The South African gold mining sector consumes 47% of the mining industry’s electricity. On a deep level gold mine, 20% of the energy is consumed by the refrigeration system. The refrigeration system cools 67 ˚C virgin rock temperatures underground. Underground cooling demand increases significantly with deeper mining activities. Various cooling systems are available for underground cooling. This study focuses on the electricity usage of an ice storage system versus a chilled water system for underground cooling. An energy-savings approach was developed to determine possible power savings on the surface refrigeration system of Mine M. The savings approach involved converting an ice storage system to a chilled water system and varying the water flow through the system. The water flow was varied by installing variable speed drives on the evaporator and condenser water pumps. The feasibility of the energy-efficiency approach was simulated with a verified simulation model. Simulation results indicated the feasibility of converting the thermal ice storage to a chilled water system and implementing the energy-efficiency approach on Mine M. Simulated results indicated a 9% electricity saving when using a chilled water system. Various problems encountered by the mine were also a motivation to convert the thermal ice storage system. Converting an ice storage facility to a chilled water system for energy efficiency on a deep level gold mine Energy management is achieved through the monitoring, controlling and reporting of the implemented savings approach. Converting the glycol plant and recommissioning the chilled water plant gave the mine an additional chiller as backup to sufficiently meet underground demand. An annual summer power saving of 1.5 MW was achieved through the conversion and control strategy. It is concluded that conversion of the thermal ice storage system on Mine M results in an energy- and cost saving. / MIng (Mechanical Engineering), North-West University, Potchefstroom Campus, 2015

The South African gold mining sector consumes 47% of the mining industry’s electricity. On a deep level gold mine, 20% of the energy is consumed by the refrigeration system. The refrigeration system cools 67 ˚C virgin rock temperatures underground. Underground cooling demand increases significantly with deeper mining activities. Various cooling systems are available for underground cooling. This study focuses on the electricity usage of an ice storage system versus a chilled water system for underground cooling. An energy-savings approach was developed to determine possible power savings on the surface refrigeration system of Mine M. The savings approach involved converting an ice storage system to a chilled water system and varying the water flow through the system. The water flow was varied by installing variable speed drives on the evaporator and condenser water pumps. The feasibility of the energy-efficiency approach was simulated with a verified simulation model. Simulation results indicated the feasibility of converting the thermal ice storage to a chilled water system and implementing the energy-efficiency approach on Mine M. Simulated results indicated a 9% electricity saving when using a chilled water system. Various problems encountered by the mine were also a motivation to convert the thermal ice storage system. Converting an ice storage facility to a chilled water system for energy efficiency on a deep level gold mine Energy management is achieved through the monitoring, controlling and reporting of the implemented savings approach. Converting the glycol plant and recommissioning the chilled water plant gave the mine an additional chiller as backup to sufficiently meet underground demand. An annual summer power saving of 1.5 MW was achieved through the conversion and control strategy. It is concluded that conversion of the thermal ice storage system on Mine M results in an energy- and cost saving. / MIng (Mechanical Engineering), North-West University, Potchefstroom Campus, 2015

Master of Science / Department of Architectural Engineering and Construction Science / Kimberly W. Kramer / Strut-and-Tie models are useful in designing reinforced concrete structures with discontinuity regions where linear stress distribution is not valid. Deep beams are typically short girders with a large point load or multiple point loads. These point loads, in conjunction with the depth and length of the members, contribute to a member with primarily discontinuity regions. ACI 318-08 Building Code Requirements for Structural Concrete provides a method for designing deep beams using either Strut-and-Tie models (STM) or Deep Beam Method (DBM). This report compares dimension requirements, concrete quantities, steel quantities, and constructability of the two methods through the design of three different deep beams. The three designs consider the same single span deep beam with varying height and loading patterns. The first design is a single span deep beam with a large point load at the center girder. The second design is the deep beam with the same large point load at a quarter point of the girder. The last design is the deep beam with half the load at the midpoint and the other half at the quarter point. These three designs allow consideration of different shear and STM model geometry and design considerations. Comparing the two different designs shows the shear or cracking control reinforcement reduces by an average 13% because the STM considers the extra shear capacity through arching action. The tension steel used for either flexure or the tension tie increases by an average of 16% from deep beam in STM design. This is due to STM taking shear force through tension in the tension reinforcement through arching action. The main advantage of the STM is the ability to decreased member depth without decreasing shear reinforcement spacing. If the member depth is not a concern in the design, the preferred method is DBM unless the designer is familiar with STMs due to the similarity of deep beam and regular beam design theory.

Static authentication, where user identity is checked once at login time, can be circumvented no matter how strong the authentication mechanism is. Through attacks such as man-in-the-middle and its variants, an authenticated session can be hijacked later after the initial login process has been completed. In the last decade, continuous authentication (CA) using biometrics has emerged as a possible remedy against session hijacking. CA consists of testing the authenticity of the user repeatedly throughout the authenticated session as data becomes available. CA is expected to be carried out unobtrusively, due to its repetitive nature, which means that the authentication information must be collectible without any active involvement of the user and without using any special purpose hardware devices (e.g. biometric readers). Stylometry analysis, which consists of checking whether a target document was written or not by a specific individual, could potentially be used for CA. Although stylometric techniques can achieve high accuracy rates for long documents, it is still challenging to identify an author for short documents, in particular when dealing with large author populations. In this dissertation, we propose a new framework for continuous authentication using authorship verification based on the writing style. Authorship verification can be checked using stylometric techniques through the analysis of linguistic styles and writing characteristics of the authors. Different from traditional authorship verification that focuses on long texts, we tackle the use of short messages. Shorter authentication delay (i.e. smaller data sample) is essential to reduce the window size of the re-authentication period in CA. We validate our method using different block sizes, including 140, 280, and 500 characters, and investigate shallow and deep learning architectures for machine learning classification. Experimental evaluation of the proposed authorship verification approach based on the Enron emails dataset with 76 authors yields an Equal Error Rate (EER) of 8.21% and Twitter dataset with 100 authors yields an EER of 10.08%. The evaluation of the approach using relatively smaller forgery samples with 10 authors yields an EER of 5.48%. / Graduate

Malware continues to be one of the primary tools employed by attackers. It is used in attacks ranging from click fraud to nation state espionage. Malware infects hosts over the network through drive-by downloads and social engineering. These infected hosts communicate with remote command and control (C&C) servers to perform tasks and exfiltrate data. Malware's reliance on the network provides an opportunity for the detection and annotation of malicious communication. This thesis presents four main contributions. First, we design and implement a novel incident investigation system, named WebWitness. It automatically traces back and labels the sequence of events (e.g., visited web pages) preceding malware downloads to highlight how users reach attack pages on the web; providing a better understanding of current attack trends and aiding in the development of more effective defenses. Second, we conduct the first systematic study of modern web based social engineering malware download attacks. From this study we develop a categorization system for classifying social engineering downloads and use it to measure attack properties. From these measurements we show that it is possible to detect the majority of social engineering downloads using features from the download path. Third, we design and implement ExecScent, a novel system for mining new malware C&C domains from live networks. ExecScent automatically learns C&C traffic models that can adapt to the deployment network's traffic. This adaptive approach allows us to greatly reduce the false positives while maintaining a high number of true positives. Lastly, we develop a new packet scheduling algorithm for deep packet inspection that maximizes throughput by optimizing for cache affinity. By scheduling for cache affinity, we are able to deploy our systems on multi-gigabit networks.

Our understanding of the pathophysiology Parkinson’s disease has transformed over the last decade as we have come to appreciate the importance of changes in neuronal firing pattern that occur within the motor network in the dopamine deficient state. These changes in firing pattern, particularly increased synchrony result in oscillations that can be recorded as local field potentials. This thesis concerns itself with the study of beta oscillations which are characteristic of Parkinson’s disease. Firstly, I investigate whether beta oscillations play a pathophysiological role in Parkinson’s disease or whether they are purely epiphenomenal by augmenting beta with low frequency deep brain stimulation. In this study I show that rigidity is increased by ~25% with low frequency stimulation providing significant further evidence for a patho-physiological role of beta in Parkinson’s disease. Next I investigate whether beta oscillations correlate with Parkinsonian severity at rest and could therefore potentially be used as a biomarker of clinical state. I demonstrate that the variability of beta amplitude recorded from the subthalamic nucleus strongly correlates with symptom severity at rest and also in response to levodopa administration. I then use beta amplitude as a biomarker for a trial of adaptive deep brain stimulation in Parkinson’s disease. I show that by using beta amplitude to control stimulation, time on stimulation is reduced by >50% but despite this, clinical outcome is improved by 25% relative to conventional continuous high frequency stimulation. Finally, I investigate the bilateral subcortical beta network and its response to levodopa. I report statistically significant bilateral functional connectivity in the beta range which is driven by phase locking and modulated by levodopa in the low beta range with implications for bilateral adaptive deep brain stimulation. These findings further our understanding of the pathophysiological role of beta oscillations in Parkinson’s disease and provide new avenues for treatment development.

Quantitative microscopy deals with the extraction of quantitative measurements from samples observed under a microscope. Recent developments in microscopy systems, sample preparation and handling techniques have enabled high throughput biological experiments resulting in large amounts of image data, at biological scales ranging from subcellular structures such as fluorescently tagged nucleic acid sequences to whole organisms such as zebrafish embryos. Consequently, methods and algorithms for automated quantitative analysis of these images have become increasingly important. These methods range from traditional image analysis techniques to use of deep learning architectures. Many biomedical microscopy assays result in fluorescent spots. Robust detection and precise localization of these spots are two important, albeit sometimes overlapping, areas for application of quantitative image analysis. We demonstrate the use of popular deep learning architectures for spot detection and compare them against more traditional parametric model-based approaches. Moreover, we quantify the effect of pre-training and change in the size of training sets on detection performance. Thereafter, we determine the potential of training deep networks on synthetic and semi-synthetic datasets and their comparison with networks trained on manually annotated real data. In addition, we present a two-alternative forced-choice based tool for assisting in manual annotation of real image data. On a spot localization track, we parallelize a popular compressed sensing based localization method and evaluate its performance in conjunction with different optimizers, noise conditions and spot densities. We investigate its sensitivity to different point spread function estimates. Zebrafish is an important model organism, attractive for whole-organism image-based assays for drug discovery campaigns. The effect of drug-induced neuronal damage may be expressed in the form of zebrafish shape deformation. First, we present an automated method for accurate quantification of tail deformations in multi-fish micro-plate wells using image analysis techniques such as illumination correction, segmentation, generation of branch-free skeletons of partial tail-segments and their fusion to generate complete tails. Later, we demonstrate the use of a deep learning-based pipeline for classifying micro-plate wells as either drug-affected or negative controls, resulting in competitive performance, and compare the performance from deep learning against that from traditional image analysis approaches.

Skanska Deep Green Cooling is a very energy efficient system for cooling of a building. The system use deep boreholes to cool the building. In the cold part of the year, when the temperature outside is lower than the temperature out from the boreholes, the boreholes will be charged with coldness and the heat in the boreholes are used to preheat the ventilation air. The system has potential to be more energy efficient by optimization of the pumps and the pressure drop in the system. Pressure drop is crucial for the pumps power requirement. In the report two methods are theoretic tested to lowering the pressure drop to lower levels. The first method work with full open control valves and the second method work with bypass of control valves. Energy saving of the two methods are 7 percent. / Skanska Deep Green Cooling är en energieffektiv metod för kontorskyla. Metoden bygger på att nyttja kylan som finns i marken för att kyla en byggnad. Under kalla delen av året så laddas berget åter med kyla samtidigt som värmen från berget förvärmer ventilationsluften. Deep Green Cooling bygger på principen att inte krångla till det i onödan, därmed har styrsystemet upprättats så enkelt som möjligt. Kylsystemet har i dag redan en mycket god verkningsgrad, men det finns möjlighet att höja den. Denna rapport tar upp två metoder för att effektivisera driften av Deep Green Cooling. Båda metoderna går ut på att minska onödiga förluster i systemet och därmed spara energi, teoretiska beräkningar visar på ungefär 7 procent i besparing.

The fundamental physics and techniques employed in Laplace transform deep level transient spectroscopy (L-DLTS) are reviewed. A Laplace-DLTS system has been constructed. The high resolving power of this system has been demonstrated experimentally. The L-DLTS system was applied to characterize the defects in undoped n-type ZnO thin film grown by the pulsed laser deposition (PLD) method. A 0.3 eV deep trap has been identified. The formations of Ec-0.39eV and Ec-0.20eVcan be enhanced when the sample surface is seriously damaged by high temperature annealing.AnEc-0.25eV trap is identified in the freshly grown samples, but would disappear after the storage of 3 months. Copper doped n-type ZnO thin film samples with low carrier concentration (n~〖10〗^16 〖cm〗^(-3)) were investigated by using both conventional and Laplace DLTS techniques. Positive DLTS signal peaks were detected that are suspected to be contributed by the minority carrier (hole carrier) emission. A physics model involving the inversion layer of a metal-insulator-semiconductor contact has been invoked to interpret the hole carrier concentration existing near the metal-semiconductor interface. Expression for the defect concentration is determined as a function of the temperature of DLTS peaks. AnEv+0.6eV defect with high concentration (N_T~〖10〗^17 〖cm〗^(-3)) was detected. The concentration of Ev+0.6eVcan be enhanced when the annealing temperature was increased from 750 to 900 degree C. / published_or_final_version / Physics / Master / Master of Philosophy

L’endométriose est définie comme la présence de tissu endométrial et de stroma en dehors de la cavité utérine. Ses localisations les plus fréquentes sont le péritoine pelvien et les ovaires. L’endométriose infiltrante est classiquement décrite comme la présence de tissu endométriotique plus de cinq millimètres sous le péritoine pelvien ou la séreuse d’un organe. Histologiquement il s’agit d’une lésion endométriotique mais qui contrairement aux lésions ovariennes ou péritonéales contient significativement plus de muscle lisse et de fibrose et est davantage associée à la douleur. Les lésions infiltrantes peuvent être responsables de dysménorrhée, dyspareunie profonde et douleurs pelviennes chroniques sévères ayant un charactère hyperalgique tel qu’on peut le retrouver dans les douleurs neuropathiques. Ces douleurs nécessitent souvent la prise de quantités importantes d’antalgiques et ont des répercussions importantes sur la vie professionnelle, quotidienne et sexuelle des femmes atteintes. L’endométriose infiltrante présente un rapport histologique étroit avec les structures nerveuses du rétropéritoine ou les nerfs des organes atteints. Dans sa localisation rectovaginale il existe une relation histologique étroite entre les lésions d’endométriose et les nerfs ainsi qu’une correlation entre l’intensité de la douleur et le nombre de structures nerveuses envahies par l’endometriose ou engaînées dans la fibrose. Ces lésions infiltrantes expriment le «nerve growth factor» (NGF), une neurotrophine qui joue un rôle clé dans la genèse de l’hyperalgie et de la douleur. Les structures nerveuses du rétropéritoine pelvien expriment quant à elles le récepteur spécifique pour la neurotrophine NGF. Le système «NGF-récepteur spécifique» peut être responsable d’un chimiotactisme tissulaire entre les tissus sécrétant du NGF et les nerfs qui expriment le récepteur pour le NGF. Le système «NGF- récepteur spécifique» au sein de la relation endométriose-nerfs pourrait rendre compte du caractère hyperalgique des lésions endométriotiques infiltrantes ainsi, qu’expliquer pourquoi les lésions nodulaires n’apparaissent que dans les sites anatomiques richement innervés (ligaments utérosacrés, lame rectovaginale, paroi du rectum ou du côlon…) et pas ailleurs. Le traitement de première intention est chirurgical. Il convient d’être suffisamment agressif sur les lésions tout en engendrant le moins de séquelles postopératoires possibles sachant que nombre de ces femmes sont stériles. En cas d’atteinte digestive basse, les modalités de l’intervention sont dictées par l’extension et le degré d’infiltration de la paroi digestive. Dans le but de réaliser dans la majorité des cas une chirurgie minimalement invasive (laparoscopique) avec des cicatrices de petites tailles, nous avons développé une stratégie de traitement basée sur le degré d’infiltration de la paroi digestive. Dans ce cadre nous avons développé une technique laparo-assistée de résection colique segmentaire et de résection antérieure du rectum.