https://ir.unisa.ac.za/handle/10500/130 2026-06-11T04:46:47Z 2026-06-11T04:46:47Z Matlakala, Icaboth Tshwarelo https://ir.unisa.ac.za/handle/10500/32573 2026-06-04T10:04:28Z 2025-11-28T00:00:00Z

Assessing the impact of landfill sites on soil and water quality in neighbouring communities : a case study of Ga-Rankuwa Township, Gauteng Province of South Africa Matlakala, Icaboth Tshwarelo Globally, solid waste generation is increasing due to accelerated population growth, rapid urbanisation, and economic activities. Consequently, the inadequate, absent, and poor planning and implementation of waste management in Ga-Rankuwa results in more waste in landfills. Although landfills carry a huge amount of waste, unmanaged waste can cause an increase in greenhouse gases, a reduction in the aesthetic of the environment, and untreated leachate pollutes surrounding water and soils, especially when proper procedures and maintenance are not followed. Regardless of how much research and awareness are done on this waste management method, environmental contamination is still evident due to inadequate/improper monitoring. The study aimed to assess the environmental impacts of the landfill site on soil and water quality in Ga-Rankuwa township. Soil samples were collected at the landfill perimeter in all 4 cardinal points and at 1 km and 2 km away from the 1st point, etcetera. Heavy metals, including Chromium, Mercury, and Lead, were assessed per sample for possible contamination by the landfill. Water samples were collected by directly dipping the containers in the nearby stream to collect water samples (upper river, mid river and lower river sections). The samples were taken to a laboratory for analysis, and further statistical tests and indices were used, and further analysis was conducted using XLSTAT. The water salinity and pollution indicators were assessed using pH, major cations (Na+, K+, Ca2+), phosphorus and heavy metals in soil and river samples near a landfill. Results showed significant variability in soil pH, with more acidic conditions closer to the landfill, likely due to leachate migration. Although heavy metal concentrations were elevated at certain sites, all values remained below WHO permissible limits and were classified as uncontaminated according to Müller's Geo-accumulation Index. River water showed slightly alkaline conditions, however, nutrient enrichment led to poor water quality. Trophic State Index (TSI) values for phosphorus exceeded 100 across all sites, classifying the river as hypereutrophic and identifying it as a pollution hotspot. While sodium values remained within acceptable limits for irrigation, microbial analysis revealed elevated amounts of E. coli and total coliforms downstream, indicating potential public health risks. It is recommended that landfill operators adopt advanced engineering solutions, such as state-of-the-art leachate collection and treatment systems, to minimise the release of harmful contaminants into adjacent soil and water bodies. Furthermore, the integration of waste segregation at source and comprehensive recycling programs should be prioritised to reduce the volume of non-biodegradable and hazardous waste entering landfills. vi

2025-11-28T00:00:00Z Oku, Deli-Bright Nii Tettey https://ir.unisa.ac.za/handle/10500/32572 2026-06-04T09:48:31Z 2025-09-01T00:00:00Z

LC–MS/MS profiling and machine learning–guided virtual screening of dual EGFR/HER2 inhibitors from Ceratonia siliqua L. pod extract for colorectal cancer Oku, Deli-Bright Nii Tettey Colorectal cancer (CRC), a malignancy that develops in the colon or rectum, is frequently associated with epidermal growth factor receptor (EGFR) overexpression, observed in up to 85% of cases, whereas human epidermal growth factor receptor 2 (HER2) amplification or overexpression is present in a smaller subset (approximately 2–6%, with increased prevalence in selected molecular subgroups). These molecular alterations highlight the therapeutic relevance of targeting EGFR and HER2 pathways in CRC management. Despite progress in targeted therapy development, most existing treatment approaches focus on inhibiting either EGFR or HER2 individually. These single-target therapies frequently demonstrate reduced efficacy because of alterations in downstream effectors such as the Kirsten rat sarcoma viral oncogene homolog (KRAS) and the activation of alternative signalling pathways that promote continued tumour growth. Consequently, designing therapeutic approaches that can concurrently block both EGFR and HER2 represents an essential and promising area of research. In this research, an innovative machine learning (ML)-driven stacking ensemble framework was established to accurately identify dual EGFR and HER2 inhibitors based on Simplified Molecular-Input Line-Entry System (SMILES) representations. A comprehensive benchmark dataset comprising active and inactive compounds targeting EGFR and HER2 was compiled from the ChEMBL database. Utilising this dataset, forty baseline models were developed and fine-tuned using various molecular descriptors and ML algorithms. The predictions from these models were then integrated through logistic regression (LR) to produce a highly reliable stacking ensemble classifier. This predictive model was further applied to natural bioactive compounds obtained from liquid chromatography–tandem mass spectrometry (LC–MS/MS) profiling of Ceratonia siliqua L. pod 9 extract, which were annotated using established spectral libraries and subsequently subjected to machine learning–guided virtual screening. The cytotoxic activity of the Ceratonia siliqua L. pod extract was confirmed experimentally using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide) assay against human colorectal carcinoma cell line (HCT116) and non-cancerous Vero cells. The extract exhibited an IC₅₀ (half maximal inhibitory concentration) value of 13.32 ± 1.09 μg/mL in HCT116 cells, underscoring its notable anti-cancer potential. To support the experimental outcomes, molecular docking and in silico ADMET (absorption, distribution, metabolism, excretion, and toxicity) evaluations were carried out on the compounds identified from the LC–MS/MS dataset using the stacking model, alongside four Food and Drug Administration (FDA) approved anticancer drugs for comparative analysis. Among all screened molecules, NCGC00385704-01, identified from LC–MS/MS spectral data through library matching, exhibited strong dual inhibitory potential against both EGFR and HER2. Overall, this study highlights Ceratonia siliqua L. as a valuable source of potential lead molecules for colorectal cancer therapy through dual EGFR/HER2 inhibition and underscores the power of integrating computational and experimental approaches in natural product-based drug discovery.

2025-09-01T00:00:00Z Strauss, Kay-Lee Elrechia https://ir.unisa.ac.za/handle/10500/32570 2026-06-02T09:55:37Z 2026-10-30T00:00:00Z

Comparative impact of antiretroviral therapy on liver function among HIV infected pregnant women with and those without pre-eclampsia in South Africa Strauss, Kay-Lee Elrechia Background: Pregnant women living with HIV (PWLWHIV) are at increased risk of adverse pregnancy outcomes, including pre-eclampsia (PE), a multisystem hypertensive disorder characterised by endothelial dysfunction, systemic inflammation, and organ damage. Both HIV infection and antiretroviral therapy (ART) are associated with hepatic impairment; their synergistic impact on maternal liver function in the context of pre-eclampsia remains unclear, especially in HIV-endemic regions. Aim: This study aimed to evaluate the effects of HIV infection and ART exposure on maternal liver function and systemic inflammation during pregnancy, with particular emphasis on the modifying role of pre-eclampsia. Method: A mixed-methods approach was utilised, combining systematic reviews and metaanalyses with observational cohort studies. Systematic reviews and meta-analyses assessed the impact of PE, HIV infection, and ART exposure on hepatic biomarkers, particularly aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and total serum bilirubin. The clinical component included cohorts stratified by PE and HIV status to evaluate inflammatory markers and liver function parameters. Plasma levels of AST, ALT, placental alkaline phosphatase (PLAP), and C-reactive protein (CRP) were measured and compared among groups to evaluate hepatic impairment, placental involvement, and systemic inflammation. Results: Meta-analytic data revealed a substantial association between PE and increased maternal liver enzymes, indicative of hepatocellular damage. HIV infection and ART exposure were independently associated with elevated AST and ALT levels in comparison to HIVnegative controls. The clinical observations validated these results, demonstrating markedly elevated AST and ALT levels in pre-eclamptic women, with the most pronounced increases noted in those with concurrent HIV infection and pre-eclampsia. PLAP levels were significantly altered in PE, especially in HIV-positive women, indicating placental dysfunction and potential placental-hepatic interplay. CRP concentrations were elevated in PE and further rose with illness severity, with HIV-positive women displaying greater baseline inflammatory levels. The simultaneous presence of HIV and PE led to the greatest inflammatory burden. Conclusion: These findings demonstrate that liver dysfunction during pregnancy is influenced by the complex interplay between HIV infection, ART exposure, and pre-eclampsia. The results indicate that hepatic abnormalities in PWLWHIV cannot be solely attributed to HIV, ART, or PE, but rather to their combined effects. Therefore, an improved monitoring of liver function and inflammatory markers in high-risk pregnancies may facilitate early identification of hepatic involvement and maternal risk assessment, especially in HIV-endemic areas like South Africa.; Boemo Basadi ba baimana bao ba phelago le HIV ba lebane le kotsi ye kgolo ya mathata a a amanago le boimana, go swana le pre-eclampsia (PE), ye e lego bolwetši bja kgatelelo ya madi bjo bo amago mekgwa ye mentši ya mmelo bjo bo akaretšago go senyega ga methapo ya madi, go ruruga ga mmele ka kakaretšo, le go senyega ga ditho. Tšoaetšo ya HIV le kalafo ya diantiretroviral (ART) ka bobedi di amahanngwa le go senyegega ga sebete; le ge go le bjalo, khuetšo ya tšona ye e kopanego godimo ga mošomo wa sebete sa bomme maemong a preeclampsia ga sešo sa hlaloswa gabotse, kudu dinageng tšeo HIV e atilego kudu. Maikemišetšo Maikemišetšo a nyakišišo ye e be e le go hlahloba ditlamorago tša tšoaetšo ya HIV le kalafo ya ART godimo ga mošomo wa sebete sa bomme le go rurušega nakong ya boimana, ka go lebanya kudu le khuetšo ya pre-eclampsia. Mokgwa Go dirišitšwe mokgwa wa mixed-methods, wo o kopantšego ditlhahlobo tša tsamaiso (systematic reviews) le meta-analyses le dithuto tše mmalwa tša di-cohort. Ditlhahlobo tša tsamaiso le meta-analyses di lekotše khuetšo ya pre-eclampsia, tšoaetšo ya HIV le go pepentšhwa go ART godimo ga di-biomarker tša sebete, e lego aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), le bilirubin ya kakaretšo ya serum. Dithuto tša kliniki di akaretša di-cohort tše mmalwa tšeo di nyakišišitšego go rurušega le mošomo wa sebete go basadi ba baimana bao ba arotšwego go ya ka maemo a PE le HIV. Maemo a plasma a AST, ALT, placental alkaline phosphatase (PLAP), le C-reactive protein (CRP) a lekantšwe le go bapetšwa magareng ga dihlopha go lekola go senyega ga sebete, go tsenela ga placenta, le go rurušega ga mmele ka kakaretšo. Dipoelo Dipoelo tša meta-analysis di bontšhitše kgokagano ye kgolo magareng ga pre-eclampsia le koketšego ya di-enzyme tša sebete sa bomme, e lego sešupo sa go senyega ga disele tša sebete. Tšoaetšo ya HIV le go šomišwa ga ART ka bonngwe di amantšwe le maemo a godimo a AST le ALT ge di bapetšwa le basadi bao ba se nago HIV. Ditemogo tša kliniki di netefaditše dipoelo tše, ka go bontšha koketšego ye kgolo ya AST le ALT go basadi bao ba nago le preeclampsia, ka maemo a godimo kudu go bao ba nago le tšoaetšo ya HIV le pre-eclampsia ka nako e tee. Maemo a PLAP a fetogile kudu go PE, kudu go basadi ba HIV-positive, se se bontšhago go se šome gabotse ga placenta le tswalano ya placenta le sebete. Maemo a CRP a be a phagame go PE gomme a oketša le go tia ga bolwetši, go basadi ba nang leHIVvi positive ba bontšha maemo a godimo a go rurušega go tšwa mathomong. Go ba gona ka nako e tee ga HIV le PE go lebile go morwalo wo mogolo kudu wa go rurušega. Mafetšo Mošomo wo o tšweleditšwego mo porojekeng ye o bontšha gore go se šome gabotse ga sebete go tšwa go tswalano ya tšoaetšo ya HIV, go šomitšwa ga ART, le boimana, gomme go ka amana gape le pre-eclampsia. Dipoelo di bontšha gore diphetogo tša sebete go basadi ba baimana bao ba phelago ka twatši ya HIV di ka se hlaloswe ka HIV, ART goba PE fela, eupša di tšwa go ditlamorago tša tšona tše di kopanego. Ka fao, go tokafatša go hlokomelwa ga mošomo wa sebete le di-marker tša go rurušega go boimana bjo bo lego kotsing go ka thuša go lemoga ka pela go tsenela ga sebete le go lekola kotsi ya bomme, kudu dinageng tšeo HIV e atilego kudu go swana le Afrika Borwa. Abstract in English and Sepedi

2026-10-30T00:00:00Z Nyamande, Tovhowani Brenda https://ir.unisa.ac.za/handle/10500/32560 2026-06-07T12:50:41Z 2026-03-01T00:00:00Z

Potential value of community-based water monitoring in water resource protection decision-making in a developing country context: South Africa Nyamande, Tovhowani Brenda Over the past two decades, most developing countries, especially in Africa, have been transitioning towards water sector reform. While new water legislation in many African countries – as in South Africa – has included the environmental protection and the Polluter Pays Principle as priorities, it has given little attention to community involvement. South Africa faces many water-related challenges, the most significant being water quality deterioration, which requires constant monitoring. Water resource monitoring is essential for protecting water resources but presents challenges that constitute research gaps this study aimed to address. Key gaps include: the lack of policy support for community stakeholder participation in water resource monitoring; the exclusion of community data from water resource protection decision-making; poor integration of monitoring programmes and information sharing among different institutions; and uncertainty about the effectiveness of stakeholder engagement in reaching the intended objectives. As a result of the identified gaps, a main research question was formulated: What is the potential value of community-based water monitoring (CBWM) in water resource protection decision-making in a developing country context such as South Africa?. The main research question was informed by the five sub- questions: 1. What are the benefits, values, pitfalls, and challenges of CBWM programmes? 2. What criteria should be considered in CBWM in developing countries? 3. Based on the opinions of governance structures and the real-life experiences of communities, to what extent do the selected catchments in South Africa, as a developing country, benefit from CBWM? 4. What are the views of governance structures on the extent to which CBWM operates in South African communities? 5. How might a conceptual framework work by enhancing the use of CBWM data in decision making for developing countries? A case study research design utilising a mixed-methods research approach, incorporating both qualitative and quantitative methods, was followed to answer the research questions. The study integrated a literature review with primary data collection through three case studies. Data were gathered through interviews and a survey questionnaire. The study’s findings indicate that sustainable CBWM programmes promote community involvement in water monitoring while encouraging public ownership and trusteeship of water resources. However, the absence of appropriate guiding policy and CBWM-related challenges have hampered progress and prevented community data from contributing to decision-making. This study demonstrates the importance of a standardised protocol as a baseline for monitoring, managing data, ensuring value for money, and securing funding. The study highlighted the extent of catchments benefiting from CBWM initiatives as an early warning system and emphasised the importance of collaboration between different community governance structures for effective operation. A conceptual framework was developed for integrating CBWM data into the decision-making process to enhance the use of CBWM data for decision-making and to enhance sustainability and establish consistent information-sharing channels among relevant institutions. Engaging with the main research question yielded important insights into the role of CBWM data in decision-making. CBWM improves the capacity for monitoring water impacts, enabling more informed decisions through increased data availability. This helps bridge the gap between scientific knowledge and local community knowledge that traditional methods involving only professional scientists have overlooked. The integration of community data in water resource protection fosters a proactive management framework, allowing community members to identify potential water issues early and optimise resource allocation and protection. A key insight gained is that CBWM participation can guide both data collection and implementation of CBWM initiatives, ultimately promoting sustainable practices. This exploration underscores that CBWM not only enhances data collection, but also supports policy development and stakeholder engagement, thus filling critical gaps identified in both knowledge and practical applications within the field of sustainable water management; Kha mahumi mavhili a miṅwaha yo fhiraho, mashango a no khou bvelela, zwihulusa a dzhango ḽa Afurika, a khou shandukela kha tshanduko ya zwa maḓi. Musi milayo miswa kha mashango manzhi a dzhango ḽa Afurika, sa Afurika Tshipembe, yo katela na u tsireledza mupo na tshikafhadzo yawo i vhidzwaho “Polluter Pays Principle”, ya dovha hafhu ya katela zwiṱukuṱuku na u dzhenelela ha vhadzulapo. Afurika Tshipembe ḽi khou livhana na khaedu nnzhi dza maḓi, zwihulusa u kuna hao zwine zwa tea u dzula zwi tshi khou poswa iṱo. U ṱola zwiko zwa maḓi ndi zwa ndeme kha u tsireledza zwiko, fhedzi zwi na khaedu kana magake ane a ṱoḓa tsedzuluso ya zwine ngudo iyi ya khou ṱoḓa u bvisela khagala. Magake aya a ndeme a katela, u shaeya ha thikhedzo ya milayo kana mbekanyamaitele ya vhafaramikovhe vha tshitshavha kha u ṱhogomela zwiko zwa maḓi; u sa dzhielwa nṱha ha mawanwa a tshitshavha musi hu tshi dzhiiwa tsheo; u shaeya ha u ṱanganya mbekanyamushumo dza u ṱhogomela na u ṋekana zwidodombedzwa uya nga u fhambana ha madzangano na u sa shumisana ha vhafaramikovhe kha u swikelela zwipikwa. Nga nṱhani ha magake o waniwaho, ho bveledzwa mbudziso nthihi khulwane ya ṱhoḓuluso ‘Ndeme khulwane ya community-based water monitoring (CBWM) kha tsheo dza u tsireledza zwiko zwa maḓi kha shango li no khou bvelela sa shango la Afurika Tshipembe?’ Mbudziso khulwane nga ha thoduluso yo tutuwedzwa nga mbudziso thanu ṱhukhu dzine dza vha: 1. Ndi mbuelo, ndeme, khombo na khaedu dzifhio dza mbekanyamushumo dza CBWM? 2. Ndi nḓila dzifhio dzine dza fanela u tevhelwa kha CBWM kha mashango a no khou bvelela? 3. Zwo disendeka kha kuhumbulele kwa zwiko zwa mavhusele, na kuhumbulele kwa tshitshavha, ndi lini hune khuvhanganyo ya maḓi Afurika Tshipembe, sa shango ļi no khou bvelela ļa bindula u bva kha CBWM? 4. Ndi mbonalele ifhio uya nga ha zwiko zwa muvhuso zwine CBWM ya khou bveledzisa? 5. CBWM data I nga shumiswa hani kha u dzhiya tsheo kha mashango ane a khou bvelela? Ngudo ya tsumbo ya tsedzuluso ye ya shumisa tsedzuluso yo ṱanganelaho, yo katela tsedzuluso ya khwaļithethivi nay a khwanthithethivi ya tevhelwa nga u fhindula mbudziso dza tsedzuluso. Ngudo iyi yo shumisa nḓiļa ya ṱhoḓuluso yo ṱanganelanaho, yo katela tsedzuluso ya maṅwalwa and mawanwa o kuvhanganyiwaho kha ṱhoḓuluso tharu dza ngudo Mawanwa o wanala nga u ita nyambedzano na vhathu vho tou nangiwaho ha dovha hafhu ha netshedzwa dzinwe mbudziso. Mawanwa a tsedzuluso a sumbedza uri mbekanyamushumo dza CBWM dzi thusa tshitshavha kha u vha tshipiḓa na kha u lavhelesa kana tsireledzo ya maḓi, na u vha na vhuḓifhinduleli. Sa zwenezwo, u sa vha na milayo na khaedu dza CBWM zwi ita u ri hu sa vhe na mvelaphanḓa. Ngudo iyi I sumbedza ndeme ya u vha na ndaulo I ṱanganedzwaho nga nnyi na nnyi sa mutheo, u ri hu kone u vha na u londola kana u ṱhogomela, u ṱhogomela mawanwa (data), na u kona u vi wana ndambedzo ya masheleni. Ngudo yo bvisela khagala u ri CBWM I khou thusa tshitshavha u swika ngafhi ya dovha ya tsivhudza vhuṱhogwa ha u shumisana kha zwitshavha u ya nga u fhambana. Ho bveledzwa nḓila ya u dzhenisa mawanwa ya CBWM musi hu tshi dzhiiwa tsheo na u khwaṱhisa u ņekana nḓivho nga zwiimiswa uya nga u fhambana. Kha mbudziso khulwane ya ṱhoḓuluso, zwo bveledza u khwiņisea kha tshipida tsha u tsireledza zwiko zwa maḓi na u shumisa mawanwa a re hone. Mbuelo idzi dzi thusa u fhungudza magake vhukati ha nḓivho ya saintsi na ya vhadzulapo ya sialala. U ṱanganyisa mawanwa a vhadzulapo kha tsireledzo ya zwiko zwa maḓi zwi ṱuṱuwedza u shuma kana u langula, zwine zwa tendela vhadzulapo u kona u vhona zwi sa khou tshimbilaho zwavhuḓi na u kona u ņetshedza na u tsireledza maḓi. Mbuelo dzo wanwaho kha u shumisana na CBWM ndi u kona u sumba nḓila kha u kuvhanganya mawanwa na u sumbedza vhurangaphanḓa, zwine zwa ṱuṱuwedza tshumisano ya tshifhinga tshilapfu. Ṱhoḓulusa iyi I sumbedza vhuṱhogwa ha CBWM kha u ṱandavhudza ṱhoḓuluso na u kuvhanganya mawanwa na u tikedza kha u bveledza mbekanyamaitele na u katela vhafaramikovhe, zwine zwa vala gake vhukati ha nḓivho na nyito. Maipfi a ndeme: Tsedzuluso ya maḓi nga vhadzulapo, tsireledzo ya zwiko zwa maḓi. muangarane wa u dzhia tsheo, tshipirioni na vhupfiwa ha vhafaramikovhe, vhuṱhogwa ha u ṱola, u bindula na dzikhaudu kha vhadzulapo, ṱhanganyo ya mawanwa, Afurika Tshipembe.; Eka makume mambirhi ya malembe lama hundzeke, matiko yo tala lama hluvukaka, ngopfungopfu eAfrika, ya vile ya hundzukela eka mpfuxeto wa sekithara ya mati. Loko milawu leyintshwa ya mati ematikweni yo tala ya Afrika – tanihi le Afrika-Dzonga – yi katsa nsirhelelo wa mbango na Nsinya wa Nawu wa Nthyakiso wa Ntshikelelo tanihi swilo leswi rhangisaka emahlweni, wu nyikile nyingiso wutsongo eka ku nghenelela ka vaaki. Afrika-Dzonga ri langutane na mintlhontlho yo tala leyi fambelanaka na mati, leyi nga ya nkoka swinene i ku hohloka ka khwalithi ya mati, leswi lavaka ku vekiwa tihlo nkarhi hinkwawo. Ku vekiwa tihlo ka switirhisiwa swa mati i swa nkoka eka ku sirhelela switirhisiwa swa mati kambe swi tisa mintlhontlho leyi vumbeke swivandla swa ndzavisiso leswi ndzavisiso lowu a wu kongomisiwile ku swi lulamisa. Swivandla swa nkoka swi katsa: ku pfumaleka ka nseketelo wa pholisi eka ku nghenelela ka vakhomaxiave va vaaki eka ku vekiwa tihlo ka switirhisiwa swa mati; ku hlongoriwa ka datha ya vaaki eka ku tekiwa ka swiboho swa nsirhelelo wa switirhisiwa swa mati; ku hlanganisiwa loku nga riki kahle ka minongonoko yo veka tihlo na ku avelana mahungu exikarhi ka swivandla swo hambana; na ku nga tiyiseki mayelana na ku humelela ka ku nghenelela ka vakhomaxiave eku fikeleleni ka swikongomelo leswi kunguhatiweke. Hikwalaho ka swivandla leswi hlawuriweke, xivutiso lexikulu xa ndzavisiso xi vumbiwile ‘Hi wihi nkoka lowu nga vaka kona wa community-based water monitoring (CBWM) eka ku tekiwa ka swiboho swa nsirhelelo wa switirhisiwa swa mati eka xiyimo xa tiko leri hluvukaka ku fana na Afrika-Dzonga?’. Xivutisonkulu xa ndzavisiso xi tivisiwile hi swivutiso leswitsongo swa ntlhanu: 1. Hi yihi mimpfuno, mimpimanyeto, mintlhamu, na mintlhontlho ya minongonoko ya CBWM? 2. Hi swihi swipimelo leswi faneleke ku tekeriwa enhlokweni eka CBWM ematikweni lama hluvukaka? 3. Hi ku ya hi mavonelo ya swivumbeko swa vulawuri na mintokoto ya xiviri ya miganga, xana swihlovo leswi hlawuriweke eAfrika-Dzonga, tanihi tiko leri hluvukaka, swi vuyeriwa ku fikela kwihi eka CBWM? 4. Hi wahi mavonelo ya swivumbeko swa vulawuri eka mpimo lowu CBWM yi tirhaka ha wona eka miganga ya Afrika-Dzonga? 5. Xana rimba ra miehleketo ri nga tirha njhani hi ku ndlandlamuxa matirhiselo ya datha ya CBWM eka ku teka swiboho eka matiko lama hluvukaka? Dizayini ya ndzavisiso wa dyondzo ya xiyimo leyi tirhisaka endlelo ra ndzavisiso wa maendlelo yo hlangana, leyi katsaka maendlelo ya xiyimo na ya nhlayo, yi landzeriwile ku hlamula swivutiso swa ndzavisiso. Dyondzo yi hlanganisa nkambisiso wa matsalwa na nhlengeleto wa datha ya masungulo hi ku tirhisa tidyondzo tinharhu ta timhaka. Data yi hlengeletiwile hi ku tirhisa mimbulavurisano na ndzavisiso. Swikumiwa swa ndzavisiso swi kombisa leswaku minongonoko ya CBWM leyi nga ta tshama nkarhi wo leha yi tlakusa ku nghenelela ka vaaki eka ku vekiwa tihlo ka mati loko yi ri karhi yi hlohlotela vun’wini bya mfumo na vutshembeki bya switirhisiwa swa mati. Hambiswiritano, ku ix pfumaleka ka pholisi yo kongomisa leyi faneleke na mintlhontlho leyi fambelanaka na CBWM swi kavanyetile nhluvuko na ku sivela datha ya vaaki ku hoxa xandla eka ku teka swiboho. Dyondzo leyi yi kombisa nkoka wa phurotokholo leyi ringaniseriweke tanihi masungulo yo veka tihlo, ku lawula datha, ku tiyisisa nkoka wa mali, na ku kuma mali. Dyondzo yi kombisile mpimo wa swihlovo leswi vuyeriwaka eka migingiriko ya CBWM tanihi sisiteme ya xitsundzuxo xa le mahlweni naswona yi kandziyisile nkoka wa ntirhisano exikarhi ka swivumbeko swo hambana swa vulawuri bya vaaki ku endlela matirhelo lama humelelaka. Rimba ra miehleketo ri tumbuluxiwile ro hlanganisa datha ya CBWM eka phurosese yo teka swiboho ku ndlandlamuxa matirhiselo ya datha ya CBWM ku teka swiboho na ku ndlandlamuxa ku kondletela na ku simeka tindlela leti nga cincekiki to avelana mahungu exikarhi ka swivandla leswi faneleke. Ku nghenelela na xivutiso xa ndzavisiso, swi humesile vutivi bya nkoka lebyi ndlandlamuxaka ku twisisa ntirho wa datha ya CBWM eka ku teka swiboho. CBWM yi antswisa vuswikoti byo langutisisa ku khumbeka ka mati, ku endla leswaku ku va na swiboho leswi nga na vutivi byo tala hi ku engetela ku kumeka ka datha. Leswi swi pfuneta ku hlanganisa xivandla exikarhi ka vutivi bya sayense na vutivi bya vaaki va ndhawu lebyi tindlela ta ndhavuko to katsa vativi va sayense va xiphurofexinali ntsena ti honiseke. Ku hlanganisiwa ka datha ya vaaki eka nsirhelelo wa switirhisiwa swa mati swi kurisa rimba ra vulawuri lebyi nga na vuxiyaxiya, ku pfumelela swirho swa vaaki ku kuma timhaka leti nga vaka kona ta mati ka ha ri na nkarhi na ku antswisa avelo na nsirhelelo wa switirhisiwa. Vutivi bya nkoka lebyi kumiweke hileswaku ku nghenelela ka CBWM ku nga kongomisa havumbirhi bya nhlengeleto wa datha na ku tirhisiwa ka migingiriko ya CBWM, eku heteleleni ku tlakusa maendlelo lama nga ta tshama nkarhi wo leha. Ku lavisisa loku ku kandziyisa leswaku CBWM a yi ndlandlamuxi ntsena nhlengeleto wa datha, kambe yi tlhela yi seketela nhluvukiso wa pholisi na ku nghenelela ka vakhomaxiave, xisweswo yi tata swivandla swa nkoka leswi hlawuriweke eka vutivi na matirhiselo lama tirhaka endzeni ka xiyenge xa vulawuri bya mati lebyi nga heriki. Marito ya nkoka: Ku vekiwa tihlo ka mati loku simekiweke eka vaaki, nsirhelelo wa switirhisiwa swa mati, ku teka swiboho, rimba ra miehleketo, mavonelo na ntokoto wa vakhomaxiave, not yak u hlawuleka, mabindza na mintlhontlho ya nhlayo, hlanganiso, Afrika-Dzonga Abstract in English with Venda and Tsonga translations

2026-03-01T00:00:00Z