This paper proposes a new method to detect bursts in District Metering Areas (DMAs) in water distribution systems. The methodology is divided into three steps. Firstly, Dynamic Time Warping was applied to study the similarity of daily water demand, extract different patterns of water demand, and remove abnormal patterns. In the second stage, according to different water demand patterns, a supervised learning algorithm was adopted for burst detection, which established a leakage identification model for each period of time, respectively, using a sliding time window. Finally, the detection process was performed by calculating the abnormal probability of flow during a certain period by the model and identifying whether a burst occurred according to the set threshold. The method was validated on a case study involving a DMA with engineered pipe-burst events. The results obtained demonstrate that the proposed method can effectively detect bursts, with a low false-alarm rate and high accuracy.

A multi-functional VOC (volatile organic compound) sensor composed of four parallel-connected quartz crystal resonators (QCRs) configured as a cascaded resonator was developed for VOCs identification and quantification. The structure of the cascaded resonator allows the high-resolution output response signals from the four QCRs to be obtained through a single measurement. Multi-parameter responses for each QCR can be calculated to form a hybrid sensor array, combining the advantages of multisensor array (MSA) and virtual sensor array (VSA), significantly enhancing the sensor’s ability to identify both similar and diverse types of VOCs. Results show that the highest sensitivity is 16.14 Hz ppm−1, with the lowest limit of detection is 0.34 ppm, exhibiting excellent sensing performance. Classification accuracy to eight VOCs is 98.68% of the hybrid sensor array. This development is of significant importance for the realization of artificial olfaction.

Terahertz technology has been widely used as a nondestructive and effective detection method. Herein, terahertz time-domain spectroscopy was used to detect drug-induced liver injury in mice. Firstly, the boxplots were used to detect abnormal data. Then the maximal information coefficient method was used to search for the features strongly correlated with the degree of injury. After that, the liver injury model was built using the random forests method in machine learning. The results show that this method can effectively identify the degree of liver injury and thus provide an auxiliary diagnostic method for detecting minor liver injury.