The eggs of the dengue fever vector Aedes aegypti possess the ability to undergo an extended quiescence period hosting a fully developed first instar larvae within its chorion. As a result of this life history stage, pharate larvae can withstand months of dormancy inside the egg where they depend on stored reserves of maternal origin. This adaptation known as pharate first instar quiescence, allows A. aegypti to cope with fluctuations in water availability. An examination of this fundamental adaptation has shown that there are trade-offs associated with it. Aedes aegypti mosquitoes are frequently associated with urban habitats that may contain metal pollution. My research has demonstrated that the duration of this quiescence and the extent of nutritional depletion associated with it affects the physiology and survival of larvae that hatch in a suboptimal habitat; nutrient reserves decrease during pharate first instar quiescence and alter subsequent larval and adult fitness. The duration of quiescence compromises metal tolerance physiology and is coupled to a decrease in metallothionein mRNA levels. My findings also indicate that even low levels of environmentally relevant larval metal stress alter the parameters that determine vector capacity. My research has also demonstrated that extended pharate first instar quiescence can elicit a plastic response resulting in an adult phenotype distinct from adults reared from short quiescence eggs. Extended pharate first instar quiescence affects the performance and reproductive fitness of the adult female mosquito as well as the nutritional status of its progeny via maternal effects in an adaptive manner, i.e., anticipatory phenotypic plasticity results as a consequence of the duration of pharate first instar quiescence and alternative phenotypes may exist for this mosquito with quiescence serving as a cue possibly signaling the environmental conditions that follow a dry period. M findings may explain, in part, A. aegypti’s success as a vector and its geographic distribution and have implications for its vector capacity and control.

Human alteration of the landscape and environment is favoring the expansion of ticks and the pathogens they vector throughout the United States. The changes in these distributions are documented through literature and the deposition of specimens in scientific collections. In southern Illinois, tick species records are sporadic and inconsistent with what is actually found in the region. This information is necessary for public health officials to develop prevention strategies against tick-borne illnesses. I conducted research from 2018-2020 to accomplish the following: (1) conduct a ticks species and tick-borne disease survey of southern Illinois, (2) create habitat maps for Amblyomma americanum and Dermacentor variabilis in the region, and (3) attempt to identify microhabitat variables influencing tick abundance. To accomplish my first objective, I sampled 26 sites from 11 counties in southern Illinois utilizing the tick drag method. Ticks were then tested for their associated pathogens using quantitative PCR. Four species of tick, Amblyomma americanum (the lone star tick), Dermacentor variabilis (the American dog tick), Ixodes scapularis (the black-legged deer tick), and Amblyomma maculatum (the Gulf Coast tick) were identified. Rickettsia rickettsii and Rickettsia parkeri causative agents of human rickettsiosis, Ehrilichia ewingii and Ehrlichia chaffeensis, causative agents of human granulocytic ehrlichiosis, and Rickettsia amblyommii whose effects are not known, were all found in the study region. The second objective, lone star and American dog tick species distribution models (SDMs), was accomplished using the popular SDM program, Maxent. Maxent uses species presence data and their associated environmental characteristics to create predictions of habitat suitability. The resulting models indicate a correlation for both species with the Shawnee National Forest, likely due to the fragmented habitat structure as indicated by the strong impact of cultivated and managed vegetation on both species models. Lastly, I attempted to identify microhabitat characteristics that influence tick species abundance. Temperature, humidity, canopy closure, and wind speed were measured and recorded and vegetation density estimates and soil moisture were recorded categorically at each site, each visit. Using a chi-squared test I proved that tick abundance is not static, and varies throughout the summer months. I then used a principal component analysis (PCA) to assess correlation between habitat variables and tick abundance, but no signal was detected. This study established a baseline for tick species and tick-borne disease presence in southern Illinois. The information resulting from this study can be used to inform public health officials and inform future tick-borne disease prevention strategies. I recommend continued surveillance of the area to monitor species distributions and resulting pathogen risk to residents.

Income inequality has been showing a steady increase for past decades and will be worsened in the future (Piketty, 2014). One of the most important factors to explain the worsening income inequality can be aging. Previous studies on aging focus on its impact on traditional issues such as health, retirement, and economic growth. This study finds the direct relationship between aging and income inequality using the vector autoregressive (VAR) model (Blanchard and Quah, 1989). The VAR model is useful to analyze the long-run response of aging on income inequality. The empirical results will verify the negative impact of aging on income inequality in the U.S. The governmental efforts to reduce the negative impact of aging on health care and pensions could delay the worsening income inequality.

No description available.

A Thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy, 2017 / The utility of lectures is directly related to the engagement of the students therein. To ensure the value of lectures, one needs to be certain that they are engaging to students. In small classes experienced lecturers develop an intuition of how engaged the class is as a whole and can then react appropriately to remedy the situation through various strategies such as breaks or changes in style, pace and content. As both the number of students and size of the venue grow, this type of contingent teaching becomes increasingly difficult and less precise. Furthermore, relying on intuition alone gives no way to recall and analyse previous classes or to objectively investigate trends over time. To address these problems this thesis presents the WITS INTELLIGENT TEACHING SYSTEM (WITS) to highlight disengaged students during class. A web-based, mobile application called Engage was developed to try elicit anonymous engagement information directly from students. The majority of students were unwilling or unable to self-report their engagement levels during class. This stems from a number of cultural and practical issues related to social display rules, unreliable internet connections, data costs, and distractions. This result highlights the need for a non-intrusive system that does not require the active participation of students. A nonintrusive, computer vision and machine learning based approach is therefore proposed. To support the development thereof, a labelled video dataset of students was built by recording a number of first year lectures. Students were labelled across a number of affects – including boredom, frustration, confusion, and fatigue – but poor inter-rater reliability meant that these labels could not be used as ground truth. Based on manual coding methods identified in the literature, a number of actions, gestures, and postures were identified as proxies of behavioural engagement. These proxies are then used in an observational checklist to mark students as engaged or not. A Support Vector Machine (SVM) was trained on Histograms of Oriented Gradients (HOG) to classify the students based on the identified behaviours. The results suggest a high temporal correlation of a single subject’s video frames. This leads to extremely high accuracies on seen subjects. However, this approach generalised poorly to unseen subjects and more careful feature engineering is required. The use of Convolutional Neural Networks (CNNs) improved the classification accuracy substantially, both over a single subject and when generalising to unseen subjects. While more computationally expensive than the SVM, the CNN approach lends itself to parallelism using Graphics Processing Units (GPUs). With GPU hardware acceleration, the system is able to run in near real-time and with further optimisations a real-time classifier is feasible. The classifier provides engagement values, which can be displayed to the lecturer live during class. This information is displayed as an Interest Map which highlights spatial areas of disengagement. The lecturer can then make informed decisions about how to progress with the class, what teaching styles to employ, and on which students to focus. An Interest Map was presented to lecturers and professors at the University of the Witwatersrand yielding 131 responses. The vast majority of respondents indicated that they would like to receive live engagement feedback during class, that they found the Interest Map an intuitive visualisation tool, and that they would be interested in using such technology. Contributions of this thesis include the development of a labelled video dataset; the development of a web based system to allow students to self-report engagement; the development of cross-platform, open-source software for spatial, action and affect labelling; the application of Histogram of Oriented Gradient based Support Vector Machines, and Deep Convolutional Neural Networks to classify this data; the development of an Interest Map to intuitively display engagement information to presenters; and finally an analysis of acceptance of such a system by educators. / XL2017

This research focuses on the development of a novel adaptive dynamical system approach to vector quantization or clustering based on only ordinary differential equations (ODEs) with potential for a real-time implementation. The ODE-based approach has an advantage in making it possible real-time implementation of the system with either electronic or photonic analog devices. This dynamical system consists of a set of energy functions which create valleys for representing clusters. Each valley represents a cluster of similar input patterns. The proposed system includes a dynamic parameter, called vigilance parameter. This parameter approximately reflects the radius of the generated valleys. Through several examples of different pattern clusters, it is shown that the model can successfully quantize/cluster these types of input patterns. Also, a hardware implementation by photonic and/or electronic analog devices is given In addition, we analyze and study stability of our dynamical system. By discovering the equilibrium points for certain input patterns and analyzing their stability, we have shown the quantizing behavior of the system with respect to its parameters. We also extend our model to include competition mechanism and vigilance dynamics. The competition mechanism causes only one label to be assigned to a group of patterns. The vigilance dynamics adjust vigilance parameter so that the cluster size or the quantizing resolution can be adaptive to the density and distribution of the input patterns. This reduces the burden of re-tuning the vigilance parameter for a given input pattern set and also better represents the input pattern space. The vigilance parameter approximately reflects the radius of the generated valley for each cluster. Making this parameter dynamic allows the bigger cluster to have a bigger radius and as a result a better cluster. Furthermore, an alternative dynamical system to our proposed system is also introduced. This system utilizes sigmoid and competitive functions. Although the results of this system are encouraging, the use of sigmoid function makes analyze and study stability of the system extremely difficult.

Data generated by complex, computational models can provide highly accurate predictions of hydrological and hydrodynamic data in multiple dimensions. Unfortunately, however, for large data sets, running these models is often timeconsuming and computationally expensive. Thus, finding a way to reduce the running time of these models, while still producing comparable results, is of notable interest. The Inference Engine is a proposed system for doing just this. It takes previously generated model data and uses them to predict additional data. Its performance, both accuracy and running time, has been compared to the performance of the actual models, in increasingly difficult data prediction tasks, and it is able, with sufficient accuracy, to quickly predict unknown model data.

No description available.

No description available.

No description available.