Pressure-assisted field-amplified sample injection with reverse migrating micelles for analyzing trace steroids in MEKC

This paper describes a novel method that applies pressure-assisted field-amplified sample injection with reverse migrating micelles (PA-FASI-RMM) for the online concentration of neutral analytes in MEKC with a low-pH BGE. After injection of a plug of water into the separation capillary, negative voltage and positive pressure were simultaneously applied to initialize PA-FASI-RMM injection. The hydrodynamic flow generated by the positive pressure compensated the reverse EOF in the water plug and allowed the water plug to remain in the capillary during FASI with reverse migrating micelles (FASI-RMM) to obtain a much longer injection time than usual, which improved stacking efficiency greatly. Equations describing this injection mode were introduced and were supported by experimental results. For a 450-s online PA-FASI-RMM injection, three orders of magnitude sample enhancement in terms of peak area could be observed for the steroids and an achievement of detection limits was between 1 and 10 ng/mL.

Measurement of 5-day biochemical oxygen demand without sample dilution or bacterial and nutrient enhancement

A direct method for measuring the 5-day biochemical oxygen demand (BODS) of aquaculture samples that does not require sample dilution or bacterial and nutrient enrichment was evaluated. The regression coefficient (R-2) between the direct method and the standard method for the analyses of 32 samples from catfish ponds was 0.996. The slope of the regression line did not differ from 1.0 or the Y-intercept from 0.0 at P = 0.05. Thus, there was almost perfect agreement between the two methods. The control limits (three standard deviations of the mean) for a standard solution containing 15 mg/L each of glutamic acid and glucose were 17.4 and 20.4 mg/L. The precision of the two methods, based on eight replicate analyses of four pond water samples did not differ at P = 0.05. (c) 2005 Elsevier B.V All rights reserved.

A derivation of the source-channel error exponent using nonidentical product distributions

This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution that depends on the class index of the source message. For discrete memoryless systems, two optimally chosen classes and product distributions are found to be sufficient to attain the sphere-packing exponent in those cases where it is tight. © 2014 IEEE.

Rapid bioassay as indicator of potentially harmful effects for dioxin-like compounds in sample of Ya-Er Lake, China: Requirements for clean-up and comparison to chemical analysis

A rapid bioassay was established measuring the extracts of wildlife samples which were taken from Ya-Er Lake area, China. In extracts of these samples containing PCDD/Fs and PCBs, bioassay and chemically derived TCDD-equivalents (TEQs) were nearly identical. Our results indicate this bioassay is an excellent complement to chemical residue analysis and a useful tool in understanding the complex interactions of halogenated hydrocarbons. However, it must be mentioned that the proper prior clean-up method is very important for using the bioassay.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.

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.

Measurement of a reciprocal four-port transmission line structure using the 16-term error model

A new method to measure reciprocal four-port structures, using a 16-term error model, is presented. The measurement is based on 5 two-port calibration standards connected to two of the ports, while the network analyzer is connected to the two remaining ports. Least-squares-fit data reduction techniques are used to lower error sensitivity. The effect of connectors is deembedded using closed-form equations. (C) 2007 Wiley Periodicals, Inc.

A 16-term error model based on linear equations of voltage and current variables

Formulation of a 16-term error model, based on the four-port ABCD-matrix and voltage and current variables, is outlined. Matrices A, B, C, and D are each 2 x 2 submatrices of the complete 4 x 4 error matrix. The corresponding equations are linear in terms of the error parameters, which simplifies the calibration process. The parallelism with the network analyzer calibration procedures and the requirement of five two-port calibration measurements are stressed. Principles for robust choice of equations are presented. While the formulation is suitable for any network analyzer measurement, it is expected to be a useful alternative for the nonlinear y-parameter approach used in intrinsic semiconductor electrical and noise parameter measurements and parasitics' deembedding.

crossmodal error correction of continuous handwriting recognition by speech

In recognition-based user interface, users’ satisfaction is determined not only by recognition accuracy but also by effort to correct recognition errors. In this paper, we introduce a crossmodal error correction technique, which allows users to correct errors of Chinese handwriting recognition by speech. The focus of the paper is a multimodal fusion algorithm supporting the crossmodal error correction. By fusing handwriting and speech recognition, the algorithm can correct errors in both character extraction and recognition of handwriting. The experimental result indicates that the algorithm is effective and efficient. Moreover, the evaluation also shows the correction technique can help users to correct errors in handwriting recognition more efficiently than the other two error correction techniques.

VLBI observations of a sample of 15 EGRET-detected AGNs at 5 GHz

We report VLBI observations of 15 EGRET-detected AGNs with European VLBI Network (EVN) at 5 GHz. All sources in the sample display core-jet structures.

A 10-bit 50-MS/s sample-and-hold circuit with low distortion sampling switches

A fully-differential switched-capacitor sample-and-hold (S/H) circuit used in a 10-bit 50-MS/s pipeline analog-to-digital converter (ADC) was designed and fabricated using a 0.35-μm CMOS process. Capacitor fliparound architecture was used in the S/H circuit to lower the power consumption. In addition, a gain-boosted operational transconductance amplifier (OTA) was designed with a DC gain of 94 dB and a unit gain bandwidth of 460 MHz at a phase margin of 63 degree, which matches the S/H circuit. A novel double-side bootstrapped switch was used, improving the precision of the whole circuit. The measured results have shown that the S/H circuit reaches a spurious free dynamic range (SFDR) of 67 dB and a signal-to-noise ratio (SNR) of 62.1 dB for a 2.5 MHz input signal with 50 MS/s sampling rate. The 0.12 mm~2 S/H circuit operates from a 3.3 V supply and consumes 13.6 mW.

Biomimetic (topological) pattern recognition - A new model of pattern recognition theory and its application

A new theoretical model of Pattern Recognition principles was proposed, which is based on "matter cognition" instead of "matter classification" in traditional statistical Pattern Recognition. This new model is closer to the function of human being, rather than traditional statistical Pattern Recognition using "optimal separating" as its main principle. So the new model of Pattern Recognition is called the Biomimetic Pattern Recognition (BPR)(1). Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Therefore, it is also called the Topological Pattern Recognition (TPR). The fundamental idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples. We experimented with the Biomimetic Pattern Recognition (BPR) by using artificial neural networks, which act through covering the high dimensional geometrical distribution of the sample set in the feature space. Onmidirectionally cognitive tests were done on various kinds of animal and vehicle models of rather similar shapes. For the total 8800 tests, the correct recognition rate is 99.87%. The rejection rate is 0.13% and on the condition of zero error rates, the correct rate of BPR was much better than that of RBF-SVM.