abstract: Negative bias temperature instability (NBTI) is a leading aging mechanism in modern digital and analog circuits. Recent NBTI data exhibits an excessive amount of randomness and fast recovery, which are difficult to be handled by conventional power-law model (tn). Such discrepancies further pose the challenge on long-term reliability prediction under statistical variations and Dynamic Voltage Scaling (DVS) in real circuit operation. To overcome these barriers, the modeling effort in this work (1) practically explains the aging statistics due to randomness in number of traps with log(t) model, accurately predicting the mean and variance shift; (2) proposes cycle-to-cycle model (from the first-principle of trapping) to handle aging under multiple supply voltages, predicting the non-monotonic behavior under DVS (3) presents a long-term model to estimate a tight upper bound of dynamic aging over multiple cycles, and (4) comprehensively validates the new set of aging models with 65nm statistical silicon data. Compared to previous models, the new set of aging models capture the aging variability and the essential role of the recovery phase under DVS, reducing unnecessary guard-banding during the design stage. With CMOS technology scaling, design for reliability has become an important step in the design cycle, and increased the need for efficient and accurate aging simulation methods during the design stage. NBTI induced delay shifts in logic paths are asymmetric in nature, as opposed to averaging effect due to recovery assumed in traditional aging analysis. Timing violations due to aging, in particular, are very sensitive to the standby operation regime of a digital circuit. In this report, by identifying the critical moments in circuit operation and considering the asymmetric aging effects, timing violations under NBTI effect are correctly predicted. The unique contributions of the simulation flow include: (1) accurate modeling of aging induced delay shift due to threshold voltage (Vth) shift using only the delay dependence on supply voltage from cell library; (2) simulation flow for asymmetric aging analysis is proposed and conducted at critical points in circuit operation; (3) setup and hold timing violations due to NBTI aging in logic and clock buffer are investigated in sequential circuits and (4) proposed framework is tested in VLSI applications such DDR memory circuits. This methodology is comprehensively demonstrated with ISCAS89 benchmark circuits using a 45nm Nangate standard cell library characterized using predictive technology models. Our proposed design margin assessment provides design insights and enables resilient techniques for mitigating digital circuit aging. / Dissertation/Thesis / Ph.D. Electrical Engineering 2012

Amifostine (WR-2721, delivered as Ethyol) is a radioprotector agent that reduces the likelihood of early and/or late biological effects by eliminating free radical particles during ionizing radiation fraction (radiotherapy). It activates in under normal tissues conditions to reduce mutation and fraction in DNA. Among 4000 prodrug compounds, amifostine is the only agent has been approved from the US Food and Drug Administration in clinical purposes. The main effective mechanisms of amifostine are based on scavenging for free radical, improving for DNA repair step and indication of cellular hypoxia. In the same time, this drug is not widely used around the world for different reasons mainly its high cost and toxicity level (lethal dose). Conjugating a monoclonal antibody with amifostine by a suitable linker is a process of Antibody Drug Conjugate producing immuno-radioprotector molecule hypothesis. Administrated molecule is an approach of targeted delivery therapy that increases the dosage uptake into particular area of treatment to minimize the dose distribution in non-targeted area in the body. In the present work, we proposed a three-compartment system model to simulate the two-pore theory pathway of an immuno-radioprotector molecule when it is crossing the physiological barriers. The model investigated its distribution and elimination in porous media (with both large and small pores) within a pharmacokinetics compartmented model approach.

This thesis is solving the issue of modeling and analysis of multi-level reward systems. The main goal is to design a solution that allows the development and testing of multi-level reward systems. The solution is based on the simulation of a dynamics reward system and the subsequent statistical analysis. The solution uses quantitative tools for simulation modeling and statistical analysis. The proposed solution eliminates the shortcomings of existing approaches, which are based strictly on methods of mathematical analysis. This invention makes possible the capture of not only the characteristics and functioning of the reward system, but also its stability and security. The output data are suitable for control and management decisions in firms that uses complex reward systems.

Biology is changing and becoming more quantitative. Research is creating new challenges that need to be addressed in education as well. New educational initiatives focus on combining laboratory procedures with mathematical skills, yet it seems that most curricula center on a single relationship between scientific knowledge and scientific method: that of the validity of knowledge claims, judged in terms of their consistency with data. Collecting data and obtaining results (however quantitative) are commonly part of science, but are not science itself. We envision that the operative use of the complete scientific method will play a critical role in providing the necessary underpinning for the integration of math and biology at various professional levels.

A Thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfillment of the requirements for the degree of Doctor of Philosophy Johannesburg, 2018 / Two dimensional gravity models and a few magnetic models limited to short profiles in the eastern sections of the BC have been used to propose conflicting geometries for the Rustenburg Layered Suite (RLS), especially between the western and eastern lobes; one school argued for completely separate intrusions, while the other proposed that the outcropping lobes are connected at depth. These competing models suggest different emplacement models. They provide a valuable starting point, but, 2.5D potential field modelling is not well suited to modelling complex three dimensional geology. Also, in previous work the magnetics or only the gravity data were modelled independently. Here I present the first full three dimensional potential field forward modelling of the central and southern Bushveld Complex (BC) which has been used to test the geometry of the Bushveld Complex in areas obscured by younger geological cover. Both gravity and magnetic data have been used to develop a geological model that is consistent with both data sets. Joint 3D modelling of regional gravity and magnetic data combined with published crustal thickness models derived from broadband seismic tomography studies were used to create a 3D model of the central and southeastern sections of the BC, as well as the southern part of the northern lobe. Eight downhole logs with more than 730 000 new density and magnetic susceptibility measurements from the RLS were combined with existing data to determine values for Bushveld Complex lithologies that were used in the model. The 3D model has a complex geometry with thick continuous RLS in most of the western lobe, thinner RLS in the south-eastern lobe, but a disrupted RLS in the eastern lobe. Large domes of Transvaal dolomite or thicker granites and granophyre in the eastern lobe interrupt the continuity of the RLS. The western and eastern lobes are therefore broadly connected, with major disruptions largely in the eastern lobe. However, they are not separate intrusions, but represent a single magma chamber affected by pre-existing and syn-magmatic updoming. The model includes the whole thickness of the crust and requires dense material at the crust mantle boundary. Thus, for the first time, I have imaged the remnants of magma staging chambers. The chambers, constrained by the extent of the diffuse crust-mantle transition as determined from teleseismic data, underlie the whole Bushveld Complex, but is thickest under the northern, south-eastern and western parts of the complex. I propose feeders that correlate with the chambers in the northern lobe near the large gravity high near Polokwane and in the south-eastern lobe. / MT 2019

<p>With increasing global renewable energy demands, there is a need for new materials with improved performance, lower cost, and less toxicity. One such application is photovoltaics, where halide perovskites (HaPs) represent the fastest growing market of absorbers owing to their impressive optoelectronic properties and excellent tunability from composition engineering and structural manipulation. However, the practically infinite composition-structure space of HaPs when considering cation and/or anion site mixing, octahedral distortion and rotation, and other forms of polymorphism, raise considerable challenges when comprehensively exploring their stability and optoelectronic properties. First principles calculations are powerful tools that can investigate large numbers of compounds and structures in a high-throughput fashion. </p><p>In my thesis, I performed high-throughput density functional theory (DFT) computations to generate a HaP dataset within a wide chemical space covering ~500 unique chemical compositions in the (pseudo-)cubic phase, across a 14-dimensional ionic space. This work explored both pure and alloyed compositions, with the latter simulated using the special quasi-random structures approach. Many critical properties were computed using the semi-local GGA-PBE and hybrid non-local HSE06 functionals, including decomposition and mixing energies, electronic band gap, and spectroscopic limited maximum efficiency (SLME), which is a theoretical surrogate for the likely absorption efficiency of the compound when used in a single-junction solar cell. Property screening over this dataset yielded 32 stable perovskite candidates with attractive optoelectronic properties.</p><p>Polymorphism in HaPs is investigated by simulating larger supercell alloys with different ionic ordering, generating compounds with random octahedral distortions and rotations, and optimizing various compositions in non-cubic phases such as tetragonal and orthorhombic. Linear correlation analysis is performed to gain a critical understanding of how properties are influenced by specific cations and anions, their mixing fractions, the perovskite phase, ionic clustering, and amount of strain or distortion in the lattice. Finally, trends, design rules, and predictive insights achieved from the DFT datasets are applied over a much larger set of thousands of hypothetical compounds, resulting in identification of more promising materials and understanding of the most important A-B-X combinations that yield multiple desired objectives.</p><p>Furthermore, a similar DFT workflow is applied for designing transition metal electrocatalysts. DFT simulations are performed to model Hydrogen adsorption, OH adsorption, and the water splitting reaction on Ni3N/Ni and Co2N/Co hybrid structures, to explore their likelihood in being used for Hydrogen Evolution Reaction (HER). The results reveal the excellent catalytic performance of transition metal and transition metal nitride hybrid structures.</p><p><br></p>

No description available.

Content augmentation strategies (CAS) are instructional methods which specify the overlaying of content objects by content augmentation objects in order to increase the effectiveness and efficiency of instruction. The goals of this research were to build a comprehensive framework around CASs, determine the experimental effects of CASs in an instructional virtual environment (VE), and make recommendations regarding the employment and further study of CASs in instructional virtual environments. The VE experiment examined the effectiveness and efficiency impact of six different content augmentation strategies which overlayed different content augmentation objects onto four immersive VE scenarios. Sixty university students, 40 men and 20 women, executed three CAS-enhanced training missions and one no-CAS test mission. The task involved the recall and correct application of specific rules for three subtasks of a military helicopter landing zone scouting mission. The strategies included a no-strategy control condition, an arrow condition, an audio coaching condition, a text coaching condition, an arrow plus audio coaching condition, and an arrow plus text coaching condition. Statistical and decision analyses were conducted on the effectiveness and efficiency performance data. Statistically significant differences were found which supported the general superiority of the audio content augmentation strategy for these tasks. This dissertation may be the first use of a decision analysis approach for analyzing the results of behavioral data for instructional design decisions. The decision analysis approach used decision trees, simulation and optimization to obtain content augmentation strategy rankings. As this approach is normally used for course of action analysis and comparing alternative system configurations, the validity of this approach in this context has yet to be determined. The decision analysis approach obtained plausible and similar, but not identical recommendations to the statistical approach. The decision analysis approach may constitute a limited instantiation of a proposed optimal stimulus set instructional design model which conceptually framed the experiment. Training guideline recommendations, experimental procedure recommendations, and a comprehensive framework for future research are also presented.

Safety training is a vital component to the well-being of individuals in all industries. With technology advancing at the current pace, conventional training methods are no longer the most effective way to communicate information. There is a strong need for safety training that incorporates new methods and forms of communication to obtain higher levels of comprehension. Virtual reality systems offer a highly customizable and interactive form of delivering information to users. This research addresses major gaps in the field of safety training using virtual reality systems and provides a design framework for creating a virtual safety-training system. A model for the virtual environment is designed and developed and the process and justification is described. The environment and an applied use case for this model is developed and verified using a sample of trainees that would use the model. This exploratory framework provides a significant contribution to the field of safety education through virtual reality systems and can be expanded with further research.

Unmanned ground vehicles (UGV) are being explored for use in military domains. Military UGVs operate in complex off-road environments. Vehicle mobility forecasting plays an important role in understanding how and where a vehicle can operate. Traditional mobility forecasting has been done using an analytical model known as the NATO Reference Mobility Model (NRMM). There has been a push to extend the forecasting capabilities of NRMM by integrating more simulation methods. Simulation enables the repeated testing of UGVs in scenarios that would be difficult or dangerous to study in real world testing. To accurately capture UGV performance in simulation, the operating environment must be accurately modeled. Current widely used methods for generating forested virtual environments rely on random methods. These methods result in forests that can appear to be realistic when visually inspected but lack the appropriate distribution of different sizes of vegetation. The size and distribution of vegetation plays a major role in the ability of a vehicle to operate in a forested environment. Therefore, there is a need for alternative forest generation algorithms that generate more realistic virtual forests. To address this, a novel environment generation model based on forest ecology was implemented. This model accurately captures vegetation growth, disbursement, and competition. Simulated UGV self-driving performance for scenes generated using the ecological model was compared to performance for scenes generated using a widely adopted random model. Resulting speeds across each scene were averaged to predict a speed made good (SMG). Vehicle SMG predictions were made in NRMM using scene descriptions matching each of the random and ecological scenes. Using a continuous vegetation override function in simulation, SMG predictions for both methods were similar to the results of NRMM. However, the predicted speeds for scenes generated with the ecological model were different from the predicted speeds for scenes generated with the random model. When examining the distribution and frequency of different sizes of trees, ecological scenes more closely match the distribution and frequency of trees that are expected for real forested environments suggesting that predictions for speed in ecological scenes better represent potential speeds for real environments.