Hydroquinone (HQ) is one of the major deleterious metabolites of benzene in the human body, which has been implicated to cause various human diseases. In order to fabricate a feasible sensor for the accurate detection of HQ, we attempted to electrochemically modify a piece of common 2B pencil lead (PL) with the conductive poly(3,4-ethylenedioxythiophene) or PEDOT film to construct a PEDOT/PL electrode. We then examined the performance of PEDOT/PL in the detection of hydroquinone with different voltammetry methods. Our results have demonstrated that PEDOT film was able to dramatically enhance the electrochemical response of pencil lead electrode to hydroquinone and exhibited a good linear correlation between anodic peak current and the concentration of hydroquinone by either cyclic voltammetry or linear sweep voltammetry. The influences of PEDOT film thickness, sample pH, voltammetry scan rate, and possible chemical interferences on the measurement of hydroquinone have been discussed. The PEDOT film was further characterized by SEM with EDS and FTIR spectrum, as well as for stability with multiple measurements. Our results have demonstrated that the PEDOT modified PL electrode could be an attractive option to easily fabricate an economical sensor and provide an accurate and stable approach to monitoring various chemicals and biomolecules.

A recurrent neural networks with context units that can handle temporal sequences is proposed. In this paper, we show an architecture whose performance is better than the architectures proposed by Jordan and Elrnan respectively using error backpropagation learning algorithms. Three learning experiiments were carried out. In the first experiment, we used the recurrent neural networks to simulate a finite state machine. In the second experiment, we use the recurrent networks to handle a combination retrieving problem. In the third experiment, we train the neural networks to recognize the periodicity in temporal sequence data. The results of three experiments showed that our system had a better performance.

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Due to complex nonlinear data pattern in time series regression, forecasting techniques had been categorized in different ways, and the literature is also full of differing opinions, thus, it is difficult to make a general conclusion. In the recent years, the support vector regression (SVR) model has been widely used to solve nonlinear time series regression problems. This investigation presents a short-term traffic forecasting model by employing SVR with genetic algorithm and simulated annealing algorithm (GA-SA) to determine the suitable parameter combination in the SVR model. Consequently, a numerical example of traffic flow values from northern Taiwan is used to demonstrate the forecasting performance of the proposed SVRGA-SA model is superior to the seasonal autoregressive integrated moving average (SARIMA) time series model. Index Terms-Support vector regression; genetic algorithm with simulated annealing (GA-SA); hybrid algorithms; SARIMA; traffic flow forecasting.

One task in the interpretation of the 2-D nuclear magnetic resonance (NMR) spectrum is to assign its signal patterns to their corresponding amino acids in proteins or polypeptides. To carry out this task of interpretation, one requires sufficient chemical knowledge and expertise to reason from a set of highly noisy data. We present a system called RUBIDIUM (a Rule-Based Identification in 2-D NMR Spectrum) to formulate the expertise and automate the process of interpretation. Given a protein or polypeptide with a known amino acid sequence and the 2-D NMR spectra (both COSY and NOESY), RUBIDIUM yields plausible assignments of lines that account for most signals observed in the spectrum and conform to prior chemical knowledge. Rules of pattern matching are used to detect plausible signal patterns. The expertise of the sequence-specific assignment task is formulated to assign a signal pattern to amino acids. To cope with ambiguities and noise, RUBIDIUM adopts various low-level data preprocessing techniques, the strategy of divide and conquer, and the relaxation technique to decrease the complexity and recover from overconstrained conditions. The polypeptides oxytocin and vasopressin are used to illustrate the performance of RUBIDIUM.