Modelling the Linkage Between Landscape Metrics and Water Quality Indices of Hydrological Units in Sihu Basin, Hubei Province, China: An Allometric Model

AbstractStudying quantitative relationships between landscape pattern and water quality is a fundamental step to assess the impacts of non-point source pollution. Many hydrological models with multi-functionality have been developed as useful tools to study several key mechanisms in non-point source pollution. In landscape ecological studies, however, the empirical modelling approaches have been dominated with emphasis on the relationships between the landscape metrics and water quality indices. The main techniques for developing those models of landscape-water quality are statistical regression analysis based on linear models. In this article, Allometric models and the traditional multiple linear regression models for estimating the linkage between landscape metrics and water quality were tested in Sihu Basin, Hubei Province, China. The models at patch class level were established in 24 hydrological units of the basin, which took nine water quality indices (EC, pH, SS, DO, COD, TN, TP, NO3--N, NH4+-N) as the dependent variables and eighteen landscape metrics calculated in FRAGSTATS 3.3 as independent variables. The results suggested that, compared with the traditional multiple linear regression models, Allometric models were more suitable for SS, DO, TP, TN, NH4+-N, in which landscape pattern metrics could explain the 80.5%, 77.7%, 58.2%, 43.9%, 67.6% of total variation, respectively. There had little difference between multiple linear regression models and Allometric models for EC and NO3--N. The coefficients of determination in Allometric models were not as strong as that obtained in the multiple linear regression models for pH and COD. The above results indicated that using Allometric model may potentially provide a new way to study the linkage between landscape metrics and water quality indices, which will help protect our regional water resources

The energy spectrum of all-particle cosmic rays around the knee region observed with the Tibet-III air-shower array

We have already reported the first result on the all-particle spectrum around the knee region based on data from 2000 November to 2001 October observed by the Tibet-III air-shower array. In this paper, we present an updated result using data set collected in the period from 2000 November through 2004 October in a wide range over 3 decades between $10^{14}$ eV and $10^{17}$ eV, in which the position of the knee is clearly seen at around 4 PeV. The spectral index is -2.68 $\pm$ 0.02(stat.) below 1PeV, while it is -3.12 $\pm$ 0.01(stat.) above 4 PeV in the case of QGSJET+HD model, and various systematic errors are under study now.Comment: 12 pages, 7 figures, accepted by Advances in space researc

Moon Shadow by Cosmic Rays under the Influence of Geomagnetic Field and Search for Antiprotons at Multi-TeV Energies

We have observed the shadowing of galactic cosmic ray flux in the direction of the moon, the so-called moon shadow, using the Tibet-III air shower array operating at Yangbajing (4300 m a.s.l.) in Tibet since 1999. Almost all cosmic rays are positively charged; for that reason, they are bent by the geomagnetic field, thereby shifting the moon shadow westward. The cosmic rays will also produce an additional shadow in the eastward direction of the moon if cosmic rays contain negatively charged particles, such as antiprotons, with some fraction. We selected 1.5 x10^{10} air shower events with energy beyond about 3 TeV from the dataset observed by the Tibet-III air shower array and detected the moon shadow at $\sim 40 \sigma$ level. The center of the moon was detected in the direction away from the apparent center of the moon by 0.23$^\circ$ to the west. Based on these data and a full Monte Carlo simulation, we searched for the existence of the shadow produced by antiprotons at the multi-TeV energy region. No evidence of the existence of antiprotons was found in this energy region. We obtained the 90% confidence level upper limit of the flux ratio of antiprotons to protons as 7% at multi-TeV energies.Comment: 13pages,4figures; Accepted for publication in Astroparticle Physic

Image Processing with Spiking Neuron Networks

International audienceArtificial neural networks have been well developed so far. First two generations of neural networks have had a lot of successful applications. Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation.Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this chapter, we present how SNN can be applied with efficacy in image clustering, segmentation and edge detection. Results obtained confirm the validity of the approach

Are protons still dominant at the knee of the cosmic-ray energy spectrum?

A hybrid experiment consisting of emulsion chambers, burst detectors and the Tibet II air-shower array was carried out at Yangbajing (4,300 m a.s.l., 606 g/cm$^2$) in Tibet to obtain the energy spectra of primary protons and heliums. From three-year operation, these energy spectra are deduced between $10^{15}$ and $10^{16}$ eV by triggering the air showers associated with a high energy core and using a neural network method in the primary mass separation. The proton spectrum can be expressed by a single power-law function with a differential index of $-3.01 \pm 0.11$ and $-3.05 \pm 0.12$ based on the QGSJET+HD and SIBYLL+HD models, respectively, which are steeper than that extrapolated from the direct observations of $-2.74 \pm 0.01$ in the energy range below $10^{14}$ eV. The absolute fluxes of protons and heliums are derived within 30% systematic errors depending on the hadronic interaction models used in Monte Carlo simulation. The result of our experiment suggests that the main component responsible for the change of the power index of the all-particle spectrum around $3 \times 10^{15}$ eV, so-called ``knee'', is composed of nuclei heavier than helium. This is the first measurement of the differential energy spectra of primary protons and heliums by selecting them event by event at the knee energy region.Comment: This paper has been accepted for publication Physics Letters B on October 19th, 2005. This paper has been accepted for publication Physics Letters B on October 19th, 200

Intelligent monitoring and recognition of the short-circuiting gas-metal arc welding process

MOE Key Lab of Liquid Structure and Heredity of Materials, Institute of Materials Joining, Shangdong University, 73 Jingshi Road, Jinan 250061, People's Republic of China This paper introduces an intelligent system for monitoring and recognition of process disturbances during short-circuiting gas-metal arc welding. It is based on the measured and statistically processed data of welding electrical parameters. A 12-dimensional array of process features is designed to describe various welding conditions and is employed as input vector of the intelligent system. Three methods, such as fuzzy c-means, neural network and fuzzy Kohonen clustering network are used to conduct process monitoring and automatic recognition. The correct recognition rates of these three methods are compared