oalib

Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99

Submit

Any time

2019 ( 21 )

2018 ( 386 )

2017 ( 340 )

2016 ( 327 )

Custom range...

Search Results: 1 - 10 of 27501 matches for " Bangqi Hu "
All listed articles are free for downloading (OA Articles)
Page 1 /27501
Display every page Item
Paleoproductivity variations in the southern Okinawa Trough since the middle Holocene: Calcareous nannofossil records
JingTao Zhao,TieGang Li,Jun Li,BangQi Hu
Chinese Science Bulletin , 2012, DOI: 10.1007/s11434-012-5276-y
Abstract: Based on 17 AMS14C age data, we reconstructed high-resolution records of sea surface primary productivity (PP) in the southern Okinawa Trough (MD05-2908) over the last 6.8 ka BP using the calcareous nannofossil carbon isotope and the relative percentage contents of Florisphaera profunda indexes. The underlying mechanism controlling the sea surface PP was then discussed. The sea surface PP, indicated by the coccolith δ 13C and %Fp conversional equations, decreased with some fluctuations since 6.8 ka BP. This decrease may be connected to the decreased terrigenous input resulting from the reduced East Asian Summer Monsoon (EASM) precipitation. Both the periods of 4-2 ka BP (PME) and 6.8–4.8 ka BP were characterized by relatively higher PP. The former was mainly controlled by the weakening of the Kuroshio Current, whereas the latter mainly resulted from the greater terrigenous input associated with the stronger EASM.
Sea surface temperature records of core ZY2 from the central mud area in the South Yellow Sea during last 6200 years and related effect of the Yellow Sea Warm Current
LiBo Wang,ZuoSheng Yang,RongPing ZHang,DeJiang Fan,MeiXun Zhao,BangQi Hu
Chinese Science Bulletin , 2011, DOI: 10.1007/s11434-011-4442-y
Abstract: Sea surface temperature (SST) records in the South Yellow Sea during the last 6200 years are reconstructed by the unsaturation index of long-chain alkenones (U 37 K′ ) in sediment core ZY2 from the central mud area. The SST records varied between 14.1 and 16.5°C (15.6°C on average), with 3 phases: (1) A high SST phase at 6.2–5.9 cal ka BP; (2) A low and intensely fluctuating SST phase at 5.9–2.3 cal ka BP; and (3) A high and stable SST phase since 2.3 cal ka BP. Variation of the SST records is similar to intensity of the Kuroshio Current (KC), and corresponds well in time to global cold climate events. However, the amplitude of the SST response to cooling events was significantly different in different phases. The SST response to global cooling event was weak while the KC was strong; and the SST response was strong while the KC was weak. The difference in amplitude of the SST response is possibly caused by the modulation effect of the Yellow Sea Warm Current which acts as a shelf branch of the KC and a compensating current induced by the East Asia winter monsoon. The warm waters brought by the Yellow Sea Warm Current cushion the SST decrease induced by climate cooling, and both the Kuroshio and East Asian winter monsoon play important roles in the modulation mechanism. The SST records display a periodicity of 1482 years. The same period was found in the KC records, indicating that variation of the SST records in the central South Yellow Sea is strongly affected by KC intensity. The same period was also found in Greenland ice cores and North Atlantic and Arabian Sea sediment cores, showing a regional response of marine environmental variability in the East China Seas to that in the global oceans.
Sedimentation in the Three Gorges Dam and the future trend of Changjiang (Yangtze River) sediment flux to the sea
Bangqi Hu, Zuosheng Yang, Houjie Wang, Xiaoxia Sun, Naishuang Bi,Guogang Li
Hydrology and Earth System Sciences (HESS) & Discussions (HESSD) , 2009,
Abstract: The Three Gorges Dam (TGD) on the upper Changjiang (Yangtze River), China, disrupts the continuity of Changjiang sediment delivery to downstream and coastal areas. In this study, which was based on 54 years of annual water and sediment data from the mainstream and major tributaries of Changjiang, sediment deposition induced by the TGD in 2003–2008 was quantified. Furthermore, we determined the theoretical trapping efficiency of the cascade reservoir upstream of the TGD. Its impact on Changjiang sediment flux in the coming decades is discussed. Results show that about 172 million tons (Mt) of sediment was trapped annually by the TGD in 2003–2008, with an averaged trapping efficiency of 75%. Most of the total sediment deposition, as induced by the TGD (88%), accumulated within the region between the TGD site and Cuntan. However, significant siltation (12% of the total sediment deposition) also occurred upstream of Cuntan as a consequence of the upstream extended backwater region of the TGD. Additionally, the Changjiang sediment flux entered a third downward step in 2001, prior to operation of the TGD. This mainly resulted from sediment reduction in the Jinshajiang tributary since the late 1990s. As the cascade reservoir is put into full operation, it could potentially trap 91% of the Jinshajiang sediment discharge and, therefore, the Jinshajiang sediment discharge would most likely further decrease to 14 Mt/yr in the coming decades. Consequently, the Changjiang sediment flux to the sea is expected to continuously decrease to below 90 Mt/yr in the near future, or only 18% of the amount observed in the 1950s. In the presence of low sediment discharge, profound impacts on the morphology of estuary, delta and coastal waters are expected.
Evaluation of Natural Radioactivity in Marine Sand Deposits from Offshore China  [PDF]
Jun Li, Bangqi Hu, Jingtao Zhao, Fenglong Bai, Yanguang Dou, Libo Wang, Liang Zou, Xue Ding
Open Journal of Marine Science (OJMS) , 2017, DOI: 10.4236/ojms.2017.73026
Abstract: Natural radioactivity is very important for the assessment of the marine sand property and usability. By using gamma spectrometry, the concentration of the natural radionuclides 226Ra, 232Th and 40K have been measured in marine sand deposits from Liaodong Bay (LDB), North Yellow Sea (NYS), Zhoushan area (ZS), Taiwan Shoal (TS) and Pearl River Mouth (PR), offshore China, which are potential marine sand mining areas. The radiation activity equivalent (Raeq), indoor gamma absorbed dose rate (DR), annual effective dose (HR), alpha index (Ia), gamma index (Ig), external radiation hazard index (Hex), internal radiation hazard index (Hin), representative level index (RLI), excess lifetime cancer risk (ELCR) and annual gonadal dose equivalent (AGDE) associated with the natural radionuclides are calculated to assess the radiation hazard of the natural radioactivity in the marine sands offshore China. From the analysis, it is found that these marine sands are safe for the constructions. The Pearson correlation coefficient reveals that the 226Ra distribution in the marine sands offshore China is controlled by the variation of the 40K concentration. Principal component analysis (PCA) yields a two-component representation of the entire data from the marine sands, wherein 98.22% of the total variance is explained. Our results provide good baseline data to expand the database of radioactivity of building materials in China and all over the world.
Middle Holocene Organic Carbon and Biomarker Records from the South Yellow Sea: Relationship to the East Asian Monsoon Middle Holocene Organic Carbon and Biomarker Records from the South Yellow Sea: Relationship to the East Asian Monsoon
ZOU Liang,HU Bangqi,LI Jun,DOU Yanguang,XIE Luhua,DONG Liang
- , 2018,
Abstract: The East Asian monsoon system influences the sedimentation and transport of organic matter in East Asian marginal seas that is derived from both terrestrial and marine sources. In this study, we determined organic carbon(OC) isotope values, concentrations of marine biomarkers, and levels of OC and total nitrogen(TN) in core YSC-1 from the central South Yellow Sea(SYS). Our objectives were to trace the sources of OC and variations in palaeoproductivity since the middle Holocene, and their relationships with the East Asian monsoon system. The relative contributions of terrestrial versus marine organic matter in core sediments were estimated using a two-end-member mixing model of OC isotopes. Results show that marine organic matter has been the main sediment constituent since the middle Holocene. The variation of terrestrial organic carbon concentration(OCter) is similar to the EASM history. However, the variation of marine organic carbon concentration(OCmar) is opposite to that of the EASM curve, suggesting OCmar is distinctly influenced by terrestrial material input. Inputs of terrestrial nutrients into the SYS occur in the form of fluvial and aeolian dust, while concentrations of nutrients in surface water are derived mainly from bottom water via the Yellow Sea circulation system, which is controlled by the East Asian winter monsoon(EAWM). Variations in palaeoproductivity represented by marine organic matter and biomarker records are, in general, consistent with the recent EAWM intensity studies, thus, compared with EASM, EAWM may play the main role to control the marine productivity variations in the SYS
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.
Page 1 /27501
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.