Puberty: Is Your Gingiva Having Mood Swings?

Objectives/aim: The purpose of this paper is to explore the effects on the different pathological changes in the oral cavity due to puberty, in both males and females. Hormonal changes caused by menstrual cycles, ovulation, the use contraceptives, and increased testosterone and estrogen levels. Methods: This topic will be analyzed by thoroughly reviewing research on articles that relate to the oral health of individuals specifically between the ages of 12-18 years old. Results: Research presents significant evidence that supports changes occurring in the oral cavity during an individual’s stage of puberty. These stages include ovulation, pre-menstruation, menstruation and males transitioning through puberty. During the puberty stage adolescents are more prone to have increased gingival crevicular fluid (GCF), gingival index, and bleeding on probing while research has shown no significant findings on plaque indexes or probing depths. Changes occurring during the menstrual cycle tend to influence the periodontium and induce inflammatory conditions as well. While the periodontium and inflammatory cytokines play a major role in the effects during puberty, changes in diet during this phase can increase the risk of developing caries as well. Conclusion: When adolescents are transitioning into adulthood, there are multiple changes their body goes through. During the literature review, many changes happen during puberty significantly affecting the oral cavity were discovered. These changes have both positive and negative effects. Variations in hormone levels and diet greatly influence the health of the oral cavity and can be a deciding factor on development or severity of oral disease.https://scholarscompass.vcu.edu/denh_student/1008/thumbnail.jp

Aerotherm charring materials ablation computer program

Ablating-surface boundary conditions involve considerations of surface thermochemistry. Several programs may be used to provide surface thermochemistry information

Aerotherm chemical equilibrium (ACE) computer program

Computer code was developed for calculating chemical quantities and qualities in equilibrium

Lithium-diffused solar cells Quarterly report, 1 Jul. - 30 Sep. 1968

Lithium diffused silicon solar cell

Development and fabrication of lithium- diffused silicon solar cells Final report, 18 Aug. - 31 Jan. 1968

Lithium-diffused p-n silicon solar cells of high conversion efficiency and improve resistance to space radiation effect

Development of radiation hardened lithium- doped solar cells Final report

Fabrication techniques to improve initial efficiency and radiation tolerance of radiation hardened lithium-diffused silicon solar cell

Evolving collective behavior in an artificial ecology

Collective behavior refers to coordinated group motion, common to many animals. The dynamics of a group can be seen as a distributed model, each “animal” applying the same rule set. This study investigates the use of evolved sensory controllers to produce schooling behavior. A set of artificial creatures “live” in an artificial world with hazards and food. Each creature has a simple artificial neural network brain that controls movement in different situations. A chromosome encodes the network structure and weights, which may be combined using artificial evolution with another chromosome, if a creature should choose to mate. Prey and predators coevolve without an explicit fitness function for schooling to produce sophisticated, nondeterministic, behavior. The work highlights the role of species’ physiology in understanding behavior and the role of the environment in encouraging the development of sensory systems

Geometric loss functions for camera pose regression with deep learning

Deep learning has shown to be effective for robust and real-time monocular image relocalisation. In particular, PoseNet is a deep convolutional neural network which learns to regress the 6-DOF camera pose from a single image. It learns to localize using high level features and is robust to difficult lighting, motion blur and unknown camera intrinsics, where point based SIFT registration fails. However, it was trained using a naive loss function, with hyper-parameters which require expensive tuning. In this paper, we give the problem a more fundamental theoretical treatment. We explore a number of novel loss functions for learning camera pose which are based on geometry and scene reprojection error. Additionally we show how to automatically learn an optimal weighting to simultaneously regress position and orientation. By leveraging geometry, we demonstrate that our technique significantly improves PoseNet's performance across datasets ranging from indoor rooms to a small city

Modelling uncertainty in deep learning for camera relocalization

We present a robust and real-time monocular six degree of freedom visual relocalization system. We use a Bayesian convolutional neural network to regress the 6-DOF camera pose from a single RGB image. It is trained in an end-to-end manner with no need of additional engineering or graph optimisation. The algorithm can operate indoors and outdoors in real time, taking under 6ms to compute. It obtains approximately 2m and 6 degrees accuracy for very large scale outdoor scenes and 0.5m and 10 degrees accuracy indoors. Using a Bayesian convolutional neural network implementation we obtain an estimate of the model's relocalization uncertainty and improve state of the art localization accuracy on a large scale outdoor dataset. We leverage the uncertainty measure to estimate metric relocalization error and to detect the presence or absence of the scene in the input image. We show that the model's uncertainty is caused by images being dissimilar to the training dataset in either pose or appearance

Thermochemical ablation of rocket nozzle insert materials Final report

Resistance of rocket nozzle throat insert materials to corrosion and meltin