STK /WST 795 Research Reports

These documents contain the honours research reports for each year for the Department of Statistics.Honours Research Reports - University of Pretoria 20XXStatisticsBSs (Hons) Mathematical Statistics, BCom (Hons) Statistics, BCom (Hons) Mathematical StatisticsUnrestricte

Effect of curing conditions and harvesting stage of maturity on Ethiopian onion bulb drying properties

The study was conducted to investigate the impact of curing conditions and harvesting stageson the drying quality of onion bulbs. The onion bulbs (Bombay Red cultivar) were harvested at three harvesting stages (early, optimum, and late maturity) and cured at three different temperatures (30, 40 and 50 oC) and relative humidity (30, 50 and 70%). The results revealed that curing temperature, RH, and maturity stage had significant effects on all measuredattributesexcept total soluble solids

UK mutual fund performance

Using a comprehensive data set on (surviving and non-surviving) UK equity mutual funds (April 1975 - December 2002), this study uses a bootstrap methodology to distinguish between `skill' and `luck' in fund performance. This methodology allows for non-normality in the idiosyncratic risks of the funds -a major issue when considering the `best' and `worst' funds and these are the funds which investors are most interested in. The study points to the existence of genuine stock picking ability among a relatively small number of top performing UK equity mutual funds (i. e. performance which is not solely due to good luck). At the negative end of the performance scale, the analysis strongly rejects the hypothesis that most poor performing funds are merely unlucky. Most of these funds demonstrate `bad skill'. The study also examines the economic and statistical significance of persistence. Sorting funds into deciles based on past raw returns or on past 4-factor alphas, strong evidence is found that past loser funds continue to perform badly in terms of their future 4-factor alphas while little evidence is found that past winner funds provide future positive risk adjusted performance. However, on investigating relatively small `fund-of-fund' portfolios of past winners, evidence of positive persistence is found. Using a cross-section bootstrap approach the study derives the empirical distribution of final wealth at a 10 year horizon and finds that if transactions costs are above 2.5% per fund round trip, a passive strategy seems at least as good as the active strategies examined while with transactions costs of 5% the passive strategy is most probably superior. The study also examines the market timing performance of the funds. Using a nonparametric test procedure the study evaluates both unconditional market timing and timing conditional on publicly available information. A relatively small number of funds (around 1%) are found to successfully time the market while market mistiming is relatively prevalent

Numerical study of a hybrid photovoltaic thermal desalination system

The world as we know it depends highly on fossil fuels. However, these resources are finite, and evidence suggests that their combustion contributes to climate change. In addition, fresh water supplies are becoming scarcer amidst instabilities in weather patterns and unsustainable water consumption levels. As such, photovoltaic (PV) systems have emerged as a potential off-grid alternative to traditional fossil fuel energy generation. However, their widespread proliferation is, in part, inhibited by their inefficiency as less than 20% of incident solar energy is converted to electrical energy. Hybrid photovoltaic thermal (PV/T) desalination systems have emerged as one way of improving the overall efficiency of PV panels as they make use of the waste heat from panels to aid the desalination process in solar stills. Solar stills have been modelled with software for the purpose of performance optimisation, but most of them do not account for the still's view factor in the calculation of internal radiative heat transfer coefficient. The aim of this study was to construct a numerical model for a hybrid PV/T desalination system and determine its accuracy. The modelling was undertaken in Matlab and was validated against experimental data from a previous study using Root Mean Square Error (RMSE) and correlation values. It was observed that the model performed adequately as a water yield RMSE value of 22.0% was found. Furthermore, it was found that the view factor reduces the RMSE of hourly water yield from 28.9% to 22.0% and improves the correlation factor from 0.9890 to 0.9896. Sensitivity analyses were performed with annual data from Stellenbosch, South Africa (33.935°S 18.7817°W) and indicated an optimal water depth of 0.02m for high water yield, and 0.04m for high electrical energy yield. Also, an optimal panel tilt angle of 30° was found for both water and electrical energy yields and optimal cover tilt angles of 40° and 60° were observed for maximum water and electrical yields respectively. The conclusion of this study was that the incorporation of a view factors does indeed improve the accuracy of hybrid PV/T desalination system models. Additionally, low basin water depth is favourable for high water yields and high basin water depth, for high electrical energy yields. Furthermore, a panel tilt angle of 30° is optimum for both types of yield. Finally, the still cover tilt angle should be set to 40° for optimal water yields, but should be as steep as possible for optimal electrical energy yields

Complexity in Economic and Social Systems

There is no term that better describes the essential features of human society than complexity. On various levels, from the decision-making processes of individuals, through to the interactions between individuals leading to the spontaneous formation of groups and social hierarchies, up to the collective, herding processes that reshape whole societies, all these features share the property of irreducibility, i.e., they require a holistic, multi-level approach formed by researchers from different disciplines. This Special Issue aims to collect research studies that, by exploiting the latest advances in physics, economics, complex networks, and data science, make a step towards understanding these economic and social systems. The majority of submissions are devoted to financial market analysis and modeling, including the stock and cryptocurrency markets in the COVID-19 pandemic, systemic risk quantification and control, wealth condensation, the innovation-related performance of companies, and more. Looking more at societies, there are papers that deal with regional development, land speculation, and the-fake news-fighting strategies, the issues which are of central interest in contemporary society. On top of this, one of the contributions proposes a new, improved complexity measure

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing