi (II), nickel (II) kanye ne-palladium (II). I-ligand chelation yenzeke ngokuchithwa okuhambisanayo kwe-amide N-H, okuholela ku-monoanionic ligand kanye ne-monocationic complexes lapho ixhunywe kuma-ion ensimbi e-divalent. Ama-ligands N-(quinolin-8-yl)-1,10-phenanthroline-2-carboxamide, HL1, kanye ne-N-(pyridin-2-ylmethyl) -1,10-phenanthroline-2-carboxamide, HL2, abenophawu lwe-NMR, IR kanye ne-UV/vis spectroscopy kanye ne-mass spectrometry. Ikilasi lesibili lama-compounds bekungama-imine-bridged copper (II) chelates. Lawa ma-chelates ahlanganiswa ngokusabela kokujiya kwesifanekiso phakathi kokuphuma kwe-salicylaldehyde okuhlukahlukene kanye ne-1,10-phenanthrolin-2-ylmethanaminium chloride, ekhiqiza ama-chelate ethusi ayisishiyagalombili (II). Ama-chelates ensimbi abonakala nge-IR, UV/vis kanye ne-EPR spectroscopy, kanye ne-mass spectrometry. I-HL1, [Cu(L4) (NO3)] kanye ne-[Cu(L7)] (NO3) zaphinde zacwaningwa nge-X-ray diffraction. Ama-chelates ethusi (II) abonisa izakhiwo ezimbili ezihlukene zesimo esiqinile ezine-ion yekhawunta ye-nitrate exhunywe esikhungweni sensimbi ku-[Cu(L4) (NO3)]], kodwa kwi-outer coordination sphere ye-[Cu(L7)] (NO3). Ama-chelates e-paramagnetic copper (II) ahlolisiswa nge-EPR spectroscopy, eqinisekisa i-square planar coordination geometries yalawa ma-chelates esixazululweni. Ama-chelates ensimbi ayeklanyelwe ukuba abe ama-chemotherapeutic agents, asebenzisa i-cytotoxicity yawo ngokusebenzisa i-DNA intercalation futhi, kuma-chelates ethusi (II), i-DNA cleavage ngokukhiqizwa okunamandla kwe-ROS. Ikhono le-copper (II) chelates lokugqugquzela ukukhiqizwa kwe-hydroxyl radical in situ lapho kukhona i-ascorbic acid ne-hydrogen peroxide yacwaningwa nge-hydroxyl radical assay kusetshenziswa i-Rhodamine B njenge-analogue yezisekelo ze-DNA enamakha. Izifundo ezibophayo ezincintisanayo zinqume ukuhambisana kwama-chelates ensimbi ku-ct-DNA, [Cu(L1)](PF6) inokuhambisana okuphezulu kakhulu okubophayo: 5.91 × 106 M-1. Izibalo ze-DFT zenziwa kuma-ligands kanye nama-chelates ensimbi ukuze kunqunywe izakhiwo ezilungiselelwe i-geometry-optimized, amaza okudlidliza, amashifu amakhemikhali e-1H kanye ne-13C NMR kanye nokuguqulwa kwe-electronic. Izinga le-B3LYP/6-311G (d,p) lethiyori lasetshenziselwa ama-ligands, ithusi(II) ne-nickel(II) chelates kanye nezinga le-B3LYP/LanL2DZ lethiyori ye-palladium(II) chelates. Indlela ye-TD-DFT isetshenziselwe izibalo zamandla. Imiphumela yokuhlolwa nebaliwe yaqhathaniswa lapho kwenzeka khona, futhi kwatholakala ukuhlobana okunengqondo. I-cytotoxicity yama-chelates amahlanu asekelwe ku-amide yahlolwa ngokumelene nemigqa yeseli yomdlavuza wabantu emine, okuyi-A549, TK-10, HT29 kanye ne-U251, kusetshenziswa i-MTT assay. Ama-chelate ahloliwe abonise umsebenzi omuhle wokulwa nomdlavuza ngamavelu amaphakathi we-IC50 ngokumelene nemigqa yeseli yomdlavuza emine kusukela ku-ca. 12 kuya ku-35 μM. Okubalulekile, kwatholakala ukuthi inhlanganisela ye-ion yethusi (II) kanye ne-ligand yayibalulekile ekuthuthukisweni kwe-cytotoxicity. Inkimbinkimbi [Cu(L1)] (PF6) ikhonjwe njengekhandidethi yesidakamizwa esihamba phambili ngokususelwe ekuhlobaneni okuphezulu kwe-DNA kanye ne-cytotoxicity. Le nhlanganisela ibiyi-cytotoxic kakhulu ibheke kumugqa weseli we-glioblastoma u-U251 onenani le-IC50 elingu-7.59 μM. Ama-chelate asekelwe ku-imine ahlolelwa imigqa emithathu yamangqamuzana omdlavuza womuntu: i-MDA-MB, i-HELA, ne-SHSY5Y, kanye nolayini wamaseli womuntu onempilo, i-HEK293. Inkomba yokukhetha yalawa ma-chelates we-neoplastic ngokumelene nomugqa weseli onempilo ibaliwe. Ama-chelates asekelwe ku-imine abonise ukukhetha okuphezulu kumdlavuza webele we-triple-negative MDA-MB, ukuhleleka kobukhulu obunobuthi obuningi engqamuzaneni yesimila kunaleyo enempilo. Le nkomba yokukhetha ithuthukiswe kakhulu kune-cisplatin. Ukuhlolwa kwe-gel mobility shift assay kuphenye ukusebenzisana phakathi kwe-copper(II) chelates ne-plasmid DNA. I-in vivo biodistribution ye-[Cu(L1)](PF6) inqunywe kusetshenziswa i-analogue ene-radiolabelled ye-copper-64 ye-[Cu(L1)]Cl ne-microPET-CT scanning. Ucwaningo lokuqala lwe-biodistribution luphakamise ukuthi inkimbinkimbi inokusimama okuhle kwe-serum futhi yabonisa ukuthi kwakungekho ukuqoqwa okuphawulekayo kunoma yiziphi izitho. Ucwaningo olwalandela lwaluhilela imodeli ye-xenograft esebenzisa ulayini weseli we-A549 futhi lwabonisa ukuthatheka okubalulekile nokugcinwa kwenkimbinkimbi esimilanjeni. I-cytotoxicity ye-chelate lapho ihlanganiswa nama-isotopes ethusi angewona ama-radioactive kanye nokuthathwa kwe-radiolabelled okulingana nemodeli yesimila kuphakamisa ukuthi le nkimbinkimbi ingaba nesicelo "njenge-ejenti yokwelapha".
Design and fabrication of tissue-like phantoms for use in biomedical imaging.
(2022) Ntombela, Lindokuhle Charles.; Chetty, Naven.; Adeleye, Bamise.
The continuous need for tissue-like samples to understand biological systems and the development of new diagnostic and therapeutic applications has led to the adoption of tissue models using potential materials. This work presents a low-cost method for manufacturing PVAslime glue-based phantoms to replicate diseased and healthy biological tissues’ optical, mechanical, and structural properties. The deformable phantoms with complex geometries are vital to model tissues’ anatomic shapes and chemical composition. Absorption and scattering properties were set by adding black India ink and aluminium oxide (Al2O3) particles in varying quantities to obtain slime phantom tissues with optical properties of the brain, malignant brain tumour, lung carcinoma, and post-menopausal uterus. The phantom properties were characterized and validated using a He-Ne laser emitting at 532 nm and 630 nm wavelengths propagated through various thicknesses of the fabricated phantom. The incident and transmitted intensity were measured to determine the absorption coefficient (a) and scattering coefficient (s). Furthermore, the effective attenuation coefficient (eff ) and penetration depth () were deduced from the reduced scattering coefficient (0s) and the anisotropy factor (g) obtained through the scattering phase function and Wolfram Mathematica. The anisotropy factor demonstrated a forward scatter, typical of strongly scattering media as real tissues. Such geometrically and optically realistic phantoms would function as effective tools for developing techniques in diagnostic and therapeutic applications such as laser ablation and PDT cancer treatment.
Determination of neonicotinoid insecticides in water, soil and sediment samples: acute and chronic risk assessment.
(2022) Ngomane, Nkosinathi Chris.; Mahlambi, Precious Nokwethemba.
Neonicotinoids are a type of insecticides pesticides widely used worldwide as a result of their low vertebrates toxicity, relative environmental stabilities, good bioavailability and high level of selectiveness. These insecticides are commonly employed in agricultural activities, in grass management and horticulture as well as in households to control domestic pet flea. Due to neonicotinoids intensive usage, they are continuously introduced to the water bodies where they can adversely affect the aquatic life and accumulate in sediments. Moreover, they can end up in drinking and unintentionally consumed by human beings resulting to health effects. With this regard, this work reports for the first time on the occurrence of neonicotinoids in sediment, soil tap, sludge, wastewater and river water samples from the province of KwaZulu-Natal. Also, the ecological risk of neonicotinoids in water sources was also assessed for the first time in the samples from this province.The liquid chromatography coupled with a photo-diode array detector (LC-PDA) method was modified and applied for the simultaneous detection of neonicotinoids (clothianidin, thiamethoxam and imidacloprid). Ultrasonic extraction (UE), soxhlet extraction (SE) and solid-phase extraction (SPE) methods were developed and applied for the extraction of nitro-guanidine neonicotinoids in water, soil and sediment samples. The SPE, SE, and UE parameters that influence the recoveries of the analytes were first optimized before application to real samples for the analytes recovery improvement. The SPE was used for the extraction of neonicotinoids in sludge and water samples, while SE and UE were both used to extract soil and sediment samples. The extraction conditions optimized for SPE were conditioning solvent and sample volume. While for the UE were extraction time, extraction solvent, and the solvent volume. And for SE method, extraction solvent and the extraction solvent volume were optimized. The LC-PDA method used for detection was also first optimized to improve peak separation, retention times, detection limits and quantification limits. The optimized parameters for the LC-PDA method were the mobile phase, flow rate, and the PDA detection wavelength. Optimum water recoveries of the neonicotinoids ranged from 79 to 112%. The detection and quantification limits of the analytes in water samples were 0.013 - 0.031 μg/L and 0.041 - 0.099 μg/L, respectively. The obtained analytes concentration ranged from 0.061 - 0.10 μg/L, 0.077- 3.76 μg/L and 0.99 - 15 μg/L in tap, river and wastewater, respectively. Analyte recoveries ranged from 85 - 102% in soil and 92 - 103% in sediment for the ultrasonic extraction method. The neonicotinoid recoveries ranged from 83 to 109% in soil and between 84 to 94% in sediment samples for the Soxhlet extraction method. The method’s detection limits and quantification limits in solid samples ranged from 40 - 80 μg/kg and 140 - 270 μg/kg, respectively. The relative standard deviation was less than 4%. The concentration determined in real environmental samples were 47 to 410 μg/kg in soil and 25 to 410 in sediment. The toxicity studies showed that clothianadin pose a high risk towards daphnia species in the river. Imidacloprid, clothianidin and thiamethoxam posed medium risk against algae, daphnia and fish species in the effluent receiving water bodies. These results imply the necessity to continuously monitor these neonicotinoids in the water sources. In South Africa there is limited data concerning the environmental occurrence of neonicotinoids, therefore this work will contribute towards the information available for the analysis of neonicotinoids. This will assist the policy makers to establish the MRL values that are precise for the African continent.
Diffuse radio emission in ACTPol clusters.
(2021) Sikhosana, Sinenhlanhla Precious.; Moodley, Kavilan.; Knowles, Kenda Leigh.; Hilton, Matthew James.
Low-frequency radio observations of galaxy clusters reveal cluster-scale diffuse emission that is not associated with individual galaxies. Studying the properties of these diffuse radio sources gives insight into astrophysical processes such as cosmic ray transportation in the intracluster medium (ICM). Observations have linked the formation of radio halos and relics with turbulence caused by cluster mergers and the formation of mini-halos to gas sloshing in cool-core clusters. Statistical studies of large galaxy cluster samples have been used to determine how the radio properties of diffuse emission scale with the mass and X-ray luminosity of the host clusters. Such studies are crucial for refining the formation theories of diffuse emission. New generation telescopes with wide bandwidths and high sensitivity such as the upgraded Giant Metrewave Radio Telescope (uGMRT) andMeerKAT are advantageous for the study of faint extended emission in large cluster samples. The main aim of this thesis was to do an in-depth study of the diffuse radio emission using a cluster sample that spans a wider mass and redshift range compared to the currently studied parameter space. We developed data reduction techniques for calibrating data from telescopes such as uGMRT and MeerKAT. The wide bandwidth of these telescopes introduces directional dependent effects (DDEs) that make the calibration process extremely complicated. However, such observations are excellent for studies of the faint diffuse emission and in-band spectral indices of this emission. In the first part of this thesis, we focused on the study of diffuse radio emission in a Sunyaev- Zeldovich (SZ) selected sample of clusters. These clusters were observed by the Atacama Cosmology Telescope’s Polarimetric extension (ACTPol). We used archival and new GMRT observations for the radio analysis of this sample. We reported newly detected diffuse emission in the following clusters: a radio halo and revived fossil plasma in ACT-CL J0137.4 0827, a radio relic in ACT-CL J2128.4+0135, and a candidate relic in ACT-CL J0022.2 0036. The radio analysis of the full sample revealed that the fraction of clusters in the sample hosting diffuse emission is 26.7% excluding candidate emission and 30% when it is included. The detection rate of the diffuse emission over all categories is lower than the detection rates reported in literature. We note that this may be because the sample comprised high redshift (z ¡ 0.5) and low mass clusters (M500c;SZ 5 1014 Md), though future more sensitive observations of these clusters could reveal fainter diffuse emission structures. We compared our results to the most recent radio halo and radio relic scaling relations. The radio halo P1:4GHz M500 scaling relation plot indicates that a few flatter spectrum radio halos are located in the region previously known to be populated by ultrasteep spectrum radio halos (USSRHs). Finally, we presented preliminary results of the uGMRT wideband backend (GWB) data reduction for ACT-CL J0034.4+0225, ACT-CL J0137.4 0827, and ACT-CL J2128.4+0135. We prioritised these clusters because the narrowband data revealed that they host diffuse emission. However, once the data reduction algorithm is improved, we will reduce the remaining clusters with non-detections. Comparing the GWB results to the narrowband GMRT data, we note that the radio halo observed in ACT-CL J0137.4 0827 is more extended in the GWB data. The diffuse emission is detected at a higher signal-to-noise ratio in the GWB images for the three clusters. We note that an improvement in the GWB reduction algorithm might reveal diffuse emission that was not detected in the narrowband data. In the second part of the thesis, we used MeerKAT observations to study diffuse emission in the Bullet Cluster (1E0657 56), RXCJ1314.4 2515, Abell 3562, and Abell 3558. We detected new extended features in the radio halos hosted by the Bullet cluster and Abell 3562. We assume that the decrement feature in the Bullet cluster might be an indication of a second wave of merger activity. The ridge feature in the peripheral region of the radio halo in Abell 3562 overlaps with the edge of the X-ray emission. Hence, we assume that the feature might be related to a shock region. We also reported the detection of a new mini-halo in Abell 3558. MeerKAT’s sensitivity and wide bandwidth enabled us to perform in-band spectral index studies and produce spectral index maps for the Bullet cluster, RXCJ1314.4 2515, and Abell 3562. The spectral index maps of the relics in the Bullet cluster and RXCJ1314.4 2515 indicate a spectral steepening towards the cluster center, while the spectral index map of the radio halo in the Bullet cluster indicates radial spectral steepening. The spectral index map of Abell 3562 indicates that the radio halo and ridge have similar spectral index variations, which suggests that the ridge feature is related to the radio halo.
Discrete time-to-event construction for multiple recurrent state transitions.
(2023) Batidzirai, Jesca Mercy.; Manda, Samuel.; Mwambi, Henry Godwell.
Recent developments in multi-state models have considered discrete time rather than continuous time in the modeling of transition intensities, whose major drawback lies in the possibility of resulting in biased parameter estimates that arise from issues of handling ties. Discrete-time models have included univariate multilevel models to account for possible dependence among specific pairwise recurrent transitions within the same subject. However, in most cases, there would be several specific pairwise transitions of interest. In such cases, there is a need to model the transitions with the aim of identifying those transitions that are correlated. This provides insight into how the transitions are related to each other. In order to investigate the interdependencies between transitions, the unique contribution of this thesis is to propose a multivariate discrete-time multi-state model with multiple state transitions. In this model, each specific recurrent transition is associated with a random effect to capture possible dependence in the transitions of the same type or different types. The random effects themselves were then modeled by a multivariate normal distribution and model parameters were estimated using maximum likelihood methods with Gaussian quadratures numerical integration. A simulation study was done to evaluate the performance of the proposed model. The model yielded satisfactory results for most fixed effects and random effects estimates. This is noticed by near-zero biases and mean square errors of the average estimates as well as high 95% coverage probabilities of the 95% confidence intervals from 1000 replications. The proposed methodology was applied to marriage formation and dissolution data from KwaZulu-Natal province, South Africa. Five transitions were considered, namely: Never Married to Married, Married to Separated, Married to Widowed, Separated to Married and Widowed to Married. The presence of very small unobserved subject-to subject heterogeneity for each transition and a weak positive correlation between transitions were produced. Statistically, the model produced smaller standard errors compared to those from univariate models, hence it is more precise on estimates. The multivariate modeling of discrete time-to-event models provides a better understanding of the evolution of all transitions simultaneously, thus in addition to covariate effects, giving an assessment of how one transition is associated with the other. Empirical results confirmed well known important socio-demographic predictors of entering and exiting a marriage. Age at sexual debut played a positive critical role in most of the transitions. More educated subjects were associated with a lower likelihood of entering a first marriage, experiencing a marital dissolution as well as remarrying after widowhood. Subjects who had a sexual debut at younger ages were more likely to experience a marital dissolution than those who started late. Age at first marriage had a negative association with marital dissolution. We may, therefore, postulate that existing programs that encourage delay in onset of sexual activity for HIV risk reduction for example, may also have a positive impact on lowering rates of marital dissolution, thus ultimately improving psychological and physical health. Iqoqa. Ukuthuthuka kwakamuva amamodeli azigaba ziningi sekubuke isikhathi esahlukene kunesikhathi esiqhubekayo ekukhombiseni ukuqina koguquko, ukubuyiseleka emuva okuncike kokungenzeka kokuphumelelisa izihlawumbiselo zomkhawuko ochemile eziphuma emsebenzini wokuphatha izihlanganiso. Amamodeli ezikhathi ezahlukene afake kuwo izigaba eziningi zoguquko ngokukodwa, ukubhekana nokungenzeka okuncika phakathi kokuthile okwenzeka ngakubili kwenqubeko yoguquko phakathi kokucwaningwayo ngakunye. Yize noma kunjalo, ezikhathini eziningi, kungaba nezinguquko ezithile zokwenzeka ngakubili. Kulezo zimo, kunesidingo ukukhombisa izinguquko ngenhloso yokuveza lezo zinguquko ezinokuhlobana. Lokhu kunikeza isibonakaliso phakathi ekutheni izinguquko zihlobene ngayinye nenye. Ukuze kuphenywe ukuncikana okungaphakathi phakathi kwezinguquko, umnikelo oqavile walo mqingombhalo ukuphakamisa imodeli yokuguquka okuningi kwezikhathi ezahlukene kwezigaba eziningi kanye nezigaba eziningi zezinguquko. Kule modeli, inguquko eqhubekayo ngayodwa ihlobene nomthelela ozenzakalelayo ukuqopha ukuncika okungenzela ezinguqukweni zohlobo olufanayo noma izinhlobo ezahlukene. Imithelela ezenzakalelayo yona qobo ikhonjiswe ngokusatshalaliswa okwejwayelekile koguquko oluningi futhi imikhawuko yemodeli yahlawumbiselwa ngokusebenzisa izindlela zokungenzeka okukhulu ngenhlanganisela yezibalo kaGaussian. Ucwaningo lwesingakwenza lwenziwa ukuhlola ukusebenza kwemodeli ephakanyisiwe. Imodeli yaveza imiphumela eyanelisayo ngemithelela enganyakazi eminingi kanye nezihlawumbiselo zemithelela ezihamba zivela. Lokhu kuqapheleka ngokungabikho nhlobo kokuchema kanye namaphutha esikwele senanimaphakathi eliwumhlawumbiselo ophakathi nendawo kanye namathuba atholakalayo aphezulu ngama-95% ezikhawu zobufakazi bama-95% ekuphindaphindekeni okuyi-1000. Indlelakwenza ephakanyisiwe isetshenzisiwe esakhiweni somshado kanye nesiphetho seminingo esiFundazwenzi saKwaZulu-Natali, eNingizimu Afrika. Izinguquko eziyisihlanu zabhekwa, nokuyilezi: Ungalinge Ushade noShadile, Ushade noHlukanisile, Ushade Nowashonelwa, Wehlukanise noShadile futhi Ushonele oShadile. Ubukhona bokuncane okungabukwana komhlanganyeli nomhlanganyeli wenhlanganiso yokungafani yoguquko ngalunye kanye nokuhlobana obuhle obusezingeni eliphansi phakathi kwezinguquko kwakhiqizwa. Ngokwezibalomidanti, imodeli yakhiqiza amaphutha ajwayelekile amancane uma kuqhathaniswa nalawo amamodeli oshintsho ngokukodwa, yize khona kuqondile ezihlawumbiselweni. Ukulinganisa amamodeli okushintsha kaningi kwezikhathi ezahlukene kuya esigamekweni kunikeza ukuqonda okungcono kokuqalisa zonke izinguquko ezenzeka kanye kanye, ngakho-ke, okunye futhi phezu kwemithelela yoshintsho oluncike kokunye, kunika ukuhlola kokuthi inguquko eyodwa ihlobene kanjani nenye. Imiphumela ebambekayo yaqinisekisa izinkomba zomphakathi nezibalo zabantu ezaziwa kakhulu futhi ezibalulekile zokungena futhi ukuphuma emshadweni. Ubudala ekuqaliseni ukuhlangana ngocansi kwaba nomthelela omkhulu ezinguqukweni eziningi. Abahlanganyeli abafunde kakhulu bahlotshaniswa nezinga eliphansi lokungakwazi ukungena emshadweni wokuqala, ngokuzithola beqeda umshado kanye nokubuye bashade futhi emva kokushonelwa. Abahlanganyeli abaqalisa ukuhlangana ngokocansi ebuncaneni babo batholakala bengasheshe bawuqede umshado kunalabo abaqala sekuhambe isikhathi. Ubudala emshadweni wokuqala kwakunokuhlobana okungekuhle nokuqeda umshado. Ngakho-ke, kungabekwa ngokuyiqiniso ukuthi izinhlelo ezikhona ezigqugquzela ukuthatha isikhathi ukuqalisa ukuzibandakanya ocansini ukwehlisa ubungozi be-HIV ngokwesibonelo, kungaba nomthelela omuhle ukwehlisa amazinga okuqeda umshado, ngakho-ke, kuthuthukise ngokushesha impilo yezengqondo kanye neyomzimba.
Exploration of ear biometrics with deep learning.
(2024) Booysens, Aimee Anne.; Viriri, Serestina.
Biometrics is the recognition of a human using biometric characteristics for identification, which may be physiological or behavioural. Numerous models have been proposed to distinguish biometric traits used in multiple applications, such as forensic investigations and security systems. With the COVID-19 pandemic, facial recognition systems failed due to users wearing masks; however, human ear recognition proved more suitable as it is visible. This thesis explores efficient deep learning-based models for accurate ear biometrics recognition. The ears were extracted and identified from 2D profiles and facial images, focusing on both left and right ears. With the numerous datasets used, with particular mention of BEAR, EarVN1.0, IIT, ITWE and AWE databases. Many machine learning techniques were explored, such as Naïve Bayes, Decision Tree, K-Nearest Neighbor, and innovative deep learning techniques: Transformer Network Architecture, Lightweight Deep Learning with Model Compression and EfficientNet. The experimental results showed that the Transformer Network achieved a high accuracy of 92.60% and 92.56% with epochs of 50 and 90, respectively. The proposed ReducedFireNet Model reduces the input size and increases computation time, but it detects more robust ear features. The EfficientNet variant B8 achieved a classification accuracy of 98.45%. The results achieved are more significant than those of other works, with the highest achieved being 98.00%. The overall results showed that deep learning models can improve ear biometrics recognition when both ears are computed.
Exploring the structure activity relationship of antiplasmodial compounds identified from the MMV Pathogen Box.
(2024) Mafuleka, Sean Manqoba.; Sithebe, Siphamandla.; Veale, Clinton Gareth Lancaster.
Over 200 million new infections are caused by malaria-causing plasmodium species. This results in over 500 000 annual deaths. These deaths are mostly young children under the age of five years. As there is an emergence of resistance to primitive first-line treatments, there is an increasing need for the development of new targets with novel scaffolds. For such advancements, we have to consider the structure-activity of antiplasmodial compounds. The Pathogen Box is a concept modelled on the Malaria Box, except the 400 drug-like compounds it contains are a diverse range of compounds which are active against numerous neglected diseases of interest, and is readily accessible. It unpacks 125 compounds of antiplasmodial activity, a lot of which have been identified from phenotypic screening of the GSK Tres Cantos Anti-Malarial Set (TCAMS). Upon request, select researchers around the globe receive a set of compounds from the Pathogen Box to help in the advancements towards neglected disease drug discovery. In turn, the researchers are requested to present, in the public domain, any data they will have generated in their work within two years. This presents an opportunity for a collaborative space for neglected disease drug research. In this project, compound MMV023227 was found to have promising antiplasmodial activity. We have therefore designed and synthesized some analogues of the hit compound with the purpose of identifying an SAR. We initiated the synthesis of the designed analogues of compound MMV023227 by successfully synthesising the three imidazole compounds that are 2-(3-bromophenyl)-4,5-dimethyl-1H-imidazole (3.1), 2-(3-bromophenyl)-4-methyl-1H-imidazole (3.2), and 2-(3-bromophenyl)-1H-imidazole (3.3) in yields between 33 – 42 %. We moved these compounds towards the desired final compounds through several stages, but we could only go as far as producing compounds N-(2-chlorobenzyl)-3-(1-(ethoxymethyl)-1H-imidazol-2-yl)aniline (3.9), N-(2 chlorobenzyl)-3-(1-(ethoxymethyl)-4-methyl-1H-imidazol-2-yl)aniline (3.10) and N-(2-chlorobenzyl)-3-(1-(ethoxymethyl)-5-methyl-1H-imidazol-2-yl)aniline (3.11) in yields between 7 – 41 %.
Extraction of pesticides using selected analytical methods from soil and maize segments : cumulative and health risks assessment.
(2024) Zondo, Sandisiwe Gladness.; Mahlambi, Precious Nokwethemba.
Increased agricultural operations result in increased usage of various pesticides to safeguard agricultural crops, however this is done without paying attention to the effects of the amounting potential harm both humans and the environment are exposed to. In this present study, a structured study was conducted to investigate the uptake of atrazine, mesotrione, 2,4- dichlorophenoxyacetic acid and glyphosate herbicides from contaminated soil and their translocation into different maize segments. Soil profile and quality of irrigation water were also assessed as they are crucial resources required in agricultural crop production due to their ability to influence the yield and quality of the agricultural products. Various physicochemical parameters were measured in an attempt to monitor the soil profile, irrigation water and maize quality harvested from Buhle farm located in Howick, KwaZulu-Natal Province. The irrigation water physicochemical parameters considered were the pH, electrical conductivity, alkalinity and chloride concentration. The soil physicochemical parameters considered were moisture content, pH, electrical conductivity, texture, total nitrogen as well Mg, Na, K, Zn, Mn, P and N elements. Maize was analysed for nutrition content and medicinal health promoting compounds. Based on the attained results, the soil texture contained high clay content (56.4%), followed by sand (40.6%) and silt (2.98%). The concentrations for total nitrogen, phosphorus and potassium which were translated to high soil fertility were 2700, 19 and 222 mg L-1, respectively. These particular elements are essential for agricultural plantation processes and consequently maize quality and maize yield. The levels of sodium, sodium adsorption ratio and electrical conductivity found in irrigation water were 0.05 mg L-1, 2 and 1.81 μS m-1, respectively. The findings showed that maize harvested from Buhle farm had high starch content of 58.6%. Fibre, protein and fat contents in maize were 23.4, 9.01 and 4.55%, respectively. Furthermore, the total anthocyanin, total flavonoids and total phenolic acid content were 8.5, 49.5 and 100 mg L-1, respectively. High amounts of phenolic acid detected indicated therapeutic ability of the maize since phenolic acids are essential for cancer prevention to the consumer. The presence of anthocyanin, flavonols and phenolic acids in maize crop is associated with its quality that could benefit livestock and human after consumption. The analysis of herbicides in soil and maize samples require sample pre-treatment due to their low concentration and complex matrix hence an ultrasonic extraction, microwave-assisted extraction (MAE), Soxhlet extraction (SE) and QuEChERS methods were investigated. The optimization and application of ultrasonic extraction, MAE, SE and QuEChERS methods were conducted for the effective extraction of pesticides from maize and their corresponding soil samples. The analysis of pesticides (atrazine, glyphosate, 2,4-dichlorophenoxyacetic acid and mesotrione) was done with gas chromatography-flame ionization detector. Factors influencing the efficiency of the extraction methods such as the extraction solvent, extraction time, solvent volume, sample wetting and spiking concentration were assessed. Under the optimum experimental conditions, the relative standard deviation (RSD), coefficient of determination (R2), limit of detection (LOD), limit of quantification (LOQ), and percentage recoveries were the quantitative characteristics of the current methods assessed. All calibration curves showed a high correlation coefficient (R2) ≥0.996, indicating good linearity. The LODs and LOQs ranged between 0.22-0.32 μg L-1 and 2.0-2.9 μg L-1 for SE , 0.1-0.25 μg L-1 and 1.1-2.2 μg L- 1 for MAE, 0.02 – 0.15 μg L-1 and 0.2 - 0.5 μg L-1 for UE and 0.01 – 0.23 μg L-1 and 0.13 – 0.8 μg L-1 for QuEChERS. The maize and soil analytes recoveries for SE, MAE, EU and QuEChERS ranged between 62-80% and 70-81%, 80-98% and 85-101%, 100-104% and 91- 97 % and 94-115% and 92-101%, respectively with the repeatability, articulated as RSD values of which are within the acceptable range as they are lower than 20%. MAE method showed higher sensitivity compared to SE while, UE and QuEChERS both showed high sensitivity for the extraction and quantification of the target analytes at low concentrations found in soil and maize cob. It was observed that 2,4-dichlorophenoxyacetic acid (2.4-D) was least absorbed by the soil, however, all the studied herbicides showed high absorption in the leafy segment of the maize plant due to the high polarity of the leaf cuticle. Glyphosate showed high absorption rate in soil, roots, stalk and leaves while mesotrione was highly absorbed in corn and tassels in all treatments. The absorption rate of analyte increased with increasing growth days. The higher treatment concentration (0.75 g L-1) showed elevated accumulation with the highest concentration (1.02 μg L-1) observed for glyphosate in leaves after 140 days and high mesotrione in corn (0.51 μg L-1) and tassel (0.42 μg L-1) observed after 120 days. Even though all maize treatment showed a pesticide toxicity index (PTI) values of <1, the health risk index (HI) data were below 100% threshold as well indicating no possible health risk linked with the intake of these crops by both adults and children.
Financial modelling of cryptocurrency: a case study of Bitcoin, Ethereum, and Dogecoin in comparison with JSE stock returns.
(2022) Kaseke, Forbes.; Ramroop, Shaun.; Mwambi, Henry Godwell.
The emergency of cryptocurrency has caused a shift in the financial markets. Although it was created as a currency for exchange, cryptocurrency has been shown to be an asset, with investors seeking to profit from it rather than using it as a medium of exchange. Despite being a financial asset, cryptocurrency has distinct, stylised facts like any other asset. Studying these stylised facts allows the creation of better-suited models to assist investors in making better data-driven decisions. The data used in this thesis was of three leading cryptocurrencies: Bitcoin, Ethereum, and Dogecoin and the Johannesburg Stock Exchange (JSE) data as a guide for comparison. The sample period was from 18 September 2017 to 27 May 2021. The goal was to research the stylised facts of cryptocurrencies and then create models that capture these stylised facts. The study developed risk-quantifying models for cryptocurrencies. The main findings were that cryptocurrency exhibits stylised facts that are well-known in financial data. However, the magnitude and frequency of these stylised facts tend to differ. For example, cryptocurrency is more volatile than stock returns. The volatility also tends to be more persistent than in stocks. The study also finds that cryptocurrency has a reverse leverage effect as opposed to the normal one, where past negative returns increase volatility more than past positive returns. The study also developed a hybrid GARCH model using the extreme value theorem for quantifying cryptocurrency risk. The results showed that the GJR-GARCH with GDP innovations could be used as an alternative model to calculate the VaR. The volatile nature of cryptocurrency was also compared with that of the JSE while accounting for structural breaks and while not accounting for them. The results showed that the cryptocurrencies’ volatility patterns are similar but differ from those of the JSE. The cryptocurrency was also found to be an inefficient market. This finding means that some investors can take advantage of this inefficiency. The study also revealed that structural breaks affect volatility persistence. However, this persistence measure differs depending on the model used. Markov switching GARCH models were used to strengthen the structural break findings. The results showed that two-regime models outperform single-regime models. The VAR and DCC-GARCH models were also used to test the spillovers amongst the assets used. The results showed short-run spillovers from Bitcoin to Ethereum and long-run spillovers based on the DCC-GARCH. Lastly, factors affecting cryptocurrency adoption were discussed. The main reasons affecting mass adoption are the complexity that comes with the use of cryptocurrency and its high volatility. This study was critical as it gives investors an understanding of the nature and behaviour of cryptocurrency so that they know when and how to invest. It also helps policymakers and financial institutions decide how to treat or use cryptocurrency within the economy.
Flexible Bayesian hierarchical spatial modeling in disease mapping.
(2022) Ayalew, Kassahun Abere.; Manda, Samuel.
The Gaussian Intrinsic Conditional Autoregressive (ICAR) spatial model, which usually has two components, namely an ICAR for spatial smoothing and standard random effects for non-spatial heterogeneity, is used to estimate spatial distributions of disease risks. The normality assumption in this model may not always be correct and misspecification of the distribution of random effects could result in biased estimation of the spatial distribution of disease risk, which could lead to misleading conclusions and policy recommendations. Limited research studies have been done where the estimation of the spatial distributions of diseases under the ICAR-normal model were compared to those obtained from fitting ICAR-nonnormal model. The results from these studies indicated that the ICAR-nonnormal models performed better than the ICAR-normal in terms of accuracy, efficiency and predictive capacity. However, these efforts have not fully addressed the effect on the estimation of spatial distributions under flexible specification of ICAR models in disease mapping. The overall aim of this PhD thesis was to develop approaches that relax the normality assumption that is often used in modeling and fitting of ICAR models in the estimation of spatial patterns of diseases. In particular, the thesis considered the skewnormal and skew-Laplace distributions under the univariate, and skew-normal for the multivariate specifications to estimate the spatial distributions of either univariable or multivariable areal data. The thesis also considered non-parametric specification of the multivariate spatial effects in the ICAR model, which is a novel extension of an earlier work. The estimation of the models was done using Bayesian statistical approaches. The performances of our suggested alternatives to the ICAR-normal model were evaluated by simulating studies as well as with practical application to the estimation of district-level distribution of HIV prevalence and treatment coverage using health survey data in South Africa. Results from the simulation studies and analysis of real data demonstrated that our approaches performed better in the prediction of spatial distributions for univariable and multivariable areal data in disease mapping approaches. This PhD has shown the limitations of relying on the ICAR-normal model for the estimations of spatial distributions for all spatial analyses, even when the data could be asymmetric and non-normal. In such scenarios, skewed-ICAR and nonparametric ICAR approaches could provide better and unbiased estimation of the spatial pattern of diseases.

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