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Search Results: 1 - 10 of 27523 matches for " Chaolang Hu "
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Productivity Formulae of an Infinite-Conductivity Hydraulically Fractured Well Producing at Constant Wellbore Pressure Based on Numerical Solutions of a Weakly Singular Integral Equation of the First Kind
Chaolang Hu,Jing Lu,Xiaoming He
Mathematical Problems in Engineering , 2012, DOI: 10.1155/2012/428596
Abstract: In order to increase productivity, it is important to study the performance of a hydraulically fractured well producing at constant wellbore pressure. This paper constructs a new productivity formula, which is obtained by solving a weakly singular integral equation of the first kind, for an infinite-conductivity hydraulically fractured well producing at constant pressure. And the two key components of this paper are a weakly singular integral equation of the first kind and a steady-state productivity formula. A new midrectangle algorithm and a Galerkin method are presented in order to solve the weakly singular integral equation. The numerical results of these two methods are in accordance with each other. And then the solutions of the weakly singular integral equation are utilized for the productivity formula of hydraulic fractured wells producing at constant pressure, which provide fast analytical tools to evaluate production performance of infinite-conductivity fractured wells. The paper also shows equipotential threads, which are generated from the numerical results, with different fluid potential values. These threads can be approximately taken as a family of ellipses whose focuses are the two endpoints of the fracture, which is in accordance with the regular assumption in Kuchuk and Brigham, 1979.
On Relationship between Pediatric Shi Ji and Fever  [PDF]
Xiangyu Hu, Lina Hu
Open Journal of Pediatrics (OJPed) , 2015, DOI: 10.4236/ojped.2015.53036
Abstract: Based on the clinical effect of the treatment on 546 Pediatric Shi Ji fever cases, the thesis tries to explore the effectiveness of Traditional Chinese Medicine(TCM) treatment on Pediatric Shi Ji and the relationship between Pediatric Shi Ji and fever. The methodology applied is a retrospective analysis on the clinical curative effect of TCM treatment on Shi Ji fever cases in our hospital from January 2008 to December 2012. And the results show that a total effective rate of 96.3% could be guaranteed through either oral Chinese Medicinal Herbs, Chinese Medicine Enema, Massage Therapy, or navel administration with TCM. The thesis holds that Pediatric Shi Ji may cause fever, which could be cured simply by applying TCM treatment (promoting digestion to eliminate stagnation) with less or no use of antibiotics.
A Variational Model for Removing Multiple Multiplicative Noises  [PDF]
Xuegang Hu, Yan Hu
Open Journal of Applied Sciences (OJAppS) , 2015, DOI: 10.4236/ojapps.2015.512075
Abstract: The problem of multiplicative noise removal has been widely studied in recent years. Many methods have been used to remove it, but the final results are not very excellent. The total variation regularization method to solve the problem of the noise removal can preserve edge well, but sometimes produces undesirable staircasing effect. In this paper, we propose a variational model to remove multiplicative noise. An alternative algorithm is employed to solve variational model minimization problem. Experimental results show that the proposed model can not only effectively remove Gamma noise, but also Rayleigh noise, as well as the staircasing effect is significantly reduced.
Training a Quantum Neural Network to Solve the Contextual Multi-Armed Bandit Problem  [PDF]
Wei Hu, James Hu
Natural Science (NS) , 2019, DOI: 10.4236/ns.2019.111003
Abstract: Artificial intelligence has permeated all aspects of our lives today. However, to make AI behave like real AI, the critical bottleneck lies in the speed of computing. Quantum computers employ the peculiar and unique properties of quantum states such as superposition, entanglement, and interference to process information in ways that classical computers cannot. As a new paradigm of computation, quantum computers are capable of performing tasks intractable for classical processors, thus providing a quantum leap in AI research and making the development of real AI a possibility. In this regard, quantum machine learning not only enhances the classical machine learning approach but more importantly it provides an avenue to explore new machine learning models that have no classical counterparts. The qubit-based quantum computers cannot naturally represent the continuous variables commonly used in machine learning, since the measurement outputs of qubit-based circuits are generally discrete. Therefore, a continuous-variable (CV) quantum architecture based on a photonic quantum computing model is selected for our study. In this work, we employ machine learning and optimization to create photonic quantum circuits that can solve the contextual multi-armed bandit problem, a problem in the domain of reinforcement learning, which demonstrates that quantum reinforcement learning algorithms can be learned by a quantum device.
Q Learning with Quantum Neural Networks  [PDF]
Wei Hu, James Hu
Natural Science (NS) , 2019, DOI: 10.4236/ns.2019.111005
Abstract: Applying quantum computing techniques to machine learning has attracted widespread attention recently and quantum machine learning has become a hot research topic. There are three major categories of machine learning: supervised, unsupervised, and reinforcement learning (RL). However, quantum RL has made the least progress when compared to the other two areas. In this study, we implement the well-known RL algorithm Q learning with a quantum neural network and evaluate it in the grid world environment. RL is learning through interactions with the environment, with the aim of discovering a strategy to maximize the expected cumulative rewards. Problems in RL bring in unique challenges to the study with their sequential nature of learning, potentially long delayed reward signals, and large or infinite size of state and action spaces. This study extends our previous work on solving the contextual bandit problem using a quantum neural network, where the reward signals are immediate after each action.
Reinforcement Learning with Deep Quantum Neural Networks  [PDF]
Wei Hu, James Hu
Journal of Quantum Information Science (JQIS) , 2019, DOI: 10.4236/jqis.2019.91001
Abstract: The advantage of quantum computers over classical computers fuels the recent trend of developing machine learning algorithms on quantum computers, which can potentially lead to breakthroughs and new learning models in this area. The aim of our study is to explore deep quantum reinforcement learning (RL) on photonic quantum computers, which can process information stored in the quantum states of light. These quantum computers can naturally represent continuous variables, making them an ideal platform to create quantum versions of neural networks. Using quantum photonic circuits, we implement Q learning and actor-critic algorithms with multilayer quantum neural networks and test them in the grid world environment. Our experiments show that 1) these quantum algorithms can solve the RL problem and 2) compared to one layer, using three layer quantum networks improves the learning of both algorithms in terms of rewards collected. In summary, our findings suggest that having more layers in deep quantum RL can enhance the learning outcome.
Receptor binding specificity and origin of 2009 H1N1 pandemic influenza virus  [PDF]
Wei Hu
Natural Science (NS) , 2011, DOI: 10.4236/ns.2011.33030
Abstract: Recently, a genetic variant of 2009 H1N1 has become the predominant virus circulating in the southern hemisphere, particularly Australia and New Zealand, and in Singapore during the winter of 2010. It was associated with several vaccine breakthroughs and fatal cases. We analyzed three reported mutations D94N, N125D, and V250A in the HA protein of this genetic variant. It appeared that the reason for D94N and V250A to occur in pairs was to maintain the HA binding to human type receptor, so the virus could replicate in humans efficiently. Guided by this interpretation, we discovered a new mutation V30A that could compensate for N125D as V250A did for D94N. We demonstrated that the presence of amino acids 30A and 125N in HA enhanced the binding to human type receptor, while 30V and 125D favored the receptors of avian type and of A/South Carolina/1/18 (H1N1). Furthermore, a combination of 94D, 125D, and 250V made the primary binding preference similar to that of A/South Carolina/1/18 (H1N1) and a combination of 94N, 125D, and 250A resulted in the primary binding affinity for avian type receptor, which clearly differed from that of A/California/07/2009 (H1N1), a strain used in the vaccine for 2009 H1N1. We also re-examined the origin of 2009 H1N1 to refine our knowledge of this important issue. Although the NP, PA, PB1, and PB2 of 2009 H1N1 were closest to North American swine H3N2 in sequence identity, their interaction patterns were closest to swine H1N1 in North America.
Highly conserved domains in hemagglutinin of influenza viruses characterizing dual receptor binding  [PDF]
Wei Hu
Natural Science (NS) , 2010, DOI: 10.4236/ns.2010.29123
Abstract: The hemagglutinin (HA) of influenza viruses in itiates virus infection by binding receptors on host cells. Human influenza viruses preferenti ally bind to receptors with α2,6 linkages to gala ctose, avian viruses prefer receptors with α2,3 linkages to galactose, and swine viruses favor both types of receptors. The pandemic H1N1 2009 remains a global health concern in 2010. The novel 2009 H1N1 influenza virus has its ge netic components from avian, human, and sw ine viruses. Its pandemic nature is characterized clearly by its dual binding to the α2,3 as well as α2,6 receptors, because the seasonal human H1N1 virus only binds to the α2,6 receptor. In pr evious studies, the informational spectrum me thod (ISM), a bioinformatics method, was appli ed to uncover highly conserved regions in the HA protein associated with the primary receptor binding preference in various subtypes. In the present study, we extended the previous work by discovering multiple domains in HA associa ted with the secondary receptor binding prefer ence in various subtypes, thus characterizing the distinct dual binding nature of these viruses. The domains discovered in the HA proteins were mapped to the 3D homology model of HA, which could be utilized as therapeutic and diag nostic targets for the prevention and treatment of influenza infection.
Subtle differences in receptor binding specificity and gene sequences of the 2009 pandemic H1N1 influenza virus  [PDF]
Wei Hu
Advances in Bioscience and Biotechnology (ABB) , 2010, DOI: 10.4236/abb.2010.14040
Abstract: A recent phylogenetic inference indicated that the 2009 pandemic H1N1 strains circulating from March 2009 to September 2009 could be divided into two closely related but distinct clusters. Cluster one contained most strains from Mexico, Texas, and California, and cluster two had most strains from New York, both of which were reported to co-circulate in all continents. The same study further revealed nine nucleotide changes in six gene segments of the new virus specific for the two clusters. In the current study, the informational spectrum method (ISM), a bioinformatics technique, was employed to study the receptor binding patterns of the two clusters. It discovered that while both groups shared the same primary human binding affinity, their secondary binding preferences were different. Cluster one favored swine binding as its secondary binding pattern, whereas cluster two mostly exhibited the binding specificity of A/South Carolina/1/18 (H1N1) (one of the 1918 flu pandemic strains) as its secondary binding pattern. Besides all the nine nucleotide changes found in the previous study, Random Forests were applied to uncover several new nucleotide polymorphisms in 10 genes of the strains between the two clusters, and several amino acid changes in the HA protein that might be accountable for the discrepancy of the secondary receptor binding patterns of the two clusters. Finally, entropy analysis was conducted to present a global view of gene sequence variations between the two clusters, which illustrated that cluster one had much higher genetic divergence than cluster two. Furthermore, it suggested a significant overall correspondence between the nucleotide positions of high importance in differentiating the two clusters and nucleotide positions of high entropy in cluster one.
Correlated mutations in the four influenza proteins essential for viral RNA synthesis, host adaptation, and virulence: NP, PA, PB1, and PB2  [PDF]
Wei Hu
Natural Science (NS) , 2010, DOI: 10.4236/ns.2010.210141
Abstract: The NP, PA, PB1, and PB2 proteins of influenza viruses together are responsible for the transcription and replication of viral RNA, and the latter three proteins comprise the viral polymerase. Two recent reports indicated that the mutation at site 627 of PB2 plays a key role in host range and increased virulence of influenza viruses, and could be compensated by multiple mutations at other sites of PB2, suggesting the association of this mutation with those at other sites. The objective of this study was to analyze the co-mutated sites within and between these important proteins of influenza. With mutual information, a set of statistically significant co- mutated position pairs (P value = 0) in NP, PA, PB1, and PB2 of avian, human, pandemic 2009 H1N1, and swine influenza were identified, based on which several highly connected networks of correlated sites in NP, PA, PB1, and PB2 were discovered. These correlation networks further illustrated the inner functional dependence of the four proteins that are critical for host adaptation and pathogenicity. Mutual information was also applied to quantify the correlation of sites within each individual protein and between proteins. In general, the inter protein correlation of the four proteins was stronger than the intra protein correlation. Finally, the correlation patterns of the four proteins of pandemic 2009 H1N1 were found to be closer to those of avian and human than to swine influenza, thus rendering a novel insight into the interaction of the four proteins of the pandemic 2009 H1N1 virus when compared to avian, human, and swine influenza and how the origin of these four proteins might affect the correlation patterns uncovered in this analysis.
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