As location-based social network (LBSN) services become increasingly popular, trip recommendation that recommends a sequence of points of interest (POIs) to visit for a user emerges as one of many important applications of LBSNs. Personalized trip recommendation tailors to users' specific tastes by learning from past check-in behaviors of users and their peers. Finding the optimal trip that maximizes user's experiences for a given time budget constraint is an NP hard problem and previous solutions do not consider two practical and important constraints. One constraint is POI availability where a POI may be only available during a certain time window. Another constraint is uncertain traveling time where the traveling time between two POIs is uncertain. This work presents efficient solutions to personalized trip recommendation by incorporating these constraints to prune the search space. We evaluated the efficiency and effectiveness of our solutions on real life LBSN data sets.

We consider a practical top-k route search problem: given a collection of points of interest (POIs) with rated features and traveling costs between POIs, a user wants to find k routes from a source to a destination and limited in a cost budget, that maximally match her needs on feature preferences. One challenge is dealing with the personalized diversity requirement where users have various trade-off between quantity (the number of POIs with a specified feature) and variety (the coverage of specified features). Another challenge is the large scale of the POI map and the great many alternative routes to search. We model the personalized diversity requirement by the whole class of submodular functions, and present an optimal solution to the top-k route search problem through indices for retrieving relevant POIs in both feature and route spaces and various strategies for pruning the search space using user preferences and constraints. We also present promising heuristic solutions and evaluate all the solutions on real life data.

As location-based social network (LBSN) services become increasingly popular, trip recommendation that recommends a sequence of points of interest (POIs) to visit for a user emerges as one of many important applications of LBSNs. Personalized trip recommendation tailors to users' specific tastes by learning from past check-in behaviors of users and their peers. Finding the optimal trip that maximizes user's experiences for a given time budget constraint is an NP-hard problem and previous solutions do not consider three practical and important constraints. One constraint is POI availability, where a POI may be only available during a certain time window. Another constraint is uncertain traveling time, where the traveling time between two POIs is uncertain. In addition, the diversity of the POIs included in the trip plays an important role in user's final adoptions. This work presents efficient solutions to personalized trip recommendation by incorporating these constraints and leveraging them to prune the search space. We evaluated the efficiency and effectiveness of our solutions on real-life LBSN datasets.

To meet the high radiation challenge for detectors in future high-energy physics, a novel 3D 4H-SiC detector was investigated. Three-dimensional 4H-SiC detectors could potentially operate in a harsh radiation and room-temperature environment because of its high thermal conductivity and high atomic displacement threshold energy. Its 3D structure, which decouples the thickness and the distance between electrodes, further improves the timing performance and the radiation hardness of the detector. We developed a simulation software—RASER (RAdiation SEmiconductoR)—to simulate the time resolution of planar and 3D 4H-SiC detectors with different parameters and structures, and the reliability of the software was verified by comparing the simulated and measured time-resolution results of the same detector. The rough time resolution of the 3D 4H-SiC detector was estimated, and the simulation parameters could be used as guideline to 3D 4H-SiC detector design and optimization.

Issues concerning natural gas pipeline leakage are becoming more prominent than ever because of the continuing expansion of natural gas pipeline networks. Although many scholars have extensively investigated generation and detection methods for pipeline leakage acoustic signals, systematic research on the characteristics of leakage and interference signals remains insufficient. Results show that the method based on the RBF kernel function is feasible for pipeline fault diagnosis, yielding 100% sensitivity, 92% specificity, and 96% accuracy.

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A fast calculation algorithm of efficiency map of permanent magnet synchronous machines using finite-element (FE) method is proposed in this paper. The control strategy of the machine can be taken into account in the whole torque-speed operating region. The optimal current vector is obtained by using analytical calculations, where the material saturation is also included. By using the proposed method, only one run of time-stepping FE analysis is required below the rated speed. The calculation time of the efficiency map is much reduced compared to the conventional calculation method. An example is tested, and calculation results are given to verify the proposed method.