MSCAEC / Department of Agricultural Economics and Agribusiness

MENVSC (Geography) / Department of Geography and Geo-Information Science / Artisanal and small-scale gold mining (ASGM) has devastating impacts on different parts of the environment and is a source of environmental degradation and contamination. ASGM degrades water resources, contaminate soil, sediments and water and lead to serious land degradation problems. ASGM activities are also associated with socio-economic issues such as child labour, prostitution and health and safety concerns. Insufficient understanding of the environmental and social problems of ASGM in Giyani Greenstone Belt has led to lack of mitigation strategies to reduce such problems. The main aim of this research was to assess and manage the environmental and socio-economic impacts of ASGM in Giyani Greenstone Belt. Remote sensing and GIS and Normalised Differential Vegetation Index were used to assess the effects of mining activities on vegetation cover. Assessment of the effects of ASGM on water, sediments and soil quality involved collection of samples in order to establish their physical and chemical properties. The concentration of toxic and trace metals were determined using Atomic Absorption Spectrometer (AAS) and X-ray Fluorescence (XRF) instruments. The pH meter was used to determine the pH level of the collected samples. Questionnaires, interviews and SPSS were used to assess socio-economic impacts of ASGM. The study culminated in devolvement of NDVI maps and this was used to assess the effects of ASGM on vegetation cover. Results showed that the mining activities in the area had caused extensive environmental degradation due to serious removal of vegetation cover in the site. ASGM had serious effects on soil, water and sediments quality such as environmental contamination by toxic and trace elements. Soil samples were found with high concentration of As, Cr, Cu, Ni, Pb and Zn as compared to the recommended South African Soil Quality and WHO threshold values for plants. It was found that Klein Letaba had high concentration of Ba, La, V, and Ce above the World Soil Averages for plants. Sediments were heavily contaminated with Cr, Ni, Pb, Zn, As and Ba as compared to the recommended standards prescribed by US EPA and WHO. The pH of water, soil and sediments samples collected from both mining sites were found to be strongly alkaline which affects the plants growth as well as aquatic flora and fauna. Socio-economic issues such as child labour, injuries, educational problems, health and safety issues, police disturbance, creation of jobs and income generation were identified at mine sites. ASGM had serious effect on vegetation cover through environmental degradation. ASGM also had serious environmental contamination by toxic and trace elements. ASGM had both positive and negative socio-economic issues at mining site which include employment opportunities, income generation, occupational health and safety, police disturbance and arrests and the use of child labour. Mine site rehabilitation is recommended in this study to reduce environmental degradation. The remediation of contaminated area by concentrated toxic and trace elements should be applied at both mining sites. ASGM should be legalised to enhance positive aspects of the mining such as increase in income generation and creation of more employment opportunities. However, there should be enforcement of mining policies to reduce social and environmental problems.

MRDV / Institute for Rural Development / See the attached abstract below

Cultural tourism is a fast-growing sector in many countries. In South Africa, it is a key growth segment of local economic development (LED). South Africa is home to many cultural institutions such as museums, art galleries, theatres, monuments and festivals that – thanks to a growing number of international and local interests – encourage entrepreneurship and help generate local business growth and employment opportunities. South Africa’s Limpopo Province is predominately known for its wildlife and hunting tourism. However, it is endowed with many cultural institutions that are contributing significantly to the regional economy – specifically in the metropole of Greater Polokwane. As yet, no study has researched the size and impact of this cultural contribution on the local economy vis a vis more well-known tourism activities. This study sketches the size and nature of the cultural tourism industry in Greater Polokwane. In the first phase of the study, a database of formal cultural institutions in Greater Polokwane was created. In the study’s second phase, interviews with staff members of these institutions using both quantitative and qualitative methods, were conducted. The data revealed that most employees, including senior managerial staff, are local Black Africans. None of these cultural institutions are state funded; they all operate privately, but some are located on state-owned land. Thus, government support for cultural tourism in this region is minimal. Insufficient funds and resources inhibit the growth of this sector. Some employees expressed dissatisfaction with their working conditions and remuneration. Nonetheless, these cultural institutions generate local economic growth and employment opportunities. / Environmental Sciences / M. Sc. (Environmental Management

Brain tumor is a disease characterized by uncontrolled growth of abnormal cells inthe brain. The brain is responsible for regulating the functions of all other organs,hence, any atypical growth of cells in the brain can have severe implications for itsfunctions. The number of global mortality in 2020 led by cancerous brains was estimatedat 251,329. However, early detection of brain cancer is critical for prompttreatment and improving patient’s quality of life as well as survival rates. Manualmedical image classification in diagnosing diseases has been shown to be extremelytime-consuming and labor-intensive. Convolutional Neural Networks (CNNs) hasproven to be a leading algorithm in image classification outperforming humans. Thispaper compares five CNN architectures namely: VGG-16, VGG-19, AlexNet, EffecientNetB7,and ResNet-50 in terms of performance and accuracy using transferlearning. In addition, the authors discussed in this paper the economic impact ofCNN, as an AI approach, on the healthcare sector. The models’ performance isdemonstrated using functions for loss and accuracy rates as well as using the confusionmatrix. The conducted experiment resulted in VGG-19 achieving best performancewith 97% accuracy, while EffecientNetB7 achieved worst performance with93% accuracy. / Hjärntumör är en sjukdom som kännetecknas av okontrollerad tillväxt av onormalaceller i hjärnan. Hjärnan är ansvarig för att styra funktionerna hos alla andra organ,därför kan all onormala tillväxt av celler i hjärnan ha allvarliga konsekvenser för dessfunktioner. Antalet globala dödligheten ledda av hjärncancer har uppskattats till251329 under 2020. Tidig upptäckt av hjärncancer är dock avgörande för snabb behandlingoch för att förbättra patienternas livskvalitet och överlevnadssannolikhet.Manuell medicinsk bildklassificering vid diagnostisering av sjukdomar har visat sigvara extremt tidskrävande och arbetskrävande. Convolutional Neural Network(CNN) är en ledande algoritm för bildklassificering som har överträffat människor.Denna studie jämför fem CNN-arkitekturer, nämligen VGG-16, VGG-19, AlexNet,EffecientNetB7, och ResNet-50 i form av prestanda och noggrannhet. Dessutom diskuterarförfattarna i studien CNN:s ekonomiska inverkan på sjukvårdssektorn. Modellensprestanda demonstrerades med hjälp av funktioner om förlust och noggrannhetsvärden samt med hjälp av en Confusion matris. Resultatet av det utfördaexperimentet har visat att VGG-19 har uppnått bästa prestanda med 97% noggrannhet,medan EffecientNetB7 har uppnått värsta prestanda med 93% noggrannhet.

La tesis engloba tres capítulos: el primero sobre fertilidad y "calidad" infantil, el segundo sobre planificación familiar y salud infantil, y el tercero sobre la estimación de un indicador de la actividad económica agregada regional. Concretamente, el primer capítulo examina empíricamente usando un modelo de diferencia-en-diferencias con efectos fijos, cómo un aumento inesperado de riqueza (proveniente de una transferencia intergeneracional) afecta a la fertilidad y la inversión de los padres en la calidad de los hijos. Se encuentra un efecto negativo en la cantidad de hijos junto a un efecto positivo en la calidad de los hijos demandados por los hogares. Estos efectos ocurren en diferentes momentos del tiempo para la muestra completa. En el corto plazo se observa una reducción de la fertilidad mientras que en el largo plazo se observa un aumento de la inversión en calidad infantil. También se encuentra un efecto positivo en la inversión en calidad infantil a corto plazo para las parejas que ya tenían hijos. En el segundo capítulo se evalúa una política, estudiando el impacto de haber sido expuesto a un programa de planificación familiar que promovió la anticoncepción quirúrgica/esterilización por primera vez en Perú (Programa de Salud Reproductiva y Planificación Familiar o PNSRPF, 1996- 2000). Los resultados muestran un mayor uso de métodos anticonceptivos temporales y permanentes entre las mujeres expuestas al programa y un menor riesgo de mortalidad infantil entre sus hijos. Este efecto es, en parte, debido a la prolongación de la lactancia materna. Finalmente, en el tercer capítulo se estima un indicador resumen de la actividad económica agregada, en frecuencia mensual, para las regiones españolas. Se utiliza un Modelo de Factores Dinámico dada la escasez de datos a nivel regional. Los indicadores estimados muestran la heterogeneidad del sistema productivo entre regiones a través de la inclusión de diferentes variables con el fin de reflejar con mayor precisión la evolución económica regional. Los indicadores obtenidos son especialmente útiles para estimar el impacto económico del reciente brote de COVID 19 a nivel regional en España. / Esta tesis ha sido elaborada con la financiación concedida por el Ministerio de Ciencia e Innovación (ECO2014-58434-P).

Brain tumor is a disease characterized by uncontrolled growth of abnormal cells in the brain. The brain is responsible for regulating the functions of all other organs, hence, any atypical growth of cells in the brain can have severe implications for its functions. The number of global mortality in 2020 led by cancerous brains was estimated at 251,329. However, early detection of brain cancer is critical for prompt treatment and improving patient’s quality of life as well as survival rates. Manual medical image classification in diagnosing diseases has been shown to be extremely time-consuming and labor-intensive. Convolutional Neural Networks (CNNs) has proven to be a leading algorithm in image classification outperforming humans. This paper compares five CNN architectures namely: VGG-16, VGG-19, AlexNet, EffecientNetB7, and ResNet-50 in terms of performance and accuracy using transfer learning. In addition, the authors discussed in this paper the economic impact of CNN, as an AI approach, on the healthcare sector. The models’ performance is demonstrated using functions for loss and accuracy rates as well as using the confusion matrix. The conducted experiment resulted in VGG-19 achieving best performance with 97% accuracy, while EffecientNetB7 achieved worst performance with 93% accuracy. / Hjärntumör är en sjukdom som kännetecknas av okontrollerad tillväxt av onormala celler i hjärnan. Hjärnan är ansvarig för att styra funktionerna hos alla andra organ, därför kan all onormala tillväxt av celler i hjärnan ha allvarliga konsekvenser för dess funktioner. Antalet globala dödligheten ledda av hjärncancer har uppskattats till 251329 under 2020. Tidig upptäckt av hjärncancer är dock avgörande för snabb behandling och för att förbättra patienternas livskvalitet och överlevnadssannolikhet. Manuell medicinsk bildklassificering vid diagnostisering av sjukdomar har visat sig vara extremt tidskrävande och arbetskrävande. Convolutional Neural Network (CNN) är en ledande algoritm för bildklassificering som har överträffat människor. Denna studie jämför fem CNN-arkitekturer, nämligen VGG-16, VGG-19, AlexNet, EffecientNetB7, och ResNet-50 i form av prestanda och noggrannhet. Dessutom diskuterar författarna i studien CNN:s ekonomiska inverkan på sjukvårdssektorn. Modellens prestanda demonstrerades med hjälp av funktioner om förlust och noggrannhets värden samt med hjälp av en Confusion matris. Resultatet av det utförda experimentet har visat att VGG-19 har uppnått bästa prestanda med 97% noggrannhet, medan EffecientNetB7 har uppnått värsta prestanda med 93% noggrannhet.

Vitiligo is a skin disease where the pigment cells that produce melanin die or stop functioning, which causes white patches to appear on the body. Although vitiligo is not considered a serious disease, there is a risk that something is wrong with a person's immune system. In recent years, the use of medical image processing techniques has grown, and research continues to develop new techniques for analysing and processing medical images. In many medical image classification tasks, deep convolutional neural network technology has proven its effectiveness, which means that it may also perform well in vitiligo classification. Our study uses four deep convolutional neural networks in order to classify images of vitiligo and normal skin. The architectures selected are VGG-19, ResNeXt101, InceptionResNetV2 and Inception V3. ROC and AUC metrics are used to assess each model's performance. In addition, the authors investigate the economic benefits that this technology may provide to the healthcare system and patients. To train and evaluate the CNN models, the authors used a dataset that contains 1341 images in total. Because the dataset is limited, 5-fold cross validation is also employed to improve the model's prediction. The results demonstrate that InceptionV3 achieves the best performance in the classification of vitiligo, with an AUC value of 0.9111, and InceptionResNetV2 has the lowest AUC value of 0.8560. / Vitiligo är en hudsjukdom där pigmentcellerna som producerar melanin dör eller slutar fungera, vilket får vita fläckar att dyka upp på kroppen. Även om Vitiligo inte betraktas som en allvarlig sjukdom, det finns fortfarande risk att något är fel på en persons immun. Under de senaste åren har användningen av medicinska bildbehandlingstekniker vuxit och forskning fortsätter att utveckla nya tekniker för att analysera och bearbeta medicinska bilder. I många medicinska bildklassificeringsuppgifter har djupa konvolutionella neurala nätverk bevisat sin effektivitet, vilket innebär att den också kan fungera bra i Vitiligo klassificering. Vår studie använder fyra djupa konvolutionella neurala nätverk för att klassificera bilder av vitiligo och normal hud. De valda arkitekturerna är VGG-19, RESNEXT101, InceptionResNetV2 och Inception V3. ROC- och AUC mätvärden används för att bedöma varje modells prestanda. Dessutom undersöker författarna de ekonomiska fördelarna som denna teknik kan ge till sjukvårdssystemet och patienterna. För att träna och utvärdera CNN modellerna använder vi ett dataset som innehåller totalt 1341 bilder. Eftersom datasetet är begränsat används också 5-faldigt korsvalidering för att förbättra modellens förutsägelse. Resultaten visar att InceptionV3 uppnår bästa prestanda i klassificeringen av Vitiligo, med ett AUC -värde på 0,9111, och InceptionResNetV2 har det lägsta AUC -värdet på 0,8560.

No description available.

La presente investigación se enfocó en el impacto que tuvo el COVID-19 en el sector servicios específicamente en el inmobiliario. Por tanto, se planteó como objetivo general analizar el impacto económico y financiero de las estrategias ante el COVID-19 de la Inmobiliaria Ingenierías Energías y Medio Ambiente SAC años 2019 a 2022. La metodología de estudio empleada fue cuantitativa, de tipo aplicada y nivel descriptivo, a su vez, un diseño experimental de corte de transversal. Así mismo, la población sujeta de estudio es la inmobiliaria teniendo como muestra a la información financiera para la elaboración de indicadores financieros, implementando estrategias para la toma de decisiones. Por ello, se utilizaron instrumentos como la entrevista y el análisis documental. Los resultados evidenciaron la elaboración de un diagnóstico de la situación económica financiera de la Inmobiliaria Ingeniería Energía y Medio Ambiente SAC, con esta información se observó una rentabilidad y liquidez no significativa; además se analizaron las medidas de reactivación económica empresarial aplicadas por la inmobiliaria durante la pandemia las cuales fueron estrategias operativas y de mercado, así pues, se evaluó el efecto por el COVID-19 en los estados financieros de los años 2021 al 2022 un ligero mejoramiento en sus ratios de rentabilidad y liquidez. Por lo tanto, la elaboración de estrategias complementarias nos va permitir enfrentar la situación económica de la Inmobiliaria Ingeniería Energía y Medio Ambiente SAC para el año 2023. / The present investigation focused on the impact that COVID-19 had on the services sector, specifically on real estate. Therefore, it has been proposed as a general objective to analyze the economic and financial impact of the strategies before the Covid-19 of the Inmobiliaria Ingenierías Energías y Medio Ambiente SAC years 2019 to 2022. The study methodology used was quantitative, of an application type and level descriptive, in turn, a cross-sectional experimental design. Likewise, the population subject to study is real estate, having as a sample the financial information for the elaboration of financial indicators, implementing strategies for decision making. For this reason, instruments such as the interview and documentary analysis were used. The results evidenced the elaboration of a diagnosis of the financial economic situation of the Inmobiliaria Ingeniería Energía y Medio Ambiente SAC, with this information a non-significant profitability and liquidity was observed; In addition, the business economic reactivation measures applied by the real estate company during the pandemic were analyzed, which were operational and market strategies, thus, the effect of Covid 19 on the financial statements for the years 2021 to 2022 was evaluated, a slight improvement in their profitability and liquidity ratios. Therefore, the elaboration of complementary strategies will allow us to face the economic situation of the Inmobiliaria Ingeniería Energía y Medio Ambiente SAC for the year 2023.