Optimization of RP-HPLC analysis of low molecular weight organic acids in soil

RP-HPLC analysis for low molecular weight organic acids in soil solution has been optimized. An Atlantis (TM) C-18 column was used for the analyses. An optimal determination for eleven organic acids in soil solution was found at room temperature (25 degrees C) and 220 nm detection wavelength, with a mobile phase of 10 mM KH2PO4 -CH3OH (955, pH 2.7), a flow rate of 0.8 mL/min and 10 mu L sample size. The detection limits ranged 3.2-619 ng/mL, the coefficients of variation ranged 1.3-4.6%, and the recoveries ranged 95.6-106.3% for soil solution with standard addition on the optimal conditions proposed.

Masonry response to tunnelling: a sensitivity study on the effect of cracking and building weight

Predicting damage to masonry structures due to tunnelling-induced ground movements remains a challenge for practising design engineers. Useful simplified procedures exist, but more detailed analysis has the potential to improve these procedures. This paper considers the use of finite element modelling, including non-linear constitutive laws for the soil and the structure, to simulate damage to a simple masonry structure subjected to tunnelling in sand. The numerical model is validated through comparison with the results of a series of centrifuge tests and used to perform a sensitivity study on the effect of building weight and masonry damage on the structural response. Results show a direct correlation between the weight of the structure, normalised to the relative stiffness between the structure and the soil, and the modification of the settlement profile. By including a cracking model for the masonry, the reduction in structural stiffness caused by progressive masonry damage is also proven to affect the building deflection.

Moisture content in Dall's porpoise (Phocoenoides dalli) tissues: a reference base for conversion factors between dry and wet weight trace element concentrations in cetaceans

Concentration of trace elements measured by dry weight basis has become more commonly used in recent studies on cetaceans than wet weight basis, which was used more in earlier studies. Because few authors present moisture content data in their papers, it is difficult to compare the concentrations of trace elements between various studies. Therefore, we felt that it would be useful if a reference conversion factor (CF) for tissue types could be found to convert between wet weight and dry weight data on trace element concentrations. We determined the moisture contents of 14 tissues of Dall's porpoise (Phocoenoides dalli), and then, calculated the CF values for those tissues. Because the moisture content of each tissue differs from other tissues, it is necessary to use a specific CIF for each tissue rather than a general CF for several tissues. We have also found that CIF values for Dall's porpoise tissues are similar to the same tissues in other cetaceans. Therefore CF values from Dall's porpoise can be reliably used to convert between wet and dry weight concentrations for other cetacean tissues as reference data. (C) 2002 Elsevier Science Ltd. All rights reserved.

The multi-weight neuron with geometry algorithm and its application

A novel approach for multi-dimension signals processing, that is multi-weight neural network based on high dimensional geometry theory, is proposed. With this theory, the geometry algorithm for building the multi-weight neuron is mentioned. To illustrate the advantage of the novel approach, a Chinese speech emotion recognition experiment has been done. From this experiment, the human emotions are classified into 6 archetypal classes: fear, anger, happiness, sadness, surprise and disgust. And the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. Compared with traditional GSVM model, the new method has its superiority. It is noted that this method has significant values for researches and applications henceforth.

Double synaptic weight neuron theory and its application

In this paper, a novel mathematical model of neuron-Double Synaptic Weight Neuron (DSWN)(l) is presented. The DSWN can simulate many kinds of neuron architectures, including Radial-Basis-Function (RBF), Hyper Sausage and Hyper Ellipsoid models, etc. Moreover, this new model has been implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. The flexibility of the DSWN has also been described in constructing neural networks. Based on the theory of Biomimetic Pattern Recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-II neurocomputer. In these two special cases, the result showed DSWN neural network had great potential in pattern recognition.

Discussion on the basic mathematical models of neurons - Double synaptic weight neuron in general-purpose neurocomputer, theory and application

Based on the introduction of the traditional mathematical models of neurons in general-purpose neurocomputer, a novel all-purpose mathematical model-Double synaptic weight neuron (DSWN) is presented, which can simulate all kinds of neuron architectures, including Radial-Basis-Function (RBF) and Back-propagation (BP) models, etc. At the same time, this new model is realized using hardware and implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. In this paper, the flexibility of the new model has also been described in constructing neural networks and based on the theory of Biomimetic pattern recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-H neurocomputer. The result showed DSWN neural network has great potential in pattern recognition.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

Circular polarization of excitonic luminescence in CdTe quantum wells with excess electrons of different densities

The circular polarization of excitonic luminescence is studied in CdTe/Cd1-xMgxTe quantum wells with excess electrons of low density in an external magnetic field. It is observed that the circular polarization of X and X- emissions has opposite signs and is influenced by the excess electron density. If the electron density is relatively high so that the emission intensity of the negatively charged excitons X- is much stronger than that of the neutral excitons X, a stronger circular polarization degree of both X and X- emissions is observed. We find that the circular polarization of both X- and X emissions is caused by the spin polarization of the excess electrons due to the electron-spin-dependent nature of the formation of X-. If the electron density is relatively low and the emission intensity of X- is comparable to that of X, the circular polarization degree of X and X- emissions is considerably smaller. This fact is interpreted as due to a depolarization of the excess electron spins, which is induced by the spin relaxation of X-.

Excess arsenic in GaAs grown at low temperatures by molecular beam epitaxy

The structural properties of GaAs grown at low temperatures by molecular beam epitaxy (LTMBE GaAs) were studied. The excess arsenic atoms in LTMBE GaAs exist in the form of arsenic interstitial couples (i,e, two ns atoms share the one host site), and cause an increase in the lattice parameter of LTMBE GaAs. Annealing at above 300 degrees C, the arsenic interstitial couples decomposed, and As precipitates formed, resulting in a decrease in the lattice parameter.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

Adaptive hormetic response of pre-exposure of mouse brain with low-dose C-12(6+) ion or Co-60 gamma-ray on growth hormone (GH) and body weight induced by subsequent high-dose irradiation

The brain of the Kun-Ming strain mice were irradiated with 0.05 Gy of C-12(6+) ion or Co-60 gamma-ray as the pre-exposure dose, and were then irradiated with 2 Gy of 12C6+ ion or Co-60 gamma-ray as challenging irradiation dose at 4 h after per-exposure. Body weight and serum growth hormone (GH) concentration were measured at 35th day after irradiation. The results showed that irradiation of mouse brain with 2 Gy of C-12(6+) ion or Co-60 gamma-ray significantly diminished mouse body weight and level of serum GH. The relative biological effectiveness values of a 2 Gy dose of C-12(6+) ion calculated with respect to Co-60 gamma-ray were 1.47 and 1.34 for body weight and serum GH concentration, respectively. Pre-exposure with a low-dose (0.05 Gy) of C-12(6+) ion or Co-60 gamma-ray significantly alleviated reductions of mouse body weight and level of serum GH induced by a subsequent high-dose (2 Gy) irradiation. The data suggested that low-dose ionizing irradiation can induce adaptive hormetic responses to the harmful effects of pituitary by subsequent high-dose exposure.