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You searched for +publisher:"Stellenbosch University" +contributor:("Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Applied Mathematics."). Showing records 1 – 9 of 9 total matches.

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1. van Jaarsveld, Jacoline. Predicting the effect of biofilm growth on the pressure drop over a biofilter.

Degree: MSc, Mathematical Sciences, 2017, Stellenbosch University

ENGLISH ABSTRACT : Hydrogen sulphide is a poisonous gas produced by several industries. It is therefore crucial to investigate and mathematically model the environmentally friendly… (more)

Subjects/Keywords: Biofilters; Biofilm growth; Air filters  – Mathematical models; Gases  – Filtration  – Mathematical models; UCTD

Stellenbosch University https://scholar.sun.ac.za vii 7 Analytical Modelling Approach… …C Final Pressure Drop Predictions 116 Stellenbosch University https://scholar.sun.ac.za… …x5B;m2 ] permeability viii Stellenbosch University https://scholar.sun.ac.za ix L… …Stellenbosch University https://scholar.sun.ac.za x Greek symbols Notation: Units: Description… …Area SSR Sum of Squared Residuals RUC Representative Unit Cell Stellenbosch University… 

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APA (6th Edition):

van Jaarsveld, J. (2017). Predicting the effect of biofilm growth on the pressure drop over a biofilter. (Masters Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/102693

Chicago Manual of Style (16th Edition):

van Jaarsveld, Jacoline. “Predicting the effect of biofilm growth on the pressure drop over a biofilter.” 2017. Masters Thesis, Stellenbosch University. Accessed September 30, 2020. http://hdl.handle.net/10019.1/102693.

MLA Handbook (7th Edition):

van Jaarsveld, Jacoline. “Predicting the effect of biofilm growth on the pressure drop over a biofilter.” 2017. Web. 30 Sep 2020.

Vancouver:

van Jaarsveld J. Predicting the effect of biofilm growth on the pressure drop over a biofilter. [Internet] [Masters thesis]. Stellenbosch University; 2017. [cited 2020 Sep 30]. Available from: http://hdl.handle.net/10019.1/102693.

Council of Science Editors:

van Jaarsveld J. Predicting the effect of biofilm growth on the pressure drop over a biofilter. [Masters Thesis]. Stellenbosch University; 2017. Available from: http://hdl.handle.net/10019.1/102693

2. Kingwill, Russell. Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices.

Degree: MSc, Mathematical Sciences, 2019, Stellenbosch University

ENGLISH ABSTRACT : South Africa has a number of fuel grades available to consumers, one of the most popular being the 95 unleaded standard. The… (more)

Subjects/Keywords: Fuel prices  – Forecasting; Machine learning; Neural networks; UCTD; Brent crude oil  – Prices; Fuel prices  – Mathematical models

Stellenbosch University https://scholar.sun.ac.za CONTENTS vii 5 Results 48 5.1… …59 References 60 Stellenbosch University https://scholar.sun.ac.za Chapter 1… …on the quality of life of millions of people [4]. 1 Stellenbosch University… …to have a poor Stellenbosch University https://scholar.sun.ac.za CHAPTER 1. 3… …recovery implies that the Stellenbosch University https://scholar.sun.ac.za CHAPTER 1… 

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APA (6th Edition):

Kingwill, R. (2019). Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices. (Masters Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/105838

Chicago Manual of Style (16th Edition):

Kingwill, Russell. “Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices.” 2019. Masters Thesis, Stellenbosch University. Accessed September 30, 2020. http://hdl.handle.net/10019.1/105838.

MLA Handbook (7th Edition):

Kingwill, Russell. “Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices.” 2019. Web. 30 Sep 2020.

Vancouver:

Kingwill R. Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices. [Internet] [Masters thesis]. Stellenbosch University; 2019. [cited 2020 Sep 30]. Available from: http://hdl.handle.net/10019.1/105838.

Council of Science Editors:

Kingwill R. Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices. [Masters Thesis]. Stellenbosch University; 2019. Available from: http://hdl.handle.net/10019.1/105838

3. Kirsten, Gerhardus Petrus. Comparison of methods for solving Sylvester systems.

Degree: MSc, Mathematical Sciences, 2018, Stellenbosch University

ENGLISH ABSTRACT :This thesis serves as a comparative study of numerical methods for solving Sylvester equations, which are linear matrix equations of the form AX… (more)

Subjects/Keywords: Lyapunov functions; Algebras, Linear; Sylvester equations; UCTD; Differential equations  – Numerical solutions

Stellenbosch University https://scholar.sun.ac.za Matrix Decompositions Cholesky… …A = U` Σ` V`T . Here U` ∈ Rn×` , Σ` ∈ R`×` and V` ∈ Rm×` . vii Stellenbosch University… …https://scholar.sun.ac.za viii Matrix Decompositions Stellenbosch University https… …49 ix Stellenbosch University https://scholar.sun.ac.za x List of Figures 6.3 Low… …75 Stellenbosch University https://scholar.sun.ac.za List of Tables 4.1 Dense linear… 

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APA (6th Edition):

Kirsten, G. P. (2018). Comparison of methods for solving Sylvester systems. (Masters Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/104855

Chicago Manual of Style (16th Edition):

Kirsten, Gerhardus Petrus. “Comparison of methods for solving Sylvester systems.” 2018. Masters Thesis, Stellenbosch University. Accessed September 30, 2020. http://hdl.handle.net/10019.1/104855.

MLA Handbook (7th Edition):

Kirsten, Gerhardus Petrus. “Comparison of methods for solving Sylvester systems.” 2018. Web. 30 Sep 2020.

Vancouver:

Kirsten GP. Comparison of methods for solving Sylvester systems. [Internet] [Masters thesis]. Stellenbosch University; 2018. [cited 2020 Sep 30]. Available from: http://hdl.handle.net/10019.1/104855.

Council of Science Editors:

Kirsten GP. Comparison of methods for solving Sylvester systems. [Masters Thesis]. Stellenbosch University; 2018. Available from: http://hdl.handle.net/10019.1/104855

4. Rakotoarisoa, Avotra Elie. Incompressible flow with variations in density.

Degree: MSc, Mathematical Sciences, 2018, Stellenbosch University

ENGLISH ABSTRACT : This study involves the investigation of incompressible flow with variable density. The fact that variable density does not necessarily imply that the… (more)

Subjects/Keywords: Density; Fluids  – Migration; Navier-Stokes equations; Fluid mechanics; Boussinesq approximation; UCTD

Stellenbosch University https://scholar.sun.ac.za List of Figures 2.1 Different types… …vii Stellenbosch University https://scholar.sun.ac.za viii LIST OF FIGURES 5.11 The… …102 Stellenbosch University https://scholar.sun.ac.za Nomenclature Symbol Description R… …specific enthalpy 1 Stellenbosch University https://scholar.sun.ac.za η The specific entropy… …U at time tn 2 2 Stellenbosch University https://scholar.sun.ac.za Qni Approximate… 

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APA (6th Edition):

Rakotoarisoa, A. E. (2018). Incompressible flow with variations in density. (Masters Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/104909

Chicago Manual of Style (16th Edition):

Rakotoarisoa, Avotra Elie. “Incompressible flow with variations in density.” 2018. Masters Thesis, Stellenbosch University. Accessed September 30, 2020. http://hdl.handle.net/10019.1/104909.

MLA Handbook (7th Edition):

Rakotoarisoa, Avotra Elie. “Incompressible flow with variations in density.” 2018. Web. 30 Sep 2020.

Vancouver:

Rakotoarisoa AE. Incompressible flow with variations in density. [Internet] [Masters thesis]. Stellenbosch University; 2018. [cited 2020 Sep 30]. Available from: http://hdl.handle.net/10019.1/104909.

Council of Science Editors:

Rakotoarisoa AE. Incompressible flow with variations in density. [Masters Thesis]. Stellenbosch University; 2018. Available from: http://hdl.handle.net/10019.1/104909

5. Briers, C. J. Data-driven river flow routing using deep learning: predicting flow along the lower Orange river, Southern Africa.

Degree: MSc, Mathematical Sciences, 2019, Stellenbosch University

ENGLISH ABSTRACT : The Vanderkloof Dam, located on the Orange River, is responsible for the water supply to consumers along its 1 400 km reach… (more)

Subjects/Keywords: Streamflow  – South Africa; Machine learning; Deep learning; UCTD; Rivers  – Mathematical models; Orange River  – South Africa; Vanderkloof Dam  – South Africa; Vaal River  – South Africa

Stellenbosch University https://scholar.sun.ac.za 4.8 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8… …vii 73 75 76 77 77 78 79 Stellenbosch University https://scholar.sun.ac.za 6.8 7.1 7.2… …85 86 87 88 89 Stellenbosch University https://scholar.sun.ac.za List of Tables 4.1 4.2… …Stellenbosch University https://scholar.sun.ac.za 7.5 7.6 Comparing validation results produced by… …seasonal data. . . . 90 x Stellenbosch University https://scholar.sun.ac.za Chapter 1… 

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APA (6th Edition):

Briers, C. J. (2019). Data-driven river flow routing using deep learning: predicting flow along the lower Orange river, Southern Africa. (Masters Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/105754

Chicago Manual of Style (16th Edition):

Briers, C J. “Data-driven river flow routing using deep learning: predicting flow along the lower Orange river, Southern Africa.” 2019. Masters Thesis, Stellenbosch University. Accessed September 30, 2020. http://hdl.handle.net/10019.1/105754.

MLA Handbook (7th Edition):

Briers, C J. “Data-driven river flow routing using deep learning: predicting flow along the lower Orange river, Southern Africa.” 2019. Web. 30 Sep 2020.

Vancouver:

Briers CJ. Data-driven river flow routing using deep learning: predicting flow along the lower Orange river, Southern Africa. [Internet] [Masters thesis]. Stellenbosch University; 2019. [cited 2020 Sep 30]. Available from: http://hdl.handle.net/10019.1/105754.

Council of Science Editors:

Briers CJ. Data-driven river flow routing using deep learning: predicting flow along the lower Orange river, Southern Africa. [Masters Thesis]. Stellenbosch University; 2019. Available from: http://hdl.handle.net/10019.1/105754

6. Ofosu Mensah, Samuel. Transcriptomic profile based cancer disease prediction and patient survival time differentiation.

Degree: Mathematical Sciences, 2018, Stellenbosch University

ENGLISH ABSTRACT : Cancer disease is an abnormal growth of cells, which may be caused by mutations in genes which, as a result, alter the… (more)

Subjects/Keywords: Cancer Genome Atlas (TCGA); Breast  – Cancer  – Research; RNA–sequencing; Survival analysis (Biometry); UCTD

Stellenbosch University https://scholar.sun.ac.za Dedications I dedicate this work to… …my dear parents Mr and Mrs Emmanuel K. Mensah. vii Stellenbosch University https… …1 1 3 3 4 4 . . . . 5 5 7 8 9 . . . . 11 11 13 13 17 Stellenbosch University https… …Stellenbosch University https://scholar.sun.ac.za List of Figures 2.1 Number of articles published… …Stellenbosch University https://scholar.sun.ac.za List of Tables 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9… 

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APA (6th Edition):

Ofosu Mensah, S. (2018). Transcriptomic profile based cancer disease prediction and patient survival time differentiation. (Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/105059

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ofosu Mensah, Samuel. “Transcriptomic profile based cancer disease prediction and patient survival time differentiation.” 2018. Thesis, Stellenbosch University. Accessed September 30, 2020. http://hdl.handle.net/10019.1/105059.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ofosu Mensah, Samuel. “Transcriptomic profile based cancer disease prediction and patient survival time differentiation.” 2018. Web. 30 Sep 2020.

Vancouver:

Ofosu Mensah S. Transcriptomic profile based cancer disease prediction and patient survival time differentiation. [Internet] [Thesis]. Stellenbosch University; 2018. [cited 2020 Sep 30]. Available from: http://hdl.handle.net/10019.1/105059.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ofosu Mensah S. Transcriptomic profile based cancer disease prediction and patient survival time differentiation. [Thesis]. Stellenbosch University; 2018. Available from: http://hdl.handle.net/10019.1/105059

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Stellenbosch University

7. Kohlakala, Aviwe. Ear-based biometric authentication.

Degree: MSc, Mathematical Sciences, 2019, Stellenbosch University

ENGLISH ABSTRACT : In this thesis novel semi-automated and fully automated ear-based biometric authentication systems are proposed. Within the context of the semiautomated system, a… (more)

Subjects/Keywords: UCTD; Deep learning; Machine learning; Biometric identification; Ear authentication

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APA (6th Edition):

Kohlakala, A. (2019). Ear-based biometric authentication. (Masters Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/105976

Chicago Manual of Style (16th Edition):

Kohlakala, Aviwe. “Ear-based biometric authentication.” 2019. Masters Thesis, Stellenbosch University. Accessed September 30, 2020. http://hdl.handle.net/10019.1/105976.

MLA Handbook (7th Edition):

Kohlakala, Aviwe. “Ear-based biometric authentication.” 2019. Web. 30 Sep 2020.

Vancouver:

Kohlakala A. Ear-based biometric authentication. [Internet] [Masters thesis]. Stellenbosch University; 2019. [cited 2020 Sep 30]. Available from: http://hdl.handle.net/10019.1/105976.

Council of Science Editors:

Kohlakala A. Ear-based biometric authentication. [Masters Thesis]. Stellenbosch University; 2019. Available from: http://hdl.handle.net/10019.1/105976


Stellenbosch University

8. Makinde, Funmilayo Lydia. Systems level cancer disease target identification using tumor microenvironment dynamics.

Degree: MSc, Mathematical Sciences, 2018, Stellenbosch University

 ENGLISH ABSTRACT : Cancer is the second driving reasons for death around the world. It is an abnormal growth of cells which can migrate and… (more)

Subjects/Keywords: Tumor microenvironment (TME); Immunocompetent cells; Tumors  – Growth; Cytokines; Cancer  – Research  – Mathematical models; Proteins  – Research  – Mathematical models; UCTD

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APA (6th Edition):

Makinde, F. L. (2018). Systems level cancer disease target identification using tumor microenvironment dynamics. (Masters Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/104950

Chicago Manual of Style (16th Edition):

Makinde, Funmilayo Lydia. “Systems level cancer disease target identification using tumor microenvironment dynamics.” 2018. Masters Thesis, Stellenbosch University. Accessed September 30, 2020. http://hdl.handle.net/10019.1/104950.

MLA Handbook (7th Edition):

Makinde, Funmilayo Lydia. “Systems level cancer disease target identification using tumor microenvironment dynamics.” 2018. Web. 30 Sep 2020.

Vancouver:

Makinde FL. Systems level cancer disease target identification using tumor microenvironment dynamics. [Internet] [Masters thesis]. Stellenbosch University; 2018. [cited 2020 Sep 30]. Available from: http://hdl.handle.net/10019.1/104950.

Council of Science Editors:

Makinde FL. Systems level cancer disease target identification using tumor microenvironment dynamics. [Masters Thesis]. Stellenbosch University; 2018. Available from: http://hdl.handle.net/10019.1/104950


Stellenbosch University

9. Fick, Carlien. Modifying and generalising the Radon transform for improved curve-sensitive feature extraction.

Degree: MSc, Mathematical Sciences, 2017, Stellenbosch University

ENGLISH ABSTRACT : In this thesis a novel and generic feature extraction protocol that is based on the well-known standard discrete Radon transform (SDRT) is… (more)

Subjects/Keywords: Curve-sensitive feature extraction; UCTD; Radon transforms; Extraction protocol (Mathematics); Biometric identification; Signatures (Writing)  – Forgeries

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APA (6th Edition):

Fick, C. (2017). Modifying and generalising the Radon transform for improved curve-sensitive feature extraction. (Masters Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/102941

Chicago Manual of Style (16th Edition):

Fick, Carlien. “Modifying and generalising the Radon transform for improved curve-sensitive feature extraction.” 2017. Masters Thesis, Stellenbosch University. Accessed September 30, 2020. http://hdl.handle.net/10019.1/102941.

MLA Handbook (7th Edition):

Fick, Carlien. “Modifying and generalising the Radon transform for improved curve-sensitive feature extraction.” 2017. Web. 30 Sep 2020.

Vancouver:

Fick C. Modifying and generalising the Radon transform for improved curve-sensitive feature extraction. [Internet] [Masters thesis]. Stellenbosch University; 2017. [cited 2020 Sep 30]. Available from: http://hdl.handle.net/10019.1/102941.

Council of Science Editors:

Fick C. Modifying and generalising the Radon transform for improved curve-sensitive feature extraction. [Masters Thesis]. Stellenbosch University; 2017. Available from: http://hdl.handle.net/10019.1/102941

.