Electro-optic probes and test generators for 500 Kv nanosecond pulses

This thesis describes a research investigation into novel probes for the measurement of ultra-fast voltage pulses with a peak value up to 500 kV, and during the course of the work five probes were constructed for voltages of between 60 kV and 500 kV. Essential requirements of the probes are that they should be immune to high-levels of electromagnetic noise and also isolate the measuring equipment from the high voltage of the test circuit. Their designs were therefore based on an electro-optic (Pockels) Cell rather than on an electromagnetic device. The first item in the probe is a capacitive divider that attenuates the high-voltage under investigation to the level that can be fed to a Pockels Cell. Light from a laser is circularly polarized and passes through the Cell, with the attenuated voltage pulse causing the Cell crystal to change its molecular shape. This produces a change in the refractive index of the crystal and the emerging light signal becomes elliptically polarized. After conversion into electrical form, a waveform is displayed on an oscilloscope that is an accurate representation of the input voltage to the capacitor. To test the performance of a probe requires a generator capable of producing the required high voltage with a very short rise time, and a number of these were developed for use with the different probes. Careful comparisons of the performance with that of several commercially available probes showed unequivocally that the new probes were far superior. The thesis concludes by presenting ideas for future probe designs and suggests what form the ultimate probe might take. Much of the work reported in the thesis has already been presented at major international conferences or in prestigious academic journals

Central Lines in Children

Children with special health care needs are increasingly prevalent in US hospitals. The pediatric hospitalist is often the primary provider of inpatient care for these patients. However, exposure to this patient population during training varies from provider to provider. No published educational curricula are specific to the inpatient care of this population. This publication is a self-directed education module on central lines in children, including indications for placement, types of lines available, and prevention and treatment of complications. The module includes a PowerPoint slide show, instructor guide, and questions for assessment. The purpose of this project is to build a multimodal educational curriculum for providers with the overall goal of improving inpatient care for this at-risk population. This curriculum is primarily composed of a series of topic-specific multimedia learning modules. Asynchronous learning modules, utilized appropriately, can augment learning by providing individualized instruction and mastery of fundamentals. This module has been piloted among pediatric hospitalists in two institutions with effectiveness measured by pre- and posttest surveys. Please see the file attachment entitled CCC Central Lines Module Pilot Data.pdf. AAMC MedEdPORTAL publication ID 10246. Link to origina

Deep R learning for continual area sweeping

In order to maintain robustness, autonomous robots need to constantly update their knowledge of the environment, which can be expensive when they are deployed in large, dynamic spaces. The continual area sweeping task formalizes the problem of a robot continually patrolling an area in a non-uniform way in order to efficiently use travel time. However, the existing problem formulation makes strong assumptions about the environment, and to date only a sub-optimal greedy approach has been proposed. We generalize the continual area sweeping formulation to include fewer environmental constraints, and propose a novel reinforcement learning approach. We evaluate our approach in an abstract simulation and in a high fidelity Gazebo simulation, which shows significant improvement upon the initial approach in general settingsComputational Science, Engineering, and Mathematic

Pharmacokinetic profile of phytoconstituent(s) isolated from medicinal plants—A comprehensive review

AbstractHerbal medicine, the backbone of traditional medicine, has played an important role in human health and welfare for a long period. Traditional therapeutic approaches of regional significance are found in Africa, South and Central America, China, India, Tibet, Indonesia, and the Pacific Islands. The considerable scientific significance and commercial potential of traditional medicines have resulted in increased international attention and global market demands for herbal medicines, especially Chinese herbal medicines. Herbal medicines currently are the primary form of health care for the poor in the developing countries, and also are widely used as a supplement or substitute for conventional drugs in developed countries. These traditional medicines have a pivotal role in the treatment of various ailments and more than 50% of drugs used in Western pharmacopoeia are isolated from herbs or derived from modifications of chemicals found in plants. Herbal medicines usually contain a complex mixture of various bioactive molecules, which make its standardization complicated, and there is little information about all compounds responsible for pharmacological activity. Several research papers have been published that claim pharmacological activity of herbal medicines but few are discussing the role of the exact phytoconstituent. Understanding the pharmacokinetic profile of such phytoconstituents is essential. Although there are research papers that deal with pharmacokinetic properties of phytoconstituents, there are a number of phytoconstituents yet to be explored for their kinetic properties. This article reviews the pharmacokinetic profile of 50 different therapeutically effective traditional medicinal plants from the year 2003 onward

Deep R-Learning for Continual Area Sweeping

This publication is by UT affiliates that was featured in the October Good Systems Network Digest in 2020.Office of the VP for Researc

NeuroCUT: A Neural Approach for Robust Graph Partitioning

Graph partitioning aims to divide a graph into $k$ disjoint subsets while optimizing a specific partitioning objective. The majority of formulations related to graph partitioning exhibit NP-hardness due to their combinatorial nature. As a result, conventional approximation algorithms rely on heuristic methods, sometimes with approximation guarantees and sometimes without. Unfortunately, traditional approaches are tailored for specific partitioning objectives and do not generalize well across other known partitioning objectives from the literature. To overcome this limitation, and learn heuristics from the data directly, neural approaches have emerged, demonstrating promising outcomes. In this study, we extend this line of work through a novel framework, NeuroCut. NeuroCut introduces two key innovations over prevailing methodologies. First, it is inductive to both graph topology and the partition count, which is provided at query time. Second, by leveraging a reinforcement learning based framework over node representations derived from a graph neural network, NeuroCut can accommodate any optimization objective, even those encompassing non-differentiable functions. Through empirical evaluation, we demonstrate that NeuroCut excels in identifying high-quality partitions, showcases strong generalization across a wide spectrum of partitioning objectives, and exhibits resilience to topological modifications

Role of nutritional supplements in the management of tendinopathies: focus on combination of type 1 collagen, vitamin C and mucopolysaccharides

Tendinopathy is a common disease that is difficult to manage due to its recurrent nature. It is associated with increased healthcare costs and significantly impacts quality of life of patients. Also, according to recent studies patients with high cholesterol and diabetes are at a higher risk of developing tendinopathy. There has been rise in the incidence of tendinopathies due to increase in sport activities, life expectancy and some other factors (environment, diet and some drug therapies). Approximately 30% of visits for musculoskeletal pain in general practice are related to tendon injury. Non-steroidal anti-inflammatory drugs (NSAIDs) and corticosteroids remain the mainstay of treatment. Despite the use of current therapies, there is need of a supportive therapy that can help in the healing process towards development of physiologically normal tendons. Nutraceuticals have been used as supportive therapy for management of tendinopathies. This review focuses on the management of tendinopathy with special attention on role of nutraceuticals such as type I collagen, mucopolysaccharides and vitamin C in the management of tendinopathy. Clinical data suggests that this combination (type I collagen, mucopolysaccharides and vitamin C) promotes the endogenous synthesis of collagen type I, avoiding the accumulation of collagen type III and aggrecan, thus interfering with the degeneration of tendon tissue. Based on the available clinical data, combination of type I collagen, mucopolysaccharides and vitamin C not only reduce the clinical symptoms but also improve structural evolution of different types of tendinopathies as well as plantar fascitis

Temporal-Logic-Based Reward Shaping for Continuing Learning Tasks

In continuing tasks, average-reward reinforcement learning may be a more appropriate problem formulation than the more common discounted reward formulation. As usual, learning an optimal policy in this setting typically requires a large amount of training experiences. Reward shaping is a common approach for incorporating domain knowledge into reinforcement learning in order to speed up convergence to an optimal policy. However, to the best of our knowledge, the theoretical properties of reward shaping have thus far only been established in the discounted setting. This paper presents the first reward shaping framework for average-reward learning and proves that, under standard assumptions, the optimal policy under the original reward function can be recovered. In order to avoid the need for manual construction of the shaping function, we introduce a method for utilizing domain knowledge expressed as a temporal logic formula. The formula is automatically translated to a shaping function that provides additional reward throughout the learning process. We evaluate the proposed method on three continuing tasks. In all cases, shaping speeds up the average-reward learning rate without any reduction in the performance of the learned policy compared to relevant baselines