Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks

Lake Dianchi is a shallow and turbid lake, located in Southwest China. Since 1985, Lake Dianchi has experienced severe cyanabacterial blooms (dominated by Microcystis spp.). In extreme cases, the algal cell densities have exceeded three billion cells per liter. To predict and elucidate the population dynamics ofMicrocystis spp. in Lake Dianchi, a neural network based model was developed. The correlation coefficient (R 2) between the predicted algal concentrations by the model and the observed values was 0.911. Sensitivity analysis was performed to clarify the algal dynamics to the changes of environmental factors. The results of a sensitivity analysis of the neural network model suggested that small increases in pH could cause significantly reduced algal abundance. Further investigations on raw data showed that the response of Microcystis spp. concentration to pH increase was dependent on algal biomass and pH level. When Microcystis spp. population and pH were moderate or low, the response of Microcystis spp. population would be more likely to be positive in Lake Dianchi; contrarily, Microcystis spp. population in Lake Dianchi would be more likely to show negative response to pH increase when Microcystis spp. population and pH were high. The paper concluded that the extremely high concentration of algal population and high pH could explain the distinctive response of Microcystis spp. population to +1 SD (standard deviation) pH increase in Lake Dianchi. And the paper also elucidated the algal dynamics to changes of other environmental factors. One SD increase of water temperature (WT) had strongest positive relationship with Microcystis spp. biomass. Chemical oxygen demand (COD) and total phosphorus (TP) had strong positive effect on Microcystis spp. abundance while total nitrogen (TN), biological oxygen demand in five days (BOD5), and dissolved oxygen had only weak relationship with Microcystis spp. concentration. And transparency (Tr) had moderate positive relationship with Microcystis spp. concentration.

The application of DBF neural networks for object recognition

On the basis of DBF nets proposed by Wang Shoujue, the model and properties of DBF neural network were discussed in this paper. When applied in pattern recognition, the algorithm and implement on hardware were presented respectively. We did experiments on recognition of omnidirectionally oriented rigid objects on the same level, using direction basis function neural networks, which acts by the method of covering the high dimensional geometrical distribution of the sample set in the feature space. Many animal and vehicle models (even with rather similar shapes) were recognized omnidirectionally thousands of times. For total 8800 tests, the correct recognition rate is 98.75%, the error rate and the rejection rate are 0.5% and 1.25% respectively. (C) 2003 Elsevier Inc. All rights reserved.

A new method based on the construction of hypothetical symmetrical networks for fixture calibration in network analyzers

At least three known standards are normally required for the full two-port test fixture calibration in vector network analyzers (VNA). In this paper, a calibration procedure using only one standard, based on establishing two hypothetical symmetrical fixtures using triple-through method, is shown. The results using the calibrating method to subtract the influence of fixtures are in accord with the directly measured data of the device-under-test (DUT) without the fixtures very well, which shows that the proposed method is very simple and accurate.

Optimization of Global Signal Networks for Island-Style FPGAs

We present a staggered buffer connection method that provides flexibility for buffer insertion while designing global signal networks using the tile-based FPGA design methodology. An exhaustive algorithm is used to analyze the trade-off between area and speed of the global signal networks for this staggered buffer insertion scheme, and the criterion for determining the design parameters is presented. The comparative analytic result shows that the methods in this paper are proven to be more efficient for FPGAs with a large array size.

Global stability of Hopfield neural networks

This paper gives a condition for the global stability of a continuous-time hopfield neural network when its activation function maybe not monotonically increasing.

Self-assembly of lambda-DNA networks/Ag nanoparticles: Hybrid architecture and active-SERS substrate

In this article, highly rough and stable surface enhanced Raman scattering (SERS)-active substrates had been fabricated by a facile layer by-layer technique. Unique lambda-DNA networks and CTAB capped silver nanoparticles (AgNP) were alternatively self-assembled on the charged mica surface until a desirable number of bilayers were reached. The as-prepared hybrid architectures were characterized by UV-vis spectroscopy, tapping mode atomic force microscopy (AFM) and confocal Raman microscopy, respectively.

Single molecule dynamics and statistical fluctuations of gene regulatory networks: A repressilator

The authors developed a time dependent method to study the single molecule dynamics of a simple gene regulatory network: a repressilator with three genes mutually repressing each other. They quantitatively characterize the time evolution dynamics of the repressilator. Furthermore, they study purely dynamical issues such as statistical fluctuations and noise evolution. They illustrated some important features of the biological network such as monostability, spirals, and limit cycle oscillation. Explicit time dependent Fano factors which describe noise evolution and show statistical fluctuations out of equilibrium can be significant and far from the Poisson distribution. They explore the phase space and the interrelationships among fluctuations, order, amplitude, and period of oscillations of the repressilators. The authors found that repressilators follow ordered limit cycle orbits and are more likely to appear in the lower fluctuating regions. The amplitude of the repressilators increases as the suppressing of the genes decreases and production of proteins increases. The oscillation period of the repressilators decreases as the suppressing of the genes decreases and production of proteins increases.

A new kind of potassium sensor based on capacitance measurement of mimic membrane

A novel type of potassium sensor based on the capacitance change of valinomycin-incorporated bilayer supported on a gold electrode has been developed and characterized. The lipid membrane was Formed by painted method and monitored simultaneously by capacitance variation. The capacitance of the electrode-supported membrane was found to be modulated by different concentrations of K+. Investigating the capacitance change allows a simple and specific technique for the measurement of potassium ion in solution. Especially, the homemade capacitance meter is, to our knowledge, used to monitor the bilayer membrane formation and detect K+ for the first time. It has been proved that this capacitance measurement is a very useful technique because it is simple and sensitive compared to the other methods.

Dynamic mechanical properties of interpenetrating polymer networks based on polyacrylates and epoxy

Interpenetrating polymer networks (IPNs) based on polyacrylate (poly(polyethylene glycol diacrylate), PEGDA) and epoxy(diglycidyl ether of bisphenol A, DGEBA) were prepared simultaneously Dynamic mechanical properties of the SINs (simultaneous interpenetrating networks) with various compositions were studied. Enhanced mechanical properties were found in this case. From the point of view of pre-swollen networks, all of the PEGDA/DGEBA IPNs were composed of the individual pre-swollen networks. A micro-phase segregation system was produced in the SIN. Glass transition temperatures shifted inward, which was attributed to molecular packing effects or mutual-entanglements of molecular segments among the individual pre-swollen networks. In accordance with the additivity of properties, namely the parallel model, the entanglement density between the two polymer networks reached its maximum at 50/50 PEGDA/DGEBA IPN.

Synthesis and characterization of interpenetrating polymer networks based on polyacrylate and epoxy

The synthesis and properties of simultaneously interpenetrating networks (SINs) based on poly(polyethylene glycol diacrylate) (PEGDA) and epoxy (diglycidyl ether of bisphenol A, DGEBA) were studied. The effect of compositional variation on the morphology and properties of products was investigated. The swelling coefficient, densities, glass transition behavior, and thermal stability of these interpenetrating networks (IPNs) are discussed. Microphase separation morphological structures were found in all PEGDA/DGEBA IPNs. Decreased swelling ratios compared to the calculated swelling coefficients based on the weight additivity of the components were obtained after the formation of IPNs. Increased density and thermal stability were also obtained in these IPNs, implying the existence of interpenetration (topological entanglements) among the component networks.