Human dehydrated normal and cancerous gastric tissues were measured using transmission time-domain terahertz spectroscopy. Based on the obtained terahertz absorption spectra, the contrasts between the two kinds of tissue were investigated and techniques for automatic identification of cancerous tissue were studied. Distinctive differences were demonstrated in both the shape and amplitude of the absorption spectra between normal and tumor tissue. Additionally, some spectral features in the range of 0.2~0.5 THz and 1~1.5 THz were revealed for all cancerous gastric tissues. To systematically achieve the identification of gastric cancer, principal component analysis combined with t-test was used to extract valuable information indicating the best distinction between the two types. Two clustering approaches, K-means and support vector machine (SVM), were then performed to classify the processed terahertz data into normal and cancerous groups. SVM presented a satisfactory result with less false classification cases. The results of this study implicate the potential of the terahertz technique to detect gastric cancer. The applied data analysis methodology provides a suggestion for automatic discrimination of terahertz spectra in other applications.
This study presents a method for detecting contamination events of sources of drinking water based on the Dempster-Shafer (D-S) evidence theory. The detection method has the purpose of protecting water supply systems against accidental and intentional contamination events. This purpose is achieved by first predicting future water-quality parameters using an autoregressive (AR) model. The AR model predicts future water-quality parameters using recent measurements of these parameters made with automated (on-line) water-quality sensors. Next, a probabilistic method assigns probabilities to the time series of residuals formed by comparing predicted water-quality parameters with threshold values. Finally, the D-S fusion method searches for anomalous probabilities of the residuals and uses the result of that search to determine whether the current water quality is normal (that is, free of pollution) or contaminated. The D-S fusion method is extended and improved in this paper by weighted averaging of water-contamination evidence and by the analysis of the persistence of anomalous probabilities of water-quality parameters. The extended D-S fusion method makes determinations that have a high probability of being correct concerning whether or not a source of drinking water has been contaminated. This paper's method for detecting water-contamination events was tested with water-quality time series from automated (on-line) water quality sensors. In addition, a small-scale, experimental, water-pipe network was tested to detect water-contamination events. The two tests demonstrated that the extended D-S fusion method achieves a low false alarm rate and high probabilities of detecting water contamination events.
An event-driven, urban, drinking water quality early warning and control system (DEWS) is proposed to cope with China's urgent need for protecting its urban drinking water. The DEWS has a web service structure and provides users with water quality monitoring functions, water quality early warning functions, and water quality accident decision-making functions. The DEWS functionality is guided by the principles of control theory and risk assessment as applied to the feedback control of urban water supply systems. The DEWS has been deployed in several large Chinese cities and found to perform well insofar as water quality early warning and emergency decision-making is concerned. This paper describes a DEWS for urban water quality protection that has been developed in China.
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