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Using Amorpha fruticosa leaves as raw materials, the polysaccharides were extracted from Amorpha fruticosa leaves by ultrasonic-assisted enzymatic hydrolysis. The effect of enzyme concentration, enzymolysis time and enzymolysis temperature on the extraction rate of polysaccharides was investigated by single factor test. The technical conditions of ultrasonic-assisted enzymatic hydrolysis for extracting polysaccharide from Amorpha fruticosa leaves were optimized by response surface methodology. The results showed that Amorpha fruticosa leaf polysaccharide was finally determined as follows: The enzyme concentration of 5%, the incubation time of 2 h, and the reaction temperature of 50 °C. The actual extraction rate of Amorpha fruticosa leaf polysaccharide was 8.31%, which was close to the theoretical simulation value of 8.40%, and the established model was true and reliable. The antioxidant experiments showed that the polysaccharides from Amorpha fruticosa leaves had a strong scavenging ability on DPPH radical and hydroxyl radical, with the maximum scavenging rates of 83.79% and 99.18%, respectively. The polysaccharide from Amorpha fruticosa leaves also had a strong inhibition on lipid oxidation, and its inhibition on β-carotene-linoleic acid oxidation system was about 83% of that of BHT. The study indicates that the extraction conditions of polysaccharide from Amorpha fruticosa leaves optimized by response surface methodology are stable and feasible. The polysaccharide has strong antioxidant activity and has high application value. [ABSTRACT FROM AUTHOR]

Clustering the trajectories of vehicles moving on road networks is a key data mining technique for understanding human mobility patterns, as well as their interactions with urban environments. The development of efficient and scalable trajectory clustering algorithms, however, still faces challenges because of the computational costs when measuring similarities among a large number of network-constrained trajectories. To address this problem, a novel trajectory clustering framework based on the well-developed Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approach is proposed. This proposed framework accurately quantifies similarities using a trajectory representation of continuous polylines in the space and time dimensions, and does not require trajectory discretization. Further, the proposed framework utilizes the space-time buffering concept to formulate ε -neighborhood queries that directly retrieve the ε -neighbors of trajectories and thus avoids computing a trajectory similarity matrix. State-of-the-art trajectory databases and index structures are incorporated to further improve trajectory clustering performance. A comprehensive case study was carried out using an open dataset of 20,161 trajectories. Results show that the proposed framework efficiently executed trajectory clustering on the large test dataset within 3 min. This was approximately 2,700 times faster than existing DBSCAN algorithms. [ABSTRACT FROM AUTHOR]

The availability of Spatiotemporal Big Data has provided a golden opportunity for time geographical studies that have long been constrained by the lack of individual-level data. However, how to store, manage, and query a huge number of time geographic entities effectively and efficiently with complex spatiotemporal characteristics and relationships poses a significant challenge to contemporary GIS platforms. In this article, a hierarchical compressed linear reference (CLR) model is proposed to transform network-constrained time geographic entities from three-dimensional (3D) (x, y, t) space into two-dimensional (2D) space. Accordingly, time geographic entities can be represented as 2D spatial entities and stored in a classical spatial database. The proposed CLR model supports a hierarchical linear reference system (LRS) including not only underlying a link-based LRS but also multiple higher-level route-based LRSs. In addition, an LRS-based spatiotemporal index structure is developed to index both time geographic entities and the corresponding hierarchical network. The results of computational experiments on large datasets of space–time paths and prisms show that the proposed hierarchical CLR model is effective at storing and managing time geographic entities in road networks. The developed index structure achieves satisfactory query performance in milliseconds on large datasets of time geographic entities. [ABSTRACT FROM AUTHOR]

Soybean is an inexpensive and high quality source of protein. Soybeans can be converted into tofu, a popular product in Asia. The quality of the tofu depends on soybean variety, soybean quality, environment and processing conditions. These processing variables include solids content of soymilk, removal of insoluble solids, coagulation temperature, type and concentration of coagulant, curd breakage, whey removal, pressing time and force, storage conditions of prepared tofu and microbial load.