11,146 research outputs found
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 individualās 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 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
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
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 āanimalā applying the same rule set. This study investigates the use of evolved sensory controllers to produce schooling behavior. A set of artificial creatures āliveā 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 speciesā 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
- ā¦