Real-time situated agents, such as characters in real-time computer games, often do not know the terrain in advance but automatically observe it within a certain range around themselves. They have to interleave searches with action executions to make the searches tractable when moving autonomously to user-specified coordinates. The searches face realtime requirements since it is important that the agents be responsive to the commands of the users and move smoothly. In this article, we compare two classes of fast heuristic search methods for these navigation tasks that speed up A* searches in different ways, namely real-time heuristic search and incremental heuristic search, to understand their advantages and disadvantages and make recommendations about when each one should be used. We first develop a competitive real-time heuristic search method. LSS-LRTA* is a version of Learning Real-Time A* that uses A* to determine its local search spaces and learns quickly. We analyze the properties of LSS-LRTA* and then compare it experimentally against the state-of-the-art incremental heuristic search method D* Lite on our navigation tasks, for which D* Lite was specifically developed, resulting in the first comparison of real-time and incremental heuristic search in the literature. We characterize when to choose each one of the two heuristic search methods, depending on the search objective and the kind of terrain. Our experimental results show that LSS-LRTA* can outperform D* Lite under the right conditions, namely when there is time pressure or the user-supplied h-values are generally not misleading.

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With the significant development of mobile commerce, privacy becomes a major concern for both customers and enterprises. Although data generalization can provide significant protection of an individual's privacy, over-generalized data may render data of little value or useless. In this paper, we devise generalization boundary techniques to maximize data usability while, minimizing disclosure of privacy. Inspired by the fact that the permissible generalization level results in a much finer level access control, we propose a privacy-aware access control model in web service environments. We also analyze how to manage a valid access process through a trust-based decision and ongoing access control policies. The extensive experiments on both real-world and synthetic data sets show that the proposed privacy aware access control model is practical and effective.

T-614 (also named as iguratimod), a novel antirheumatic drug, could attenuate joint inflammation and articular damage in rheumatoid arthritis (RA) patients, providing a new therapy for RA. Here, we tested the role T-614 on the IL-6-induced receptor activator of nuclear factor κB ligand (RANKL)/osteoprotegerin (OPG), IL-17, and MMP-3 expression in synovial fibroblasts from rheumatoid arthritis (RASFs) patients. T-614 decreased RANKL expression and RANKL/OPG ratio in IL-6-induced RASFs. We confirmed this effect by a decrease of the mRNA and protein RANKL and mRNA RANKL/OPG in RASFs exposed in vitro to T-614 or MTX. Markedly decreased levels of IL-17, retinoid-related orphan receptor C (RORc), and MMP-3 mRNA expression were also observed in IL-6-induced RASFs in the presence of T-614 or MTX compared with those in its absence. Furthermore, T-614 blocked expression of p-ERK1/2 protein without affecting ERK1/2 expression, indicating that the way that T-614 regulated RANKL expression might be ERK1/2 pathway. Our results suggest that T-614 yields a strong improvement in arthritis via exact suppression of RANKL/OPG, IL-17, and MMP-3 expression in RASFs.