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Turquoise is one of the key ingredients in some magical Tibetan medicines, and its quality and content directly affect the medicine's effectiveness. In this paper, laser-induced breakdown spectroscopy (LIBS) technology was first applied to detect the raw materials of Tibetan medicine. The traditional data analysis methods could not meet the practical requirements of modern Tibetan medicine factories due to matrix effects. The concept of correlation coefficient (ρ) in pattern recognition technique was introduced as an evaluation index, and the model was established based on the intensities of the four characteristic Al and Cu spectral lines of the samples for different contents of turquoise, which was applied to estimate the contents of turquoise in the samples to be tested. We detected the LIBS on 126 samples of raw ore from 42 areas in China and evaluated the turquoise content using self-developed software with an error of <10%. This paper's technical testing process and methods can also be applied to test other mineral compositions and provide technical support for modernizing and standardizing Tibetan medicines. Graphical Abstract This is a visual representation of the abstract. [ABSTRACT FROM AUTHOR]

Summary: Structural health monitoring (SHM) has been widely employed to reveal the hidden safety information and to diagnose the safety status in dam engineering fields. As one of the most important parameters of SHM, crack opening displacement (COD) is often used to evaluate the cracks or joints of concrete dams. In this paper, a new dam health analytic perspective is introduced by integrating the data mining method into SHM field, focusing on revealing the association rules in COD monitoring data. The association rules are investigated systematically, considering the cause–effect relations between external loads and structural response, the temporal characteristics of time series for a single sensor, the spatial characteristics of monitoring data for multisensors, and the abnormal characteristics for different items of structural responses. The association relation is quantified by proposing the quantitative indexes, including support degree, confidence degree, and promotion degree. The methods are used in the COD monitoring data of the Baishan concrete gravity‐arch dam, which is located in a severely cold area in northeastern China. Results show that 4 out of 24 cause–effect association rules are extracted by calculating the association degree of monitored COD values, and 21 out of 24 crack sensors present a temporal association relationship, among which the confidence degree of two sensors reaches 100%. The variation trend of COD values is relevant with the locations of the crack sensors. These results are consistent with the dam safety monitoring theories and models, which would be very useful for extracting the SHM information between different sensors, predicting the trend of COD value and repairing the monitoring data series of COD sensors, or even for discovering an abnormal signal for the operation safety of dams. [ABSTRACT FROM AUTHOR]

Short-termintense precipitation (SIP; i.e., convective precipitation exceeding 20mmh21) nowcasting is important for urban flood warning and natural hazards management. This paper presents an algorithm for coupling automatic weather station data and single-polarization S-band radar data with a graph model and a random forest for the nowcasting of SIP. Different from the pixel-by-pixel precipitation nowcasting algorithm, this algorithm takes the convective cells as the basic units to consider their interactions and focuses on multicell convective systems. In particular, the following question could be addressed: Will a multicell convective system cause SIP events in the next hour? First, a method based on spatiotemporal superposition between cells is proposed for multicell systems identification. Then, the graph model is used to represent cell physical attributes and the spatial distribution of the entire system. For each graph model, a fusion operation is used to form a 42-dimensional graph feature vector. Finally, combined with the machine learning approaches, a random forest classifier is trained with the graph feature vector to predict the precipitation. In the experiment, this algorithm achieves a probability of detection (POD) of 79.2% and a critical success index (CSI) of 68.3% with the data between 2015 and 2016 in North China. Compared with other precipitation nowcasting algorithms, the graph model and random forest could predict SIP events more accurately and produce fewer false alarms. [ABSTRACT FROM AUTHOR]

The rapid development of information and communication technologies, the availability of various traveling information, and the wide use of social networks facilitate the selection of the appropriate mode of transport in urban traveling. To better understand such selection processes, this paper presents a hierarchical clustering analysis of the transport data collected from some major cities in China for exploring the relationship between information use, social networks, and cooperation consciousness of individual travelers. This leads to the identification of three underlying patterns of cooperation consciousness behaviors including the pro-social relation, the diversity average relation and the homogeneous random relation. An analysis of such patterns shows that there is much to be done for enhancing the social and environmental awareness of travelers in individual travel choices for the sustainable development of urban traveling. This study contributes to the transportation study through the provision of better understanding of the influence of information use and social networks on conscious cooperation behaviors in urban traveling. The findings are beneficial for developing a sustainable travel culture through information sharing and conscious cooperation between and among travelers. [ABSTRACT FROM AUTHOR]

• The first contribution of our manuscript is: we overcomed the problem of poor practicability when the traditional clustering methods were applied for online analysis of PQ monitoring data with complex structures. The traditional clustering results of PQ monitoring data with imbalanced structure needs improvement for the real application. • The existing researches do not consider the imbalanced structure of data when applying clustering algorithm to PQ monitoring data. Quantum clustering is a more suitable clustering algorithm for analyzing data with imbalanced structure. It can effectively avoid the interference of subjective factors and recognize the patterns in large amounts of PQ monitoring data with complex structure. The clustering results can contribute to the effective application of PQ monitoring data. • Therefore, this contribution is novel. To the best of our knowledge, it has not been published in the existing literatures. • The second contribution of our manuscript is: multiple clusters with diverse characteristics are obtained and the patterns of clusters are analyzed from the PQ point of view. The cluster severity index is defined from the patterns in the clusters to represent the PQ performance in the clusters. The region severity index is defined from the PQ monitoring data of regions in different clusters, which represent the PQ performance of the regions. The region severity index is a relative value which can be used to compare the PQ performance in different regions. The indices we proposed enable an easy comparison among regions. Applied to the PQ monitoring data of 11 regions in a large city in China, the method can effectively distill diverse abnormal patterns from substantially imbalanced monitoring data, which is beneficial for easy and automatic detection of regions having issues with PQ indices exceeding defined limits. • Therefore, this contribution is novel and practical. To the best of our knowledge, it has not been published in the existing literatures. Steady-state power quality (PQ) indices seldom exceed the limits in power systems. Therefore, steady-state PQ monitoring data show a distinct imbalanced structure (i.e. unequal distribution). To ensure the efficient assessment of PQ performance, new and more appropriate tools for analyzing these data are needed. In this paper, an analysis method based on Quantum clustering is proposed, aiming to address the imbalanced structure of monitoring data. Firstly, the imbalanced structure of PQ monitoring data is analyzed. Secondly, Quantum clustering is performed on the PQ monitoring data and several disparate patterns of clusters are recognized. Thirdly, cluster severity index of cluster and the region severity index of region are defined to quantify the PQ performance of a cluster and a region, respectively. The cluster severity index is defined according to the sum of the proportions between cluster centers and limit for the respective PQ index. The region severity index is defined according to the proportions of PQ monitoring data of each region belonging to the different clusters with the corresponding cluster severity index. By sorting the region severity index in descending order, regions with poorer PQ performance become a higher-ranking place, which enables an easy comparison among regions. Finally, the proposed method is applied to the PQ monitoring data of 11 regions in a large city in China. The method can effectively distill diverse abnormal patterns from substantially imbalanced monitoring data, which is beneficial for easy and automatic detection of regions having issues with PQ indices exceeding defined limits. [ABSTRACT FROM AUTHOR]

Short-term traffic forecasting has played a key role in supporting the need of proactive and dynamic traffic control system. K-nearest neighbor (KNN) nonparametric regression models have been widely used in traffic prediction. KNN models give predictions based on the future state of traffic speed that is completely determined by the current state, but with no dependence on the past sequences of traffic speed that produced the current state. In fact, traffic speed is not completely random in nature, and some patterns repeat in the traffic stream. In this paper, we proposed a methodology called bi-pattern recognition KNN model (BKNN) which uses pattern recognition technique twice in the searching process to predict the future traffic state. Then the proposed BKNN model is applied to predict one day real traffic speed series of two sites, which are located near the North 2nd and 3rd Ring Road in Beijing, respectively. With the optimal neighbor and pattern size, the BKNN model provides good predictions. Moreover, in comparison with the KNN model, PKNN model (a modified model based on KNN), seasonal autoregressive integrated moving average (SARIMA) and the artificial neural networks (ANN), the BKNN model appears to be the most promising and robust of the five models to provide better short-term traffic prediction. [ABSTRACT FROM AUTHOR]

Camellia oil (CA), mainly produced in southern China, has always been called Oriental olive oil (OL) due to its similar physicochemical properties to OL. The high nutritional value and high selling price of CA make mixing it with other low-quality oils prevalent, in order to make huge profits. In this paper, the transverse relaxation time (T2) distribution of different brands of CA and OL, and the variation in transverse relaxation parameters when adulterated with corn oil (CO), were assessed via low field nuclear magnetic resonance (LF-NMR) imagery. The nutritional compositions of CA and OL and their quality indices were obtained via high field NMR (HF-NMR) spectroscopy. The results show that the fatty acid evaluation indices values, including for squalene, oleic acid, linolenic acid and iodine, were higher in CA than in OL, indicating the nutritional value of CA. The adulterated CA with a content of CO more than 20% can be correctly identified by principal component analysis or partial least squares discriminant analysis, and the blended oils could be successfully classified by orthogonal partial least squares discriminant analysis, with an accuracy of 100% when the adulteration ratio was above 30%. These results indicate the practicability of LF-NMR in the rapid screening of food authenticity. [ABSTRACT FROM AUTHOR]

Positioning devices allow users' movement to be recorded. The GPS (Global Positioning System) trajectory data typically consists of spatiotemporal points, which make up the major part of the big data concerning urban life. Existing knowledge extraction methods about the trajectory share a general limitation—they only investigate data from a spatiotemporal aspect, but fail to take the semantic information of trajectories into consideration. Therefore, extracting the semantic information of trajectories with the context of big data is challenging pattern recognition task that has practical application prospects. In this paper, a system is proposed to extract the semantic trajectory patterns of positioning device users. Firstly, a spatiotemporal threshold and clustering based pre-processing model is proposed to process the raw data. Then, we design a probabilistic generative model to annotate the semantic information of each trajectory after the pre-processing procedure. Finally, we apply the PrefixSpan algorithm to mine the semantic trajectory patterns. We verify our system on a large dataset of users' real trajectories over a period of 5 years in Beijing, China. The results of the experiment indicate that our system produces meaningful patterns. [ABSTRACT FROM AUTHOR]